bluuebunny/crossref_metadata_2025 huggingface dataset:
Gordonia zhenghanii sp. nov. and Gordonia liuliyuniae sp. nov., isolated from bat faeces
Authors: Huang Yuyuan , Gong Jian , Dong Lingzhi , Yang Jing , Lu Shan , Lai Xin-He , Pu Ji , Jin Dong , Xu Jianguo
Published: October 2022

Four mesophilic actinobacteria (HY002T, HY442, HY366T and HY285) isolated from the faeces of bats collected in southern China were found to be strictly aerobic, non-motile, rod-shaped, oxidase-negative, Gram-stain-positive and catalase-positive. Strains HY002T and HY366T contained meso-diaminopimelic acid as the diagnostic diamino acid and MK-9(H2) the sole respiratory quinone. Arabinose, galactose and ribose were detected in the whole-cell hydrolysates of both type strains. The main cellular fatty acids (> 10.0%) of all strains were C16 : 0, C18 : 1
 ω9c, 10-methyl-C18 : 0 and summed feature 3. Strains HY002T and HY366T contained diphosphatidylglycerol, phosphatidylethanolamine, phosphatidylinositol and phosphatidyl inositol mannosides as the major polar lipids. The phylogenetic/phylogenomic analyses based on 16S rRNA gene and genomic sequence comparison revealed that the four strains belong to the genus

Gordonia

, most closely related to

G. neofelifaecis

NRRL B-59395T(98.2–98.3% sequence similarity) on the EzBioCloud database. The G+C contents of strains HY002T and HY366T based on genomic DNA were 66.5 and 66.9%, respectively. The DNA–DNA relatedness values between the two types strains and members of the genus

Gordonia

were far below 70 % (18.6–23.1 %). All genotypic and phenotypic data indicated that the four strains are representatives of two novel separate species, for which the names Gordonia zhenghanii sp. nov. and Gordonia liuliyuniae sp. nov. are proposed, with HY002T (=CGMCC 4 7757T=JCM 34 878T) and HY366T (=CGMCC 1 19146T=JCM 34 879T) as the respective type strains.
Benchmarking taxonomic classifiers with Illumina and Nanopore sequence data for clinical metagenomic diagnostic applications
Authors: Govender Kumeren N. , Eyre David W.
Published: October 2022

Culture-independent metagenomic detection of microbial species has the potential to provide rapid and precise real-time diagnostic results. However, it is potentially limited by sequencing and taxonomic classification errors. We use simulated and real-world data to benchmark rates of species misclassification using 100 reference genomes for each of the ten common bloodstream pathogens and six frequent blood-culture contaminants (n=1568, only 68 genomes were available for

Micrococcus luteus

). Simulating both with and without sequencing error for both the Illumina and Oxford Nanopore platforms, we evaluated commonly used classification tools including Kraken2, Bracken and Centrifuge, utilizing mini (8 GB) and standard (30–50 GB) databases. Bracken with the standard database performed best, the median percentage of reads across both sequencing platforms identified correctly to the species level was 97.8% (IQR 92.7:99.0) [range 5:100]. For Kraken2 with a mini database, a commonly used combination, median species-level identification was 86.4% (IQR 50.5:93.7) [range 4.3:100]. Classification performance varied by species, with

Escherichia coli

being more challenging to classify correctly (probability of reads being assigned to the correct species: 56.1–96.0%, varying by tool used). Human read misclassification was negligible. By filtering out shorter Nanopore reads we found performance similar or superior to Illumina sequencing, despite higher sequencing error rates. Misclassification was more common when the misclassified species had a higher average nucleotide identity to the true species. Our findings highlight taxonomic misclassification of sequencing data occurs and varies by sequencing and analysis workflow. To account for ‘bioinformatic contamination’ we present a contamination catalogue that can be used in metagenomic pipelines to ensure accurate results that can support clinical decision making.
Microbial Gene Ontology informed deep neural network for microbe functionality discovery in human diseases
Authors: Liu Yunjie , Zhang Yao-zhong , Imoto Seiya
Published: October 2022

Background

The human microbiome plays a crucial role in human health and is associated with a number of human diseases. Determining microbiome functional roles in human diseases remains a biological challenge due to the high dimensionality of metagenome gene features. However, existing models were limited in providing biological interpretability, where the functional role of microbes in human diseases is unexplored. Here we propose to utilize a neural network-based model incorporating Gene Ontology (GO) relationship network to discover the microbe functionality in human diseases.

Results

We use four benchmark datasets, including diabetes, liver cirrhosis, inflammatory bowel disease, and colorectal cancer, to explore the microbe functionality in the human diseases. Our model discovered and visualized the novel candidates’ important microbiome genes and their functions by calculating the important score of each gene and GO term in the network. Furthermore, we demonstrate that our model achieves a competitive performance in predicting the disease by comparison with other non-Gene Ontology informed models.

