bluuebunny/crossref_metadata_2025_split huggingface dataset:
Operationalizing cultural adaptation to climate change: contemporary examples from United States agriculture
Authors: Waring Timothy M. , Niles Meredith T. , Kling Matthew M. , Miller Stephanie N. , Hébert-Dufresne Laurent , Sabzian Hossein , Gotelli Nicholas , McGill Brian J. 
Published: September 2023

It has been proposed that climate adaptation research can benefit from an evolutionary approach. But related empirical research is lacking. We advance the evolutionary study of climate adaptation with two case studies from contemporary United States agriculture. First, we define ‘cultural adaptation to climate change’ as a mechanistic process of population-level cultural change. We argue this definition enables rigorous comparisons, yields testable hypotheses from mathematical theory and distinguishes adaptive change, non-adaptive change and desirable policy outcomes. Next, we develop an operational approach to identify ‘cultural adaptation to climate change’ based on established empirical criteria. We apply this approach to data on crop choices and the use of cover crops between 2008 and 2021 from the United States. We find evidence that crop choices are adapting to local trends in two separate climate variables in some regions of the USA. But evidence suggests that cover cropping may be adapting more to the economic environment than climatic conditions. Further research is needed to characterize the process of cultural adaptation, particularly the routes and mechanisms of cultural transmission. Furthermore, climate adaptation policy could benefit from research on factors that differentiate regions exhibiting adaptive trends in crop choice from those that do not.
This article is part of the theme issue ‘Climate change adaptation needs a science of culture’.
A comprehensive analysis of Transcribed Ultra Conserved Regions uncovers important regulatory functions of novel non-coding transcripts in gliomas
Authors: Gibert Myron K , Zhang Ying , Saha Shekhar , Marcinkiewicz Pawel , Dube Collin , Hudson Kadie , Sun Yunan , Bednarek Sylwia , Chagari Bilhan , Sarkar Aditya , Roig-Laboy Christian , Neace Natalie , Saoud Karim , Setiady Initha , Hanif Farina , Schiff David , Kumar Pankaj , Kefas Benjamin , Hafner Markus , Abounader Roger 
Published: September 2023

ABSTRACTTranscribed Ultra-Conserved Regions (TUCRs) represent a severely understudied class of putative non-coding RNAs (ncRNAs) that are 100% conserved across multiple species. We performed the first-ever analysis of TUCRs in glioblastoma (GBM) and low-grade gliomas (LGG). We leveraged large human datasets to identify the genomic locations, chromatin accessibility, transcription, differential expression, correlation with survival, and predicted functions of all 481 TUCRs, and identified TUCRs that are relevant to glioma biology. Of these, we investigated the expression, function, and mechanism of action of the most highly upregulated intergenic TUCR, uc.110, identifying it as a new tumor enhancer. Uc.110 was highly overexpressed in GBM and LGG, where it promoted malignancy and tumor growth. Uc.110 activated the WNT pathway by upregulating the expression of membrane frizzled-related protein (MFRP), by sponging the tumor suppressor microRNA miR-544. This pioneering study shows important roles for TUCRs in gliomas and provides an extensive database and novel methods for future TUCR research.
Machine-Learning Identifies a Strategy for Differentiation Therapy in Solid Tumors
Authors: Sinha Saptarshi , Alcantara Joshua , Perry Kevin , Castillo Vanessa , Espinoza Celia R. , Taheri Sahar , Vidales Eleadah , Tindle Courtney , Adel Adel , Amirfakhri Siamak , Sawires Joseph R. , Yang Jerry , Bouvet Michael , Sahoo Debashis , Ghosh Pradipta 
Published: September 2023

