Miracle-dz/UniFAR-FTUs huggingface dataset:
A New Method to Obtain the Complete Genome Sequence of Multiple-Component Circular ssDNA Viruses by Transcriptome Analysis

Circular single-stranded DNA (ssDNA) viruses are widely distributed globally, infecting diverse hosts ranging from bacteria, archaea, and eukaryotes. Among these, the genome of Banana bunchy top virus (BBTV) comprises at least six circular, ssDNA components that are ∼1 kb in length. Its genome is usually amplified and obtained at the DNA level. However, RNA-based techniques to obtain the genome sequence of such multi-component viruses have not been reported. In this study, transcriptome sequencing analysis showed that the full-length of BBTV each genomic component was transcribed into viral mRNA (vmRNA). Accordingly, the near-complete genome of BBTV B2 isolate was assembled using transcriptome sequencing data from virus-infected banana leaves. Assembly analysis of BBTV-derived reads indicated that the full-length sequences were obtained for DNA-R, DNA-U3, DNA-S, DNA-M, DNA-N, NewS2, and Sat4 components, while two gaps (73 and 25 nt) missing in the DNA-C component which was further filled by reverse transcription-PCR (RT-PCR). The RT-qPCR analysis indicated that the vmRNA levels of coding regions were 3.19–103.53 folds higher than those of non-coding regions, implying that the integrity of genome assembly depended on the transcription level of non-coding region. In conclusion, this study proposes a new approach to obtain the genome of nanovirids, and provides insights for studying the transcriptional mechanism of the family Nanoviridae, Genomoviridae, and Geminiviridae.
Seasonal Variation of Bacterial Diversity Along the Marine Particulate Matter Continuum

Seasonal dynamics of ocean prokaryotic communities in the free-living fraction have been widely described, but less is known about the seasonality of prokaryotes inhabiting marine particles. We describe the seasonality of bacterial communities in the particulate matter continuum by sampling monthly over two years in a temperate oligotrophic coastal ecosystem and using a serial filtration (including six size-fractions spanning from 0.2 to 200 μm). We observed that bacterial communities in the particulate matter continuum had annual changes following harmonic seasonal oscillations, where alpha, beta, and gamma diversity increased during the warm period and decreased during the cold period. Communities in each size-fraction changed gradually over time, being the communities in larger size-fractions the ones with stronger annual changes. Annual community changes were driven mainly by day length and sea surface temperature, and each size-fraction was additionally affected by other variables (e.g., smaller size-fractions by dissolved PO4 and larger size-fractions by turbidity). While some taxonomic groups mantained their preference for a given size fraction during most of the year, others varied their distribution into different size fractions over time, as e.g., SAR11, which increased its presence in particles during the cold period. Our results indicate that the size-fractionation scheme provides novel seasonal patterns that are not possible to unveil by analyzing only free-living bacteria, and that help to better understand the temporal dynamics of prokaryotes.
Bayesian Multi-objective Hyperparameter Optimization for Accurate, Fast, and Efficient Neural Network Accelerator Design

In resource-constrained environments, such as low-power edge devices and smart sensors, deploying a fast, compact, and accurate intelligent system with minimum energy is indispensable. Embedding intelligence can be achieved using neural networks on neuromorphic hardware. Designing such networks would require determining several inherent hyperparameters. A key challenge is to find the optimum set of hyperparameters that might belong to the input/output encoding modules, the neural network itself, the application, or the underlying hardware. In this work, we present a hierarchical pseudo agent-based multi-objective Bayesian hyperparameter optimization framework (both software and hardware) that not only maximizes the performance of the network, but also minimizes the energy and area requirements of the corresponding neuromorphic hardware. We validate performance of our approach (in terms of accuracy and computation speed) on several control and classification applications on digital and mixed-signal (memristor-based) neural accelerators. We show that the optimum set of hyperparameters might drastically improve the performance of one application (i.e., 52–71% for Pole-Balance), while having minimum effect on another (i.e., 50–53% for RoboNav). In addition, we demonstrate resiliency of different input/output encoding, training neural network, or the underlying accelerator modules in a neuromorphic system to the changes of the hyperparameters.
Exogenous Nitro-Oleic Acid Treatment Inhibits Primary Root Growth by Reducing the Mitosis in the Meristem in Arabidopsis thaliana

