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# Cell Press Summary, Highlights, and eTOC Examples
Examples of Cell Press-specific elements including Summary (abstract), Highlights, and eTOC blurb.
---
## Complete Example 1: Senescence and Aging
### Summary (150 words max)
```
Cellular senescence is a stress response that prevents damaged cell
proliferation but can drive tissue dysfunction through the senescence-
associated secretory phenotype (SASP). How senescent cells resist
apoptosis despite expressing pro-apoptotic p53 has remained unclear.
Here, we identify FOXO4 as a pivotal mediator of senescent cell viability.
FOXO4 is highly expressed in senescent cells and directly interacts with
p53, retaining it in the nucleus and preventing p53-mediated apoptosis.
A cell-permeable peptide that disrupts FOXO4-p53 interaction selectively
induces p53 nuclear exclusion and apoptosis in senescent cells without
affecting proliferating cells. In vivo, this FOXO4 peptide neutralizes
doxorubicin-induced senescent cells and restores fitness, fur density,
and renal function in naturally aged mice. These findings establish
FOXO4-mediated p53 sequestration as a senescence-specific survival
pathway and demonstrate the therapeutic potential of targeted senescent
cell elimination.
```
### Highlights (≤85 characters each)
```
• FOXO4 is selectively upregulated in senescent cells and binds p53
• FOXO4-p53 interaction retains p53 in the nucleus, preventing apoptosis
• A FOXO4-targeting peptide induces apoptosis specifically in senescent cells
• FOXO4 peptide treatment restores fitness and organ function in aged mice
```
### eTOC Blurb (30-50 words)
```
Baar et al. identify FOXO4 as a critical mediator of senescent cell survival
through p53 sequestration. A peptide disrupting FOXO4-p53 interaction
selectively eliminates senescent cells and restores tissue function in
aged mice, establishing proof-of-concept for targeted senolytic therapy.
```
### In Brief (1 sentence)
```
A FOXO4-targeting peptide selectively eliminates senescent cells by
releasing p53, restoring tissue function in aged mice.
```
---
## Complete Example 2: Genome Organization
### Summary (150 words max)
```
The three-dimensional organization of chromosomes within the nucleus
influences gene expression, DNA replication, and genome stability.
Phase separation has emerged as a potential mechanism for organizing
nuclear contents, but whether condensates can shape chromosome
structure in vivo remains unknown. Here, we show that the transcriptional
coactivator BRD4 forms liquid-like condensates at super-enhancers that
organize associated chromatin into hub structures. Optogenetic induction
of BRD4 condensates is sufficient to remodel chromosome topology and
activate transcription within minutes. Conversely, disruption of BRD4
condensates with the small molecule JQ1 dissolves chromatin hubs and
rapidly silences super-enhancer-controlled genes. Single-molecule
tracking reveals that condensate formation increases the local
concentration of transcription machinery 100-fold, explaining the
transcriptional potency of super-enhancers. These results establish
phase separation as a mechanism for chromatin organization and
transcriptional control with implications for understanding and
targeting oncogenic super-enhancers.
```
### Highlights
```
• BRD4 forms liquid condensates at super-enhancers in living cells
• BRD4 condensates organize chromatin into transcriptionally active hubs
• Optogenetic condensate induction rapidly remodels chromatin topology
• Condensates concentrate transcription machinery 100-fold locally
```
### eTOC Blurb
```
Sabari et al. demonstrate that BRD4 forms phase-separated condensates
at super-enhancers that organize chromatin into hub structures and
concentrate transcription machinery. Optogenetic manipulation reveals
that condensate formation directly drives chromatin remodeling and
transcriptional activation.
```
---
## Complete Example 3: Metabolism and Immunity
### Summary (150 words max)
```
Immune cells undergo dramatic metabolic reprogramming upon activation,
switching from oxidative phosphorylation to aerobic glycolysis. This
metabolic shift is thought to support the biosynthetic demands of
rapid proliferation, but whether specific metabolites directly regulate
immune cell function remains largely unexplored. Here, we show that
the glycolytic metabolite phosphoenolpyruvate (PEP) sustains T cell
receptor signaling by inhibiting sarco/endoplasmic reticulum Ca²⁺-ATPase
(SERCA) activity. PEP accumulates in activated T cells and directly
binds SERCA, preventing calcium reuptake and prolonging store-operated
calcium entry. Genetic or pharmacological enhancement of PEP levels
augments T cell effector function and anti-tumor immunity in vivo.
Conversely, tumor-derived lactate suppresses PEP levels and impairs
T cell calcium signaling, contributing to tumor immune evasion. These
findings reveal an unexpected signaling role for a glycolytic
intermediate and suggest metabolic strategies to enhance T cell
responses in cancer immunotherapy.
```
### Highlights
```
• Phosphoenolpyruvate (PEP) accumulates during T cell activation
• PEP directly binds and inhibits SERCA to sustain calcium signaling
• Enhancing PEP levels augments anti-tumor T cell immunity
• Tumor lactate suppresses T cell PEP levels and calcium signaling
```
### eTOC Blurb
```
Ho et al. discover that the glycolytic metabolite phosphoenolpyruvate
directly regulates T cell calcium signaling by inhibiting SERCA. This
metabolic-signaling link is exploited by tumors through lactate
secretion and offers new targets for cancer immunotherapy.
```
---
## Graphical Abstract Description Examples
### For Senescence Paper
```
"Graphical abstract for Cell paper on FOXO4 and senescence:
Left panel: Senescent cell (enlarged, irregular shape) with FOXO4 (blue
oval) binding p53 (green oval) in nucleus, preventing apoptosis. Label:
'FOXO4 sequesters p53 → Senescent cell survival'
Center panel: Same senescent cell with FOXO4 peptide (red wedge)
disrupting FOXO4-p53 interaction. p53 moves to mitochondria (orange
organelles). Label: 'FOXO4 peptide disrupts interaction'
Right panel: Senescent cell undergoing apoptosis (fragmenting). Label:
'Selective senescent cell death'
Bottom: Aged mouse (grey, hunched) → Treatment arrow → Rejuvenated mouse
(brown, active). Label: 'Restored fitness in aged mice'
Color scheme: Blue for FOXO4, green for p53, red for peptide, grey
background for cells."
```
### For Chromatin Paper
```
"Graphical abstract for Cell paper on BRD4 condensates:
Top row: Diagram showing BRD4 molecules (purple dots) clustering at
super-enhancer (yellow region on DNA strand), forming condensate
(purple droplet). Transcription factors (orange, green, blue small
circles) accumulate inside condensate.
Middle: Chromatin fibers (grey) being pulled into hub structure around
condensate. Arrow showing '100× local concentration increase'
Bottom: Two panels - Left shows 'JQ1' treatment dissolving condensate
and chromatin hub dispersing. Right shows 'Optogenetic activation'
creating new condensate with chromatin reorganization. Gene expression
indicators (up arrow, down arrow) for each condition."
```
---
## Writing Tips for Cell Elements
### Summary Tips
1. **First sentence**: Establish the biological context
2. **Second sentence**: State what was unknown (the gap)
3. **"Here, we show/identify/demonstrate"**: Clear transition to your work
4. **Middle sentences**: Key findings with mechanism
5. **Final sentence**: Significance and implications
### Highlights Tips
- **Start with a noun or verb**: "FOXO4 forms..." or "Activation of..."
- **One finding per bullet**: Don't combine multiple points
- **Be specific**: Include the protein/gene/pathway name
- **Check character count**: Strictly ≤85 characters including spaces
- **Cover different findings**: Don't repeat the same point
### eTOC Blurb Tips
- **Start with author names**: "Smith et al. show that..."
- **One or two sentences only**: Keep it punchy
- **Include the key mechanism**: Not just the finding
- **End with significance**: Why readers should care
---
## Character Counting for Highlights
Use this to check your highlights:
```
• This highlight is exactly 52 characters long including sp
↑ Count: 52 characters ✓ (under 85)
• This highlight is getting close to the maximum allowed character limit
↑ Count: 73 characters ✓ (under 85)
• This highlight demonstrates what happens when you try to include way too much info
↑ Count: 88 characters ✗ (over 85 - need to shorten)
```
---
## See Also
- `cell_press_style.md` - Comprehensive Cell Press writing guide
- `nature_abstract_examples.md` - Compare with Nature abstract style

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# Medical Journal Structured Abstract Examples
Examples of structured abstracts for NEJM, Lancet, JAMA, and BMJ showing the labeled section format expected at medical journals.
---
## NEJM Style (250 words max)
### Example 1: Clinical Trial
```
BACKGROUND
Sodium-glucose cotransporter 2 (SGLT2) inhibitors reduce cardiovascular
events in patients with type 2 diabetes and established cardiovascular
disease. Whether these benefits extend to patients with heart failure and
reduced ejection fraction, regardless of diabetes status, is unknown.
METHODS
We randomly assigned 4,744 patients with heart failure and an ejection
fraction of 40% or less to receive dapagliflozin (10 mg once daily) or
placebo, in addition to recommended therapy. The primary outcome was a
composite of worsening heart failure (hospitalization or urgent visit
requiring intravenous therapy) or cardiovascular death.
RESULTS
Over a median of 18.2 months, the primary outcome occurred in 386 of
2,373 patients (16.3%) in the dapagliflozin group and in 502 of 2,371
patients (21.2%) in the placebo group (hazard ratio, 0.74; 95% confidence
interval [CI], 0.65 to 0.85; P<0.001). A first worsening heart failure
event occurred in 237 patients (10.0%) in the dapagliflozin group and
in 326 patients (13.7%) in the placebo group (hazard ratio, 0.70; 95%
CI, 0.59 to 0.83). Death from cardiovascular causes occurred in 227
patients (9.6%) and 273 patients (11.5%), respectively (hazard ratio,
0.82; 95% CI, 0.69 to 0.98). Effects were similar in patients with and
without diabetes. Serious adverse events were similar between groups.
CONCLUSIONS
Among patients with heart failure and a reduced ejection fraction,
dapagliflozin reduced the risk of worsening heart failure or
cardiovascular death, regardless of the presence of diabetes.
```
**Key Features**:
- Four labeled sections (BACKGROUND, METHODS, RESULTS, CONCLUSIONS)
- Background: 2 sentences (problem + gap)
- Methods: Study design, population, intervention, primary outcome
- Results: Primary outcome with HR and 95% CI, key secondary outcomes
- Conclusions: Clear, measured statement of findings
---
### Example 2: Observational Study
```
BACKGROUND
Long-term use of proton-pump inhibitors (PPIs) has been associated with
adverse outcomes in observational studies, but causality remains uncertain.
The relationship between PPI use and chronic kidney disease is unclear.
METHODS
We conducted a prospective cohort study using data from 10,482 participants
in the Atherosclerosis Risk in Communities study who were free of kidney
disease at baseline. PPI use was ascertained at baseline and follow-up
visits. The primary outcome was incident chronic kidney disease, defined
as an estimated glomerular filtration rate less than 60 ml per minute per
1.73 m² of body-surface area.
RESULTS
Over a median follow-up of 13.9 years, incident chronic kidney disease
occurred in 56.0 per 1000 person-years among PPI users and in 42.0 per
1000 person-years among non-users (adjusted hazard ratio, 1.50; 95%
confidence interval [CI], 1.14 to 1.96). The association persisted after
adjustment for potential confounders, including indication for PPI use
and baseline kidney function. Sensitivity analyses using propensity-score
matching yielded similar results. No association was observed for
histamine H2-receptor antagonist use (hazard ratio, 1.08; 95% CI, 0.87
to 1.34).
