bluuebunny/crossref_metadata_2025_split huggingface dataset:
Factors Predicting Dependence on Waterpipe Smoking Among Waterpipe Exclusive Smokers
Authors: Rayan Ahmad , Eshah Nidal F. , Froelicher Erika S. 
Published: March 2023

Abstract
The current study targeted a sample of waterpipe (WP) exclusive smokers to identify the levels and predictors of dependence on this smoking pattern and to examine the relationship between WP smoking (WPS) dependence and depressive symptoms. A cross-sectional research design was employed with a community sample of 270 subjects who are currently WP exclusive smokers. Assessment of WPS dependence and depressive symptoms was performed using self-report measures. About 62.6% of the subjects were WPS dependent. The unique significant predictors of WPS dependence were age, depression levels, age of starting WPS, duration of WPS, income, smoking WP daily, the belief of being attracted to WPS, and the desire to stop WPS. These predictors explained 35% of the variance in WPS dependence. WPS dependence was associated with increased depressive symptoms. Although few studies have been conducted, the WPS dependence prevalence appears to be alarmingly high among WP exclusive smokers. Factors associated with WPS dependence provide useful information that can be used to tailor WP prevention interventions.
The Experience of Women With Opioid Use Disorder Accessing Methadone Treatment
Authors: Keenan Lizette C. , Ojeda Maria M. , Valdez Anna 
Published: March 2023

Abstract
The number of women experiencing opioid use disorder (OUD) in Canada has increased exponentially. In Canada, healthcare is socialized and free for all citizens and, often, medications like methadone are free as well, yet few individuals with OUD access treatment services. The purpose of this study was to describe the lived experiences of Canadian women with OUD who were receiving methadone treatment. Interpretive phenomenology was used to investigate the treatment experiences of seven women with OUD. The conceptual framework of self-care of chronic illness was used to examine this phenomenon. Data were analyzed using a seven-step process of interpretive phenomenological analysis. Four major themes emerged: learning how to be you again, reaching out for help, finding your way to methadone, and going down the path of methadone. Women's experiences were influenced by family, friends, and healthcare providers. Accessibility and self-determination were important factors in entering and sustaining treatment. This study contributes to the discipline of nursing by providing accurate information regarding women's experiences with OUD and uncovering practice changes that can attract and retain women in treatment.
“Peripheral Nerve Matrix hydrogel promotes recovery after nerve transection and repair.”
Authors: Bernard Megan , McOnie Rebecca , Tomlinson Joy E. , Blum Ethan , Prest Travis A. , Sledziona Mike , Willand Mike , Gordon Tessa , Borschel Gregory H. , Soletti Lorenzo , Brown Bryan N. , Cheetham Jonathan 
Published: March 2023

Background:
Nerve transection is the most common form of peripheral nerve injury. Treatment of peripheral nerve injury has primarily focused on stabilization and mechanical cues to guide extension of the regenerating growth cone across the site of transection. Here we investigate the effects of a peripheral nerve matrix (PNM) hydrogel on recovery following nerve transection.

Methods:
We use rodent models to determine the effect of PNM on axon extension, electrophysiological nerve conduction, force generation and neuromuscular junction formation after nerve transection and repair. We complemented this work with in vivo and in vitro FACS and immunohistochemistry approaches to determine the effects of PN on critical cell populations early after repair.

Results:
Extension of axons from the proximal stump and overall GFP+ axon volume within the regenerative bridge were increased in the presence of PNM compared with an empty conduit (p< 0.005) 21 days after repair. PNM increased electrophysiological conduction (CMAP amplitude) across the repair site (p<0.05) and neuromuscular junction formation (p=0.04) 56 days after repair. PNM produced a shift in macrophage phenotype in vitro and in vitro (p<0.05) and also promoted regeneration in a murine model used to characterize the early immune response to PNM (p<0.05).

Conclusions:
PNM, delivered by subepineural injection, promoted recovery following nerve transection with immediate repair, supporting a beneficial macrophage response, axon extension and downstream remodeling using a range of clinically relevant outcome measures.
AB-Gen: Antibody Library Design with Generative Pre-trained Transformer and Deep Reinforcement Learning
Authors: Xu Xiaopeng , Xu Tiantian , Zhou Juexiao , Liao Xingyu , Zhang Ruochi , Wang Yu , Zhang Lu , Gao Xin 
Published: March 2023

