bluuebunny/crossref_metadata_2025_split huggingface dataset:
Dynamics, Electrostatics, and Thermodynamics of Base Pairing at theLTR-IIIQuadruplex:Duplex Junction
Authors: Michel Haley M. , Lemkul Justin A. 
Published: January 2024

ABSTRACTG-quadruplexes (GQs) play key regulatory roles within the human genome and have also been identified to play similar roles in other eukaryotes, bacteria, archaea, and viruses. Human immunodeficiency virus 1 (HIV-1), the etiological agent of acquired immunodeficiency syndrome (AIDS), can form two GQs in its long terminal repeat (LTR) promoter region, each of which act to regulate viral gene expression in opposing manners. The major LTR GQ, calledLTR-III, is a distinct hybrid GQ containing a 12-nucleotide duplex loop attached to the quadruplex motif. The resulting quadruplex:duplex junction (QDJ) has been hypothesized to serve as a selective drug targeting site. To better understand the dynamics of this QDJ, we performed conventional and enhanced-sampling molecular dynamics simulations using the Drude-2017 force field. We observed unbiased and reversible formation of additional base pairs in the QDJ, between Ade4:Thy14 and Gua3:Thy14. Both base pairs were electrostatically favored but geometric constraints within the junction may drive the formation of, and preference for, the Ade4:Thy14 base pair. Finally, we demonstrated that the base pairs are separated only by small energy barriers that may enable transitions between both base-paired states. Together, these simulations provide new insights into the dynamics, electrostatics, and thermodynamics of theLTR-IIIQDJ.SIGNIFICANCEHere, we characterize the quadruplex:duplex junction of the HIV-1LTR-IIIG-quadruplex. We find that two additional base pairs can form in the junction and are driven by electrostatic, thermodynamic, and geometric factors. G-quadruplexes containing such junctions are rather recent discoveries, and it has been proposed that these junctions can act as selective targets for drugs. These results further identify distinct chemical and electrostatic characteristics that can be used to guide drug design studies.
Cross-institutional HER2 assessment via a computer-aided system using federated learning and stain composition augmentation
Authors: Yang Chia-Hung , Chen Yung-An , Chang Shao-Yu , Hsieh Yu-Han , Hung Yu-Ling , Lin Yi-Wen , Lee Yi-Hsuan , Lin Ching-Hung , Lin Yu-Chieh , Lu Yen-Shen , Lin Yen-Yin 
Published: January 2024

AbstractThe rapid advancement of precision medicine and personalized healthcare has heightened the demand for accurate diagnostic tests. These tests are crucial for administering novel treatments like targeted therapy. To ensure the widespread availability of accurate diagnostics with consistent standards, the integration of computer-aided systems has become essential. Specifically, computer-aided systems that assess biomarker expression have thrusted through the widespread application of deep learning for medical imaging. However, the generalizability of deep learning models has usually diminished significantly when being confronted with data collected from different sources, especially for histological imaging in digital pathology. It has therefore been challenging to effectively develop and employ a computer-aided system across multiple medical institutions. In this study, a biomarker computer-aided framework was proposed to overcome such challenges. This framework incorporated a new approach to augment the composition of histological staining, which enhanced the performance of federated learning models. A HER2 assessment system was developed following the proposed framework, and it was evaluated on a clinical dataset from National Taiwan University Hospital and a public dataset coordinated by the University of Warwick. This assessment system showed an accuracy exceeding 90% for both institutions, whose generalizability outperformed a baseline system developed solely through the clinical dataset by 30%. Compared to previous works where data across different institutions were mixed during model training, the HER2 assessment system achieved a similar performance while it was developed with guaranteed patient privacy via federated learning.
Exploring Mechanisms of Lipid Nanoparticle-Mucus Interactions in Healthy and Cystic Fibrosis Conditions
Authors: Tafech Belal , Rokhforouz Mohammad-Reza , Leung Jerry , Sung Molly MH , Lin Paulo JC , Sin Don D , Lauster Daniel , Block Stephan , Quon Bradley S. , Tam Ying , Cullis Pieter , Feng James J , Hedtrich Sarah 
Published: January 2024