CONCLUSIONS
PPI use was associated with an increased risk of incident chronic kidney
disease in this community-based cohort. These findings warrant cautious
use of PPIs and further investigation to establish causality.
```
**Key Features**:
- Appropriate hedging for observational study ("associated with")
- Incidence rates provided (per 1000 person-years)
- Sensitivity analyses mentioned
- Negative control (H2-receptor antagonists)
- Cautious conclusion acknowledging limitation
---
## Lancet Style (300 words max)
### Example 3: Clinical Trial with Summary Box
```
BACKGROUND
Dexamethasone has been shown to reduce mortality in hospitalized patients
with COVID-19 requiring respiratory support. We aimed to evaluate whether
higher doses of corticosteroids would provide additional benefit in
patients with severe COVID-19 pneumonia.
METHODS
In this randomized, controlled, open-label trial conducted at 18 hospitals
in Brazil, we assigned patients with moderate-to-severe COVID-19 (PaO2/FiO2
≤200 mm Hg) to receive high-dose dexamethasone (20 mg once daily for 5
days, then 10 mg once daily for 5 days) or standard dexamethasone (6 mg
once daily for 10 days). The primary outcome was ventilator-free days
at 28 days.
FINDINGS
Between June 17, 2020, and September 20, 2021, we enrolled 299 patients
(151 assigned to high-dose dexamethasone and 148 to standard
dexamethasone). The mean number of ventilator-free days at 28 days was
14·2 (SD 10·8) in the high-dose group and 15·5 (SD 10·4) in the standard
group (difference, −1·3 days; 95% CI, −3·9 to 1·3; P=0·32). There was
no significant difference in 28-day mortality (high dose 35·8% vs
standard 31·8%; hazard ratio 1·16; 95% CI, 0·79 to 1·70). Hyperglycemia
requiring insulin was more frequent with high-dose dexamethasone (66·0%
vs 53·4%; P=0·027).
INTERPRETATION
In patients with moderate-to-severe COVID-19 pneumonia, high-dose
dexamethasone did not improve ventilator-free days and was associated
with increased hyperglycemia compared with standard-dose dexamethasone.
These findings do not support the use of high-dose corticosteroids in
COVID-19.
FUNDING
Ministry of Health of Brazil.
```
**Key Features**:
- Lancet uses "Findings" instead of "Results"
- Lancet uses "Interpretation" instead of "Conclusions"
- Includes funding statement in abstract
- Decimal point (·) instead of period in numbers (Lancet style)
---
## JAMA Style (350 words max)
### Example 4: Diagnostic Study
```
IMPORTANCE
Lung cancer screening with low-dose computed tomography (CT) reduces
mortality but identifies many indeterminate pulmonary nodules, leading
to unnecessary invasive procedures. Improved risk prediction could
reduce harms while preserving benefits.
OBJECTIVE
To develop and validate a deep learning model for predicting malignancy
risk of lung nodules detected on screening CT.
DESIGN, SETTING, AND PARTICIPANTS
This retrospective cohort study included 14,851 participants with
lung nodules from the National Lung Screening Trial (NLST) for model
development and 5,402 participants from an independent multi-site
validation cohort (2016-2019). Data analysis was performed from
January to November 2022.
EXPOSURES
Deep learning model prediction of malignancy risk based on CT imaging.
MAIN OUTCOMES AND MEASURES
The primary outcome was lung cancer diagnosis within 2 years. Model
performance was assessed by area under the receiver operating
characteristic curve (AUC), sensitivity, specificity, and comparison
with radiologist assessments.
RESULTS
In the validation cohort (median age, 65 years; 57% male), 312 nodules
(5.8%) were diagnosed as lung cancer within 2 years. The deep learning
model achieved an AUC of 0.94 (95% CI, 0.92-0.96), compared with 0.85
(95% CI, 0.82-0.88) for the Lung-RADS categorization used by radiologists
(P<0.001). At 95% sensitivity, the model achieved 68% specificity compared
with 38% for Lung-RADS, corresponding to a 49% reduction in false-positive
nodules requiring follow-up. The model's performance was consistent across
subgroups defined by nodule size, location, and patient demographics.
CONCLUSIONS AND RELEVANCE
A deep learning model for lung nodule malignancy prediction outperformed
current clinical standards and could substantially reduce false-positive
findings in lung cancer screening, decreasing unnecessary surveillance
and invasive procedures.
```
**Key Features**:
- JAMA-specific sections (IMPORTANCE, OBJECTIVE, DESIGN...)
- "Importance" section required (2-3 sentences on why this matters)
- Detailed design section
- "Exposures" clearly stated
- "Main Outcomes and Measures" explicit
---
## BMJ Style (300 words max)
### Example 5: Cohort Study
```
OBJECTIVE
To examine the association between statin use and risk of Parkinson's
disease in a large population-based cohort.
DESIGN
Prospective cohort study.
SETTING
UK Biobank, 2006-2021.
PARTICIPANTS
402,251 adults aged 40-69 years without Parkinson's disease at baseline.
MAIN OUTCOME MEASURES
Incident Parkinson's disease identified through hospital admissions,
primary care records, and death certificates. Hazard ratios were
estimated using Cox regression, adjusted for age, sex, education,
smoking, alcohol, physical activity, body mass index, and comorbidities.
RESULTS
Over a median follow-up of 12.3 years, 2,841 participants developed
Parkinson's disease (incidence rate 5.7 per 10,000 person-years).
Statin use at baseline was not associated with incident Parkinson's
disease (adjusted hazard ratio 0.95, 95% confidence interval 0.87 to
1.04). Results were consistent across analyses stratified by statin
type (lipophilic vs hydrophilic), dose, and duration of use, and in
sensitivity analyses accounting for reverse causation. No protective
association was observed in analyses restricted to participants with
high cardiovascular risk or in propensity-score matched cohorts.
CONCLUSIONS
In this large prospective cohort, statin use was not associated with
reduced risk of Parkinson's disease, contrary to findings from some
previous observational studies. The null findings were robust across
multiple sensitivity analyses. These results do not support a
neuroprotective effect of statins against Parkinson's disease.
WHAT IS ALREADY KNOWN ON THIS TOPIC
Previous observational studies have yielded inconsistent results
regarding statin use and Parkinson's disease risk.
WHAT THIS STUDY ADDS
This large prospective study with long follow-up found no evidence
that statin use protects against Parkinson's disease.
```
**Key Features**:
- BMJ uses abbreviated section headers
- Includes "What is already known" and "What this study adds" boxes
- Design, Setting, and Participants as separate sections
- Clear Main Outcome Measures section
---
## Key Differences Between Journals
| Element | NEJM | Lancet | JAMA | BMJ |
|---------|------|--------|------|-----|
| **Word limit** | 250 | 300 | 350 | 300 |
| **Results label** | RESULTS | FINDINGS | RESULTS | RESULTS |
| **Conclusions label** | CONCLUSIONS | INTERPRETATION | CONCLUSIONS AND RELEVANCE | CONCLUSIONS |
| **Unique sections** | — | Funding in abstract | IMPORTANCE | What is known/adds |
| **Decimal style** | Period (.) | Centered dot (·) | Period (.) | Period (.) |
---
## Essential Elements for All Medical Abstracts
### Background/Context
- Disease burden or clinical problem (1 sentence)
- Knowledge gap or rationale for study (1 sentence)
### Methods
- Study design (RCT, cohort, case-control)
- Setting (number of sites, country/region)
- Participants (N, key inclusion criteria)
- Intervention or exposure
- Primary outcome with definition
### Results
- Number enrolled and analyzed
- Primary outcome with effect size and 95% CI
- Key secondary outcomes
- P-values for primary comparisons
- Adverse events (if applicable)
### Conclusions
- Clear statement of main finding
- Appropriate hedging based on study design
- Clinical implication (optional, 1 sentence)
---
## Common Mistakes in Medical Abstracts
❌ **Missing confidence intervals**: "HR 0.75, P=0.02" → include 95% CI
❌ **Relative risk only**: Add absolute risk reduction, NNT
❌ **Causal language for observational studies**: "PPIs cause kidney disease"
❌ **Overstated conclusions**: Claims exceeding evidence
❌ **Missing sample sizes**: Always include N for each group
❌ **Vague outcomes**: "Improved outcomes" without specific definition
---
## See Also
- `medical_journal_styles.md` - Comprehensive medical writing guide
- `venue_writing_styles.md` - Style comparison across venues

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# Nature/Science Abstract Examples
Examples of well-crafted abstracts for high-impact multidisciplinary journals. These demonstrate the flowing paragraph style with broad accessibility expected at Nature, Science, and related venues.
---
## Example 1: Molecular Biology / Cell Biology
**Topic**: CRISPR gene editing discovery
```
The ability to precisely edit DNA sequences in living cells has transformed
biological research and holds promise for treating genetic diseases. However,
current genome editing tools can introduce unwanted mutations at off-target
sites, limiting their clinical potential. Here we describe prime editing, a
versatile and precise genome editing method that directly writes new genetic
information into a specified DNA site using a reverse transcriptase fused to a
CRISPR nickase. Prime editing can make all 12 types of point mutations, as
well as small insertions and deletions, with minimal off-target editing and
without requiring double-strand breaks or donor DNA templates. In human cells,
we used prime editing to correct the primary genetic causes of sickle cell
disease and Tay-Sachs disease, and to install protective mutations that
reduce risk of prion disease. Prime editing expands the scope and capabilities
of genome editing and may address approximately 89% of known human genetic
disease variants.
```
**Why this works**:
- Opens with broad significance (genetic disease treatment)
- States the problem clearly (off-target mutations)
- Describes the approach accessibly ("writes new genetic information")
- Includes specific results (all 12 point mutations, specific diseases)
- Ends with quantified impact (89% of variants)
---
## Example 2: Neuroscience
**Topic**: Memory consolidation mechanism
```
Sleep is essential for memory consolidation, yet how the sleeping brain
transforms labile memories into stable long-term representations remains
poorly understood. We used multi-site electrophysiology in freely behaving
mice to record the activity of thousands of neurons across hippocampus and
cortex during learning and subsequent sleep. We discovered that specific
neurons that encode a newly learned memory reactivate in precisely timed
sequences during slow-wave sleep, with hippocampal reactivation preceding
cortical reactivation by 10-15 milliseconds. Optogenetic disruption of this
temporal coordination impaired memory retention by 78%, whereas artificial
enhancement of the temporal relationship strengthened memories beyond normal
levels. These results reveal that the temporal ordering of hippocampal-cortical
replay is not merely correlative but causally necessary for memory
consolidation. Our findings suggest new therapeutic approaches for memory
disorders based on optimizing the temporal dynamics of sleep.
```
**Why this works**:
- Connects to well-known phenomenon (sleep and memory)
- States what was unknown
- Describes approach (multi-site recordings)
- Key finding with specific number (10-15 ms)
- Causal evidence (disruption and enhancement experiments)
- Broader implications (therapeutic approaches)
---
## Example 3: Climate Science
**Topic**: Carbon cycle feedback
```
Arctic permafrost contains approximately 1,500 billion tonnes of organic
carbon—twice the amount currently in the atmosphere. As the Arctic warms,
this carbon may be released to the atmosphere, accelerating global warming
through a positive feedback loop. However, the magnitude and timing of this
feedback remain highly uncertain because microbial decomposition rates in
thawing permafrost are poorly constrained. Here we present a 15-year
field experiment across 25 sites spanning the Arctic, tracking carbon
fluxes in warming permafrost under natural conditions. We find that
microbial respiration increases exponentially with temperature until soils
reach 3°C, then plateaus due to substrate limitation—a threshold effect
not captured by current Earth system models. Our results suggest that
permafrost carbon feedback will be 30-50% lower than current projections
during this century, providing more time to limit warming, but will
accelerate dramatically if deep permafrost begins to thaw.
