bluuebunny/crossref_metadata_2025_split huggingface dataset:
How soon should patients be eligible for differentiated service delivery models for antiretroviral treatment? Evidence from Zambia
Authors: Jamieson Lise , Rosen Sydney , Phiri Bevis , Grimsrud Anna , Mwansa Muya , Shakwelele Hilda , Haimbe Prudence , Mwenechanya Mpande M , Lumano-Mulenga Priscilla , Chimboma Innocent , Nichols Brooke E 
Published: August 2021

AbstractIntroductionAttrition from HIV treatment is high during patients’ first 6 months after antiretroviral therapy (ART) initiation and patients with less than 6 months on ART are systematically excluded from most differentiated service delivery (DSD) models, which are intended to reduce attrition. Despite eligibility criteria requiring greater than 6 months on ART, some patients enroll earlier. Using routinely-collected medical record data in Zambia, we compared loss to follow-up (LTFU) among patients enrolling in DSD models early (<6 months on ART) to LTFU among those who enrolled according to guidelines (≥6 months on ART) in order to assess whether the ART experience eligibility criterion is necessary.MethodsWe extracted data from electronic medical records for adults (≥15 years) who initiated ART between 01/01/2019 and 31/12/2019 and evaluated LTFU, defined as >90 days late for last scheduled medication pickup, at 18 months for “early enrollers” (DSD enrolment after <6 months on ART) and “established enrollers” (DSD enrolment after ≥6 months on ART). We used a log-binomial model to compare LTFU risk between groups, adjusting for age, sex, urban/rural status, ART refill interval and DSD model.ResultsFor 6,340 early enrollers and 25,857 established enrollers there were no important differences between the groups in sex (61% female), age (median 37 years), or setting (65% urban). ART refill intervals were longer for established vs early enrollers (72% vs 55% were given 4–6-month refills). LTFU at 18 months was 3% (192/6,340) for early enrollers and 5% (24,646/25,857) for established enrollers. Early enrollers were 41% less likely to be LTFU than established patients (adjusted risk ratio [95% confidence interval] 0.59 [0.50-0.68]).ConclusionsPatients enrolled in DSD models in Zambia with < 6 months on ART were more likely to be retained in care than patients referred after they were established on ART. A limitation of the analysis is that early enrollers may have been selected for DSD participation due to providers’ and patients’ expectations about future retention. Offering DSD model entry to at least some ART patients <6 months after ART initiation may help address high attrition during the early treatment period.
Cognitive functioning in anxiety and depression: Results from the ALSPAC cohort
Authors: Suddell Steph , Mahedy Liam , Skirrow Caroline , Penton-Voak Ian S. , Munafò Marcus R. , Wootton Robyn E. 
Published: August 2021

AbstractBackgroundUnderstanding the nature of cognitive deficits in anxiety and depression may identify intervention targets and help prevent functional decline. This study used observational and genetic methods to investigate the association of anxiety and depression with emotion recognition, response inhibition, and working memory, in young adulthood.MethodsThe Avon Longitudinal Study of Parents and Children (ALSPAC) is a large prospective birth cohort study. Participants completed regular postal questionnaires and in-clinic assessments, starting from September 6, 1990. Data collection is ongoing. Linear regression was used to assess 1) cross-sectional associations between anxiety, depression, and cognition at age 24 (n = 2,187) and 2) prospective associations between anxiety and depression at age 18 and cognition at age 24 (n = 1,855). Mendelian randomization analyses were conducted to assess causal pathways between anxiety, depression, and cognition.ResultsPrimary analyses were conducted on 3,087 participants following multiple imputation. There was evidence for anxiety being associated with a decreased recognition of happiness (b = -0.27, 95% CI -0.54 to 0.01, p = .045), and depression being associated with an increased recognition of sadness (b = 0.35, 95% CI 0.07 to 0.64, p = .016). Anxiety was negatively associated with working memory (b = -0.14, 95% CI -0.24 to 0.04, p = .005), but no association was found for depression (b = 0.06, 95% CI -0.05 to 0.16, p = .284). There was no evidence for any association with response inhibition. Results from Mendelian randomization analyses were inconclusive, likely due to low statistical power.ConclusionsThere was little evidence that anxiety and depression are associated with significant impairments in executive functioning. However, both anxiety and depression were associated with altered emotion recognition. This may inform the development of interventions that target psychosocial functioning.
Microenvironmental correlates of immune checkpoint inhibitor response in human melanoma brain metastases revealed by T cell receptor and single-cell RNA sequencing
Authors: Alvarez-Breckenridge Christopher A. , Markson Samuel C. , Stocking Jackson H. , Nayyar Naema , Lastrapes Matthew , Strickland Matthew R. , Kim Albert E. , de Sauvage Magali , Dahal Ashish , Larson Juliana M , Mora Joana L. , Navia Andrew W. , Kuter Benjamin M. , Gill Corey M. , Bertalan Mia Solana , Shaw Brian , Kaplan Alexander , Subramanian Megha , Jain Aarushi , Kumar Swaminathan , Danish Husain , White Michael , Shahid Osmaan , Pauken Kristen E. , Miller Brian C. , Frederick Dennie T. , Herbert Christine , Shaw McKenzie , Martinez-Lage Maria , Frosch Matthew P. , Wang Nancy , Gerstner Elizabeth R. , Nahed Brian V. , Curry William T. , Carter Bob S. , Cahill Daniel P. , Boland Genevieve Marie , Izar Benjamin , Davies Michael , Sharpe Arlene , Suvà Mario L. , Sullivan Ryan J. , Brastianos Priscilla K. , Carter Scott L. 
Published: August 2021

