Miracle-dz/UniFAR-FTUs huggingface dataset:
Rift Valley Fever Virus: Propagation, Quantification, and Storage

Rift Valley fever virus (RVFV) is an arthropod‐borne, zoonotic disease endemic to sub‐Saharan Africa and the Arabian Peninsula. Outbreaks of Rift Valley fever have had up to 100% mortality rates in fetal and neonatal sheep. Upon infection of ruminant and human hosts alike, RVFV infection causes an at times severe hepatitis and pathology in many other organs. The enveloped virion contains a tripartite, predominantly negative‐sense, single‐stranded RNA genome, which codes for the proteins the virus needs to replicate both in mammalian hosts and insect vectors. Endemic countries often use attenuated RVFV strains for vaccination of livestock but there are no commercially licensed vaccines for humans or livestock in non‐endemic areas. In the laboratory, RVFV can be readily propagated and manipulated in vitro using cell culture systems. Presented in this article are techniques routinely used in RVFV research that have proven successful in our laboratories. © 2019 by John Wiley & Sons, Inc.
Collective dynamics in complex networks for machine learning

VERRI, F. A. N. Collective dynamics in complex networks for machine learning. 2018. 95 p. Tese (Doutorado em Ciências – Ciências de Computação e Matemática Computacional) – Instituto de Ciências Matemáticas e de Computação, Universidade de São Paulo, São Carlos – SP, 2018. Machine learning enables machines to learn automatically from data. In literature, graph-based methods have received increasing attention due to their ability to learn from both local and global information. In these methods, each data instance is represented by a vertex and is linked to other vertices according to a predefined affinity rule. However, they usually have unfeasible time cost for large problems. To overcome this problem, techniques can employ a heuristic to find suboptimal solutions in a feasible time. Early heuristic optimization methods exploit nature-inspired collective processes, such as ants looking for food sources and swarms of bees. Nowadays, advances in the field of complex systems provide powerful tools to assess and to understand dynamical systems. Complex networks, which are graphs with nontrivial topology, are among these theoretical tools capable of describing the interplay of topology, structure, and dynamics of complex systems. Therefore, machine learning methods based on complex networks and collective dynamics have been proposed. They encompass three steps. First, a complex network is constructed from the input data. Then, the simulation of a distributed collective system in the network generates rich information. Finally, the collected information is used to solve the learning problem. The coordination of the individuals in the system permit to achieve dynamics that is far more complex than the behavior of single individuals. In this research, I have explored collective dynamics in machine learning tasks, both in unsupervised and semi-supervised scenarios. Specifically, I have proposed a new collective system of competing particles that shifts the traditional vertex-centric dynamics to a more informative edge-centric one. Moreover, it is the first particle competition system applied in machine learning task that has deterministic behavior. Results show several advantages of the edge-centric model, including the ability to acquire more information about overlapping areas, a better exploration behavior, and a faster convergence time. Also, I have proposed a new network formation technique that is not based on similarity and has low computational cost. Since addition and removal of samples in the network is cheap, it can be used in real-time application. Finally, I have conducted analytical investigations of a flocking-like system that was needed to guarantee the expected behavior in community detection tasks. In conclusion, the result of the research contributes to many areas of machine learning and complex systems.
An Automatic Classification and Clustering Algorithm for Online Learning Goals Based on Cognitive Thinking

To improve the learning effect of online learning, an online learning target automatic classification and clustering analysis algorithm based on cognitive thinking was proposed. It was applied to a multi-dimensional learning community. A new form of virtual learning community concept was proposed. The design ideas of its multi-dimensional learning environment were elaborated. Ontology technology was used to collect student learning process data. A cognitive diagnostic model for assessing student learning status was generated. Finally, through the cluster analysis technology, the registered students in the curriculum center were automatically divided into different levels of community groups. The results showed that the proposed algorithm for automatic classification and clustering of online learning targets had a good application effect in the learning community. Therefore, this method has practical application value.
Surface association induces cytotoxic alkyl-quinolones in Pseudomonas aeruginosa