```
**Why this works**:
- Opens with striking number (1,500 billion tonnes)
- Clear problem statement (feedback uncertainty)
- Specific methodology (15 years, 25 sites)
- Novel finding (threshold at 3°C)
- Implications both reassuring and cautionary
---
## Example 4: Physics / Materials Science
**Topic**: Room-temperature superconductivity
```
Superconductivity—the flow of electricity without resistance—has been
confined to extremely low temperatures since its discovery over a century
ago, limiting practical applications. The recent demonstration of
superconductivity in hydrogen-rich materials at high pressure has raised
hopes for higher transition temperatures, but achieving room-temperature
superconductivity at ambient pressure has remained elusive. Here we report
superconductivity at 21°C (294 K) in a nitrogen-doped lutetium hydride
(Lu-N-H) compound at pressures of approximately 1 GPa—nearly ambient
conditions. Electrical resistance drops to zero below the transition
temperature with a sharp transition width of 2 K, and we observe the Meissner
effect confirming bulk superconductivity. Density functional theory
calculations suggest that nitrogen incorporation stabilizes the high-symmetry
structure that enables strong electron-phonon coupling. These results
establish a pathway toward practical room-temperature superconductors.
```
**Why this works**:
- Opens with accessible explanation of significance
- Historical context (century-old limitation)
- Precise results (21°C, 1 GPa, 2 K transition width)
- Multiple lines of evidence (resistance + Meissner effect)
- Theoretical explanation briefly included
- Forward-looking conclusion
---
## Example 5: Evolution / Ecology
**Topic**: Rapid evolution in response to climate
```
Climate change is driving rapid shifts in the geographic distributions of
species, but whether organisms can adapt quickly enough to keep pace with
warming remains a critical question for biodiversity conservation. Here we
document real-time evolution in wild populations of a widespread forest tree,
Scots pine, along a 1,000 km latitudinal gradient in Scandinavia. By combining
whole-genome sequencing with phenotypic measurements across 25 common gardens,
we detect signatures of selection at 47 loci associated with cold tolerance,
phenology, and drought resistance over just 50 years—approximately
five tree generations. Alleles conferring warmer-adapted phenotypes have
increased in frequency by 4-12% across northern populations, matching
predictions from models of climate-driven selection. However, migration of
warm-adapted genotypes from the south appears limited by geographic barriers.
These results demonstrate that trees can evolve rapidly in response to
climate change but suggest that assisted gene flow may be necessary to
prevent local maladaptation.
```
**Why this works**:
- Opens with pressing question (climate adaptation)
- Specific system (Scots pine) and scale (1,000 km)
- Methods described briefly (genomics + common gardens)
- Quantitative results (47 loci, 4-12% frequency shift, 5 generations)
- Mechanism identified (limited migration)
- Conservation implications stated
---
## Common Elements Across Examples
### Structure (Implicit)
1. **Hook**: Why this matters broadly (1-2 sentences)
2. **Gap**: What was unknown or problematic (1 sentence)
3. **Approach**: What was done (1 sentence)
4. **Findings**: Key results with numbers (2-3 sentences)
5. **Significance**: Why this matters going forward (1 sentence)
### Style Features
- **Active voice**: "We discovered," "We find," "We report"
- **Specific numbers**: Exact values, not vague quantities
- **Accessible language**: Minimal jargon, explained when needed
- **Compelling opening**: Broad hook before technical details
- **Strong close**: Implications or future directions
### Word Count
- Nature: 150-200 words (examples above: 185-210 words)
- Science: ≤125 words (would need tightening)
---
## What to Avoid
❌ **Too technical opening**:
> "The CRISPR-Cas9 system with guide RNA targeting PAM sequences..."
✅ **Better opening**:
> "The ability to precisely edit DNA in living cells..."
---
❌ **Vague results**:
> "Our method significantly outperformed existing approaches..."
✅ **Better results**:
> "Our method reduced off-target editing by 78% compared to standard Cas9..."
---
❌ **Weak significance statement**:
> "These findings may have implications for the field..."
✅ **Better significance**:
> "These findings suggest new therapeutic approaches for memory disorders..."
---
## See Also
- `nature_science_style.md` - Comprehensive Nature/Science writing guide
- `venue_writing_styles.md` - Style comparison across venues

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# NeurIPS/ICML Introduction Example
This example demonstrates the distinctive ML conference introduction structure with numbered contributions and technical precision.
---
## Full Introduction Example
**Paper Topic**: Efficient Long-Context Transformers
---
### Paragraph 1: Problem Motivation
```
Large language models (LLMs) have demonstrated remarkable capabilities in
natural language understanding, code generation, and reasoning tasks [1, 2, 3].
These capabilities scale with both model size and context length—longer
contexts enable processing of entire documents, multi-turn conversations,
and complex reasoning chains that span many steps [4, 5]. However, the
standard Transformer attention mechanism [6] has O(N²) time and memory
complexity with respect to sequence length N, creating a fundamental
bottleneck for processing long sequences. For a context window of 100K
tokens, computing full attention requires 10 billion scalar operations
and 40 GB of memory for the attention matrix alone, making training and
inference prohibitively expensive on current hardware.
```
**Key features**:
- States why this matters (LLM capabilities)
- Connects to scaling (longer contexts = better performance)
- Specific numbers (O(N²), 100K tokens, 10 billion ops, 40 GB)
- Citations to establish credibility
---
### Paragraph 2: Limitations of Existing Approaches
```
Prior work has addressed attention efficiency through three main approaches.
Sparse attention patterns [7, 8, 9] reduce complexity to O(N√N) or O(N log N)
by restricting attention to local windows, fixed stride patterns, or learned
sparse masks. Linear attention approximations [10, 11, 12] reformulate
attention using kernel feature maps that enable O(N) computation, but
sacrifice the ability to model arbitrary pairwise interactions. Low-rank
factorizations [13, 14] approximate the attention matrix as a product of
smaller matrices, achieving efficiency at the cost of expressivity. While
these methods reduce theoretical complexity, they introduce approximation
errors that compound in deep networks, often resulting in 2-5% accuracy
degradation on long-range modeling benchmarks [15]. Perhaps more importantly,
they fundamentally change the attention mechanism, making it difficult to
apply advances in standard attention (e.g., rotary positional embeddings,
grouped-query attention) to efficient variants.
```
**Key features**:
- Organized categorization of prior work
- Complexity stated for each approach
- Limitations clearly identified
- Quantified shortcomings (2-5% degradation)
- Deeper issue identified (incompatibility with advances)
---
### Paragraph 3: Your Approach (High-Level)
```
We take a different approach: rather than approximating attention, we
accelerate exact attention by optimizing memory access patterns. Our key
observation is that on modern GPUs, attention is bottlenecked by memory
bandwidth, not compute. Reading and writing the N × N attention matrix to
and from GPU high-bandwidth memory (HBM) dominates runtime, while the GPU's
tensor cores remain underutilized. We propose LongFlash, an IO-aware exact
attention algorithm that computes attention block-by-block in fast on-chip
SRAM, never materializing the full attention matrix in HBM. By carefully
orchestrating the tiling pattern and fusing the softmax computation with
matrix multiplications, LongFlash reduces HBM accesses from O(N²) to
O(N²d/M) where d is the head dimension and M is the SRAM size, achieving
asymptotically optimal IO complexity.
```
**Key features**:
- Clear differentiation from prior work ("different approach")
- Key insight stated explicitly
- Technical mechanism explained
- Complexity improvement quantified
- Method name introduced
---
### Paragraph 4: Contributions (CRITICAL)
```
Our contributions are as follows:
• We propose LongFlash, an IO-aware exact attention algorithm that achieves
2-4× speedup over FlashAttention [16] and up to 9× over standard PyTorch
attention on sequences from 1K to 128K tokens (Section 3).
• We provide theoretical analysis proving that LongFlash achieves optimal
IO complexity of O(N²d/M) among all algorithms that compute exact
attention, and analyze the regime where our algorithm provides maximum
benefit (Section 3.3).
• We introduce sequence parallelism techniques that enable LongFlash to
scale to sequences of 1M+ tokens across multiple GPUs with near-linear
weak scaling efficiency (Section 4).
• We demonstrate that LongFlash enables training with 8× longer contexts
on the same hardware: we train a 7B parameter model on 128K token
contexts using the same memory that previously limited us to 16K tokens
(Section 5).
• We release optimized CUDA kernels achieving 80% of theoretical peak
FLOPS on A100 and H100 GPUs, along with PyTorch and JAX bindings, at
[anonymous URL] (Section 6).
```
**Key features**:
- Numbered/bulleted format
- Each contribution is specific and quantified
- Section references for each claim
- Both methodological and empirical contributions
- Code release mentioned
- Self-contained bullets (each makes sense alone)
---
## Alternative Opening Paragraphs
### For a Methods Paper
```
Scalable optimization algorithms are fundamental to modern machine learning.
Stochastic gradient descent (SGD) and its variants [1, 2, 3] have enabled
training of models with billions of parameters on massive datasets. However,
these first-order methods exhibit slow convergence on ill-conditioned
problems, often requiring thousands of iterations to converge on tasks
where second-order methods would converge in tens of iterations [4, 5].
```
### For an Applications Paper
```
Drug discovery is a costly and time-consuming process, with the average new
drug requiring 10-15 years and $2.6 billion to develop [1]. Machine learning
offers the potential to accelerate this process by predicting molecular
properties, identifying promising candidates, and optimizing lead compounds
computationally [2, 3]. Recent successes in protein structure prediction [4]
and molecular generation [5] have demonstrated that deep learning can
capture complex chemical patterns, raising hopes for ML-driven drug discovery.
```
### For a Theory Paper
```
Understanding why deep neural networks generalize well despite having more
parameters than training examples remains one of the central puzzles of
modern machine learning [1, 2]. Classical statistical learning theory
predicts that such overparameterized models should overfit dramatically,
yet in practice, large networks trained with SGD achieve excellent test
accuracy [3]. This gap between theory and practice has motivated a rich
literature on implicit regularization [4], neural tangent kernels [5],
and feature learning [6], but a complete theoretical picture remains elusive.
```
---
## Contribution Bullet Templates
### For a New Method
```
• We propose [Method Name], a novel [type of method] that [key innovation]
achieving [performance improvement] over [baseline] on [benchmark].
```
### For Theoretical Analysis
```
• We prove that [statement], providing the first [type of result] for
[problem setting]. This resolves an open question from [prior work].
```
### For Empirical Study
```
• We conduct a comprehensive evaluation of [N] methods across [M] datasets,
revealing that [key finding] and identifying [failure mode/best practice].
```
### For Code/Data Release
```
• We release [resource name], a [description] containing [scale/scope],
available at [URL]. This enables [future work/reproducibility].
```
---
## Common Mistakes to Avoid
### Vague Contributions
❌ **Bad**:
```
• We propose a novel method for attention
• We show our method is better than baselines
• We provide theoretical analysis
```
✅ **Good**:
```
• We propose LongFlash, achieving 2-4× speedup over FlashAttention
• We prove LongFlash achieves optimal O(N²d/M) IO complexity
• We enable 8× longer context training on fixed hardware budget
```
### Missing Quantification
❌ **Bad**: "Our method significantly outperforms prior work"
✅ **Good**: "Our method improves accuracy by 3.2% on GLUE and 4.1% on SuperGLUE"
### Overlapping Bullets
❌ **Bad**:
```
• We propose a new attention mechanism
• We introduce LongFlash attention
• Our novel attention approach...
