Miracle-dz/UniFAR-FTUs huggingface dataset:
Heart rate as a measure of emotional arousal in evolutionary biology

How individuals interact with their environment and respond to changes is a key area of research in evolutionary biology. A physiological parameter that provides an instant proxy for the activation of the automatic nervous system, and can be measured relatively easily, is modulation of heart rate. Over the past four decades, heart rate has been used to assess emotional arousal in non-human animals in a variety of contexts, including social behaviour, animal cognition, animal welfare and animal personality. In this review, I summarize how measuring heart rate has provided new insights into how social animals cope with challenges in their environment. I assess the advantages and limitations of different technologies used to measure heart rate in this context, including wearable heart rate belts and implantable transmitters, and provide an overview of prospective research avenues using established and new technologies, with a special focus on implications for applied research on animal welfare. This article is part of the theme issue ‘Measuring physiology in free-living animals (Part II)’.
Machine translation and post-editing for user generated content: an LSP perspective

User Generated Content (UGC) is a new and exciting content type for Langauge Service providers (LSP) and it poses its own distinctive challenges for a machine translation workflow: UGC requires more pre-editing steps than any other content type we process with MT and it demands non-traditional approaches to post-editing, resourcing and quality evaluations. We discuss the most common quality level requirements that we have observed in our work with UGC and ways to achieve them using specific post-editing methodologies for this content type. We will also touch on the subjects of resource selection, our experience around MT engine evaluations and customization for this content type and the importance of using the appropriate evaluation method for different use cases.
Density Guarantee on Finding Multiple Subgraphs and Subtensors

Dense subregion (subgraph & subtensor) detection is a well-studied area, with a wide range of applications, and numerous efficient approaches and algorithms have been proposed. Approximation approaches are commonly used for detecting dense subregions due to the complexity of the exact methods. Existing algorithms are generally efficient for dense subtensor and subgraph detection, and can perform well in many applications. However, most of the existing works utilize the state-or-the-art greedy 2-approximation algorithm to capably provide solutions with a loose theoretical density guarantee. The main drawback of most of these algorithms is that they can estimate only one subtensor, or subgraph, at a time, with a low guarantee on its density. While some methods can, on the other hand, estimate multiple subtensors, they can give a guarantee on the density with respect to the input tensor for the first estimated subsensor only. We address these drawbacks by providing both theoretical and practical solution for estimating multiple dense subtensors in tensor data and giving a higher lower bound of the density. In particular, we guarantee and prove a higher bound of the lower-bound density of the estimated subgraph and subtensors. We also propose a novel approach to show that there are multiple dense subtensors with a guarantee on its density that is greater than the lower bound used in the state-of-the-art algorithms. We evaluate our approach with extensive experiments on several real-world datasets, which demonstrates its efficiency and feasibility.
Improved Customer Lifetime Value Prediction With Sequence-To-Sequence Learning and Feature-Based Models

The prediction of the Customer Lifetime Value (CLV) is an important asset for tool-supported marketing by customer relationship managers. Since standard methods based on purchase recency, frequency, and past profit and revenue statistics often have limited predictive power, advanced machine learning (ML) techniques were applied to this task in recent years. However, existing approaches are often not fully capable of modeling certain temporal patterns that can be commonly found in practice, such as periodic purchasing behavior of customers. To address these shortcomings, we propose a novel method for CLV prediction based on a combination of several ML techniques. At its core, our method consists of a tailored deep learning approach based on encoder–decoder sequence-to-sequence recurrent neural networks with augmented temporal convolutions. This model is then combined with gradient boosting machines (GBMs) and a set of novel features in a hybrid framework. Empirical evaluations based on real-world data from a larger e-commerce company and a public dataset from the domain of online retail show that already the sequence-based model leads to competitive performance results. Stacking it with the GBM model is synergistic and further improves accuracy, indicating that the two models capture different patterns in the data.
Augmented Virtual Reality Meditation

