Miracle-dz/UniFAR-FTUs huggingface dataset:
Impacts of Small RNAs and Their Chaperones on Bacterial Pathogenicity

Bacterial small RNAs (sRNAs) are critical post-transcriptional regulators that exert broad effects on cell physiology. One class of sRNAs, referred to as trans-acting sRNAs, base-pairs with mRNAs to cause changes in their stability or translation. Another class of sRNAs sequesters RNA-binding proteins that in turn modulate mRNA expression. RNA chaperones play key roles in these regulatory events by promoting base-pairing of sRNAs to mRNAs, increasing the stability of sRNAs, inducing conformational changes on mRNA targets upon binding, or by titrating sRNAs away from their primary targets. In pathogenic bacteria, sRNAs and their chaperones exert broad impacts on both cell physiology and virulence, highlighting the central role of these systems in pathogenesis. This review provides an overview of the growing number and roles of these chaperone proteins in sRNA regulation, highlighting how these proteins contribute to bacterial pathogenesis.
A Graph Theory Approach to Clarifying Aging and Disease Related Changes in Cognitive Networks

In accordance with the physiological networks that underlie it, human cognition is characterized by both the segregation and interdependence of a number of cognitive domains. Cognition itself, therefore, can be conceptualized as a network of functions. A network approach to cognition has previously revealed topological differences in cognitive profiles between healthy and disease populations. The present study, therefore, used graph theory to determine variation in cognitive profiles across healthy aging and cognitive impairment. A comprehensive neuropsychological test battery was administered to 415 participants. This included three groups of healthy adults aged 18–39 (n = 75), 40–64 (n = 75), and 65 and over (n = 70) and three patient groups with either amnestic (n = 75) or non-amnestic (n = 60) mild cognitive impairment or Alzheimer’s type dementia (n = 60). For each group, cognitive networks were created reflective of test-to-test covariance, in which nodes represented cognitive tests and edges reflected statistical inter-nodal significance (p < 0.05). Network metrics were derived using the Brain Connectivity Toolbox. Network-wide clustering, local efficiency and global efficiency of nodes showed linear differences across the stages of aging, being significantly higher among older adults when compared with younger groups. Among patients, these metrics were significantly higher again when compared with healthy older controls. Conversely, average betweenness centralities were highest in middle-aged participants and lower among older adults and patients. In particular, compared with controls, patients demonstrated a distinct lack of centrality in the domains of semantic processing and abstract reasoning. Network composition in the amnestic mild cognitive impairment group was similar to the network of Alzheimer’s dementia patients. Using graph theoretical methods, this study demonstrates that the composition of cognitive networks may be measurably altered by the aging process and differentially impacted by pathological cognitive impairment. Network alterations characteristic of Alzheimer’s disease in particular may occur early and be distinct from alterations associated with differing types of cognitive impairment. A shift in centrality between domains may be particularly relevant in identifying cognitive profiles indicative of underlying disease. Such techniques may contribute to the future development of more sophisticated diagnostic tools for neurodegenerative disease.
Role of Physicochemical and Interfacial Properties on the Binary Coalescence of Crude Oil Drops in Synthetic Produced Water

An important aspect of efficient treatment of produced water is to promote the coalescence of dispersed oil drops. Therefore, a fundamental understanding of the relations between interfacial and physicochemical properties of crude oils and the coalescence process is essential, and elucidating these relationships was the aim of this investigation. Nine crude oils and fractions of two crude oils, where acids, bases, and asphaltenes were selectively extracted, were included in the studies. The dynamic interfacial tension and interfacial rheology of the oils were followed in synthetic produced water. The binary coalescence of oil drops was followed by a micropipette setup, and characteristic times for three phenomena occurring during the coalescence process were identified: the time of film thinning, the time for rupture of the thin film, and the apparent time of merger of the two drops. Decreasing the density of the crude oils and increasing the strength of the interfacial layer were determined to influence .
Continuous Speech Recognition using Advanced Deep Neural Networks