AbstractMelanoma-derived brain metastases (MBM) represent an unmet clinical need due to central nervous system (CNS) progression as a frequent, end-stage site of disease. Immune checkpoint inhibition (ICI) represents a clinical opportunity against MBM; however, the MBM tumor microenvironment (TME) has not been fully elucidated in the context of ICI. To dissect unique MBM-TME elements and correlates of MBM-ICI response, we collected 32 fresh MBM and performed single cell RNA sequencing of the MBM-TME and T cell receptor clonotyping on T cells from MBM and matched blood and extracranial lesions. We observed myeloid phenotypic heterogeneity, most notably multiple distinct neutrophil states including an IL-8 expressing population that correlated with malignant cell epithelial-to-mesenchymal transition. Additionally, we observe significant relationships between intracranial T cell phenotypes and the distribution of T cell clonotypes intracranially and peripherally. We found that the phenotype, clonotype, and overall number of MBM-infiltrating T cells were associated with response to ICI, suggesting that ICI-responsive MBMs interact with peripheral blood in a manner similar to extracranial lesions. These data demonstrate unique features of the MBM-TME, which may represent potential targets to improve clinical outcomes for patients with MBM.
Fiscore Package: Effective Protein Structural Data Visualisation and Exploration
Authors: Kanapeckaite Auste 
Published: August 2021

AbstractThe lack of bioinformatics tools to quickly assess protein conformational and topological features motivated to create an integrative and user-friendly R package. Moreover, the Fiscore package implements a pipeline for Gaussian mixture modelling making such machine learning methods readily accessible to non-experts. This is especially important since probabilistic machine learning techniques can help with a better interpretation of complex biological phenomena when it is necessary to elucidate various structural features that might play a role in protein function. Thus, Fiscore builds on the mathematical formulation of protein physicochemical properties that can aid in drug discovery, target evaluation, or relational database building. In addition, the package provides interactive environments to explore various features of interest. Finally, one of the goals of this package was to engage structural bioinformaticians and develop more robust R tools that could help researchers not necessarily specialising in this field. Package Fiscore (v.0.1.3) is distributed via CRAN and Github.
Synergistic Enzyme Mixtures to Realize Near-Complete Depolymerization in Biodegradable Polymer/Additive Blends
Authors: DelRe Christopher , Chang Boyce , Jayapurna Ivan , Hall Aaron , Wang Ariel , Zolkin Kyle , Xu Ting 
Published: August 2021