Conclusions

The discovered candidates’ important microbiome genes and their functions provide novel insights into microbe functional contribution.
Anatomical drivers of stomatal conductance in sorghum lines with different leaf widths grown under different temperatures
Authors: Al-Salman Yazen , Cano Francisco Javier , Pan Ling , Koller Fiona , Piñeiro Juan , Jordan David , Ghannoum Oula
Published: October 2022

Improvements in leaf water use efficiency (iWUE) can maintain crop productivity in water limited environments under rising temperatures. We investigated the leaf anatomical traits which underpin our recently identified link between leaf width (LW) andiWUE.

Ten sorghum lines with varyingLWwere grown under three temperatures to expand the range of variation of bothLWand gas exchange rates. Leaf gas exchange, surface morphology and cross-sectional anatomy were measured and analysed using structural equations modelling.

Narrower leaves had lower stomatal conductance (gs) and higheriWUEacross growth temperatures. They also had smaller intercellular airspaces, stomatal size, percentage of open stomatal aperture relative to maximum, hydraulic pathway, mesophyll thickness, and leaf mass per area. Structural modelling revealed a developmental association among leaf anatomical traits that underpinnedgsvariation in sorghum.

Growing temperature andLWboth impacted leaf gas exchange rates, but onlyLWdirectly impacted leaf anatomy. Wider leaves may be more productive under well-watered conditions, but consume more water for growth and development, which is detrimental under water stress.

Highlight

Coordination between leaf width and leaf anatomy underpins stomatal conductance variation in sorghum grown under different temperatures.
Microbial nanowires with genetically modified peptide ligands to sustainably fabricate electronic sensing devices
Authors: Lekbach Yassir , Ueki Toshiyuki , Liu Xiaomeng , Woodard Trevor , Yao Jun , Lovley Derek R.
Published: October 2022

Nanowires have substantial potential as the sensor component in electronic sensing devices. However, surface functionalization of traditional nanowire and nanotube materials with short peptides that increase sensor selectivity and sensitivity requires complex chemistries with toxic reagents. In contrast, microorganisms can assemble pilin monomers into protein nanowires with intrinsic conductivity from renewable feedstocks, yielding an electronic material that is robust and stable in applications, but also biodegradable. Here we report that the sensitivity and selectivity of protein nanowire-based sensors can be modified with a simple plug and play genetic approach in which a short peptide sequence, designed to bind the analyte of interest, is incorporated into the pilin protein that is microbially assembled into nanowires. We employed a scalableEscherichia colichassis to fabricate protein nanowires that displayed either a peptide previously demonstrated to effectively bind ammonia, or a peptide known to bind acetic acid. Sensors comprised of thin films of the nanowires amended with the ammonia-specific peptide had a ca. 100-fold greater response to ammonia than sensors made with unmodified protein nanowires. Protein nanowires with the peptide that binds acetic acid yielded a 4-fold higher response than nanowires without the peptide. The results demonstrate that protein nanowires with enhanced sensor response for analytes of interest can be fabricated with a flexible genetic strategy that sustainably eliminates the energy, environmental, and health concerns associated with other common nanomaterials.
Elasmobranch genome sequencing reveals evolutionary trends of vertebrate karyotype organization
Authors: Yamaguchi Kazuaki , Uno Yoshinobu , Kadota Mitsutaka , Nishimura Osamu , Nozu Ryo , Murakumo Kiyomi , Matsumoto Rui , Sato Keiichi , Kuraku Shigehiro
Published: October 2022

Genomic studies of vertebrate chromosome evolution have long been hindered by the scarcity of chromosome-scale DNA sequences of some key taxa. One of those limiting taxa has been the elasmobranchs (sharks and rays), which harbor species often with numerous chromosomes and enlarged genomes. Here, we report the chromosome-scale genome assembly for the zebra sharkStegostoma tigrinum, an endangered species that has the smallest genome sequenced to date among sharks (3.71 Gb), as well as for the whale sharkRhincodon typus. Our analysis employing a male–female comparison identified an X chromosome, the first genomically characterized shark sex chromosome. The X chromosome harbors a Hox C cluster whose intact linkage has not been shown for an elasmobranch fish. The sequenced shark genomes exhibit a gradualism of chromosome length with remarkable length-dependent characteristics—shorter chromosomes tend to have higher GC content, gene density, synonymous substitution rate, and simple tandem repeat content as well as smaller gene length, which resemble the edges of longer chromosomes. This pattern of intragenomic heterogeneity, previously recognized as peculiar to species with so-called microchromosomes, occurs in more vertebrates including elasmobranchs. We challenge the traditional binary classification of karyotypes as with and without microchromosomes, as even without microchromosomes, shorter chromosomes tend to have higher contents of GC and simple tandem repeats and harbor shorter and more rapid-evolving genes. Such characteristics also appear on the edges of longer chromosomes. Our investigation of elasmobranch karyotypes underpins their unique characteristics and provides clues for understanding how vertebrate karyotypes accommodate intragenomic heterogeneity to realize a complex readout.
Dietary restriction fails to extend life in stressful environments
Authors: Zajitschek Felix , Zajitschek Susanne R.K. , Vasconcelos Ana C.O. , Bonduriansky Russell
Published: October 2022

