bluuebunny/crossref_metadata_2025 huggingface dataset:
Nonlinear acoustics of an aperture under grazing flow
Authors: Stoychev Alexander K. , Pedergnana Tiemo , Noiray Nicolas
Published: February 2024

This work presents a mathematical model of a dynamically forced, acoustically compact aperture subject to one-sided mean grazing flow in two or three dimensions. By contrast to other simplified theoretical representations of a grazed aperture, the one proposed in this contribution considers some of the nonlinear effects a reduced order model should naturally inherit from the conservation equations governing the primary system’s dynamics. Furthermore, unlike other nonlinear developments, this one is able to reproduce the acoustic forcing amplitude dependence of the fundamental-frequency-based impedance, measured in recent experiments, without relying on empirical parameters. This nonlinear model offers further insight into the dominant physical mechanisms determining the aforementioned behaviour and allows reasonable
a priori
estimates of the aeroacoustic dynamics of the aperture. This could be used as a building block of more sophisticated systems or for the derivation of even simpler representations suitable for acoustic network modelling.
Adaptive evolution and loss of a putative magnetoreceptor in passerines
Authors: Langebrake Corinna , Manthey Georg , Frederiksen Anders , Lugo Ramos Juan S. , Dutheil Julien Y. , Chetverikova Raisa , Solov'yov Ilia A. , Mouritsen Henrik , Liedvogel Miriam
Published: February 2024

Migratory birds possess remarkable accuracy in orientation and navigation, which involves various compass systems including the magnetic compass. Identifying the primary magnetosensor remains a fundamental open question. Cryptochromes (Cry) have been shown to be magnetically sensitive, and Cry4a from a migratory songbird seems to show enhanced magnetic sensitivity
in vitro
compared to Cry4a from resident species. We investigate Cry and their potential involvement in magnetoreception in a phylogenetic framework, integrating molecular evolutionary analyses with protein dynamics modelling. Our analysis is based on 363 bird genomes and identifies different selection regimes in passerines. We show that Cry4a is characterized by strong positive selection and high variability, typical characteristics of sensor proteins. We identify key sites that are likely to have facilitated the evolution of an optimized sensory protein for night-time orientation in songbirds. Additionally, we show that Cry4 was lost in hummingbirds, parrots and Tyranni (Suboscines), and thus identified a gene deletion, which might facilitate testing the function of Cry4a in birds. In contrast, the other avian Cry (Cry1 and Cry2) were highly conserved across all species, indicating basal, non-sensory functions. Our results support a specialization or functional differentiation of Cry4 in songbirds which could be magnetosensation.
An image segmentation pipeline optimized for human microglia uncovers sources of morphological diversity in Alzheimer’s disease
Authors: De Jager Robert M. , Lee Annie J. , Sigalov Alina , Taga Mariko
Published: February 2024

Alzheimer’s disease (AD) is a neurodegenerative disorder characterized by cognitive decline and the accumulation of beta-amyloid plaques and neurofibrillary tangles. Microglia, the resident immune cells in the brain, play a crucial role in AD pathology, particularly in relation to amyloid plaques. However, the exact role of plaque-associated microglia and their morphological changes in AD progression remains under debate. In this study, we aimed to establish an automated image segmentation and analysis pipeline optimized for the detection of human microglia, and we assessed its utility by systematically investigating topological relationships between microglia morphology and amyloid plaques.

We accessed post-mortem brain tissue samples from persons with AD and utilized immunofluorescence staining to label microglia (IBA1) and amyloid plaques (Aβ1-42). The acquired images were processed using CellProfiler, an open-source image analysis software, to automatically segment microglia and measure their morphological features. Specifically, since activated (stage III) microglia have a condensed morphology with retracted processes, we prioritized a morphological parameter called “compactness” in our analyses to capture shape changes in microglial found in proximity to amyloid plaques.

Our results revealed that microglia unassociated with plaques (Mip-) were more abundant than microglia associated with plaques (Mip+) in the Dorsolateral Prefrontal Cortex (DLPFC) of both men and women with AD. Furthermore, we observed that Mip+exhibited a significantly more ramified shape and had a higher expression of the IBA1 microglial marker gene compared to Mip. There were no significant differences in microglia morphology between men and women.

Our study highlights the utility of automated image analysis in characterizing detailed microglia morphology at the single-cell level and its relationship with AD pathology.
Toward Sustainable Biocultural Tourism: An Integrated Spatial Analysis of Cultural and Biodiversity Richness in Colombia
Authors: Echeverri Alejandra , Batista Natasha M. , Wolny Stacie , Herrera-R Guido A. , Andrade-Rivas Federico , Bailey Allison , Cardenas-Navarrete Anaid , Dávila Arenas Armando , Díaz-Salazar Andres Felipe , Hernandez Katherine Victoria , Langhans Kelley E. , Mateus-Aguilar Bryam E. , Levey Dallas , Neill Andrew , Nguyen Oliver , Suárez-Castro Andres Felipe , Zapata Felipe , Ocampo-Peñuela Natalia
Published: February 2024

Tourism plays a vital role in both economic development and depending on the scale, it can also aid environmental conservation. Tourism planning often considers culture-based and nature-based tourism separately, failing to recognize the synergies between them, with the potential to market locations as biocultural destinations.