Nitric oxide (NO) is a second messenger that regulates a broad range of physiological processes in plants. NO-derived molecules called reactive nitrogen species (RNS) can react with unsaturated fatty acids generating nitrated fatty acids (NO2-FA). NO2-FA work as signaling molecules in mammals where production and targets have been described under different stress conditions. Recently, NO2-FAs were detected in plants, however their role(s) on plant physiological processes is still poorly known. Although in this work NO2-OA has not been detected in any Arabidopsis seedling tissue, here we show that exogenous application of nitro-oleic acid (NO2-OA) inhibits Arabidopsis primary root growth; this inhibition is not likely due to nitric oxide (NO) production or impaired auxin or cytokinin root responses. Deep analyses showed that roots incubated with NO2-OA had a lower cell number in the division area. Although this NO2-FA did not affect the hormonal signaling mechanisms maintaining the stem cell niche, plants incubated with NO2-OA showed a reduction of cell division in the meristematic area. Therefore, this work shows that the exogenous application of NO2-OA inhibits mitotic processes subsequently reducing primary root growth.
Volitional Modulation of the Left DLPFC Neural Activity Based on a Pain Empathy Paradigm—A Potential Novel Therapeutic Target for Pain

The ability to perceive and feel another person' pain as if it were one's own pain, e.g., pain empathy, is related to brain activity in the “pain-matrix” network. A non-core region of this network in Dorsolateral Prefrontal Cortex (DLPFC) has been suggested as a modulator of the attentional-cognitive dimensions of pain processing in the context of pain empathy. We conducted a neurofeedback experiment using real-time functional magnetic resonance imaging (rt-fMRI-NF) to investigate the association between activity in the left DLPFC (our neurofeedback target area) and the perspective assumed by the participant (“first-person”/“Self” or “third-person”/“Other” perspective of a pain-inducing stimulus), based on a customized pain empathy task. Our main goals were to assess the participants' ability to volitionally modulate activity in their own DLPFC through an imagery task of pain empathy and to investigate into which extent this ability depends on feedback. Our results demonstrate participants' ability to significantly modulate brain activity of the neurofeedback target area for the “first-person”/”Self” and “third-person”/”Other” perspectives. Results of both perspectives show that the participants were able to modulate (with statistical significance) the activity already in the first run of the session, in spite of being naïve to the task and even in the absence of feedback information. Moreover, they improved modulation throughout the session, particularly in the “Self” perspective. These results provide new insights on the role of DLPFC in pain and pain empathy mechanisms and validate the proposed protocol, paving the way for future interventional studies in clinical populations with empathic deficits.
Above-ground resource use increases with plant species richness in experimental grassland ecosystems

1. Diversity effects on canopy structure and the use of space and light were investigated in experimental grassland communities of one, two, four, eight or 32 species and of one, two or three functional groups (grasses, legumes and non-leguminous forbs) in north-western Switzerland. 2. Plant species and functional group richness improved the establishment of grassland assemblages and increased mean vegetation cover from 64% in monocultures to 100% in 32-species mixtures. 3. Above-ground biomass and leaf area index increased linearly with the logarithm of plant species number in all 3 years of the experiment. The most species-rich communities produced 143% more biomass than the mean of all monocultures and 25% more biomass than the most productive monoculture in the third year of the experiment. 4. Plant light absorbance per unit ground area increased by 44% with diversity (32-species mixtures compared with monocultures) for two reasons. (i) The lack of species complementarity in terms of success of seedling establishment and plant size in vegetation with low species diversity increased the gap area in the canopy, and consequently more light reached bare ground. (ii) Diverse ecosystems absorbed more light within their closed canopies (+20%) than did less diverse ecosystems, because of better three-dimensional space filling and greater biomass density due to complementarity in plant architecture, particularly between grasses and dicots. 5. The overall canopy height was increased by 46% and the centre of gravity of the canopy was raised by up to 10 cm over the range of experimental diversity treatments from one to 32 species. 6. These results cannot be explained solely by a higher likelihood of the inclusion of more productive dominant species in the more diverse communities (sampling effect), which suggests that biomass production may be determined by the most productive species present in a community. For all important canopy characteristics of multispecies communities, we show that with increasing diversity, mixtures perform better than the best monoculture. This is a strong indication that part of the positive effect of diversity on biomass production is driven by complementary resource use.
Essential Role for CD30-Transglutaminase 2 Axis in Memory Th1 and Th17 Cell Generation