This thesis presents Automatic Speech Recognition (ASR) systems based on deep neural networks (DNN) with advanced structures and their implementations for the English and Czech languages using the Kaldi toolkit. The experiments use the newest Kaldi DNN nnet3 setup, which supports mentioned advanced DNN types. Moreover, two nnet3 models are adopted the standard nnet3 models and the chain models, which are implemented as a part of the nnet3 DNN setup with the intention to decrease decoding time. The implementations work with DNN-HMM architecture with two neural network types, Time Delay Neural Network (TDNN) and Long Short-Term Memory (LSTM), using both nnet3 and chain models. Additionally, the Convolutional Neural Network (CNN) neural network structure is adopted using the chain model. Experiments compare the accuracy of the DNN-HMM ASR models with the standard GMM-HMM approach, and the best accuracy was achieved with TDNN network with World Error Rate (WER) of 2.69 % for the English language, which was about 5% improvement over the standard GMM-HMM model. The best result for the Czech language was also accomplished with the TDNN network with a WER of 10.78 % (approximately 9% improvement over the GMM-HMM model). Secondly, the performance of DNN-HMM systems using GMM-HMM models trained with trigram and fourgram language models (LM) was analyzed as well as with a dictionary with and without silence and pronunciation probabilities. Finally, the accuracy improvements and the overall processing speed of the chain models over the standard nnet3 models were tested. The enhancement of accuracy was achieved both in TDNN and LSTM-TDNN chain models when more extensive improvements registered the TDNN chain model. The decoding time decreased for both DNN chain models when LSTM-TDNN decoded almost nine times faster than standard nnet3 implementation. LSTM-TDNN chain model also reduces the training time when the model was trained about 20 % faster. Nevertheless, the TDNN chain model had worse training speed when the network trained more than twice slower than the nnet3 variant.
Recognized and Potentially New Biomarkers—Their Role in Diagnosis and Prognosis of Cardiovascular Disease

Atherosclerosis and its consequences are the leading cause of mortality in the world. For this reason, we have reviewed atherosclerosis biomarkers and selected the most promising ones for review. We focused mainly on biomarkers related to inflammation and oxidative stress, such as the highly sensitive C-reactive protein (hs-CRP), interleukin 6 (IL-6), and lipoprotein-associated phospholipase A2 (Lp-PLA2). The microRNA (miRNA) and the usefulness of the bone mineralization, glucose, and lipid metabolism marker osteocalcin (OC) were also reviewed. The last biomarker we considered was angiogenin (ANG). Our review shows that due to the multifactorial nature of atherosclerosis, no single marker is known so far, the determination of which would unambiguously assess the severity of atherosclerosis and help without any doubt in the prognosis of cardiovascular risk.
Fundamentals of Lossless, Reciprocal Bianisotropic Metasurface Design

Lossless, reciprocal bianisotropic metasurfaces have the ability to control the amplitude, phase, and polarization of electromagnetic wavefronts. However, producing the responses that are necessary for achieving this control with physically realizable surfaces is a challenging task. Here, several design approaches for bianisotropic metasurfaces are reviewed that produce physically realizable metasurfaces using cascaded impedance sheets. In practice, three or four impedance sheets are often used to realize bianisotropic responses, which can result in narrowband designs that require the unit cells to be optimized in order to improve the performance of the metasurface. The notion of a metasurface quality factor is introduced for three-sheet metasurfaces to address these issues in a systematic manner. It is shown that the quality factor can be used to predict the bandwidth of a homogeneous metasurface, and it can also be used to locate problematic unit cells when designing inhomogeneous metasurfaces. Several design examples are provided to demonstrate the utility of the quality factor, including an impedance matching layer with maximal bandwidth and a gradient metasurface for plane wave refraction. In addition to these examples, several metasurfaces for polarization control are also reported, including an isotropic polarization rotator and an asymmetric circular polarizer.
OP-GBEV200081 750..763

