bluuebunny/crossref_metadata_2025 huggingface dataset:
Adolescent gulls have the opportunity for social development at breeding colonies
Authors: Taylor Liam U.
Published: May 2023

Most seabirds delay reproduction for multiple years. The long-standing ecological hypothesis is that seabirds delay reproduction until they pass a foraging efficiency threshold. This foraging development hypothesis is puzzling for seabirds with progressive delayed plumage maturation, such as American Herring Gulls (Larus argentatus smithsonianus). Young American Herring Gulls pass through a distinct series of predefinitive plumages in their early years, suggesting a process of adolescence rather than a binary switch between energetic immaturity and maturity. Drawing on facts from both colonial seabirds and tropical lekking birds, I propose an additional life history hypothesis: young gulls undergo a phase of social development—rather than foraging development alone—during which time predefinitive plumages function as signals that reduce the costs of social engagement at breeding colonies. I tested one facet of the hypothesis: predefinitive gulls have an opportunity for social development at colonies before breeding. A unique prediction is that predefinitive gulls are common at breeding colonies, socially engaged, and not breeding. I conducted counts, quantitative tests of territoriality and conflict, and qualitative behavioral observations of American Herring Gulls at two northwest Atlantic breeding colonies in the summer of 2022. Results supported all three prediction criteria. Birds in an advanced predefinitive plumage stage were common at colonies (2.0-5.8% of census) even while birds in earlier plumage stages were nearly absent (generally <1% of census). These predefinitive birds were socially engaged while loafing on—and losing fights in—foreign territories. Yet only one out of hundreds of predefinitive birds held a territory or nest. This phenomenon suggests the social conditions of breeding colonies can set the stage for social development that, in turn, sets the stage for life history and plumage evolution.
Glaucoma-associated Optineurin mutations increase transmitophagy in a vertebrate optic nerve
Authors: Jeong Yaeram , Davis Chung-ha O. , Muscarella Aaron M. , Deshpande Viraj , Kim Keun-Young , Ellisman Mark H. , Marsh-Armstrong Nicholas
Published: May 2023

We previously described a process referred to as transmitophagy where mitochondria shed by retinal ganglion cell (RGC) axons are transferred to and degraded by surrounding astrocytes in the optic nerve head of mice. Since the mitophagy receptor Optineurin (OPTN) is one of few large- effect glaucoma genes and axonal damage occurs at the optic nerve head in glaucoma, here we explored whether OPTN mutations perturb transmitophagy. Live-imaging ofXenopus laevisoptic nerves revealed that diverse human mutant but not wildtype OPTN increase stationary mitochondria and mitophagy machinery and their colocalization within, and in the case of the glaucoma-associated OPTN mutations also outside of, RGC axons. These extra-axonal mitochondria are degraded by astrocytes. Our studies support the view that in RGC axons under baseline conditions there are low levels of mitophagy, but that glaucoma-associated perturbations in OPTN result in increased axonal mitophagy involving the shedding and astrocytic degradation of the mitochondria.

Graphical Abstract
Multimodal subspace identification for modeling discrete-continuous spiking and field potential population activity
Authors: Ahmadipour Parima , Sani Omid G. , Pesaran Bijan , Shanechi Maryam M.
Published: May 2023

Learning dynamical latent state models for multimodal spiking and field potential activity can reveal their collective low-dimensional dynamics and enable better decoding of behavior through multimodal fusion. Toward this goal, developing unsupervised learning methods that are computationally efficient is important, especially for real-time learning applications such as brain-machine interfaces (BMIs). However, efficient learning remains elusive for multimodal spike-field data due to their heterogeneous discrete-continuous distributions and different timescales. Here, we develop a multiscale subspace identification (multiscale SID) algorithm that enables computationally efficient modeling and dimensionality reduction for multimodal discrete-continuous spike-field data. We describe the spike-field activity as combined Poisson and Gaussian observations, for which we derive a new analytical subspace identification method. Importantly, we also introduce a novel constrained optimization approach to learn valid noise statistics, which is critical for multimodal statistical inference of the latent state, neural activity, and behavior. We validate the method using numerical simulations and spike-LFP population activity recorded during a naturalistic reach and grasp behavior. We find that multiscale SID accurately learned dynamical models of spike-field signals and extracted low-dimensional dynamics from these multimodal signals. Further, it fused multimodal information, thus better identifying the dynamical modes and predicting behavior compared to using a single modality. Finally, compared to existing multiscale expectation-maximization learning for Poisson-Gaussian observations, multiscale SID had a much lower computational cost while being better in identifying the dynamical modes and having a better or similar accuracy in predicting neural activity. Overall, multiscale SID is an accurate learning method that is particularly beneficial when efficient learning is of interest.
Flexible Analysis of Spatial Transcriptomics Data (FAST): A Deconvolution Approach
Authors: Zhang Meng , Liu Yiwen , Parker Joel , An Lingling , Sun Xiaoxiao
Published: May 2023

Motivation

Spatial transcriptomics is a state-of-art technique that allows researchers to study gene expression patterns in tissues over the spatial domain. As a result of technical limitations, the majority of spatial transcriptomics techniques provide bulk data for each sequencing spot. Consequently, in order to obtain high-resolution spatial transcriptomics data, performing deconvolution becomes essential. Deconvolution enables the determination of the proportions of different cell types along with the corresponding gene expression levels for each cell type within each spot. Most existing deconvolution methods rely on reference data (e.g., single-cell data), which may not be available in real applications. Current reference-free methods encounter limitations due to their dependence on distribution assumptions, reliance on marker genes, or the absence of leveraging histology and spatial information. Consequently, there is a critical demand for the development of highly adaptable, robust, and user-friendly reference-free deconvolution methods capable of unifying or leveraging case-specific information in the analysis of spatial transcriptomics data.