Surface attachment, an early step in the colonization of multiple host environments, activates the virulence of the human pathogen P. aeruginosa. However, the signaling pathways and downstream toxins specifically induced by surface association to stimulate P. aeruginosa virulence are not fully understood. Here, we demonstrate that alkyl-quinolone (AQ) secondary metabolites are rapidly induced upon surface association and represent a major class of surface-dependent cytotoxins. AQ cytotoxicity is direct and independent of other AQ functions like quorum sensing or PQS-specific activities like iron sequestration. Furthermore, the regulation of AQ production can explain the surface-dependent virulence regulation of the quorum sensing receptor, LasR, and the pilin-associated candidate mechanosensor, PilY1. PilY1 regulates surface-induced AQ production by repressing the AlgR-AlgZ two-component system. AQs also contribute to the known cytotoxicity of secreted outer membrane vesicles. These findings collectively explain previously mysterious aspects of virulence regulation and provide new avenues for the development of anti-infectives.
SDQIM: Software-Defined Quadcopter Interdomain Management Protocol With Decentralized Inductive Power Transfer (DIPT) Platform

Due to the flexible deployment, low-cost and management, unmanned aerial vehicles, quadcopters have attracted significant interest lately in expanding wireless communications to areas that lack to cellular coverage network. Currently, software-defined network (SDN) is considered a viable alternative that improves network scalability and management. Earlier in 2018, we designed a novel software-defined quadcopter (SDQ) with a mobile Internet of Things vehicle to measure the best efficient wind power location for effective future wind turbine placement. However, large wind farms require complex and scalable wind measurement platform that consists of multiple quadcopters and ground mobile nodes that operate under a decision-making softwarized protocol. To this end, this paper proposes a novel cognitive routing protocol called SDQ interdomain management (SDQIM) Protocol. The SDQIM structure consists of multiple SDN controllers with failover capability. Our proposed protocol operate with multiple phases as each phase is used to cover a specific scenario that could occur in the interdomain network. The SDQ-main will have a total overview of the network topology and the SDQ-slave will be responsible for communication management with all other local subdomains. However, for large-scale farmland, quadcopter power level is essential for the continuity of the system services. Thus, we additionally propose a decentralized inductive power transfer (DIPT) platform that operates autonomously using software defined controlling approach. In essence, The DIPT platform is considered to be automated and low-cost approach that encompasses a custom-designed SDN controller for all interdomain quadcopters. Our system has been implemented by using an experimental testbed. The experimental results showed that scalability of the SDQ system can be increased to cover large wind farms with uninterrupted power management platform.
Population genetics and molecular phylogeography of Thamnaconus modestus (Tetraodontiformes, Monachanthidae) in Northwestern Pacific inferred from variation of the mtDNA control region

In order to study the genetic diversity of Thamnaconus modestus, a species of great commercial importance in Southeast Asia, the 5′-end hypervariable regions (423 bp) of the mitochondrial control region of T. modestus in nine geographical populations (248 individuals) were sequenced and analysed in this study. The target sequence fragment contained large numbers of polymorphic sites (87) involved in high levels of haplotype diversity (h = 0.97 ± 0.01) and nucleotide diversity (π = 0.0285 ± 0.0143). The genetic variations within populations (92.71%) were significantly larger than those among populations (7.29%). No significant genetic divergences were detected among the wild populations owing to their gregarious habits, strong moving ability, r-selection strategy. Significant genetic divergences were found between the cultured and wild populations, probably resulting from kin selection and aquacultural environment. Three significant phylogenetic lineages were identified, and the variation among lineages (56.90%) was greater than that among individuals within the lineages (43.10%), with the significant ΦST value (ΦST = 0.57, P = 0.0000). The results showed great and significant genetic differentiations among these three lineages, indicating that they may have independent phylogenetic dynamics. Dominant shared haplotypes that included individuals from each population and the median-joining network of haplotypes presented a star-like structure. Historic demographic analysis of each lineage showed that population expansion occurred after the Pleistocene glacial period. At the last glacial maximum, T. modestus in China seas was scattered across variable refuges, including Central South China Sea and Okinawa Trough.
Triceps surae torque-length relationships relevant for walking activity levels with and without an ankle exoskeleton

