Miracle-dz/UniFAR-FTUs huggingface dataset:
Speech Emotion Recognition among Couples using the Peak-End Rule and Transfer Learning

Extensive couples? literature shows that how couples feel after a conflict is predicted by certain emotional aspects of that conversation. Understanding the emotions of couples leads to a better understanding of partners? mental well-being and consequently their relationships. Hence, automatic emotion recognition among couples could potentially guide interventions to help couples improve their emotional well-being and their relationships. It has been shown that people's global emotional judgment after an experience is strongly influenced by the emotional extremes and ending of that experience, known as the peak-end rule. In this work, we leveraged this theory and used machine learning to investigate, which audio segments can be used to best predict the end-of-conversation emotions of couples. We used speech data collected from 101 Dutch-speaking couples in Belgium who engaged in 10-minute long conversations in the lab. We extracted acoustic features from (1) the audio segments with the most extreme positive and negative ratings, and (2) the ending of the audio. We used transfer learning in which we extracted these acoustic features with a pre-trained convolutional neural network (YAMNet). We then used these features to train machine learning models - support vector machines - to predict the end-of-conversation valence ratings (positive vs negative) of each partner. The results of this work could inform how to best recognize the emotions of couples after conversation-sessions and eventually, lead to a better understanding of couples? relationships either in therapy or in everyday life.
Uncovering patterns in heavy-tailed networks : A journey beyond scale-free

Real-world heavy-tailed networks are claimed to be scale-free, meaning that the degree distributions follow the classical power-law. But it is evident from a closer observation that there exists a clearly identifiable non-linear pattern in the entire degree distribution in a log-log scale. Thus, the classical power-law distribution is often inadequate to fit the large-scale complex network data sets. The presence of this non-linearity can also be linked to the recent debate on scarcity versus the universality of scale-free networks. The search, therefore, continues to develop probabilistic models that can efficiently capture the crucial aspect of heavy-tailed and long-tailed behavior of the entire degree distribution of real-world complex networks. This paper proposes a new variant of the popular Lomax distribution, termed as modified Lomax (MLM) distribution, which can efficiently fit the entire degree distribution of real-world networks. The newly introduced MLM distribution arises from a hierarchical family of Lomax distributions and belongs to the basin of attraction of Frechet distribution. Some interesting statistical properties of MLM including characteristics of the maximum likelihood estimates have been studied. Finally, the proposed MLM model is applied over several real-world complex networks to showcase its excellent performance in uncovering the patterns of these heavy-tailed networks.
Identification of Food Quality Descriptors in Customer Chat Conversations using Named Entity Recognition

Chatbots are increasingly being used for providing customer support. One of the fundamental challenges for a bot, or for that matter any human agent, is to understand the context of a customer message. Chat conversations are typically associated with agrammatical structure, spelling mistake/variants, informal and slang words, and code-mixing, i.e., the use of words from more than one language. We focused on a use case related to the conversations between customers and agents regarding issues with the quality of food delivered by an online food delivery company. Accurate identification of words that describe the poor quality of food can immensely benefit prompt resolution of the issue and also provide vital feedback to the company and its partner restaurants. This feature can be used in a chatbot to effectively resolve customers’ food quality related concerns. This paper presents a named entity recognition (NER) approach to identify the food quality descriptors in a given message. On an internal benchmark dataset, we achieved an F1 score of 0.93 while outperforming classical baseline approaches in NER.
Seroprevalence of SARS‐CoV‐2 infections among children visiting a hospital

In this study, we retrospectively investigated the seroprevalence of severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) antibodies within serum samples from children in Beijing, China. These findings provide preliminary guidance regarding population susceptibility to SARS‐CoV‐2, which will aid in establishing policy toward coronavirus disease 2019 (COVID‐19) prevention and control.
Using a virtual digital board to organize student's cooperative learning

The article substantiates the importance of using a virtual digital board to organize student’s cooperative learning in the conditions of distance education, incl. social distance (for the quarantine period 2020). The main advantages of using a virtual digital board are outlined and their functions for the organization of cooperative education are compared. An analysis of the benefits of using virtual digital boards and a survey of experts made it possible to identify the most popular virtual digital boards: Wiki-Wall, Glogster, PadLet, Linoit, Twidla, Trello, Realtimeboard (Miro), Rizzoma. The comparison of the functions of virtual digital boards outlines their ability to organize students’ cooperative learning. The structure of the module E-Learning “Creating education content with tools of virtual digital board Padlet” is presented in the system LMS Moodle. The results of the experiment are presented, which show the effectiveness of the use of instruments of the virtual digital board to organize student’s cooperative learning. Perspectives of researches in developing methods of using a virtual digital board by students of natural-mathematical specialties are determined.
Recognition ability of temperature responsive molecularly imprinted polymer hydrogels

