Miracle-dz/UniFAR-FTUs huggingface dataset:
Distributed Network Optimization for Secure Operation of Interdependent Complex Networks

The optimal operation of complex networks and critical infrastructures requires solving various large-scale decision-making problems. These problems usually are formulated as optimization problems with several variables and constraints. This leads to the high computational complexity of solving the underlying optimization problem. Hence, we require efficient methods to first model the operational objective function and constraints of the complex networks, and how they can leverage available computational resources to achieve the optimal operation of the entire system. We further need to ensure data security of decision-making entities, e.g., network flow problems, and their impact on the secure operation of the system. The proposed framework and algorithms in this paper include distributed intelligence among heterogeneous agents in a complex network represented by a graph of nodes and edges among them. Our utilized methods act as efficient computational algorithms to solve the underlying optimization problems of these networks in a computationally-efficient fashion. In order to evaluate the introduced distributed algorithm for linear-constrained optimization with a quadratic cost function, we used a random network with different numbers of nodes and edges. We illustrate the run-time and convergence of the distributed method over various networks.
Impacts of COVID-19 and Social Measure on Behavioral Intention in the context of Online Learning Software

Since the COVID-19 outbreaks, countries around the world have implemented social measures to prevent the spread of COVID-19. To inhibit infection, governments are conducting social distancing, wearing a mask, and vaccination. In the field of education, non-face-to-face lectures are being carried out. With these backgrounds, the importance of online lectures has increased. The purpose of this study is to understand the behavioral intention of university students in the context of online learning software. Data were collected from 318 Vietnamese university students taking classes through online education software. We analyzed the sample by using the partial least squares (PLS) technique. The results indicated that attitude has a significant effect on behavioral intention. The findings showed that subjective norms do not affect attitude. The research results revealed that perceived behavioral control, regulatory environment, and affective risk perception are significantly related to attitude. The results of this study will provide meaningful guidelines for implementing effective education within COVID-19 in the future.
TwinDroid: A Dataset of Android app System call traces and Trace Generation Pipeline

System call traces are an invaluable source of information about a program's runtime behavior and be particularly useful for malware detection in Android apps. However, the paucity of publicly available high-quality datasets hinders the development of the field. In this paper, we introduce TwinDroid, a dataset of over 1000 system calls traces, from both benign and infected Android apps. A large part of the apps used to create the dataset is from benign-malicious app pairs, identical apart from the inclusion of malware in the latter. This makes TwinDroid an ideal basis for security research, and an earlier version of TwinDroid has already been used for this purpose. In addition to a dataset of traces, TwinDroid includes a fully automated traces generation pipeline, which allows users to generate new traces in a standardized manner seamlessly. This pipeline will enable the dataset to remain up-to-date and relevant despite the rapid pace of change that characterizes Android security.
Finite Element Analysis Study of FBG Magnetostrictive Composite Flux Sensor Geometry for Electric Motor Air-gap Flux Monitoring

With the progressively more prominent usage of electric motors in a wide range of applications, more advanced monitoring of their operating parameters is becoming increasingly important. The in-service observation of the air-gap magnetic flux can be very useful for monitoring the status of permanent magnets, which are key to most modern motors' performance. This was recently shown to be possible through the in-situ application of magnetostrictive composite fibre optic flux sensors. This paper reports an electromagnetic finite element model study exploring the performance of various such sensor configurations and positions in the air-gap of a commercial motor design. The aim of the model study is to identify sensor design and positioning features that can yield an enhanced response in observation of rotor flux. It is shown that rectangular cross-sectional flux sensor geometries provide a considerable advantage in this respect.
Abstractive Text Summarization Using Generative Adversarial Network and Relation Extraction

