dataesr/scientific-paragraphs-categorization huggingface dataset:
These data suggest that MM cell adaptation to replicative stress through Wee1 and Chk1 upregulation may decrease the activation of the cell-intrinsic innate immune response and the response to immunotherapy. Of interest, a recent study reported that Chk1 inhibitor treatment promotes proinflammatory cytokine expression, innate cell immune response and tumor regression in melanoma models  (50). Yet, the toxicity of Chk1 and Wee1 inhibitors' association need to be carefully addressed using in vivo models to assess the benefits and risks of this combination in MM. The author(s) declare financial support was received for the research, authorship, and/or publication of this article. The JM research group was supported by grants from INCA PLBIO19.
Bioinformatics requirements 1. Basic knowledge of Bash language is needed to run the pipeline. 2. Dependencies are: FastQC  (19) , NGmerge (20), Bowtie 2 (21), Samtools  (22)  Bedtools  (23) , MACS2  (24) , MSPC, MEME Suite  (25) , Python and R scripts available on the GitHub page provided (https://github. com/Bioinfo-LPCV-RDF/DAPseq_TFanalysis) and optionally SRA Toolkit if DAP-seq sequence reads are downloaded from the NCBI.
9. Use paired-end sequencing of 150 cycles for 10-20 million reads per lane. 10 )-[Figure  10  near here] Processing of DAP-seq sequence reads to identify bound regions and determine TFs or TF complex binding sites can be decomposed in five main steps (Figure  10 ). A bioinformatics pipeline encapsulated in a Conda environment is provided at https://github. com/Bioinfo-LPCV-RDF/DAPseq_TFanalysis. The pipeline provides an example DAP-seq sequencing dataset for BIM2, an Arabidopsis thaliana TF, with sequencing data from an ampDAP-seq experiment with paired end reads with two replicates and an input ampDAP DNA library as control. An example of a successful and unsuccessful seq-DAP-seq experiment are shown in Figure  11.
21. If possible positive and negative qPCR controls should be performed prior to deep sequencing (if known binding sites are available from ChIP data, for example). If there is no difference in amplification of the positive vs. negative control, this is an indication that the seq-DAP-seq protocol has failed. We have not had a case where the NGS gave useable data if the positive control qPCR verification failed. We recommend optimizing the binding buffer for seq-DAP-seq and repeating the protocol. We have found that amplicon sizes of 150 bp and 5-10 positive and 5-10 control regions is sufficient for library verification. 1 µl of library diluted to 50-100 µl is sufficient for qPCR verification. Enrichment of 4-fold or higher (CT cycle differences of at least 2 for positive and control qPCR) is a good indication that the experiment worked (Figure  9 ).
Data Collection Acoustic data were collected from dusk until between 4:00 am and 4:30 am (beyond which we ran the risk of equipment theft) at the same six locations (Fig. 1 ) simultaneously over 48 non-consecutive nights between 27 August and 9 December 2019. Six recorders operating simultaneously over the 48 nights collected a total of 2576. 5 h of data. Recordings were made using Audiomoth 1. 0. 0 full spectrum recorders  (Hill et al. 2018 ). The recorders were put into plastic zip-locked bags and placed in shallow (4 cm) baskets attached to bamboo poles, with their microphones pointing upward. The sampling rate was set to the detectors' maximum of 384 kHz, the gain at medium, and the recorders were programmed to record for tenmin intervals, separated by a one second break. The six selected locations were east of the village.
Acoustic data analysis The raw acoustic data were processed using custom written code in Python version 2. 7  (Rossum et al. 1995)  to isolate bat calls and extract key measurements of the call, as follows. (1) Frequency of maximum energy (FMAXE): the frequency containing the most energy in the call  (Wordley et al. 2014). (  2 ) Minimum and maximum frequencies: calculated as the lowest and highest frequencies that contained 5% of FMAXE. (3) Bandwidth: the difference between the minimum and maximum frequency. (4) Call length: the time between when the call first crosses 5% FMAXE, and when it last crosses 30% FMAXE. (  5 ) Average amplitude: the average amplitude of the call. Once extracted, calls were classified into sonotypes using a MatLab Classification learner (The Math-Works Inc. 2019).
