Miracle-dz/UniFAR-FTUs huggingface dataset:
Distribution of Aminoglycoside Resistance Genes in Coagulase-Negative Staphylococci Isolated From Hospitalized Patients

Background: Theincidenceof coagulase-negativestaphylococci(CNS)isolates,ascausesof hospital-acquiredinfections,isincreas-ing annually and it is a truly global challenge. Objectives: The current study aimed at assessing aminoglycoside resistance genes among CNS isolated from hospitalized patients and investigating the prevalence of methicillin and aminoglycoside resistance genes using molecular methods. Methods: A total of 103 species of coagulase negative staphylococci were isolated from clinical specimens from August 2013 to November 2014. All the specimens were identiﬁed using conventional microbiological methods including colony morphology, Gram staining, catalase, and slide and tube coagulase. The isolates were subjected to the API-20 Staph identiﬁcation kit. Antibiotic susceptibility tests were performed using Kirby-Bauer disc diﬀusion method. The methicillin and aminoglycoside resistant genes among coagulase negative staphylococci were detected by polymerase chain reaction (PCR) and sequencing methods. Results: Themostfrequentclinicallyisolatedcoagulasenegativestaphylococcispecieswere Staphylococcushominis and S.haemolyti-cus (51.9%), S. epidermidis (21.7%), S. lugdunensis (9.8%), S. intermedius (4.9%), S. saprophyticus and S. simulans (3.9%), S. warneri (2.9%), as well as S. chromogenes , S. sciuri , S. caprae , S. schleiferi , and S. xylosus (0.98%). The overall rate of methicillin resistance among CNS species in the present study was 89 (86.4%). The resistance of CNS isolates against tested antibiotics was as follows: 74 (71.8%) to cefoxitin, 54 (52.4%) to kanamycin, 51 (49.5%) to gentamicin, 45 (43.7%) to tobramycin, and 30 (29.1%) to amikacin. The prevalence of aminoglycoside resistance genes such as ant (4’)-Ia, aac (6’)/ aph (2") and aph (3’)-IIIa were 89 (86.4%), 87(84.5%), and 68(66%), respec-tively. Conclusions: The presence of antibiotic resistance among coagulase negative Staphylococcus species, which cause nosocomial infections, has increased. Therefore, identiﬁcation of coagulase negative staphylococci within 24 hours after development of enzy-matictestswouldbeveryusefulinanyclinicalmicrobiologylaboratoryforeﬀectivetreatment. Resistancetoantibiotics,including aminoglycosides, develops quickly in CNS species where these antimicrobial agents are widely used. Thus, a better understanding of antibiotic resistance of CNS species is essential to eliminate antibiotic resistance in hospitals and society.
Behavioral and Genomic Sensory Adaptations Underlying the Pest Activity of Drosophila suzukii

Studying how novel phenotypes originate and evolve is fundamental to the field of evolutionary biology as it allows us to understand how organismal diversity is generated and maintained. However, determining the basis of novel phenotypes is challenging as it involves orchestrated changes at multiple biological levels. Here, we aim to overcome this challenge by using a comparative species framework combining behavioral, gene expression and genomic analyses to understand the evolutionary novel egg-laying substrate-choice behavior of the invasive pest species Drosophila suzukii. First, we used egg-laying behavioral assays to understand the evolution of ripe fruit oviposition preference in D. suzukii as compared to their closely related species: D. subpulchrella and D. biarmipes, as well as D. melanogaster. We show that D. subpulchrella and D. biarmipes lay eggs on both ripe and rotten fruits, suggesting that the transition to ripe fruit preference was gradual. Secondly, using two-choice oviposition assays, we studied how D. suzukii, D. subpulchrella, D. biarmipes and D. melanogaster differentially process key sensory cues distinguishing ripe from rotten fruit during egg-laying. We found that D. suzukii’s preference for ripe fruit is in part mediated through an aversion to acetic acid and preference for stiff substrates. Lastly, we identified candidate genes involved in D. suzukii’s ability to seek out and target ripe fruits. Our results provide detail to the stepwise evolution of pest activity in D. suzukii, indicating important cues used by this species when finding a host, and the molecular mechanisms potentially underlying their adaptation to a new ecological niche.
Androgen and glucocorticoid receptor direct distinct transcriptional programs by receptor-specific and shared DNA binding sites