In this paper, we propose a new adversarial model to solve some common problems existing in Generative Adversarial Network for text summarization. we simultaneously train a generative model G, a discriminative model D, and a model which auxiliary generator to generate a high quality summary . This module is like a teacher to guide G to quickly optimize himself and then against D, so we call it Teacher model. Teacher is an entity relationship extraction model to extract the triples of the real summary. The words in the triples are defined as keywords, and the keywords are revealed to the generator like the teacher points out the key points, and the summary generated by the guiding generator contains more Keywords. Model achieves competitive ROUGE scores with the baseline on CNN/Daily Mail dataset.
A novel fully-automated system for lifelong continuous phenotyping of mouse cognition and behaviour

Comprehensive ethologically-relevant behavioural phenotyping in rodent experiments is essential for deciphering the neural basis of animal cognition. Automated home-cage monitoring systems present a valuable tool to fulfil this need. However, they often involve complex animal training routines, water or food deprivation, and probe a limited range of behaviours. Here, we present a new fully automated AI-driven home-cage system for cognitive and behavioural phenotyping in mice. The system incorporates spontaneous alternation T-maze, novel-object recognition and object-in-place recognition tests combined with monitoring of an animal’s position, water consumption, quiescence and locomotion patterns, all carried out continuously and simultaneously in an unsupervised fashion over long periods of time. Mice learnt the tasks rapidly without any need for water or food restrictions. We applied ethomics approach to show that combined statistical properties of multiple behaviours can be used to discriminate between mice with hippocampal, medial entorhinal and sham lesions and accurately predict genotype of Alzheimer’s disease mouse models on an individual animal level, surpassing the performance of several gold standard cognitive tests. This technology could enable large-scale behavioural screening for genes and neural circuits underlying spatial memory and other cognitive processes.
The influence of sponge-dwelling gobies (Elacatinus horsti) on feeding efficiency of Caribbean sponge hosts, Aplysina lacunosa and Aplysina archeri

Mutualistic associations between benthic marine invertebrates and reef taxa are common. Sponge-dwelling gobies benefit from protection within sponge tubes and greater food availability. Sponge-dwelling gobies are hypothesized to increase sponge pump rates by consuming polychaete parasites, but such increases have not yet been demonstrated. We investigated the association between sponge-dwelling gobies (Elacatinus horsti) and two species of tube sponge (Aplysina lacunosa and Aplysina archeri) in Bonaire, Caribbean Netherlands. We visually assessed goby presence in sponges and used in situ methods with fluorescein dye to estimate feeding rates via pump rates. Aplysina archeri were more likely to host a goby than A. lacunosa. For both sponge species, pump rates of tubes with gobies were higher on average than those of tubes without gobies. Our observations, therefore, suggest that E. horsti associations with Aplysina are likely mutualistic relationships in which sponges benefit from higher feeding rates when gobies are present.
Breathing on Chip: Dynamic flow and stretch tune cellular composition and accelerate mucociliary maturation of airway epithelium in vitro

Human lung function is intricately linked to blood flow and breathing cycles, but it remains unknown how these dynamic cues shape human airway epithelial biology. Here we report a state-of-the-art protocol for studying effects of physiological airflow and stretch on differentiation, cellular composition, and mucociliary clearance of human primary airway epithelial cells cultured on a perfused airway chip. Perfused epithelial tissue cultures developed a large airway-like cellular composition with accelerated maturation and polarization of mucociliary clearance when compared to traditional (static) culture methods. Additional application of airflow and stretch to the airway chip resulted in a cellular composition more comparable to the small(er) airways, reduced baseline secretion of interleukin-8 and other inflammatory proteins, and reduced gene expression of matrix metalloproteinase (MMP) 9, fibronectin, and other extracellular matrix factors. These results indicate that breathing-like mechanical stimuli are important modulators of airway epithelial cell differentiation and homeostasis and that their fine-tuned application could generate models of specific epithelial regions, pathologies, and mucociliary (dys)function.
MLL family members regulate H3K4 methylation to ensure CENP-A assembly at human centromeres