Moderate dietary restriction often prolongs life in laboratory animals, and this response has been interpreted as an adaptive strategy that promotes survival during famine. However, dietary restriction can also increase frailty, and it therefore remains unclear whether restricted diets prolong life under stressful conditions like those experienced by wild animals. We manipulated adult dietary protein ofDrosophila melanogasteracross a gradient of ambient temperature. We found that protein restriction increased longevity of both sexes at benign ambient temperatures (25-27°C), but failed to extend or even reduced longevity of flies maintained in cold (21-23°C) or hot (29°C) conditions. Protein restriction also generally reduced reproductive performance, and did not consistently enhance performance of F1, F2 or F3 descendants. Our results challenge the long-held idea that extended longevity of diet-restricted laboratory animals represents an adaptive survival strategy in natural populations, and suggest instead that this response is an artefact of benign laboratory conditions.
Loss of Linker Histone H1 in the Maternal Genome Influences DEMETER-Mediated Demethylation and Affects the Endosperm DNA Methylation Landscape
Authors: Han Qiang , Hung Yu-Hung , Zhang Changqing , Bartels Arthur , Rea Matthew , Yang Hanwen , Park Christine , Zhang Xiang-Qian , Fischer Robert L , Xiao Wenyan , Hsieh Tzung-Fu
Published: October 2022

TheArabidopsisDEMETER (DME) DNA glycosylase demethylates the central cell genome prior to fertilization. This epigenetic reconfiguration of the female gamete companion cell establishes gene imprinting in the endosperm and is essential for seed viability. DME demethylates small and genic-flanking transposons as well as intergenic and heterochromatin sequences, but how DME is recruited to these target loci remains unknown. H1.2 was identified as a DME-interacting protein in a yeast two-hybrid screen, and maternal genome H1 loss affects DNA methylation and expression of selected imprinted genes in the endosperm. Yet, the extent to which how H1 influences DME demethylation and gene imprinting in theArabidopsisendosperm has not been investigated. Here, we showed that unlike in the vegetative cell, both canonical histone H1 variants are present in the central cell. Our endosperm methylome analysis revealed that without the maternal linker histones, DME-mediated demethylation is facilitated, particularly in the heterochromatin regions, indicating that H1-containing nucleosomes are barriers for DME demethylation. Loss of H1 in the maternal genome has a very limited effect on gene transcription or gene imprinting regulation in the endosperm; however, it variably influences euchromatin TE methylation and causes a slight hypermethylation and a reduced expression in selected imprinted genes. We conclude that loss of maternal H1 indirectly influences DME-mediated demethylation and endosperm DNA methylation landscape but does not appear to affect endosperm gene transcription and overall imprinting regulation.
Atomistic Insights Into The Mechanism of Dual Affinity Switching In Plant Nitrate Transporter NRT1.1
Authors: Selvam Balaji , Feng Jiangyan , Shukla Diwakar
Published: October 2022

Improving nitrogen use efficiency is critical to enhancing agricultural productivity and to mitigate environmental pollution. To overcome the fluctuations in soil nitrate concentration, plants have evolved an elaborate nitrate transporting mechanism that switches between high and low affinity. In plants, NRT1.1, a root-associated nitrate transporter, switches its affinity upon phosphorylation at Thr101. However, the molecular basis of this unique functional behavior known as dual-affinity switching remains elusive. Crystal structures of the NRT1.1 nitrate transporter have provided evidence for the two competing hypotheses to explain the origin of dual-affinity switching. It is not known how the interplay between transporter phosphorylation and dimerization regulates the affinity switching. To reconcile the different hypotheses, we have performed extensive simulations of nitrate transporter in conjunction with Markov state models to elucidate the molecular origin for a dual-affinity switching mechanism. Simulations of monomeric transporter reveal that phosphorylation stabilizes the outward-facing state and accelerates dynamical transitions for facilitating transport. On the other hand, phosphorylation of the transporter dimer decouples dynamic motions of dimer into independent monomers and thus facilitates substrate transport. Therefore, the phosphorylation-induced enhancement of substrate transport and dimer decoupling not only reconcile the competing experimental results but also provide an atomistic view of how nitrate transport is regulated in plants.
Estimating LFT and qPCR test-sensitivity over time since infection from a human challenge study
Authors: Davis Emma L , Hollingsworth T Deirdre
Published: October 2022

Testing has been central to global policy throughout the SARS-CoV-2 pandemic. Understanding how test sensitivity changes after exposure is crucial for the interpretation of test outcomes and the design of testing-based interventions. Using data from a human challenge study, we derive temporal test sensitivity profiles for lateral flow tests (LFT), quantitative polymerase chain reaction (qPCR) tests and viable virus, measured by focus-forming assay (FFA). The median time to detectability was 2 days (throat swab) and 3 days (nasal) for qPCR and 4 days for LFT (both swabs), and there was strong positive correlation between first LFT positive and first FFA positive for both throat (p=0.00019) and nasal (p=0.00032) swabs, supporting the use of LFTs as a method of detecting infectiousness. Peak LFT sensitivity was 82.4% (67.0%-91.8%) for throat samples, occurring 6 days post-exposure and 93.3% (85.1%-98.0%) for nasal, 7 days post-exposure. These temporal profiles provide quantification of the mean behaviour of these tests and individual-based variability and could inform a framework for investigating future testing-based interventions.
Towards 3D Deep Learning for neuropsychiatry: predicting Autism diagnosis using an interpretable Deep Learning pipeline applied to minimally processed structural MRI data
Authors: Garcia Mélanie , Kelly Clare
Published: October 2022