AbstractBackgroundAlthough differentiation therapy can ‘cure’ some hematologic malignancies, its curative potential remains unrealized in solid tumors. This is because conventional computational approaches succumb to the thunderous noise of inter-/intratumoral heterogeneity. Using colorectal cancers (CRCs) as an example, here we outline a machine learning(ML)-based approach to track, differentiate, and selectively target cancer stem cells (CSCs).MethodsA transcriptomic network was built and validated using healthy colon and CRC tissues in diverse gene expression datasets (∼5,000 human and >300 mouse samples). Therapeutic targets and perturbation strategies were prioritized using ML, with the goal of reinstating the expression of a transcriptional identifier of the differentiated colonocyte, CDX2, whose loss in poorly differentiated (CSC-enriched) CRCs doubles the risk of relapse/death. The top candidate target was then engaged with a clinical-grade drug and tested on 3 models: CRC linesin vitro, xenografts in mice, and in a prospective cohort of healthy (n = 3) and CRC (n = 23) patient-derived organoids (PDOs).ResultsThe drug shifts the network predictably, inducesCDX2and crypt differentiation, and shows cytotoxicity in all 3 models, with a high degree of selectivity towards allCDX2-negative cell lines, xenotransplants, and PDOs. The potential for effective pairing of therapeutic efficacy (IC50) and biomarker (CDX2-low state) is confirmed in PDOs using multivariate analyses. A 50-gene signature of therapeutic response is derived and tested on 9 independent cohorts (∼1700 CRCs), revealing the impact ofCDX2-reinstatement therapy could translate into a ∼50% reduction in the risk of mortality/recurrence.ConclusionsFindings not only validate the precision of the ML approach in targeting CSCs, and objectively assess its impact on clinical outcome, but also exemplify the use of ML in yielding clinical directive information for enhancing personalized medicine.
Motivational Interviewing Interventions for Harmful Substance Use Amongst Adults in Low- and Middle-Income Countries (LMICs): A Systematic Review
Authors: Adams Amy S. , Williams Taryn , Stein Dan J. , Sibeko Goodman , Shoptaw Steven , Rollnick Stephen 
Published: September 2023

ABSTRACTBackgroundHarmful substance use is a major challenge globally with far reaching detrimental effects on social, economic and health systems. This burden weighs more heavily on underdeveloped countries as they do not have the infrastructure needed to manage the consequences of substance use. In high-income countries, several evidence-based brief intervention models are used for HSU including Motivational Interviewing (MI). However, fewer studies have been conducted in low-resource settings.AimIn this review, we systematically reviewed studies utilising motivational interviewing as a component of interventions with adults presenting with harmful substance use in LMICs.MethodsAdhering to Cochrane and PRISMA guidelines, we systematically searched for studies published between 2010 and 2022. The search was conducted in PubMed, EBSCOhost, Web of Science, the Cochrane Library,clinicaltrials.govand the GSK Clinical Study Register. A narrative analysis was done, and The Joanna Briggs Institute (JBI) Critical Appraisal Tools were used to assess the quality of the studies included.ResultsThe search yielded 518 publications. Eleven studies from five different countries met inclusion criteria. These consisted mainly of RCTs with some employing qualitative or mixed methods approaches. The focus of the interventions varied, with most targeting alcohol use, while a few addressed opioids, and other drugs. The interventions used different strategies, including MI blended with other interventions (such as CBT, BA, and imaginal desensitization) or using MI-informed approaches. Overall, MI was shown to improve substance use outcomes in seven of the 11 studies with significant reduction in substance use based on outcomes measured.ConclusionsThis review highlights that, interventions incorporating MI demonstrated positive effects in improving substance use outcomes in the majority of the studies reviewed. Further research with larger sample sizes and more rigorous study designs is necessary to strengthen the evidence base and address potential sources of bias.
Accelerated neurodevelopment of reward anticipation processing in adolescent girls with depression
Authors: Baranger David AA , Lindenmuth Morgan , Yoon Leehyun , Guyer Amanda E. , Keenan Kate , Hipwell Alison E , Forbes Erika E 
Published: September 2023

