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Notícias não são apenas contagens imperfeitas de apreensões. Quando tratadas sistematicamente, podem revelar rotas, atores, métodos, mercados, espécies v…

Notícias não são apenas contagens imperfeitas de apreensões. Quando tratadas sistematicamente, podem revelar rotas, atores, métodos, mercados, espécies visadas e períodos de maior pressão sobre populações silvestres. O desafio é combinar esse contexto com a escala dos registros oficiais.

This dataset is associated with the paper titled "Where Did the Repair First Go Wrong? Localizing the Origins of Silent Failures in Agentic Vulnerability Repair". It consists of a Microsoft Excel file…

This dataset is associated with the paper titled "Where Did the Repair First Go Wrong? Localizing the Origins of Silent Failures in Agentic Vulnerability Repair". It consists of a Microsoft Excel file with eleven worksheets, described in items (1)–(11), and the raw execution traces, provided as five compressed archives and described in item (12). (1) The 'Overview' worksheet lists the worksheets, the main counts of the dataset, the identifiers of tasks, agent frameworks, and base models, and the correspondence between the values used in the data and the terms used in the paper. (2) The 'By framework' worksheet reports the verification outcomes for each of the six agent frameworks, broken down by base model: the number of valid traces, traces that passed L0–L3, traces that failed L0 or L1, and silent failure candidates. (3) The 'By model' worksheet reports the same verification outcomes for each of the six base models, broken down by agent framework. (4) The 'Selected_Tasks_SecurityEval' worksheet lists the 75 sampled SecurityEval tasks, with one row per task. Each task records its identifier, CWE, a short description, its source, and whether each base model and each agent framework produced at least one valid trace. (5) The 'Selected_Tasks_CVEfixes' worksheet lists the 75 sampled CVEfixes tasks, with the same columns. (6) The 'Raw_Verification_Units' worksheet contains the 3,684 valid traces, with one row per trace. Each trace records its task, dataset, agent framework, base model, the result of each verification level (L0–L3), and the resulting outcome. (7) The 'RQ1' worksheet reports the SAGE localization output for the 95 confirmed silent failures (RQ1). It contains summary tables for the localization status, code provenance by origin type, the origin turn relative to the introducing write, and missed detection opportunities, followed by a case-level table with the status, introducing write, code provenance, reconstruction condition, origin turn, origin type, confirmed deficiency, defect location, scores at the origin turn, and missed detection opportunities of each case. (8) The 'RQ2_summary' worksheet reports the consistency of the SAGE output across three scoring runs of the primary judge on the 60-case subset (RQ2): the number of cases localized in all three runs, agreement on the origin type and the origin turn, and Krippendorff's alpha. (9) The 'RQ2_Units_results' worksheet contains the case-level SAGE output of the three scoring runs for the 60-case subset. (10) The 'RQ2_CrossJudge_results' worksheet reports the output of the second judge on the same subset and its agreement with the primary judge (RQ2): the localization status of both judges, agreement on the origin type and the origin turn, score differences at the origin turn, and agreement across all four ratings, followed by the case-level table. (11) The 'RQ3' worksheet reports the agreement between SAGE and the two annotators (R1 and R2) on code provenance and the introducing write (RQ2, RQ3): by confirmed deficiency, reconstruction condition, agent framework, and base model; the overall percent agreement and Cohen's kappa; the confusion matrices; the consensus subset; and the case-level labels of R1, R2, and SAGE. It also reports the consistency of the origin across the three scoring runs and between the two judges for the same subgroups. (12) 'SAGE_execution_traces_batch1.tar.gz' to 'SAGE_execution_traces_batch5.tar.gz' contain the raw execution traces. Extracting all five yields a single folder, 'SAGE_execution_traces', with one directory per execution, organized as batch / base model / agent framework / task identifier. Note: For anonymity, user names, local paths, host names, and folder names were replaced with placeholders (e.g., 'user', 'hpc.example.org', 'folder_1'), and API keys with '<REDACTED_SECRET>'.   