In a novel experimental setting, we augmented a variation of traditional compassion meditation with our custom-built VR environment for multiple concurrent users. The presence of another user’s avatar in shared virtual space supports social interactions and provides an active target for evoked compassion. The system incorporates respiration and brainwave-based biofeedback to enable closed-loop interaction of users based on their shared physiological state. Specifically, we enhanced interoception and the deep empathetic processes involved in compassion meditation with real-time visualizations of: breathing rate, level of approach motivation assessed from EEG frontal asymmetry, and dyadic synchrony of those signals between two users. We manipulated these interventions across eight separate conditions (dyadic or solo meditation; brainwave, breathing, both or no biofeedback) in an experiment with 39 dyads (N=8), observing the effect of conditions on self-reported experience and physiological synchrony. We found that each different shared biofeedback type increased users’ self-reported empathy and social presence, compared to no-biofeedback or solo conditions. Our study illustrates how dyadic synchrony biofeedback can expand the possibilities of biofeedback in affective computing and VR solutions for health and wellness.
AP-4 regulates neuronal lysosome composition, function, and transport via regulating export of critical lysosome receptor proteins at the trans-Golgi network

The adaptor protein complex-4 or AP-4 is known to mediate autophagosome maturation through regulating sorting of transmembrane cargo such as ATG9A at the Golgi. There is a need to understand AP-4 function in neurons, as mutations in any of its four subunits cause a complex form of hereditary spastic paraplegia (HSP) with intellectual disability. While AP-4 has been implicated in regulating trafficking and distribution of cargo such as ATG9A and APP, little is known about its effect on neuronal lysosomal protein traffic, lysosome biogenesis and function. In this study, we demonstrate that in human iPSC-derived neurons AP-4 regulates lysosome composition, function and transport via regulating export of critical lysosomal receptors, including Sortilin 1, from the trans-Golgi network to endo-lysosomes. Additionally, loss of AP-4 causes endo-lysosomes to stall and build up in axonal swellings potentially through reduced recruitment of retrograde transport machinery to the organelle. These findings of axonal lysosome build-up are highly reminiscent of those observed in Alzheimer’s disease as well as in neurons modelling the most common form of HSP, caused by spastin mutations. Our findings implicate AP-4 as a critical regulator of neuronal lysosome biogenesis and altered lysosome function and axonal endo-lysosome transport as an underlying defect in AP-4 deficient HSP.
Using dual EEG to analyse event-locked changes in child-adult neural connectivity

Current approaches typically measure the connectivity between interacting physiological systems as a time-invariant property. This approach obscures crucial information about how connectivity between interacting systems is established and maintained. Here, we describe methods, and present computational algorithms, that will allow researchers to address this deficit. We focus on how two different approaches to measuring connectivity, namely concurrent (e.g., power correlations, phase locking) and sequential (e.g., Granger causality), can be applied to three aspects of the brain signal, namely amplitude, power, and phase. We guide the reader through worked examples using mainly simulated data on how to leverage these methods to measure changes in interbrain connectivity between adults and children/infants relative to events identified within continuous EEG data during a free-flowing naturalistic interaction. For each, we aim to provide a detailed explanation of the interpretation of the analysis and how they can be usefully used when studying early social interactions.
Application of a convolutional neural network to the quality control of MRI defacing

Large-scale neuroimaging datasets present unique challenges for automated processing pipelines. Mo-tivated by a large clinical trials dataset of Multiple Sclerosis with over 235,000 MRI scans, we consider the challenge of defacing - anonymisation to remove identifying facial features. The defacing process must undergo quality control (QC) checks to ensure that the facial features have been adequately anonymised and that the brain tissue is left completely intact. Visual QC checks – particularly on a project of this scale – are time-consuming and can cause delays in preparing data. We have developed a convolutional neural network (CNN) that can assist with the QC of the application of MRI defacing; our CNN is able to distinguish between scans that are correctly defaced, and can also classify defacing failures into three sub-types to facilitate parameter tuning during remedial re-defacing. Since integrating the CNN into our anonymisation pipeline, over 75,000 scans have been processed. Strict thresholds have been applied so that ambiguous classifications are referred for visual QC checks, however all scans still undergo a time-efficient verification check before being marked as passed. After applying the thresholds, our network is 92% accurate and is able to classify nearly half of the scans without the need for protracted manual checks. Even with the introduction of the verification checks, incorporation of the CNN has reduced the time spent undertaking QC checks by 42% during initial defacing, and by 35% overall. With the help of the CNN, we have been able to successfully deface 96% of the scans in the project whilst maintaining high QC standards. In a similarly sized new project of this scale, we would expect the model to reduce the time spent on manual QC checks by 125 hours. Our approach is applicable to other projects with the potential to greatly improve the efficiency of imaging anonymisation pipelines.
utR.annotation: a tool for annotating genomic variants that could influence post-transcriptional regulation

