Search For Candidates Research Articles

A new look at the extragalactic very high energy sky: Searching for TeV-emitting candidates among the X-ray-bright, non-Fermi-detected blazar population

A new look at the extragalactic very high energy sky: Searching for TeV-emitting candidates among the X-ray-bright, non-Fermi-detected blazar population

An optically dark merging system at z ∼ 6 detected by JWST

Context. Near- to mid-infrared observations (from Spitzer and JWST) have revealed a hidden population of galaxies at redshift z = 3 − 6 called optically dark objects, which are believed to be massive and dusty star-formers. They contribute substantially to the cosmic star-formation rate (SFR) density at z ∼ 4 − 5 (up to 30 − 40%). Aims. While optically dark sources are widely recognized as a significant component of the stellar mass function, the history of their stellar mass assembly (and the evolution of their interstellar medium) remains unexplored. However, they are thought to be the progenitors of the more massive early-type galaxies found in present-day groups and clusters. It is thus important to examine the possible connection between dark sources and merging events in order to understand the environment in which they live. Methods. Here, we report our search for close companions in a sample of 19 optically-dark objects identified in the SMACS0723 JWST deep field. They were selected in the NIRCam F444W band and undetected below 2 μm. We restricted our analysis to the reddest (i.e., F277W–F444W > 1.3) and brightest (F444W < 26 mag) objects. Results. We identified KLAMA, an optically dark source showing a very close companion (angular distance < 0.5″). The spatially resolved SED fitting procedure indicates that all components lying within 1.5″ of who is it the dark source are indeed at z ∼ 5.7. Tidal features (leading to a whale-shaped morphology) corroborate the hypothesis that KLAMA is the most massive (log(M⋆/M⊙) > 10.3) and dusty (AV ∼ 3 at the core) system of an ongoing merger with a mass ratio of ∼10. Thus, around ten similar merger events would be required to double the stellar mass of KLAMA. Merging systems with properties similar to KLAMA are identified in the SERRA simulations, allowing us to reconstruct their stellar-mass assembly history and predict their molecular gas properties (in particular, the [CII] emission for the simulated system). Conclusions. The discovery of mergers within dark galaxies at the end of the Epoch of Reionization highlights the importance of conducting a statistical search for additional candidates in deep NIRCam fields. Such research will aid in our understanding of the significance of merging processes during the obscured phase of stellar-mass accumulation.

Updating information of mycoses and antifungal medicines to find effective platforms for the production of new thiazole compounds

Introduction. Information about mycoses and antifungal drugs of the modern type is often fragmentary and unsystematic, which requires correction.Aim. To systematize the latest information about fungi and mycoses, means of combating them and the problems encountered along the way, to highlight the latest achievements in the field of synthesis and research of the activities of 1,2,4-triazole as a potential fungicidal compound.Materials and methods. The materials were the authors' research published in advanced scientific journals, conducted around the world in the field of studying fungi, antifungal drugs, and the search for new antifungal agents.Results and discussion. The article provides an overview of current information on the morphobiological features of fungi, updating their taxonomy and nomenclature, shows the role of fungi in nature and the development of fungal infections in humans and animals, structured information on the classification of mycoses and their pathogens. The latest information is also provided on the pharmacological properties of the main currently existing antifungal drugs, the mechanisms of formation of resistance to them in fungi, in a comparative aspect with other groups of antimycotic drugs, the prospects of azoles and their derivatives as new fungicidal drugs and disinfectants are shown. Literature studies have shown that 1,2,4-triazole derivatives have a wide range of antifungal activity, which extends to other types of activity.Conclusion. It was noted that the significant prospects of triazoles are due, among other things, to the fungicidal effect on resistant strains of fungi-pathogens of human and animal infections. The advantage of the new derivatives is the low level of concentrations suppressing fungi, low cytotoxicity, which allows them to be used for intravenous administration in the future. Updating information on the effectiveness of new antifungal compounds will help researchers systematize knowledge about the properties of azoles, which can contribute to the search and development of new potential candidates for antifungal drugs with high efficiency and selectivity, and contribute to the formation of new research directions for the search for effective means of combating mycoses in various fields of human activity.