```
(These say the same thing three times)
### Buried Contributions
❌ **Bad**: Contribution bullets at the end of page 2
✅ **Good**: Contribution bullets clearly visible by end of page 1
---
## See Also
- `ml_conference_style.md` - Comprehensive ML conference guide
- `venue_writing_styles.md` - Style comparison across venues

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% NIH Specific Aims Page Template
% THE MOST CRITICAL PAGE OF YOUR NIH PROPOSAL
% 1 page maximum - strictly enforced
% Last updated: 2024
\documentclass[11pt,letterpaper]{article}
% Formatting
\usepackage[margin=0.5in]{geometry} % 0.5 inch minimum margins
\usepackage{helvet} % Arial-like font
\renewcommand{\familydefault}{\sfdefault}
\usepackage{setspace}
\usepackage{color}
\usepackage{soul} % For highlighting (remove in final version)
% Remove page numbers (optional)
\pagestyle{empty}
\begin{document}
% Optional: Highlight template text to remind yourself to replace
% Remove \hl{} and color in final version
\definecolor{highlight}{RGB}{255,255,200}
\sethlcolor{highlight}
% ====================
% SPECIFIC AIMS PAGE
% ====================
\begin{center}
\textbf{\large Your Project Title Here: Concise and Descriptive}
\end{center}
\vspace{0.3cm}
% OPENING PARAGRAPH: The Hook and Gap
% 2-3 sentences establishing significance and the knowledge gap
\textbf{[Disease/condition]} affects \textbf{[number]} people worldwide and results in \textbf{[burden: mortality, morbidity, cost]}. \textbf{[Current treatment/understanding]} has improved outcomes, but \textbf{[limitation/gap]} remains a critical barrier to \textbf{[desired outcome]}. Understanding \textbf{[specific mechanism/relationship]} is essential for \textbf{[future advance: therapy, prevention, diagnosis]}.
\vspace{0.2cm}
% LONG-TERM GOAL
% 1 sentence on your overarching research vision
Our \textbf{long-term goal} is to \textbf{[overarching vision: develop cure, understand mechanism, improve treatment]} for \textbf{[disease/population]}.
\vspace{0.2cm}
% OBJECTIVE AND CENTRAL HYPOTHESIS
% 1-2 sentences on what THIS proposal will accomplish
The \textbf{objective} of this proposal is to \textbf{[specific objective for this project]}. Our \textbf{central hypothesis} is that \textbf{[clearly stated, testable hypothesis]}.
\vspace{0.2cm}
% RATIONALE
% 2-3 sentences explaining WHY you expect success (preliminary data!)
This hypothesis is based on our \textbf{preliminary data} showing that \textbf{[key preliminary finding 1]} and \textbf{[key preliminary finding 2]}. These findings suggest that \textbf{[mechanistic explanation or expected outcome]}.
\vspace{0.2cm}
% TRANSITION TO AIMS
% 1 sentence introducing the specific aims
To test this hypothesis and achieve our objective, we will pursue the following \textbf{Specific Aims}:
\vspace{0.3cm}
% ====================
% SPECIFIC AIM 1
% ====================
\noindent\textbf{Specific Aim 1: [Concise, active verb title describing what you'll do].}
\textit{Working Hypothesis:} \hl{State testable hypothesis for this aim.}
We will \textbf{[approach/method]} to determine \textbf{[what you'll learn]}. We will use \textbf{[model system/approach]} to test whether \textbf{[specific prediction]}.
\textbf{Expected Outcome:} We expect to find that \textbf{[predicted result]}. This outcome will demonstrate that \textbf{[significance of finding]} and will be \textbf{[positive/negative/innovative/transformative]} because \textbf{[why it matters]}.
\vspace{0.3cm}
% ====================
% SPECIFIC AIM 2
% ====================
\noindent\textbf{Specific Aim 2: [Title of second aim].}
\textit{Working Hypothesis:} \hl{Testable hypothesis for Aim 2.}
Building on Aim 1, we will \textbf{[approach]} to \textbf{[objective]}. We will employ \textbf{[method/technique]} in \textbf{[model/population]} to test the hypothesis that \textbf{[specific prediction]}.
\textbf{Expected Outcome:} These studies will reveal \textbf{[predicted finding]}. This is significant because \textbf{[impact on field/understanding]}.
\vspace{0.3cm}
% ====================
% SPECIFIC AIM 3 (OPTIONAL)
% ====================
\noindent\textbf{Specific Aim 3: [Title of third aim].}
\textit{Working Hypothesis:} \hl{Testable hypothesis for Aim 3.}
To translate findings from Aims 1-2, we will \textbf{[approach]} to determine \textbf{[translational objective]}. We will \textbf{[method]} using \textbf{[clinically relevant model/patient samples]} to test whether \textbf{[translational prediction]}.
\textbf{Expected Outcome:} We anticipate that \textbf{[result]}, which will provide \textbf{[proof-of-concept/validation/mechanism]} for \textbf{[therapeutic/diagnostic/preventive strategy]}.
\vspace{0.3cm}
% ====================
% PAYOFF PARAGRAPH
% ====================
% 2-3 sentences on IMPACT, INNOVATION, and FUTURE DIRECTIONS
\textbf{Impact and Innovation:} This project is \textbf{innovative} because it \textbf{[novel aspect: new concept, method, approach, application]}. The proposed research is \textbf{significant} because it will \textbf{[advance the field by...]} and will ultimately lead to \textbf{[long-term impact: improved treatment, new therapeutic target, diagnostic tool]}. Upon completion of these studies, we will be positioned to \textbf{[next steps: clinical trial, mechanistic studies, therapeutic development]}.
\vspace{0.5cm}
% ====================
% ALTERNATIVE STRUCTURE (if preferred)
% ====================
% Some successful Specific Aims pages use this alternative structure:
% - Open with hook (same as above)
% - State long-term goal and objective (same)
% - Present central hypothesis with 2-3 supporting pieces of preliminary data
% - Then state: "We will test this hypothesis through three Specific Aims:"
% - List aims more concisely (1-2 sentences each, plus expected outcome)
% - Conclude with payoff paragraph emphasizing innovation, significance, impact
\end{document}
% ====================
% TIPS FOR WRITING SPECIFIC AIMS
% ====================
% 1. START WITH A HOOK
% - Open with the big picture: disease burden, societal cost, mortality
% - Use compelling statistics
% - Make it clear why anyone should care
% 2. IDENTIFY THE GAP
% - What's currently known?
% - What's the critical barrier or unknown?
% - Why does it matter?
% 3. STATE YOUR HYPOTHESIS EXPLICITLY
% - Clear, testable hypothesis
% - Not "We hypothesize that we will study..." (that's not a hypothesis!)
% - "We hypothesize that [mechanism] causes [outcome]"
% 4. SHOW PRELIMINARY DATA
% - Demonstrate feasibility
% - Prove you're not starting from scratch
% - Build confidence in your approach
% 5. THREE AIMS (TYPICALLY)
% - Can be 2 or 4, but 3 is most common
% - Aims should be related but somewhat independent
% - Failure of one aim shouldn't sink the whole project
% - Aims can build on each other (Aim 1 → Aim 2 → Aim 3)
% 6. EACH AIM SHOULD HAVE:
% - Clear title (active verb)
% - Working hypothesis
% - Approach/method
% - Expected outcome
% - Significance/impact
% 7. END WITH PAYOFF
% - Innovation: What's new/different?
% - Significance: Why does it matter?
% - Impact: What will change?
% - Future: Where does this lead?
% 8. COMMON MISTAKES TO AVOID
% - Too much background (this is not a mini-review)
% - Vague hypotheses or objectives
% - Missing expected outcomes
% - No preliminary data mentioned
% - Too ambitious (can't do it all in 5 years)
% - Not addressing innovation and significance
% - Poor logical flow between aims
% - Exceeding 1 page (auto-reject!)
% 9. FORMATTING RULES (STRICTLY ENFORCED)
% - 1 page maximum (including all text, no figures typically)
% - Arial 11pt minimum (or equivalent)
% - 0.5 inch margins minimum
% - Any spacing (single, 1.5, double acceptable)
% - No smaller fonts allowed (even for superscripts/subscripts)
% 10. REVISION STRATEGY
% - Write, get feedback, revise 10+ times
% - Every word must earn its place
% - Test on non-specialist colleagues
% - Read aloud to check flow
% - Have it reviewed by successful R01 holders
% - Mock study section review
% ====================
% EXAMPLES OF STRONG OPENING SENTENCES
% ====================
% DISEASE BURDEN APPROACH:
% "Alzheimer's disease (AD) affects 6.7 million Americans and will cost $345 billion in 2023,
% yet no disease-modifying therapies exist."
% MECHANISTIC GAP APPROACH:
% "Despite decades of research, the molecular mechanisms driving metastasis remain poorly understood,
% limiting our ability to develop effective therapies for the 90% of cancer deaths caused by metastatic disease."
% TRANSLATIONAL APPROACH:
% "Current immunotherapies fail in 70% of patients with melanoma, largely because we cannot predict
% who will respond, highlighting an urgent need for biomarkers of treatment response."
% ====================
% REMEMBER
% ====================
% The Specific Aims page is often the ONLY page reviewers read carefully before
% forming their initial opinion. A weak Specific Aims page can doom an otherwise
% excellent proposal. Invest the time to make it compelling, clear, and concise.
% Get feedback from:
% - Successful R01 awardees in your field
% - Grant writing office at your institution
% - Colleagues who've served on NIH study sections
% - Non-specialists (if they can't understand it, reviewers may struggle too)

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% NSF Research Proposal Template
% For NSF Standard Grant Proposals
% Last updated: 2024
% Based on NSF PAPPG (Proposal & Award Policies & Procedures Guide)
\documentclass[11pt,letterpaper]{article}
% Required formatting
\usepackage[margin=1in]{geometry} % 1 inch margins required
\usepackage{times} % Times Roman font (11pt minimum)
\usepackage{graphicx}
\usepackage{amsmath}
\usepackage{amssymb}
\usepackage{cite}
\usepackage{hyperref}
% Single spacing (NSF allows single spacing)
\usepackage{setspace}
\singlespacing
% Page numbers
\usepackage{fancyhdr}
\pagestyle{fancy}
\fancyhf{}
\rhead{\thepage}
\renewcommand{\headrulewidth}{0pt}
\begin{document}
% ====================
% PROJECT SUMMARY (1 page maximum)
% ====================
\section*{Project Summary}
\subsection*{Overview}
Provide a concise 1-2 paragraph description of the proposed research. This should be understandable to a scientifically literate reader who is not a specialist in your field.
\subsection*{Intellectual Merit}
Describe how the project advances knowledge within its field and across different fields. Address:
\begin{itemize}
\item How the project advances understanding in the field
\item Innovative aspects of the research
\item Qualifications of the research team
\item Adequacy of resources
\end{itemize}
\subsection*{Broader Impacts}
Describe the potential benefits to society and contributions to desired societal outcomes. Address one or more of the following:
\begin{itemize}
\item Advancing discovery and understanding while promoting teaching and learning
\item Broadening participation of underrepresented groups in STEM
\item Disseminating broadly to enhance scientific and technological understanding
\item Benefits to society (economic development, health, quality of life, national security, etc.)
\item Developing the scientific workforce and enhancing research infrastructure
\end{itemize}
\newpage
% ====================
% PROJECT DESCRIPTION (15 pages maximum)
% ====================
\section*{Project Description}
\section{Introduction and Background}
\subsection{Current State of Knowledge}
Provide context for your proposed research. Review relevant literature and establish what is currently known in the field.