Memory helper T (Th) cells are crucial for secondary immune responses against infectious microorganisms but also drive the pathogenesis of chronic inflammatory diseases. Therefore, it is of fundamental importance to understand how memory T cells are generated. However, the molecular mechanisms governing memory Th cell generation remain incompletely understood. Here, we identified CD30 as a molecule heterogeneously expressed on effector Th1 and Th17 cells, and CD30hi effector Th1 and Th17 cells preferentially generated memory Th1 and Th17 cells. We found that CD30 mediated signal induced Transglutaminase-2 (TG2) expression, and that the TG2 expression in effector Th cells is essential for memory Th cell generation. In fact, Cd30-deficiency resulted in the impaired generation of memory Th1 and Th17 cells, which can be rescued by overexpression of TG2. Furthermore, transglutaminase-2 (Tgm2)-deficient CD4 T cells failed to become memory Th cells. As a result, T cells from Tgm2-deficient mice displayed impaired antigen-specific antibody production and attenuated Th17-mediated allergic responses. Our data indicate that CD30-induced TG2 expression in effector Th cells is essential for the generation of memory Th1 and Th17 cells, and that CD30 can be a marker for precursors of memory Th1 and Th17 cells.
Influence of the Acoustic Environment in Hospital Wards on Patient Physiological and Psychological Indices

Patients in general wards are often exposed to excessive levels of noise and activity, and high levels of noise have been associated with depression and anxiety. Previous studies have found that an appropriate acoustic environment is beneficial to the patient’s therapeutic and treatment process; however, the soundscape is rarely intentionally designed or operated to improve patient recovery, especially for psychological rehabilitation. To gain the most accurate, and least variable, estimate of acoustic environmental stimuli/properties, virtual reality (VR) technology should be used to ensure that other environmental factors are stable and uniform in order to reduce the stimulation of other environmental factors. Therefore, this study aims to discuss the influence of the acoustic environment on patient physiological/psychological indicators and the mechanism of the effect on recovery using VR technology. A digital three-dimensional (3D) model of a hospital room was constructed, and experimental subjects wore VR glasses to visualize a real ward scene. Four typical sound categories were selected to analyze the effect of the acoustic environment on recovery; physiological indicators were monitored, and psychological factors were subjectively evaluated. The results show that music plays an important role in reducing stress as it can aid in a patient’s physiological (skin conduction levels) and psychological stress recovery. Furthermore, mechanical and anthropogenic sounds exert negative effects on a patient’s stress recovery. However, the effect is only limited to psychological stress indicators. The interaction effects of demographic characteristics and the acoustic environment are not significant, and future studies could consider the social–economic characteristics of patients. Based on these findings, we provide evidence that indicates that a hospital’s acoustic environment is an important influencing factor on the stress recovery of patients and can serve as a reference for healthcare architects and policy makers.
Positive Affect Over Time and Emotion Regulation Strategies: Exploring Trajectories With Latent Growth Mixture Model Analysis