Using Colombia as a case study, we created metrics of taxonomic biological diversity as measured by vertebrate species richness (including birds, mammals, freshwater fishes, reptiles, and amphibians) and institutionalized cultural richness (by counting the number of UNESCO world heritage sites, intangible cultural heritage sites, museums, endemic music festivals, Afro-Colombian territories, and Indigenous reserves), and evaluated the spatial correlations between them.

To determine biocultural tourism potential, we evaluated whether biocultural richness was accessible, and mapped potential biocultural tourism supply. By mapping areas of sports fisheries, birdwatching destinations, and airport arrivals we also estimated spatial demand. We also analyzed the difference between demand and supply to assess the realized and untapped potential for biocultural destinations.

While biocultural richness is high in the Amazon, Pacific, and Caribbean regions, we found that there are no win-win-win locations where culture, species richness, and accessibility are all high. Areas with great potential for biocultural tourism development largely coincide with designated Indigenous reserves and Afro-Colombian territories.

This study underscores the power of integrating cultural and biological variables to reshape the tourism sector. Our paper offers practical recommendations for policymakers, conservation organizations, and local communities seeking to create transformative and inclusive tourism experiences.
Towards a Survival Risk Prediction Model for Metastatic NSCLC Patients on Durvalumab Using Whole-Lung CT Radiomics
Authors: Patwardhan Kedar , RaviPrakash Harish , Nikolaou Nikos , Gonzalez-García Ignacio , Salazar José Domingo , Metcalfe Paul , Reischl Joachim
Published: February 2024

Background

Existing criteria for predicting patient survival from immunotherapy are primarily centered on the PD-L1 status of patients. We tested the hypothesis that noninvasively captured baseline whole-lung radiomics features from CT images, baseline clinical parameters, combined with advanced machine learning approaches, can help to build models of patient survival that compare favorably with PD-L1 status for predicting ‘less-than-median-survival risk’ in the metastatic NSCLC setting for patients on durvalumab. With a total of 1062 patients, inclusive of model training and validation, this is the largest such study yet.

Methods

To ensure a sufficient sample size, we combined data from treatment arms of three metastatic NSCLC studies. About 80% of this data was used for model training, and the remainder was held-out for validation. We first trained two independent models; Model-C trained to predict survival using clinical data; and Model-R trained to predict survival using whole-lung radiomics features. Finally, we created Model-C+R which leveraged both clinical and radiomics features.

Results

The classification accuracy (for median survival) of Model-C, Model-R, and Model-C+R was 63%, 55%, and 68% respectively. Sensitivity analysis of survival prediction across different training and validation cohorts showed concordance indices ([95 percentile]) of 0.64 ([0.63, 0.65]), 0.60 ([0.59, 0.60]), and 0.66 ([0.65,0.67]), respectively. We additionally evaluated generalization of these models on a comparable cohort of 144 patients from an independent study, demonstrating classification accuracies of 65%, 62%, and 72% respectively.

Conclusion

Machine Learning models combining baseline whole-lung CT radiomic and clinical features may be a useful tool for patient selection in immunotherapy. Further validation through prospective studies is needed.
OligoFormer: an accurate and robust prediction method for siRNA design
Authors: Bai Yilan , Zhong Haochen , Wang Taiwei , Lu Zhi John
Published: February 2024

Motivation

RNA interference(RNAi) has become a widely used experimental approach for post-transcriptional regulation and is increasingly showing its potential as future targeted drugs. However, the prediction of highly efficient siRNAs(small interfering RNA) is still hindered by dataset biases, the inadequacy of prediction methods, and the presence of off-target effects. To overcome these limitations, we propose an accurate and robust prediction method, OligoFormer, for siRNA design.

Results

OligoFormer comprises three different modules including thermodynamic calculation, RNA-FM module, and Oligo encoder. Oligo encoder is the core module based on the transformer encoder. Taking siRNA and mRNA sequences as input, OligoFormer can obtain thermodynamic parameters, RNA-FM embedding, and Oligo embedding through these three modules, respectively. We carefully benchmarked OligoFormer against 5 comparable methods on siRNA efficacy datasets. OligoFormer outperforms all the other methods, with an average improvement of 9% in AUC and 10.7% in F1 score in our inter-dataset validation. We also provide a comprehensive pipeline with prediction of siRNA efficacy and off-target effects using PITA score and TargetScan score. The ablation study shows RNA-FM module and thermodynamic parameters improved the performance and accelerated convergence of OligoFormer. The saliency map by gradient backpropagation shows certain base preferences in initial and terminal region of siRNAs.
Characterization Of Bitter Taste Receptor Dependent Autophagy in Oral Epithelial Cells
Authors: Singh Nisha , Ghavami Saeid , Chelikani Prashen
Published: February 2024

Microbial dysbiosis is an important trigger in the development of oral diseases. Oral keratinocytes or gingival epithelial cells (GECs) offer protection against various microbial insults. Recent studies suggest GECs expressed higher level of bitter taste receptor 14 (T2R14) compared to other taste receptors and toll-like receptors and acts as innate immune sentinels. Macroautophagy or autophagy is a cellular conserved process involved in the regulation of host innate immune responses against microbial infection. Here, we describe a robust method for evaluation of T2R14-dependent autophagy flux in GECs. Autophagy flux was detected using western blot analysis in GECs and further was confirmed using Acridine Orange dependent flow cytometry analysis.