Training data for each sonotype were created by extracting the parameters of manually identified calls. These training data were used by the classification learner in a linear discriminant analysis to classify all the extracted calls into sonotypes. Following this, the classified calls were run through a final round of processing in R version 3. 63 (R Core Team 2020), where hard limits on FMAXE and bandwidth were used to reclassify calls that had been misclassified into a similar sonotype. The calls were then filtered to exclude undesirable sections of the audio, such as during heavy rain, instrumental failure resulting in static, or bats recorded prior to the recorders being in position. Due to the absence of a complete acoustic library for Indian insectivorous bats, calls were classified to the sonotype level instead of species.
2019), using a template created by manually identifying calls and extracting relevant parameters. Every recording was divided into five second intervals and a bat pass was defined as any interval with more than two calls of the same sonotype  (Millon et al. 2015). We undertook a manual check of 10% of the ~ 20,000 extracted passes, selected by a random number generator, to verify the accuracy of the classification. The classifier accurately identified over 90% of all passes. Code for the classifier can be found at https:// github. com/ Iqbal Bhalla/ BatCl assifi er. git.
Statistical analysis All statistical analysis was conducted in R. We used generalised linear mixed model (GLMM) to analyse the relationship between bat activity and its drivers  (McCullagh and Nedler 2019). A total of 267 sampling units were used in each statistical model (6 recorders over 48 nights, minus nights where the recorders failed) Prior to building the model, outliers in the response variable 'bat activity' were identified and deleted using the 'check outliers' function from the 'Performance' R package  (Lüdecke et al. 2020). All the variables were then standardised to zero mean and unit variance before analysis so that their effect sizes were comparable  (Schielzeth 2010). The following variables were considered relevant drivers of bat activity. (1) Temperature: minimum nightly temperature was included to test for the effect of temperature on bat activity.
For those sonotypes which showed a quadratic relationship between activity and minimum nightly temperature, minimum nightly temperature squared was included as a variable in their supermodel. (2) Moonlight: the percentage of the moon visible on each night, was included to test for the effect of moonlight intensity on bat activity. Cloud cover would impact moonlight intensity, but as we were unable to quantify this at a relevant resolution, we included percentage of the moon visible as a measure that strongly correlated with brightness. Temperature and moonlight data for Puthimari village were obtained, with permission, from the World Weather Online database (WorldWeatherOnline. com). (3) Sampling effort: the recorders failed unexpectedly throughout the field season, leaving gaps in the dataset ranging from one to ten hours.
: (0123456789) (Spearman's rank correlation: r = -0. 90). Therefore, both day of the season and its quadratic-'day of the season squared'-as well as all relevant interactions were removed from further analysis. The remaining variables and interactions were included in a global model implemented with the 'glmmTMB' package  (Brooks et al. 2017). The model was initially tested with a Poisson error distribution, since the response variable took the form of count data, but this was changed to a quasi-Poisson when the model tested positive for overdispersion  (Bolker et al. 2009 ). An information theoretic approach was then implemented to capture the best set of variables explaining bat activity by fitting all possible models and calculating Akaike's information criterion corrected for sample size (AIC c ) for each model; the best model being the one with the smallest AIC c  (Burnham and Anderson 2002).
This was done using the 'dredge' function from the 'MuMIn' package  (Bartoń 2022) , which compared all possible combinations of variables in the global model. Note that during the selection procedure, an interaction was included in a model only if its composite variables were also included, even if the variables independently were not statistically significant. All models with a ΔAIC c value < 2 (the difference between each model's AIC c and the lowest AIC c ) were considered as receiving equal statistical support  (Burnham and Anderson 2002). When the 'dredge' function returned more than one model having an AICc within two units of the best model, the model with fewest variables was chosen  (Symonds and Moussalli 2011). The top ten models of each sonotype can be found in Supplementary Material Table  S.
I-S. VII. One 'best model' was identified for each sonotype, as well as one model for all the sonotypes together. All best models were tested for overdispersion and zero inflation using the 'testZeroInflation' and 'test-Dispersion' functions from the DHARMa package  (Hartig 2022 ). The residuals were analysed using the 'simulateResiduals' function, also from the DHARMa package. Residuals were checked for obvious patterns (heteroscedasticity, linear/quadratic trends), as well as deviance in the Kolgomorov-Smirnov test. A pseudo R-squared value based on the likelihood ratio test was calculated using the function 'r. squaredLR' from the 'MuMIn' package. Finally, for a given model, the absolute values of the standardised regression coefficients were rescaled to sum to one to derive a measure of relative importance for each predictor.