Mean recent growth and gut contents from larval English sole (Parophrys vetulus) collected in the northern California Current in 2018, 2019, 2022, and 2023.  Mean recent growth was calculate…

Mean recent growth and gut contents from larval English sole (Parophrys vetulus) collected in the northern California Current in 2018, 2019, 2022, and 2023.  Mean recent growth was calculated from otolith microstructure-based estimates. The sagittal otoliths of each English sole larvae was removed from the cranium, placed in immersion oil, and read under 400x magnification. Complete increments corresponded to age, and the widths between successive increments to growth. Mean recent growth was calculated as the mean of the last 3 days of growth, and detrended following (Sponaugle 2010).  Gut contents estimates were derived from dissections of gut contents under a microscope. The entire gastrointestinal tract of each larvae was removed, and placed in a drop of glycerin. Prey were teased out with minutien pins, measured to the nearest 0.01mm and identified to the lowest taxonomic level possible. Appendicularian prey were identified by their golden-brown ovoid fecal pellets. Appendicularian ingested prey biomass was then estimated by converting fecal pellet length (mm) to trunk length (mm) following Gadomski and Boehlert (1984), and then trunk length converted to dry weight (mg) following Paffenhöfer (1976). All other prey items were converted to dry weight in mg following published length-mass regressions from temperature regions (Lasker 1966, Reeve and Walter 1976, Hirche 1990, Hay et al. 1991, Gannefors et al. 2005).  larval_EnglishSole_growth_diet_data.csv contains the individual larva level detrended mean recent growth, metadata, and gut contents dry weight and standardized biomass. A full description of all the fields in the dataset is available in larval_EnglishSole_growth_diet_data_FIELD_DESCRIPTIONS.csv.  References: Gadomski D, Boehlert G. Feeding ecology of pelagic larvae of English sole Parophrys vetulus and butter sole Isopsetta isolepis off the Oregon coast. Mar Ecol Prog Ser 1984;20:1–12. https://doi.org/10.3354/meps020001.  Gannefors C, Böer M, Kattner G et al. The Arctic sea butterfly Limacina helicina: lipids and life strategy. Mar Biol 2005;147(1):169–77. https://doi.org/10.1007/s00227-004-1544-y.  Hay SJ, Kiørboe T, Matthews A. Zooplankton biomass and production in the North Sea during the Autumn Circulation experiment, October 1987–March 1988. Cont Shelf Res 1991;11(12):1453–76. https://doi.org/10.1016/0278-4343(91)90021-W. Hirche HJ. Egg production of Calanus finmarchicus at low temperature. Mar Biol 1990;106(1):53–8. https://doi.org/10.1007/BF02114674. Lasker R. Feeding, growth, respiration, and carbon utilization of a Euphausiid crustacean. J Fish Res Board Can 1966;23(9):1291–317. https://doi.org/10.1139/f66-121. Paffenhöfer GA. On the biology of Appendicularia of the southeastern North Sea. Proc 10th Eur Symp Mar Biol 1976;2:437–55. Reeve MR, Walter MA. A large-scale experiment on the growth and predation potential of ctenophore populations. In: Mackie GO (ed.), Coelenterate Ecology and Behavior. Boston, MA: Springer US, 1976, 187–99. https://doi.org/10.1007/978-1-4757-9724-4_20. Sponaugle S. Otolith microstructure reveals ecological and oceanographic processes important to ecosystem-based management. Environ Biol Fishes 2010;89(3–4):221–38. https://doi.org/10.1007/s10641-010-9676-z.   

Optimizing Oxylipin Analysis with Liquid Chromatography Mass Spectrometry through Bio-Inert Systems and Ion Funnel Adjustments

 Ushbu maqolada pedagogika oliy ta’lim muassasalari bitiruvchilarida boshqaruvchilik kompetensiyalarini rivojlantirishda taym-menejment (vaqtni boshqarish) texnologiyalarining o‘rni v…

 Ushbu maqolada pedagogika oliy ta’lim muassasalari bitiruvchilarida boshqaruvchilik kompetensiyalarini rivojlantirishda taym-menejment (vaqtni boshqarish) texnologiyalarining o‘rni va ahamiyati tizimli tahlil qilingan. Bo‘lajak pedagog-menejerlarning vaqt resurslaridan samarali foydalanish ko‘nikmalari ularning kasbiy strategik fikrlashi, prioritizatsiyalash hamda stressga chidamlilik darajasini oshirish kontekstida ochib berilgan.

