dataesr/scientific-paragraphs-categorization huggingface dataset:
2% and an average R²= 0. 208 were achieved. For comparison, the average accuracy and R² for the exact same predictions with a morphospace obtained using disconnectomes computed by the BCBToolkit software reached 83. 7% and R² = 0. 191, respectively. Hence, computing the disconnectomes using the deep-learning model and obtaining a corresponding morphospace for predicting neuropsychological scores significantly increases the accuracy of the resulting scores by at least 1. 5%; (t(85) = -1. 663, p =. 009) (Figure  2b ). To perform an out-of-sample validation, N=20 stroke survivors from dataset 3 were excluded from the prediction training phase and used as independent model validation. We outputted the deep-disconnectomes from the segmented binary stroke lesion (as reported in 4. 2) and predicted their corresponding neuropsychological scores (as reported in 4.
The efficacy of an algorithm heavily relies on the quality and representativeness of the training data it receives  (Taylor & Nitschke, 2018). Consequently, ensuring a diverse and comprehensive dataset becomes crucial for maximizing the model's ability to generalize effectively. Within the realm of medical diagnosis, this challenge is particularly pronounced, exacerbated by biases and stringent privacy policies that restrict data availability as well as typical bias in the distribution of clinical data  (Gianfrancesco et al. , 2018). precise outcomes achieved by AI models, there remains a prevailing mistrust among individuals regarding their reliability in healthcare diagnoses. While statistics demonstrate the better performance of AI models and the number of FDA-approved AI algorithms is steadily growing, concerns stem from the inherent opacity of these "black box" models, which oftentimes do not provide sufficient explanations for their results  (Bzdok, Krzywinski & Altman, 2017).
Datasets for model training and testing Our deep-learning framework was trained on a dataset of N=1333 synthetic lesions and their corresponding disconnectomes (Dataset 1) and subsequently tested for real stroke lesions (Dataset 2). The synthetic stroke lesions were initially computed by Thiebaut de Schotten,  Foulon, and Nachev (2020)  to assess the spatial distribution of stroke impact on cognition. For that, they produced more than one million synthetic stroke lesion masks to sample out 1333 lesions matching in hemispheric lateralization and voxel size with natural lesions but randomly distributed in the brain. The probability of disconnectivity for each voxel in the brain was calculated with the BCBToolkit  (Foulon et al. , 2018) , and the obtained disconnectomes were used as ground truth to train our model.
Using synthetic lesions had two main benefits: Firstly, we circumvented the eventual problem of insufficient training data since we could produce any desired number of synthetic lesion-disconnectome pairs. Secondly, the diversity of the training data increased, theoretically also increasing the generalizability of the trained model. During the training of our 3D U-Net, we used a split 80%/20% of synthetic data (Dataset 1) for, respectively, training and validation. Subsequently, we used the N=1333 lesion masks segmented from patients recruited by the University College London Hospitals (Dataset 2) to test our trained deep-learning model. This patient cohort was recruited as part of a study approved by the West London and GTAC Research Ethics Committee. MRI scans (1. 5 T and 3 T) of patients with acute ischemic stroke were acquired across several scanners between 1-2 weeks following the stroke  (Xu et al.
Deep-disconnectomes and conventional disconnectomes comparison To assess the level of similarities between deep-disconnectome and conventional disconnectomes, we first produced a Pearson R² revealing the percentage of the variance of disconnection reproduced by the 3D U-Net (i. e. , the goodness of fit). To explore any systematic differences in terms of disconnected fibers, we ran a voxelwise paired t-test for dataset 2, comparing the conventional disconnectomes and their corresponding deep-disconnectomes. To circumvent any differences in the ranges of the intensity values (i. e. , deep-disconnectomes typically do not produce 0 or 1 values), both disconnectomes were binarized using a threshold of 0. 0001 (Figure  1b ). Subsequently, the embedding properties were compared across the two disconnectome methods.