Abstract Ankle exoskeletons have been developed to assist walking by offloading the plantar flexors work requirements, which reduces muscle activity level. However, reduced muscle activity alters plantar flexor muscle-tendon unit dynamics in a way that is poorly understood. We therefore evaluated torque-fascicle length properties of the soleus and lateral gastrocnemius during voluntary contractions at simulated activity levels typical during late stance with and without an ankle exoskeleton. Soleus activity levels (100, 30, and 22% maximal voluntary activity) were produced by participants via visual electromyography feedback at ankle angles ranging from −10° plantar flexion to 35° dorsiflexion. Using dynamometry and ultrasound imaging, torque-fascicle length data of the soleus and lateral gastrocnemius were produced. The results indicate that muscle activity reductions observed with an exoskeleton shift the torque-angle and torque-fascicle length curves to more dorsiflexed ankle angles and longer fascicle lengths where no descending limb is physiologically possible. This shift is in line with previous simulations that predicted a similar increase in the operating fascicle range when wearing an exoskeleton. These data suggest that a small reduction in muscle activity causes changes to torque-fascicle length properties, which has implications for the design and testing of future ankle exoskeletons for assisted walking. Significance Statement Assistive lower-limb exoskeletons reduce the metabolic cost of walking by reducing the positive work requirements of the plantar flexor muscles. However, if the exoskeleton reduces plantar flexor muscle activity too much, then the metabolic benefit is lost. The biological reasons for this are unclear and hinder further exoskeleton development. This research study is the first to directly evaluate if a reduction in plantar flexor muscle activity similar to that caused by wearing an exoskeleton affects muscle function. We found that reduced muscle activity changes the torque-length properties of two plantar flexors, which could explain why reducing muscle activity too much can increase metabolic cost.
Characteristic and phylogenetic analyses of chloroplast genome for an endangered species Manglietia lucida (Magnoliaceae)

Abstract Manglietia lucida is a threatened horticultural plant in Yunnan province of China. Now, the complete chloroplast (cp) genome of M. lucida was assembled. The cp genome of M. lucida was 160,134 bp in length and contained two short inverted repeat regions (26,595 bp), which were separated by a small single copy region (18,825 bp) and a large single copy region (88,119 bp). The cp genome encodes 108 unique genes, including 74 protein-coding genes, 30 transfer RNA genes and 4 ribosomal RNA genes. The topology of the phylogenetic tree showed that M. lucida was not formed a monophyletic clade but clustered together with genus Magnolia.
A new species of the genus Stedocys (Araneae: Scytodidae), from Japan

― A new scytodid species of the genus Stedocys is described as Stedocys amamiensis sp. nov. from Amami-Ôshima, Japan. This species is similar to S. gaolingensis Wu & Li 2017 from China, but can be distinguished from it by having stouter embolus and abdominal markings on the midline. The genus Stedocys is newly recorded from Japan, and this record is the northeasternmost area for the genus. For future study, partial sequence data of mt-COI gene of the new species is documented.
Antibacterial activity of the lyophilized aqueous leaf extract of the Philippine green-leafed Acalypha amentacea Roxb. (Maslakot-Ambulong) against selected human bacterial pathogens