AbstractObjectiveTo test the hypothesis that depression is associated with differential neurodevelopment of reward circuitry in adolescence.MethodsAdolescent girls (N=183, 58 with MDD in early or late adolescence) underwent MRI scans from ages 16-20 (1-4 scans/participant, 477 scans total) and completed a card-guessing fMRI task with monetary rewards. Mixed-effect models tested the effect of age and the moderating effect of MDD on whole-brain regional activation during reward anticipation.ResultsEighty of 414 regions showed age effects (pFDR<0.05), consisting primarily of increasing activation with increasing age. Most significant regions were in dorsal attention, salience, and somatomotor networks, and also included the bilateral putamen, pallidum, and right nucleus accumbens. MDD moderated age effects in 40 regions (pFDR<0.05), including the right putamen, medial orbitofrontal cortex, and amygdala, and regions in control and dorsal attention networks. MDD x linear and quadratic age effects were negative, suggesting that MDD was associated with accelerated neurodevelopment.ConclusionsTheories of reward processing’s contribution to adolescent risk for depression focus primarily on core reward regions, yet a host of regions beyond these continue to develop during late adolescence. Findings demonstrate differing regional patterns of age-related changes in relation to MDD in girls, suggesting that depression involves disruption of a wide range of regions during reward anticipation processing across adolescence. Childhood and adolescent MDD is associated with accelerated neurodevelopment of attention and cognitive control regions during reward anticipation processing, which may have consequences both for cognitive function and the emergence of reward-system-specific disruptions.
Discrimination, Smoking, and Cardiovascular Disease Risk: A Moderated Mediation Analysis with the Multi-Ethnic Study of Atherosclerosis
Authors: Cook Stephanie H. , Wood Erica P. , Stein James H. , McClelland Robyn L. 
Published: September 2023

AbstractBackgroundRace is a social determinant of cardiovascular (CVD) risk and the American Heart Association has called for increased research to understand how this social determinants of health (SDoH) impacts CVD risk. Carotid intima-media thickness (cIMT) and carotid plaque are reliable indicators of cardiovascular risk. Further, research highlights that disparities exist in these indicators of cardiovascular risk such that racial/ethnic minorities generally exhibit greater characteristics of cardiovascular risk with respect to these indices (e.g., greater cIMT and higher carotid plaque scores) than white individuals due, in part, to exacerbated stress experienced from racial discrimination. At present, the mechanisms driving these racial/ethnic disparities is poorly understood.Methods and ResultsData came from the baseline examination of the Multi-Ethnic Study of Atherosclerosis (MESA). 6,814 participants aged 45-84 free of clinical CVD completed assessments on health behavior, perceived discrimination, CVD-related risk factors, and sociodemographics. Four biological sex-stratified moderated mediation models were used to examine the associations between discrimination, cigarette smoking, and mean cIMT and plaque, modeled separately. We hypothesized that cigarette use would mediate the association between discrimination and carotid artery disease features and that these associations would be moderated by race/ethnicity. While results did not fully support our study hypothesis, racial/ethnic differences were observed across biological sex. Significant indirect effects of discrimination on plaque scores were observed among Hispanic females such that increased discrimination was associated with more cigarette use which, in turn, was associated with higher plaque (b=.04, CI=.01, .08). Similar findings were observed among Hispanic males in relation to mean cIMT (b=.003, CI=.00, .01) and among white (b=.04, CI=.01, .08) and Hispanic males (b=.03, CI=.004, .07) and plaque. No other racial/ethnic differences were observed.ConclusionsResults suggest that cardiovascular risk disparities should be examined within frameworks that consider the importance of the intersection of multiple identities (e.g., race and gender). To better address the American Heart Association’s call to examine social determinants of cardiovascular health, researchers must incorporate the complexity of the intersection of various social positions in future work.
VirEvol platform : accurate prediction and visualization of SARS-CoV-2 evolutionary trajectory based on protein language model, structural information and immunological recognition mechanism
Authors: Zeng Xincheng , Zhang Linghao , Ning Zhenyu , Qiu Yusong , Dong Ruobing , Li Xiangyi , Lv Lijun , Xu Hanlin , Wang Yanjing , Ma Buyong 
Published: September 2023