For the comparison of the coordinates in the disconnectome morphospace, we computed the average Euclidean distances for 139 coordinate pairs (corresponding to the 119 data points of dataset 3, and 20 data points of dataset 4) across deep-disconnectomes and conventional disconnectomes. This comparison allowed us to explore the relative distances between data points in the embedding space. Subsequently, we visualized the results in a Bland Altman plot showing the differences in morphospace distances between conventional and deep-disconnectomes. In this visualization, a negative difference indicates a bigger distance between the data points, while a positive distance indicates a smaller distance between the data points. By comparing the distribution of the data points, we gain insights into the differences between the two types of disconnectomes that can be relevant to explain differences in the prediction of neuropsychological scores.
Prediction of neuropsychological scores one year after stroke Achieving clinical endpoints is the main goal of our investigation of stroke disconnections. In order to test and compare the potential in clinical score prediction, we compared the deepdisconnectomes prediction and the conventional disconnectomes performances. For this purpose, a third dataset was used (Dataset 3, N=119 stroke survivors). Neuropsychological score predictions were performed on datasets 3 and 4, recruited at the School of Medicine at Washington University in St. Louis  (Corbetta et al. , 2015). N=86 scores were collected, spanning from motor and language abilities to visuospatial, memory, and sickness reports. Details of the acquired scores can be found in  Talozzi et al. (2023). The peculiarity of these datasets is to have neuroimaging within 2 weeks from the stroke event, whereas neuropsychological scores were evaluated on average one year later hence allowing to test the long-term predictive power of acute neuroimaging.
This information could be key for informing behavioral therapies. To test the predictive quality of the deep-disconnectomes obtained from the 3D U-Net model, we replicated the analysis and validation procedures of  Talozzi et al. 's (2023)  work. The authors were able to accurately predict neuropsychological scores 1 year after stroke by creating a latent disconnectome morphospace using the UMAP algorithm for dimensionality reduction  (McInnes, Healy & Melville, 2018). First, 1333 disconnectomes of dataset 2 (corresponding to the 'real' lesions used in the presented testing procedure of our 3D U-Net) were used to compute the 2D UMAP morphospace based on the standard computation of disconnectomes through the BCBToolkit software. Following this step, disconnectome maps from dataset 3 (n = 119) were imported into the UMAP morphospace, and their localization was statistically correlated to the individual neuropsychological scores on the N=86 behavioral and cognitive scores.
To handle multiple clusters of voxels that survived the threshold for medium effect size (R>0. 2), a principal component analysis (PCA) was run to capture the greatest source of patient coordinate variability. Based on the first three PCA components, a multiple regression model (Python scikit-learn package) was evaluated to predict each neuropsychological score. For more detailed information on the methods, see  Talozzi et al. (2023). Note that the datasets used in the present paper are the same as in  Talozzi et al. (2023) , the only difference being the method of how the disconnectomes were obtained: in Talozzi's work using BCBToolkit software, here.
Spikes were then threshed using a Wintersteiger LD350 Laboratory Thresher (Wintersetiger, Ried, Austria) and grains were weighed after drying for 48 h at 70 • C, and 200 grains were counted and weighed to determine thousand grain weight (TGW). Each plot was machine-harvested in an area ranging from 3. 2 to 5. 6 m 2 after borders were removed to avoid any border effect, and values were adjusted to the moisture percentage measured in each plot. These data were then used to calculate harvest index (HI, proportion of above-ground DM in the grain), above-ground DM (AGDM PM , GY/HI), grain number per m 2 (GN, GY/TGW), and fruiting efficiency (FE A+7 , ratio of grain number to spike DM at anthesis + 7 days, or FE chaff , ratio of grain number to chaff DM at maturity).
Statistical analysis In the glasshouse experiments, genotype means in individual years were estimated from the Analysis of Variance (ANOVA) using Genstat 19th edition  (VSN International, 2021) , where the genotypes were considered as fixed effects and replicates as random effects. Genotype means across years were estimated from the cross-year ANOVA, where the genotypes were considered as fixed effects and replicates and years as random effects using Genstat 19th edition. In the field experiment, genotype means and standard deviations were calculated in R v 4. 3. 1 using the package Psych  (Revelle, 2023). Statistical significance is considered throughout at the 5% level (P < 0. 05). We have also commented on trends for effects which were between the 5-10% significance level. A covariate for days from sowing to anthesis was included in the ANOVA when it had a significant effect, to take account of any environmental change with development.