Genomic analysis of many nonmodel specieshasuncovered an incredible diversity of sex chromosome systems, making it possible to empirically test the rich body of evolutionary theory that describes each stage of sex chromosome evolution. Classic theory predicts that sex chromosomes originate from a pair of homologous autosomes and recombination between them is suppressed via inversions to resolve sexual conflict. The resulting degradation of the Y chromosome gene content creates the need for dosage compensation in the heterogametic sex. Sex chromosome theory also implies a linear process, starting from sex chromosome origin and progressing to heteromorphism. Despite many convergent genomic patterns exhibited by independently evolved sex chromosome systems, and many case studies supporting these theoretical predictions, emerging data provide numerous interesting exceptions to these long-standing theories, and suggest that the remarkable diversity of sex chromosomes is matched by a similar diversity in their evolution. For example, it is clear that sex chromosome pairs are not always derived from homologous autosomes. In addition, both the cause and the mechanism of recombination suppression between sex chromosome pairs remain unclear, and it may be that the spread of recombination suppression is a more gradual process than previously thought. It is also clear that dosage compensationcanbeachieved inmanyways, anddisplaysa rangeofefficacy indifferent systems. Finally, the remarkable turnoverof sex chromosomes in many systems, as well as variation in the rate of sex chromosome divergence, suggest that assumptions about the inevitable linearity of sex chromosome evolution are not always empirically supported, and the drivers of the birth–death cycle of sex chromosome evolution remain to be elucidated. Here, we concentrate on how the diversity in sex chromosomes across taxa highlights an equal diversity in each stage of sex chromosome evolution.
Building Trust in Artificial Conversational Agents

The notion of trust on human and its construction mechanisms have been widely studies in the two last decades. However, research on this topic has focused on human interactions, on organizations or on some specific technologies such as the Internet or automation. With the increasing popularity of Artificial Conversational Agents (ACA), there is a growing interested to study trust in AI. In this paper, we present a new model of building trust in human-ACA interactions. Our contribution aims at proposing a theoretical heuristic for building trust in ACAs by combining multi-disciplinary research on human-to-human trust, trust in organizations and trust in technologies. We claim that an ACA is at the core of these three dimensions because it can be perceived as a technology by its nature, as a service provided to an individual with specific processes by its role, and as a social agent with its own set of intentions due to its human-like interaction model. We believe that this diversity in trust drivers is key to have a holistic approach of trust in ACAs.
In Vitro Cell Toxicity and Intracellular Uptake of Doxorubicin Exposed as a Solution or Liposomes: Implications for Treatment of Hepatocellular Carcinoma

Cytostatic effects of doxorubicin in clinically applied doses are often inadequate and limited by systemic toxicity. The main objective of this in vitro study was to determine the anti-tumoral effect (IC50) and intracellular accumulation of free and liposomal doxorubicin (DOX) in four human cancer cell lines (HepG2, Huh7, SNU449 and MCF7). The results of this study showed a correlation between longer DOX exposure time and lower IC50 values, which can be attributed to an increased cellular uptake and intracellular exposure of DOX, ultimately leading to cell death. We found that the total intracellular concentrations of DOX were a median value of 230 times higher than the exposure concentrations after exposure to free DOX. The intracellular uptake of DOX from solution was at least 10 times higher than from liposomal formulation. A physiologically based pharmacokinetic model was developed to translate these novel quantitative findings to a clinical context and to simulate clinically relevant drug concentration–time curves. This showed that a liver tumor resembling the liver cancer cell line SNU449, the most resistant cell line in this study, would not reach therapeutic exposure at a standard clinical parenteral dose of doxorubicin (50 mg/m2), which is serious limitation for this drug. This study emphasizes the importance of in-vitro to in-vivo translations in the assessment of clinical consequence of experimental findings.
Constellation Design With Deep Learning for Down Link Non-Orthogonal Multiple Access