Constraints on simplified dark matter models involving an s-channel mediator with the ATLAS detector in pp collisions at s=13\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sqrt{s} = 13$$\\end{document} TeV

This paper reports a summary of searches for a fermionic dark matter candidate in the context of theoretical models characterised by a mediator particle exchange in the s-channel. The data sample considered consists of pp collisions delivered by the Large Hadron Collider during its Run 2 at a centre-of-mass energy of s=13TeV\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sqrt{s} = 13\\,\ extrm{TeV}$$\\end{document} and recorded by the ATLAS detector, corresponding to up to 140 fb-1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{-1}$$\\end{document}. The interpretations of the results are based on simplified models where the new mediator particles can be spin-0, with scalar or pseudo-scalar couplings to fermions, or spin-1, with vector or axial-vector couplings to fermions. Exclusion limits are obtained from various searches characterised by final states with resonant production of Standard Model particles, or production of Standard Model particles in association with large missing transverse momentum.

An Image-based Search for Pulsar Candidates in the MeerKAT Bulge Survey

We report on the results of an image-based search for pulsar candidates toward the Galactic bulge. We used mosaic images from the MeerKAT radio telescope that were taken as part of a 173 deg2 survey of the bulge and Galactic center of our Galaxy at L band (856–1712 MHz) in all four Stokes I, Q, U, and V. The image rms noise levels of 12–17 μJy ba−1 represent a significant increase in sensitivity over past image-based pulsar searches. Our primary search criterion was circular polarization, but we used other criteria, including linear polarization, in-band spectral index, compactness, variability, and multiwavelength counterparts to select pulsar candidates. We first demonstrate the efficacy of this technique by searching for polarized emission from known pulsars and comparing our results with measurements from the literature. Our search resulted in a sample of 75 polarized sources. Bright stars or young stellar objects were associated with 28 of these sources, including a small sample of highly polarized dwarf stars with pulsar-like steep spectra. Comparing the properties of this sample with the known pulsars, we identified 30 compelling candidates for pulsation follow-up, including two sources with both strong circular and linear polarization. The remaining 17 sources are either pulsars or stars, but we cannot rule out an extragalactic origin or image artifacts among the brighter, flat-spectrum objects.

Semisynthetic Ecdysteroid Cinnamate Esters and tert-Butyl Oxime Ether Derivatives with Trypanocidal Activity.

The parasite Trypanosoma cruzi is the causative agent of Chagas disease, a neglected tropical disease that affects the lives of millions of indigenous people in Latin America. As medications to treat Chagas disease are limited to the application of benznidazole and nifurtimox, which are not ideal treatments for the chronic stage of the disease, the search for new antichagasic drug candidates is an important need. Ecdysone has previously been shown to interfere with the life cycle of T. cruzi. Here, we report the biological profiling and subsequent semisynthetic structure optimization of 47 ecdysteroids against T. cruzi with the aim of identifying selective trypanocidal ecdysteroids. Two moderately trypanocidal pharmacophores were identified: ecdysteroids containing a 6-tert-butyl oxime ether and a cinnamic ester moiety. These functional groups were combined into the structures of four new semisynthetic ecdysteroids (44-47), among which 44 exerted potent and selective trypanocidal activity (IC50 < 2 μM). Cellular infection assays showed that ecdysteroid 44 potently and efficiently inhibited amastigote replication as determined by trypomastigote release after cellular infection with an IC50 of 2.7 ± 0.1 μM. The compound was similarly potent to benznidazole (IC50 = 3.8 ± 0.7 μM) and more than 5-fold more cytotoxic toward T. cruzi over RAW264.7 host macrophages. Overall, the ecdysteroid cinnamate ester 44 is a novel trypanocidal lead structure that needs to be further characterized in follow-up studies.