Workflow

Graphical abstract

Schematic showing the methodology (Western blot and flow cytometry) used for assessment of autophagy flux in GEC (created with Biorender). GEC: Gingival epithelial cells, BafA1: Bafilomycin, Rapa: Rapamycin, LC3-II: microtubule associated light chain protein, p62: sequestosome 1
The spread of pESI-mediated extended-spectrum cephalosporin resistance inSalmonellaserovars - Infantis, Senftenberg, and Alachua isolated from food animal sources in the United States
Authors: Li Cong , Tate Heather , Huang Xinyang , Hsu Chih-Hao , Harrison Lucas B. , Zhao Shaohua , Fortenberry Gamola Z. , Dessai Uday , McDermott Patrick F. , Strain Errol A.
Published: February 2024

The goal of this study is to investigate the origin, prevalence, and evolution of the pESI megaplasmid inSalmonellaisolated from animals, foods, and humans. We queried 510,097Salmonellagenomes under the National Center for Biotechnology Information (NCBI) Pathogen Detection (PD) database for the presence of potential sequences containing the pESI plasmid in animal, food, and environmental sources. The presence of the pESI megaplasmid was confirmed by using seven plasmid-specific markers (rdA,pilL,SogS,TrbA,ipf,ipr2 andIncFIB(pN55391)). The plasmid and chromosome phylogeny of these isolates was inferred from single nucleotide polymorphisms (SNPs). Our search resolved sixSalmonellaclusters carrying the pESI plasmid. Four were emergentSalmonellaInfantis clusters, and one each belonged to serovar Senftenberg and Alachua. The Infantis cluster with a pESI plasmid carryingblaCTX-M-65gene was the biggest of the four emergent Infantis clusters, with over 10,000 isolates. This cluster was first detected in South America and has since spread widely in United States. Over time the composition of pESI in United States has changed with the average number of resistance genes showing a decrease from 9 in 2014 to 5 in 2022, resulting from changes in gene content in two integrons present in the plasmid. A recent and emerging cluster of Senftenberg, which carries theblaCTX-M-65gene and is primarily associated with turkey sources, was the second largest in the United States. SNP analysis showed that this cluster likely originated in North Carolina with the recent acquisition of the pESI plasmid. A single Alachua isolate from turkey was also found to carry the pESI plasmid containingblaCTX-M-65gene. The study of the pESI plasmid, its evolution and mechanism of spread can help us in developing appropriate strategies for the prevention and further spread of this multi-drug resistant plasmid inSalmonellain poultry and humans.
Different types of social links contrastingly shape reproductive wellbeing in a multi-level society of wild songbirds
Authors: Gokcekus Samin , Firth Josh A. , Cole Ella F. , Sheldon Ben C. , Albery Gregory F.
Published: February 2024

The social environment has diverse consequences for individuals’ welfare, health, reproductive success, and survival. This environment consists of different kinds of dyadic bonds that exist at different levels; in many social species, smaller social units come together in larger groups, creating multilevel societies. In great tits (Parus major), individuals have four major types of dyadic bonds: pair mates, breeding neighbours, flockmates, and spatial associates, all of which have been previously linked to fitness outcomes. Here, we show that these different types of dyadic bonds are differentially linked with subsequent reproductive success metrics in this wild population and that considering spatial effects provides further insights into these relationships. We provide evidence that more social individuals had a higher number of fledglings, and individuals with more spatial associates had smaller clutch sizes. We also show individuals with stronger bonds with their pair mate had earlier lay dates. Our study highlights the importance of considering different types of dyadic relationships when investigating the relationship between wellbeing and sociality, and the need for future work aimed at experimentally testing these relationships, particularly in spatially structured populations.
TPPB modulates PKC activity to attenuate neuroinflammation and ameliorate experimental multiple sclerosis
Authors: Shanmukha Shruthi , Godfrey Wesley H. , Gharibani Payam , Lee Judy J. , Guo Yu , Deng Xiaojing , Wender Paul A. , Kornberg Michael D. , Kim Paul M.
Published: February 2024

Protein kinase C (PKC) plays a key role in modulating the activities of the innate immune cells of the central nervous system (CNS). A delicate balance between pro-inflammatory and regenerative activities by microglia and CNS-associated macrophages is necessary for the proper functioning of the CNS. Thus, a maladaptive activation of these CNS innate immune cells results in neurodegeneration and demyelination associated with various neurologic disorders, such as multiple sclerosis (MS) and Alzheimer’s disease. Prior studies have demonstrated that modulation of PKC activity by bryostatin-1 (bryo-1) and its analogs (bryologs) attenuates the pro-inflammatory processes by microglia/CNS macrophages and alleviates the neurologic symptoms in experimental autoimmune encephalomyelitis (EAE), an MS animal model. Here, we demonstrate that (2S,5S)-(E,E)-8-(5-(4-(trifluoromethyl)phenyl)-2,4-pentadienoylamino)benzolactam (TPPB), a structurally distinct PKC modulator, has a similar effect to bryo-1 on CNS innate immune cells both in vitro and in vivo, attenuating neuroinflammation and resulting in CNS regeneration and repair. This study identifies a new structural class of PKC modulators, which can therapeutically target CNS innate immunity as a strategy to treat neuroinflammatory and neurodegenerative disorders.
Commonly used software tools produce conflicting and overly-optimistic AUPRC values
Authors: Chen Wenyu , Miao Chen , Zhang Zhenghao , Fung Cathy Sin-Hang , Wang Ran , Chen Yizhen , Qian Yan , Cheng Lixin , Yip Kevin Y. , Tsui Stephen Kwok-Wing , Cao Qin
Published: February 2024

