Miracle-dz/UniFAR-FTUs huggingface dataset:
Transcriptional Landscape of a blaKPC-2 Plasmid and Response to Imipenem Exposure in Escherichia coli TOP10

The diffusion of KPC-2 carbapenemase is closely related to the spread of Klebsiella pneumoniae of the clonal-group 258 and linked to IncFIIK plasmids. Little is known about the biology of multi-drug resistant plasmids and the reasons of their successful dissemination. Using E. coli TOP10 strain harboring a multi-replicon IncFIIK-IncFIB blaKPC−2-gene carrying plasmid pBIC1a from K. pneumoniae ST-258 clinical isolate BIC-1, we aimed to identify basal gene expression and the effects of imipenem exposure using whole transcriptome approach by RNA sequencing (RNA-Seq). Independently of the antibiotic pressure, most of the plasmid-backbone genes were expressed at low levels. The most expressed pBIC1a genes were involved in antibiotic resistance (blaKPC−2, blaTEM and aph(3′)-I), in plasmid replication and conjugation, or associated to mobile elements. After antibiotic exposure, 34% of E. coli (pBIC1a) genome was differentially expressed. Induction of oxidative stress response was evidenced, with numerous upregulated genes of the SoxRS/OxyR oxydative stress regulons, the Fur regulon (for iron uptake machinery), and IscR regulon (for iron sulfur cluster synthesis). Nine genes carried by pBIC1a were up-regulated, including the murein DD-endopeptidase mepM and the copper resistance operon. Despite the presence of a carbapenemase, we observed a major impact on E. coli (pBIC1a) whole transcriptome after imipenem exposure, but no effect on the level of transcription of antimicrobial resistance genes. We describe adaptive responses of E. coli to imipenem-induced stress, and identified plasmid-encoded genes that could be involved in resistance to stressful environments.
A Method for RNA Structure Prediction Shows Evidence for Structure in lncRNAs

To compare the secondary structures of RNA molecules we developed the CROSSalign method. CROSSalign is based on the combination of the Computational Recognition Of Secondary Structure (CROSS) algorithm to predict the RNA secondary structure at single-nucleotide resolution using sequence information, and the Dynamic Time Warping (DTW) method to align profiles of different lengths. We applied CROSSalign to investigate the structural conservation of long non-coding RNAs such as XIST and HOTAIR as well as ssRNA viruses including HIV. In a pool of sequences with the same secondary structure CROSSalign accurately recognizes repeat A of XIST and domain D2 of HOTAIR and outperforms other methods based on covariance modelling. CROSSalign can be applied to perform pair-wise comparisons and is able to find homologues between thousands of matches identifying the exact regions of similarity between profiles of different lengths. The algorithm is freely available at the webpage http://service.tartaglialab. com//new_submission/CROSSalign.
Myokine Response to High-Intensity Interval vs. Resistance Exercise: An Individual Approach

Purpose: This study aimed to compare the response to acute exercise of several myokines/hormones involved in metabolic function between two types of training sessions that are growing in popularity for their purported cardiometabolic benefits, high-intensity interval (HIIT) and resistance training (RT). Methods: Seventeen healthy, non-athletic men (23 ± 3 years) participated in this cross-over study. They randomly performed a HIIT [with short (HIIT1) or long (HIIT2) intervals] or a RT session. The concentration of fibroblast-growth factor (FGF) 21, follistatin, ghrelin, interleukin-15, irisin, myostatin, and peptide YY was measured at baseline and 0, 1, 3, 24, 48, and 72 h post-exercise. An individual approach was adopted to determine the rate of responsiveness to each specific cytokine and training mode. Results: A significant condition (session type) by time interaction (p = 0.004) effect was observed for FGF21, with RT eliciting a greater area under the curve (AUC) concentration than HIIT1 (p = 0.02). The AUC for follistatin was significantly greater after HIIT2 compared with RT (p = 0.02). Individual responsiveness to all session types ranged between 19 and 93% depending on the cytokine. However, most subjects (71–100%) responded positively for all cytokines (except for irisin, with only 53% of responders) after 1+ session type. Conclusion: Except for FGF21, our results show no overall differences in the myokine response to HIIT or RT. A considerable individual variability was observed, with some subjects responding to some but not other training session types. Notwithstanding, most responded to at least one training session. Thus, it is mostly the individual response of each subject rather than general recommendations on type of training session (i.e., RT vs. HIIT or HIIT subtypes) that must be taken into consideration for maximizing cardiometabolic benefits in the context of personalized exercise prescription.
Lightweight and Efficient Image Super-Resolution with Block State-based Recursive Network