ABSTRACTMucus forms the first defense line of human lungs, and as such hampers the efficient delivery of therapeutics to the underlying epithelium. This holds particularly true for genetic cargo such as CRISPR-based gene editing tools which cannot readily surmount the mucosal barrier. While lipid nanoparticles (LNPs) emerged as versatile non-viral gene delivery systems that could help overcome the delivery challenge, many knowledge gaps remain, especially for diseased states such as cystic fibrosis (CF).This study provides fundamental insights into Cas9 mRNA or ribonucleoprotein-loaded LNP-mucus interactions in healthy and diseased states by assessing the impact of the genetic cargo, mucin sialylation, mucin concentration, ionic strength, pH, and polyethylene glycol (PEG) concentration and nature on LNP diffusivity leveraging experimental approaches and Brownian dynamics simulations.Taken together, this study identifies key mucus and LNP characteristics that are critical to enabling a rational LNP design for transmucosal delivery.Graphical Abstract
Evaluation of Deep Learning for predicting rice traits using structural and single-nucleotide genomic variants
Authors: Vourlaki Ioanna-Theoni , Ramos-Onsins Sebastián E. , Pérez-Enciso Miguel , Castanera Raúl 
Published: January 2024

AbstractStructural variants (SVs) such as deletions, inversions, duplications, and Transposable Element (TE) Insertion Polymorphisms (TIPs) are prevalent in plant genomes and have played an important role in evolution and domestication, as they constitute a significant source of genomic and phenotypic variability. Nevertheless, most methods in quantitative genetics focusing on crop improvement, such as genomic prediction, consider Single Nucleotide Polymorphisms (SNPs) as the only type of genetic marker. Here, we used rice to investigate whether combining the structural and nucleotide genome-wide variation can improve prediction ability of traits when compared to using only SNPs. Moreover, we also examine the potential advantage of Deep Learning (DL) networks over Bayesian Linear models, which have been widely applied in genomic prediction. Specifically, the performance of BayesC and a Bayesian Reproducible Kernel Hilbert space regressions were compared to two different DL architectures, the Multilayer Perceptron, and the Convolution Neural Network. We further explore their prediction ability by using various marker input strategies and found that exploiting structural and nucleotide variation improves prediction ability on complex traits in rice. Also, DL models outperformed Bayesian models in 75% of the studied cases. Finally, DL systematically improved prediction ability of binary traits against the Bayesian models.
Magnetic Microtweezers for High-Throughput Bioseparation in Sub-Nanoliter Droplets
Authors: Dumas Simon , Alexandre Lucile , Richerd Mathilde , Serra Marco , Descroix Stéphanie 
Published: January 2024

AbstractMultiomics studies at single-cell level require small volume manipulation, high throughput analysis and multiplexed detection, characteristics that droplet microfluidics can tackle. However, the initial step of molecules bioseparation remains challenging. Here, we describe a unique magnetic device to trap and extract magnetic particles in sub-nanoliter droplets, for compartmentalisation of detection steps. Relying on electrodeposition of NiFe structures and microfluidic manipulation, this technology has allowed the purification of genetic material at single-cell scale and was able to reach an extraction rate of 72% for a sample of purified oligonucleotides.
ProtHyena: A fast and efficient foundation protein language model at single amino acid Resolution
Authors: Zhang Yiming , Okumura Manabu 
Published: January 2024

