Miracle-dz/UniFAR-FTUs huggingface dataset:
Molecular Basis of Bicyclic Boronate β-Lactamase Inhibitors of Ultrabroad Efficacy – Insights From Molecular Dynamics Simulation Studies

β-Lactam antibiotics represent about 70% of all antibacterial agents in clinical use. They are safe and highly effective drugs that have been used for more than 50 years, and, in general, well tolerated by most patients. However, its usefulness has been dramatically reduced with the spread and dissemination worldwide of multi-drug resistant bacteria. These pathogens elude the therapeutic action of these antibiotics by expressing β-lactamase enzymes that catalyze the hydrolysis of their β-lactam ring to give inactive products, which is one of the most relevant resistance mechanisms in deadly pathogens such as Pseudomonas aeruginosa, Acinetobacter baumannii, and Enterobacteriaceae. From the drug development point of view, the design of an efficient β-lactamase inhibitor able to block this antibiotic resistance mechanism and restore β-lactam antibiotics efficacy is challenging. This is due to: (1) the huge structural diversity of these enzymes in both the amino acid sequence and architecture of the active site; (2) the distinct hydrolytic capability against different types of substrates; (3) the variety of enzyme mechanisms of action employed, either involving covalent catalyzed processes (serine hydrolases) or non-covalent catalysis (zinc-dependent hydrolases); and (4) the increasing emergence and spread of bacterial pathogens capable of simultaneously producing diverse β-lactamases. Hence, a long-pursued goal has been the development of ultrabroad-spectrum inhibitors able to inhibit both serine- and metallo-β-lactamases. The recent development of taniborbactam (formerly VNRX-5133) and QPX7728, which are bicyclic boronate inhibitors currently under clinical development, represents a huge step forward in this goal. In this article, the molecular basis of the ultrabroad-spectrum of activity of these boron-based inhibitors is analyzed by molecular dynamics simulation studies using the available crystal structures in complex with both inhibitors, or the models constructed from wild-type forms. The efficacy of taniborbactam and QPX7728 is compared with the cyclic boronate inhibitor vaborbactam, which is the first boron-based β-lactamase inhibitor approved by the FDA in combination with meropenem for the treatment of complicated urinary tract infections.
A Bulk Retrospective Study of Robot-Assisted Stereotactic Biopsies of Intracranial Lesions Guided by Videometric Tracker

Background: Biopsies play an important role in the diagnosis of intracranial lesions, and robot-assisted procedures are increasingly common in neurosurgery centers. This research investigates the diagnoses, complications, and technology yield of 700 robotic frameless intracranial stereotactic biopsies conducted with the Remebot system. Method: This research considered 700 robotic biopsies performed between 2016 and 2020 by surgeons from the Department of Functional Neurosurgery in Beijing's Tiantan Hospital. The data collected included histological diagnoses, postoperative complications, operation times, and the accuracy of robotic manipulation. Results: Among the 700 surgeries, the positive rate of the biopsies was 98.2%. The most common histological diagnoses were gliomas, which accounted for 62.7% of cases (439/700), followed by lymphoma and germinoma, which accounted for 18.7% (131/700) and 7.6% (53/700). Bleeding was found in 14 patients (2%) by post-operation computed tomography scans. A total of 29 (4.14%) patients had clinical impairments after the operation, and 9 (1.29%) experienced epilepsy during the operation. The post-biopsy mortality rate was 0.43%. Operation time—from marking the cranial point to suturing the skin—was 16.78 ± 3.31 min (range 12–26 min). The target error was 1.13 ± 0.30 mm, and the entry point error was 0.99 ± 0.24 mm. Conclusion: A robot-assisted frameless intracranial stereotactic biopsy guided by a videometric tracker is an efficient, safe, and accurate method for biopsies.
Under the Hood: Skeletal Muscle Determinants of Endurance Performance