Recently, several deep learning-based image super-resolution methods have been developed by stacking massive numbers of layers. However, this leads too large model sizes and high computational complexities, thus some recursive parameter-sharing methods have been also proposed. Nevertheless, their designs do not properly utilize the potential of the recursive operation. In this paper, we propose a novel, lightweight, and efficient super-resolution method to maximize the usefulness of the recursive architecture, by introducing block state-based recursive network. By taking advantage of utilizing the block state, the recursive part of our model can easily track the status of the current image features. We show the benefits of the proposed method in terms of model size, speed, and efficiency. In addition, we show that our method outperforms the other state-of-the-art methods.
Parsing R-CNN for Instance-Level Human Analysis

Instance-level human analysis is common in real-life scenarios and has multiple manifestations, such as human part segmentation, dense pose estimation, human-object interactions, etc. Models need to distinguish different human instances in the image panel and learn rich features to represent the details of each instance. In this paper, we present an end-to-end pipeline for solving the instance-level human analysis, named Parsing R-CNN. It processes a set of human instances simultaneously through comprehensive considering the characteristics of region-based approach and the appearance of a human, thus allowing representing the details of instances. Parsing R-CNN is very flexible and efficient, which is applicable to many issues in human instance analysis. Our approach outperforms all state-of-the-art methods on CIHP (Crowd Instance-level Human Parsing), MHP v2.0 (Multi-Human Parsing) and DensePose-COCO datasets. Based on the proposed Parsing R-CNN, we reach the 1st place in the COCO 2018 Challenge DensePose Estimation task. Code and models are publicly available.
Domain-Invariant Adversarial Learning for Unsupervised Domain Adaption

Unsupervised domain adaption aims to learn a powerful classifier for the target domain given a labeled source data set and an unlabeled target data set. To alleviate the effect of `domain shift', the major challenge in domain adaptation, studies have attempted to align the distributions of the two domains. Recent research has suggested that generative adversarial network (GAN) has the capability of implicitly capturing data distribution. In this paper, we thus propose a simple but effective model for unsupervised domain adaption leveraging adversarial learning. The same encoder is shared between the source and target domains which is expected to extract domain-invariant representations with the help of an adversarial discriminator. With the labeled source data, we introduce the center loss to increase the discriminative power of feature learned. We further align the conditional distribution of the two domains to enforce the discrimination of the features in the target domain. Unlike previous studies where the source features are extracted with a fixed pre-trained encoder, our method jointly learns feature representations of two domains. Moreover, by sharing the encoder, the model does not need to know the source of images during testing and hence is more widely applicable. We evaluate the proposed method on several unsupervised domain adaption benchmarks and achieve superior or comparable performance to state-of-the-art results.
High Intensity Jump Exercise Preserves Posture Control, Gait, and Functional Mobility During 60 Days of Bed-Rest: An RCT Including 90 Days of Follow-Up