Temperature responsive imprinted polymer hydrogels were synthesized and characterized, which recognize target molecule, 4-amino pyridine (Apy), via electrostatic interaction and hydrogen bonding. The interaction between Apy and functional monomer methacrylic acid (MAA) was investigated by FTIR and 1H-NMR. Adsorption and release of Apy were determined at 60 °C and 20 °C, respectively. The amount of adsorption of Apy in the imprinted hydrogels was 2–3 times higher than that in the non-imprinted hydrogels. At 20 °C, 82% of Apy is released by the imprinted hydrogels, but only 60% by the non-imprinted hydrogels. The unique aspect of these imprinted polymer hydrogels is that they combine molecular recognition with thermo-responsiveness; reversible recognition and release of target molecule are realized by changing temperature. Selective recognition tests of the imprinted polymer hydrogels were carried out using three compounds structurally related to Apy, pyridine, 4-methylpyridine and 2-aminopyridine. The imprinted hydrogels exhibited the highest selectivity for Apy, while the non-imprinted hydrogels showed similarly low adsorption for all the substrates. As the imprinted polymer hydrogels have good temperature response, selectivity and reusability, they have great potential for application in separation field and in drug controlled release systems.
TUGDA: task uncertainty guided domain adaptation for robust generalization of cancer drug response prediction from in vitro to in vivo settings

Over the last decade, large-scale cancer omics studies have highlighted the diversity of patient molecular profiles and the importance of leveraging this information to deliver the right drug to the right patient at the right time. Key challenges in learning predictive models for this include the high-dimensionality of omics data, limitations in the number of data points available, and heterogeneity in biological and clinical factors affecting patient response. The use of multi-task learning (MTL) techniques has been widely explored to address dataset limitations for in vitro drug response models, while domain adaptation (DA) has been employed to extend them to predict in vivo response. In both of these transfer learning settings, noisy data for some tasks (or domains) can substantially reduce the performance for others compared to single-task (domain) learners, i.e. lead to negative transfer (NT). We describe a novel multi-task unsupervised domain adaptation method (TUGDA) that addresses these limitations in a unified framework by quantifying uncertainty in predictors and weighting their influence on shared domain/task feature representations. TUGDA’s ability to rely more on predictors with low-uncertainty allowed it to notably reduce cases of negative transfer for in vitro models (63% for drugs with limited data and 94% overall) compared to state-of-the-art methods. For domain adaptation to in vivo settings, TUGDA improved performance for 6 out of 12 drugs in patient-derived xenografts, and 7 out of 22 drugs in TCGA patient datasets, despite being trained in an unsupervised fashion. TUGDA’s ability to avoid negative transfer thus provides a key capability as we try to integrate diverse drug-response datasets to build consistent predictive models with in vivo utility. Availability https://github.com/CSB5/TUGDA
Stem cell derived astrocytes with POLG mutations and mitochondrial dysfunction including abnormal NAD+ metabolism is toxic for neurons

The inability to reliably replicate mitochondrial DNA (mtDNA) by mitochondrial DNA polymerase gamma (POLG) leads to a subset of common mitochondrial diseases associated with neuronal death and depletion of neuronal mtDNA. Defining disease mechanisms remains difficult due to the limited access to human tissue. Astrocytes are highly abundant in the brain, playing a crucial role in the support and modulation of neuronal function. Astrocytes also respond to insults affecting the brain. Following damage to the center neural system, which can be hypoxia, inflammation or neurodegeneration, astrocytes become activated and lose their supportive role and gain toxic functions that induce rapid death of neurons and oligodendrocytes. The role of astrocyte reactivation and the consequences this has for neuronal homeostasis in mitochondrial diseases has not been explored. Here, using patient cells carrying POLG mutations, we generated iPSCs and then differentiated into astrocytes. We demonstrated that POLG-astrocytes exhibited both mitochondrial dysfunctions, including loss of mitochondrial membrane potential, energy failure, complex I and IV defects, disturbed NAD+/NADH metabolism, and mtDNA depletion. Further, POLG derived astrocytes presented an A1-like reactive phenotype with increased proliferation, invasion, upregulation of pathways involved in response to stimulus, immune system process, cell proliferation and cell killing. Under direct and indirect co-culture with neurons, POLG-astrocytes exhibited a toxic effect leading to the death of neurons. Our findings demonstrate that mitochondrial dysfunction caused by POLG mutations leads not only to intrinsic defects in energy metabolism affecting both neurons and astrocytes, but also to neurotoxic damage driven by astrocytes. Our studies provide a robust astroglia-neuronal interaction model for future investigation of mitochondrial involvement in neurogenesis and neurodegenerative diseases. Highlights ▪ Patient-specific astrocytes harbouring a POLG mutation showed lower mitochondrial membrane potential and mtDNA depletion. ▪ POLG-astrocytes generated elevated L-lactate as the end glycolytic product. ▪ Patient-specific astrocytes with POLG mutations exhibited mitochondrial respiratory chain disruption accompanied with abnormal UCP2/SirT1/SirT3 mediated NAD+ metabolism. ▪ Suppressed complex I and IV-driven respiration contributed to the pathological mechanisms in POLG-related disease. ▪ POLG-astrocytes exhibit A1-reactive phenotype and neurotoxic potential. Graphic abstract
Discovery of synthetic lethal and tumor suppressive paralog pairs in the human genome