In the past decades, researchers have extensively studied (elite) athletes' physiological responses to understand how to maximize their endurance performance. In endurance sports, whole-body measurements such as the maximal oxygen consumption, lactate threshold, and efficiency/economy play a key role in performance. Although these determinants are known to interact, it has also been demonstrated that athletes rarely excel in all three. The leading question is how athletes reach exceptional values in one or all of these determinants to optimize their endurance performance, and how such performance can be explained by (combinations of) underlying physiological determinants. In this review, we advance on Joyner and Coyle's conceptual framework of endurance performance, by integrating a meta-analysis of the interrelationships, and corresponding effect sizes between endurance performance and its key physiological determinants at the macroscopic (whole-body) and the microscopic level (muscle tissue, i.e., muscle fiber oxidative capacity, oxygen supply, muscle fiber size, and fiber type). Moreover, we discuss how these physiological determinants can be improved by training and what potential physiological challenges endurance athletes may face when trying to maximize their performance. This review highlights that integrative assessment of skeletal muscle determinants points toward efficient type-I fibers with a high mitochondrial oxidative capacity and strongly encourages well-adjusted capillarization and myoglobin concentrations to accommodate the required oxygen flux during endurance performance, especially in large muscle fibers. Optimisation of endurance performance requires careful design of training interventions that fine tune modulation of exercise intensity, frequency and duration, and particularly periodisation with respect to the skeletal muscle determinants.
Gene Set Enrichment Analysis Reveals Individual Variability in Host Responses in Tuberculosis Patients

Group-aggregated responses to tuberculosis (TB) have been well characterized on a molecular level. However, human beings differ and individual responses to infection vary. We have combined a novel approach to individual gene set analysis (GSA) with the clustering of transcriptomic profiles of TB patients from seven datasets in order to identify individual molecular endotypes of transcriptomic responses to TB. We found that TB patients differ with respect to the intensity of their hallmark interferon (IFN) responses, but they also show variability in their complement system, metabolic responses and multiple other pathways. This variability cannot be sufficiently explained with covariates such as gender or age, and the molecular endotypes are found across studies and populations. Using datasets from a Cynomolgus macaque model of TB, we revealed that transcriptional signatures of different molecular TB endotypes did not depend on TB progression post-infection. Moreover, we provide evidence that patients with molecular endotypes characterized by high levels of IFN responses (IFN-rich), suffered from more severe lung pathology than those with lower levels of IFN responses (IFN-low). Harnessing machine learning (ML) models, we derived gene signatures classifying IFN-rich and IFN-low TB endotypes and revealed that the IFN-low signature allowed slightly more reliable overall classification of TB patients from non-TB patients than the IFN-rich one. Using the paradigm of molecular endotypes and the ML-based predictions allows more precisely tailored treatment regimens, predicting treatment-outcome with higher accuracy and therefore bridging the gap between conventional treatment and precision medicine.
Grass Growth and N2O Emissions From Soil After Application of Jellyfish in Coastal Areas

The supply of nutrients for agricultural production faces enormous challenges as food security and sustainability goals have to be ensured. Processing of marine biomass has high potential to provide nutrients for agricultural purposes in coastal areas. One underexplored resource are jellyfish, which occur as blooms and by-catch of the fishing industry. In this context, a pot experiment investigated the effects of jellyfish as a fertilizer on biomass accumulation of annual ryegrass (Lolium multiflorum), and its effect on the important greenhouse gas N2O as a sustainability indicator of novel fertilizers. Dried and ground jellyfish was applied [3 species: Aurelia aurita (AA), Cyanea capillata (CC), Periphylla periphylla (PP)] and compared with an unfertilized and a mineral fertilized (calcium-ammonium-nitrate, CAN) treatment. Dried jellyfish and CAN were applied at equal N rates of 5 g N per m2. The N2O-fluxes from soil were measured over 56 days after fertilizer application. Grass dry matter yields, when using CC and PP treatments, were not significantly different to the CAN treatment (p > 0.05). After reducing its salinity, AA also showed no differences to CAN on plant growth and the lowest coefficient of variation for dry matter yield as an indicator for yield stability. Accumulated N2O-emissions were lowest in the control and were 3-times higher in AA and CC compared to CAN (p < 0.05). If salinity levels are moderate, jellyfish application to soil can compete with artificial mineral fertilizers in terms of N-supply for above- and belowground yield response, regardless of jellyfish species used. However, elevated N2O-emissions are likely to affect its suitability for large-scale application. Nevertheless, if energy-efficient methods of drying and desalination of jellyfish can be developed, in coastal areas dried jellyfish is a valuable fertilizer in coastal areas, particularly in situations where nutrient supplies for agriculture are limited.
Pharmacological Manipulation of Wnt/β-Catenin Signaling Pathway in Human Neural Precursor Cells Alters Their Differentiation Potential and Neuronal Yield