The precision-recall curve (PRC) and the area under it (AUPRC) are useful for quantifying classification performance. They are commonly used in situations with imbalanced classes, such as cancer diagnosis and cell type annotation. We evaluated 10 popular tools for plotting PRC and computing AUPRC, which were collectively used in>3,000 published studies. We found the AUPRC values computed by the tools rank classifiers differently and some tools produce overly-optimistic results.
DNA N-gram Analysis Framework (DNAnamer): A generalized N-gram frequency analysis framework for the supervised classification of DNA sequences
Authors: Malamon John S.
Published: February 2024

In 1948, Claude Shannon published a mathematical system describing the probabilistic relationships between the letters of a natural language and their subsequent order or syntax structure. By counting unique, reoccurring sequences of letters called N-grams, this language model was used to generate recognizable English sentences from N-gram frequency probability tables. More recently, N-gram analysis methodologies have been successful in addressing many complex problems in a variety of domains, from language processing to genomics. One such example is the common use of N-gram frequency patterns and supervised classification models to determine authorship and plagiarism. In this methodology, DNA is a language model where nucleotides are analogous to the letters of a word and nucleotide N-grams are analogous to the words of a sentence. Because DNA contains highly conserved and identifiable nucleotide sequence frequency patterns, this approach can be applied to a variety of classification and data reduction problems, such as identifying species based on unknown DNA segments. Other useful applications of this methodology include the identification of functional gene elements, sequence contamination, and sequencing artifacts. To this end, I present DNAnamer, a generalized and extensible methodological framework and analysis toolkit for the supervised classification of DNA sequences based on their N-gram frequency patterns.
Inferring the Joint Distribution of Structural and Functional Connectivity in the Human Brain using UNIT-DDPM
Authors: Canamedi Varun
Published: February 2024

Abstract

The structural wiring of the brain is expected to produce a repertoire of functional networks, across time, context, individuals and vice versa. Therefore, a method to infer the joint distribution of structural and functional connectomes would be of immense value. However, existing approaches only provide deterministic snapshots of the structure-function relationship. Here we use an unpaired image translation method, UNIT-DDPM, that infers a joint distribution of structural and functional connectomes. Our approach allows estimates of variability of function for a given structure and vice versa. Furthermore, we found a significant improvement in prediction accuracy among individual brain networks, implicating a tighter coupling of structure and function than previously understood. Also, our approach has the ad-vantage of not relying on paired samples for training. This novel approach provides a means for identifying regions of consistent structure-function coupling.
Explicit and implicit locomotor learning in individuals with chronic hemiparetic stroke
Authors: Wood Jonathan M. , Thompson Elizabeth , Wright Henry , Festa Liam , Morton Susanne M. , Reisman Darcy S. , Kim Hyosub E.
Published: February 2024

Motor learning involves both explicit and implicit processes that are fundamental for acquiring and adapting complex motor skills. However, stroke may damage the neural substrates underlying explicit and/or implicit learning, leading to deficits in overall motor performance. While both learning processes are typically used in concert in daily life and rehabilitation, no gait studies have determined how these processes function together after stroke when tested during a task that elicits dissociable contributions from both. Here, we compared explicit and implicit locomotor learning in individuals with chronic stroke to age- and sex-matched neurologically intact controls. We assessed implicit learning using split-belt adaptation (where two treadmill belts move at different speeds). We assessed explicit learning (i.e., strategy-use) using visual feedback during split-belt walking to help individuals explicitly correct for step length errors created by the split-belts. The removal of visual feedback after the first 40 strides of split-belt walking, combined with task instructions, minimized contributions from explicit learning for the remainder of the task. We utilized a multi-rate state-space model to characterize individual explicit and implicit process contributions to overall behavioral change. The computational and behavioral analyses revealed that, compared to controls, individuals with chronic stroke demonstrated deficits in both explicit and implicit contributions to locomotor learning, a result that runs counter to prior work testing each process individually during gait. Since post-stroke locomotor rehabilitation involves interventions that rely on both explicit and implicit motor learning, future work should determine how locomotor rehabilitation interventions can be structured to optimize overall motor learning.