The Non-Orthogonal Multiple Access (NOMA) is considered a key technology for the next generation of cellular systems. NOMA is expected to meet the rapid increase in the demand of high data rate, massive connectivity, and high reliability for the fifth generation (5G) wireless networks. In downlink NOMA, superconstellation scheme for the transmission is designed by the superposition of the constellation schemes of each individual user. The super-constellation scheme at transmitter end needs to be designed carefully, in order to ensure the complete recovery of data intended for the corresponding receiver. The paper under consideration [1] proposed the novel deep learning based approach that design the optimized constellation scheme for the downlink NOMA. Auto-encoder network is trained in this approach, where Enocder is trained to map data bits to symbol, and Decoder is trained to map received symbol to data bits. The network is trained with synthetic data, and then trained bit to symbol mapping is extracted to get the optimized super-constellation scheme for downlink NOMA. The purposed scheme can be integrated into current communication systems and readily use for a practical purpose. Moreover, the scheme can be readily combined with iterative error-correction devices such as turbo codes or LDPC. Simulation results with synthetic data have verified the effectiveness of this deep learning based approach in designing the super-constellation scheme that allows transmission of data to multiple users simultaneously.
Biomechanical effects of the addition of a precision constraint on a collective load carriage task

Team lifting is a complex and collective motor task that possesses both motor and cognitive components. The purpose of this study was to investigate to what extent the biomechanics of a collective load carriage is affected when a dyad of individuals is performing a carrying task with an additional accuracy constraint. Ten dyads performed a first condition in which they collectively transported a load (CC), and a second one in which they transported the same load while maintaining a ball in a target position on its top (PC). The recovery-rate, amplitude, and period of the center-of-mass (COM) trajectory were computed for the whole system (dyad + table = PACS). We analyzed the forces and moments exerted at each joint of the upper limbs of the subjects. We observed a decrease in the overall performance of the dyads when the Precision task was added, i.e., i) the velocity and amplitude of CoMPACS decreased by 1,7% and 5,8%, respectively, ii) inter-subject variability of the Moment-Cost-Function decreased by 95% and recovery rate decreased by 19,2% during PC. A kinetic synergy analysis showed that the subjects reorganized their coordination in the PC. Our results demonstrate that adding a precision task affects the economy of collective load carriage. Notwithstanding, the joint moments at the upper-limbs are better balanced and co-vary more across the paired subjects during the precision task. Our study results may find applications in domains such as Ergonomics, Robotics-developments, and Rehabilitation.
Non-Deterministic Solvers and Explainable AI through Trajectory Mining

Traditional methods of creating explanations from complex systems involving the use of AI have resulted in a wide variety of tools available to users to generate explanations regarding algorithm and network designs. This however has traditionally been aimed at systems that mimic the structure of human thought such as neural networks. The growing adoption of AI systems in industries has led to research and roundtables regarding the ability to extract explanations from other systems such as Non-Deterministic algorithms. This family of algorithms can be analysed but the explanation of events can often be difficult for non-experts to understand. Mentioned is a potential path to the generation of explanations that would not require expert-level knowledge to be correctly understood.
Subfractional Spectrum of Serum Lipoproteins and Gut Microbiota Composition in Healthy Individuals

Aim: To reveal the relationship between gut microbiota composition and subfractional spectrum of serum lipoproteins and metabolic markers in healthy individuals from Moscow. Methods: The study included 304 participants (104 were men), who underwent thorough preclinical assessment to exclude any chronic disease as well as cardiovascular pathology. Lipoprotein subfractional distribution was analyzed by Lipoprint LDL System (Quantimetrix, Redodno Beach, CA, USA). Gut microbiota composition was assessed by 16S rRNA sequencing of V3-V4 regions. Results: High gut microbiota diversity was positively associated with HDL-cholesterol (C) level and negatively associated with abdominal obesity, BMI, and dyslipidemia. According to selbal analysis, excessive representation of Prevotella spp. was positively associated with IDL-C and LDL-2-C. VLDL-C correlated with Ruminococcus_u/Faecalibacterium_prausnitzii balance. An unexpected positive relationship between LDL-C level and Bifidobacteriaceae_u/Christensenellaceae_u to Bifidobacterium_u balance was found, which may reflect the importance of the integrative microbiota assessment. Low microbiota diversity was associated with obesity, abdominal obesity and low HDL-C level. Conclusions: Gut microbiota imbalance may be one of the components involved in metabolic disorders. The balance of microorganisms and the microbiota diversity may play a more significant role in human health than individual bacterial genera.
Dyadic Joint Visual Attention Interaction in Face-to-face Collaborative Problem-solving at K-12 Maths Education: A Multimodal Approach