\subsection{Knowledge Gap}
Clearly identify the gap in current knowledge or understanding that your project will address. Explain why this gap is significant.
\subsection{Preliminary Work and Feasibility}
Describe any preliminary work that demonstrates the feasibility of your approach. Highlight your team's qualifications and prior accomplishments.
\section{Research Objectives and Hypotheses}
\subsection{Overall Goal}
State the overarching long-term goal of your research program.
\subsection{Specific Objectives}
List 2-4 specific, measurable objectives for this project:
\begin{enumerate}
\item \textbf{Objective 1:} Clearly stated objective
\item \textbf{Objective 2:} Second objective
\item \textbf{Objective 3:} Third objective
\end{enumerate}
\subsection{Hypotheses}
State your testable hypotheses explicitly.
\section{Research Plan}
\subsection{Objective 1: [Title]}
\subsubsection{Rationale}
Explain why this objective is important and how it addresses the knowledge gap.
\subsubsection{Approach and Methods}
Describe in detail how you will accomplish this objective. Include:
\begin{itemize}
\item Experimental design or computational approach
\item Methods and procedures
\item Data collection and analysis
\item Controls and validation
\end{itemize}
\subsubsection{Expected Outcomes}
Describe what results you expect and how they will advance the field.
\subsubsection{Potential Challenges and Alternatives}
Identify potential obstacles and describe alternative approaches.
\subsection{Objective 2: [Title]}
[Repeat same structure as Objective 1]
\subsection{Objective 3: [Title]}
[Repeat same structure as Objective 1]
\section{Timeline and Milestones}
Provide a detailed timeline showing when each objective will be addressed:
\begin{center}
\begin{tabular}{|l|p{3cm}|p{3cm}|p{3cm}|}
\hline
\textbf{Activity} & \textbf{Year 1} & \textbf{Year 2} & \textbf{Year 3} \\
\hline
Objective 1 activities & Months 1-6: ... & & \\
\hline
Objective 2 activities & Months 7-12: ... & Months 13-18: ... & \\
\hline
Objective 3 activities & & Months 19-24: ... & Months 25-36: ... \\
\hline
Publications & & Submit paper 1 & Submit papers 2-3 \\
\hline
\end{tabular}
\end{center}
\section{Broader Impacts}
\textit{Note: Broader Impacts must be substantive, not perfunctory. Integrate throughout proposal.}
\subsection{Educational Activities}
Describe specific educational activities integrated with the research:
\begin{itemize}
\item Curriculum development
\item Training of graduate and undergraduate students
\item K-12 outreach programs
\item Public science communication
\end{itemize}
\subsection{Broadening Participation}
Describe concrete efforts to broaden participation of underrepresented groups:
\begin{itemize}
\item Recruitment strategies
\item Mentoring programs
\item Partnerships with minority-serving institutions
\item Measurable outcomes
\end{itemize}
\subsection{Dissemination and Outreach}
Describe plans for broad dissemination:
\begin{itemize}
\item Open-access publications
\item Data and code sharing (repositories, licenses)
\item Conference presentations and workshops
\item Public engagement activities
\end{itemize}
\subsection{Societal Benefits}
Explain potential benefits to society:
\begin{itemize}
\item Economic development
\item Health and quality of life improvements
\item Environmental sustainability
\item National security (if applicable)
\end{itemize}
\subsection{Assessment of Broader Impacts}
Describe how you will measure the success of broader impacts activities. Include specific, measurable outcomes.
\section{Results from Prior NSF Support}
\textit{Required if PI or co-PI has received NSF funding in the past 5 years}
\subsection{Award Title and Number}
Award Number: NSF-XXXXX, Amount: \$XXX,XXX, Period: MM/YY - MM/YY
\subsection{Intellectual Merit}
Summarize research accomplishments and findings from prior award.
\subsection{Broader Impacts}
Describe broader impacts activities and outcomes from prior award.
\subsection{Publications}
List publications resulting from prior NSF support (up to 5 most significant):
\begin{enumerate}
\item Author, A.A., et al. (Year). Title. \textit{Journal}, vol(issue), pages.
\end{enumerate}
\newpage
% ====================
% REFERENCES CITED (No page limit)
% ====================
\section*{References Cited}
\begin{thebibliography}{99}
\bibitem{ref1}
Author, A.A., \& Author, B.B. (2023). Article title. \textit{Journal Name}, \textit{45}(3), 123-145.
\bibitem{ref2}
Author, C.C., Author, D.D., \& Author, E.E. (2022). Book title. Publisher.
\bibitem{ref3}
Author, F.F., et al. (2021). Another article. \textit{Nature}, \textit{590}, 234-238.
% Add more references as needed
\end{thebibliography}
\newpage
% ====================
% BUDGET JUSTIFICATION (3-5 pages typical)
% Note: Budget is submitted separately in NSF's systems
% This justifies the budget requests
% ====================
\section*{Budget Justification}
\subsection*{A. Senior Personnel}
\textbf{PI Name (X\% academic year, Y summer months):} Justify percent effort and role in project. Summer salary calculated as X/9 of academic year salary.
\textbf{Co-PI Name (X\% academic year):} Justify role and effort.
\subsection*{B. Other Personnel}
\textbf{Postdoctoral Researcher (1.0 FTE, Years 1-3):} Justify need for postdoc, qualifications required, and role in project. Salary: \$XX,XXX/year.
\textbf{Graduate Student (2 students, Years 1-3):} Justify need, training opportunities, and project contributions. Stipend: \$XX,XXX/year per student.
\textbf{Undergraduate Researchers (2 students/year):} Describe research training opportunities. Hourly wage: \$XX/hour.
\subsection*{C. Fringe Benefits}
List fringe benefit rates for each personnel category as determined by institution.
\subsection*{D. Equipment (\$5,000+)}
\textbf{Instrument Name (\$XX,XXX):} Justify need, explain why existing equipment inadequate, describe how it enables proposed research.
\subsection*{E. Travel}
\textbf{Domestic Conference Travel (\$X,XXX/year):} Justify conference attendance for dissemination (1-2 conferences/year for PI and students).
\textbf{Field Work Travel (\$X,XXX):} If applicable, justify field site visits.
\subsection*{F. Participant Support Costs}
\textit{If hosting workshop, summer program, etc.}
Stipends, travel, and per diem for XX participants attending [workshop/program name].
\subsection*{G. Other Direct Costs}
\textbf{Materials and Supplies (\$X,XXX/year):} Itemize major categories (e.g., chemicals, consumables, software licenses).
\textbf{Publication Costs (\$X,XXX):} Budget for open-access publication fees (estimate X papers @ \$X,XXX each).
\textbf{Subaward to Partner Institution (\$XX,XXX):} Justify collaboration and subaward amount.
\textbf{Other:} Justify any other costs.
\subsection*{H. Indirect Costs}
Calculated at XX\% of Modified Total Direct Costs (institution's negotiated rate).
\newpage
% ====================
% DATA MANAGEMENT PLAN (2 pages maximum)
% ====================
\section*{Data Management Plan}
\subsection*{Types of Data}
Describe the types of data to be generated by the project:
\begin{itemize}
\item Experimental data (e.g., measurements, observations)
\item Computational data (e.g., simulation results, models)
\item Metadata describing data collection and processing
\end{itemize}
\subsection*{Data and Metadata Standards}
Describe standards to be used for data format and metadata:
\begin{itemize}
\item File formats (e.g., HDF5, NetCDF, CSV)
\item Metadata standards (e.g., Dublin Core, domain-specific standards)
\item Documentation of data collection and processing
\end{itemize}
\subsection*{Policies for Access and Sharing}
Describe how data will be made accessible:
\begin{itemize}
\item Repository for data deposition (e.g., Dryad, Zenodo, domain-specific archive)
\item Timeline for public release (immediately upon publication, or within X months)
\item Access restrictions (if any) and justification
\item Embargo periods (if applicable)
\end{itemize}
\subsection*{Policies for Re-use, Redistribution}
Describe terms for re-use:
\begin{itemize}
\item Licensing (e.g., CC0, CC-BY, specific data use agreement)
\item Attribution requirements
\item Restrictions on commercial use (if any)
\end{itemize}
\subsection*{Plans for Archiving and Preservation}
Describe long-term preservation strategy:
\begin{itemize}
\item Repository selection (long-term, stable repositories)
\item Preservation period (minimum 3-5 years post-project)
\item Data formats for long-term preservation
\item Institutional commitments
\end{itemize}
\subsection*{Roles and Responsibilities}
Identify who is responsible for data management implementation.
\end{document}
% ====================
% ADDITIONAL DOCUMENTS (submitted separately in NSF system)
% ====================
% 1. BIOGRAPHICAL SKETCH (3 pages per person)
% - Use NSF-approved format (SciENcv or NSF template)
% - Professional preparation
% - Appointments
% - Products (up to 5 most relevant, up to 5 other significant)
% - Synergistic activities
% 2. CURRENT AND PENDING SUPPORT
% - All current and pending support for all senior personnel
% - Use NSF format
% - Check for overlap with proposed project
% 3. FACILITIES, EQUIPMENT, AND OTHER RESOURCES
% - Describe available facilities and equipment
% - Computational resources
% - Laboratory space
% - Other resources supporting the project
% ====================
% FORMATTING CHECKLIST
% ====================
% ☐ Margins: 1 inch on all sides
% ☐ Font: Times Roman 11pt or larger (or equivalent)
% ☐ Line spacing: Single spacing acceptable
% ☐ Project Summary: 1 page, includes Overview, Intellectual Merit, Broader Impacts
% ☐ Project Description: 15 pages maximum
% ☐ References Cited: No page limit, consistent formatting
% ☐ Biographical Sketches: 3 pages per person, NSF-approved format
% ☐ Budget Justification: Detailed and reasonable
% ☐ Data Management Plan: 2 pages maximum
% ☐ Current & Pending: Complete and accurate
% ☐ Facilities: Adequate resources described
% ☐ Broader Impacts: Substantive and integrated throughout
% ☐ All required sections included
% ====================
% SUBMISSION NOTES
% ====================
% 1. Submit through Research.gov or Grants.gov
% 2. Follow your institution's internal deadlines (usually 3-5 days before NSF deadline)
% 3. Obtain institutional approval before submission
% 4. Ensure all senior personnel have NSF IDs
% 5. Budget prepared in NSF's system (separate from this document)
% 6. Check program-specific requirements (may differ from standard grant)
% 7. Contact Program Officer for guidance (encouraged but not required)

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% Nature Journal Article Template
% For submission to Nature family journals
% Last updated: 2024
\documentclass[12pt]{article}
% Packages
\usepackage[margin=2.5cm]{geometry}
\usepackage{times}
\usepackage{graphicx}
\usepackage{amsmath}
\usepackage{amssymb}
\usepackage{hyperref}
\usepackage{lineno} % Line numbers for review
\usepackage[super]{natbib} % Superscript citations
% Line numbering (required for submission)
\linenumbers
% Title and Authors
\title{Insert Your Title Here: Concise and Descriptive}
\author{
First Author\textsuperscript{1}, Second Author\textsuperscript{1,2}, Third Author\textsuperscript{2,*}
}
\date{}
\begin{document}
\maketitle
% Affiliations
\noindent
\textsuperscript{1}Department Name, Institution Name, City, State/Province, Postal Code, Country \\
\textsuperscript{2}Second Department/Institution \\
\textsuperscript{*}Correspondence: [email protected]
% Abstract
\begin{abstract}
\noindent
Write a concise abstract of 150-200 words summarizing the main findings, significance, and conclusions of your work. The abstract should be self-contained and understandable without reading the full paper. Focus on what you did, what you found, and why it matters. Avoid jargon and abbreviations where possible.