AbstractEmbedding catalysts inside of plastics affords accelerated chemical modification with programmable latency and pathways. Nanoscopically embedded enzymes can lead to near complete degradation of polyesters via chain-end mediated processive depolymerization. The overall degradation rate and pathways have a strong dependence on the morphology of semi-crystalline polyesters. Yet, most studies to date focus on pristine polymers instead of mixtures with additives and other components despite their nearly universal uses in plastic production. Here, additives are introduced to purposely change the morphology of polycaprolactone (PCL) by increasing the bending and twisting of crystalline lamellae. These morphological changes immobilize chain-ends preferentially at the crystalline/amorphous interfaces and limit chain-end accessibility by the embedded processive enzyme. This chain end redistribution reduces the polymer-to-monomer conversion from >95% to less than 50%, causing formation of highly crystalline plastic pieces including microplastics. By synergizing both random chain scission and processive depolymerization, it is feasible to navigate morphological changes in polymer/additive blends and to achieve near complete depolymerization. The random scission enzymes in the amorphous domains create new chain ends that are subsequently bound and depolymerized by processive enzymes. Present studies further highlight the importance to consider host polymer morphological effects on the reactions catalyzed by embedded catalytic species.
Amphetamine alters an EEG marker of reward processing in humans and mice
Authors: Cavanagh James F. , Olguin Sarah , Talledo Jo A , Kotz Juliana E. , Roberts Benjamin Z. , Nungaray John A , Sprock Joyce , Gregg David , Bhakta Savita G. , Light Gregory A. , Swerdlow Neal R. , Young Jared W. , Brigman Jonathan L. 
Published: August 2021

AbstractThe bench-to-bedside development of pro-cognitive therapeutics for psychiatric disorders has been mired by translational failures. This is in part due to the absence of pharmacologically-sensitive cognitive biomarkers common to humans and rodents. Here, we describe a cross-species translational marker of reward processing that is sensitive to the dopamine agonist, d-amphetamine. Motivated by human electroencephalographic (EEG) findings, we recently reported that frontal midline delta-band power is also an electrophysiological biomarker of reward surprise in mice. In the current series of experiments, we determined the impact of parametric doses of d-amphetamine on this reward-related EEG response from humans (n=23) and mice (n=28) performing a probabilistic learning task. In humans, d-amphetamine (placebo, 10 mg, 20 mg) boosted the Reward Positivity event-related potential (ERP) component as well as the spectral delta-band representations of this signal. In mice, d-amphetamine (placebo, 0.1 mg/kg, 0.3 mg/kg, 1.0 mg/kg) boosted both reward and punishment ERP features, yet there was no modulation of spectral activities. In sum, the present results confirm the role of dopamine in the generation of the Reward Positivity in humans, and paves the way towards a pharmacologically valid biomarker of reward sensitivity across species.
Plucking a string or playing a G? Choice type impacts human reinforcement learning
Authors: Rmus Milena , Zou Amy , Collins Anne G.E. 
Published: August 2021

AbstractIn reinforcement learning (RL) experiments, participants learn to make rewarding choices in response to different stimuli; RL models use outcomes to estimate stimulus-response values which change incrementally. RL models consider any response type indiscriminately, ranging from more concretely defined motor choices (e.g. pressing a key with the index finger), to more general choices that can be executed in a number of ways (e.g. getting dinner at the restaurant). But does the learning process vary as a function of the choice type? In Experiment 1, we show that it does: participants were slower and less accurate in learning correct choices of a general format compared to learning more concrete, motor actions. Using computational modeling, we show that two mechanisms contribute to this. First, there was evidence of irrelevant credit assignment: the values of motor actions interfered with the values of other choice dimensions, resulting in more incorrect choices when the correct response is not defined by a single motor action; second, information integration for relevant general choices was slower. In Experiment 2, we replicated and further extended the findings from Experiment 1, by showing that slowed learning was attributable to weaker working memory use, rather than slowed RL learning. In both experiments we ruled out the explanation that the difference in performance between two condition types was driven by difficulty/different levels of complexity. We conclude that defining a more abstract choice space used by multiple learning systems for credit assignment recruits executive resources, limiting how much such processes then contribute to fast learning.
Fractionation of neural reward processing into independent components by novel decoding principle
Authors: Xiang Shitong , Jia Tianye , Xie Chao , Zhu Zhichao , Cheng Wei , Schumann Gunter , Robbins Trevor W. , Feng Jianfeng 
Published: August 2021