The canonical Wnt/β-catenin pathway is a master-regulator of cell fate during embryonic and adult neurogenesis and is therefore a major pharmacological target in basic and clinical research. Chemical manipulation of Wnt signaling during in vitro neuronal differentiation of stem cells can alter both the quantity and the quality of the derived neurons. Accordingly, the use of Wnt activators and blockers has become an integral part of differentiation protocols applied to stem cells in recent years. Here, we investigated the effects of the glycogen synthase kinase-3β inhibitor CHIR99021, which upregulates β-catenin agonizing Wnt; and the tankyrase-1/2 inhibitor XAV939, which downregulates β-catenin antagonizing Wnt. Both drugs and their potential neurogenic and anti-neurogenic effects were studied using stable lines human neural precursor cells (hNPCs), derived from embryonic stem cells, which can be induced to generate mature neurons by chemically-defined conditions. We found that Wnt-agonism by CHIR99021 promotes induction of neural differentiation, while also reducing cell proliferation and survival. This effect was not synergistic with those of pro-neural growth factors during long-term neuronal differentiation. Conversely, antagonism of Wnt by XAV939 consistently prevented neuronal progression of hNPCs. We show here how these two drugs can be used to manipulate cell fate and how self-renewing hNPCs can be used as reliable human in vitro drug-screening platforms.
Long Noncoding RNAs in Neurodegenerative Diseases: Pathogenesis and Potential Implications as Clinical Biomarkers

Neurodegenerative diseases (NDDs), including Alzheimer’s disease (AD), Parkinson’s disease (PD), Huntington’s disease (HD), and amyotrophic lateral sclerosis (ALS), are progressive and ultimately fatal. NDD onset is influenced by several factors including heredity and environmental cues. Long noncoding RNAs (lncRNAs) are a class of noncoding RNA molecules with: (i) lengths greater than 200 nucleotides, (ii) diverse biological functions, and (iii) highly conserved structures. They directly interact with molecules such as proteins and microRNAs and subsequently regulate the expression of their targets at the genetic, transcriptional, and post-transcriptional levels. Emerging studies indicate the important roles of lncRNAs in the progression of neurological diseases including NDDs. Additionally, improvements in detection technologies have enabled quantitative lncRNA detection and application to circulating fluids in clinical settings. Here, we review current research on lncRNAs in animal models and patients with NDDs. We also discuss the potential applicability of circulating lncRNAs as biomarkers in NDD diagnostics and prognostics. In the future, a better understanding of the roles of lncRNAs in NDDs will be essential to exploit these new therapeutic targets and improve noninvasive diagnostic methods for diseases.
Electrophysiological Correlates of Character Transposition in the Left and Right Visual Fields

This study examined the brain activity elicited by the hemispheric asymmetries and morpheme transposition of two-character Chinese words (canonical and transposed word) and pseudowords using event-related potentials (ERPs) with a dual-target rapid serial visual presentation (RSVP) task. Electrophysiological results showed facilitation effects for canonical words with centrally presented visual field (CVF) and right visual field (RVF) presentations but not with left visual field (LVF) presentations, as reflected by less negative N400 amplitudes. Moreover, more positive late positive component (LPC) amplitudes were observed for both canonical words and transposed words irrespective of the visual fields. More importantly, transposed words elicited a more negative N400 amplitude and a less positive LPC amplitude compared with the amplitudes elicited by canonical words for CVF and RVF presentations. For LVF presentations, transposed words elicited a less negative N250 amplitude compared with canonical words, and there was no significant difference between canonical words and transposed words in the N400 effect. Taken together, we concluded that character transposition facilitated the mapping of whole-word orthographic representation to semantic information in the LVF, as reflected by the N250 component, and such morpheme transposition influenced whole-word semantic processing in CVF and RVF presentations, as reflected by N400 and LPC components.
Associations of Thyroid Hormones Profile During Normal Pregnancy and Postpartum With Anxiety, Depression, and Obsessive/Compulsive Disorder Scores in Euthyroid Women