Results

We propose a novel reference-free method based on regularized non-negative matrix factorization (NMF), named Flexible Analysis of Spatial Transcriptomics (FAST), that can effectively incorporate gene expression data, spatial coordinates, and histology information into a unified deconvolution framework. Compared to existing methods, FAST imposes fewer distribution assumptions, utilizes the spatial structure information of tissues, and encourages interpretable factorization results. These features enable greater flexibility and accuracy, making FAST an effective tool for deciphering the complex cell-type composition of tissues and advancing our understanding of various biological processes and diseases. Extensive simulation studies have shown that FAST outperforms other existing reference-free methods. In real data applications, FAST is able to uncover the underlying tissue structures and identify the corresponding marker genes.
Protein-protein interaction prediction via structure-based deep learning
Authors: Liu Yucong , Li Zhenhai
Published: May 2023

Protein-protein interactions (PPIs) play an essential role in life activities. Many machine learning algorithms based on protein sequence information have been developed to predict PPIs. However, these models have difficulty dealing with various sequence lengths and suffer from low generalization and prediction accuracy. In this study, we proposed a novel end-to-end deep learning framework, RSPPI, combining Residual Neural Network (ResNet) and Spatial Pyramid Pooling (SPP), to predict PPIs based on the protein sequence physicochemistry properties and spatial structural information. In the RSPPI model, ResNet was employed to extract the structural and physicochemical information from the protein 3D structure and primary sequence; the SPP layer was used to transform feature maps to a single vector and avoid the fixed-length requirement. The RSPPI model possessed excellent cross-species performance and outperformed several state-of-the-art methods based either on protein sequence or gene ontology in most evaluation metrics. The RSPPI model provides a novel strategic direction to develop an AI PPI prediction algorithm.
ElecFeX: A user-friendly toolkit for efficient feature extraction from single-cell electrophysiological recordings
Authors: Ma Xinyue , Miraucourt Loïs S. , Qiu Haoyi , Xu Mengyi , Sharif-Naeini Reza , Khadra Anmar
Published: May 2023

Motivation

Profiling neurons by their electrophysiological phenotype is essential for understanding their roles in information coding within and beyond the nervous systems. Technological development has unleashed our power to record neurons more than ever before, yet the booming size of the dataset poses new challenges for data analysis. Current software tools require users to have either significant programming knowledge or to devote great time and effort, which impedes their prevalence and adoption among experimentalists. To address this problem, here we present ElecFeX, a MATLAB-based graphical user interface designed for a more accessible and efficient analysis of single-cell electrophysiological recordings. ElecFeX has a simple and succinct graphical layout to enable effortless handling of large datasets. This tool includes a set of customizable methods for most common electrophysiological features, and these methods can process multiple files all at once in a reliable and reproducible manner. The output is assembled in a properly formatted file which is exportable for further analysis such as statistical comparison and clustering. By providing such a streamlined and user-friendly open-sourced interface, we hope ElecFeX can benefit broader users for their studies associated with neural activity.

Characterizing neurons by their electrophysiological phenotypes is essential for understanding the neural basis of behavioral and cognitive functions. Recent developments in electrode technologies have enabled the collection of hundreds of neural recordings; that necessitated the development of new toolkits capable of performing feature extraction efficiently. To address this urgent need for a powerful and accessible tool, we present ElecFeX, an open-source MATLAB-based toolbox that (1) has a succinct and intuitive graphical user interface, (2) provides generalized methods for wide-ranging electrophysiological features, (3) processes large-size dataset effortlessly, and (4) yields formatted output for further analysis such as neuronal characterization and classification. We implemented the toolbox on a diverse set of neural recordings and demonstrated its functionality, efficiency, and versatility in capturing features that can well-distinguish neuronal subgroups across brain regions and species. ElecFeX is thus presented as a powerful tool to significantly promote future studies on neuronal electrical activity.
Lyssa excreta: Defining parameters for fecal samples as a rabies virus surveillance method
Authors: Walker Faith M. , Upton Jordyn R. , Erickson Daryn , Barrand Zachary A. , Brock Breezy , Valentine Michael , Federman Emma L. , Froehlich Emma M. , Van Pelt Lolita , Hastings Lias , Bergman David L. , Engelthaler David M. , Hepp Crystal M.
Published: May 2023

