bluuebunny/crossref_metadata_2025_split huggingface dataset:
Effects of ketamine on frontoparietal interactions during working memory in macaque monkeys
Authors: Ma Liya , Katyare Nupur , Johnston Kevin , Everling Stefan 
Published: May 2023

ABSTRACTSchizophrenia is known as a syndrome of dysconnection among brain regions. As a model for this syndrome, low doses of N-methyl-D-aspartate (NMDA) receptor antagonists, such as ketamine, produce schizophrenia-like symptoms and cognitive deficits in healthy humans and animals. One of such deficits is impaired working memory, a process that engages an extended network of both frontal and parietal areas. While ketamine is known to disrupt working memory by altering both spiking and oscillatory activities in the lateral prefrontal cortex (lPFC), it remains unknown whether NMDA receptor antagonists also produce frontoparietal dysconnection during working memory processes. Here, we simultaneously recorded both single unit activities and local field potentials from lPFC and posterior parietal cortex (PPC) in macaque monkeys during a rule-based working memory task. Like previous work in the lPFC alone, we found that ketamine compromised delay-period rule coding in single neurons and reduced low-frequency oscillations in the PPC. Furthermore, ketamine reduced task-related connectivity in both fronto-parietal and parieto-frontal directions. Consistent with this, ketamine also weakened interareal coherence between spiking and low-frequency oscillatory activities. Our findings demonstrate the utility of acute NMDA receptor antagonist in simulating a syndrome of dysconnection and support this model in its potential for the exploration of novel treatment strategies for schizophrenia.
A comparison of behavior paradigms assessing spatial memory in tree shrew
Authors: Li Cheng-Ji , Hui Yi-Qing , Zhang Rong , Zhou Hai-Yang , Cai Xing , Lu Li 
Published: May 2023

AbstractSpatial cognition enables animals to navigate the environment. Impairments in spatial navigation are often preclinical signs of Alzheimer’s disease (AD) in human. Therefore, evaluating spatial memory deficits is valuable when assessing incipient AD in animal models. The Chinese tree shrew, a close relative of primates, possesses many features that make it suitable for AD research. However, there is a scarcity of reliable behavior paradigms to monitor changes in spatial cognition in this species. To address this, we established reward-based paradigms in the radial-arm maze and the cheeseboard maze for tree shrew, and tested spatial memory of a group of twelve male animals in both tasks, along with a control water maze test, before and after bilateral lesions to the hippocampus, the brain region essential for spatial navigation. Tree shrews memorized target positions during training, and their task performance improved gradually until reaching a plateau in all three mazes. After the lesion, spatial learning was compromised in both newly-developed tasks, whereas memory retrieval was impaired in the water maze. Furthermore, individual task performance in both dry-land paradigms depended heavily on the size of remaining hippocampal tissue. Notably, all lesioned animals displayed spatial memory deficits in the cheeseboard task, but not in the other two paradigms. Our results suggest that the cheeseboard task currently represents the most sensitive paradigm for assessing spatial memory in tree shrew, with the potential to monitor progressive cognitive declines in aged or genetically modified animals developing AD-like symptoms.Significance StatementCognitive tests that monitor impairments in spatial memory play a crucial role in evaluating animal models with early-stage Alzheimer’s disease (AD). The Chinese tree shrew possesses many features suitable for an AD model, yet behavior tests assessing spatial cognition in this species are lacking. Here we developed novel behavior paradigms tailored to measure spatial memory in tree shrews and evaluated their sensitivity to changes in spatial learning by examining a group of hippocampus-lesioned animals. Our results indicate that the cheeseboard task effectively detects impairments in spatial memory and holds potential for monitoring the progressive cognitive decline in aged or genetically modified tree shrews that develop AD-like symptoms. This research may facilitate the use of tree shrew model in AD research.
GWAIS-Web: A Fast and Secure Web Service for Epistasis Detection in Genome-wide Association Interaction Studies
Authors: Wienbrandt Lars , Prieß Christoph , Ellinghaus David 
Published: May 2023