New and noteworthy

Motor learning involves both implicit and explicit processes, the underlying neural substrates of which could be damaged by after stroke. While both learning processes are typically used in concert in daily life and rehabilitation, no gait studies have determined how these processes function together after stroke. Using a locomotor task that elicits dissociable contributions from both processes and computational modeling, we found evidence that chronic stroke causes deficits in both explicit and implicit locomotor learning.
Frequency of Dengue Virus-Specific T Cells is related to Infection Outcome in Endemic Settings
Authors: Gálvez Rosa Isela , Martínez-Pérez Amparo , Alexandar Escarrega E. , Singh Tulika , Zambrana José Víctor , Balmaseda Ángel , Harris Eva , Weiskopf Daniela
Published: February 2024

Dengue is widespread in tropical and subtropical regions globally and leads to a considerable burden of disease. Annually, dengue virus (DENV) causes up to 400 million infections, of which ~25% present with clinical symptoms ranging from mild to fatal. Despite its significance as a growing public health concern, the development of effective DENV vaccines has been highly challenging. One of the reasons is the lack of comprehensive understanding of the influence exerted by prior DENV infections and immune responses with cross-reactive properties. To investigate this, we collected samples from a pediatric cohort study in dengue-endemic Managua, Nicaragua. We characterized T cell responses in a group of 71 healthy children who had previously experienced one or more natural DENV infections and who, within one year after sample collection, had a subsequent DENV infection that was either symptomatic (n=25) or inapparent (n=46, absence of clinical disease). Thus, our study was designed to investigate the impact of pre-existing DENV specific T cell responses on the clinical outcomes of subsequent DENV infection. We assessed the DENV specific T cell responses using an activation-induced marker assay (AIM). Children who had experienced only one prior DENV infection displayed heterogeneous DENV specific CD4+and CD8+T cell frequencies. In contrast, children who had experienced two or more DENV infections showed significantly higher frequencies of DENV specific CD4+and CD8+T cells that were associated with inapparent as opposed to symptomatic outcomes in the subsequent DENV infection. Taken together, these findings demonstrate the protective role of DENV specific T cells against symptomatic DENV infection and constitute an advancement toward identifying protective immune correlates against dengue fever and clinical disease.

Graphical Abstract
Feasibility of decoding covert speech in ECoG with a Transformer trained on overt speech
Authors: Komeiji Shuji , Mitsuhashi Takumi , Iimura Yasushi , Suzuki Hiroharu , Sugano Hidenori , Shinoda Koichi , Tanaka Toshihisa
Published: February 2024

Several attempts for speech brain–computer interfacing (BCI) have been made to decode phonemes, sub-words, words, or sentences using invasive measurements, such as the electrocorticogram (ECoG), during auditory speech perception, overt speech, or imagined (covert) speech. Decoding sentences from covert speech is a challenging task. Sixteen epilepsy patients with intracranially implanted electrodes participated in this study, and ECoGs were recorded during overt speech and covert speech of eight Japanese sentences, each consisting of three tokens. In particular, Transformer neural network model was applied to decode text sentences from covert speech, which was trained using ECoGs obtained during overt speech. We first examined the proposed Transformer model using the same task for training and testing, and then evaluated the model’s performance when trained with overt task for decoding covert speech. The Transformer model trained on covert speech achieved an average token error rate (TER) of 46.6% for decoding covert speech, whereas the model trained on overt speech achieved a TER of 46.3% (p >0.05;d= 0.07). Therefore, the challenge of collecting training data for covert speech can be addressed using overt speech. The performance of covert speech can improve by employing several overt speeches.
DecipheringStaphylococcus aureus-host dynamics using dual activity-based protein profiling of ATP-interacting proteins
Authors: Ahator Stephen Dela , Hegstad Kristin , Lentz Christian S. , Johannessen Mona
Published: February 2024

The utilization of ATP within cells plays a fundamental role in cellular processes that are essential for the regulation of host-pathogen dynamics and the subsequent immune response. This study focuses on ATP-binding proteins to dissect the complex interplay betweenStaphylococcus aureusand human cells, particularly macrophages (THP-1) and keratinocytes (HaCaT), during an intracellular infection. A snapshot of the various protein activity and function is provided using a desthiobiotin-ATP probe, which targets ATP-interacting proteins. InS. aureus, we observe enrichment in pathways required for nutrient acquisition, biosynthesis and metabolism of amino acids and energy metabolism when located inside human cells. Additionally, the direct profiling of the protein activity revealed specific adaptations ofS. aureusto the keratinocytes and macrophages. Mapping the differentially activated proteins to biochemical pathways in the human cells with intracellular bacteria revealed cell-type specific adaptations to bacterial challenges where THP-1 cells prioritized immune defenses, autophagic cell death, and inflammation. In contrast, HaCaT cells emphasized barrier integrity and immune activation. We also observe bacterial modulation of host processes and metabolic shifts. These findings offer valuable insights into the dynamics ofS. aureus-host cell interactions, shedding light on modulating host immune responses toS. aureus, which could involve developing immunomodulatory therapies.

Importance

This study uses a chemoproteomics approach to target active ATP-interacting proteins and examines the dynamic proteomic interactions betweenS. aureusand human cell lines THP-1 and HaCaT. It uncovers the distinct responses of macrophages and keratinocytes during bacterial infection.S. aureusdemonstrated a tailored response to the intracellular environment of each cell type and adaptation during exposure to professional and non-professional phagocytes. It also highlights strategies employed byS. aureusto persist within host cells. This study offers significant insights into the human cell response toS. aureusinfection, illuminating the complex proteomic shifts that underlie the defense mechanisms of macrophages and keratinocytes. Notably, the study underscores the nuanced interplay between the host’s metabolic reprogramming and immune strategy, suggesting potential therapeutic targets for enhancing host defense and inhibiting bacterial survival. The findings enhance our understanding of host-pathogen interactions and can inform the development of targeted therapies againstS. aureusinfections.