\end{abstract}
% Main Text
\section*{Introduction}
% 2-3 paragraphs setting the context
Provide background on the research area, establish the importance of the problem, and identify the knowledge gap your work addresses. Nature papers should emphasize broad significance beyond a narrow specialty.
State your main research question or objective clearly.
Briefly preview your approach and key findings.
\section*{Results}
% Primary results section
% Organize by finding, not by experiment
% Reference figures/tables as you describe results
\subsection*{First major finding}
Describe your first key result. Reference Figure~\ref{fig:example} to support your findings.
\begin{figure}[ht]
\centering
% Include your figure here
% \includegraphics[width=0.7\textwidth]{figure1.pdf}
\caption{{\bf Figure title in bold.} Detailed figure caption explaining what is shown, experimental conditions, sample sizes (n), statistical tests, and significance levels. Panels should be labeled (a), (b), etc. if multiple panels are present.}
\label{fig:example}
\end{figure}
\subsection*{Second major finding}
Describe your second key result objectively, without interpretation.
\subsection*{Third major finding}
Describe additional results as needed.
\section*{Discussion}
% Interpret results, compare to literature, acknowledge limitations
\subsection*{Main findings and interpretation}
Summarize your key findings and explain their significance. How do they advance our understanding?
\subsection*{Comparison to previous work}
Compare and contrast your results with existing literature\cite{example2023}.
\subsection*{Implications}
Discuss the broader implications of your work for the field and beyond.
\subsection*{Limitations and future directions}
Honestly acknowledge limitations and suggest future research directions.
\section*{Conclusions}
Provide a concise conclusion summarizing the main take-home messages of your work.
\section*{Methods}
% Detailed methods allowing reproducibility
% Can be placed after main text in Nature
\subsection*{Experimental design}
Describe overall experimental design, including controls.
\subsection*{Sample preparation}
Detail procedures for sample collection, preparation, and handling.
\subsection*{Data collection}
Describe instrumentation, measurement procedures, and data collection protocols.
\subsection*{Data analysis}
Explain analytical methods, statistical tests, and software used. State sample sizes, replication, and significance thresholds.
\subsection*{Ethical approval}
Include relevant ethical approval statements (human subjects, animal use, biosafety).
\section*{Data availability}
State where data supporting the findings can be accessed (repository, supplementary files, available on request).
\section*{Code availability}
If applicable, provide information on code availability (GitHub, Zenodo, etc.).
\section*{Acknowledgements}
Acknowledge funding sources, technical assistance, and other contributions. List grant numbers.
\section*{Author contributions}
Describe contributions of each author using CRediT taxonomy or similar (conceptualization, methodology, investigation, writing, etc.).
\section*{Competing interests}
Declare any financial or non-financial competing interests. If none, state "The authors declare no competing interests."
% References
\bibliographystyle{naturemag} % Nature bibliography style
\bibliography{references} % Your .bib file
% Alternatively, manually format references:
\begin{thebibliography}{99}
\bibitem{example2023}
Smith, J. D., Jones, M. L. \& Williams, K. R. Groundbreaking discovery in the field. \textit{Nature} \textbf{600}, 123--130 (2023).
\bibitem{author2022}
Author, A. A. \& Coauthor, B. B. Another important paper. \textit{Nat. Methods} \textbf{19}, 456--
460 (2022).
% Add more references as needed
\end{thebibliography}
% Figure Legends (if not included with figures)
\section*{Figure Legends}
\textbf{Figure 1 | Figure title.} Comprehensive figure legend describing all panels, experimental conditions, sample sizes, and statistical analyses.
\textbf{Figure 2 | Second figure title.} Another detailed legend.
% Extended Data Figures (optional - supplementary figures)
\section*{Extended Data}
\textbf{Extended Data Figure 1 | Supplementary data title.} Description of supplementary figure supporting main findings.
\end{document}
% Notes for Authors:
% 1. Nature articles are typically ~3,000 words excluding Methods, References, Figure Legends
% 2. Use superscript numbered citations (1, 2, 3)
% 3. Figures should be high resolution (300+ dpi for photos, 1000 dpi for line art)
% 4. Submit figures as separate files (TIFF, EPS, or PDF)
% 5. Double-space the manuscript for review
% 6. Include line numbers using \linenumbers
% 7. Follow Nature's specific author guidelines for your target journal
% 8. Methods section can be quite detailed and placed after main text
% 9. Check word limits and specific requirements for your Nature family journal

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% NeurIPS Conference Paper Template
% For submission to Neural Information Processing Systems (NeurIPS)
% Last updated: 2024
% Note: Use the official neurips_2024.sty file from the conference website
\documentclass{article}
% Required packages (neurips_2024.sty provides most formatting)
\usepackage{neurips_2024} % Official NeurIPS style file (download from conference site)
% Recommended packages
\usepackage{amsmath}
\usepackage{amssymb}
\usepackage{amsthm}
\usepackage{graphicx}
\usepackage{algorithm}
\usepackage{algorithmic}
\usepackage{hyperref}
\usepackage{url}
\usepackage{booktabs} % For better tables
\usepackage{multirow}
\usepackage{microtype} % Improved typography
% Theorems, lemmas, etc.
\newtheorem{theorem}{Theorem}
\newtheorem{lemma}{Lemma}
\newtheorem{proposition}{Proposition}
\newtheorem{corollary}{Corollary}
\newtheorem{definition}{Definition}
% Title and Authors
\title{Your Paper Title: Concise and Descriptive \\ (Maximum Two Lines)}
% Authors - ANONYMIZED for initial submission
% For initial submission (double-blind review):
\author{
Anonymous Authors \\
Anonymous Institution(s) \\
}
% For camera-ready version (after acceptance):
% \author{
% First Author \\
% Department of Computer Science \\
% University Name \\
% City, State, Postal Code \\
% \texttt{first.author@university.edu} \\
% \And
% Second Author \\
% Company/Institution Name \\
% Address \\
% \texttt{second.author@company.com} \\
% \And
% Third Author \\
% Institution \\
% \texttt{third.author@institution.edu}
% }
\begin{document}
\maketitle
\begin{abstract}
Write a concise abstract (150-250 words) summarizing your contributions. The abstract should clearly state: (1) the problem you address, (2) your approach/method, (3) key results/findings, and (4) significance/implications. Make it accessible to a broad machine learning audience.
\end{abstract}
\section{Introduction}
\label{sec:introduction}
Introduce the problem you're addressing and its significance in machine learning or AI. Motivate why this problem is important and challenging.
\subsection{Background and Motivation}
Provide necessary background for understanding your work. Explain the gap in current methods or knowledge.
\subsection{Contributions}
Clearly state your main contributions as a bulleted list:
\begin{itemize}
\item First contribution: e.g., "We propose a novel architecture for..."
\item Second contribution: e.g., "We provide theoretical analysis showing..."
\item Third contribution: e.g., "We demonstrate state-of-the-art performance on..."
\end{itemize}
\subsection{Paper Organization}
Briefly describe the structure of the remainder of the paper.
\section{Related Work}
\label{sec:related}
Discuss relevant prior work and how your work differs. Organize by themes or approaches rather than chronologically. Be fair and accurate in describing others' work.
Cite key papers \cite{lecun2015deep, vaswani2017attention, devlin2019bert} and explain how your work builds upon or differs from them.
\section{Problem Formulation}
\label{sec:problem}
Formally define the problem you're solving. Include mathematical notation and definitions.
\subsection{Notation}
Define your notation clearly. For example:
\begin{itemize}
\item $\mathcal{X}$: input space
\item $\mathcal{Y}$: output space
\item $f: \mathcal{X} \rightarrow \mathcal{Y}$: function to learn
\item $\mathcal{D} = \{(x_i, y_i)\}_{i=1}^n$: training dataset
\end{itemize}
\subsection{Objective}
State your learning objective formally, e.g.:
\begin{equation}
\min_{\theta} \mathbb{E}_{(x,y) \sim \mathcal{D}} \left[ \mathcal{L}(f_\theta(x), y) \right]
\end{equation}
where $\mathcal{L}$ is the loss function and $\theta$ are model parameters.
\section{Method}
\label{sec:method}
Describe your proposed method in detail. This is the core technical contribution of your paper.
\subsection{Model Architecture}
Describe the architecture of your model with sufficient detail for reproduction. Include figures if helpful.
\begin{figure}[t]
\centering
% \includegraphics[width=0.8\textwidth]{architecture.pdf}
\caption{Model architecture diagram. Describe the key components and data flow. Use colorblind-safe colors.}
\label{fig:architecture}
\end{figure}
\subsection{Training Procedure}
Explain how you train the model, including:
\begin{algorithm}[t]
\caption{Training Algorithm}
\label{alg:training}
\begin{algorithmic}[1]
\STATE \textbf{Input:} Training data $\mathcal{D}$, learning rate $\alpha$
\STATE \textbf{Output:} Trained parameters $\theta$
\STATE Initialize $\theta$ randomly
\FOR{epoch $= 1$ to $T$}
\FOR{batch $(x, y)$ in $\mathcal{D}$}
\STATE Compute loss: $\mathcal{L} = \mathcal{L}(f_\theta(x), y)$
\STATE Update: $\theta \leftarrow \theta - \alpha \nabla_\theta \mathcal{L}$
\ENDFOR
\ENDFOR
\RETURN $\theta$
\end{algorithmic}
\end{algorithm}
\subsection{Key Components}
Describe key technical innovations or components in detail.
\section{Theoretical Analysis}
\label{sec:theory}
If applicable, provide theoretical analysis of your method.
\begin{theorem}
\label{thm:main}
State your main theoretical result here.
\end{theorem}
\begin{proof}
Provide proof or sketch of proof. Full proofs can go in the appendix.
\end{proof}
\section{Experiments}
\label{sec:experiments}
Describe your experimental setup and results.
\subsection{Experimental Setup}
\textbf{Datasets:} Describe datasets used (e.g., ImageNet, CIFAR-10, etc.).
\textbf{Baselines:} List baseline methods for comparison.
\textbf{Implementation Details:} Provide implementation details including hyperparameters, hardware, training time.
\textbf{Evaluation Metrics:} Define metrics used (accuracy, F1, AUC, etc.).
\subsection{Main Results}
Present your main experimental results.
\begin{table}[t]
\centering
\caption{Performance comparison on benchmark datasets. Bold indicates best performance. Results reported as mean ± std over 3 runs.}
\label{tab:main_results}
\begin{tabular}{lcccc}
\toprule
Method & Dataset 1 & Dataset 2 & Dataset 3 & Average \\
\midrule
Baseline 1 & 85.3 ± 0.5 & 72.1 ± 0.8 & 90.2 ± 0.3 & 82.5 \\
Baseline 2 & 87.2 ± 0.4 & 74.5 ± 0.6 & 91.1 ± 0.5 & 84.3 \\
\textbf{Our Method} & \textbf{91.7 ± 0.3} & \textbf{79.8 ± 0.5} & \textbf{94.3 ± 0.2} & \textbf{88.6} \\
\bottomrule
\end{tabular}
\end{table}
\subsection{Ablation Studies}
Conduct ablation studies to understand which components contribute to performance.
\subsection{Analysis}
Provide deeper analysis of results, failure cases, limitations, etc.
\section{Discussion}
\label{sec:discussion}
Discuss your findings, limitations, and broader implications.
\subsection{Limitations}
Honestly acknowledge limitations of your work.
\subsection{Broader Impacts}
Discuss potential positive and negative societal impacts (required by NeurIPS).
\section{Conclusion}
\label{sec:conclusion}
Summarize your main contributions and findings. Suggest future research directions.