CRISPR knockout screens have accelerated the discovery of important cancer genetic dependencies. However, traditional CRISPR-Cas9 screens are limited in their ability to assay the function of redundant or duplicated genes. Paralogs in multi-gene families constitute two-thirds of the protein-coding genome, so this blind spot is the rule, not the exception. To overcome the limitations of single gene CRISPR knockout screens, we developed paired guide RNAs for Paralog gENetic interaction mapping (pgPEN), a pooled CRISPR/Cas9 approach which targets over a thousand duplicated human paralogs in single knockout and double knockout configurations. We applied pgPEN to two cell lineages and discovered that over 10% of human paralogs exhibit synthetic lethality in at least one cellular context. We recovered known synthetic lethal paralogs such as MAP2K1/MAP2K2, important drug targets such as CDK4/CDK6, and numerous other synthetic lethal pairs such as CCNL1/CCNL2. In addition, we identified ten tumor suppressive paralog pairs whose compound loss promotes cell growth. These findings identify a large number of previously unidentified essential gene families and nominate new druggable targets for oncology drug discovery. Highlights Comprehensive genetic interaction mapping of 1,030 human duplicated paralogs using a dual targeting CRISPR/Cas9 approach Duplicated paralogs are highly enriched for genetic interactions Synthetic lethal paralogs include CCNL1/CCNL2, CDK4/CDK6, and GSK3A/GSK3B Tumor suppressor paralog pairs include CDKN2A/CDKN2B and FBXO25/FBXO32
The 3D architecture and molecular foundations of de novo centriole assembly via bicentrioles

Centrioles are structurally conserved organelles, composing both centrosomes and cilia. In animal cycling cells, centrioles often form through a highly characterized process termed canonical duplication. However, a large diversity of eukaryotes form centrioles de novo through uncharacterized pathways. This unexplored diversity is key to understanding centriole assembly mechanisms and how they evolved to assist specific cellular functions. Here, combining electron microscopy and tomography, we show that during spermatogenesis of the moss Physcomitrium patens, centrioles are born as a co-axially oriented centriole pair united by a cartwheel. We observe that microtubules emanate from those bicentrioles, which localize to the spindle poles during cell division. Thereafter, each bicentriole breaks apart, and the two resulting sister centrioles mature asymmetrically, elongating specific microtubule triplets and a naked cartwheel. Subsequently, two cilia are assembled which are capable of beating asynchronously. We further show that conserved cartwheel and centriole wall components, SAS6, BLD10 and POC1 are expressed during spermatogenesis and are required for this de novo biogenesis pathway. Our work supports a scenario where centriole biogenesis is more diverse than previously thought and that conserved molecular modules underlie diversification of this essential pathway.
Single mosquito metatranscriptomics identifies vectors, emerging pathogens and reservoirs in one assay

Mosquitoes are major infectious disease-carrying vectors. Assessment of current and future risks associated with the mosquito population requires knowledge of the full repertoire of pathogens they carry, including novel viruses, as well as their blood meal sources. Unbiased metatranscriptomic sequencing of individual mosquitoes offers a straightforward, rapid, and quantitative means to acquire this information. Here, we profile 148 diverse wild-caught mosquitoes collected in California and detect sequences from eukaryotes, prokaryotes, 24 known and 46 novel viral species. Importantly, sequencing individuals greatly enhanced the value of the biological information obtained. It allowed us to (a) speciate host mosquito, (b) compute the prevalence of each microbe and recognize a high frequency of viral co-infections, (c) associate animal pathogens with specific blood meal sources, and (d) apply simple co-occurrence methods to recover previously undetected components of highly prevalent segmented viruses. In the context of emerging diseases, where knowledge about vectors, pathogens, and reservoirs is lacking, the approaches described here can provide actionable information for public health surveillance and intervention decisions.
Shape changes and cooperativity in the folding of the central domain of the 16S ribosomal RNA