It is not possible to systematically screen the environment for rabies virus (RABV) using current approaches. We sought to determine under what conditions RABV is detectable from feces and other accessible samples from infected wildlife to broaden the number of biological samples that could be used to test for RABV. We employed a recently-developed quantitative RT-PCR assay called the “LN34 panlyssavirus real-time RT-PCR assay”, which is highly sensitive and specific for all variants of RABV. We harvested and tested brain tissue, fecal, and/or mouth swab samples from 25 confirmed RABV positive bats of six species. To determine if rabies RNA lasts in feces sufficiently long post-defecation to use it as a surveillance tool, we tested fecal samples from 10 bats at the time of sample collection and after 24 hours of exposure to ambient conditions, with an additional test on six bats out to 72 hours. To assess whether we could pool fecal pellets and still detect a positive, we generated dilutions of known positives at 1:1, 1:10, 1:50, and 1:200. For six individuals for which matched brain, mouth swab, and fecal samples were tested, results were positive for 100%, 67%, and 67%, respectively. For the first time test to 24 hours, 63% of feces that were positive at time 0 were still positive after 24 hours, and 50% of samples at 72 hours were positive across all three replicates. Pooling tests revealed that fecal positives were detected at 1:10 dilution, but not at 1:50 or 1:200. Our preliminary results suggest that fecal samples hold promise for a rapid and non-invasive environmental screening system.
Slow ramping emerges from spontaneous fluctuations in spiking neural networks
Authors: Gavenas Jake , Rutishauser Ueli , Schurger Aaron , Maoz Uri
Published: May 2023

Highlights

1. We reveal a mechanism for slow-ramping signals before spontaneous voluntary movements.

2. Slow synapses stabilize spontaneous fluctuations in spiking neural network.

3. We validate model predictions in human frontal cortical single-neuron recordings.

4. The model recreates the readiness potential in an EEG proxy signal.

5. Neurons that ramp together had correlated activity before ramping onset.

The capacity to initiate actions endogenously is critical for goal-directed behavior. Spontaneous voluntary actions are typically preceded by slow-ramping activity in medial frontal cortex that begins around two seconds before movement, which may reflect spontaneous fluctuations that influence action timing. However, the mechanisms by which these slow ramping signals emerge from single-neuron and network dynamics remain poorly understood. Here, we developed a spiking neural-network model that produces spontaneous slow ramping activity in single neurons and population activity with onsets ∼2 seconds before threshold crossings. A key prediction of our model is that neurons that ramp together have correlated firing patterns before ramping onset. We confirmed this model-derived hypothesis in a dataset of human single neuron recordings from medial frontal cortex. Our results suggest that slow ramping signals reflect bounded spontaneous fluctuations that emerge from quasi-winner-take-all dynamics in clustered networks that are temporally stabilized by slow-acting synapses.
Non-Markovian memory in a bacterium
Authors: Joshi Kunaal , Ziegler Karl F , Roy Shaswata , Wright Charles S , Gandhi Rhea , Stonecipher Jack , Biswas Rudro R , Iyer-Biswas Srividya
Published: May 2023

Do individual bacterial cells retain memories of the history of environmental conditions experienced in previous generations? Here we directly address this question through a synthesis of physics theory and high-precision experiments on statistically identical, non-interacting individual bacterial cells, which grow and divide with intrinsic stochasticity in precisely controlled conditions. From these data, we extract “emergent simplicities” in the seemingly complex interplay between history dependence, persistence, and transience in the stochastic memories of the dynamic environments experienced by individuals over multiple generations. First, we find that the instantaneous single-cell growth rate is the key physiologically relevant quantity where intergenerational memory is stored. In contrast, the cell size dynamics are memory free, or Markovian, over intergenerational timescales. Next, we find that the effect of experiencing dynamic environments can be captured quantitatively by recal-ibrating the cellular unit of time by the measured mean instantaneous growth rate; the dynamically rescaled cell age distributions undergo a scaling collapse. Moreover, in a given condition, an individual bacterial cell retains history-dependent, or non-Markovian, memory of its growth rate over tens of generations. We derive from first principles a physically-motivated metric to quantify the degree of non-Markovianity. Furthermore, when conditions change, the instantaneous single-cell growth distribution becomes bimodal, as the bacterium’s memory of past environment encountered is reset stochastically and plastically, prior to achieving a new homeostasis.
WITHDRAWN: Neurophysiology of Reciprocal Influence of Cognition and Emotion in Pianistic Performances
Authors: Higuchi Marcia K. K. , Sanchez Tiago A. , Ramos Lucas R. , Fornari N. José E. , Araújo Dráulio B. , Leite João P.
Published: May 2023

Withdrawal Statement

This manuscript has been withdrawn by bioRxiv as it was submitted and made public without the full consent of all the authors. Therefore, the authors do not wish this work to be cited as reference for the project. If you have any questions, please contact the corresponding author
Unraveling the timeline of gene expression: a pseudo-temporal trajectory analysis of single-cell RNA sequencing data
Authors: Cheng Jinming , Smyth Gordon K. , Chen Yunshun
Published: May 2023