AbstractAntibody leads must fulfill multiple desirable properties to be clinical candidates. Primarily due to the low throughput in the experimental procedure, the need for such multi-property optimization causes the bottleneck in preclinical antibody discovery and development, because addressing one issue usually causes another. We developed a reinforcement learning (RL) method, named AB-Gen, for antibody library design using a generative pre-trained Transformer (GPT) as the policy network of the RL agent. We showed that this model can learn the antibody space of heavy chain complementarity determining region 3 (CDRH3) and generate sequences with similar property distributions. Besides, when using HER2 as the target, the agent model of AB-Gen was able to generate novel CDRH3 sequences that fulfill multi-property constraints. 509 generated sequences were able to pass all property filters and three highly conserved residues were identified. The importance of these residues was further demonstrated by molecular dynamics simulations, which consolidated that the agent model was capable of grasping important information in this complex optimization task. Overall, the AB-Gen method is able to design novel antibody sequences with an improved success rate than the traditional propose-then-filter approach. It has the potential to be used in practical antibody design, thus empowering the antibody discovery and development process.
Comparison of genomic-enabled cross selection criteria for the improvement of inbred line breeding populations
Authors: des Déserts Alice Danguy , Durand Nicolas , Servin Bertrand , Goudemand-Dugué Ellen , Alliot Jean-Marc , Ruiz Daniel , Charmet Gilles , Elsen Jean-Michel , Bouchet Sophie 
Published: March 2023

AbstractA crucial step in inbred plant breeding is the choice of mating design to derive high-performing inbred varieties while also maintaining a competitive breeding population to secure sufficient genetic gain in future generations. In practice, the mating design usually relies on crosses involving the best parental inbred lines to ensure high mean progeny performance. This excludes crosses involving lower performing but more complementary parents in terms of favorable alleles. We predicted crosses with putative outstanding progenies (high mean and high variance progeny distribution) using genomic prediction models to assess the value of top progeny. This study compared the benefits and drawbacks of seven genomic cross selection criteria (CSC) in terms of genetic gain for one trait and genetic diversity in the next generation. Six CSC were already published and we have proposed an improved CSC that can estimate the proportion of progeny above a threshold defined for the whole mating plan. We simulated mating designs optimized using different CSC and 835 elite parents from a real breeding program that were evaluated between 2000 and 2016. We applied constraints on parental contributions and genetic similarities between parents according to usual breeder practices. Our results showed that CSC based on progeny variance estimation increased the genetic value of superior progenies by up to 5% in the next generation compared to CSC based on the progeny mean estimation (i.e. parental genetic values) alone. It also increased the genetic gain (up to 4%) and/or maintained more genetic diversity at QTLs (up to 4% more genic variance when the marker effects were perfectly estimated).
Efficient Hi-C inversion facilitates chromatin folding mechanism discovery and structure prediction
Authors: Schuette Greg , Ding Xinqiang , Zhang Bin 
Published: March 2023

AbstractGenome-wide chromosome conformation capture (Hi-C) experiments have revealed many structural features of chromatin across multiple length scales. Further understanding genome organization requires relating these discoveries to the mechanisms that establish chromatin structures and reconstructing these structures in three dimensions, but both objectives are difficult to achieve with existing algorithms that are often computationally expensive. To alleviate this challenge, we present an algorithm that efficiently converts Hi-C data into contact energies, which measure the interaction strength between genomic loci brought into proximity. Contact energies are local quantities unaffected by the topological constraints that correlate Hi-C contact probabilities. Thus, extracting contact energies from Hi-C contact probabilities distills the biologically unique information contained in the data. We show that contact energies reveal the location of chromatin loop anchors, support a phase separation mechanism for genome compartmentalization, and parameterize polymer simulations that predict three-dimensional chromatin structures. Therefore, we anticipate that contact energy extraction will unleash the full potential of Hi-C data and that our inversion algorithm will facilitate the widespread adoption of contact energy analysis.Significance StatementThe three-dimensional organization of the genome is essential to many DNA-templated processes, and numerous experimental techniques have been introduced to characterize its features. High-throughput chromosome conformation capture experiments, or Hi-C, have proven particularly useful, reporting the interaction frequency between pairs of DNA segmentsin vivoand genome-wide. However, the polymer topology of chromosomes complicates Hi-C data analysis, which often employs sophisticated algorithms without explicitly accounting for the disparate processes affecting each interaction frequency. In contrast, we introduce a computational framework based on polymer physics arguments that efficiently removes the correlation between Hi-C interaction frequencies and quantifies how each local interaction influences genome folding globally. This framework facilitates the identification of mechanistically important interactions and the prediction of three-dimensional genome structures.
How much data do we need? Reliability and data deficiency in global vertebrate biodiversity trends
Authors: Dove Shawn , Böhm Monika , Freeman Robin , McRae Louise , Murrell David J. 
Published: March 2023