1

By capitalizing on the power of multivariate analyses of large datasets, predictive modeling approaches are enabling progress toward robust and reproducible brain-based markers of neuropsychiatric conditions. While Deep Learning offers a particularly promising avenue to further advance progress, there are challenges related to implementation in 3D (best for MRI) and interpretability. Here, we address these challenges and describe an interpretable predictive pipeline for inferring Autism diagnosis using 3D Deep Learning applied to minimally processed structural MRI scans. We trained 3D Deep Learning models to predict Autism diagnosis using the openly available ABIDE I and II datasets (n = 1329, split into training, validation, and test sets). Importantly, we did not perform transformation to template space, to reduce bias and maximize sensitivity to structural alterations associated with Autism. Our models attained predictive accuracies equivalent to those of previous Machine Learning studies, while side-stepping the time- and resource-demanding requirement to first normalize data to a template, thus minimizing the time required to generate predictions. Further, our interpretation step, which identified brain regions that contributed most to accurate inference, revealed regional Autism-related alterations that were highly consistent with the literature, such as in a left-lateralized network of regions supporting language processing. We have openly shared our code and models to enable further progress towards remaining challenges, such as the clinical heterogeneity of Autism, and to enable the extension of our method to other neuropsychiatric conditions.
Cumulative COVID-19 incidence indicators should account for the population-at-risk dynamics
Authors: Masjuan Pere , Trias-Llimós Sergi , Rojas Camilo , Prakash Arul , Gullón Pedro , Tobias Aurelio , López-Gay Antonio
Published: October 2022

Risk estimators for COVID-19 propagation based on the incidence rate of new cases can be misleading as they usually fail to account for the fraction of population immunized by infection or vaccination.This misconception yields different incidence rates, as we illustrate using the daily number of COVID-19 reported cases in Spain during the pre-vaccine period, between 15/01/2020 and 11/07/2021. An increase in the incidence rate of about 7% is found when properly accounting for the population at risk. Our results demonstrate that accounting for dynamic changes to the immunized fraction of the population is necessary for accurate risk estimation. We hope that our findings can lead to more effective strategies for pandemic response.
Challenges and advances in measuring phenotypic convergence
Authors: Grossnickle David M. , Brightly William H. , Weaver Lucas N. , Stanchak Kathryn E. , Roston Rachel A. , Pevsner Spencer K. , Stayton C. Tristan , Polly P. David , Law Chris J.
Published: October 2022

Tests of phenotypic convergence can provide evidence of adaptive evolution, and the popularity of such studies has grown in recent years due to the development of novel, quantitative methods for identifying and measuring convergence. These methods include the commonly appliedC1–C4 measures of Stayton (2015), which measure morphological distances between lineages, and Ornstein-Uhlenbeck (OU) evolutionary model-fitting analyses, which test whether lineages convergently evolved toward adaptive peaks. We test the performance ofC-measures and other convergence measures under various evolutionary scenarios and reveal a critical issue withC-measures: they often misidentify divergent lineages as convergent. We address this issue by developing novel convergence measures—Ct1–Ct4-measures—that measure distances between lineages at specific points in time, minimizing the possibility of misidentifying divergent taxa as convergent.Ct-measures are most appropriate when focal lineages are of the same or similar geologic ages (e.g., extant taxa), meaning that the lineages’ evolutionary histories include considerable overlap in time. BeyondC-measures, we find that all convergence measures are influenced by the position of focal taxa in phenotypic space, with morphological outliers often statistically more likely to be measured as strongly convergent by chance. Further, we mimic scenarios in which researchers assess convergence using OU models witha prioriregime assignments (e.g., classifying taxa by ecological traits), and we find that multiple-regime OU models with phenotypically divergent lineages assigned to a shared selective regime often outperform simpler models. This highlights that model support for these multiple-regime OU models should not be assumed to always reflect convergence among focal lineages of a shared regime. Our newCt1–Ct4-measures provide researchers with an improved comparative tool, but we emphasize that all available convergence measures are imperfect, and researchers should recognize the limitations of these methods and use multiple lines of evidence when inferring and measuring convergence.
Spatial Transcriptomics of Intraductal Papillary Mucinous Neoplasms of The Pancreas Identifies NKX6-2 as a Driver of Gastric Differentiation and Indolent Biological Potential
Authors: Sans Marta , Makino Yuki , Min Jimin , Rajapakshe Kimal I. , Yip-Schneider Michele , Schmidt C Max , Hurd Mark W. , Burks Jared K. , Gomez Javier A. , Thege Fredrik I. , Fahrmann Johannes , Wolff Robert A. , Kim Michael P. , Guerrero Paola A. , Maitra Anirban
Published: October 2022

Intraductal Papillary Mucinous Neoplasms (IPMNs) of the pancreas arebona fideprecursor lesions of pancreatic ductal adenocarcinoma (PDAC). The most common subtype of IPMNs harbor a gastric foveolar-type epithelium, and these low-grade mucinous neoplasms are harbingers of IPMNs with high-grade dysplasia and cancer. The molecular underpinning of gastric differentiation in IPMNs is unknown, although identifying drivers of this indolent phenotype might enable opportunities for intercepting progression to high-grade IPMN and cancer. We conducted spatial transcriptomics on a cohort of IPMNs, followed by orthogonal and cross species validation studies, which established the transcription factor NKX6-2 as a key determinant of gastric cell identity in low-grade IPMNs. Loss ofNKX6-2expression is a consistent feature of IPMN progression, while re-expression ofNKX6-2in murine IPMN lines recapitulates the aforementioned gastric transcriptional program and glandular morphology. Our study identifies NKX6-2 as a previously unknown transcription factor driving indolent gastric differentiation in IPMN pathogenesis.