Physical inactivity causes a deconditioning of the human body. Concerns due to chronic bed-rest include deficits in posture and gait control, predisposing individuals to an increased fall and injury risk. This study assessed the efficiency of a high-load jump exercise (JUMP) as a countermeasure to prevent detrimental effects on gait, posture control and functional mobility. In an RCT (23 males), the effect of 60 days bed-rest without training was compared to JUMP. JUMP is characterized by plyometric executed as a high intensity interval training. Typical trainings session consisted of 4 × 10 countermovement jumps and 2 × 10 hops in a sledge jump system. We assessed sway path and muscle activity in monopedal stance, spatiotemporal, kinematic, and variability characteristics in gait, functional mobility with repeated chair-rises and Timed Up and Go (TUG). Results revealed: The JUMP group showed no significant changes after bed-rest, whereas the control group exhibited substantial deteriorations: an increased sway path (+104%, p < 0.05) was accompanied by increased co-contractions of antagonistic muscles encompassing the ankle (+32%, p < 0.05) and knee joint (45%, p < 0.05). A reduced locomotor speed (−22%, p < 0.05) was found concomitant with pathological gait rhythmicity (p < 0.05), reduced joint excursions (ankle −8%, knee −29%, p < 0.05) and an increased gait variability (p < 0.05). Chair-rising was slowed (+28%, p < 0.05) with reduced peak power (+18%, p < 0.05), and more time was needed to accomplish TUG (+39%, p < 0.05). The effects persisted for a period of 1 month after bed-rest. Increases in sway path were correlated to decreases in gait speed. The JUMP effectively preserved the neuromuscular system's ability to safely control postural equilibrium and perform complex locomotor movements, including fast bipedal gait with turns and rises. We therefore recommend JUMP as an appropriate strategy combatting functional deconditioning.
The Adhesion G Protein-Coupled Receptor GPR97/ADGRG3 Is Expressed in Human Granulocytes and Triggers Antimicrobial Effector Functions

The adhesion family of G protein-coupled receptors (aGPCRs) comprises 33 members in human, several of which are distinctly expressed and functionally involved in polymorphonuclear cells (PMNs). As former work indicated the possible presence of the aGPCR GPR97 in granulocytes, we studied its cellular distribution, molecular structure, signal transduction, and biological function in PMNs. RNA sequencing and mass-spectrometry revealed abundant RNA and protein expression of ADGRG3/GPR97 in granulocyte precursors and terminally differentiated neutrophilic, eosinophilic, and basophilic granulocytes. Using a newly generated GPR97-specific monoclonal antibody, we confirmed that endogenous GPR97 is a proteolytically processed, dichotomous, N-glycosylated receptor. GPR97 was detected in tissue-infiltrating PMNs and upregulated during systemic inflammation. Antibody ligation of GPR97 increased neutrophil reactive oxygen species production and proteolytic enzyme activity, which is accompanied by an increase in mitogen-activated protein kinases and IκBα phosphorylation. In-depth analysis of the GPR97 signaling cascade revealed a possible switch from basal Gαs/cAMP-mediated signal transduction to a Gαi-induced reduction in cAMP levels upon mutation-induced activation of the receptor, in combination with an increase in downstream effectors of Gβγ, such as SRE and NF-κB. Finally, ligation of GPR97 increased the bacteria uptake and killing activity of neutrophils. We conclude that the specific presence of GPR97 regulates antimicrobial activity in human granulocytes.
On the Performance of Reed-Muller Codes with respect to Random Errors and Erasures

This work proves new results on the ability of binary Reed-Muller codes to decode from random errors and erasures. We obtain these results by proving improved bounds on the weight distribution of Reed-Muller codes of high degrees. Specifically, given weight $\beta \in (0,1)$ we prove an upper bound on the number of codewords of relative weight at most $\beta$. We obtain new results in two different settings: for weights $\beta < 1/2$ and for weights that are close to $1/2$. Our new bounds on the weight distribution imply that RM codes with $m$ variables and degree $\gamma m$, for some explicit constant $\gamma$, achieve capacity for random erasures (i.e. for the binary erasure channel) and for random errors (for the binary symmetric channel). Earlier, it was known that RM codes achieve capacity for the binary symmetric channel for degrees $r = o(m)$. For the binary erasure channel it was known that RM codes achieve capacity for degree $o(m)$ or $r \in [m/2 \pm O(\sqrt{m})]$. Thus, our result provide a new range of parameters for which RM achieve capacity for these two well studied channels. In addition, our results imply that for every $\epsilon > 0$ (in fact we can get up to $\epsilon = \Omega\left(\sqrt{\frac{\log m}{m}}\right)$) RM codes of degree $r<(1/2-\epsilon)m$ can correct a fraction of $1-o(1)$ random erasures with high probability. We also show that, information theoretically, such codes can handle a fraction of $1/2-o(1)$ random errors with high probability. Thus, for example, given noisy evaluations of a degree $0.499m$ polynomial, it is possible to interpolate it even if a random $0.499$ fraction of the evaluations were corrupted, with high probability. While the $o(1)$ terms are not the correct ones to ensure capacity, these results show that RM codes of such degrees are in some sense close to achieving capacity.
Global-optimization-based model decomposition for support-free multi-DOF 3D printing