Reproduction package for the paper: Stars Would Remember a Recent Gravitational Transition: Testing G-step Resolutions of the Hubble Tension with Red Dwarfs

This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/23110671. 1. Introduction and research objectives We find t…

This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/23110671. 1. Introduction and research objectives We find the research question relevant, particularly the attempt to make predictions about students' mental health more interpretable. Comparing logistic regression with a random forest is a sensible starting point: it allows the authors to examine whether a more flexible model offers a useful advantage over a simpler baseline. The intended application is less clear. The introduction discusses continuous monitoring and the limitations of self-report, yet the predictors come from a questionnaire. What would this model add to administering the PHQ-9 directly? Clarifying this point would help readers understand the practical problem the study is intended to solve. The title also promises a comparison of explainable AI approaches, while the analysis compares two classifiers using the same explanation framework. A narrower title would better reflect the work presented. 2. Suitability and reporting of the methods Our main concern is the limited information available about the data and the analytical procedure. The choice of algorithms is reasonable for an exploratory study, but the present description does not allow the results to be reproduced or their reliability to be assessed adequately. For the 94 respondents, we would need to know how participants were recruited, the eligibility criteria, the sample characteristics, and the number of participants in each outcome category. The exact training and test counts, handling of missing or duplicate responses, questionnaire items, and coding scheme should be reported. The meaning and direction of the scales for "academic impact" and "digital dependency" are especially important. A single 80:20 split is a fragile basis for comparing models in this sample. Repeated stratified cross-validation, if the class counts allow it, would give a better indication of how dependent the findings are on the partition. Both models should use the same splits. Any tuning and preprocessing must be confined to the appropriate training data, with model selection separated from final evaluation. The manuscript also needs the model settings, software versions, random seeds, and an account of ethical approval or exemption, consent, and data protection. We raise the ethical reporting issue because these details are needed to assess the study; their absence from the text does not establish that safeguards were absent in practice. 3. Support for the conclusions The reported accuracy needs to be put into perspective. If the test set contained 19 participants, the results correspond to 18 correct classifications for logistic regression and 19 for the random forest. The difference would therefore rest on one case. Under that assumption, the exact two-sided 95% binomial confidence interval for 19 correct classifications out of 19 is approximately 82.4–100%. This is an illustrative calculation based on an inferred test-set size, not an interval reported in the manuscript. It also leaves out uncertainty associated with model selection and recruitment. We therefore find the claim of superior random-forest performance insufficiently supported. A perfect result on this test set neither establishes overfitting nor demonstrates a reliable advantage in capturing nonlinear relationships. The clinical interpretation needs similar restraint. The outcome is a category derived from a PHQ-9 threshold. The analysis does not establish a clinical diagnosis or predict future onset of depression. Its conclusions should remain tied to the questionnaire outcome actually measured. 4. Presentation of results and visualisations The SHAP plots are a useful inclusion. However, one apparent inconsistency needs to be resolved: in Figure 1, higher encoded values of "digital dependency" have negative SHAP values, whereas the text associates greater dependency with a positive contribution to predicted risk. Reverse coding could explain this. Without the coding scheme and the identity of the explained class, the reader cannot tell whether the problem lies in the wording or in the interpretation. Table 1 is also incomplete relative to the stated evaluation plan. Precision, recall, and F1-score should be reported alongside confusion matrices, class counts, and uncertainty estimates. Sensitivity, specificity, and a majority-class baseline would make the practical meaning of the results clearer. If the model is intended to provide individual probabilities, calibration should be examined as well. For the SHAP analysis, the explainer, reference data, explained observations, and output scale need to be specified. The authors should also explain how Table 2's feature-importance scores were obtained and how they differ from the SHAP summaries. 