There has been a rapid development in single cell RNA sequencing (scRNA-seq) technologies in recent years. Droplet-based single cell platforms such as the 10x Genomics’ Chromium system enable gene expression profiling of tens of thousands of cells per sample. The goal of a typical scRNA-seq analysis is to identify different cell subpopulations and their respective marker genes. Trajectory analysis can also be used to infer the developmental or differentiation trajectories of cells by ordering them along a putative lineage tree based on their gene expression profiles. This analysis positions cells and cell clusters along a pseudotime trajectory that represents a biological process such as cell differentiation, development, or disease progression. Here we demonstrate a time-course analysis to identify genes that are significantly associated with pseudotime. The article demonstrates a comprehensive workflow for performing trajectory inference and time course analysis on a multi-sample single cell RNA-seq experiment of the mouse mammary gland. The workflow uses open-source R software packages and covers all steps of the analysis pipeline, including quality control, doublet prediction, normalization, integration, dimension reduction, cell clustering, trajectory inference, and pseudobulk time course analysis. Sample integration and cell clustering follows theSeuratpipeline while the trajectory inference is conducted using themonocle3package. The pseudo-bulk time course analysis uses the quasi-likelihood framework ofedgeR.
Structural characterization of a pathogenic antibody underlying vaccine-induced immune thrombotic thrombocytopenia (VITT)
Authors: Nguyen Son N. , Le Si-Hung , Ivanov Daniil G. , Ivetic Nikola , Nazy Ishac , Kaltashov Igor A.
Published: May 2023

Vaccine-induced immune thrombotic thrombocytopenia (VITT) is a rare but extremely dangerous side effect that has been reported for several adenoviral (Ad)-vectored COVID-19 vaccines. VITT pathology had been linked to production of antibodies that recognize platelet factor 4 (PF4), an endogenous chemokine. In this work we characterize anti-PF4 antibodies obtained from a VITT patient’s blood. Intact-mass MS measurements indicate that a significant fraction of this ensemble is comprised of antibodies representing a limited number of clones. MS analysis of large antibody fragments (the light chain, as well as the Fc/2 and Fd fragments of the heavy chain) confirms the monoclonal nature of this component of the anti-PF4 antibodies repertoire, and reveals the presence of a fully mature complex biantennary N-glycan within its Fd segment. Peptide mapping using two complementary proteases and LC-MS/MS analysis were used to determine the amino acid sequence of the entire light chain and over 98% of the heavy chain (excluding a short N-terminal segment). The sequence analysis allows the monoclonal antibody to be assigned to IgG2 subclass and verify that the light chain belongs to the λ-type. Incorporation of enzymatic de-N-glycosylation into the peptide mapping routine allows theN-glycan in the Fab region of the antibody to be localized to the framework 3 region of the VHdomain. This novelN-glycosylation site (absent in the germline sequence) is a result of a single mutation giving rise to an NDT motif in the antibody sequence. Peptide mapping also provides a wealth of information on lower-abundance proteolytic fragments derived from the polyclonal component of the anti-PF4 antibody ensemble, revealing the presence of all four subclasses (IgG1 through IgG4) and both types of the light chain (λ and κ). The structural information reported in this work will be indispensable for understanding the molecular mechanism of VITT pathogenesis.
Vigilance & Cognitive Flexibility: The Effect of Martial Arts Experience on Task Switching
Authors: Johnstone Ashleigh , Marí-Beffa Paloma
Published: May 2023

Martial Arts can be considered both a sport and a therapy, combining physical exercise with intense periods of concentration (similar to meditation). Previous research suggests that both exercise and meditation can lead to improvements in executive functions (i.e., cognitive flexibility) in adult participants. Results from our lab have previously suggested that martial artists show improvements in the alerting attentional network – a measure of vigilance. The current research aims to investigate the impact of Martial Arts training on a task-switching protocol to measure both vigilance and cognitive flexibility in typical adults. Here we recruited adult martial artists with at least two years of experience, and control participants with no experience. Participants had to respond to either the shape or the colour of a figure in pure and mixed blocks to provide measures of mixing costs (sustained vigilance) and switching costs (cognitive flexibility). Results demonstrated martial artists did not differ from controls in the pure block, but displayed improved performance in the mixed block, revealing an improvement in mixing costs (vigilance). These benefits in vigilance mirror those previously found in attentional tasks, providing convergent evidence on the impact of Martial Arts training on vigilance.
Intestinal microbiota modulates the anti-tumor effect of oncolytic virus vaccine in colorectal cancer
Authors: Chen Xia , Wang Guang-Jun , Qin Ling , Hu Bing , Li Jun
Published: May 2023

Background

Immunotherapy such as oncolytic virus has become a powerful cancer treatment but only a part of cancer patients can benefit from it, especially to advanced-stage cancer patients are required new therapeutic strategies to facilitate extended survival. Intestinal microbiota may contribute to colorectal cancer (CRC) carcinogenesis and response to immunotherapy. However, whether and how the modulating effect of intestinal microbiota on oncolytic virus vaccine (OVV) in CRC remains to be investigated.