% Acknowledgments (add after acceptance, not in submission version)
\section*{Acknowledgments}
Thank collaborators, funding sources (with grant numbers), and compute resources. Not included in double-blind submission.
% References
\bibliographystyle{plainnat} % or other NeurIPS-compatible style
\bibliography{references} % Your .bib file
% Appendix (optional, unlimited pages)
\appendix
\section{Additional Proofs}
\label{app:proofs}
Provide full proofs of theorems here.
\section{Additional Experimental Results}
\label{app:experiments}
Include additional experiments, more ablations, qualitative results, etc.
\section{Hyperparameters}
\label{app:hyperparameters}
List all hyperparameters used in experiments for reproducibility.
\begin{table}[h]
\centering
\caption{Hyperparameters used in all experiments}
\begin{tabular}{ll}
\toprule
Hyperparameter & Value \\
\midrule
Learning rate & 0.001 \\
Batch size & 64 \\
Optimizer & Adam \\
Weight decay & 0.0001 \\
Epochs & 100 \\
\bottomrule
\end{tabular}
\end{table}
\section{Code and Data}
\label{app:code}
Provide links to code repository (anonymized for review, e.g., anonymous GitHub):
\begin{itemize}
\item Code: \url{https://anonymous.4open.science/r/project-XXXX}
\item Data: Available upon request / at [repository]
\end{itemize}
\end{document}
% Notes for Authors:
% 1. Main paper: 8 pages maximum (excluding references and appendix)
% 2. References: unlimited pages
% 3. Appendix: unlimited pages (reviewed at discretion of reviewers)
% 4. Use double-blind anonymization for initial submission
% 5. Include broader impact statement
% 6. Code submission strongly encouraged (anonymous for review)
% 7. Use official neurips_2024.sty file (download from NeurIPS website)
% 8. Font: Times, 10pt (enforced by style file)
% 9. Figures should be colorblind-friendly
% 10. Ensure reproducibility: report seeds, hyperparameters, dataset splits

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% PLOS ONE Article Template
% For submission to PLOS ONE and other PLOS journals
% Last updated: 2024
\documentclass[10pt,letterpaper]{article}
% Packages
\usepackage[top=0.85in,left=2.75in,footskip=0.75in]{geometry}
\usepackage{amsmath,amssymb}
\usepackage{changepage}
\usepackage[utf8]{inputenc}
\usepackage{textcomp,marvosym}
\usepackage{cite}
\usepackage{nameref,hyperref}
\usepackage[right]{lineno}
\usepackage{microtype}
\usepackage{graphicx}
\usepackage[table]{xcolor}
\usepackage{array}
\usepackage{authblk}
% Line numbering
\linenumbers
% Set up authblk for PLOS format
\renewcommand\Authfont{\fontsize{12}{14}\selectfont}
\renewcommand\Affilfont{\fontsize{9}{11}\selectfont}
% Title
\title{Your Article Title Here: Concise and Descriptive}
% Authors and Affiliations
\author[1]{First Author}
\author[1,2]{Second Author}
\author[2,$\dagger$]{Third Author}
\affil[1]{Department of Biology, University Name, City, State, Country}
\affil[2]{Institute of Research, Institution Name, City, Country}
% Corresponding author
\affil[$\dagger$]{Corresponding author. E-mail: [email protected]}
\date{}
\begin{document}
\maketitle
% Abstract
\begin{abstract}
\noindent
Write a structured or unstructured abstract of 250-300 words. The abstract should be accessible to a broad readership and should clearly state: (1) background/rationale, (2) objectives, (3) methods, (4) principal findings with key data, and (5) conclusions and significance. Do not include citations in the abstract.
\end{abstract}
% Introduction
\section*{Introduction}
Provide background and context for your study. The introduction should:
\begin{itemize}
\item Present the rationale for your study
\item Clearly state what is currently known about the topic
\item Identify the knowledge gap your study addresses
\item State your research objectives or hypotheses
\item Explain the significance of the research
\end{itemize}
Review relevant literature \cite{smith2023,jones2022}, setting your work in context.
State your main research question or objective at the end of the introduction.
% Materials and Methods
\section*{Materials and Methods}
Provide sufficient detail to allow reproduction of your work.
\subsection*{Study Design}
Describe the overall study design (e.g., prospective cohort, randomized controlled trial, observational study, etc.).
\subsection*{Participants/Samples}
Describe your study population, sample collection, or experimental subjects:
\begin{itemize}
\item Sample size and how it was determined (power analysis)
\item Inclusion and exclusion criteria
\item Demographic information
\item For animal studies: species, strain, age, sex, housing conditions
\end{itemize}
\subsection*{Procedures}
Detail all experimental procedures, measurements, and interventions. Include:
\begin{itemize}
\item Equipment and reagents (with manufacturer, catalog numbers)
\item Protocols and procedures (step-by-step if novel)
\item Controls used
\item Blinding and randomization (if applicable)
\end{itemize}
\subsection*{Data Collection}
Describe how data were collected, including instruments, assays, and measurements.
\subsection*{Statistical Analysis}
Clearly describe statistical methods used:
\begin{itemize}
\item Software and version (e.g., R 4.3.0, Python 3.9 with scipy 1.9.0)
\item Statistical tests performed (e.g., t-tests, ANOVA, regression)
\item Significance level ($\alpha$, typically 0.05)
\item Corrections for multiple testing
\item Sample size justification
\end{itemize}
\subsection*{Ethical Approval}
Include relevant ethical approval statements:
\begin{itemize}
\item Human subjects: IRB approval, protocol number, consent procedures
\item Animal research: IACUC approval, protocol number, welfare considerations
\item Field studies: Permits and permissions
\end{itemize}
Example: "This study was approved by the Institutional Review Board of University Name (Protocol \#12345). All participants provided written informed consent."
% Results
\section*{Results}
Present your findings in a logical sequence. Refer to figures and tables as you describe results. Do not interpret results in this section (save for Discussion).
\subsection*{First Major Finding}
Describe your first key result. Statistical results should include effect sizes and confidence intervals in addition to p-values.
As shown in Figure~\ref{fig:results1}, we observed a significant increase in [outcome variable] (mean $\pm$ SD: 45.2 $\pm$ 8.3 vs. 32.1 $\pm$ 6.9; t = 7.42, df = 48, p < 0.001).
\begin{figure}[!ht]
\centering
% \includegraphics[width=0.75\textwidth]{figure1.png}
\caption{{\bf Figure 1. Title of first figure.}
Detailed figure legend describing what is shown. Include: (A) Description of panel A. (B) Description of panel B. Sample sizes (n), error bars represent [SD, SEM, 95\% CI], and statistical significance indicated by asterisks (* p < 0.05, ** p < 0.01, *** p < 0.001). Statistical test used should be stated.}
\label{fig:results1}
\end{figure}
\subsection*{Second Major Finding}
Describe your second key result, referencing Table~\ref{tab:results1}.
\begin{table}[!ht]
\centering
\caption{{\bf Table 1. Title of table.}}
\label{tab:results1}
\begin{tabular}{lccc}
\hline
\textbf{Condition} & \textbf{Measurement 1} & \textbf{Measurement 2} & \textbf{p-value} \\
\hline
Control & 25.3 $\pm$ 3.1 & 48.2 $\pm$ 5.4 & -- \\
Treatment A & 32.7 $\pm$ 2.8 & 55.1 $\pm$ 4.9 & 0.003 \\
Treatment B & 41.2 $\pm$ 3.5 & 62.8 $\pm$ 6.2 & < 0.001 \\
\hline
\end{tabular}
\begin{flushleft}
Values shown as mean $\pm$ standard deviation (n = 20 per group). P-values from one-way ANOVA with Tukey's post-hoc test comparing to control.
\end{flushleft}
\end{table}
\subsection*{Additional Results}
Present additional findings as needed.
% Discussion
\section*{Discussion}
Interpret your results and place them in the context of existing literature.
\subsection*{Principal Findings}
Summarize your main findings concisely.
\subsection*{Interpretation}
Interpret your findings and explain their significance. How do they advance understanding of the topic? Compare and contrast with previous studies \cite{brown2021,williams2020}.
\subsection*{Strengths and Limitations}
Discuss both strengths and limitations of your study honestly:
\textbf{Strengths:}
\begin{itemize}
\item Large sample size with adequate statistical power
\item Rigorous methodology with appropriate controls
\item Novel approach or finding
\end{itemize}
\textbf{Limitations:}
\begin{itemize}
\item Cross-sectional design limits causal inference
\item Generalizability may be limited to [specific population]
\item Potential confounding variables not measured
\end{itemize}
\subsection*{Implications}
Discuss the practical or theoretical implications of your findings.
\subsection*{Future Directions}
Suggest directions for future research.
% Conclusions
\section*{Conclusions}
Provide a concise conclusion summarizing the main findings and their significance. Avoid repeating the abstract.
% Acknowledgments
\section*{Acknowledgments}
Acknowledge individuals who contributed but do not meet authorship criteria, technical assistance, and writing assistance. Example: "We thank Dr. Jane Doe for technical assistance with microscopy and Dr. John Smith for helpful discussions."
% References
\section*{References}
% Using BibTeX
\bibliographystyle{plos2015}
\bibliography{references}
% Or manually formatted (Vancouver style, numbered):
\begin{thebibliography}{99}
\bibitem{smith2023}
Smith JD, Johnson ML, Williams KR. Title of article. Journal Abbrev. 2023;45(3):301-318. doi:10.1371/journal.pone.1234567.
\bibitem{jones2022}
Jones AB, Brown CD. Another article title. PLoS ONE. 2022;17(8):e0234567. doi:10.1371/journal.pone.0234567.
\bibitem{brown2021}
Brown EF, Davis GH, Wilson IJ, Taylor JK. Comprehensive study title. Nat Commun. 2021;12:1234. doi:10.1038/s41467-021-12345-6.
\bibitem{williams2020}
Williams LM, Anderson NO. Previous work on topic. Science. 2020;368(6489):456-460. doi:10.1126/science.abc1234.
\end{thebibliography}
% Supporting Information
\section*{Supporting Information}
List all supporting information files (captions provided separately during submission):
\paragraph{S1 Fig.}
\textbf{Title of supplementary figure 1.} Brief description.
\paragraph{S2 Fig.}
\textbf{Title of supplementary figure 2.} Brief description.
\paragraph{S1 Table.}
\textbf{Title of supplementary table 1.} Brief description.
\paragraph{S1 Dataset.}
\textbf{Raw data.} Complete dataset used in analysis (CSV format).
\paragraph{S1 File.}
\textbf{Supplementary methods.} Additional methodological details.
% Author Contributions (CRediT taxonomy recommended)
\section*{Author Contributions}
Use CRediT (Contributor Roles Taxonomy):
\begin{itemize}
\item \textbf{Conceptualization:} FA, SA
\item \textbf{Data curation:} FA
\item \textbf{Formal analysis:} FA, SA
\item \textbf{Funding acquisition:} TA
\item \textbf{Investigation:} FA, SA
\item \textbf{Methodology:} FA, SA, TA
\item \textbf{Project administration:} TA
\item \textbf{Resources:} TA
\item \textbf{Software:} FA
\item \textbf{Supervision:} TA
\item \textbf{Validation:} FA, SA
\item \textbf{Visualization:} FA
\item \textbf{Writing – original draft:} FA
\item \textbf{Writing – review \& editing:} FA, SA, TA
\end{itemize}
(FA = First Author, SA = Second Author, TA = Third Author)
% Data Availability Statement (REQUIRED)
\section*{Data Availability}
Choose one of the following:
\textbf{Option 1 (Public repository):}
All data are available in the [repository name] repository at [URL/DOI].