AbstractHow to retrieve latent neurobehavioral processes from complex neurobiological signals is an important yet unresolved challenge. Here, we develop a novel approach, orthogonal-Decoding multi-Cognitive Processes (DeCoP), to reveal underlying latent neurobehavioral processing and show that its performance is superior to traditional non-orthogonal decoding in terms of both false inference and robustness. Processing value and salience information are two fundamental but mutually confounded pathways of reward reinforcement essential for decision making. During reward/punishment anticipation, we applied DeCoP to decode brain-wide responses into spatially overlapping, yet functionally independent, evaluation and readiness processes, which are modulated differentially by meso-limbic vs nigro-striatal dopamine systems. Using DeCoP, we further demonstrated that most brain regions only encoded abstract information but not the exact input, except for dACC and insula. Furthermore, our novel analytical principle could be applied generally to decode multiple latent neurobehavioral processes and thus advance both the design and hypothesis testing for cognitive tasks.
Tensor-decomposition–based unsupervised feature extraction in single-cell multiomics data analysis
Authors: Taguchi Y-h. , Turki Turki 
Published: August 2021

AbstractAnalysis of single-cell multiomics datasets is a novel topic and is considerably challenging because such datasets contain a large number of features with numerous missing values. In this study, we implemented a recently proposed tensor-decomposition (TD)–based unsupervised feature extraction (FE) technique to address this difficult problem. The technique can successfully integrate single-cell multiomics data composed of gene expression, DNA methylation, and accessibility. Although the last two have large dimensions, as many as ten million, containing only a few percentages of non-zero values, TD-based unsupervised FE can integrate three omics datasets without filling missing values. Together with UMAP, which is used frequently when embedding single-cell measurements into two-dimensional space, TD-based unsupervised FE can produce two-dimensional embedding coincident with classification when integrating single-cell omics datasets. Genes selected based on TD-based unsupervised FE were also significantly related to reasonable biological roles.
Brain Activation in Chronic Nonspecific Low Back Pain : A Systematic review and ALE Meta-analysis
Authors: Hazra Sandipan , Sahu Samantak , Nayak Prasun Priya , Sarkar Koushik , Srikumar V , Handa Gita 
Published: August 2021

AbstractPain, a protective mechanism turns into a pathologic response when it becomes chronic. Recent evidences are pointing towards neuroplastic brain changes as the primary factor for the persisting pain in chronic nonspecific low back pain (cLBP). To summarise the previous fMRI studies, a coordinate-based ALE meta-analysis of resting functional brain imaging studies is carried out to identify the clusters activated in the brain in cLBP.Literature survey: ‘PubMed’, ‘Scopus’ and ‘Sleuth’ were searched for studies with resting functional whole-brain imaging in cLBP. Till October 2020; 258, 238, and 7 studies were found respectively after search. The activity pattern was documented in ‘without stimulation’ and ‘with stimulation’ groups. The risk of bias was assessed by Joanna Briggs Institute critical appraisal checklist for analytical cross-section studies. Total seven (224 cLBP patients, 110 activation foci) and six studies (106 cLBP patients, 66 activation foci) were selected among 277 studies for metanalysis in the ‘without stimulation’ and ‘with stimulation’ group respectively. In the ‘without stimulation’ group 8 statistically significant clusters were found. The clusters are distributed in the prefrontal cortex, primary somatosensory cortex, and primary motor cortex, anterior cingulate cortex, insular cortex, putamen, claustrum, amygdala, and associated white matters in both hemispheres. On the other group, 3 statistically significant clusters were found in the frontal cortex, Parietal cortex, and Insula. In the ‘with stimulation’ group, significant lateralization was observed and most of the clusters were in the right hemisphere. The white matter involvement was more in the ‘with stimulation’ group (78.62% Vs 38.21%). The statistically significant clusters found in this study indicate a probable imbalance in GABAergic modulation of brain circuit and dysfunction in descending pain modulation system. This disparity in pain neuro-matrix is the source of spontaneous and persisting pain in cLBP.
Best templates outperform homology models in predicting the impact of mutations on protein stability
Authors: Pak Marina A. , Ivankov Dmitry N. 
Published: August 2021