Methods

We generated a MC38-gp33 CRC mouse model and treated with OVV-gp33 in early- and advanced-stages. Probiotics, fecal microbiota transplantation (FMT) and antibiotics (ABX) were treated to regulate the microbial composition of CRC mice of advanced stage. The tumor growth rate and survival time of mice were recorded. 16S rDNA sequencing analyzed the microbial composition and flow cytometry detected the T cells subsets activity.

Results

OVV-gp33 treatment led to inhibited tumor growth and prolonged survival in the early stage of CRC but did not have a significant effect on the advanced stage of CRC. Moreover, 16S rDNA sequence analysis and flow cytometry showed significant differences in intestinal microbiota composition, microbial metabolites and T-cell subsets in early- and advanced-stage CRC. Probiotic and FMT treatment significantly enhanced the antitumor effect of OVV in advanced stage of CRC with an increased abundance of activated CD8+T cells and a decreased ratio of Treg cells, while depletion of the microbiota by ABX eliminated the antitumor activity of OVV with decreased CD8+T-cell activation and upregulated Treg cells.

Conclusions

These results indicate that intestinal microbiota and microbial metabolites play an important role in the OVV antitumor effect in CRC, furthermore, altering the intestinal microbiota composition can modulate the antitumor and immunomodulatory effect of OVV in CRC.
Environmental radiation exposure at Chornobyl has not systematically affected the genomes or mutagen tolerance phenotypes of local worms
Authors: Tintori Sophia C. , Çağlar Derin , Ortiz Patrick , Chyzhevskyi Ihor , Mousseau Timothy A. , Rockman Matthew V.
Published: May 2023

The 1986 disaster at the Chornobyl Nuclear Power Plant transformed the surrounding region into the most radioactive landscape known on the planet. Questions remain regarding whether this sudden environmental shift selected for species, or even individuals within a species, that are naturally more resistant to radiation exposure.

We collected, cultured, and cryopreserved 298 wild nematodes isolates from areas varying in radioactivity within the Chornobyl Exclusion Zone. We sequenced and assembled genomesde novofor 20Oschieus tipulaestrains, analyzed their genomes for evidence of recent mutation acquisition in the field and saw no evidence of an association between mutation and radiation level at the sites of collection. Multigenerational exposure of each of these strains to several mutagens in the lab revealed that strains vary heritably in tolerance to each mutagen, but mutagen tolerance cannot be predicted based on the radiation levels at collection sites.
Advancing scaffold biomimicry: engineering mechanics in microfiber scaffolds with independently controlled architecture using melt electrowriting
Authors: Devlin Brenna L. , Pickering Edmund , Allenby Mark C. , Paxton Naomi C. , Woodruff Maria A.
Published: May 2023

Melt electrowriting (MEW) is an additive manufacturing technique characterized by its ability to fabricate micronscale fibers from molten polymers into highly controlled 3D microfiber scaffolds. This emerging technique is gaining traction in tissue engineering and biofabrication research, however limitations in the ability to develop advanced coding to program MEW printers to fabricate scaffolds with complex fiber architectures has inhibited the development of structures with tunable and biomimetic mechanical properties. This study reports a series of non-straight scaffold architectures with combinations of independently controlled X & Y fiber spacing, corrections for MEWjet lag, and characterizations of their influences on scaffold mechanics. Polycaprolactone scaffolds with an elastic modulus ranging from 0.3 to 7.3 MPa were fabricated utilizing scaffolds manufactured from 5 layers of 55 μm fibers. The inclusion of scaffold design corrections in the gcode to compensate for decreasing deposition accuracy with increasing layer height enabled us to correct for discontinuous stress-strain mechanics and improved scaffold fabrication reproducibility. This study provides a comparison between a series of highly reproducible MEW scaffold architectures with non-straight fibers compared to the common crosshatch design to inform the development of more biomimetic scaffolds applicable to a variety of clinical applications. It further illustrates the significant effect toolpath correction has on reducing poor stress-strain mechanics, therefore improving the control, reproducibility, and biomimetic capacity of the MEW technique.
DNA-guided transcription factor cooperativity shapes face and limb mesenchyme
Authors: Kim Seungsoo , Morgunova Ekaterina , Naqvi Sahin , Bader Maram , Koska Mervenaz , Popov Alexander , Luong Christy , Pogson Angela , Claes Peter , Taipale Jussi , Wysocka Joanna
Published: May 2023

Transcription factors (TFs) can define distinct cellular identities despite nearly identical DNA-binding specificities. One mechanism for achieving regulatory specificity is DNA-guided TF cooperativity. Althoughin vitrostudies suggest it may be common, examples of such cooperativity remain scarce in cellular contexts. Here, we demonstrate how ‘Coordinator’, a long DNA motif comprised of common motifs bound by many basic helix-loop-helix (bHLH) and homeodomain (HD) TFs, uniquely defines regulatory regions of embryonic face and limb mesenchyme. Coordinator guides cooperative and selective binding between the bHLH family mesenchymal regulator TWIST1 and a collective of HD factors associated with regional identities in the face and limb. TWIST1 is required for HD binding and open chromatin at Coordinator sites, while HD factors stabilize TWIST1 occupancy at Coordinator and titrate it away from HD-independent sites. This cooperativity results in shared regulation of genes involved in cell-type and positional identities, and ultimately shapes facial morphology and evolution.
Investigating individual variability in microstructural-functional coupling in the human cortex
Authors: Patel Raihaan , Dai Alyssa , Valk Sofie L. , Desrosiers-Grégoire Gabriel , Devenyi Gabriel A. , Chakravarty M. Mallar
Published: May 2023