Graphical Abstract
Deep Normative Tractometry for Identifying Joint White Matter Macro- and Micro-structural Abnormalities in Alzheimer’s Disease
Authors: Feng Yixue , Chandio Bramsh Q. , Villalon-Reina Julio E. , Benavidez Sebastian , Chattopadhyay Tamoghna , Chehrzadeh Sasha , Laltoo Emily , Thomopoulos Sophia I. , Joshi Himanshu , Venkatasubramanian Ganesan , John John P. , Jahanshad Neda , Thompson Paul M.
Published: February 2024

This study introduces the Deep Normative Tractometry (DNT) framework, that encodes the joint distribution of both macrostructural and microstructural profiles of the brain white matter tracts through a variational autoencoder (VAE). By training on data from healthy controls, DNT learns the normative distribution of tract data, and can delineate along-tract micro- and macro-structural abnormalities. Leveraging a large sample size via generative pre-training, we assess DNT’s generalizability using transfer learning on data from an independent cohort acquired in India. Our findings demonstrate DNT’s capacity to detect widespread diffusivity abnormalities along tracts in mild cognitive impairment and Alzheimer’s disease, aligning closely with results from the Bundle Analytics (BUAN) tractometry pipeline. By incorporating tract geometry information, DNT may be able to distinguish disease-related abnormalities in anisotropy from tract macrostructure, and shows promise in enhancing fine-scale mapping and detection of white matter alterations in neurodegenerative conditions.
Sleep need driven oscillation of glutamate synaptic phenotype
Authors: Vogt K.E. , Kulkarni A. , Pandey R. , Dehnad M. , Konopka G. , Greene R.W.
Published: February 2024

Sleep loss increases AMPA-synaptic strength and number in the neocortex. However, this is only part of the synaptic sleep loss response. We report increased AMPA/NMDA EPSC ratio in frontal-cortical pyramidal neurons of layers 2-3. Silent synapses are absent, decreasing the plastic potential to convert silent NMDA to active AMPA synapses. These sleep loss changes are recovered by sleep. Sleep genes are enriched for synaptic shaping cellular components controlling glutamate synapse phenotype, overlap with autism risk genes and are primarily observed in excitatory pyramidal neurons projecting intra-telencephalically. These genes are enriched with genes controlled by the transcription factor, MEF2c and its repressor, HDAC4. Sleep genes can thus provide a framework within which motor learning and training occurs mediated by sleep-dependent oscillation of glutamate-synaptic phenotypes.
Identifying Interpretable Latent Factors with Sparse Component Analysis
Authors: Zimnik Andrew J. , Ames K. Cora , An Xinyue , Driscoll Laura , Lara Antonio H. , Russo Abigail A. , Susoy Vladislav , Cunningham John P. , Paninski Liam , Churchland Mark M. , Glaser Joshua I.
Published: February 2024

In many neural populations, the computationally relevant signals are posited to be a set of ‘latent factors’ – signals shared across many individual neurons. Understanding the relationship between neural activity and behavior requires the identification of factors that reflect distinct computational roles. Methods for identifying such factors typically require supervision, which can be suboptimal if one is unsure how (or whether) factors can be grouped into distinct, meaningful sets. Here, we introduce Sparse Component Analysis (SCA), an unsupervised method that identifies interpretable latent factors. SCA seeks factors that are sparse in time and occupy orthogonal dimensions. With these simple constraints, SCA facilitates surprisingly clear parcellations of neural activity across a range of behaviors. We applied SCA to motor cortex activity from reaching and cycling monkeys, single-trial imaging data fromC. elegans, and activity from a multitask artificial network. SCA consistently identified sets of factors that were useful in describing network computations.
Combining Unity with machine vision to create low latency, flexible, and simple virtual realities
Authors: Ogawa Yuri , Aoukar Raymond , Leibbrandt Richard , Manger Jake S , Bagheri Zahra M , Turnbull Luke , Johnston Chris , Kaushik Pavan K , Hemmi Jan M , Nordström Karin
Published: February 2024

In recent years, virtual reality arenas have become increasingly popular for quantifying visual behaviors. By using the actions of a constrained animal to control the visual scenery, the animal is provided the perception of moving through a simulated environment. As the animal is constrained in space, this allows detailed behavioral quantification. Additionally, as the world is generally computer-generated this allows for mechanistic quantification of visual triggers of behavior.

We created a novel virtual arena combining machine vision with the gaming engine Unity. For tethered flight, we enhanced an existing multi-modal virtual reality arena, MultiMoVR (Kaushik et al., 2020) but tracked hoverfly wing movements using DeepLabCut-live (DLC-live, Kane et al., 2020). For trackball experiments, we recorded the motion of a ball that a tethered crab was walking on using FicTrac (Moore et al., 2014). In both cases, real-time tracking was interfaced with Unity to control the movement of the tethered animals’ avatars in the virtual world. We developed a user-friendly Unity Editor interface, CAVE, to simplify experimental design and data storage without the need for coding.