AbstractThe emergence of self-supervised deep language models has revolutionized natural language processing tasks and has recently extended its applications to biological sequence analysis. Traditional models, primarily based on the Transformer and BERT architectures, demonstrate substantial effectiveness in various applications. However, these models are inherently constrained by the attention mechanism’s quadratic computational complexityO(L2), limiting their efficiency and the length of context they can process. Addressing these limitations, we introduceProtHyena, a novel approach that leverages the Hyena operator. This innovative methodology circumvents the constraints imposed by attention mechanisms, thereby reducing the time complexity to a subquadratic, enabling the modeling of extra-long protein sequences at the single amino acid level without the need to compress data. ProtHyena is able to achieve, and in many cases exceed, state-of-the-art results in various downstream tasks with only 10% of the parameters typically required by attention-based models. The architecture of ProtHyena presents a highly efficient solution for training protein predictors, offering a promising avenue for fast and efficient analysis of biological sequences.
Cell Type-Agnostic Optical Perturbation Screening Using Nuclear In-Situ Sequencing (NIS-Seq)
Authors: Fandrey Caroline I. , Konopka Peter , Jentzsch Marius , Zackria Afraa , Maasewerd Salie , Latz Eicke , Schmid-Burgk Jonathan L. 
Published: January 2024

ABSTRACTGenome-scale perturbation screening is widely used to identify disease-relevant cellular proteins serving as potential drug targets. However, most biological processes are not compatible with commonly employed perturbation screening methods, which rely on FACS- or growth-based enrichment of cells. Optical pooled screening instead uses fluorescence microscopy to determine the phenotype in single cells, and subsequently to identify individual perturbagens in the same cells. Published methods rely on cytosolic detection of endogenously expressed barcoded transcripts, which limits application to large, transcriptionally active cell types, and often relies on local clusters of clonal cells for unequivocal barcode assignment, thus precluding genome-scale screening for many biological processes. Nuclear In-Situ Sequencing (NIS-Seq) solves these shortcomings by creating bright sequencing signals directly from nuclear genomic DNA, enabling screening any nucleus-containing cell type at high density and high library complexity. We benchmark NIS-Seq by performing three genome-scale optical screens in live cells, identifying key players of inflammation-related cellular pathways.
Orthogonalized human protease control of secreted signals
Authors: Aldrete Carlos A. , Call Connor C. , Sant’Anna Lucas E. , Vlahos Alexander E. , Pei Jimin , Cong Qian , Gao Xiaojing J. 
Published: January 2024

AbstractSynthetic circuits that regulate protein secretion in human cells could support cell-based therapies by enabling control over local environments. While protein-level circuits enable such potential clinical applications, featuring orthogonality and compactness, their non-human origin poses a potential immunogenic risk. Here, we developed Humanized Drug Induced Regulation of Engineered CyTokines (hDIRECT) as a platform to control cytokine activity exclusively using human-derived proteins. We sourced a specific human protease and its FDA-approved inhibitor. We engineered cytokines (IL-2, IL-6, and IL-10) whose activities can be activated and abrogated by proteolytic cleavage. We utilized species specificity and re-localization strategies to orthogonalize the cytokines and protease from the human context that they would be deployed in. hDIRECT should enable local cytokine activation to support a variety of cell-based therapies such as muscle regeneration and cancer immunotherapy. Our work offers a proof of concept for the emerging appreciation of humanization in synthetic biology for human health.
Penalized Semiparametric Estimation for Causal Inference with Possibly Invalid Instruments
Authors: Cao Yunlong , Wang Yuquan , Shi Dapeng , Chen Dong , Hu Yue-Qing 
Published: January 2024

SummaryInferring causal effects with unmeasured confounder is a main challenge in causal inference. Many researchers impose parametric assumptions on the distribution of unmeasured confounder. However, due to the unobservable nature of the unmeasured confounder, it is more reasonable to leave its distribution unrestricted. Another key challenge in causal inference is the involvement of invalid instrumental variables, which may lead to biased inference and possibly misleading scientific conclusions. To this end, we employ a flexible semiparametric model that allows for possibly invalid instruments without specifying the distribution of unmeasured confounder in this work. A penalized semiparametric estimator for causal effects is constructed and its oracle and asymptotic properties are well established for statistical inference. We evaluate the performance of the estimator through simulation studies, revealing that our proposed estimator exhibits asymptotic unbiasedness and robustness in estimating causal effects, along with consistent selection of invalid instruments. We also demonstrate its application using Atherosclerosis Risk in Communities Study data set, which further validates its robustness in the presence of invalid instruments. Additionally, we have implemented the proposed method in R, and the corresponding R code is available for free download.
Development of a Seizure Matching System for Clinical Decision Making in Epilepsy Surgery
Authors: Thomas John , Abdallah Chifaou , Jaber Kassem , Aron Olivier , Doležalová Irena , Gnatkovsky Vadym , Mansilla Daniel , Nevalainen Päivi , Pana Raluca , Schuele Stephan , Singh Jaysingh , Suller-Marti Ana , Urban Alexandra , Hall Jeffery , Dubeau François , Gotman Jean , Frauscher Birgit 
Published: January 2024