AbstractMotivationGenome-wide association interaction studies (GWAIS) are becoming increasingly important as estimates of genetic interactions at the genome-wide level using genome-wide data from hundreds of thousands of individuals from large biobanks showed that non-additive genetic variance plays a role in complex human traits in addition to additive genetic effects identified in genome-wide association studies (GWAS). However, a comprehensive genome-wide search for all combinations of second-order (SNPxSNP) or third-order (SNPxSNPxSNP) associations using millions of SNP markers is a very computationally intensive task, especially when hundreds of thousands or, in the near future, even millions of individuals can be studied with GWAS datasets. The runtime so far exceeds years, even if the search is performed on a multicore CPU server system.ResultsWe developedGWAIS-Web, a web service for fast analysis of genome-wide interactions with case-control GWAS datasets. By using a hybrid combination of graphics-processing units (GPUs) and field-programmable gate arrays (FPGAs),GWAIS-Webspeeds up epistasis detection methods for binary traits by a factor of more than 2000, allowing an exhaustive SNP-SNP GWAIS with a GWAS data set of one million SNPs and 500,000 individuals to complete within one day, which would take more than five years on a regular CPU server system. The user can choose between different methods for epistasis detection, such as logistic regression, BOOST, mutual information (MI) and others, with calculations in double precision and including on-the-fly filtering of correlated results based on linkage disequilibrium (LD). Due to the underlying common data structure ofGWAIS-Web, all methods can be combined and processed together on-the-fly without increasing the runtime. The user can choose between 2nd order (pairwise) and 3rd order tests and can also limit the search to selected chromosomal regions.GWAIS-Weboffers a high level of security through optional 2-factor authentication, encrypted connections and the protection of GWAS/user account data in accordance with the European General Data Protection Regulation (GDPR).AvailabilityGWAIS-Webis freely available athttps://hybridcomputing.ikmb.uni-kiel.de. The stand-alone softwareHybridGWAIScan be downloaded athttps://github.com/ikmb/hybridgwais.Supplementary informationSupplementary data are available online.
Sense and Screen-ability: Development of tuneable, biosensor-based screening platforms for abscisic acid
Authors: Otto Maximilian , Dabirian Yasaman , David Florian , Siewers Verena 
Published: May 2023

AbstractThe activities of heterologous enzymes often limit the production titers, rates and yields of cell factories. With the help of biosensors, large random mutagenesis libraries can be screened for improved enzyme variants in a high-throughput manner, even if the enzyme-of-interest is poorly characterised.We previously constructed aSaccharomyces cerevisiaecell factory for the heterologous production of abscisic acid (ABA), a high-value product with a broad range of applications in medicine, agriculture and nutrition. In the current study, we developed high-throughput screening platform strains for two rate-limiting cytochrome P450 monooxygenases, BcABA1 and BcABA2, in the ABA biosynthetic pathway. The screening platforms are designed to minimize the occurrence of false positives during screening experiments.We thoroughly characterised two plant protein-based ABA biosensor candidates. Furthermore, we designed a simple genetic switch, based on the thiamine-repressible promoter pTHI4, to regulate the expression level of enzyme variants. We demonstrated that ABA production can be fine-tuned by varying thiamine concentration in the media. In-depth analysis of the platform strains revealed that screening conditions can be optimized by varying thiamine concentration and cultivation time, making it possible to utilize the full dynamic and operational range of the biosensor. In the future, the constructed strains can be used to screen for improved BcABA1 and BcABA2 variants.
Parameter Inference for an Astrocyte Model using Machine Learning Approaches
Authors: Fritschi Lea , Lenk Kerstin 
Published: May 2023

AbstractAstrocytes are the largest subset of glial cells and perform structural, metabolic, and regulatory functions. They are directly involved in the communication at neuronal synapses and the maintenance of brain homeostasis. Several disorders, such as Alzheimer’s, epilepsy, and schizophrenia, have been associated with astrocyte dysfunction. Computational models on various spatial levels have been proposed to aid in the understanding and research of astrocytes. The difficulty of computational astrocyte models is to fastly and precisely infer parameters. Physics informed neural networks (PINNs) use the underlying physics to infer parameters and, if necessary, dynamics that can not be observed. We have applied PINNs to estimate parameters for a computational model of an astrocytic compartment. The addition of two techniques helped with the gradient pathologies of the PINNS, the dynamic weighting of various loss components and the addition of Transformers. To overcome the issue that the neural network only learned the time dependence but did not know about eventual changes of the input stimulation to the astrocyte model, we followed an adaptation of PINNs from control theory (PINCs). In the end, we were able to infer parameters from artificial, noisy data, with stable results for the computational astrocyte model.
Icarust, a real-time simulator for Oxford Nanopore adaptive sampling
Authors: Munro Rory , Payne Alexander , Loose Matthew 
Published: May 2023