For the two genotypes where different plant components (flag leaf, peduncle, basal spikelets, central spikelets and apical spikelets) were measured for plant hormone levels, a separate ANOVA was carried out where the genotypes were considered as fixed effects and plant component position and replicates as random effects. Broad-sense heritability (H 2 ) was calculated as described in Eq. 1, where σ 2 g and σ 2 e are the genotypic and environmental variance, respectively, and σ 2 ge is the genotype × environment interaction. The number of environments and replicates are represented by e and r, respectively. H 2 = σ 2 g σ 2 g + σ 2 ge e + σ 2 e re (1) Genstat 19th edition was used for calculating linear regressions and the R v 4. 3. 1 package Hmisc  (Harell, 2022)  for calculating Pearson's correlation coefficients using the genotype means from the ANOVA.
Study design and participant countries We conducted an ecological multi-national study worldwide. We registered the presence of alcohol-related PHPs enforced in 2010 in each country (Table  S1 ). Data were also obtained from the WHO Global Information System of Alcohol and Health (GISAH). Three independent investigators collected all the data (MC, DV, and AM), and disagreements were resolved by a fourth reviewer (MPM). Policies were categorized according to the 2018 WHO classification. 11  We excluded 24 countries that did not have complete information on PHPs (Table  S2 ). All the information was aggregated at a country level, and we relied on publicly available data. Therefore, this study was considered exempt from ethics review by the Pontificia Universidad Católica de Chile institutional review board (ID: 230304001).
The socio-demographic information was collected from the World Bank Open Data source (http://databank. worldbank. org). Data on alcohol consumption measures and their harmful health consequences in each country were collected from the WHO GISAH and the Global Burden of Disease databases (between 2010 and 2019). 12  We included the alcohol per capita consumption (APC) in liters of pure alcohol per year and the prevalence of AUD (including alcohol dependence and harmful use of alcohol), according to the Diagnostic and Statistical Manual of Mental Disorders, fifth edition. Thus, we gathered information on outcomes related to the disease burden due to alcohol, and all outcomes were registered annually from 2010 to 2019 (Tables  S3-12 ). 12  This study adheres to the GATHER (Guidelines for Accurate and Transparent Health Estimates Reporting) statement.
Statistical analysis We used multiple correspondence analysis (MCA) to estimate an alcohol preparedness index (API) for each country based on the scores obtained from the 5-item instrument. 15  MCA identifies composite dimensions in datasets consisting of categorical variables (i. e. , the strength of alcohol-related PHPs in each item). This approach combines all the information observed in the categorical values for the 5-item instrument into a single factor that acts as a weighted summary of each possible different level indicator combination for each country. The full description of MCA is available in the supplementary methods. The weighted summary has an assigned weight for each individual level of indicator, which combines to give the full score of the country (Table  S13 ). We standardized the obtained indicator in order to be able to compare the performance among countries easily.
Subsequently, we introduced an interaction term between the API and time to assess the relationship over time. As a sensitivity analysis, we assessed the association between API and AUD incidence, ALD prevalence, and DALYs due to ALD. In models assessing quartiles, we added a cubic function to models, since the linear trend breaks up at the third and fourth quartiles. We also assessed a falsification endpoint (agerelated hearing loss) to ensure that associations were unrelated to other socioeconomic variables. The supplementary methods contain an in-depth description of the models used in this study. The analyses were performed with STATA software version 17 (StataCorp, College Station, Texas), and a p value <0. 05 was considered statistically significant.