AbstractBackground and ObjectivesThe proportion of patients becoming seizure-free after epilepsy surgery has stagnated. Large multi-center stereo-electroencephalography datasets can potentially allow comparing a new patient to past similar cases and make clinical decisions with the knowledge of how similar cases were treated in the past. However, the complexity of these evaluations makes the manual search for similar patients in a large database impractical. We aim to develop an automated system that electrographically and anatomically matches seizures from a patient to those in a database. In addition, since we do not know what features define seizure similarity, particularly considering the various stereo-electroencephalography implantation schemes, we evaluate the agreement and features among experts in classifying seizure similarity.MethodsWe utilized SEEG seizures from consecutive patients who underwent stereo-electroencephalography for epilepsy surgery. Eight international experts evaluated seizure-pair similarity using a four-level similarity score through a graphical user interface. As our primary outcome, we developed and validated an automated seizure matching system by employing a leave-one-expert-out approach. Secondary outcomes included the inter-rater agreement and features for classifying seizure similarity.Results320 SEEG seizures from 95 patients were utilized. The seizure matching system achieved an area-under-the-curve of 0.82 (95% CI, 0.819-0.822), indicating its feasibility. Six distinct seizure similarity features were identified and proved effective: onset region, onset pattern, propagation region, duration, extent of spread, and propagation speed. Among these features, the onset region showed the strongest correlation with expert scores (Spearman’s rho=0.75,p<0.001). Additionally, the moderate inter-rater agreement confirmed the practicality of our approach: for the four-level classification, median agreement was 73.9% (interquartile range, 7%), and beyond-chance Gwet’s kappa was 0.45 (0.16); for the binary classification of similar vs. not related, agreement stood at 71.9% (4.7%) with a kappa of 0.46 (0.13).DiscussionWe demonstrate the feasibility and validity of a stereo-electroencephalography seizure matching system across patients, effectively mirroring the expertise of epileptologists. This novel system can identify patients with seizures similar to that of a patient being evaluated, thus optimizing the treatment plan by considering the treatment and the results of treating similar patients in the past, potentially resulting in an improved surgery outcome.
Metagenomic Analysis and Taxonomy of Bacteria Identified in Primary Endodontic Infections
Authors: Konjhodzic Alma , Brankovic Lajla Hasic , Tahmiscija Irmina , Korac Samra , Dzankovic Aida 
Published: January 2024

ABSTRACTThe study explores the polymicrobial nature of primary endodontic infections using Illumina Next Generation Sequencing. Samples involved in research have been collected from root canals of the patients suffering from pulp and periapical inflammations with no history of endodontic interventions on affected teeth. The study revealed prevalence of different bacterial phyla, classes, orders, and species. Further work will show potential correlations between individual microbiotas and clinical diagnosis.
A nutritional immunity blockade controls extracellular bacterial replication inLegionella pneumophilainfections
Authors: Torres-Escobar Ascención , Wilkins Ashley , Juárez-Rodríguez María D , Circu Magdalena , Latimer Brian , Dragoi Ana-Maria , Ivanov Stanimir S. 
Published: January 2024