AbstractPredicting the mutation direction of SARS-CoV-2 using exploratory computational methods presents a challenging, yet prospective, research avenue. However, existing research methods often ignore the effects of protein structure and multi-source viral information on mutation prediction, making it difficult to accurately predict the evolutionary trend of the SARS-CoV-2 S protein receptor-binding domain (RBD). To overcome this limitation, we proposed an interpretable language model combining structural, sequence and immune information. The dual utility of this model lies in its ability to predict SARS-CoV-2’s affinity for the ACE2 receptor, and to assess its potential for immune evasion. Additionally, it explores the mutation trend of SARS-CoV-2 via a genetic algorithm-directed evolution. The model exhibits high accuracy in both regards and has displayed promising early warning capabilities, effectively identifying 13 out of 14 high-risk strains, marking a success rate of 93%.”. This study provides a novel method for discerning the molecular evolutionary pattern, as well as predicting the evolutionary trend of SARS-CoV-2 which is of great significance for vaccine design and drug development of new coronaviruses. We further developed VirEvol, a unique platform designed to visualize the evolutionary trajectories of novel SARS-CoV-2 strains, thereby facilitating real-time predictive analysis for researchers. The methodologies adopted in this work may inspire new strategies and offer technical support for addressing challenges posed by other highly mutable viruses.
Assessment of behavioral flexibility in mice with conditional deletion of metabotropic glutamate receptor 2 fromEmx1-lineage neurons
Authors: Chang Doris S. , Littlepage-Saunders Mydirah , Hochstein Michael J. , Matteo Christopher J. , Amelga Kidus , Homanics Gregg E. , Johnson Kari A. 
Published: September 2023

AbstractConvergent lines of evidence from animal models suggest that disrupted metabotropic glutamate receptor 2 (mGlu2) function promotes high levels of drug consumption for a variety of psychoactive drugs including alcohol, opioids, and psychostimulants. In both rodents and humans, impaired behavioral flexibility prior to first drug use correlates with high levels of drug consumption later in life. Thus, we posited that deletion of mGlu2from brain regions that contribute to behavioral flexibility, including cortical regions, could predispose animals to high levels of drug consumption by impairing behavioral flexibility. To evaluate the role of mGlu2in behavioral flexibility, we generated mice with a floxedGrm2allele (Grm2f/f) and selectively disrupted mGlu2expression in neurons of theEmx1lineage (primarily telencephalonic projection neurons) by crossing these mice with an Emx1-IRES-Cre driver line. Behavioral flexibility, including sensitivity to change in either outcome value or action-outcome contingency, was evaluated in adult male and female mice trained to press a lever for a food reinforcer. Contrary to our hypothesis, mGlu2deletion did not facilitate habitual responding assessed by devaluation, contingency degradation, or omission tests. MaleGrm2f/f;Emx1-IRES-Cre+/-mice showed modest impairment in reversal learning compared with littermate controls. Finally, we saw a sex-specific effect of mGlu2deletion on response vigor in male mice trained on a random ratio reinforcement schedule. However, we did not find evidence of a general reduction in motivation in a progressive ratio test. These findings suggest that loss of mGlu2from cortical circuitry is unlikely to create a predisposition to inflexible behavior that facilitates excessive drug consumption.
cdsBERT - Extending Protein Language Models with Codon Awareness
Authors: Hallee Logan , Rafailidis Nikolaos , Gleghorn Jason P. 
Published: September 2023

AbstractRecent advancements in Protein Language Models (pLMs) have enabled high-throughput analysis of proteins through primary sequence alone. At the same time, newfound evidence illustrates that codon usage bias is remarkably predictive and can even change the final structure of a protein. Here, we explore these findings by extending the traditional vocabulary of pLMs from amino acids to codons to encapsulate more information inside CoDing Sequences (CDS). We build upon traditional transfer learning techniques with a novel pipeline of token embedding matrix seeding, masked language modeling, and student-teacher knowledge distillation, called MELD. This transformed the pretrained ProtBERT into cdsBERT; a pLM with a codon vocabulary trained on a massive corpus of CDS. Interestingly, cdsBERT variants produced a highly biochemically relevant latent space, outperforming their amino acid-based counterparts on enzyme commission number prediction. Further analysis revealed that synonymous codon token embeddings moved distinctly in the embedding space, showcasing unique additions of information across broad phylogeny inside these traditionally “silent” mutations. This embedding movement correlated significantly with average usage bias across phylogeny. Future fine-tuned organism-specific codon pLMs may potentially have a more significant increase in codon usage fidelity. This work enables an exciting potential in using the codon vocabulary to improve current state-of-the-art structure and function prediction that necessitates the creation of a codon pLM foundation model alongside the addition of high-quality CDS to large-scale protein databases.
Deep learning-Based Correlation Analysis of Pelvic and Spinal Sequences for Enhanced Sagittal Spinal Alignment Prediction
Authors: Song Kai , Qi Huan , Ma Chi , Chi Pengfei , Lin Junyu , Yang Qiang , Yang Cao , Wang Bing , Li Fangcai , Zhu Zezhang , Li Weishi , Zhang Jianguo , Lu William , Zheng Wang 
Published: September 2023