In 3D printing, using supporting materials as few as possible is critical for efficiency and material saving. Although both model partition methods and 3D printers with multi-DOF(degree of freedom) have been developed independently to tackle the problem, only a few existing approaches combined partition methods with multi-DOF printers. We present a global-optimization-based model decomposition method for multi-DOF 3D printers to achieve consistent printing with the least supporting materials. We solve the minimization of the surface area that need supporting where the printing sequence is determined inherently by global single-objective optimization. We first describe the printing constraints for using a multi-DOF 3D printer, then propose an optimization framework that meets the constraints. Experiments show the merits of our method.
Pharmacological Applications of Bile Acids and Their Derivatives in the Treatment of Metabolic Syndrome

Apart from well-known functions of bile acids in digestion and solubilization of lipophilic nutrients and drugs in the small intestine, the emerging evidence from the past two decades identified the role of bile acids as signaling, endocrine molecules that regulate the glucose, lipid, and energy metabolism through complex and intertwined pathways that are largely mediated by activation of nuclear receptor farnesoid X receptor (FXR) and cell surface G protein-coupled receptor 1, TGR5 (also known as GPBAR1). Interactions of bile acids with the gut microbiota that result in the altered composition of circulating and intestinal bile acids pool, gut microbiota composition and modified signaling pathways, are further extending the complexity of biological functions of these steroid derivatives. Thus, bile acids signaling pathways have become attractive targets for the treatment of various metabolic diseases and metabolic syndrome opening the new potential avenue in their treatment. In addition, there is a significant effort to unveil some specific properties of bile acids relevant to their intrinsic potency and selectivity for particular receptors and to design novel modulators of these receptors with improved pharmacokinetic and pharmacodynamic profiles. This resulted in synthesis of few semi-synthetic bile acids derivatives such as 6α-ethyl-chenodeoxycholic acid (obeticholic acid, OCA), norursodeoxycholic acid (norUDCA), and 12-monoketocholic acid (12-MKC) that are proven to have positive effect in metabolic and hepato-biliary disorders. This review presents an overview of the current knowledge related to bile acids implications in glucose, lipid and energy metabolism, as well as a potential application of bile acids in metabolic syndrome treatment with future perspectives.
LEARN Codes: Inventing Low-Latency Codes via Recurrent Neural Networks

Designing channel codes under low latency constraints is one of the most demanding requirements in 5G standards. However, sharp characterizations of the performances of traditional codes are only available in the large block lengths limit. Code designs are guided by those asymptotic analyses and require large block lengths and long latency to achieve the desired error rate. Furthermore, when the codes designed for one channel (e.g. Additive White Gaussian Noise (AWGN) channel) are used for another (e.g. non-AWGN channels), heuristics are necessary to achieve any non trivial performance — thereby severely lacking in robustness as well as adaptivity. Obtained by jointly designing recurrent neural network (RNN) based encoder and decoder, we propose an end-to-end learned neural code which outperforms canonical convolutional code under block settings. With this gained experience of designing a novel neural block code, we propose a new class of codes under low latency constraint — Low-latency Efficient Adaptive Robust Neural (LEARN) codes, which outperform the state-of-the-art low latency codes as well as exhibit robustness and adaptivity properties. LEARN codes show the potential of designing new versatile and universal codes for future communications via tools of modern deep learning coupled with communication engineering insights.
Combined Volumetric and Density Analyses of Contrast-Enhanced CT Imaging to Assess Drug Therapy Response in Gastroenteropancreatic Neuroendocrine Diffuse Liver Metastasis