Temporal distribution of nightly activity Bat activity was also analysed for patterns over the night. Each sonotype was analysed separately, with calls being aggregated into ten-min intervals starting from dusk. First, a clustering algorithm based on a parameterised finite Gaussian mixture model was fitted to the data to identify clusters within the nightly activity data using the package and function 'Mclust' in R  (Scrucca et al. 2016). Second, the mean and sigma of each cluster recovered from the previous analyses were then used in multimodal analyses to fit multiple normal distributions that best describe the nightly activity pattern of each sonotype. This was implemented with the 'mix' function from the 'MixDist' package  (Macdonald and Pitcher 1979; Macdonald and Du 2018). To standardise for changing dusk times, data from each night were clipped to begin 20 min post-dusk and end 620 min post-dusk.
The strength of this effect suggests that the bats recorded at our sites were responding to changes in rice pest abundance by preferentially hunting over rice fields when insect activity was high. While insect activity was a major driver of bat activity, it was not the only one. Although temporal heterothermy in Indian bats has not been studied, data from other tropical and subtropical bats suggest that insectivorous bats are likely to be heterothermic and capable of entering torpor to reduce energy Fig. 4  The temporal distribution of nightly activity of six sonotypes. Data were collected at six locations over 48 nights of sampling in a rice field in Puthimari, Assam. Every night was divided into ten-min intervals starting at dusk and ending ten hours post dusk (blue bars). The number of passes of each sonotype in each ten-min post-dusk interval was summed up over all 48 nights of sampling.
Acknowledgements We thank  Raju Sharma ,  Nirmala Devi ,  Seemanta Jyoti Baishya  and  Manas Jyoti Talukdar  for unwavering support in the field, the  Bhagabati  family for logistical and emotional succour,  Uma Ramakrishnan  and  Pilot Dovih  for bat advice and training, and  Rita Banerjee  for local coordination. We thank  Adrià López-Baucells ,  Thomas Cherico Wanger ,  Rohit Chakravarty , and  Danielle Linton  for advice early in the project. IB thanks his funders, the  Rufford Trust  ( 27620-1 ) and the  Rhodes Trust  for supporting and funding this project, as well as  Jesus College Oxford  and the  School of Geography and the Environment, University of Oxford , for providing financial support. OR was funded through a  Natural Environment Research Council   Independent Research Fellowship  ( NE/ M018660/3 ).
| GWAS samples Independent GWAS data were obtained in the form of summary statistics ( p values and effect sizes) (Table  1 ). For OUD, data from the Million Veteran Program (MVP), Yale-Penn and the Study of Addiction: Genetics and Environment (SAGE)  21  were used. In the MVP cohort, case and control status was defined using electronic health record data. There were 8529 affected European American (EA) individuals and 71 200 opioid-exposed EA controls, 4032 affected African American (AA) individuals and 26 029 opioid-exposed AA controls. The Yale-Penn and SAGE datasets included 2015 EA cases and 963 controls and 1180 AA cases and 847 controls from a previous GWAS. 32 To ensure compatibility of the linkage disequilibrium (LD) pattern, our main analysis on OUD focused on individuals of EA ancestry and included a meta-analysis of a total number of 10 544 cases and 72 163 opioid-exposed controls.
| Sign-consistency test We used the sign-consistency test  38  on independent datasets (Table  1 ) to validate the shared loci, which produced one of two concordant variants (50%; binomial p = 0. 75) for SCZ, two of two (100%; binomial p = 0. 25) concordant variants for BD, six of seven (86%; binomial p = 0. 06) concordant variants for MD and four of eight (50%; binomial p = 0. 64) concordant variants for OUD. We further assessed the validity of the lead SNPs in loci identified at conjFDR < 0. 05 by testing for presence in independent datasets at p < 0. 05. One lead variant (rs4702 at FURIN) was present in the SCZ GWAS of East Asian ancestry. 30 No lead variants were present in the BD GWAS of European ancestry (https://r5. finngen. fi/). For MD, three of seven lead SNPs (rs28524171 at RN7SKP157, rs6929812 within MHC and rs1800498 at DRD2) were validated in the independent MD GWAS of European ancestry.