AbstractBackgroundPelvic Incidence (PI) plays a crucial role in surgical planning. However, it is insufficient for accurately predicting spinal alignment parameters, including Sacral Slope, Pelvic Tilt, and Lumbar Lordosis. We have devised an AI-based method for predicting sagittal spinal alignments with enhanced precision.MethodsWe have developed an AI-based system utilizing a Seq2Seq framework to model the spatial correlation between pelvic and spinal key points. This system was trained on a dataset of 337 cases and evaluated using 51 cases obtained from a multi-centre hospital. To address the issue of pelvic rotation, we introduced an Angle Correlation Network. We compared the performance of our AI-based system in predicting spinal alignment against the traditional PI-based method. This comparison was conducted using Mean Absolute Error (MAE) and the Correlation Coefficient (R value) as evaluation metrics.ResultsWe evaluated the performance of our AI-based system for predicting Sacral Slope (SS), Pelvic Tilt (PT), and Lumbar Lordosis (LL) values. The Pearson correlation coefficient of the AI-based method surpassed that of the PI-based method (0.80 vs 0.67 for SS, 0.73 vs 0.52 for PT, and 0.76 vs 0.48 for LL), indicating a more robust linear relationship between AI predictions and actual values. Additionally, the AI-based method exhibited a lower Mean Absolute Error (MAE) compared to the PI-based method for LL (5.52 vs 6.69), signifying enhanced prediction accuracy.ConclusionsIn this study, we demonstrated the potential of an AI-based approach for predicting sagittal spinal alignments with improved precision compared to the traditional PI-based method. The AI-based system, utilizing a Seq2Seq framework and an Angle Correlation Network, exhibited a stronger linear relationship between predicted and actual values for Sacral Slope, Pelvic Tilt, and Lumbar Lordosis, as well as a reduced Mean Absolute Error for Lumbar Lordosis. These findings support the integration of AI in spinal surgery planning and personalized medicine for sagittal alignment evaluation and management.
AMYGDALA VOLUME IS ASSOCIATED WITH ADHD RISK AND SEVERITY BEYOND COMORBIDITIES IN ADOLESCENTS: CLINICAL TESTING OF BRAIN CHART REFERENCE STANDARDS
Authors: Nárai Ádám , Hermann Petra , Rádosi Alexandra , Vakli Pál , Weiss Béla , Réthelyi János M. , Bunford Nóra , Vidnyánszky Zoltán 
Published: September 2023

AbstractBackground.Understanding atypicalities in ADHD brain correlates is a step towards better understanding ADHD etiology. Efforts to map atypicalities at the level of brain structure have been hindered by the absence of normative reference standards. Recent publication of brain charts allows for assessment of individual variation relative to age- and sex-adjusted reference standards and thus estimation not only of case-control differences but also of intraindividual prediction.Methods.Aim was to examine, whether brain charts can be applied in a sample of adolescents (N=140, 38% female) to determine whether atypical brain subcortical and total volumes are associated with ADHD at-risk status and severity of parentrated symptoms, accounting for self-rated anxiety and depression, and parent-rated oppositional defiant disorder (ODD) as well as motion.Results.Smaller bilateral amygdala volume was associated with ADHD at-risk status, beyond effects of comorbidities and motion, and smaller bilateral amygdala volume was associated with inattention and hyperactivity/impulsivity, beyond effects of comorbidities except for ODD symptoms, and motion.Conclusions.Individual differences in amygdala volume meaningfully add to estimating ADHD risk and severity. Conceptually, amygdalar involvement is consistent with behavioral and functional imaging data on atypical reinforcement sensitivity as a marker of ADHD-related risk. Methodologically, results show that brain chart reference standards can be applied to address clinically informative, focused and specific questions.