AbstractGlobal biodiversity is facing a crisis, which must be solved through effective policies and on-the-ground conservation. But governments, NGOs, and scientists need reliable indicators to guide research, conservation actions, and policy decisions. Developing reliable indicators is challenging because the data underlying those tools is incomplete and biased. For example, the Living Planet Index tracks the changing status of global vertebrate biodiversity, but taxonomic, geographic and temporal gaps and biases are present in the aggregated data used to calculate trends. But without a basis for real-world comparison, there is no way to directly assess an indicator’s accuracy or reliability. Instead, a modelling approach can be used.We developed a model of trend reliability, using simulated datasets as stand-ins for the "real world", degraded samples as stand-ins for indicator datasets (e.g. the Living Planet Database), and a distance measure to quantify reliability by comparing sampled to unsampled trends. The model revealed that the proportion of species represented in the database is not always indicative of trend reliability. Important factors are the number and length of time series, as well as their mean growth rates and variance in their growth rates, both within and between time series. We found that many trends in the Living Planet Index need more data to be considered reliable, particularly trends across the global south. In general, bird trends are the most reliable, while reptile and amphibian trends are most in need of additional data. We simulated three different solutions for reducing data deficiency, and found that collating existing data (where available) is the most efficient way to improve trend reliability, and that revisiting previously-studied populations is a quick and efficient way to improve trend reliability until new long-term studies can be completed and made available.
Using DNA flow-stretching assay as a tool to validate the tagging of DNA-binding proteins for single-molecule experiments
Authors: Molina Miranda , Way Lindsey E. , Ren Zhongqing , Liao Qin , Wang Xindan , Kim HyeongJun 
Published: March 2023

SummaryDue to the enhanced labeling capability of maleimide-based fluorescent probes, lysine-cysteine-lysine (KCK) tags are frequently added to proteins for visualization. In this study, we employedin vitrosingle-molecule DNA flow-stretching assay as a sensitive way to assess the impact of the KCK-tag on the property of DNA-binding proteins. UsingBacillus subtilisParB as an example, we show that, although no noticeable changes were detected byin vivofluorescence imaging and chromatin immunoprecipitation (ChIP) assays, the KCK-tag substantially altered ParB’s DNA compaction rates, its response to nucleotide binding and to the presence of the specific sequence (parS) on the DNA. While it is typically assumed that short peptide tags minimally perturb protein function, our results urge researchers to carefully validate the use of tags for protein labeling. Our comprehensive analysis can be expanded and used as a guide to assess the impacts of other tags on DNA-binding proteins in single-molecule assays.MotivationSingle-molecule fluorescence microscopy has been extensively used in modern biology to define the molecular action of proteins. Appending short peptide tags is a common strategy to enhance fluorescence labeling. In this Resources article, we evaluate the impact of a commonly used tag, the lysine-cysteine-lysine (KCK) tag, on protein behavior in single-molecule DNA flow-stretching assay, which is a sensitive and versatile method to understand the action of DNA-binding proteins. Our motivation is to provide researchers with an experimental framework to validate the fluorescently labeled DNA-binding proteins in single-molecule methods.
Predicting the Distribution of Serotonergic Axons: A Supercomputing Simulation of Reflected Fractional Brownian Motion in a 3D-Mouse Brain Model
Authors: Janušonis Skirmantas , Haiman Justin H. , Metzler Ralf , Vojta Thomas 
Published: March 2023

ABSTRACTThe self-organization of the brain matrix of serotonergic axons (fibers) remains an unsolved problem in neuroscience. The regional densities of this matrix have major implications for neuroplasticity, tissue regeneration, and the understanding of mental disorders, but the trajectories of single fibers are strongly stochastic and require novel conceptual and analytical approaches. In a major extension to our previous studies, we used a supercomputing simulation to model 1000 serotonergic fibers as paths of superdiffusive fractional Brownian motion (FBM), a continuous-time stochastic process. The fibers produced long walks in a complex, threedimensional shape based on the mouse brain and reflected at the outer (pial) and inner (ventricular) boundaries. The resultant regional densities were compared to the actual fiber densities in the corresponding neuroanatomically-defined regions. The relative densities showed strong qualitative similarities in the forebrain and midbrain, demonstrating the predictive potential of stochastic modeling in this system. The current simulation does not respect tissue heterogeneities, but can be further improved with novel models of multifractional FBM. The study demonstrates that serotonergic fiber densities can be strongly influenced by the geometry of the brain, with implications for brain development, plasticity, and evolution.
An analysis of NHS 111 demand for primary care services
Authors: Pilbery Richard , Smith Madeleine , Green Jonathan , Chalk Daniel 
Published: March 2023