Objective We propose a computer-aided method to assess response to drug treatment, using CT imaging-based volumetric and density measures in patients with gastroenteropancreatic neuroendocrine tumors (GEP-NETs) and diffuse liver metastases. Methods Twenty-five patients with GEP-NETs with diffuse liver metastases were enrolled. Pre- and posttreatment CT examinations were retrospectively analyzed. Total tumor volume (volume) and mean volumetric tumor density (density) were calculated based on tumor segmentation on CT images. The maximum axial diameter (tumor size) for each target tumor was measured on pre- and posttreatment CT images according to Response Evaluation Criteria In Solid Tumors (RECIST). Progression-free survival (PFS) for each patient was measured and recorded. Results Correlation analysis showed inverse correlation between change of volume and density (Δ(V + D)), change of volume (ΔV), and change of tumor size (ΔS) with PFS (r = −0.653, P=0.001; r = −0.617, P=0.003; r = −0.548, P=0.01, respectively). There was no linear correlation between ΔD and PFS (r = −0.226, P=0.325). Conclusion The changes of volume and density derived from CT images of all lesions showed a good correlation with PFS and may help assess treatment response.
Genetic Interaction Between Two VNTRs in the SLC6A4 Gene Regulates Nicotine Dependence in Vietnamese Men

Nicotine dependence is an addiction to tobacco products and a global public health concern. Association between the SLC6A4 polymorphisms and nicotine dependence is controversial. Two variable number tandem repeat (VNTR) domains, termed HTTLPR and STin2, in the SLC6A4 gene are well characterized transcriptional regulatory elements. Their polymorphism in the copy number of the repeat correlates with the particular personality and psychiatric traits. We analyzed nicotine dependence in 1,804 participants from Central Vietnam. The Fagerström Test (FTND) was used to evaluate the nicotine dependence and PCR was used to determine the SLC6A4 HTTLPR and STin2 VNTRs. The HTTLPR VNTR was associated with difficulties to refrain from smoking in a prohibiting environment. The STIn2 10/10 genotype was associated with (1) years of smoking, (2) difficulties to quit the first cigarette, and (3) higher number of cigarettes smoked per day (CPD). Stratification analysis was used to find the genetic interaction between these two VNTRs in nicotine dependence as they may synergistically regulate the SLC6A4 expression. Smokers with the S/S HTTLPR genotypes showed a much stronger association between STin2 10/10 variant and CPD. This finding is consistent with the molecular evidence for the functional interaction between HTTLPR and STin2 in cell line models, where STin2 has described as a stronger expressional regulator. Similarly, we found that STin2 is a much stronger modifier of smoking with 10/10 genotype related to higher nicotine dependence. The present study supports genetic interaction between HTTLPR and STin2 VNTRs in the regulation of nicotine dependence with the dominance of the STin2 effects. This finding could be explained by their differential effect on the SLC6A4 expression.
Dual Language Competencies of Turkish–German Children Growing Up in Germany: Factors Supportive of Functioning Dual Language Development

This paper is about the first (L1) and second language (L2) skills of Turkish–German dual language learners (DLLs), the interrelatedness of the L1 and L2 skills, and their relation to other selected child and family variables. The first aim of the study was to examine L1 and L2 performance and the relation between the languages. Second, the study sought to explore the conditions in which functioning dual language development can be achieved, while trying to predict the extent to which child and environmental factors are related to the DLLs’ language competencies. L1 and L2 language skills of N = 69 bilingually developing 3–5 years old Turkish–German children were assessed via standardized tests. In addition, information on the children’s sociodemographic variables and home language environments was obtained by means of parental questionnaires. Correlational analyses were used to examine the interrelations between L1 and L2 skills and multiple regression analyses were conducted in order to predict the children’s language competencies. The children showed age-appropriate language skills in L1 (Turkish) and lower language skills in L2 (German). Whereas their phonological memory abilities were positively correlated with L1 and L2 skills, their expressive vocabulary in L1 was negatively correlated with L2 skills. Our findings also indicated that phonological memory was a strong predictor of language abilities. Concerning family variables, both early daycare entry and stimulating home language environment were significant predictors of better L2 skills. Lastly, balanced use of both languages at home had no negative consequences on language competencies. Although more research is needed, this study shows the benefits of using a combined language measure including both L1 and L2 skills to predict DLLs’ language competencies without disregarding either of their languages.