The influence of Positive Affect (PA) on people’s well-being and happiness and the related positive consequences on everyday life have been extensively described by positive psychology in the past decades. This study shows an application of Latent Growth Mixture Modeling (LGMM) to explore the existence of different trajectories of variation of PA over time, corresponding to different groups of people, and to observe the effect of emotion regulation strategies on these trajectories. We involved 108 undergraduates in a 1-week daily on-line survey, assessing their PA. We also measured their emotion regulation strategies before the survey. We identified three trajectories of PA over time: a constantly high PA profile, an increasing PA profile, and a decreasing PA profile. Considering emotion regulation strategies as covariates, reappraisal showed an effect on trajectories and class membership, whereas suppression regulation strategy did not.
Focusing on the Neuro-Psycho-Biological and Evolutionary Underpinnings of the Imposter Syndrome

University Research Institute of Maternal and Child Health and Precision Medicine, and UNESCO Chair on Adolescent Health Care, “Aghia Sophia” Children’s Hospital, National and Kapodistrian University of Athens, Athens, Greece, 2 Public Health Laboratories, Hellenic Pasteur Institute, Athens, Greece, Department of Microbiology, School of Medicine, University Hospital of Larissa, University of Thessaly, Larissa, Greece, Department of Neurology, School of Medicine, University Hospital of Larissa, University of Thessaly, Larissa, Greece
Quantification of Lung Fibrosis in IPF-Like Mouse Model and Pharmacological Response to Treatment by Micro-Computed Tomography

Idiopathic pulmonary fibrosis (IPF) is a chronic progressive degenerative lung disease leading to respiratory failure and death. Although anti-fibrotic drugs are now available for treating IPF, their clinical efficacy is limited and lung transplantation remains the only modality to prolong survival of IPF patients. Despite its limitations, the bleomycin (BLM) animal model remains the best characterized experimental tool for studying disease pathogenesis and assessing efficacy of novel potential drugs. In the present study, the effects of oropharyngeal (OA) and intratracheal (IT) administration of BLM were compared in C57BL/6 mice. The development of lung fibrosis was followed in vivo for 28 days after BLM administration by micro-computed tomography and ex vivo by histological analyses (bronchoalveolar lavage, histology in the left lung to stage fibrosis severity and hydroxyproline determination in the right lung). In a separate study, the antifibrotic effect of Nintedanib was investigated after oral administration (60 mg/kg for two weeks) in the OA BLM model. Lung fibrosis severity and duration after BLM OA and IT administration was comparable. However, a more homogeneous distribution of fibrotic lesions among lung lobes was apparent after OA administration. Quantification of fibrosis by micro-CT based on % of poorly aerated tissue revealed that this readout correlated significantly with the standard histological methods in the OA model. These findings were further confirmed in a second study in the OA model, evaluating Nintedanib anti-fibrotic effects. Indeed, compared to the BLM group, Nintedanib inhibited significantly the increase in % of poorly aerated areas (26%) and reduced ex vivo histological lesions and hydroxyproline levels by 49 and 41%, respectively. This study indicated that micro-computed tomography is a valuable in vivo technology for lung fibrosis quantification, which will be very helpful in the future to better evaluate new anti-fibrotic drug candidates.
Cannabis Improves Obsessive-Compulsive Disorder—Case Report and Review of the Literature