Significance

Improved understanding of the molecular features driving IPMN development and differentiation is critical to prevent cancer progression and enhance risk stratification. Our study employed spatial profiling technologies to characterize the epithelium and microenvironment of IPMN, which revealed a previously unknown link betweenNKX6-2expression and gastric differentiation in IPMNs, the latter associated with an indolent biological potential.
Phase transition in atomistic simulations of model membrane with thylakoid lipids of red algae
Authors: Rathod Arun K. , Chavda Dhruvil , Manna Moutusi
Published: October 2022

Marine algae are diverse photosynthetic organisms, profoundly rich in bioactive compounds. Temperature is a major factor in algal cultivation and biomass production. At the cellular level, the change of temperature is reflected in oscillating algal lipid/fatty acid profile and inhibition of photosynthetic activities. The function of thylakoid membrane system is intimately dependent on its lipid matrix, however the molecular organization of these lipid membranes and particularly their adaptive arrangements under temperature stress remain largely unexplored. The present work employing extensive atomistic simulations provides the first atomistic view of the phase transition and domain coexistence in model membrane composed of thylakoid lipids of a marine alga, between 10-40 °C. The model membrane undergoes a transition from a gel-like phase at 10-15 °C to a homogeneous liquid-disordered phase at 40 °C. Clear evidences of spontaneous phase separation into coexisting nanoscale domains are detected at intermediate temperatures. Particularly at 25-30 °C, we identified the formation of a stable rippled phase, where the gel-like domains rich in saturated and nearly hexagonally packed lipids separated from fluid-like domains enriched in lipids containing polyunsaturated chains. Cholesterol impairs the phase transition and the emergence of domains, and induces a fairly uniform liquid-ordered phase in the membrane over the temperatures studied. The results have implications in understanding the role of lipids in temperature adaptation in algal.
Genome sequencing of the Monkeypox virus 2022 outbreak with Amplicon-based Oxford Nanopore MinION sequencing
Authors: Brinkmann Annika , Pape Katharina , Uddin Steven , Woelk Niklas , Förster Sophie , Jessen Heiko , Michel Janine , Kohl Claudia , Schaade Lars , Nitsche Andreas
Published: October 2022

We present an amplicon-based assay for MinION Nanopore sequencing of monkeypox virus genomes from clinical specimens, obtaining high-quality results for up to 99 % of the genomes, even for samples with high Ct values of up to 30 which are not suitable for shotgun sequencing.
Automated neuronal reconstruction with super-multicolour fluorescence imaging
Authors: Leiwe Marcus N. , Fujimoto Satoshi , Baba Toshikazu , Moriyasu Daichi , Saha Biswanath , Sakaguchi Richi , Inagaki Shigenori , Imai Takeshi
Published: October 2022

Fluorescence imaging is widely used for the mesoscopic mapping of neuronal connectivity. However, neurite reconstruction is challenging, especially when neurons are densely labelled. Here we report a strategy for the fully automated reconstruction of densely labelled neuronal circuits. Firstly, we established stochastic “super-multicolour” labelling with up to seven different fluorescent proteins using the Tetbow method. With this method, each neuron was labelled with a unique combination of fluorescent proteins, which were then imaged and separated by linear unmixing. We also established an automated neurite reconstruction pipeline based on the quantitative analysis of multiple dyes (QDyeFinder). To classify colour combinations, we used a newly developed unsupervised clustering algorithm, dCrawler, in which data points in multi-dimensional space were clustered based on a given threshold distance. Our new strategy allows for the reconstruction of neurites for up to hundreds of neurons at a millimetre scale without manual tracing.
GOProFormer: A Multi-modal Transformer Method for Gene Ontology Protein Function Prediction
Authors: Kabir Anowarul , Shehu Amarda
Published: October 2022

Protein Language Models (PLMs) are shown capable of learning sequence representations useful for various prediction tasks, from subcellular localization, evolutionary relationships, family membership, and more. They have yet to be demonstrated useful for protein function prediction. In particular, the problem of automatic annotation of proteins under the Gene Ontology (GO) framework remains open. This paper makes two key contributions. It debuts a novel method that leverages the transformer architecture in two ways. A sequence transformer encodes protein sequences in a task-agnostic feature space. A graph transformer learns a representation of GO terms while respecting their hierarchical relationships. The learned sequence and GO terms representations are combined and utilized for multi-label classification, with the labels corresponding to GO terms. The method is shown superior over recent representative GO prediction methods. The second major contribution in this paper is a deep investigation of different ways of constructing training and testing datasets. The paper shows that existing approaches under- or over-estimate the generalization power of a model. A novel approach is proposed to address these issues, resulting a new benchmark dataset to rigorously evaluate and compare methods and advance the state-of-the-art.
Inference of Presynaptic Connectivity from Temporally Blurry Spike Trains by Supervised Learning
Authors: Vareberg Adam D. , Eizadi Jenna , Ren Xiaoxuan , Hai Aviad
Published: October 2022