Objective Thyroid dysfunction (overt and subclinical) has been consistently linked to pregnancy adversity and abnormal fetal growth and development. Mood disorders such as anxiety, depression, and obsessive-compulsive disorder (OCD) are frequently diagnosed during pregnancy and at postpartum, and emerging evidence suggests association with impaired offspring neurodevelopment and growth. This study aimed to examine potential associations between thyroid function and mood symptoms during pregnancy and postpartum. Design This is a prospective study measuring thyroid hormones and assessing mood symptoms by employing specific questionnaires in the same cohort of 93 healthy pregnant women at the 24th (2nd trimester) and 36th (3rd trimester) gestational weeks and at the 1st postpartum week. Methods Serum thyroid hormones, TSH, anti-TPO, and anti-Tg antibodies were measured at the 24th (2nd trimester) and 36th (3rd trimester) gestational weeks and at the 1st postpartum week. Specific validated questionnaires were employed at the same time-points to assess separately symptoms of anxiety [Generalized Anxiety Disorder Inventory (GADI), Penn State Worry Questionnaire (PSWQ), STAI-State Anxiety inventory (STAI-S), STAI-Trait Anxiety Inventory (STAI-T)], depression [Edinburgh Postnatal Depression Scale (EPDS), Stein’s Blues Scale (BLUES), Beck Depression Inventory (BDI)], and obsessive compulsive disorder (OCD) [Yale-Brown Obsessive Compulsive scale (Y-BOCS)]. Results At the 2nd trimester, GADI score correlated negatively with FT3 (p < 0.010, r = −0.545) and positively with TSH (p < 0.050, r = 0.837) concentrations; GADI, PSWQ, EPDS and Y-BOCS scores correlated negatively with FT4 concentrations (p < 0.010, r = −0.768; p < 0.010, r = −0.384; p < 0.050, r = −0.364; p < 0.010, r = −0.544, respectively). At the 3rd trimester, BLUES score correlated positively with rT3 concentrations (p = 0.00, r = 0.89); GADI, EPDS, and Y-BOCS scores correlated negatively with FT4 concentrations (p = 0.001, r = − 0.468; p = 0.036, r = −0.39; p = 0.001, r = −0.625, respectively); GADI, STAI-S, and Y-BOCS scores correlated positively with TSH concentrations (p = 0.015, r = 0.435; p = 0.024, r = 0.409 p = 0.041, r = 0.389, respectively). At postpartum, PSWQ, STAI-T, EPDS, and BDI scores correlated positively with rT3 concentrations (p = 0.024, r = 0.478; p = 0.014, r = 0.527; p = 0.046, r = 0.44; p = 0.021, r = 0.556, respectively, Y-BOCS score correlated positively with TSH (p = 0.045, r = 0.43), and BLUES score correlated positively with anti-TPO antibody concentrations (p = 0.070, r = 0.586). Conclusion The reported findings demonstrate positive associations between low-normal thyroid function at the 2nd and 3rd trimesters of pregnancy and postpartum with anxiety, depression, and OCD scores.
Diversity, Distribution, and Density of Marine Mammals Along the Saudi Waters of the Arabian Gulf: Update From a Multi-Method Approach