Although several lines of evidence support the hypothesis of a dysregulation of serotoninergic neurotransmission in the pathophysiology of obsessive-compulsive disorder (OCD), there is also evidence for an involvement of other pathways such as the GABAergic, glutamatergic, and dopaminergic systems. Only recently, data obtained from a small number of animal studies alternatively suggested an involvement of the endocannabinoid system in the pathophysiology of OCD reporting beneficial effects in OCD-like behavior after use of substances that stimulate the endocannabinoid system. In humans, until today, only two case reports are available reporting successful treatment with dronabinol (tetrahydrocannabinol, THC), an agonist at central cannabinoid CB1 receptors, in patients with otherwise treatment refractory OCD. In addition, data obtained from a small open uncontrolled trial using the THC analogue nabilone suggest that the combination of nabilone plus exposure-based psychotherapy is more effective than each treatment alone. These reports are in line with data from a limited number of case studies and small controlled trials in patients with Tourette syndrome (TS), a chronic motor and vocal tic disorder often associated with comorbid obsessive compulsive behavior (OCB), reporting not only an improvement of tics, but also of comorbid OCB after use of different kinds of cannabis-based medicines including THC, cannabis extracts, and flowers. Here we present the case of a 22-year-old male patient, who suffered from severe OCD since childhood and significantly improved after treatment with medicinal cannabis with markedly reduced OCD and depression resulting in a considerable improvement of quality of life. In addition, we give a review of current literature on the effects of cannabinoids in animal models and patients with OCD and suggest a cannabinoid hypothesis of OCD.
Why would we get attacked? An analysis of attacker's aims behind DDoS attacks

Reliable availability to the internet and internet-based services is crucial in today’s world. DDoS attacks pose a severe threat to the availability of such online resources – especially owing to booters – virtually everyone can execute them nowadays. In order to appropriately protect oneself against such attacks, it is essential to have a good insight into the threats that exist. This paper proposes a novel hybrid model that combines postulates from various models on crime opportunity, analyzing the targeted victim and the targeted infrastructure in conjunction. We apply this model to analyze 27 distinct attack events that occurred in 2016. To construct this dataset, we utilize a longitudinal news database specific to DDoS-related events, aiding to select relevant attack events. We outline the procedure to replicate the dataset construction process. Looking at DDoS attacks solely as a technical issue is not enough, news articles can be an important resource in providing contextual relevance to this problem. Our analysis reveals several motives underlying DDoS attacks; economic reasons are but one of the possible aims. For this reason, we advise companies to also monitor the socio-cultural and political environment. In terms of infrastructure, visibility and accessibility are the main instigators for an attack. A holistic perspective is imperative to accurately map the threats that companies face and to take appropriate protective measures.
Nonstationary Model of Oxygen Transport in Brain Tissue

The paper addresses the mathematical study of a nonstationary continuum model describing oxygen propagation in cerebral substance. The model allows to estimate the rate of oxygen saturation and stabilization of oxygen concentration in relatively large parts of cerebral tissue. A theoretical and numerical analysis of the model is performed. The unique solvability of the underlying initial-boundary value problem for a system of coupled nonlinear parabolic equations is proved. In the numerical experiment, the tissue oxygen saturation after hypoxia is analyzed for the case when a sufficient amount of oxygen begins to flow into the capillary network. A fast stabilization of the tissue oxygen concentration is demonstrated. The reliability of the results of the numerical simulation is discussed.
Capturing Human Factors to Optimize Crowdsourced Label Acquisition through Active Learning

The goal of this article is to propose an optimization framework by acknowledging human factors to enable label acquisition through active learning . In particular, we are interested to investigate tasks, such as, providing (collecting or acquiring) and validating labels, or comparing data using active learning techniques. Our basic approach is to take a set of existing active learning techniques for a few well known supervised and unsupervised algorithms, but study them in the context of crowdsourcing, especially considering worker-centric optimization (i,e., human factors) . Our innovation lies in designing optimization functions that appropriately capture these two fundamental yet complementary facets, performing systematic investigation to understand the complexity of such optimization problems, and designing efﬁcient solutions with theoretical guarantees.
Ontology-Based Modelling of State Machines for Production Robots in Smart Manufacturing Systems

The integration of modern IT technologies in production equipment does not only enable them to acquire information from different sources and provide it to others but also to make decisions depending on the situation. Due to the limited processing power of such equipment, usage of state machine to describe and program it is considered a promising direction. However, the necessity of intensive interaction of the equipment units causes problems related to interoperability, which are usually solved with the usage of ontologies. The objective of the presented research is to model state machines of production robots via ontologies. The results are demonstrated on the example of a fragment of an automated production line.