Reconstruction of neural network connectivity is a central focus of neuroscience. The ability to use neuronal connection information to predict activity at single unit resolution and decipher its effect on whole systems can provide critical information about behavior and cognitive processing. Neuronal sensing modalities come in varying forms, but there is yet to exist a modality that can deliver readouts that sufficiently address the spatiotemporal constraints of biological nervous systems. This necessitates supplementary approaches that rely on mathematical models to mitigate physical limitations and decode network features. Here, we introduce a simple proof-of-concept model that addresses temporal constraints by reconstructing presynaptic connections from temporally blurry data. We use a variation of the perceptron algorithm to process firing rate information at multiple time constraints for a heterogenous feed-forward network of excitatory, inhibitory, and unconnected presynaptic units. We evaluate the performance of the algorithm under these conditions and determine the optimal learning rate, firing rate, and the ability to reconstruct single unit spikes for a given degree of temporal blur. We then test our method on a physiologically relevant configuration by sampling network subpopulations of leaky integrate-and-fire neuronal models displaying bursting firing patterns and find comparable learning rates for optimized reconstruction of network connectivity. Our method provides a recipe for reverse engineering neural networks based on limited data quality that can be extended to more complicated readouts and connectivity distributions relevant to multiple brain circuits.
Molecular basis ofFAAH-OUT-associated human pain insensitivity
Authors: Mikaeili Hajar , Habib Abdella M. , Yeung Charlix , Santana-Varela Sonia , Luiz Ana P. , Panteleeva Kseniia , Zuberi Sana , Athanasiou-Fragkouli Alkyoni , Houlden Henry , Wood John N. , Okorokov Andrei L. , Cox James J.
Published: October 2022

Chronic pain affects millions of people worldwide. Studying pain insensitive individuals helps to identify novel analgesic strategies. Here we report how the recently discoveredFAAH-OUTlncRNA-encoding gene, which was found from studying a pain insensitive patient with reduced anxiety and fast wound healing, regulates the adjacent key endocannabinoid system geneFAAH, which encodes the anandamide-degrading fatty acid amide hydrolase enzyme. We demonstrate that the disruption inFAAH-OUTlncRNA transcription leads to DNMT1-dependent DNA methylation within theFAAHpromoter. In addition,FAAH-OUTcontains a conserved regulatory element, FAAH-AMP, that acts as an enhancer forFAAHexpression. Furthermore, using transcriptomic analyses we have uncovered a network of genes that are dysregulated from disruption of theFAAH-FAAH-OUTaxis, thus providing a coherent mechanistic basis to understand the human phenotype observed and a platform for development of future gene and small molecule therapies.
Constructed Synchronized Multimodal Signalling in Great Bowerbirds
Authors: Endler John A. , Meehan Selina , Rodrigues Aida
Published: October 2022

Great bowerbird males build bowers for attracting females for mating. Bowers consist of a thatched twig tunnel (avenue) which opens onto two flat courts covered with objects. Male displays on a court are seen by a female from within the avenue. She sees and hears displays through the avenue entrance but can only see the male’s head and objects in his bill as it passes repeatedly across the entrance. We investigated bower acoustic properties by playing standard sounds from multiple court positions and recorded the resulting sounds at the female’s typical avenue head position within the avenue. Bower geometry significantly affects both his acoustic and visual display components and physically synchronizes them as he repeatedly moves in and out of the female’s view. Consequently, complex neural circuitry is unnecessary for linking sound to vision. Experimentally removing bower objects shows that objects significantly increase higher frequencies, hence bandwidth and loudness received inside the avenue. Great Bowerbird bowers produce a synchronized multimodal signal to females which may increase male attractiveness more than if a single sensory mode were used. This multimodal signal is unusual in that it is constructed rather than being part of the body and synchronized as a result of physics rather than neurons.

Summary Statement

Bowerbird bower geometry jointly effects both visual and auditory signal components and synchronizes them without the need for additional neural circuitry.
Magnetic neutron scattering from spherical nanoparticles with Néel surface anisotropy: analytical treatment
Authors: Adams Michael P. , Michels Andreas , Kachkachi Hamid
Published: October 2022