The glucocorticoid (GR) and androgen (AR) receptors execute unique functions in vivo, yet have nearly identical DNA binding specificities. To identify mechanisms that facilitate functional diversification among these transcription factor paralogs, we studied AR and GR in an equivalent cellular context. Analysis of chromatin and sequence features suggest that divergent binding, and corresponding gene regulation, are driven by different abilities of AR and GR to interact with relatively inaccessible chromatin. Divergent genomic binding patterns can also be the results of subtle differences in DNA binding preference between AR and GR. Furthermore, the sequence composition of large regions (>10 kb) surrounding selectively occupied binding sites differs significantly, indicating a role for the sequence environment in selectively guiding AR and GR to distinct binding sites. The comparison of binding sites that are shared between AR and GR shows that the specificity paradox can also be resolved by differences in the events that occur downstream of receptor binding. Specifically, we find that shared binding sites display receptor-specific enhancer activity, cofactor recruitment and changes in histone modifications. Genomic deletion of shared binding sites demonstrates their contribution to directing receptor-specific gene regulation. Together, these data suggest that differences in genomic occupancy as well as divergence in the events that occur downstream of receptor binding direct functional diversification among transcription factor paralogs.
Multimodal Gated Information Fusion for Emotion Recognition from EEG Signals and Facial Behaviors

Emotions associated with neural and behavioral responses are detectable through scalp electroencephalogram (EEG) signals and measures of facial expressions. We propose a multimodal deep representation learning approach for emotion recognition from EEG and facial expression signals. The proposed method involves the joint learning of a unimodal representation aligned with the other modality through cosine similarity and a gated fusion for modality fusion. We evaluated our method on two databases: DAI-EF and MAHNOB-HCI. The results show that our deep representation is able to learn mutual and complementary information between EEG signals and face video, captured by action units, head and eye movements from face videos, in a manner that generalizes across databases. It is able to outperform similar fusion methods for the task at hand.
Behavior of hydrogen at Fe/W interface: a first-principle calculation study

ABSTRACT The dissolution behavior of hydrogen (H) at Fe/W interface and the effect of H on the strength of Fe/W interface are investigated by means of first-principle calculation. The dissolution of H atom at Fe/W interface would be easier than in bulk Fe or W due to the low charge density at Fe/W interface caused by the distortion of lattice. Hydrogen atom prefers to stay at Fe side of Fe/W interface due to its lower dissolution energy in Fe lattice at the interface region. However, H atom could be trapped by vacancy at Fe/W interface. In addition, H and vacancy reduce the strength of the interface; however, H atom at the specific site seems to increase the strength of Fe/W interface slightly. Furthermore, the electronic structures of the interface are analyzed to give a physical understanding of the effect of H on Fe/W interface.
Introducing the Treatment Hierarchy Question in Network Meta-Analysis

Abstract Comparative effectiveness research using network meta-analysis can present a hierarchy of competing treatments, from the most to the least preferable option. However, in published reviews, the research question associated with the hierarchy of multiple interventions is typically not clearly defined. Here we introduce the novel notion of a treatment hierarchy question that describes the criterion for choosing a specific treatment over one or more competing alternatives. For example, stakeholders might ask which treatment is most likely to improve mean survival by at least 2 years, or which treatment is associated with the longest mean survival. We discuss the most commonly used ranking metrics (quantities that compare the estimated treatment-specific effects), how the ranking metrics produce a treatment hierarchy, and the type of treatment hierarchy question that each ranking metric can answer. We show that the ranking metrics encompass the uncertainty in the estimation of the treatment effects in different ways, which results in different treatment hierarchies. When using network meta-analyses that aim to rank treatments, investigators should state the treatment hierarchy question they aim to address and employ the appropriate ranking metric to answer it. Following this new proposal will avoid some controversies that have arisen in comparative effectiveness research.
Cloning and Sequence Analysis of Gene Encoding psbZ from Silybum marianum (L.) Gaertn