AbstractThe accidental human pathogenLegionella pneumophila(Lp) is the etiological agent for a severe atypical pneumonia known as Legionnaires’ disease. In human infections and animal models of disease alveolar macrophages are the primary cellular niche that supports bacterial replication within a unique intracellular membrane-bound organelle. The Dot/Icm apparatus – a type IV secretion system that translocates ∼300 bacterial proteins within the cytosol of the infected cell – is a central virulence factor required for intracellular growth. Mutant strains lacking functional Dot/Icm apparatus are transported to and degraded within the lysosomes of infected macrophages. The early foundational work from Dr. Horwitz’s group unequivocally established thatLegionelladoes not replicate extracellularly during infection – a phenomenon well supported by experimental evidence for four decades. Our data challenges the dogma in the field by demonstrating that macrophages and monocytes provide the necessary nutrients and support robustLegionellaextracellular replication. We show that the previously reported lack of Lp extracellular replication is not a bacteria intrinsic feature but rather a result of robust restriction by serum-derived nutritional immunity factors. Specifically, the host iron-sequestering protein Transferrin was identified as a critical suppressor of Lp extracellular replication in an iron-dependent manner. In iron-overload conditions or in the absence of Transferrin, Lp bypasses growth restriction by IFNψ-primed macrophages though extracellular replication. It is well established that certain risk factors associated with development of Legionnaires’ disease, such as smoking, produce a chronic pulmonary environment of iron-overload. Our work indicates that iron-overload could be an important determinant of severe infection by allowing Lp to overcome nutritional immunity and replicate extracellularly, which in turn would circumvent intracellular cell intrinsic host defenses. Thus, we provide evidence for nutritional immunity as a key underappreciated host defense mechanism inLegionellapathogenesis.
Stability and Volatility of Human Rest-Activity Rhythms: Insights from Very Long Actograms (VLAs)
Authors: Adhyapak Nandani , Abboud Mark A. , Rao Pallavi S.K. , Kar Ananya , Mignot Emmanuel , Delucca Gianluigi , Smagula Stephen F. , Krishnan Vaishnav 
Published: January 2024

AbstractImportanceWrist-worn activity monitors provide biomarkers of health by non-obtrusively measuring the timing and amount of rest and physical activity (rest-activity rhythms, RARs). The morphology and robustness of RARs vary by age, gender, and sociodemographic factors, and are perturbed in various chronic illnesses. However, these are cross-sectionally derived associations from recordings lasting 4-10 days, providing little insights into how RARs vary with time.ObjectiveTo describe how RAR parameters can vary or evolve with time (∼months).Design, Setting and Participants48 very long actograms (“VLAs”, ≥90 days in duration) were identified from subjects enrolled in the STAGES (Stanford Technology, Analytics and Genomics in Sleep) study, a prospective cross-sectional, multisite assessment of individuals > 13 years of age that required diagnostic polysomnography to address a sleep complaint. A single 3-year long VLA (author GD) is also described.Exposures/InterventionNone planned.Main Outcomes and MeasuresFor each VLA, we assessed the following parameters in 14-day windows: circadian/ultradian spectrum, pseudo-F statistic (“F”), cosinor amplitude, intradaily variability, interdaily stability, acrophase and estimates of “sleep” and non-wearing.ResultsIncluded STAGES subjects (n = 48, 30 female) had a median age of 51, BMI of 29.4kg/m2, Epworth Sleepiness Scale score (ESS) of 10/24 and a median recording duration of 120 days. We observed marked within-subject undulations in all six RAR parameters, with many subjects displaying ultradian rhythms of activity that waxed and waned in intensity. When appraised at the group level (nomothetic), averaged RAR parameters remained remarkably stable over a ∼4 month recording period. Cohort-level deficits in average RAR robustness associated with unemployment or high BMI (>29.4) also remained stable over time.Conclusions and RelevanceThrough an exemplary set of months-long wrist actigraphy recordings, this study quantitatively depicts the longitudinal stability and dynamic range of human rest-activity rhythms. We propose that continuous and long-term actigraphy may have broad potential as a holistic, transdiagnostic and ecologically valid monitoring biomarker of changes in chronobiological health. Prospective recordings from willing subjects will be necessary to precisely define contexts of use.