Summary Whole genome sequencing of patient populations is identifying thousands of new variants in UnTranslated Regions(UTRs). While the consequences of UTR mutations are not as easily predicted from primary sequence as coding mutations are, there are some known features of UTRs modulate their function. utR.annotation is an R package that can be used to annotate potential deleterious variants in the UTR regions for both human and mouse species. Given a CSV or VCF format variant file, utR.annotation provides information of each variant on whether and how it alters known translational regulators including:upstream Open Reading Frames (uORFs), upstream Kozak sequences, polyA signals, the Kozak sequence at the annotated translation initiation site, start codon, and stop codon, conservation scores in the variant position, and whether and how it changes ribosome loading based on a model from empirical data. Availability and implementation utR.annotation is freely available on Bitbucket (https://bitbucket.org/jdlabteam/utr.annotation/src/master/) and CRAN (to be updated) Supplementary information Supplementary data are available at https://wustl.box. com/s/yye99bryfin89nav45gv91l5k35fxo7z
GraphOmics: an interactive platform to explore and integrate multi-omics data

An increasing number of studies now produce multiple omics measurements that require using sophisticated computational methods for analysis. While each omics data can be examined separately, jointly integrating multiple omics data allows for deeper understanding and insights to be gained from the study. In particular, data integration can be performed horizontally, where biological entities from multiple omics measurements are mapped to common reactions and pathways. However, data integration remains a challenge due to the complexity of the data and the difficulty in interpreting analysis results. Here we present GraphOmics, a user-friendly platform to explore and integrate multiple omics datasets and support hypothesis generation. Users can upload transcriptomics, proteomics and metabolomics data to GraphOmics. Relevant entities are connected based on their biochemical relationships, and mapped to reactions and pathways from Reactome. From the Data Browser in GraphOmics, mapped entities and pathways can be ranked, sorted and filtered according to their statistical significance (p values) and fold changes. Context-sensitive panels provide information on the currently selected entities, while interactive heatmaps and clustering functionalities are also available. As a case study, we demonstrated how GraphOmics was used to interactively explore multi-omics data and support hypothesis generation using two complex datasets from existing Zebrafish regeneration and Covid-19 human studies. GraphOmics is fully open-sourced and freely accessible from https://graphomics.glasgowcompbio. org/. It can be used to integrate multiple omics data horizontally by mapping entities across omics to reactions and pathways. Our demonstration showed that by using interactive explorations from GraphOmics, interesting insights and biological hypotheses could be rapidly revealed.
Sensory and Choice Responses in MT Distinct from Motion Encoding

The macaque middle temporal (MT) area is well known for its visual motion selectivity and relevance to motion perception, but the possibility of it also reflecting higher-level cognitive functions has largely been ignored. We tested for effects of task performance distinct from sensory encoding by manipulating subjects' temporal evidence-weighting strategy during a direction discrimination task while performing electrophysiological recordings from groups of MT neurons in rhesus macaques (one male, one female). This revealed multiple components of MT responses that were, surprisingly, not interpretable as behaviorally relevant modulations of motion encoding, or as bottom-up consequences of the readout of motion direction from MT. The time-varying motion-driven responses of MT were strongly affected by our strategic manipulation—but with time courses opposite the subjects' temporal weighting strategies. Furthermore, large choice-correlated signals were represented in population activity distinct from its motion responses, with multiple phases that lagged psychophysical readout and even continued after the stimulus (but which preceded motor responses). In summary, a novel experimental manipulation of strategy allowed us to control the time course of readout to challenge the correlation between sensory responses and choices, and population-level analyses of simultaneously recorded ensembles allowed us to identify strong signals that were so distinct from direction encoding that conventional, single-neuron-centric analyses could not have revealed or properly characterized them. Together, these approaches revealed multiple cognitive contributions to MT responses that are task related but not functionally relevant to encoding or decoding of motion for psychophysical direction discrimination, providing a new perspective on the assumed status of MT as a simple sensory area. SIGNIFICANCE STATEMENT This study extends understanding of the middle temporal (MT) area beyond its representation of visual motion. Combining multineuron recordings, population-level analyses, and controlled manipulation of task strategy, we exposed signals that depended on changes in temporal weighting strategy, but did not manifest as feedforward effects on behavior. This was demonstrated by (1) an inverse relationship between temporal dynamics of behavioral readout and sensory encoding, (2) a choice-correlated signal that always lagged the stimulus time points most correlated with decisions, and (3) a distinct choice-correlated signal after the stimulus. These findings invite re-evaluation of MT for functions outside of its established sensory role and highlight the power of experimenter-controlled changes in temporal strategy, coupled with recording and analysis approaches that transcend the single-neuron perspective.
Successional Stages in Infant Gut Microbiota Maturation

