Miracle-dz/UniFAR-FTUs huggingface dataset:
Toxicicity and Histological Changes Caused by Insecticides in Spodoptera frugiperda (Lepidoptera: Noctuidae) Eggs

Abstract Insecticides typically are used to control Spodoptera frugiperda (J.E. Smith) (Lepidoptera: Noctuidae) larvae in corn crops; however, both eggs and larvae are affected by these applications. The purpose of this study was to evaluate the effects of 9 insecticides commonly used on corn crops in Brazil on ovicide and embryonic development of S. frugiperda. The insecticides were applied with an airbrush to the outer surface of eggs at 72, 96, 120, 144, and 168 h after oviposition. Larval emergence rates then were calculated. Eggs in the control and the alpha-cypermethrin and methomyl + novaluron treatment were evaluated by light microscopy to investigate possible histological changes in the embryos. The insecticides methomyl + novaluron, chlorantraniliprole + lambda-cyhalothrin, and deltamethrin reduced the emergence rate of S. frugiperda larvae. A mixture of alpha-cypermethrin and methomyl + novaluron did not affect the embryonic development of S. frugiperda; however, methomyl + novaluron-treated larvae did not emerge. Therefore, the insecticides methomyl + novaluron, chlorantraniliprole + lambda-cyhalothrin, and deltamethrin have an ovicidal effect and may be recommended for managing S. frugiperda.
From Argument Search to Argumentative Dialogue: A Topic-independent Approach to Argument Acquisition for Dialogue Systems

Despite the remarkable progress in the field of computational argumentation, dialogue systems concerned with argumentative tasks often rely on structured knowledge about arguments and their relations. Since the manual acquisition of these argument structures is highly time-consuming, the corresponding systems are inflexible regarding the topics they can discuss. To address this issue, we propose a combination of argumentative dialogue systems with argument search technology that enables a system to discuss any topic on which the search engine is able to find suitable arguments. Our approach utilizes supervised learning-based relation classification to map the retrieved arguments into a general tree structure for use in dialogue systems. We evaluate the approach with a state of the art search engine and a recently introduced dialogue model in an extensive user study with respect to the dialogue coherence. The results vary between the investigated topics (and hence depend on the quality of the underlying data) but are in some instances surprisingly close to the results achieved with a manually annotated argument structure.
Numerical methods for time-fractional convection-diffusion problems with high-order accuracy

Abstract In this paper, we consider the numerical method for solving the two-dimensional time-fractional convection-diffusion equation with a fractional derivative of order α \alpha ( 1 < α < 2 1\lt \alpha \lt 2 ). By combining the compact difference approach for spatial discretization and the alternating direction implicit (ADI) method in the time stepping, a compact ADI scheme is proposed. The unconditional stability and H 1 {H}^{1} norm convergence of the scheme are proved rigorously. The convergence order is O ( τ 3 − α + h 1 4 + h 2 4 ) O\left({\tau }^{3-\alpha }+{h}_{1}^{4}+{h}_{2}^{4}) , where τ \tau is the temporal grid size and h 1 {h}_{1} , h 2 {h}_{2} are spatial grid sizes in the x x and y y directions, respectively. It is proved that the method can even attain ( 1 + α ) \left(1+\alpha ) order accuracy in temporal for some special cases. Numerical results are presented to demonstrate the effectiveness of theoretical analysis.
DoS Attacks in Available MQTT Implementations: Investigating the Impact on Brokers and Devices, and supported Anti-DoS Protections

The Internet of Things is a widely adopted and pervasive technology, but also one of the most conveniently attacked given the volume of shared data and the availability of affordable but insecure products. This paper investigates two classes of denial of service (DoS) attacks that target the handling of message queues in MQTT, one of the most broadly used IoT protocols. The first attack attempts to saturate the MQTT broker resources, while the second exploits the broker to perform an amplification attack against the connected clients. We demonstrate the effectiveness of the attacks and indicate the parameters that would hinder the capabilities of a DoS attacker in three open-source MQTT implementations: Mosquitto, VerneMQ and EMQ X. To improve the security awareness in MQTT-based deployments, we integrate the attacks and mitigations in MQTTSA, a tool that detects MQTT misconfigurations and provides security-oriented recommendations and configuration snippets.
Fast Video Facial Expression Recognition by a Deeply Tensor-Compressed LSTM Neural Network for Mobile Devices