We show that both the DLC-live-Unity and the FicTrac-Unity configurations close the feedback loop effectively with small delays, less than 50 ms. Our FicTrac-Unity integration highlighted the importance of closed-loop feedback by reducing behavioral artifacts exhibited by the crabs in open-loop scenarios. We show thatEristalis tenaxhoverflies, using the DLC-live-Unity integration, navigate towards flowers. The effectiveness of our CAVE interface is shown by implementing experimental sequencing control based on avatar proximity to virtual structures.

Our results show that combining Unity with machine vision tools such as DLC-live and FicTrac provides an easy and flexible virtual reality (VR) environment that can be readily adjusted to new experiments and species. This can be implemented programmatically in Unity, or by using our new tool CAVE, which allows users to design and implement new experiments without programming in code. We provide resources for replicating experiments and our interface CAVE via GitHub, together with user manuals and instruction videos, for sharing with the wider scientific community.
Percutaneous Nephrostomy Guidance by a Convolutional Neural Network Based Endoscopic Optical Coherence Tomography System
Authors: Wang Chen , Calle Paul , Yan Feng , Zhang Qinghao , Fung Kar-ming , Pan Chongle , Tang Qinggong
Published: February 2024

Percutaneous nephrostomy (PCN) is a commonly used procedure for kidney surgeries. However, difficulties persist in precisely locating the PCN needle tip during its insertion into the kidney. Challenges for PCN needle guidance exist in two aspects: 1) Accurate tissue recognition, and 2) Renal blood vessel detection. In this study, we demonstrated an endoscopic optical coherence tomography (OCT) system for PCN needle guidance. Human kidney samples are utilized in the experiments. Different renal tissues including: 1) cortex, 2) medulla, 3) calyx, 4) fat, and 5) pelvis can be clearly distinguished based on their OCT imaging features. We conduct kidney perfusion experiments to mimic the renal blood flow. Our system can efficiently detect the blood flow in front of PCN needle using Doppler OCT function. To improve surgical guidance efficiency and alleviate the workload of radiologists, we employ convolutional neural network (CNN) methods to automate the procedure. Three CNN models including ResNet50, InceptionV3, and Xception were applied for tissue classification. All of them demonstrate promising prediction results, with InceptionV3 achieving the highest recognition accuracy of 99.6%. For automatic blood vessel detection, nnU-net was applied, and it exhibited intersection over unions (IoU) values of 0.8917 for blood vessel and 0.9916 for background.
Functional networks of reward and punishment processing and their molecular profiles predicting the severity of young adult drinking
Authors: Li Yashuang , Yang Lin , Hao Dongmei , Chen Yu , Li Bao , Liu Youjun , Ye-Lin Yiyao , Li Chiang-Shan R. , Li Guangfei
Published: February 2024

Background

Alcohol misuse is associated with altered punishment and reward processing. Here, we investigated neural network responses to reward and punishment and the molecular profiles of the connectivity features predicting alcohol use severity in young adults.

Methods

We curated the Human Connectome Project data and employed connectome-based predictive modeling (CPM) to examine how functional connectivity (FC) features during wins and losses associated with alcohol use severity in 981 young adults. Alcohol use severity was quantified by the first principal component of principal component analysis of all drinking measures of the Semi-Structured Assessment for the Genetics of Alcoholism. We combined the CPM findings and JuSpace toolbox to characterize the molecular profiles of the network connectivity features of alcohol use severity.

Results

The connectomics predicting alcohol use severity appeared specific, comprising less than 0.12% of all connectivity features. These connectivities featured the medial frontal, motor/sensory, and cerebellum/brainstem networks during punishment processing and medial frontal, fronto-parietal, and motor/sensory networks during reward processing. Spatial correlation analyses showed that these networks were associated predominantly with serotonergic and GABAa signaling.

Conclusions

A distinct pattern of network connectivity predicted alcohol use severity in young adult drinkers. These network features were associated with the serotonergic and GABAa signaling. These “neural fingerprints” help in elucidating the impact of alcohol misuse on the brain and providing evidence of new targets for future intervention.
αKG-mediated carnitine synthesis promotes homologous recombination via histone acetylation
Authors: Uboveja Apoorva , Huang Zhentai , Buj Raquel , Amalric Amandine , Wang Hui , Tangudu Naveen Kumar , Cole Aidan R. , Megill Emily , Kantner Daniel , Chatoff Adam , Ahmad Hafsah , Marcinkiewicz Mariola M. , Disharoon Julie A. , Graff Sarah , Dahl Erika S. , Hempel Nadine , Stallaert Wayne , Sidoli Simone , Bitler Benjamin G. , Long David T. , Snyder Nathaniel W. , Aird Katherine M.
Published: February 2024