AbstractBackgroundThe NHS 111 service triages over 16,650,745 calls per year and approximately 48% of callers are triaged to a primary care disposition, such as a telephone appointment with a general practitioner (GP). However, there has been little assessment of the ability of primary care services to meet this demand. If a timely service cannot be provided to patients, it could result in patients calling 999 or attending emergency departments (ED) instead.This study aimed to explore the patient journey for callers who were triaged to a primary care disposition, and the ability of primary care services to meet this demand.MethodsWe obtained routine, retrospective data from the Connected Yorkshire research database, and identified all 111 calls between the 1st January 2021 and 31st December 2021 for callers registered with a GP in the Bradford or Airedale region of West Yorkshire, who were triaged to a primary care disposition. Subsequent healthcare system access (111, 999, primary and secondary care) in the 72 hours following the index 111 call was identified, and a descriptive analysis of the healthcare trajectory of patients was undertaken.ResultsThere were 56,102 index 111 calls, and a primary care service was the first interaction in 26,690/56,102 (47.6%) of cases, with 15,470/26,690 (58%) commenced within the specified triage time frame. Calls to 999 were higher in the cohort who had no prior contact with primary care (58% vs 42%) as were ED attendances (58.2% vs 41.8), although the proportion of avoidable ED attendances was similar (10.5% vs 11.8%).ConclusionLess than half of 111 callers triaged to a primary care disposition make contact with a primary care service, and even when they do, call triage time frames are frequently not met, suggesting that current primary care provision cannot meet the demand from 111.
Body size as a metric for the affordable world
Authors: Feng Xinran , Xu Shan , Li Yuannan , Liu Jia 
Published: March 2023

AbstractThe physical body of an organism serves as a vital interface for interactions with its environment. Here we investigated the impact of human body size on the perception of action possibilities (affordances) offered by the environment. We found that the body size delineated a distinct boundary on affordances, dividing objects of continuous real-world sizes into two discrete categories with each affording distinct action sets. Additionally, the boundary shifted with imagined body sizes, suggesting a causal link between body size and affordance perception. Intriguingly, ChatGPT, a large language model lacking physical embodiment, exhibited a modest yet comparable affordance boundary at the scale of human body size, suggesting the boundary is not exclusively derived from organism-environment interactions. A subsequent fMRI experiment revealed evidence of affordance processing exclusively for objects within the body size range, but not for those beyond. This suggests that only objects capable of being manipulated are the objects capable of offering affordance in the eyes of an organism. In summary, our study suggests a novel definition of object-ness in an affordance-based context, advocating the concept of embodied cognition in understanding the emergence of intelligence constrained by an organism’s physical attributes.
A Framework for Systematically Evaluating the Representations Learned by A Deep Learning Classifier from Raw Multi-Channel Electroencephalogram Data
Authors: Ellis Charles A. , Sattiraju Abhinav , Miller Robyn L. , Calhoun Vince D. 
Published: March 2023

ABSTRACTThe application of deep learning methods to raw electroencephalogram (EEG) data is growing increasingly common. While these methods offer the possibility of improved performance relative to other approaches applied to manually engineered features, they also present the problem of reduced explainability. As such, a number of studies have sought to provide explainability methods uniquely adapted to the domain of deep learning-based raw EEG classification. In this study, we present a taxonomy of those methods, identifying existing approaches that provide insight into spatial, spectral, and temporal features. We then present a novel framework consisting of a series of explainability approaches for insight into classifiers trained on raw EEG data. Our framework provides spatial, spectral, and temporal explanations similar to existing approaches. However, it also, to the best of our knowledge, proposes the first explainability approaches for insight into spatial and spatio-spectral interactions in EEG. This is particularly important given the frequent use and well-characterized importance of EEG connectivity measures for neurological and neuropsychiatric disorder analysis. We demonstrate our proposed framework within the context of automated major depressive disorder (MDD) diagnosis, training a high performing one-dimensional convolutional neural network with a robust cross-validation approach on a publicly available dataset. We identify interactions between frontal and central electrodes and other electrodes and identify differences in frontal δ, θ, β, and γlowbetween healthy controls and individuals with MDD. Our study represents a significant step forward for the field of deep learning-based raw EEG classification, providing new capabilities in interaction explainability and providing direction for future innovations through our proposed taxonomy.