Mobile devices usually suffer from limited computation and storage resources, which seriously hinders them from deep neural network applications. In this article, we introduce a deeply tensor-compressed long short-term memory (LSTM) neural network for fast video-based facial expression recognition on mobile devices. First, a spatio-temporal facial expression recognition LSTM model is built by extracting time-series feature maps from facial clips. The LSTM-based spatio-temporal model is further deeply compressed by means of quantization and tensorization for mobile device implementation. Based on datasets of Extended Cohn-Kanade (CK+), MMI, and Acted Facial Expression in Wild 7.0, experimental results show that the proposed method achieves 97.96%, 97.33%, and 55.60% classification accuracy and significantly compresses the size of network model up to 221× with reduced training time per epoch by 60%. Our work is further implemented on the RK3399Pro mobile device with a Neural Process Engine. The latency of the feature extractor and LSTM predictor can be reduced 30.20× and 6.62× , respectively, on board with the leveraged compression methods. Furthermore, the spatio-temporal model costs only 57.19 MB of DRAM and 5.67W of power when running on the board.
Knowledge-aware Multi-modal Adaptive Graph Convolutional Networks for Fake News Detection

In this article, we focus on fake news detection task and aim to automatically identify the fake news from vast amount of social media posts. To date, many approaches have been proposed to detect fake news, which includes traditional learning methods and deep learning-based models. However, there are three existing challenges: (i) How to represent social media posts effectively, since the post content is various and highly complicated; (ii) how to propose a data-driven method to increase the flexibility of the model to deal with the samples in different contexts and news backgrounds; and (iii) how to fully utilize the additional auxiliary information (the background knowledge and multi-modal information) of posts for better representation learning. To tackle the above challenges, we propose a novel Knowledge-aware Multi-modal Adaptive Graph Convolutional Networks (KMAGCN) to capture the semantic representations by jointly modeling the textual information, knowledge concepts, and visual information into a unified framework for fake news detection. We model posts as graphs and use a knowledge-aware multi-modal adaptive graph learning principal for the effective feature learning. Compared with existing methods, the proposed KMAGCN addresses challenges from three aspects: (1) It models posts as graphs to capture the non-consecutive and long-range semantic relations; (2) it proposes a novel adaptive graph convolutional network to handle the variability of graph data; and (3) it leverages textual information, knowledge concepts and visual information jointly for model learning. We have conducted extensive experiments on three public real-world datasets and superior results demonstrate the effectiveness of KMAGCN compared with other state-of-the-art algorithms.
Buoyant convection in heterogeneous porous media with an inclined permeability jump: an experimental investigation of filling box-type flows

Abstract We investigate experimentally the outflow that occurs when a plume of dense fluid strikes a sloping permeability jump in a saturated two-layered porous medium of finite extent. Contact of the plume with the jump results in a pair of leaky (primary) gravity currents that propagate up- and downdip. Previous studies have considered a thin or thick lower layer; here, we identify the non-trivial dynamics that arises for the intermediate thickness case. For instance, the primary gravity currents become arrested when the volume supplied by the plume matches that lost by basal draining. However, fluid draining into the lower layer will eventually contact the bottom boundary leading to the formation of secondary gravity currents. As these secondary gravity currents propagate left and right, they ‘tug’ upon the primary gravity currents causing remobilization. Of course, the motion of the primary and secondary gravity currents must eventually become impeded by the sidewall boundaries leading, thereafter, to layer filling. We categorize the overall filling process as either simultaneous (upper and lower layers fill concurrently) or sequential (lower layer fills first). Factors influencing the filling mode include the permeability jump angle and the plume source conditions/location. Owing to the asymmetric manner in which the upper layer fills, the plume, originally vertical, must become deflected. We categorize this deflection and identify scenarios where, somewhat counterintuitively, the plume bends towards the lower (lower permeability) layer. The significance of our measurements relative to real geophysical flows is highlighted with particular attention paid to geological sequestration and storage.
Screening and Identification of Muscle-Specific Candidate Genes via Mouse Microarray Data Analysis

Muscle tissue is involved with every stage of life activities and has roles in biological processes. For example, the blood circulation system needs the heart muscle to transport blood to all parts, and the movement cannot be separated from the participation of skeletal muscle. However, the process of muscle development and the regulatory mechanisms of muscle development are not clear at present. In this study, we used bioinformatics techniques to identify differentially expressed genes specifically expressed in multiple muscle tissues of mice as potential candidate genes for studying the regulatory mechanisms of muscle development. Mouse tissue microarray data from 17 tissue samples was selected from the GEO database for analysis. Muscle tissue as the treatment group, and the other 16 tissues as the control group. Genes expressed in the muscle tissue were different to those in the other 16 tissues and identified 272 differential genes with highly specific expression in muscle tissue, including 260 up-regulated genes and 12 down regulated genes. is the genes were associated with the myofibril, contractile fibers, and sarcomere, cytoskeletal protein binding, and actin binding. KEGG pathway analysis showed that the differentially expressed genes in muscle tissue were mainly concentrated in pathways for AMPK signaling, cGMP PKG signaling calcium signaling, glycolysis, and, arginine and proline metabolism. A PPI protein interaction network was constructed for the selected differential genes, and the MCODE module used for modular analysis. Five modules with Score > 3.0 are selected. Then the Cytoscape software was used to analyze the tissue specificity of differential genes, and the genes with high degree scores collected, and some common genes selected for quantitative PCR verification. The conclusion is that we have screened the differentially expressed gene set specific to mouse muscle to provide potential candidate genes for the study of the important mechanisms of muscle development.
A cooperative network at the nuclear envelope counteracts LINC-mediated forces during oogenesis in C. elegans