Aims: The specific aim of this study was to evaluate for the first time the phytochemical constituents, functional group assignment, and antibacterial activities of the Philippine green-leafed Acalypha amentacea Roxb. (Maslakot-Ambulong), a wildcrafted medicinal plant of local traditional healers in the southern most region of Mindoro province. Methodology and results: Aqueous leaf extracts of A. amentacea Roxb. were lyophilized and subjected to qualitative phytochemical screening and FT-IR analysis. The antibacterial activity of the plant using agar-well diffusion assay revealed highest Zone of Inhibition (ZOI) in 500 mg/mL concentration for Staphylococcus aureus (21.78 mm), Escherichia coli, (21.36 mm), Serratia marcescens (21.90 mm), Klebsiella pneumoniae (21.44 mm), and Enterococcus faecalis (20.52 mm) among other concentrations suggesting a dose dependent bioactivity. Also, compared to the antibiotic Rifampicin, A. amentacea Roxb. demonstrated better bioactivity against all the selected bacteria except S. aureus (p<0.05) and comparable to Ofloxacin when against E. faecalis. The minimum inhibitory concentration (MIC) of the extract was found to be at 15.6 mg/mL for all the bacteria except for S. marcescens with 31.25 mg/mL as MIC. The bioactivity of the plant may be accounted to the presence of alkaloid, phenol, flavonoid, tannin, and saponin which were supported by its functional groups like carboxylic acid, alcohols, amine, conjugated alkene, aromatic esters, and alkyl aryl ether. Conclusion, significance and impact of study: The results of this investigation, proved that A. amentacea Roxb. has bioactive antibacterial principles against the selected microorganisms. This also confirms its potentiality as a new source of antibacterial agents.
Characterization of the complete mitochondrial genome of Amphioplus laevis (Ophiuroidea, Amphiuridae) with phylogenetic analysis

Abstract The complete mitochondrial genome of Amphioplus laevis was 16,084 bp in length (Genbank accession: MN276320). It contained 13 protein-coding genes, 2 ribosomal RNA and 22 transfer RNA. The GC contents of A. laevis was 37.59%. The gene order was similar with species in the same family. Phylogenetic relationships within known ophiuroids reconstructed by 13 protein-coding mitochondrial genes, which showed that it was mostly related to Amphiopholis squamata, corresponding to the gene order. These results could provide a novel insight to the phylogeny of Ophiuroidea.
A hierarchical key pre‐distribution scheme for fog networks

Security in fog computing is multi‐faceted, and one particular challenge is establishing a secure communication channel between fog nodes and end devices. This emphasizes the importance of designing efficient and secret key distribution scheme to facilitate fog nodes and end devices to establish secure communication channels. Existing secure key distribution schemes designed for hierarchical networks may be deployable in fog computing, but they incur high computational and communication overheads and thus consume significant memory. In this paper, we propose a novel hierarchical key pre‐distribution scheme based on “Residual Design” for fog networks. The proposed key distribution scheme is designed to minimize storage overhead and memory consumption while increasing network scalability. The scheme is also designed to be secure against node capture attacks. We also demonstrate that, in an equal‐size network, our scheme reduces node storage overhead significantly. Our research paves the way for building an efficient key management framework for secure communication within the hierarchical network of fog nodes and end devices.
Regulation of expression site and generalizability of experience-dependent plasticity in visual cortex

The experience-dependent decrease in stimulus detection thresholds that underly perceptual learning can be induced by repetitive exposure to a visual stimulus. Robust stimulus-selective potentiation of visual responses is induced in the primary mouse visual cortex by repetitive low frequency visual stimulation (LFVS). How the parameters of the repetitive visual stimulus impact the site and specificity of this experience-dependent plasticity is currently a subject of debate. Here we demonstrate that the stimulus selective response potentiation induced by repetitive low frequency (1 Hz) stimulation, which is typically limited to layer 4, shifts to superficial layers following manipulations that enhance plasticity in primary visual cortex. In contrast, repetitive high frequency (10 Hz) visual stimulation induces response potentiation that is expressed in layers 4 and 5/6, and generalizes to novel visual stimuli. Repetitive visual stimulation also induces changes in the magnitude and distribution of oscillatory activity in primary visual cortex, however changes in oscillatory power do not predict the locus or specificity of response potentiation. Instead we find that robust response potentiation is induced by visual stimulation that resets the phase of ongoing gamma oscillations. Furthermore, high frequency, but not low frequency, repetitive visual stimulation entrains oscillatory rhythms with enhanced sensitivity to phase reset, such that familiar and novel visual stimuli induce similar visual response potentiation.
UDSMProt: universal deep sequence models for protein classification