The active state of centromeres is epigenetically defined by the presence of CENP-A interspersed with histone H3 nucleosomes. While the importance of dimethylation of H3K4 mark for centromeric transcription has been highlighted in various studies, the identity of the enzyme(s) depositing these marks on the centromere is still unknown. The MLL (KMT2) family play a crucial role in RNA polymerase II (Pol II)-mediated gene regulation by methylating H3K4. Here, we report that MLL family regulate transcription of human centromeres. CRISPR-mediated downregulation of MLL causes loss of H3K4me2, resulting in an altered epigenetic chromatin state of the centromeres. Intriguingly, our results reveal that loss of MLL, but not SETD1A, increases co-transcriptional R-loop formation, and Pol II accumulation at the centromeres. Finally we report that the presence of MLL and SETD1A is crucial for kinetochore maintenance. Altogether, our data reveals a novel molecular framework where both the H3K4 methylation mark and the methyltransferases regulate stability and identity of the centromere.
Adjuvant Discovery via a High Throughput Screen using Human Primary Mononuclear Cells

We describe a methodology utilizing high-throughput and high-content screening for novel adjuvant candidates that was used to screen a library of ∼2,500 small molecules via a 384-well quantitative combined cytokine and flow cytometry immunoassay in primary human peripheral blood mononuclear cells (PBMCs) from 4 healthy adult study participants. Hits were identified based on their induction of soluble cytokine (TNF, IFNγ and IL-10) secretion and PBMC maturation (CD 80/86, Ox40, and HLA-DR) in at least two of the four donors screened. From an initial set of 197 compounds identified using these biomarkers—an 8.6% hit rate—we downselected to five scaffolds that demonstrated robust efficacy and potency in vitro and evaluated the top hit, vinblastine sulfate, for adjuvanticity in vivo. Vinblastine sulfate significantly enhanced murine humoral responses to recombinant SARS-CoV-2 spike protein, including a four-fold enhancement of IgG titer production when compared to treatment with the spike antigen alone. Overall, we outline a methodology for discovering immunomodulators with adjuvant potential via high-throughput screening of PBMCs in vitro that yielded a lead compound with in vivo adjuvanticity. Motivation Vaccines are a key biomedical intervention to prevent the spread of infectious diseases, but their efficacy can be limited by insufficient immunogenicity and nonuniform reactogenic profiles. Adjuvants are molecules that potentiate vaccine responses by inducing innate immune activation. However, there are a limited number of adjuvants in approved vaccines, and current approaches for preclinical adjuvant discovery and development are inefficient. To enhance adjuvant identification, we developed a protocol based on in vitro screening of human primary leukocytes.
multEvol: An interactive pipeline for multitrait evolutionary analysis in a phylogenetic framework

Comparative phylogenetic studies based on 2D image data can reveal macroevolutionary patterns and processes by integrating phylogenetic and ecological analyses. The advent of high-throughput imaging facilities has led to the accumulation of large image datasets; however, large-scale data extraction and analysis using these resources can be labour- and computation-intensive. Moreover, there are few standardized methods to analyse multitrait image-based data in a phylogenetic framework. Several packages in the R statistical environment offer utilities for processing and analysing large-scale image data. Techniques for the comparative phylogenetic analysis of multitrait data have also been introduced in other packages. However, most of these approaches are specialized for discrete purposes, and there is a lack of streamlined and efficient methodological pipelines integrating features across packages. Here, we present multEvol, an interactive pipeline for the extraction and phylogenetic analysis of shape and colour pattern data from 2D images in the R statistical computing environment. Using this pipeline, shape outlines and colour pattern data for multiple colours can be extracted, visualised, and compared in a phylogenetic framework. Using various utilities provided in the phytools, RNiftyReg, Momocs, patternize, convevol, and geiger packages, we introduce an interactive interface that allows user inputs for iterative data extraction. This pipeline provides an efficient method for the bulk processing of images of biological specimens from museum collections. As such, it has potential applications in the large-scale morphometric analysis of museum specimens to reveal macroevolutionary patterns in ecology, evolution and integrative biology.
Binding of a blast fungus Zinc-finger fold effector to a hydrophobic pocket in the host exocyst subunit Exo70 modulates immune recognition in rice