Oogenesis involves transduction of mechanical forces from the cytoskeleton to the nuclear envelope (NE). In Caenorhabditis elegans, oocyte nuclei lacking the single lamin protein LMN-1 are vulnerable to collapse under forces mediated through LINC (linker of nucleoskeleton and cytoskeleton) complexes. Here, we use cytological analysis and in vivo imaging to investigate the balance of forces that drive this collapse and protect oocyte nuclei. We also use a mechano-node-pore sensing device to directly measure the effect of genetic mutations on oocyte nuclear stiffness. We find that nuclear collapse is not a consequence of apoptosis. It is promoted by dynein, which induces polarization of a LINC complex composed of Sad1 and UNC-84 homology 1 (SUN-1) and ZYGote defective 12 (ZYG-12). Lamins contribute to oocyte nuclear stiffness and cooperate with other inner nuclear membrane proteins to distribute LINC complexes and protect nuclei from collapse. We speculate that a similar network may protect oocyte integrity during extended oocyte arrest in mammals.
An approximate stochastic optimal control framework to simulate nonlinear neuro-musculoskeletal models in the presence of noise

Optimal control simulations have shown that both musculoskeletal dynamics and physiological noise are important determinants of movement. However, due to the limited efficiency of available computational tools, deterministic simulations of movement focus on accurately modelling the musculoskeletal system while neglecting physiological noise, and stochastic simulations account for noise while simplifying the dynamics. We took advantage of recent approaches where stochastic optimal control problems are approximated using deterministic optimal control problems, which can be solved efficiently using direct collocation. We were thus able to extend predictions of stochastic optimal control as a theory of motor coordination to include muscle coordination and movement patterns emerging from non-linear musculoskeletal dynamics. In stochastic optimal control simulations of human standing balance, we demonstrated that the inclusion of muscle dynamics can predict muscle co-contraction as minimal effort strategy that complements sensorimotor feedback control in the presence of sensory noise. In simulations of reaching, we demonstrated that nonlinear multi-segment musculoskeletal dynamics enables complex perturbed and unperturbed reach trajectories under a variety of task conditions to be predicted. In both behaviors, we demonstrated how interactions between task constraint, sensory noise, and the intrinsic properties of muscle influence optimal muscle coordination patterns, including muscle co-contraction, and the resulting movement trajectories. Our approach enables a true minimum effort solution to be identified as task constraints, such as movement accuracy, can be explicitly imposed, rather than being approximated using penalty terms in the cost function. Our approximate stochastic optimal control framework predicts complex features, not captured by previous simulation approaches, providing a generalizable and valuable tool to study how musculoskeletal dynamics and physiological noise may alter neural control of movement in both healthy and pathological movements.
Physiologically Based Modeling of the Effect of Physiological and Anthropometric Variability on Indocyanine Green Based Liver Function Tests

Accurate evaluation of liver function is a central task in hepatology. Dynamic liver function tests (DLFT) based on the time-dependent elimination of a test substance provide an important tool for such a functional assessment. These tests are used in the diagnosis and monitoring of liver disease as well as in the planning of hepatobiliary surgery. A key challenge in the evaluation of liver function with DLFTs is the large inter-individual variability. Indocyanine green (ICG) is a widely applied test compound used for the evaluation of liver function. After an intravenous administration, pharmacokinetic (PK) parameters are calculated from the plasma disappearance curve of ICG which provide an estimate of liver function. The hepatic elimination of ICG is affected by physiological factors such as hepatic blood flow or binding of ICG to plasma proteins, anthropometric factors such as body weight, age, and sex, or the protein amount of the organic anion-transporting polypeptide 1B3 (OATP1B3) mediating the hepatic uptake of ICG. Being able to account for and better understand these various sources of inter-individual variability would allow to improve the power of ICG based DLFTs and move towards an individualized evaluation of liver function. Within this work we systematically analyzed the effect of various factors on ICG elimination by the means of computational modeling. For the analysis, a recently developed and validated physiologically based pharmacokinetics (PBPK) model of ICG distribution and hepatic elimination was utilized. Key results are (i) a systematic analysis of the variability in ICG elimination due to hepatic blood flow, cardiac output, OATP1B3 abundance, liver volume, body weight and plasma bilirubin level; (ii) the evaluation of the inter-individual variability in ICG elimination via a large in silico cohort of n=100000 subjects based on the NHANES cohort with special focus on stratification by age, sex, and body weight; (iii) the evaluation of the effect of various degrees of cirrhosis on variability in ICG elimination. The presented results are an important step towards individualizing liver function tests by elucidating the effects of confounding physiological and anthropometric parameters in the evaluation of liver function via ICG.
RADseq data reveal a lack of admixture in a mouse lemur contact zone contrary to previous microsatellite results