Countries with higher incomes could have more effectively implemented PHPs for longer periods while having better health indicators. Also, data from policies were collected by the WHO using a questionnaire and relied on accurate reporting by the country teams. Another relevant aspect involves the impact of cultural attitudes to alcohol, which can vary notably among countries (i. e. , the acceptance of social drinking or religious restrictions, among others), and it is difficult to capture in these models. Since alcohol-attributable conditions are associated with a large degree of stigma, several consequences due to alcohol misuse could be coded as non-alcoholic and, therefore, underestimated. Along with that, the use of codes could vary within countries and regions during the observed period, affecting the estimation of outcomes.
Although some countries obtained a maximum score of 100 because they had better preparedness than others, they still had room for improvement in policies and implementation. Our models do not include data on specific restrictions to alcohol access in some places and regions, nor the pattern of alcohol consumption (i. e. , heavy drinkers and binge drinkers). Also, some policies (such as MUP or health warning labels on alcoholic beverages) and the tax range for alcoholic beverages per country were not included in the instrument nor the models because the WHO did not include them in the GISAH repository in 2010. Important variability in terms of establishment and implementation of PHPs could have been observed in some countries (i. e. , the United States or the United Kingdom), and other confounders, such as population density, ethnicity, medical comorbidities, and educational level were not included in our models.
However, we included the human development index and healthcare access and quality index in these models to partially overcome that risk. Although ecological fallacy is one of the main risks of an ecological study, a prior study conducted in the United States, a heterogeneous country in many aspects, demonstrated an association between alcohol-related policies and lower ALD mortality. 21  Finally, our study did not evaluate data during the COVID-19 pandemic, since the pandemic may alter the interpretation of public policies, strategies, and collaborative efforts to reduce alcohol-related health consequences. In conclusion, we developed the API to assess the establishment and strength of alcohol-related PHPs worldwide. Lower mortality due to ALD, cancer, alcohol-attributable HCC, and cardiovascular diseases was observed in countries with a higher API, with substantial regional differences.
Financial support This article was partially supported by the  Chilean government  through the  Fondo Nacional de Desarrollo Científico y Tecnológico  ( FONDECYT 1200227  to JPA and  1191145  to MA). Luis Antonio Díaz  is recipient of a grant from the  Colegio Médico de Chile (COLMED) Regional Santiago. Ramón Bataller  is recipient of  NIAAA U01AA021908  and  U01AA020821.
A C T Benzodiazepines increase plasma brain-derived neurotrophic factor (BDNF) level which, in turn, may improve survival in colorectal cancer (CRC) patients. This study aimed to evaluate the associations between benzodiazepine and benzodiazepine-related drugs (BZRD) use and outcomes of patients operated for CRC. This is a retrospective cohort study including patients operated for CRC at Limoges' University Hospital between 2010 and 2019. Data were collected from two sources: medical records of patients in the digestive, general and endocrine surgery department at Limoges University Hospital and from the Haute-Vienne general cancer registry. Patients were divided into benzodiazepine users and non-users. Outcomes were overall survival (OS) and recurrence-free survival (RFS). Among 504 patients who underwent surgery for CRC, 125 (24.
Endpoints The overall survival (OS) was the primary endpoint and was compared between benzodiazepine/BZRD users and non-users. The OS was defined as the time from CRC surgery to death from any cause. Data on the date of death were collected from the general cancer registry of the Haute-Vienne and from the death certificate in patient's computerized medical record at Limoges University hospital. When the information was not available in either source, it was verified by consulting the files of deceased person available on the website of the National Institute of Statistics and Economic Studies (INSEE)  [23]. The secondary endpoint was recurrence-free survival (RFS), defined as the time between CRC surgery and the first documented local or distant (metastasis) CRC recurrence or death. Recurrence of CRC was defined as recurrence at the site of anastomosis, pelvic cavity structure, systemic lymph nodes, liver, lungs, peritoneum, bones, and brain  [24].