Exocytosis plays an important role in plant-microbe interactions, both in pathogenesis and symbiosis. Exo70 proteins are integral components of the exocyst, an octameric complex that mediates tethering of vesicles to membranes in eukaryotes. Although plant Exo70s are known to be targeted by pathogen effectors, the underpinning molecular mechanisms and the impact of this interaction on infection is poorly understood. Here, we show the molecular basis of the association between the effector AVR- Pii of the blast fungus Maganaporthe oryzae and rice Exo70 alleles OsExo70F2 and OsExo70F3, which is sensed by the immune receptor pair Pii via an integrated RIN4/NOI domain. The crystal structure of AVR-Pii in complex with OsExo70F2 reveals that the effector binds to a conserved hydrophobic pocket in Exo70, defining a new effector/target binding interface. Structure-guided and random mutagenesis validates the importance of AVR-Pii residues at the Exo70 binding interface to sustain protein association and disease resistance in rice when challenged with fungal strains expressing effector mutants. Further, the structure of AVR-Pii defines a novel Zinc- finger effector fold (ZiF) distinct from the MAX fold previously described for the majority of characterized M. oryzae effectors. Our data suggests that blast fungus ZiF effectors bind a conserved Exo70 interface to manipulate plant exocytosis and that these effectors are also baited by plant immune receptors, pointing to new opportunities for engineering disease resistance. Significance statement Plant diseases destroy ∼20-30% of annual crop production, contributing to global food insecurity. Discovering how pathogen effectors target host proteins to promote virulence is essential for understanding pathogenesis and can be used for developing disease resistant crops. Here, we reveal the structural basis of how an effector from the blast pathogen (AVR-Pii) binds a specific host target (rice Exo70), and how this underpins immune recognition. This has implications for understanding the molecular mechanisms of blast disease and for the engineering of new recognition specificities in plant immune receptors to confer resistance to a major crop pathogen.
Lignin Composites with Sustained Oxygenation and Reactive Oxygen Species-Scavenging Improve Neovascularization and Healing of Diabetic Wounds

Although delayed wound healing is an important clinical complication in diabetic patients, few targeted treatments are available, and it remains challenging to promote diabetic wound healing. Impaired neovascularization is one of the prime characteristics of the diabetic phenotype of delayed wound healing. Additionally, increased levels of reactive oxygen species (ROS) and chronic low-grade inflammation and hypoxia are associated with diabetes, which disrupts mechanisms of wound healing. We developed lignin composites with multiple wound healing-promotive functions, including pro-angiogenesis, sustained oxygenation from calcium peroxide (CaO2)-based oxygen releasing nanoparticles and ROS–scavenging with thiolated lignosulfonate that captures the elevated ROS in diabetic wounds. The sustained release of oxygen and ROS-scavenging by the lignin composites promoted endothelial cell branching and their reorganization into characteristic network formation in vitro, promoted angiogenic growth factor expression and angiogenesis in full thickness skin wounds tested in a diabetic murine model of delayed wound healing, and decreased hypoxia inducible factor-1α (HIF-1α) expression. These effects significantly increased the granulation tissue deposition and tissue repair. Our findings demonstrate that lignin composites promote diabetic wound healing without use of other drugs and show the potential of functionalized lignosulfonate for wound healing applications requiring balanced antioxidation and controlled oxygen release.
Type III CRISPR–Cas provides resistance against nucleus-forming jumbo phages via abortive infection