After birth, microbial colonization of the infant intestinal tract is important for health later in life. However, this initial process is highly dynamic and influenced by many factors. ABSTRACT Disturbances in the primary colonization of the infant gut can result in lifelong consequences and have been associated with a range of host conditions. Although early-life factors have been shown to affect infant gut microbiota development, our current understanding of human gut colonization in early life remains limited. To gain more insights into the unique dynamics of this rapidly evolving ecosystem, we investigated the microbiota over the first year of life in eight densely sampled infants (n = 303 total samples). To evaluate the gut microbiota maturation transition toward an adult configuration, we compared the microbiome composition of the infants to that of the Flemish Gut Flora Project (FGFP) population (n = 1,106). We observed the infant gut microbiota to mature through three distinct, conserved stages of ecosystem development. Across these successional gut microbiota maturation stages, the genus predominance was observed to shift from Escherichia over Bifidobacterium to Bacteroides. Both disease and antibiotic treatment were observed to be associated occasionally with gut microbiota maturation stage regression, a transient setback in microbiota maturation dynamics. Although the studied microbiota trajectories evolved to more adult-like constellations, microbiome community typing against the background of the FGFP cohort clustered all infant samples within the (in adults) potentially dysbiotic Bacteroides 2 (Bact2) enterotype. We confirmed the similarities between infant gut microbial colonization and adult dysbiosis. Profound knowledge about the primary gut colonization process in infants might provide crucial insights into how the secondary colonization of a dysbiotic adult gut can be redirected. IMPORTANCE After birth, microbial colonization of the infant intestinal tract is important for health later in life. However, this initial process is highly dynamic and influenced by many factors. Studying this process in detail requires a dense longitudinal sampling effort. In the current study, the bacterial microbiota of >300 stool samples was analyzed from 8 healthy infants, suggesting that the infant gut microbial population matures along a path involving distinct microbial constellations and that the timing of these transitions is infant specific and can temporarily retrace upon external events. We also showed that the infant microbial populations show similarities to suboptimal bacterial populations in the guts of adults. These insights are crucial for a better understanding of the dynamics and characteristics of a “healthy gut microbial population” in both infants and adults and might allow the identification of intervention targets in cases of microbial disturbances or disease.
Gene-teratogen interactions influence the penetrance of birth defects by altering Hedgehog signaling strength

Birth defects result from interactions between genetic and environmental factors, but the mechanisms remain poorly understood. We find that mutations and teratogens interact in predictable ways to cause birth defects by changing target cell sensitivity to Hedgehog (Hh) ligands. These interactions converge on a membrane protein complex, the MMM complex, that promotes degradation of the Hh transducer Smoothened (SMO). Deficiency of the MMM component MOSMO results in elevated SMO and increased Hh signaling, causing multiple birth defects. In utero exposure to a teratogen that directly inhibits SMO reduces the penetrance and expressivity of birth defects in Mosmo-/- embryos. Additionally, tissues that develop normally in Mosmo-/- embryos are refractory to the teratogen. Thus, changes in the abundance of the protein target of a teratogen can change birth defect outcomes by quantitative shifts in Hh signaling. Consequently, small molecules that re-calibrate signaling strength could be harnessed to rescue structural birth defects.
AFEchidna is an R package for genetic evaluation of plant and animal breeding datasets

Progeny tests play important roles in plant and animal breeding programs, and mixed linear models are usually performed to estimate variance components of random effects, estimate the fixed effects (Best Linear Unbiased Estimates, BLUEs) and predict the random effects (Best Linear Unbiased Predictions, BLUPs) via restricted maximum likehood (REML) methods in progeny test datasets. The current pioneer software for genetic assessment is ASReml, but it is commercial and expensive. Although there is free software such as Echidna or the R package sommer, the Echidna syntax is complex and the R package functionality is limited. Therefore, this study aims to develop an R package named AFEchidna based on Echidna software. The mixed linear models are conveniently implemented for users through the AFEchidna package to solve variance components, genetic parameters and the BLUP values of random effects, and the batch analysis of multiple traits, multiple variance structures and multiple genetic parameters can be also performed, as well as comparison between different models and genomic BLUP analysis. The AFEchidna package is free, please email us (yzhlinscau@163.com) to get a copy if reader is interested for it. The AFEchidna package is developed to expand free genetic assessment software with the expectation that its efficiency could be close to the commercial software.