Despite being one of the most fundamental biological processes, the process of speciation remains poorly understood in many groups of organisms. Mouse lemurs are a species-rich genus of small primates endemic to Madagascar, whose diversity has only recently been uncovered using genetic data and is primarily found among morphologically cryptic, allopatric populations. To assess to what extent described species represent reproductively isolated entities, studies are needed in areas where mouse lemur taxa come into contact. Hybridization has previously been reported in a contact zone between two closely related mouse lemur species (Microcebus murinus and M. griseorufus) based on microsatellite data. Here, we revisit this system using RADseq data for populations in, near, and far from the contact zone, including many of the individuals that had previously been identified as hybrids. Surprisingly, we find no evidence for admixed nuclear ancestry in any of the individuals. Re-analyses of microsatellite data and simulations suggest that previously inferred hybrids were false positives and that the program NewHybrids can be particularly sensitive to erroneously inferring hybrid ancestry. Using coalescent-bases analyses, we also show an overall lack of recent gene flow between the two species, and low levels of ancestral gene flow. Combined with evidence for local syntopic occurrence, these data indicate that M. murinus and M. griseorufus are reproductively isolated. Finally, we estimate that they diverged less than a million years ago, suggesting that completion of speciation is relatively rapid in mouse lemurs. Future work should focus on the underpinnings of reproductive isolation in this cryptic primate radiation, which are mostly unknown. Our study also provides a cautionary tale for the inference of hybridization with microsatellite data.
A B-cell actomyosin arc network couples integrin co-stimulation to mechanical force-dependent immune synapse formation

B-cell activation and immune synapse (IS) formation with membrane-bound antigens are actin-dependent processes that scale positively with the strength of antigen-induced signals. Importantly, ligating the B-cell integrin, LFA-1, with ICAM-1 promotes IS formation when antigen is limiting. Whether the actin cytoskeleton plays a specific role in integrin-dependent IS formation is unknown. Here we show using super-resolution imaging of mouse primary B cells that LFA-1: ICAM-1 interactions promote the formation of an actomyosin network that dominates the B-cell IS. This network is created by the formin mDia1, organized into concentric, contractile arcs by myosin 2A, and flows inward at the same rate as B-cell receptor (BCR): antigen clusters. Consistently, individual BCR microclusters are swept inward by individual actomyosin arcs. Under conditions where integrin is required for synapse formation, inhibiting myosin impairs synapse formation, as evidenced by reduced antigen centralization, diminished BCR signaling, and defective signaling protein distribution at the synapse. Together, these results argue that a contractile actomyosin arc network plays a key role in the mechanism by which LFA-1 co-stimulation promotes B-cell activation and IS formation.
Solar cell efficiency tables (version 51)

Consolidated tables showing an extensive listing of the highest independently confirmed efficiencies for solar cells and modules are presented. Guidelines for inclusion of results into these tables are outlined and new entries since July 2017 are reviewed, together with progress over the last 25 years. Appendices are included documenting area definitions and also listing recognised test centres.
Organization and dynamics of the cortical complexes controlling insulin secretion in β-cells

Insulin secretion in pancreatic β-cells is regulated by cortical complexes that are enriched at the sites of adhesion to extracellular matrix facing the vasculature. Many components of these complexes, including Bassoon, RIM, ELKS and liprins, are shared with neuronal synapses. Here, we show that insulin secretion sites also contain non-neuronal proteins LL5β and KANK1, which in migrating cells organize exocytotic machinery in the vicinity of integrin-based adhesions. Depletion of LL5β or focal adhesion disassembly triggered by myosin II inhibition perturbed the clustering of secretory complexes and attenuated the first wave of insulin release. While previous analyses in vitro and in neurons suggested that secretory machinery might assemble through liquid-liquid phase separation, analysis of endogenously labeled ELKS in pancreatic islets indicated that its dynamics is inconsistent with such a scenario. Instead, fluorescence recovery after photobleaching and single molecule imaging showed that ELKS turnover is driven by binding and unbinding to low-mobility scaffolds. Both the scaffold movements and ELKS exchange were stimulated by glucose treatment. Our findings help to explain how integrin-based adhesions control spatial organization of glucose-stimulated insulin release.