The magnetization profile and the related magnetic small-angle neutron scattering cross section of a single spherical nanoparticle with Néel surface anisotropy are analytically investigated. A Hamiltonian is employed that comprises the isotropic exchange interaction, an external magnetic field, a uniaxial magnetocrystalline anisotropy in the core of the particle and the Néel anisotropy at the surface. Using a perturbation approach, the determination of the magnetization profile can be reduced to a Helmholtz equation with Neumann boundary condition, whose solution is represented by an infinite series in terms of spherical harmonics and spherical Bessel functions. From the resulting infinite series expansion, the Fourier transform, which is algebraically related to the magnetic small-angle neutron scattering cross section, is analytically calculated. The approximate analytical solution for the spin structure is compared with the numerical solution using the Landau–Lifshitz equation, which accounts for the full nonlinearity of the problem. The signature of the Néel surface anisotropy can be identified in the magnetic neutron scattering observables, but its effect is relatively small, even for large values of the surface anisotropy constant.
AMX – the highly automated macromolecular crystallography (17-ID-1) beamline at the NSLS-II
Authors: Schneider Dieter K. , Soares Alexei S. , Lazo Edwin O. , Kreitler Dale F. , Qian Kun , Fuchs Martin R. , Bhogadi Dileep K. , Antonelli Steve , Myers Stuart S. , Martins Bruno S. , Skinner John M. , Aishima Jun , Bernstein Herbert J. , Langdon Thomas , Lara John , Petkus Robert , Cowan Matt , Flaks Leonid , Smith Thomas , Shea-McCarthy Grace , Idir Mourad , Huang Lei , Chubar Oleg , Sweet Robert M. , Berman Lonny E. , McSweeney Sean , Jakoncic Jean
Published: October 2022

The highly automated macromolecular crystallography beamline AMX/17-ID-1 is an undulator-based high-intensity (>5 × 1012 photons s−1), micro-focus (7 µm × 5 µm), low-divergence (1 mrad × 0.35 mrad) energy-tunable (5–18 keV) beamline at the NSLS-II, Brookhaven National Laboratory, Upton, NY, USA. It is one of the three life science beamlines constructed by the NIH under the ABBIX project and it shares sector 17-ID with the FMX beamline, the frontier micro-focus macromolecular crystallography beamline. AMX saw first light in March 2016 and started general user operation in February 2017. At AMX, emphasis has been placed on high throughput, high capacity, and automation to enable data collection from the most challenging projects using an intense micro-focus beam. Here, the current state and capabilities of the beamline are reported, and the different macromolecular crystallography experiments that are routinely performed at AMX/17-ID-1 as well as some plans for the near future are presented.
Investigation of polycapillary half lenses for quantitative confocal micro-X-ray fluorescence analysis
Authors: Iro Michael , Ingerle Dieter , Radtke Martin , Guilherme Buzanich Ana , Kregsamer Peter , Streli Christina
Published: October 2022

The use of polycapillary optics in confocal micro-X-ray fluorescence analysis (CMXRF) enables the destruction-free 3D investigation of the elemental composition of samples. The energy-dependent transmission properties, concerning intensity and spatial beam propagation of three polycapillary half lenses, which are vital for the quantitative interpretation of such CMXRF measurements, are investigated in a monochromatic confocal laboratory setup at the Atominstitut of TU Wien, and a synchrotron setup on the BAMline beamline at the BESSY II Synchrotron, Helmholtz-Zentrum-Berlin. The empirically established results, concerning the intensity of the transmitted beam, are compared with theoretical values calculated with the polycap software package and a newly presented analytical model for the transmission function. The resulting form of the newly modelled energy-dependent transmission function is shown to be in good agreement with Monte Carlo simulated results for the complete energy regime, as well as the empirically established results for the energy regime between 6 keV and 20 keV. An analysis of possible fabrication errors was conducted via pinhole scans showing only minor fabrication errors in two of the investigated polycapillary optics. The energy-dependent focal spot size of the primary polycapillary was investigated in the laboratory via the channel-wise evaluation of knife-edge scans. Experimental results are compared with data given by the manufacturer as well as geometric estimations for the minimal focal spot size. Again, the resulting measurement points show a trend in agreement with geometrically estimated results and manufacturer data.
Piecewise-linear embeddings of decussate extended θ graphs and tetrahedra
Authors: O'Keeffe Michael , Treacy Michael M. J.
Published: October 2022

An nθ graph is an n-valent graph with two vertices. From symmetry considerations, it has vertex–edge transitivity 1 1. Here, they are considered extended with divalent vertices added to the edges to explore the simplest piecewise-linear tangled embeddings with straight, non-intersecting edges (sticks). The simplest tangles found are those with 3n sticks, transitivity 2 2, and with 2⌊(n − 1)/2⌋ ambient-anisotopic tangles. The simplest finite and 1-, 2- and 3-periodic decussate structures (links and tangles) are described. These include finite cubic and icosahedral and 1- and 3-periodic links, all with minimal transitivity. The paper also presents the simplest tangles of extended tetrahedra and their linkages to form periodic polycatenanes. A vertex- and edge-transitive embedding of a tangled srs net with tangled and polycatenated θ graphs and vertex-transitive tangled diamond (dia) nets are described.
Managerial ability and informative earnings management: the role of CEO-commissioner relationship and board independence
Authors: Putra Adhitya Agri
Published: October 2022

Purpose

The purpose of this study is to examine the effect of managerial ability on informative earnings management (hereafter IEM) and to examine the moderating role of the chief executive officer and board of commissioner relationship (hereafter CEO-commissioner relationship) and board independence between managerial ability and IEM.