Bacteria have diverse defences against phages. In response, jumbo phages evade multiple DNA-targeting defences by protecting their DNA inside a nucleus-like structure. We previously demonstrated that RNA-targeting type III CRISPR–Cas systems provide jumbo phage immunity by recognising viral mRNA exported from the nucleus for translation. Here, we demonstrate that recognition of phage mRNA by the type III system activates a cyclic triadenylate-dependent accessory nuclease, NucC. Although unable to access phage DNA in the nucleus, NucC degrades the bacterial chromosome, triggers cell death, and disrupts phage replication and maturation. Hence, type III-mediated jumbo phage immunity occurs via abortive infection, with suppression of the viral epidemic protecting the population. We further show that type III systems targeting jumbo phages have diverse accessory nucleases, including RNases that provide immunity. Our study demonstrates how type III CRISPR–Cas systems overcome the inaccessibility of jumbo phage DNA to provide robust immunity.
The domain-separation low-dimensional language network dynamics in the resting-state support the flexible functional segregation and integration during language and speech processing

Modern linguistic theories and network science propose that the language and speech processing is organized into hierarchical, segregated large-scale subnetworks, with a core of dorsal (phonological) stream and ventral (semantic) stream. The two streams are asymmetrically recruited in receptive and expressive language or speech tasks, which showed flexible functional segregation and integration. We hypothesized that the functional segregation of the two streams was supported by the underlying network segregation. A dynamic conditional correlation approach was employed to construct frame-wise time-varying language networks and investigate the temporal reoccurring patterns. We found that the time-varying language networks in the resting-state robustly clustered into four low-dimensional states, which dynamically reconfigured following a domain-separation manner. Spatially, the hub distributions of the first three states highly resembled the neurobiology of primary auditory processing and lexical-phonological processing, motor and speech production processing, and semantic processing, respectively. The fourth state was characterized by the weakest functional connectivity and subserved as a baseline state. Temporally, the first three states appeared exclusively in limited time bins (∼15%), and most of the time (> 55%), the language network kept inactive in state 4. Machine learning-based dFC-linguistics prediction analyses showed that dFCs of the four states significantly predicted individual linguistic performance. These findings suggest a domain-separation manner of language network dynamics in the resting-state, which forms a dynamic “meta-networking” (network of networks) framework. Highlights The time-varying language network in the resting-state is robustly clustered into four low-dimensional states. Spatially, the first three dFC states are cognitively meaningful, which highly resemble the neurobiology of primary auditory processing and lexical-phonological representation, speech production processing, and semantic processing, respectively. Temporally, the first three states appeared exclusively in limited time bins (∼15%), and most of the time (> 55%), the language network kept inactive in state 4. A dynamic “meta-networking” framework of language network in the resting-state is proposed.
Mammalian-like developmental changes are present in the neural 1 signatures of avian sleep

of rapid (REM) and slow wave sleep (SWS). the structure of not remain constant during lifetime, but undergoes radical changes during development. Our current understanding of these age-related variations in neural signature of sleep is limited to studies in a small number of mammalian species. 5-7 How does the neural signature of sleep evolve during brain development in an avian brain? To answer this, we recorded multi-channel EEG from developing juvenile and adult zebra finches for several nights. We compared the structure of sleep in adults and juveniles using the ratio of slow oscillations to gamma oscillations (“depth of sleep”, DOS). We observed a significantly larger DOS, as well as less- frequent state transitions in juveniles compared to adults. Furthermore, multichannel EEG revealed local slow oscillations , i n addition to global (wide-spread) slow waves. The occurrence of these local waves was correlated with DOS and was higher in frontal sites. In addition, we observed that the functional connectivity increased rapidly during development and was significantly higher in adults than in juveniles. Complementarily, a graph theory analysis of highly-correlated networks revealed numerous small networks in juveniles compared to adults, underscoring the lower connectivity observed in juveniles. Overall, our results reveal significant changes in the neural signature of sleep during development in an avian brain that are remarkably similar to age-dependent changes that occur in mammals.