Photosystem II is known as the core of photosynthesis and among of the photosystem II protein complex, PsbZ protein is very important because of its role in connection PSII and LHCII. For identifying and sequencing of the psbZ gene in Silybum marianum plant, seeds were prepared from botanical garden in Tabriz, Iran. DNA was extracted by CTAB method and by using the PCR amplification the fragment length was estimated to be 705 bp. The amplified fragment was cloned in pGEM-T vector and E.coli transformation was carried out. The plasmid was extracted and the sequence was recorded at NCBI with accession number of GQ225868. BLAST of psbZ, glycine tRNA and psbZ-tRNA GLY intergenic spacer showed that each region is highly conserved among species. BLAST of predicted PsbZ protein also represents a severe conservation among species. Because of the importance of PsbZ protein in photosynthesis, it is necessary to create targeted mutations to determine the active sites of proteins.
GC-MS-MS analysis of Trichilia connaroides (Wight and Arn.) Bentv (Meliaceae): A tree of ethnobotanical records

GC-MS-MS analysis was carried out for the identification of the phytochemicals present in the methanolic leaf extract of Trichilia connaroides. It indicates that the leaf extract is rich in phenolics, fatty acids, flavonoids and antioxidants. Many phytochemcial compounds such as Naphthalene, Palmitic acid, Oleic acid, Isotridecanol, phytol, Lycopersen, Isochiapin B and Serverogenin acetate have been identified. These compounds have been found to possess anti-insect, antimicrobial, antioxidant, anticancer, and antiulcerogenic activities.
Non-Radiation Based Early Pain Relief Treatment Options for Patients With Non-Small Cell Lung Cancer and Cancer Induced Bone Pain: A Systematic Review

Introduction Cancer induced bone pain (CIBP) is frequent in patients with non-small cell lung cancer (NSCLC). Radiation therapy continues to be the gold standard for treatment of painful bone metastases, however only a limited number of metastases can be irradiated. We evaluated non-radiation based early CIBP relief options in NSCLC through a systematic review. Methods Systematic review including all prospective articles published between 01-1994 and 06-2020 on Pubmed, Cochrane Library and ClinicalTrials.gov database. Inclusion: non-radiation based trials evaluating CIBP early pain relief options (initially defined as pain score evaluated within two weeks, because of no randomized trials, later inclusion broadened to pain score evaluated within six weeks) in ≥10 NSCLC patients. Radioisotope trials were excluded as these treatments have interactions with systemic anticancer therapy. Results 188 articles were found; 10 articles (6 randomized controlled (4 double blinded), 1 phase II single-arm, and 3 prospective trials) fulfilled the inclusion criteria. Six of these trials consisted of ≥2 treatment arms, whereas the others were single-arm studies. In total, 554 NSCLC patients were evaluated in these trials. The included trials were very heterogeneous regarding evaluated treatment options, methods of pain measuring, and endpoints. No high-level evidence for specific early pain relief treatment options was found. Discussion Non-radiation based studies evaluating treatment options to rapidly reduce CIBP in NSCLC are scarce. This systematic review shows that there is no high-level evidence to recommend a specific treatment for early pain relief. Future research should focus on early pain relief treatment options for CIBP in NSCLC.
Mitochondrial Fission Factor (MFF) Inhibits Mitochondrial Metabolism and Reduces Breast Cancer Stem Cell (CSC) Activity