31 Finally, no lead variants in the shared loci for OUD and psychiatric disorders had a p value of < 0. 05 in the independent AA GWAS dataset on OUD. 21 A total of four of the OUD risk loci identified by conjFDR were identified in recent larger GWAS on OUD, specifically the PPP6C, 48 NCAM1, 45 DRD2 45 and FURIN  45, 46, 48  loci; however there is considerable overlap in the datasets in these studies and ours, and therefore, further validation will depend on sufficiently powered new samples.
| DISCUSSION In the current study, we used the cond/conjFDR analytical method and leveraged cross-trait enrichment between OUD, and SCZ, BD and MD to improve the statistical power for discovery of shared genetic loci to shed light on their genetic relationships. The weaker cross-trait enrichment observed for psychiatric disorders conditional on OUD is expected given the smaller sample size and reduced statistical power of the OUD GWAS dataset compared to the other psychiatric disorders. We identified polygenic overlap beyond genetic correlation. Specifically, we discovered 14 novel OUD loci and 7 novel loci jointly associated with OUD and psychiatric disorders (Figure  2 ; Tables  2  and 3 ). There was a concordant allelic direction of effects in all the shared loci, indicating that these genetic variants increase the risk of both OUD and the respective psychiatric disorders.
g. , for substance use disorders) or a general vulnerability to psychiatric symptoms in general  54  is still an open question. We also included PGS and LDSC analyses in the study, to complement the conjFDR analysis. Corroborating prior studies,  21  we find significant positive genetic correlations between OUD and SCZ, BD and MD, indicating shared genetic effects. The PGS analysis, however, in which we tested the association between OUD PGS and case control status of SCZ, BD and MD in the TOP sample,  55  was insufficiently powered. As larger OUD datasets become available, analytical tools such as MiXeR  56  will be able to quantify the genetic overlap in thousands of SNPs, beyond genetic correlation and most likely with mixed effect directions. This is not feasible today, due to insufficient statistical power.
Our findings warrant experimental validation to elucidate the pathophysiological mechanisms of the reward circuitry underlying comorbid SUDs and SMDs. The current study has some limitations. We cannot exclude sporadic, non-systematic sample overlap; however, we calculated LDSC genetic covariance intercepts (OUD vs. SCZ = 0. 01 [SE = 0. 01], p = 0. 05; OUD vs. BD = 0. 01 [SE = 0. 01], p = 0. 11; OUD vs. MD = 0. 01 [SE = 0. 01], p = 0. 22), and neither are significant, suggesting no significant effect of overlap between the samples. We included datasets from a diverse set of populations, but larger datasets for other cohorts than Europeans are needed to uncover more of the genetic underpinnings of OUD and SMDs. To conclude, our study suggests overlapping genetic architecture between OUD and SCZ, BD and MD, indicating similarities in the genetic architectures of these debilitating disorders.
ACKNOWLEDGEMENTS We thank the  Psychiatric Genomics Consortium , the  Million Veteran Program , FinnGen biobank and 23andMe for access to data and the many people who provided DNA samples. The work was supported by the  Research Council of Norway  ( 324499 ,  324252 ,  273291  and  223273 ),  South-Eastern Norway Regional Health Authority  ( 2019-108 ),  European Union 's  Horizon 2020 Research and Innovation Programme (Horizon 2020 Framework Programme ) Grant No. 847776  (CoMorMent), and  KG Jebsen Stiftelsen (Stiftelsen Kristian Gerhard Jebsen ,  SKGJ-MED-021 ). A. M. D. was supported by the  Foundation for the National Institutes of Health , Grant  U24DA041123. This work was performed on the TSD (Tjeneste for Sensitive Data) facilities, owned by the  University of Oslo , operated and developed by the  TSD service group at the University of Oslo ,  IT-Department (USIT).