AbstractMotivationPrediction of protein stability change upon mutation (ΔΔG) is crucial for facilitating protein engineering and understanding of protein folding principles. Robust prediction of protein folding free energy change requires the knowledge of protein three-dimensional (3D) structure. Unfortunately, protein 3D structure is not always available. In this case, one can still predict the protein stability change by constructing a homology model of the protein; however, the accuracy of homology model-based ΔΔG predictions is unknown. The perspectives of using 3D structures of the best templates are also unclear.ResultsTo investigate these questions, we used the most popular and accurate publicly available tools: FoldX for stability change prediction and I-Tasser for homology modeling. We found that both homology models and best templates worsen the ΔΔG prediction, with best templates performing 1.5 times better than homology models. For AlphaFold models, we also found that the best templates seem to outperform protein models. Our findings imply using the 3D structures of the best templates for ΔΔG prediction if the 3D protein structure is unavailable.Contactd.ivankov@skoltech.ru
Oral Glucosamine Ameliorates Aggravated Neurological Phenotype in Mucopolysaccharidosis III Type C Mouse Model Expressing Misfolded HGSNAT Variant
Authors: Pan Xuefang , Taherzadeh Mahsa , Bose Poulomee , Heon-Roberts Rachel , Nguyen Annie L. A. , Xu TianMeng , Pará Camila , Yamanaka Yojiro , Priestman David A. , Platt Frances M. , Khan Shaukat , Fnu Nidhi , Tomatsu Shunji , Morales Carlos R. , Pshezhetsky Alexey V. 
Published: August 2021

AbstractObjectiveOver 55% of mucopolysaccharidosis IIIC (MPS IIIC) patients have at least one allelic missense variant responsible for misfolding of heparan sulfate acetyl-CoA:α-glucosaminide N- acetyltransferase (HGSNAT). These variants are potentially treatable with pharmacological chaperones, such as a competitive HGSNAT inhibitor, glucosamine. Since the constitutive HGSNAT knockout mice, we generated previously cannot be used to test such strategyin vivo,we generated a novel model, theHgsnatP304Lstrain, expressing misfolded mutant HGSNAT with human missense mutation Pro311Leu (Pro304Leu in the mouse enzyme).ResultsHgsnatP304Lmice present deficits in short-term (novel object recognition test) and working/spatial (Y-maze test) memory at 4 months of age, 2-4 months earlier than previously described gene-targetedHgsnat-Geomice, which lack HGSNAT protein.HgsnatP304Lmice also show increased severity of synaptic deficits in CA1 neurons, and accelerated course of CNS pathology including neuronal storage of heparan sulfate, accumulation of misfolded proteins, increase of simple gangliosides, and neuroinflammation as compared withHgsnat-Geomice. Expression of misfolded human Pro311Leu HGSNAT protein in cultured hippocampalHgsnat- Geoneurons aggravated reduction of synaptic proteins. Memory deficits and majority of pathological changes in the brain were rescued in mice receiving daily doses of oral glucosamine.InterpretationAltogether, our data for the first time demonstrate dominant-negative effects of the misfolded HGSNAT Pro304Leu variant and show that these effects are treatable by oral administration of glucosamine, suggesting that patients, affected with missense mutations preventing normal folding of the enzyme, could benefit from chaperone therapy.
Hi-C-LSTM: Learning representations of chromatin contacts using a recurrent neural network identifies genomic drivers of conformation
Authors: Dsouza Kevin B. , Maslova Alexandra , Al-Jibury Ediem , Merkenschlager Matthias , Bhargava Vijay K. , Libbrecht Maxwell W. 
Published: August 2021

AbstractDespite the availability of chromatin conformation capture experiments, understanding the relationship between regulatory elements and conformation remains a challenge. We propose Hi-C-LSTM, a method that produces low-dimensional latent representations that summarize intra-chromosomal Hi-C contacts via a recurrent long short-term memory (LSTM) neural network model. We find that these representations contain all the information needed to recreate the original Hi-C matrix with high accuracy, outperforming existing methods. These representations enable the identification of a variety of conformation-defining genomic elements, including nuclear compartments and conformation-related transcription factors. They furthermore enable in-silico perturbation experiments that measure the influence of cis-regulatory elements on conformation.