Elevated mitochondrial biogenesis and metabolism represent key features of breast cancer stem cells (CSCs), whose propagation is conducive to disease onset and progression. Therefore, interfering with mitochondria biology and function may be regarded as a useful approach to eradicate CSCs. Here, we used the breast cancer cell line MCF7 as a model system to interrogate how mitochondrial fission contributes to the development of mitochondrial dysfunction toward the inhibition of metabolic flux and stemness. We generated an isogenic MCF7 cell line transduced with Mitochondrial Fission Factor (MCF7-MFF), which is primarily involved in mitochondrial fission. We evaluated the biochemical, molecular and functional properties of MCF7-MFF cells, as compared to control MCF7 cells transduced with the empty vector (MCF7-Control). We observed that MFF over-expression reduces both mitochondrial mass and activity, as evaluated using the mitochondrial probes MitroTracker Red and MitoTracker Orange, respectively. The analysis of metabolic flux using the Seahorse XFe96 revealed the inhibition of OXPHOS and glycolysis in MCF7-MFF cells, suggesting that increased mitochondrial fission may impair the biochemical properties of these organelles. Notably, CSCs activity, assessed by 3D-tumorsphere assays, was reduced in MCF7-MFF cells. A similar trend was observed for the activity of ALDH, a well-established marker of stemness. We conclude that enhanced mitochondrial fission may compromise CSCs propagation, through the impairment of mitochondrial function, possibly leading to a quiescent cell phenotype. Unbiased proteomic analysis revealed that proteins involved in mitochondrial dysfunction, oxidative stress-response, fatty acid metabolism and hypoxia signaling are among the most highly up-regulated in MCF7-MFF cells. Of note, integrated analysis of top regulatory networks obtained from unbiased proteomics in MCF7-MFF cells predicts that this cell phenotype activates signaling systems and effectors involved in the inhibition of cell survival and adhesion, together with the activation of specific breast cancer cell death programs. Overall, our study shows that unbalanced and abnormal activation of mitochondrial fission may drive the impairment of mitochondrial metabolic function, leading to inhibition of CSC propagation, and the activation of quiescence programs. Exploiting the potential of mitochondria to control pivotal events in tumor biology may, therefore, represent a useful tool to prevent disease progression.
Force transduction creates long-ranged coupling in ribosomes stalled by arrest peptides

Force-sensitive arrest peptides regulate protein biosynthesis by stalling the ribosome as they are translated. Synthesis can be resumed when the nascent arrest peptide experiences a pulling force of sufficient magnitude to break the stall. Efficient stalling is dependent on the specific identity of a large number of amino acids, including amino acids which are tens of angstroms away from the peptidyl transferase center (PTC). The mechanism of force-induced restart and the role of these essential amino acids far from the PTC is currently unknown. We use hundreds of independent molecular dynamics trajectories spanning over 120 μs in combination with kinetic analysis to characterize the barriers along the force-induced restarting pathway for the arrest peptide SecM. We find that the essential amino acids far from the PTC play a major role in controlling the transduction of applied force. In successive states along the stall-breaking pathway, the applied force propagates up the nascent chain until it reaches the C-terminus of SecM and the PTC, inducing conformational changes that allow for restart of translation. A similar mechanism of force propagation through multiple states is observed in the VemP stall-breaking pathway, but secondary structure in VemP allows for heterogeneity in the order of transitions through intermediate states. Results from both arrest peptides explain how residues that are tens of angstroms away from the catalytic center of the ribosome impact stalling efficiency by mediating the response to an applied force and shielding the amino acids responsible for maintaining the stalled state of the PTC. Significance Statement As nascent proteins are synthesized by the ribosome, their interactions with the environment can create pulling forces on the nascent protein that can be transmitted to the ribosome’s catalytic center. These forces can affect the rate and even the outcome of translation. We use simulations to characterize the pathway of force transduction along arrest peptides and discover how secondary structure in the nascent protein and its interactions with the ribosome exit tunnel impede force propagation. This explains how amino acids in arrest peptides that are tens of angstroms away from the ribosome’s catalytic center contribute to stalling, and, more broadly, suggests how structural features in the nascent protein dictate the ribosome’s ability to functionally respond to its environment.
The Usefulness of Imaging Quantification in Discriminating Non-Calcified Pulmonary Hamartoma From Adenocarcinoma

Background Patients with non-calcified hamartoma were more susceptible to surgery or needle biopsy for the tough discrimination from lung adenocarcinoma. Radiomics have the ability to quantify the lesion features and potentially improve disease diagnosis. Thus, this study aimed to discriminate non-calcified hamartoma from adenocarcinoma by employing imaging quantification and machine learning. Methods Forty-two patients with non-calcified hamartoma and 49 patients with adenocarcinoma were retrospentation; Manual lesion segmentation, feature quantification (e.g., texture features), and artificial neural network were performed consecutively. Independent t-test was used to conduct the inter-group comparisons of those imaging features. Receiver operating characteristic curve was performed to investigate the discriminating efficacy. Results Significantly higher contrast, cluster prominence, cluster shade, dissimilarity, energy, and entropy in non-calcified hamartoma were observed compared with lung adenocarcinoma. Texture-grey-level co-occurrence matrix showed a well discrimination between non-calcified hamartoma and adenocarcinoma as the detection sensitivity, specificity, accuracy, and the area under the curve were 87.22% ± 9.07%, 82.64% ± 8.07%, 85.11% ± 5.40%, and 0.942, respectively. Conclusion Quantifying imaging features is a potentially useful tool for clinical diagnosis. This study demonstrated that non-calcified hamartoma has a heterogeneous distribution of attenuations probably resulting from its complex organizations. Based on this property, imaging quantification could improve discrimination of non-calcified hamartoma from adenocarcinoma.
LCD – Line Clustering and Description for Place Recognition