Understanding the relationship between the structural and functional architecture of the human brain remains a key question in neuroscience. In this regard variation in cortical myelin may provide key insights into the functional organization. Previous findings have demonstrated that regions sharing myeloarchitectonic features are also likely to be structurally and functionally connected. However, this association is not uniform for all regions. For example, the strength of the association, or ‘coupling’, between microstructure and function is regionally heterogeneous, with strong coupling in primary cortices but weaker coupling in higher order transmodal cortices. However, the bases of these observations have been typically made at the group level, leaving much to be understood regarding the individual-level behavioural relevance of microstructural-functional coupling variability. To examine this critical question, we apply a multivariate framework to a combination of high-resolution structural, diffusion, and functional magnetic resonance imaging (MRI) data in a sample of healthy young adults. We identify four distinct patterns of coupling variation that vary across individuals. Remarkably, we find that while microstructural-functional coupling is consistently strong in primary cortices, significant variation in transmodal cortices exists. Importantly, we identified coupling variability maps and their association with behaviour that demonstrate the existence of latent dimensions of variability related to inter-individual performance on cognitive tasks. These findings suggest that the existence of behaviourally relevant coupling variation is a key principle for brain organization.
Inhibition of proprotein convertase subtilisin-like kexin type 9 (PCSK9) potentiates anti-angiogenic therapy in colorectal cancer liver metastases
Authors: Rada Miran , Reynolds Andrew R. , Lazaris Anthoula , Seidah Nabil , Metrakos Peter
Published: May 2023

Colorectal cancer liver metastatic (CRCLM) tumours present as two main histopathological growth patterns (HGPs) including desmoplastic HGP (DHGP) and replacement HGP (RHGP). The DHGP tumours obtain their blood supply by sprouting angiogenesis, whereas the RHGP tumours utilize an alternative vascularisation known as vessel co-option. In vessel co-option, the cancer cells hijack the mature sinusoidal vessels to obtain blood supply. Vessel co-option has been reported as an acquired mechanism of resistance to anti-angiogenic treatment in CRCLM. Herein, we showed that inhibiting proprotein convertase subtilisin-like kexin type 9 (PCSK9) via clinically approved PCSK9-neutralizing antibody (Evolocumab) can boost the response of vessel co-opting tumours to anti-angiogenic therapy. Mechanistically, we found that PCSK9 inhibition downregulates runt related transcription factor-1 (RUNX1) expression levels in CRCLM cancer cells in vivo, which its expression positively correlates with the development of vessel co-option. Collectively, these results suggest that inhibiting PCSK9 is a promising way to improve the efficacy of anti-angiogenic therapy against vessel co-opting tumours in CRCLM.
ULDNA: Integrating Unsupervised Multi-Source Language Models with LSTM-Attention Network for Protein-DNA Binding Site Prediction
Authors: Zhu Yi-Heng , Yu Dong-Jun
Published: May 2023

Accurate identification of protein-DNA interactions is critical to understand the molecular mechanisms of proteins and design new drugs. We proposed a novel deeplearning method, ULDNA, to predict DNA-binding sites from protein sequences through a LSTM-attention architecture embedded with three unsupervised language models pretrained in multiple large-scale sequence databases. The method was systematically tested on 1287 proteins with DNA-binding site annotation from Protein Data Bank. Experimental results showed that ULDNA achieved a significant increase of the DNA-binding site prediction accuracy compared to the state-of-the-art approaches. Detailed data analyses showed that the major advantage of ULDNA lies in the utilization of three pre-trained transformer language models which can extract the complementary DNA-binding patterns buried in evolution diversity-based feature embeddings in residue-level. Meanwhile, the designed LSTM-attention network could further enhance the correlation between evolution diversity and protein-DNA interaction. These results demonstrated a new avenue for high-accuracy deep-learning DNA-binding site prediction that is applicable to large-scale protein-DNA binding annotation from sequence alone.
Designer genes courtesy of artificial intelligence
Authors: Hoffmann Alexander
Published: May 2023

The core promoter determines not only where gene transcription initiates but also the transcriptional activity in both basal and enhancer-induced conditions. Multiple short sequence elements within the core promoter have been identified in different species, but how they function together and to what extent they are truly species-specific has remained unclear. In this issue ofGenes & Development, Vo ngoc and colleagues (pp. 377–382) report undertaking massively parallel measurements of synthetic core promoters to generate a large data set of their activities that informs a statistical learning model to identify the sequence differences of human andDrosophilacore promoters. This machine learning model was then applied to design gene core promoters that are particularly specific for the human transcriptional machinery.
Extremely fast construction and querying of compacted and colored de Bruijn graphs with GGCAT
Authors: Cracco Andrea , Tomescu Alexandru I.
Published: May 2023