5. Interpretation of findings and future research We appreciate the acknowledgement of the small sample and possible overfitting, but the discussion needs to engage more closely with what the models actually show. The differing feature rankings are particularly relevant. Are those rankings stable across resampling, and could correlations between predictors help explain the disagreement? The dominance of "academic impact" in the random forest deserves closer examination. Does this variable capture perceived effects of technology use on studying, or academic difficulties more generally? Comparing the full model with a model using only this variable, and with a model excluding it, would help establish what the remaining predictors contribute. The authors should confirm that no predictor was derived from PHQ-9 scores or outcome labels. Overlap in the content of questionnaire items, including sleep-related questions, should also be discussed. Such overlap is not automatically data leakage, but it matters when judging whether the model supplies independent information. SHAP explanations concern the model's predictions. They do not establish that the highlighted behaviours cause depressive symptoms or that modifying those behaviours would improve mental health. In our view, stronger internal evaluation should come first, followed by independent external and prospective studies before practical screening or monitoring claims are made. 6. Contribution to academic knowledge The work has potential as a pilot study. Including a simple baseline and examining explanations for both models are useful choices. At present, however, the contribution is mainly exploratory: applying established classifiers with SHAP does not itself amount to a methodological advance. We would encourage the authors to state more clearly what this particular sample, set of measurements, or comparison adds to the literature. A reproducible analysis would make that contribution easier to judge. The TRIPOD+AI checklist could help identify missing reporting details, although completing it would not resolve the limitations of the design. 7. Language and editorial improvements The manuscript is readable. The more pressing editorial task is to make the terminology and claims precise. "Depression," "depression risk," and a PHQ-9-defined symptom category should not be used interchangeably. The predictors should consistently be described as self-reported measures, and statements about model superiority should reflect the uncertainty of the comparison. The references need checking against the claims they support. In the introduction, reference [3], the foundational SHAP paper, is used to support an empirical association between digital behaviour and depressive symptoms. An appropriate empirical source is needed there. 8. Recommendation to other readers We would recommend reading this preprint as an exploratory contribution to research on student mental health and explainable machine learning. Its main interest lies in the question it raises and the analyses that could follow from it. Readers should be cautious about treating the reported accuracy as evidence of clinical performance. The study does not yet provide a sufficient basis for institutional screening or decisions about individual students. 9. Readiness for wider consideration Our assessment is that the manuscript needs substantial revision before its broader conclusions can be supported. The immediate priorities are a fuller account of the measurements and analysis, a more reliable assessment of model performance, complete reporting of the evaluation metrics, and a resolution of the SHAP interpretation issue. These are substantive concerns, but they also give the authors a clear route for improving the paper. With appropriate reanalysis and more restrained conclusions, the study could become a useful and transparent pilot investigation. Competing interests The authors declare that they have no competing interests. Use of Artificial Intelligence (AI) The authors declare that they did not use generative AI to come up with new ideas for their review.