Current research on visual place recognition mostly focuses on aggregating local visual features of an image into a single vector representation. Therefore, high-level information such as the geometric arrangement of the features is typically lost. In this paper, we introduce a novel learning-based approach to place recognition, using RGB-D cameras and line clusters as visual and geometric features. We state the place recognition problem as a problem of recognizing clusters of lines instead of individual patches, thus maintaining structural information. In our work, line clusters are defined as lines that make up individual objects, hence our place recognition approach can be understood as object recognition. 3D line segments are detected in RGB-D images using state-of-the-art techniques. We present a neural network architecture based on the attention mechanism for frame-wise line clustering. A similar neural network is used for the description of these clusters with a compact embedding of 128 floating point numbers, trained with triplet loss on training data obtained from the InteriorNet dataset. We show experiments on a large number of indoor scenes and compare our method with the bag-of-words image-retrieval approach using SIFT and SuperPoint features and the global descriptor NetVLAD. Trained only on synthetic data, our approach generalizes well to real-world data captured with Kinect sensors, while also providing information about the geometric arrangement of instances.
Clinical outcomes of pre-loaded ultra-thin DSAEK and pre-loaded DMEK

Objective To compare clinical outcomes and complications between pre-loaded ultra-thin Descemet stripping automated endothelialkeratoplasty (pl-UT-DSAEK) and pre-loaded Descemet membrane endothelial keratoplasty (pl-DMEK). Methods and analysis Comparative study in patients with endothelial dysfunction associated with Fuchs endothelial corneal dystrophy and pseudophakic bullous keratopathy who underwent pl-UT-DSAEK or pl-DMEK transplants. For both groups, the tissues were pre-loaded at the Fondazione Banca degli Occhi del Veneto (Venice, Italy) and shipped to The Royal Liverpool University Hospital (Liverpool, UK). Best corrected visual acuity (BCVA) and re-bubbling rates were the main outcome measures. Results 56 eyes of 56 patients were included. 31 received pl-UT-DSAEK and 25 received pl-DMEK. At 12 months, BCVA (LogMAR) was significantly better for pl-DMEK (0.17±0.20 LogMAR) compared with pl-UT-DSAEK (0.37±0.37 LogMAR, p<0.01). The percentage of people that achieved ≥20/30 was significantly higher in the pl-DMEK group. The rate of re-bubbling, however, was significantly higher for pl-DMEK (44.0%) than for Pl-UT-DSAEK (12.9%), p<0.01. Conclusion Pl-DMEK offers better BCVA than pl-UT-DSAEK. The higher re-bubbling rate associated with pre-loaded DMEK is of concern.
Taming Discrete Integration via the Boon of Dimensionality

Discrete integration is a fundamental problem in computer science that concerns the computation of discrete sums over exponentially large sets. Despite intense interest from researchers for over three decades, the design of scalable techniques for computing estimates with rigorous guarantees for discrete integration remains the holy grail. The key contribution of this work addresses this scalability challenge via an efficient reduction of discrete integration to model counting. The proposed reduction is achieved via a significant increase in the dimensionality that, contrary to conventional wisdom, leads to solving an instance of the relatively simpler problem of model counting. Building on the promising approach proposed by Chakraborty et al, our work overcomes the key weakness of their approach: a restriction to dyadic weights. We augment our proposed reduction, called DeWeight, with a state of the art efficient approximate model counter and perform detailed empirical analysis over benchmarks arising from neural network verification domains, an emerging application area of critical importance. DeWeight, to the best of our knowledge, is the first technique to compute estimates with provable guarantees for this class of benchmarks.