Compacted de Bruijn graphs are one of the most fundamental data structures in computational genomics. Colored compacted de Bruijn graphs are a variant built on acollectionof sequences and associate to eachk-mer the sequences in which it appears. We present GGCAT, a tool for constructing both types of graphs, based on a new approach merging thek-mer counting step with the unitig construction step, as well as on numerous practical optimizations. For compacted de Bruijn graph construction, GGCAT achieves speed-ups of 3× to 21× compared with the state-of-the-art tool Cuttlefish 2. When constructing the colored variant, GGCAT achieves speed-ups of 5× to 39× compared with the state-of-the-art tool BiFrost. Additionally, GGCAT is up to 480× faster than BiFrost for batch sequence queries on colored graphs.
Discordant calls across genotype discovery approaches elucidate variants with systematic errors
Authors: Atkinson Elizabeth G. , Artomov Mykyta , Loboda Alexander A. , Rehm Heidi L. , MacArthur Daniel G. , Karczewski Konrad J. , Neale Benjamin M. , Daly Mark J.
Published: May 2023

Large-scale high-throughput sequencing data sets have been transformative for informing clinical variant interpretation and for use as reference panels for statistical and population genetic efforts. Although such resources are often treated as ground truth, we find that in widely used reference data sets such as the Genome Aggregation Database (gnomAD), some variants pass gold-standard filters, yet are systematically different in their genotype calls across genotype discovery approaches. The inclusion of such discordant sites in study designs involving multiple genotype discovery strategies could bias results and lead to false-positive hits in association studies owing to technological artifacts rather than a true relationship to the phenotype. Here, we describe this phenomenon of discordant genotype calls across genotype discovery approaches, characterize the error mode of wrong calls, provide a list of discordant sites identified in gnomAD that should be treated with caution in analyses, and present a metric and machine learning classifier trained on gnomAD data to identify likely discordant variants in other data sets. We find that different genotype discovery approaches have different sets of variants at which this problem occurs, but there are characteristic variant features that can be used to predict discordant behavior. Discordant sites are largely shared across ancestry groups, although different populations are powered for the discovery of different variants. We find that the most common error mode is that of a variant being heterozygous for one approach and homozygous for the other, with heterozygous in the genomes and homozygous reference in the exomes making up the majority of miscalls.
Structure–function studies of a novel laccase-like multicopper oxidase fromThermothelomyces thermophilaprovide insights into its biological role
Authors: Kosinas Christos , Zerva Anastasia , Topakas Evangelos , Dimarogona Maria
Published: May 2023

Multicopper oxidases are promiscuous biocatalysts with great potential for the production of industrial compounds. This study is focused on the elucidation of the structure–function determinants of a novel laccase-like multicopper oxidase from the thermophilic fungusThermothelomyces thermophila(TtLMCO1), which is capable of oxidizing both ascorbic acid and phenolic compounds and thus is functionally categorized between the ascorbate oxidases and fungal ascomycete laccases (asco-laccases). The crystal structure ofTtLMCO1, determined using anAlphaFold2 model due to a lack of experimentally determined structures of close homologues, revealed a three-domain laccase with two copper sites, lacking the C-terminal plug observed in other asco-laccases. Analysis of solvent tunnels highlighted the amino acids that are crucial for proton transfer into the trinuclear copper site. Docking simulations showed that the ability ofTtLMCO1 to oxidizeortho-substituted phenols stems from the movement of two polar amino acids at the hydrophilic side of the substrate-binding region, providing structural evidence for the promiscuity of this enzyme.
Investigating the relationship between supply chain finance and supply chain collaborative factors
Authors: Zaman Syed Imran , Khan Sharfuddin Ahmed , Kusi-Sarpong Simonov
Published: June 2023

Purpose

It is important to understand the factors that are significant in supply chain (SC) collaboration decision making and whether supply chain collaborative factors that are considered in the literature are still valid. To date, SC collaboration has not been extensively studied in the literature with supply chain finance (SCF) factors to evaluate SCF performance. Therefore, in this paper, the authors investigate the interrelationships between SCF and supply chain collaborative (SCC) factors for achieving SCF performance. The authors identified the most important factors from the literature on SCF and SCC and with inputs from experts in the textile industry in Pakistan.

Design/methodology/approach

The authors employed the Gray-Decision Making Trial and Evaluation Laboratory approach to help examine the cause-and-effect relationship between the factors and identify the influence of each factor on the others.

Findings

The findings showed that the most prominent factors of the study are “level of digitalization”, “information sharing”, and “collaborative communication”, and “most effect factors of this study are incentive alignment” and “information quality”. Furthermore, the “Level of digitalization” was identified as the factor with the central role and most significant correlation with other factors.

Research limitations/implications

The major implication of the study is that textile industries should effectively develop their supply chain decisions after analyzing their internal and external factors, which will help in developing strategies that will facilitate better management of SCF relationships. The limitations of the study are that only 15 SCF and supply chain collaborative factors were considered, and time and scope are also limited. This study is only applied in the textile industry, so generalization may be limited.