Despite the important role of marine mammals in marine ecosystems and the imperative for their conservation, there is still a great lack of information on the diversity, distribution, and density of these animals in the Saudi waters of the Arabian Gulf. To fill this gap, an integrative data-collection approach including fishermen’s questionnaires, opportunistic sighting reports, and directed boat-based surveys, was undertaken between 2016 and 2020, leading to the first scientific report of marine mammal diversity, distribution, and density in the region. The results of the different approaches carried out during the study confirmed a high diversity of cetaceans on the west coast of the study area, with bottlenose dolphins (Tursiops aduncus), humpback dolphins (Sousa plumbea) and dugongs (Dugong dugon) as the most common species. While the two dolphin species were found to be widely distributed in both coastal and offshore waters, D. dugon appears to occur exclusively in coastal waters in the southern part of the study area, mainly in the Gulf of Salwah. The presence of both species of dolphins increased during the summer months and in the vicinity of the numerous oil and gas facilities in this region. The distribution of the observed dolphins was found mostly within a 10–20 km radius around each facility. Other cetacean species observed less frequently in the area include Bryde’s whale (Balaenoptera edeni), killer whale (Orcinus orca), common dolphin (Delphinus delphis) and finless porpoise (Neophocaena phocaenoides). Regarding the density of marine mammals in the region, boat-based surveys confirmed the results of fishermen’s questionnaires and reports of opportunistic sightings, with bottlenose and humpback dolphins being the most abundant species. These results provide a baseline for policies oriented to the conservation of mammals in the Saudi waters of the Arabian Gulf.
Eagle

Object sensitivity is widely used as a context abstraction for computing the points-to information context-sensitively for object-oriented programming languages such as Java. Due to the combinatorial explosion of contexts in large object-oriented programs, k-object-sensitive pointer analysis (under k-limiting), denoted k-obj, is often inefficient even when it is scalable for small values of k, where k ⩽ 2 holds typically. A recent popular approach for accelerating k-obj trades precision for efficiency by instructing k-obj to analyze only some methods in a program context-sensitively, determined heuristically by a pre-analysis. In this article, we investigate how to develop a fundamentally different approach, Eagle, for designing a pre-analysis that can make k-obj run significantly faster while maintaining its precision. The novelty of Eagle is to enable k-obj to analyze a method with partial context sensitivity (i.e., context-sensitively for only some of its selected variables/allocation sites) by solving a context-free-language (CFL) reachability problem based on a new CFL-reachability formulation of k-obj. By regularizing one CFL for specifying field accesses and using another CFL for specifying method calls, we have formulated Eagle as a fully context-sensitive taint analysis (without k-limiting) that is both effective (by selecting the variables/allocation sites to be analyzed by k-obj context-insensitively so as to reduce the number of context-sensitive facts inferred by k-obj in the program) and efficient (by running linearly in terms of the number of pointer assignment edges in the program). As Eagle represents the first precision-preserving pre-analysis, our evaluation focuses on demonstrating its significant performance benefits in accelerating k-obj for a set of popular Java benchmarks and applications, with call graph construction, may-fail-casting, and polymorphic call detection as three important client analyses.
Determination of ultra-trace levels of cobalt in water and wheat flour samples using cloud point extraction coupled with laser thermal lens spectrometry

A highly sensitive method for the determination of ultra-trace levels of cobalt using cloud point extraction (CPE) coupled with thermal lens spectrometry was proposed. It is based on the complexation of cobalt with a suitable chelating agent, 2-(5-iodio-2-pyridylazo)-5-dimethylaminoaniline (5-I-PADMA), and subsequent micelle-mediated extraction of the product using non-ionic surfactant octylphenoxypolyethoxyethanol (Triton X-114) as an extracting agent. The effects of experimental conditions such as pH, concentration of 5-I-PADMA and Triton X-114, equilibration temperature and time on CPE were studied. After phase separation, the surfactant rich phase containing the complex was dissolved with HCl–acetone solution prior to its analysis by thermal lens spectrometry. A single mode He–Ne laser with λ = 632.8 nm was used to produce both excitation and probe beams. Under optimal conditions, the calibration plot was linear in the range of 0.2–6.0 ng mL−1 cobalt, with a correlation coefficient of 0.9975. The detection limit obtained under optimum conditions was 0.02 ng mL−1. The sensitivity was enhanced by 331 times compared to that of the conventional spectrophotometric method. The proposed method was successfully applied to the extraction and determination of cobalt in water and wheat flour samples.