Data collection Data collection was based on patients' electronic medical records from two sources: -Medical records of CRC patients from the digestive, general and endocrine surgery department of Limoges University Hospital using the hospital systems. -Patients records from the Haute-Vienne general cancer registry in France. The sources used for data collection in the cancer registry were as follows: • Medical Information Departments of public and private facilities, • The medical services of the health insurance of the Haute-Vienne, • The public and private anatomical pathology, hematology, molecular biology and cytogenetics laboratories and cytogenetic laboratories of the Haute-Vienne, • Summaries of Multidisciplinary Consultation Meetings, organized by the Limousin Regional Cancer Network, Limousin Hematology Oncology Network and the Haute Vienne Cancer Coordination Center, • General physicians and specialists, and other cancer registries.
Ethical considerations This study has received the validation and approval of the Ethics Committee of Limoges University Hospital (N0 479-2021-135). In Limoges, the cancer registry is authorized by the French Data Protection Authority (CNIL) to collect data for epidemiological surveillance purposes. For the present study, which corresponds to a re-use of data, data collection and analysis were performed in accordance with reference methodology MR004. In addition, patients at Limoges University Hospital were informed of the potential use of their data for research purposes when they were admitted.
Descriptive analyses: patients' characteristics After exclusion of 12 patients who did not meet the eligibility criteria for this study, 504 patients undergoing CRC surgery were included in this study. 125 (24. 8%) patients were treated with benzodiazepine/ BZRD drugs of which 86 (68. 8%) were treated with benzodiazepine alone, 23 (18. 4%) were treated with BZRD alone, and 16 (12. 8%) were treated with benzodiazepine in association with BZRD (Fig. 1 ). The benzodiazepine types most frequently used were alprazolam (22. 4%), zolpidem (19. 2%) and bromazepam (18. 4%) while the least frequently used were clobazam (1. 6%), diazepam (4%) and clotiazepam (4%). Tables  1  and 2  present the demographic and clinical data as well as CRC characteristics of the patients. The median age of patients was 73.
The effect of benzodiazepines/BZRDs on CRC survival also varied according to the Anatomical Therapeutic Chemical (ATC) classification (anxiolytics vs. hypnotics) of the molecule. We found that only benzodiazepine molecules classified as anxiolytics improved survival in CRC patients. Hypnotics such as zolpidem and zopiclone have a shorter halflife, which may not allow them to exert a sufficient effect on BDNF levels and improve survival in CRC patients  [15]. This was further validated by our subgroup analysis, whereby only CRC patients suffering from a psychiatric disorder and using benzodiazepines had significantly improved survival. This association was not observed among BZRD (zolpidem, zopiclone) users. The strengths of this study include the integration of multiple sources for data collection using the general cancer registry at Limoges University Hospital and medical records of the digestive, general and endocrine surgery department at Limoges University Hospital, which reduces information bias by enhancing the accuracy of the data collected.
An accurate estimate of true exposure to this class of drugs is difficult to obtain, given the considerable and heterogeneous patient non-compliance, which was not controlled for in this study. In line with other pharmacoepidemiological studies, we recognize the possibility of non-adherence to treatment. Due to the retrospective design of the study, which relies solely on patients' medical records as data sources, a possible information bias may be present. Consequently, certain variables such as alcohol consumption and diagnosis of psychiatric disorders may have been underestimated. This cohort included patients living in France who may be ethnically diverse and have genetic variability influencing CRC survival, response to cancer treatment, as well as drug metabolism and individual responses to benzodiazepines.
Background and aims: In colorectal cancer (CRC), HER2 targeting is a promising treatment and immune infiltrate is an important area of research and strategy. Data regarding HER2 status and immune infiltrate are lacking. The aim of this study was to compare the immune infiltrate between HER2 amplified and non-amplified categories in proficient MisMatchRepair (pMMR)/microsatellite stable (MSS) CRC. Methods: HER2 immunohistochemistry (IHC) and fluorescence in situ hybridization were performed in a retrospective series of 654 CRC. Lymphocyte infiltrate was analysed by anti-CD3, CD8 and CD4 IHC and evaluated digitally using QuPath software. Results: Among the 654 CRC, we first observed a decreased CD3 + and CD8 + infiltrate between HER2 amplified (all IHC 3 + except one 2 + ) and non-amplified HER2 2 + IHC CRC ( p = 0.