1AbstractSummaryOxford Nanopore Technologies (ONT) sequencers enable real-time generation of sequence data, which allows for concurrent analysis during a run. Adaptive sampling leverages this real-time capabilityin extremis, rejecting or accepting reads for sequencing based on assessment of the sequence from the start of each read. This functionality is provided by ONT’s software, MinKNOW (https://community.nanoporetech.com/downloads). Designing and developing software to take advantage of adaptive sampling can be expensive due to the cost of using real sequencing consumables, using precious samples and preparing sequencing libraries. MinKNOW addresses this issue in part by providing the ability to replay previously sequenced runs for testing, using its playback functionality. However, as we have illustrated here, the sequencing output only partially changes in response to adaptive sampling instructions. Here we present Icarust, a tool enabling more accurate approximations of sequencing runs. Icarust recreates all the required endpoints of MinKNOW to perform adaptive sampling and writes output compatible with current base-callers and analysis pipelines. Icarust is capable of serving nanopore signal to simulate a MinION or PromethION flow cell experiment from any reference genome using either R9 or R10 pore signal. We show that simulating sequencing runs with Icarust provides a realistic testing and development environment for software exploiting the real-time nature of Nanopore sequencing.Availability and ImplementationAll code is open source and freely available here -https://github.com/LooseLab/Icarust. Icarust is implemented in Rust, with an easy to install and use docker container available.Supplementary InformationSupplementary information is available here -https://github.com/Adoni5/Icarust_paper.
Cutin-Derived Oligomers Act as Damage-Associated Molecular Patterns inArabidopsis thaliana
Authors: Moreira Carlos J.S. , Escórcio Rita , Bento Artur , Bjornson Marta , Tomé Ana S. , Martins Celso , Fanuel Mathieu , Martins Isabel , Bakan Benedicte , Zipfel Cyril , Pereira Cristina Silva 
Published: May 2023

AbstractThe cuticle constitutes the outermost defensive barrier of most land plants. It comprises a polymeric matrix – cutin, surrounded by soluble waxes. Moreover, the cuticle constitutes the first line of defense against pathogen invasion, while also protecting the plant from many abiotic stresses. Aliphatic monomers in cutin have been suggested to act as immune elicitors in plants. This study analyses the potential of tomato cutin oligomers to act as damage-associated molecular patterns (DAMPs) able to induce a rapid immune response in the model plantArabidopsis. Cutin oligomeric mixtures led to Ca2+influx and MAPK activation inArabidopsis. Comparable responses were measured for cutin, which was also able to induce a reactive oxygen species (ROS) burst. Furthermore, treatment ofArabidopsiswith cutin oligomers resulted in a unique transcriptional reprogramming profile, having many archetypal features of pattern-triggered immunity (PTI). Targeted spectroscopic and spectrometric analyses of the cutin oligomers suggest that the elicitors compounds consist mostly of two up to three 10,16-dihydroxyhexadecanoic acid monomers linked together through ester bonds. This study demonstrates that cutin breakdown products can act as DAMPs; a novel class of elicitors deserving further characterization.
Comparison of phage-derived recombinases for genetic manipulation ofPseudomonasspecies
Authors: Kalb Madison J. , Grenfell Andrew W. , Jain Abhiney , Fenske-Newbart Jane , Gralnick Jeffrey A. 
Published: May 2023