\textbf{Option 2 (Supporting Information):}
All relevant data are within the paper and its Supporting Information files.
\textbf{Option 3 (Available on request):}
Data cannot be shared publicly because of [reason]. Data are available from the [institution/contact] (contact via [email]) for researchers who meet the criteria for access to confidential data.
\textbf{Option 4 (Third-party):}
Data are available from [third party] (contact: [details]) for researchers who meet criteria for access.
% Funding Statement (REQUIRED)
\section*{Funding}
State all funding sources including grant numbers. If no funding, state "The authors received no specific funding for this work."
Example: "This work was supported by the National Science Foundation (NSF) [grant number 123456 to TA] and the National Institutes of Health (NIH) [grant number R01-234567 to TA]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript."
% Competing Interests (REQUIRED)
\section*{Competing Interests}
Declare any financial or non-financial competing interests. If none, state: "The authors have declared that no competing interests exist."
If competing interests exist, declare them explicitly: "Author TA is a consultant for Company X. This does not alter our adherence to PLOS ONE policies on sharing data and materials."
\end{document}
% Notes for Authors:
% 1. PLOS ONE has no length limit - be concise but thorough
% 2. Use Vancouver style for citations [1], [2], [3]
% 3. Figures: TIFF or EPS format, 300-600 dpi
% 4. All data must be made available (data availability statement required)
% 5. Include line numbers for review
% 6. PLOS ONE focuses on scientific rigor, not novelty or impact
% 7. Reporting guidelines encouraged (CONSORT, STROBE, PRISMA, etc.)
% 8. Ethical approval required for human/animal studies
% 9. All authors must agree to submission
% 10. Submit via PLOS online submission system

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% Academic Research Poster Template using beamerposter
% For conference presentations
% Last updated: 2024
\documentclass[final]{beamer}
% Poster size and scale
% Common sizes: a0, a1, a2, a3, a4
% Custom size: size=custom,width=XX,height=YY
\usepackage[size=a0,scale=1.24,orientation=portrait]{beamerposter}
% Packages
\usepackage[utf8]{inputenc}
\usepackage{amsmath,amsthm,amssymb,latexsym}
\usepackage{graphicx}
\usepackage{booktabs,array}
\usepackage{multirow}
\usepackage{qrcode} % For QR codes
\usepackage{tikz}
\usepackage{lipsum} % For placeholder text (remove in final version)
% Beamer theme
\usetheme{Berlin}
% Other themes: default, AnnArbor, Antibes, Bergen, Berkeley, Berlin, Boadilla, CambridgeUS, Copenhagen, Darmstadt, Dresden, Frankfurt, Goettingen, Hannover, Ilmenau, JuanLesPins, Luebeck, Madrid, Malmoe, Marburg, Montpellier, PaloAlto, Pittsburgh, Rochester, Singapore, Szeged, Warsaw
% Color theme
\usecolortheme{seahorse}
% Other color themes: default, albatross, beaver, beetle, crane, dolphin, dove, fly, lily, orchid, rose, seagull, seahorse, whale, wolverine
% Custom colors (Okabe-Ito colorblind-safe palette)
\definecolor{OIorange}{RGB}{230,159,0}
\definecolor{OIblue}{RGB}{86,180,233}
\definecolor{OIgreen}{RGB}{0,158,115}
\definecolor{OIyellow}{RGB}{240,228,66}
\definecolor{OIdarkblue}{RGB}{0,114,178}
\definecolor{OIvermillion}{RGB}{213,94,0}
\definecolor{OIpurple}{RGB}{204,121,167}
% Set custom colors
\setbeamercolor{block title}{fg=white,bg=OIdarkblue}
\setbeamercolor{block body}{fg=black,bg=white}
\setbeamercolor{block alerted title}{fg=white,bg=OIvermillion}
\setbeamercolor{block alerted body}{fg=black,bg=white}
% Fonts
\setbeamerfont{title}{size=\VERYHuge,series=\bfseries}
\setbeamerfont{author}{size=\Large}
\setbeamerfont{institute}{size=\large}
\setbeamerfont{block title}{size=\large,series=\bfseries}
\setbeamerfont{block body}{size=\normalsize}
% Remove navigation symbols
\setbeamertemplate{navigation symbols}{}
% Title, authors, and affiliations
\title{Your Research Title Here:\\A Concise and Descriptive Title}
\author{First Author\inst{1}, Second Author\inst{1,2}, Third Author\inst{2}}
\institute[shortinst]{
\inst{1} Department of Science, University Name, City, State, Country\\
\inst{2} Institute of Research, Institution Name, City, Country
}
% Footer
\setbeamertemplate{footline}{
\leavevmode%
\hbox{%
\begin{beamercolorbox}[wd=.33\paperwidth,ht=4ex,dp=2ex,left]{author in head/foot}%
\hspace{1em}\usebeamerfont{author in head/foot}Contact: [email protected]
\end{beamercolorbox}%
\begin{beamercolorbox}[wd=.34\paperwidth,ht=4ex,dp=2ex,center]{title in head/foot}%
\usebeamerfont{title in head/foot}Conference Name 2024
\end{beamercolorbox}%
\begin{beamercolorbox}[wd=.33\paperwidth,ht=4ex,dp=2ex,right]{date in head/foot}%
\usebeamerfont{date in head/foot}University Logo\hspace{1em}
\end{beamercolorbox}}%
\vskip0pt%
}
\begin{document}
\begin{frame}[t]
\begin{columns}[t]
% Left Column
\begin{column}{.48\textwidth}
% Introduction/Background
\begin{block}{Introduction}
\begin{itemize}
\item \textbf{Background:} Provide context for your research. What is the broader problem or area of study?
\item \textbf{Gap:} What is currently unknown or inadequately addressed?
\item \textbf{Objective:} Clearly state your research question or hypothesis
\item \textbf{Significance:} Why does this work matter?
\end{itemize}
\vspace{0.5cm}
\textbf{Hypothesis:} State your main hypothesis clearly in one sentence.
\end{block}
\vspace{1cm}
% Methods
\begin{block}{Methods}
\textbf{Study Design:} Brief description of overall approach.
\vspace{0.5cm}
\textbf{Participants/Samples:}
\begin{itemize}
\item Sample size: n = XX
\item Key characteristics
\item Inclusion/exclusion criteria
\end{itemize}
\vspace{0.5cm}
\textbf{Procedures:}
\begin{enumerate}
\item Data collection procedure
\item Experimental intervention or measurement
\item Analysis approach
\end{enumerate}
\vspace{0.5cm}
% Optional: Methods flowchart
\begin{center}
\begin{tikzpicture}[node distance=1.5cm, auto,
box/.style={rectangle, draw, fill=OIblue!20, text width=8cm, text centered, minimum height=1cm}]
\node [box] (step1) {Step 1: Participant Recruitment};
\node [box, below of=step1] (step2) {Step 2: Baseline Assessment};
\node [box, below of=step2] (step3) {Step 3: Intervention};
\node [box, below of=step3] (step4) {Step 4: Follow-up Assessment};
\node [box, below of=step4] (step5) {Step 5: Data Analysis};
\draw [->] (step1) -- (step2);
\draw [->] (step2) -- (step3);
\draw [->] (step3) -- (step4);
\draw [->] (step4) -- (step5);
\end{tikzpicture}
\end{center}
\textbf{Statistical Analysis:}
\begin{itemize}
\item Statistical test used (e.g., t-test, ANOVA, regression)
\item Software: R 4.3.0, Python 3.9
\item Significance level: $\alpha = 0.05$
\end{itemize}
\end{block}
\end{column}
% Right Column
\begin{column}{.48\textwidth}
% Results
\begin{block}{Results}
\textbf{Finding 1: Main Result}
\vspace{0.5cm}
% Figure 1
\begin{figure}
\centering
% \includegraphics[width=0.9\textwidth]{figure1.pdf}
\caption{Figure 1. Main result showing significant effect. Error bars represent standard deviation. * p < 0.05, ** p < 0.01, *** p < 0.001.}
\end{figure}
\vspace{0.5cm}
\textbf{Finding 2: Secondary Analysis}
\vspace{0.5cm}
% Table or second figure
\begin{table}
\centering
\caption{Summary of key results}
\begin{tabular}{lcccc}
\toprule
\textbf{Condition} & \textbf{Mean} & \textbf{SD} & \textbf{n} & \textbf{p-value} \\
\midrule
Control & 25.3 & 3.1 & 30 & -- \\
Treatment A & 32.7 & 2.8 & 30 & 0.003 \\
Treatment B & 41.2 & 3.5 & 30 & < 0.001 \\
\bottomrule
\end{tabular}
\end{table}
\vspace{0.5cm}
\textbf{Finding 3: Additional Observation}
Describe third key finding with reference to supporting data.
\end{block}
\vspace{1cm}
% Discussion/Conclusions
\begin{block}{Discussion \& Conclusions}
\textbf{Main Findings:}
\begin{itemize}
\item Summary of first key result
\item Summary of second key result
\item Summary of third key result
\end{itemize}
\vspace{0.5cm}
\textbf{Interpretation:}
\begin{itemize}
\item How do these findings advance understanding?
\item How do they compare to previous work?
\item What are the mechanisms or explanations?
\end{itemize}
\vspace{0.5cm}
\textbf{Limitations:}
\begin{itemize}
\item Acknowledge key limitations honestly
\item Discuss how they might affect interpretation
\end{itemize}
\vspace{0.5cm}
\textbf{Future Directions:}
\begin{itemize}
\item Next steps for research
\item Potential applications
\end{itemize}
\vspace{0.5cm}
\begin{alertblock}{Key Takeaway}
\textbf{One-sentence summary of most important finding or implication.}
\end{alertblock}
\end{block}
\vspace{1cm}
% References and QR Code
\begin{block}{References \& Contact}
\begin{minipage}[t]{0.65\textwidth}
\small
\textbf{Selected References:}
\begin{enumerate}
\item Smith et al. (2023). \textit{Journal Name}, 45:123-130.
\item Jones \& Brown (2022). \textit{Another Journal}, 12:456-467.
\item Williams et al. (2021). \textit{Third Journal}, 8:789-801.
\end{enumerate}
\vspace{0.3cm}
\textbf{Acknowledgments:} Funding from [Agency] Grant \#12345. Thanks to [collaborators].
\end{minipage}
\hfill
\begin{minipage}[t]{0.3\textwidth}
\begin{center}
\qrcode[height=3cm]{https://yourlab.university.edu/paper}\\
\small Scan for full paper\\and supplementary materials
\end{center}
\end{minipage}
\end{block}
\end{column}
\end{columns}
\end{frame}
\end{document}
% Notes for Poster Design:
% 1. Font sizes (for A0 poster):
% - Title: 80-100pt
% - Authors: 60pt
% - Section headers: 50-60pt
% - Body text: 32-36pt (set by beamerposter scale)
% - Captions: 28-32pt
%
% 2. Use colorblind-safe colors (Okabe-Ito palette provided)
%
% 3. Keep text minimal - use bullets, not paragraphs
%
% 4. Make figures large and clear
%
% 5. Use white space effectively - don't crowd
%
% 6. Test readability from 6 feet (2 meters) away
%
% 7. Include QR code linking to paper, lab website, or supplementary materials
%
% 8. Print at professional print shop (FedEx Office, university print center)
%
% 9. Common poster sizes:
% - A0: 841 × 1189 mm (33.1 × 46.8 in)
% - 36" × 48" (914 × 1219 mm)
% - Check conference requirements!
%
% 10. Compile with: pdflatex beamerposter_academic.tex