Design/methodology/approach

Sample consists of 864 firm-years listed on the Indonesian Stock Exchange. Informative earnings management is measured by the relationship between discretionary accruals and earnings growth. Managerial ability is measured by data envelopment analysis. This research uses firm-effect logistic regression to perform the data analysis.

Findings

Based on firm-effect logistic regression, managerial ability increases IEM. It confirms the managers’ stewardship behavior where managers tend to engage in IEM and provide higher quality information for shareholders. The result also shows that the absence of a CEO-commissioner relationship and higher board independence leads higher ability managers to engage more in IEM. It confirms the role of corporate governance to reduce managers-shareholders conflict (in the context of agency theory) or to facilitate higher ability managers to act as both controlling and minority shareholders’ stewards (in the context of stewardship theory) by engaging more in IEM and providing higher-quality information.

Originality/value

This research contributes to filling the previous studies gap that provides conflicting results on managerial ability and earnings management by considering earnings management motivations, CEO-commissioner relationship and board independence. This research also contributes to providing new evidence of managerial ability, IEM, CEO-commissioner relationship and board independence, especially in Indonesia.
Unions' role in intersectional age discrimination litigation cases
Authors: Katz Marsha , LaVan Helen
Published: October 2022

Purpose

How older workers pursue their employment discrimination claims is the focus of this study. This is part of the broader question of how older, unionized employees pursuing their statutory rights fare in litigation and how unions might organize and subsequently better protect older workers who have been discriminated against.

Design/methodology/approach

This study is a random sample of 1889 litigated age discrimination cases in Federal courts using NVivo to conduct a content analysis on unionization, individual, organizational and legal variables.

Findings

An analysis of case characteristics and outcomes in cases filed under the Age Discrimination in Employment Act indicated that older unionized workers pursuing their claims are more likely to have rulings in their favor. Other demographic characteristics of the cases in which unionized workers prevailed include seniority, disability, filing under Title VII and cases involving retaliation.

Research limitations/implications

Importantly, empirical legal scholarship, using the case as the unit of analysis, will significantly add to the understanding of how age discrimination might be reduced through litigation. Empirical legal scholarship strategies would also suggest identifying labor tribunal or arbitration cases. Using content analysis would allow for a deep understanding at the micro level of the context that led to the charges of age (or other types) of discrimination.

Practical implications

This study offers evidence that union representation can add value when older employees are pursuing their rights in litigation.

Originality/value

This research focuses on individuals who have actually been discriminated against using empirical legal scholarship, content analysis and big data analytics.
Measuring consumer perception of overall brand equity drivers for m-payments
Authors: Sankaran Raja , Chakraborty Shibashish
Published: October 2022

Purpose

The objective of this study was to investigate the relationship between various drivers of consumer-based brand equity (CBBE) for m-payments and to demonstrate practical relevance. The study examined the indirect relationship between m-payment drivers and satisfaction and their subsequent association with brand equity drivers in the context of m-payments.

Design/methodology/approach

A survey was administered to a total of 725 respondents. Structural equation modeling, SPSS AMOS and a multi-mediation model using process macros were used to analyze the primary data.

Findings

The results of this study corroborate the post-use driver (satisfaction) and trust mediates drivers of m-payments (perceived usefulness and perceived ease of use) with overall brand equity. Satisfaction and trust exert a positive influence on overall brand equity, and this research will help organizations devise strategies to retain consumers, offer loyalty schemes and brand effectively to bundle services.

Originality/value

Novelty was achieved in this study by extending the technology acceptance model to determine the association between m-payment drivers and satisfaction and their subsequent association with overall brand equity, thus providing practical implications.
Big data and big disaster: a mechanism of supply chain risk management in global logistics industry
Authors: Li Lixu , Gong Yeming , Wang Zhiqiang , Liu Shan
Published: October 2022

Purpose

Although big data may enhance the visibility, transparency, and responsiveness of supply chains, whether it is effective for improving supply chain performance in a turbulent environment, especially in mitigating the impact of COVID-19, is unclear. The research question the authors addressed is: How do logistics firms improve the supply chain performance in COVID-19 through big data and supply chain integration (SCI)?

Design/methodology/approach

The authors used a mixed-method approach with four rounds of data collection. A three-round survey of 323 logistics firms in 26 countries in Europe, America, and Asia was first conducted. The authors then conducted in-depth interviews with 55 logistics firms.

Findings

In the first quantitative study, the authors find mediational mechanisms through which big data analytics technology capability (BDATC) and SCI influence supply chain performance. In particular, BDATC and SCI are two second-order capabilities that help firms develop three first-order capabilities (i.e. proactive capabilities, reactive capabilities, and resource reconfiguration) and eventually lead to innovation capability and disaster immunity that allow firms to survive in COVID-19 and improve supply chain performance. The results of the follow-up qualitative analysis not only confirm the inferences from the quantitative analysis but also provide complementary insights into organizational culture and the institutional environment.

Originality/value

The authors contribute to supply chain risk management by developing a three-level hierarchy of capabilities framework and finding a mechanism with the links between big data and big disaster. The authors also provide managerial implications for logistics firms to address the new management challenges posed by COVID-19.