DATA AVAILABILITY STATEMENT Cond/conjFDR analyses were run with MATLAB 2018b, and the code is available online (at https://github. com/precimed/pleiofdr). Version 1. 3. 7 of the FUMA web tool was used (https://fuma. ctglab. nl/ updates). We used the 25 February 2022 release of the OpenTargets web tool (https://genetics. opentargets. org/). Scripts to standardize summary statistics and to produce Manhattan plots were run with python version 3. 7. 4 (https://github. com/precimed/python_convert). We used an LDSC command line tool  22, 37  for genetic correlations and PRSice-2  36  for PRS calculations; see the supplementary information. For the FDR to p-value conversion, please see the original publication. 39.
org and the geometry of the layer 5 pyramidal cell was downloaded from the LFPY github repository (https://github. com/LFPy). In all cases, the simulations were performed with a passive cell model with the following properties: a membrane capacitance of 1 µF/cm 2 , an axial resistance of 150 Ω cm, and a membrane resistance   [23, 22, 24]. The figure also shows the different depths at which the cortical layer divisions were set corresponding to the layer size of the cat visual cortex. b) Generation of a cortical patch. Neurons were spatially spread within a 1 mm diameter column with their somas always inside their corresponding layers. The same layer size displayed in a) was used resulting in a GM thickness of 1640 µm. c) Schematic of the axisymmetric geometry used to simulate a pair of SEEG electrode contacts using FEM.
The aforementioned approach was implemented using the finite elements method (FEM). The electric potential distribution was calculated by solving the Laplace equation with the geometry described above, ⃗ ∇ • (σ ⃗ ∇Φ) = 0 (4) with boundary conditions of a floating potential and a unit ±1 A current (positive on the top electrode and negative on the bottom one) at the contact surfaces. The problem described was solved using COMSOL Multiphysics 5. 3a (Stockholm, Sweden) for different depths of the SEEG electrode, z el. The solutions obtained with this FEM model were used to interpolate the voltage at a grid of points (ρ, z) inside the GM. In the radial direction these points were generated every 10 µm beginning at a distance of 100 µm from the surface of the contacts (to account for the fact that scar tissue, and, therefore, no neuronal activity, is expected near the contacts  [27] ) and extended up to ACCEPTED MANUSCRIPT / CLEAN COPY 1 cm away from them.
Methods Data source and preparation HPAI H5 genomes with at least HA gene and sample information, including sample collection date, location, and host species, were obtained from the Global Initiative on Sharing All Influenza Data (GISAID) (https://platform. epicov. org/) and NCBI Influenza Virus Resource (https://www. ncbi. nlm. nih. gov/genomes/FLU/) databases on 11 July 2022. After removing duplicate isolates, laboratory-derived and mixed-subtype isolates, sequences with less than 85% gene coverage  (69) , and sequences with incomplete collection dates, H5 clades based on the WHO gs/Gd H5N1 nomenclature system (7) were determined using LABEL v0. 6. 3 (70). Clade 2. 3. 4. 4 was further assigned into subclades 2. 3. 4. 4a-h based on phylogenetic relationships to WHO H5 candidate vaccine viruses with known clade assignment estimated using a maximum likelihood phylogenetic tree generated using the Jukes-Cantor nucleotide substitution model in FastTree v2.
1. 1. Avian hosts were classified as domestic and wild birds using strain names, associated metadata, and original publications (https://github. com/vjlab/episodic-h5). Location coordinates were used to analyze diffusion in continuous space and were accurate, at least to the province/state level for China, Russia, and the United States. For discrete analysis and visualization, locations were classified into countries and regions according to the countryregion list in NextStrain  (71). To mitigate sampling bias for phylogeographic analyses, curated datasets were subsampled by either epidemiological information (including country, host, and sampling date) or phylogenetic relationship. HPAI H5 HA datasets were subsampled randomly with at most two sequences for clade 2. The R package 'ggstream' v.
Phylogenetic analyses Gene sequences were aligned using MAFFT v7. 490  (72)  and trimmed using trimAL v1. 4  (73)  with a 50% gap threshold followed by manual trimming to the open-reading frame. Maximum-likelihood (ML) trees were generated using IQ-TREE v2. 1. 4 (74)  with the best-fit nucleotide substitution model. The ML phylogenies were used to check each dataset for molecular clock outliers (sequences that have disproportionately too much or too little rootto-tip divergence for its sampling time) using TempEst v1. 5. 3  (75). The time-scaled ML trees in this study were performed using TreeTime  (54). HA phylogeny showing sampling location (country/region), host and clade details of all sequences are available as NextStrain  (71)  builds at https://nextstrain. org/community/vjlab/episodic-h5/H5.