AbstractSeveral strains in thePseudomonasgenus are categorized as plant growth promoting rhizobacteria (PGPR). Although several of these strains are strong candidates for applications as biofertilizers or biopesticides, known genome editing approaches are generally limited and require further development. Editing genomes in PGPR could enable more robust agricultural applications, persistence and biosafety measures. In this study, we investigate the use of five phage-encoded recombinases to develop a recombineering workflow in 3 PGPR strains:P. protegensPf-5,P. protegensCHA0, andP. putidaKT2440. Using point mutations in therpoBgene, we reach maximum recombineering efficiencies of 1.5 x 10-4, 3 x 10-4, and 5 x 10-5, respectively, in these strains using λ-Red Beta recombinase fromE. coli. We further examine recombineering efficiencies across these strains as a function of selected mutation, editing template concentration, and phosphorothiolate bond protection. This work validates the use of these tools across several environmentally and biotechnologically relevant strains to expand the possibilities of genetic manipulation in thePseudomonasgenus.ImportanceThePseudomonasgenus contains many members currently being investigated for applications in biodegradation, biopesticides, biocontrol and synthetic biology. Though several strains have been identified with beneficial properties, in situ genetic manipulations to further improve these strains for commercial applications have been limited due to lack of efficient genetic tools that have been tested across this genus. Here we test the recombineering efficiencies of 5 phage-derived recombinases across 3 biotechnologically relevantPseudomonasstrains:P. putidaKT2440,P. protegensPf-5, andP. protegensCHA0. These results demonstrate a method to generate targeted mutations quickly and efficiently across these strains, ideally introducing a method that can be implemented across thePseudomonasgenus and a strategy that may be applied to develop analogous systems in other non-model bacteria.
Enhancing AlphaFold-Multimer-based Protein Complex Structure Prediction with MULTICOM in CASP15
Authors: Liu Jian , Guo Zhiye , Wu Tianqi , Roy Raj S. , Quadir Farhan , Chen Chen , Cheng Jianlin 
Published: May 2023

AbstractAlphaFold-Multimer has emerged as the state-of-the-art tool for predicting the quaternary structure of protein complexes (assemblies or multimers) since its release in 2021. To further enhance the AlphaFold-Multimer-based complex structure prediction, we developed a new quaternary structure prediction system (MULTICOM) to improve the input fed to AlphaFold-Multimer and evaluate and refine the outputs generated by AlphaFold2-Multimer. Specifically, MULTICOM samples diverse multiple sequence alignments (MSAs) and templates for AlphaFold-Multimer to generate structural models by using both traditionalsequencealignments and new Foldseek-basedstructurealignments, ranks structural models through multiple complementary metrics, and refines the structural models via a Foldseek structure alignment-based refinement method. The MULTICOM system with different implementations was blindly tested in the assembly structure prediction in the 15th Critical Assessment of Techniques for Protein Structure Prediction (CASP15) in 2022 as both server and human predictors. Our server (MULTICOM_qa) ranked 3rdamong 26 CASP15 server predictors and our human predictor (MULTICOM_human) ranked 7thamong 87 CASP15 server and human predictors. The average TM-score of the first models predicted by MULTICOM_qa for CASP15 assembly targets is ∼0.76, 5.3% higher than ∼0.72 of the standard AlphaFold-Multimer. The average TM-score of the best of top 5 models predicted by MULTICOM_qa is ∼0.80, about 8% higher than ∼0.74 of the standard AlphaFold-Multimer. Moreover, the novel Foldseek Structure Alignment-based Model Generation (FSAMG) method based on AlphaFold-Multimer outperforms the widely used sequence alignment-based model generation. The source code of MULTICOM is available at:https://github.com/BioinfoMachineLearning/MULTICOM3.
Deep learning and host variable embedding augment microbiome-based simultaneous detection of multiple diseases
Authors: Wu Shunyao , Li Zhiruo , Chen Yuzhu , Zhang Mingqian , Sun Yangyang , Xing Jieqi , Zhao Fengyang , Huang Shi , Knight Rob , Su Xiaoquan 
Published: May 2023