Collaborative problem-solving (CPS) is an essential skill in the workplace in the 21st century, but the assessment and support of the CPS process with scientifically objective evidence are challenging. This research aims to understand in-class CPS interaction by investigating the change of a dyad’s cognitive engagement during a mathematics lesson. Here, we propose a multimodal evaluation of joint visual attention (JVA) based on eye gazes and eye blinks data as non-verbal indicators of dyadic cognitive engagement. Our results indicate that this multimodal approach can bring more insights into students’ CPS process than unimodal evaluations of JVA in temporal analysis. This study contributes to the field by demonstrating the value of nonverbal multimodal JVA temporal analysis in CPS assessment and the utility of eye physiological data in improving the interpretation of dyadic cognitive engagement. Moreover, a method is proposed for capturing gaze convergence by considering eye fixations and the overlapping time between two eye gazes. We conclude the paper with our preliminary findings from a pilot study investigating the proposed approach in a real-world teaching context.
Pre-Orientale Southwest Peak-Ring Basin: Gravity Structure, Geologic Characteristics, and Influence on Orientale Basin Ring Formation and Ejecta Emplacement

The Orientale impact basin is the youngest and most well-preserved of the lunar multi-ring basins. The generally well-preserved ring structures and basin facies are distinctly anomalous in the southwestern quadrant; the outer Cordillera ring extends significantly outward, the Outer and Inner Rook mountain rings are more poorly developed and show anomalous characteristics, and the Montes Rook Formation varies widely from its characteristics elsewhere in the basin interior. Based on the gravity, image, and topography data, we confirmed that the southwest region of the Orientale basin represents the location of a pre-existing ~320 km rim–crest diameter peak–ring basin centered at 108.8°W, 28.4°S, and characterized by an ~170 km peak–ring diameter. We model the structure and morphology of this large pre-Orientale peak–ring basin (about one-third the diameter of Orientale) and show that its presence and negative relief had a distinctive influence on the development of the basin rings (disrupting the otherwise generally circular continuity and causing radial excursions in their locations) and the emplacement of ejecta (causing filling of the low region represented by the peak–ring basin, creating anomalous surface textures, and resulting in late stage ejecta movement in response to the pre-existing peak–ring basin topography. The location and preservation of the peak–ring basin Bouguer anomaly strongly suggest that the rim crest of the Orientale basin excavation cavity lies at or within the Outer Rook Mountain ring.
Rapid divergence in vegetative morphology of a wind‐pollinated plant between populations at contrasting densities

Plant sexual dimorphism is thought to evolve in response to sex-specific selection associated with competition for access to mates or resources, both of which will often be density-dependent. In wind-pollinated plants in particular, vegetative traits can have an important influence on both resource acquisition and the pollen dispersal and receipt, with potential conflict between these two components of fitness. We evaluated the role of plant density in shaping plant traits by measuring evolutionary responses in experimental populations of the sexually dimorphic wind-pollinated plant Mercurialis annua. After three generations of evolution, we observed divergence between high- and low-density populations in several vegetative traits, whereas there was no divergence for reproductive traits. A reversal in the direction of sexually dimorphic traits expressed in young plants evolved in both low- and high-density populations compared to the original population (stored as seeds). Compared to the source population, males at high density evolved to be taller when young, whereas at low density young females tended to become smaller. These results demonstrate that a simple change in plant density can induce rapid, age-dependent and sex-specific evolution in the ontogeny of vegetative organs, and illustrates the power of experimental evolution for investigating plant trait evolution.