Homologous recombination (HR) deficiency enhances sensitivity to DNA damaging agents commonly used to treat cancer. In HR-proficient cancers, metabolic mechanisms driving response or resistance to DNA damaging agents remain unclear. Here we identified that depletion of alpha-ketoglutarate (αKG) sensitizes HR-proficient cells to DNA damaging agents by metabolic regulation of histone acetylation. αKG is required for the activity of αKG-dependent dioxygenases (αKGDDs), and prior work has shown that changes in αKGDD affect demethylases. Using a targeted CRISPR knockout library consisting of 64 αKGDDs, we discovered that Trimethyllysine Hydroxylase Epsilon (TMLHE), the first and rate-limiting enzyme inde novocarnitine synthesis, is necessary for proliferation of HR-proficient cells in the presence of DNA damaging agents. Unexpectedly, αKG-mediated TMLHE-dependent carnitine synthesis was required for histone acetylation, while histone methylation was affected but dispensable. The increase in histone acetylation via αKG-dependent carnitine synthesis promoted HR-mediated DNA repair through site- and substrate-specific histone acetylation. These data demonstrate for the first time that HR-proficiency is mediated through αKG directly influencing histone acetylation via carnitine synthesis and provide a metabolic avenue to induce HR-deficiency and sensitivity to DNA damaging agents.
Microbiome-derived metabolite effects on intestinal barrier integrity and immune cell response to infection
Authors: Adams Lauren , Li Xiang , Burchmore Richard , Goodwin Richard , Wall Daniel M.
Published: February 2024

The gut microbiota exerts a significant influence on human health and disease. While compositional changes in the gut microbiota in specific diseases can easily be determined, we lack a detailed mechanistic understanding of how these changes exert effects at the cellular level. However, the putative local and systemic effects on human physiology that are attributed to the gut microbiota are clearly being mediated through molecular communication. Here we determined the effects of a number of gut microbiome-derived metabolites on the first line of defence in the gut. Usingin vitromodels of intestinal barrier integrity, and studying the interaction of macrophages with pathogenic and non-pathogenic bacteria, we could ascertain the influence of these metabolites at the cellular level at physiologically relevant concentrations. Many metabolites exerted competing influences on intestinal epithelial or immune cells, specific metabolite effects were noted on barrier function, polarised cytokine release and the lifespan of metabolite treated cells. Our findings reiterate the complexity of understanding microbiome effects on host physiology with many metabolites having contrasting effects on host cells. However, our results underline that microbiome metabolites are crucial mediators of barrier function and the innate response to infection. Understanding the effects of these metabolites at the cellular level will allow us to move towards a better mechanistic understanding of microbiome influence over host physiology, a crucial step in advancing microbiome research.
A pharmacological toolkit for human microglia identifies Topoisomerase I inhibitors as immunomodulators for Alzheimer’s disease
Authors: Haage Verena , Tuddenham John F. , Comandante-Lou Natacha , Bautista Alex , Monzel Anna , Chiu Rebecca , Fujita Masashi , Garcia Frankie G. , Bhattarai Prabesh , Patel Ronak , Buonfiglioli Alice , Idiarte Juan , Herman Mathieu , Rinderspacher Alison , Mela Angeliki , Zhao Wenting , Argenziano Michael G. , Furnari Julia L. , Banu Matei A. , Landry Donald W. , Bruce Jeffrey N. , Canoll Peter , Zhang Ya , Nuriel Tal , Kizil Caghan , Sproul Andrew A. , de Witte Lotje D. , Sims Peter A. , Menon Vilas , Picard Martin , De Jager Philip L.
Published: February 2024

Abstract

While efforts to identify microglial subtypes have recently accelerated, the relation of transcriptomically defined states to function has been largely limited toin silicoannotations. Here, we characterize a set of pharmacological compounds that have been proposed to polarize human microglia towards two distinct states – one enriched for AD and MS genes and another characterized by increased expression of antigen presentation genes. Using different model systems including HMC3 cells, iPSC-derived microglia and cerebral organoids, we characterize the effect of these compounds in mimicking human microglial subtypesin vitro. We show that the Topoisomerase I inhibitor Camptothecin induces a CD74high/MHChighmicroglial subtype which is specialized in amyloid beta phagocytosis. Camptothecin suppressed amyloid toxicity and restored microglia back to their homeostatic state in a zebrafish amyloid model. Our work provides avenues to recapitulate human microglial subtypesin vitro, enabling functional characterization and providing a foundation for modulating human microgliain vivo.
Lytic bacteriophages interact with respiratory epithelial cells and induce the secretion of antiviral and proinflammatory cytokines
Authors: Zamora Paula F. , Reidy Thomas G. , Armbruster Catherine R. , Sun Ming , Tyne Daria Van , Turner Paul E. , Koff Jonathan L. , Bomberger Jennifer M.
Published: February 2024

Phage therapy is a therapeutic approach to treat multidrug resistant infections that employs lytic bacteriophages (phages) to eliminate bacteria. Despite the abundant evidence for its success as an antimicrobial in Eastern Europe, there is scarce data regarding its effects on the human host. Here, we aimed to understand how lytic phages interact with cells of the airway epithelium, the tissue site that is colonized by bacterial biofilms in numerous chronic respiratory disorders. We determined that interactions between phages and epithelial cells depend on specific phage properties as well as physiochemical features of the microenvironment. Although poor at internalizing phages, the airway epithelium responds to phage exposure by changing its transcriptional profile and secreting antiviral and proinflammatory cytokines that correlate with specific phage families. Overall, our findings indicate that mammalian responses to phages are heterogenous and could potentially alter the way that respiratory local defenses aid in bacterial clearance during phage therapy. Thus, besides phage receptor specificity in a particular bacterial isolate, the criteria to select lytic phages for therapy should be expanded to include mammalian cell responses.