Originality/value

To date, this study is the only one that has taken into consideration SCC with SCF factors to evaluate supply chain performance. This paper therefore makes this initial attempt and original contribution to this discussion, which can be helpful for those working to enhance supply chain performance, such as practitioners and policymakers.
Coming to work with an illness: the role of high-involvement work systems and individual competence on presenteeism
Authors: Islam Md Shamirul , Amin Muslim , Feranita Feranita , Winterton Jonathan
Published: May 2023

Purpose

This paper aims to examine the effect of high-involvement work systems (HIWSs) on completing work and avoiding distraction as two dimensions of presenteeism. It also investigates competence as a mediator of the effect of HIWS on presenteeism.

Design/methodology/approach

Data were collected from 343 Bangladeshi bank employees using an online survey. The partial least squares-structural equation modelling (PLS-SEM) was employed to assess the abovementioned linkages.

Findings

The findings demonstrate HIWS directly avoid distraction but do not significantly impact the completing work dimension of presenteeism. The findings also indicate that competence mediates the effect of HIWS on completing work but not on avoiding distraction.

Originality/value

Drawing on the job demands-resources (JD-R) model, this study empirically demonstrates the contrasting role of HIWS in completing work and avoiding distraction related to presenteeism. It also provides a novel perspective on the unexplored mediating mechanism of competence on the relationship between HIWS and presenteeism and offers new directions for HIWS and presenteeism research.
Asymmetric impact of income and price on kerosene consumption in Cameroon: new evidence from NARDL
Authors: Noumga Jeunesse , Sapnken Flavian Emmanuel , Kinfack Jeutsa Aubin , Tamba Jean Gaston
Published: May 2023

Purpose

This research paper aims to examine the asymmetric impact of income and price on household consumption of kerosene in Cameroon.

Design/methodology/approach

The methodological approach consists of testing for stationarity using the augmented Dickey–Fuller and Andrews and Zivot tests, determining cointegration using nonlinear autoregressive distributed lag (NARDL) test approach and finally examining asymmetry using the Wald test.

Findings

Results of the stationarity tests reveal that variables are all integrated of order less than two I(2). The NARDL approach indicates that the (positive and negative) income shock and the positive price boom negatively influence consumption in the long- and short-run. The same is true for the negative price shock, but the latter remains insignificant. Furthermore, the Wald test carried out in the study confirms that the cumulative effects of the positive and negative income and price shocks are asymmetric.

Originality/value

The increase in the price of kerosene due to the lifting of subsidies has led to a decrease in household consumption and an unfortunate increase in the loss of tree cover in Cameroon. According to the results, this phenomenon will persist even if the price is reduced. Actions aimed at reducing its production at the expense of liquefied petroleum gas, electricity and renewable energy should be encouraged to limit the loss of vegetation cover. Thus, this study could contribute to solving the problem of deforestation and desertification in Cameroon.
Employee participation in total productive maintenance – a bottom-up perspective
Authors: Yang Ying , Yang Biao
Published: May 2023

Purpose

This study emphasises the importance of employee participation in total productive maintenance and identifies the key factors influencing employee participation. The Motivation-Opportunity-Ability (MOA) framework is adopted to identify and categorise key factors.

Design/methodology/approach

An embedded case study with a power plant service provider in England was conducted with a variety of research methods, for example interviews and questionnaire surveys, to gain a wide range of data.

Findings

Following the MOA framework, this study shows various key aspects of employees' motivation, opportunity and ability when participating in total productive maintenance. It also compares first-line machine operators and maintenance specialists in terms of the drivers and barriers to total productive maintenance for them, and reveals that they need different mechanical skills in order to participate in total productive maintenance.

Originality/value

The study extends the applications of the MOA framework to total productive maintenance initiatives and provides managers with guidance on how to correctly consider and prioritise employee participation in their implementation. Moreover, this is the first study to identify differences between first-line machine operators and maintenance specialists, in terms of their willingness to participate in total productive maintenance.
Hybrid-disturbance-observer-based interaction control for a fully actuated UAV with tether-based positioning system
Authors: Jiao Ran , Rong Yongfeng , Dong Mingjie , Li Jianfeng
Published: May 2023

Purpose

This paper aims to tackle the problem for a fully actuated unmanned aerial vehicle (FUAV) to perform physical interaction tasks in the Global Positioning System-denied environments without expensive motion capture system (like VICON) under disturbances.

Design/methodology/approach

A tether-based positioning system consisting of a universal joint, a tether-actuated absolute position encoder and an attitude sensor is designed to provide reliable position feedback for the FUAV. To handle the disturbances, including the tension force caused by the taut tether, model uncertainties and other external disturbances such as aerodynamic disturbance, a hybrid disturbance observer (HDO) combining the position-based method and momentum-based technology with force sensor feedback is designed for the system. In addition, an HDO-based impedance controller is built to allow the FUAV interacting with the environment and meanwhile rejecting the disturbances.

Findings

Experimental validations of the proposed control algorithm are implemented on a real FUAV with the result of nice disturbance rejection capability and physical interaction performance.

Originality/value

A cheap alternative to indoor positioning system is proposed, with which the FUAV is able to interact with external environment and meanwhile reject the disturbances under the help of proposed hybrid disturbance observer and the impedance controller.