Motivation Inferring the properties of a protein from its amino acid sequence is one of the key problems in bioinformatics. Most state-of-the-art approaches for protein classification tasks are tailored to single classification tasks and rely on handcrafted features such as position-specific-scoring matrices from expensive database searches. We argue that this level of performance can be reached or even be surpassed by learning a task-agnostic representation once, using self-supervised language modeling, and transferring it to specific tasks by a simple finetuning step. Results We put forward a universal deep sequence model that is pretrained on unlabeled protein sequences from Swiss-Prot and finetuned on protein classification tasks. We apply it to three prototypical tasks, namely enzyme class prediction, gene ontology prediction and remote homology and fold detection. The proposed method performs on par with state-of-the-art algorithms that were tailored to these specific tasks or, for two out of three tasks, even outperforms them. These results stress the possibility of inferring protein properties from the sequence alone and, on more general grounds, the prospects of modern natural language processing methods in omics. Availability Source code is available under https://github.com/nstrodt/UDSMProt. Contact firstname.lastname@hhi.fraunhofer. de
Purification and Characterization of NDH-2 Protein and Elucidating Its Role in Extracellular Electron Transport and Bioelectrogenic Activity

In microbial electrochemical systems, transport of electrons from bacteria to an electrode is the key to its functioning. However, the roles of several electron transport proteins, especially the membrane-bound dehydrogenases which link cellular metabolism to EET pathway are yet to be identified. NDH-2 is a non-proton pumping NADH dehydrogenase located in the inner membrane of several bacteria like Bacillus subtilis, Escherichia coli, etc. Unlike NADH dehydrogenase I, NDH-2 is not impeded by a high proton motive force thus helping in the increase of metabolic flux and carbon utilization. In the current study, NADH dehydrogenase II protein (NDH-2) was heterologously expressed from B. subtilis into E. coli BL21 (DE3) for enhancing electron flux through EET pathway and to understand its role in bioelectrogenesis. We found that E. coli expressing NDH-2 has increased the electron flux through EET and has shown a ninefold increase in current (4.7 μA) production when compared to wild strain with empty vector (0.52 μA). Furthermore, expression of NDH-2 also resulted in increased biofilm formation which can be corroborated with the decrease in charge transfer resistance of NDH-2 strain and increased NADH oxidation. It was also found that NDH-2 strain can reduce ferric citrate at a higher rate than wild type strain suggesting increased electron flux through electron transport chain due to NADH dehydrogenase II activity. Purified NDH-2 was found to be ∼42 kDa and has FAD as a cofactor. This work demonstrates that the primary dehydrogenases like NADH dehydrogenases can be overexpressed to increase the electron flux in EET pathway which can further enhance the microbial fuel cells performance.
Predicting mechanism of action of cellular perturbations with pathway activity signatures

Motivation Misregulation of signaling pathway activity is etiologic for many human diseases, and modulating activity of signaling pathways is often the preferred therapeutic strategy. Understanding the mechanism of action (MOA) of bioactive chemicals in terms of targeted signaling pathways is the essential first step in evaluating their therapeutic potential. Changes in signaling pathway activity are often not reflected in changes in expression of pathway genes which makes MOA inferences from transcriptional signatures a difficult problem. Results We developed a new computational method for implicating pathway targets of bioactive chemicals and other cellular perturbations by integrated analysis of pathway network topology, the LINCS transcriptional signatures of genetic perturbations of pathway genes and the transcriptional signature of the perturbation. Our methodology accurately predicts signaling pathways targeted by the perturbation when current pathway analysis approaches utilizing only a transcriptional signature of the perturbation fail. Availability and Implementation Open source R package paslincs is available at https://github.com/uc-bd2k/paslincs.