AbstractMicrobiome has emerged as a promising indicator or predictor of human diseases. However, previous studies typically labeled each specimen as either healthy or with a specific disease, ignoring the prevalence of complications or comorbidities in actual cohorts, which may confound the microbial-disease associations. For instance, a patient may suffer from multiple diseases, making it challenging to detect their health status accurately. Furthermore, host phenotypes such as physiological characteristics and lifestyles can alter the microbiome structure, but this information has not yet been fully utilized in data models. To address these issues, we propose a highly explainable deep learning (DL) method called Meta-Spec. Using a deep neural network (DNN) based approach, it encodes and embeds the refined host variables with microbiome features, enabling the detection of multiple diseases and their correlations simultaneously. Our experiments showed that Meta-Spec outperforms regular machine learning (ML) strategies for multi-label disease screening in several cohorts. More importantly, Meta-Spec can successfully detect comorbidities that are often missed by regular ML approaches. In addition, due to its high interpretability, Meta-Spec captures key factors that shape disease patterns from host variables and microbial members. Hence, these efforts improve the feasibility and sensitivity of microbiome-based disease screening in practical scenarios, representing a significant step towards personalized medicine and better health outcomes.
Practice makes imperfect: stronger implicit interference with practice in individuals at high risk of developing Alzheimer’s disease
Authors: Hung Shao-Min , Adams Sara W. , Molloy Cathleen , Wu Daw-An , Shimojo Shinsuke , Arakaki Xianghong 
Published: May 2023

AbstractEarly screening to determine patient risk of developing Alzheimer’s will allow better interventions and planning but necessitates accessible methods such as behavioral biomarkers. Previously, we showed that cognitively healthy older individuals whose cerebrospinal fluid amyloid / tau ratio indicates high risk of cognitive decline experienced implicit interference during a high-effort task, signaling early changes in attention. To further investigate attention’s effect on implicit interference, we analyzed two experiments completed sequentially by the same high- and low-risk individuals. We hypothesized that if attention modulates interference, practice would affect the influence of implicit distractors. Indeed, while both groups experienced a strong practice effect, the association between practice and interference effects diverged between groups: stronger practice effects correlated with more implicit interference in high-risk participants, but less interference in low-risk individuals. Furthermore, low-risk individuals showed a positive correlation between implicit interference and EEG low-range alpha event-related desynchronization when switching from high-to low-load tasks. These results demonstrate how attention impacts implicit interference and highlight early differences in cognition between high- and low-risk individuals.
RNA- and ATAC-sequencing Reveals a UniqueCD83+Microglial Population Focally Depleted in Parkinson’s Disease
Authors: Chatila Z.K. , Yadav A. , Mares J. , Flowers X. , Yun T.D. , Rashid M. , Talcoff R. , Pelly Z. , Zhang Ya , De Jager P.L. , Teich A. , Costa R. , Gomez E. Area , Martins G. , Alcalay R. , Vonsattel J.P. , Menon V. , Bradshaw E.M. , Przedborski S. 
Published: May 2023

AbstractAll brain areas affected in Parkinson’s disease (PD) show an abundance of microglia with an activated morphology together with increased expression of pro-inflammatory cytokines, suggesting that neuroinflammation may contribute to the neurodegenerative process in this common and incurable disorder. We applied a single nucleus RNA- and ATAC-sequencing approach using the 10x Genomics Chromium platform to postmortem PD samples to investigate microglial heterogeneity in PD. We created a multiomic dataset using substantia nigra (SN) tissues from 19 PD donors and 14 non-PD controls (NPCs), as well as three other brain regions from the PD donors which are differentially affected in this disease: the ventral tegmental area (VTA), substantia inominata (SI), and hypothalamus (HypoTs). We identified thirteen microglial subpopulations within these tissues as well as a perivascular macrophage and a monocyte population, of which we characterized the transcriptional and chromatin repertoires. Using this data, we investigated whether these microglial subpopulations have any association with PD and whether they have regional specificity. We uncovered several changes in microglial subpopulations in PD, which appear to parallel the magnitude of neurodegeneration across these four selected brain regions. Specifically, we identified that inflammatory microglia in PD are more prevalent in the SN and differentially express PD-associated markers. Our analysis revealed the depletion of aCD83andHIF1A-expressing microglial subpopulation, specifically in the SN in PD, that has a unique chromatin signature compared to other microglial subpopulations. Interestingly, this microglial subpopulation has regional specificity to the brainstem in non-disease tissues. Furthermore, it is highly enriched for transcripts of proteins involved in antigen presentation and heat-shock proteins, and its depletion in the PD SN may have implications for neuronal vulnerability in disease.