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This dataset contains the training, validation, and test data used for transcription factor (TF)–DNA binding prediction in the revised TFBindFormer framework. It integrates genomic DNA sequence …

This dataset contains the training, validation, and test data used for transcription factor (TF)–DNA binding prediction in the revised TFBindFormer framework. It integrates genomic DNA sequence data, transcription factor protein sequence and structural information, processed TF representations, and metadata to support reproducible model training, evaluation, and zero-shot testing on unseen transcription factors. DNA Sequence Data (dna_data/) The dna_data directory contains one-hot-encoded genomic DNA sequence data and corresponding TF-binding labels, organized into three mutually exclusive dataset splits: train/ – training data train_data.npy train_labels.npy val/ – validation data val_data.npy val_labels.npy test/ – held-out test data test_data.npy test_labels.npy The DNA inputs correspond to genomic sequence windows used by TFBindFormer for TF-binding prediction. The label arrays contain the corresponding TF-binding annotations for each DNA sequence. The genomic data are partitioned using chromosome-based splits. The training set contains chromosomes other than chr4, chr7, chr8, chr9, and chrY. Chromosomes chr4 and chr7 are used for validation, while chr8 and chr9 are reserved for testing. Each input sequence represents a 1,000-bp genomic window centered on a 200-bp genomic bin. A TF-binding label is considered positive when at least 50% of the corresponding 200-bp bin overlaps a ChIP-seq narrowPeak region. Metadata (metadata/) The metadata directory contains metadata describing the TF–cell-type prediction tasks and the cell-type identifiers used by the model. seen_tf_metadata.tsv – metadata for TF–cell-type tasks associated with transcription factors represented during model training. unseen_tf_metadata.tsv – metadata for TF–cell-type tasks associated with transcription factors excluded from training and reserved for zero-shot evaluation. seen_cell_type_ids.npy – encoded cell-type identifiers corresponding to the seen-TF tasks. unseen_cell_type_ids.npy – encoded cell-type identifiers corresponding to the unseen-TF tasks. The metadata files provide the mapping between transcription factors, cell types or experimental conditions, and the corresponding prediction tasks. The revised TFBindFormer dataset contains: 100 seen TFs corresponding to 422 TF–cell-type tasks. 8 unseen TFs corresponding to 35 TF–cell-type tasks. The unseen TFs are completely excluded from model training and are used to evaluate zero-shot generalization to previously unseen transcription factors. Transcription Factor Data (tf_data/) The tf_data directory contains the transcription factor protein sequence, structural, and processed representation data used by the TFBindFormer protein encoder. Amino-Acid Sequences (aa_sequences/) The aa_sequences directory contains amino-acid FASTA sequences for the transcription factors(seen+unseen) included in the dataset. These protein sequences provide the sequence-based information used to generate pretrained TF representations. Protein Structures (pdb_structures/) The pdb_structures directory contains protein structure files in PDB format for the transcription factors(seen+unseen). These structures are used to derive Foldseek 3Di structural representations. Foldseek 3Di Structural Sequences (foldseek_3Di_ss.fasta) foldseek_3Di_ss.fasta contains precomputed Foldseek 3Di structural token sequences derived from the TF protein structures. The 3Di representation converts local three-dimensional protein environments into discrete structural tokens, providing structure-aware information that complements the amino-acid sequence representation. ProstT5 Embeddings (prostt5_embeddings/) The prostt5_embeddings directory contains precomputed TF protein embeddings(seen+unseen) generated using ProstT5-based sequence and structural representations. These embeddings are stored before conversion to the fixed-length protein representation used directly by TFBindFormer. Fixed-Length TF Representations (fixed_length_200/) TF proteins vary in sequence length. Before being provided to the TFBindFormer model, their protein representations are standardized to a fixed length of 200 tokens. The fixed_length_200 directory contains the final model-ready TF representations and is divided into seen and unseen TF sets: Seen TFs (fixed_length_200/seen_tf/) fixed_tf_embs.pt fixed_tf_masks.pt tf_names_in_label_order.tsv fixed_tf_embs.pt contains the fixed-length protein embeddings for TFs represented during model training. fixed_tf_masks.pt contains the corresponding masks used to distinguish valid protein-token positions from padded positions. tf_names_in_label_order.tsv records the TF ordering corresponding to the protein tensor and TF-binding label ordering. This file ensures that each protein representation is correctly matched to the corresponding prediction task. Unseen TFs (fixed_length_200/unseen_tf/) fixed_tf_embs.pt fixed_tf_masks.pt tf_names_in_label_order.tsv These files contain the corresponding fixed-length representations, masks, and TF ordering information for the transcription factors held out from training. The unseen-TF representations are used only for zero-shot evaluation. TF Protein Processing Workflow The TF protein data follow the general processing workflow: Amino-acid sequences ↓ ProstT5 ↓ sequence-based representations Protein structures ↓ Foldseek ↓ 3Di structural sequences ↓ structure-aware representations Sequence + structural representations ↓ fixed-length processing ↓ 200-token TF representations ↓ seen TF / unseen TF sets Seen and unseen TFs are processed together during the initial protein preprocessing stages. They are separated only at the final fixed-length representation stage used by the model. Intended Use This dataset is intended for: Training and evaluating TF–DNA binding prediction models Reproducing the experiments reported in the revised TFBindFormer study Evaluating zero-shot generalization to unseen transcription factors Studying protein-conditioned DNA binding specificity Investigating sequence- and structure-aware TF representations Developing multimodal models integrating genomic DNA sequence, TF protein information, and cell-type context The dataset is provided for research and academic use and is intended to support reproducible model training and evaluation in the TFBindFormer framework.