Significance Ribosomes are complexes between ribosomal RNA (rRNA) and a number of proteins. Because ribosome assembly begins with rRNA folding, we simulated the molecular details of Mg2+-driven folding of the central domain of the bacterial rRNA. Good agreement with experiments on the folding of the three-way junction in the center of the rRNA validates the model. Coupling of rRNA folding and Mg2+ binding shows that ions interact with rRNA segments in a coordinated manner. The shape of rRNA changes from a sphere in the unfolded state to a prolate ellipsoid at high Mg2+ concentration, which is the opposite of what transpires when a globular protein folds. Our study provides the needed framework for undertaking ion-driven folding of large RNA molecules. Both the small and large subunits of the ribosome, the molecular machine that synthesizes proteins, are complexes of ribosomal RNAs (rRNAs) and a number of proteins. In bacteria, the small subunit has a single 16S rRNA whose folding is the first step in its assembly. The central domain of the 16S rRNA folds independently, driven either by Mg2+ ions or by interaction with ribosomal proteins. To provide a quantitative description of ion-induced folding of the ∼350-nucleotide rRNA, we carried out extensive coarse-grained molecular simulations spanning Mg2+ concentration between 0 and 30 mM. The Mg2+ dependence of the radius of gyration shows that globally the rRNA folds cooperatively. Surprisingly, various structural elements order at different Mg2+ concentrations, indicative of the heterogeneous assembly even within a single domain of the rRNA. Binding of Mg2+ ions is highly specific, with successive ion condensation resulting in nucleation of tertiary structures. We also predict the Mg2+-dependent protection factors, measurable in hydroxyl radical footprinting experiments, which corroborate the specificity of Mg2+-induced folding. The simulations, which agree quantitatively with several experiments on the folding of a three-way junction, show that its folding is preceded by formation of other tertiary contacts in the central junction. Our work provides a starting point in simulating the early events in the assembly of the small subunit of the ribosome.
Deregulation of epigenetic marks is correlated to differential exon usage of developmental genes

Alternative exon usage is known to affect a large portion of genes in mammalian genomes. Importantly, different splice forms sometimes lead to distinctly different protein functions. We analyzed data from the Human Epigenome Atlas (version 9) whereby we connected the differential usage of exons in various developmental stages of human cells/tissues to differential epigenetic modifications at the exon level. In total, we analyzed 19 human tissues, adult cells, and cultured cells that mimic early developmental stages. We found that the differential occurrence of protein isoforms across developmental stages was often associated with changes in histone marks at exon boundary regions. Many of the genes that are differentially regulated at the exon level were found to be functionally associated with development and metabolism.
The interaction of curiosity and reward on long-term memory in younger and older adults

Long-term memory is sensitive to both intrinsic and extrinsic motivation, but little is known about the relative influence of these two sources of motivation on memory performance across the adult lifespan. The study examined the effects of extrinsic motivation, manipulated via monetary reward, and curiosity, a form of intrinsic motivation, on long-term memory in healthy younger and older adults. During the incidental encoding phase on Day 1, 60 younger and 53 older participants viewed high- and low-curiosity trivia items as well as unrelated face stimuli. Half of the participants in each age group received financial rewards for correctly guessing trivia answers. On Day 2, participants completed a trivia recall test and an old-new recognition test for the face stimuli. Both curiosity and reward were associated with enhanced trivia recall, but the effects were interactive, such that only low-curiosity items benefitted from monetary reward. Neither curiosity nor reward affected face recognition performance in either age group. These findings indicate that individual and joint effects of intrinsic and extrinsic motivation on long-term memory are relatively preserved in healthy aging, a finding that highlights the viability of motivational strategies for memory enhancement into old age. Identifying conditions under which memory for unrelated information benefits from motivational spillover effects in younger and older adults is a priority for future research.
The ENIGMA Toolbox: Cross-disorder integration and multiscale neural contextualization of multisite neuroimaging datasets

Among ‘big data’ initiatives, the ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis) Consortium—a worldwide alliance of over 2,000 scientists diversified into over 50 Working Groups—has yielded some of the largest studies of the healthy and diseased brain. Integration of multisite datasets to assess transdiagnostic similarities and differences and to contextualize findings with respect to neural organization, however, have been limited. Here, we introduce the ENIGMA Toolbox, a Python/Matlab ecosystem for (i) accessing 100+ ENIGMA datasets, facilitating cross-disorder analysis, (ii) visualizing data on brain surfaces, and (iii) contextualizing findings at the microscale (postmortem cytoarchitecture and gene expression) and macroscale (structural and functional connectomes). Our Toolbox equips scientists with tutorials to explore molecular, histological, and network correlates of noninvasive neuroimaging markers of brain disorders. Moreover, our Toolbox bridges the gap between standardized data processing protocols and analytic workflows and facilitates cross-consortia initiatives. The Toolbox is documented and openly available at http://enigma-toolbox.readthedocs. io.