Development and validation of a geographic search filter for MEDLINE (PubMed) to identify studies about Germany.

While geographic search filters exist, few of them are validated and there are currently none that focus on Germany. We aimed to develop and validate a highly sensitive geographic search filter for MEDLINE (PubMed) that identifies studies about Germany. First, using the relative recall method, we created a gold standard set of studies about Germany, dividing it into 'development' and 'testing' sets. Next, candidate search terms were identified using (i) term frequency analyses in the 'development set' and a random set of MEDLINE records; and (ii) a list of German geographic locations, compiled by our team. Then, we iteratively created the filter, evaluating it against the 'development' and 'testing' sets. To validate the filter, we conducted a number of case studies (CSs) and a simulation study. For this validation we used systematic reviews (SRs) that had included studies about Germany but did not restrict their search strategy geographically. When applying the filter to the original search strategies of the 17 SRs eligible for CSs, the median precision was 2.64% (interquartile range [IQR]: 1.34%-6.88%) versus 0.16% (IQR: 0.10%-0.49%) without the filter. The median number-needed-to-read (NNR) decreased from 625 (IQR: 211-1042) to 38 (IQR: 15-76). The filter achieved 100% sensitivity in 13 CSs, 85.71% in 2 CSs and 87.50% and 80% in the remaining 2 CSs. In a simulation study, the filter demonstrated an overall sensitivity of 97.19% and NNR of 42. The filter reliably identifies studies about Germany, enhancing screening efficiency and can be applied in evidence syntheses focusing on Germany.

Fast Exclusion Candidate Identification Based on Sparse Estimation for ARAIM Fault Exclusion Process

Advanced receiver autonomous integrity monitoring (ARAIM) is an integrity technique for a global navigation satellite system (GNSS), centered on the multiple hypothesis solution separation (MHSS) test, which assesses the consistency between a subset and the all-in-view solution. Successful fault exclusion (FE) in ARAIM relies on identifying exclusion candidates that ensure no faults among the remaining satellites, a process requiring computationally expensive MHSS tests. The existing methods guide exclusion candidate searches based on the size of the normalized solution separation statistics, i.e., the normalized absolute difference between the subset solution and the all-in-view solution. However, in scenarios involving more than one satellite fault, these statistics can become unreliable due to fault diversity and interactions, perhaps misleading the FE process and causing its failure. To overcome this issue, our study proposes employing sparse estimation to simply identify satellite faults in one go, leveraging the sparsity of faulty satellites compared to the total number of observations in civil aviation GNSSs. Unlike the existing methods, which infer the fault likelihood indirectly through solution separation statistics, our approach represents an improvement that directly indicates potential exclusion candidates. Our experiments demonstrate that this method is fast and accurate. As a fundamentally different approach, it serves as a valuable complement or an alternative to the existing methods, enhancing the success and efficiency of the ARAIM FE process.

Rapid Targeted Screening and Identification of Active Ingredients in Herbal Extracts through Ligand-Detected NMR and Database Matching.

Herbal extracts are rich sources of active compounds that can be used for drug screening due to their diverse and unique chemical structures. However, traditional methods for screening these compounds are notably laborious and time-consuming. In this manuscript, we introduce a new high-throughput approach that combines nuclear magnetic resonance (NMR) spectroscopy with a tailored database and algorithm to rapidly identify bioactive components in herbal extracts. This method distinguishes characteristic signals and structural motifs of active constituents in the raw extracts through a relaxation-weighted technique, particularly utilizing the perfect echo Carr-Purcell-Meiboom-Gill (peCPMG) sequence, complemented by precise 2D spectroscopic strategies. The cornerstone of our approach is a customized database designed to filter potential compounds based on defined parameters, such as the presence of CHn segments and unique chemical shifts, thereby expediting the identification of promising compounds. This innovative technique was applied to identifying substances interacting with choline kinase α (ChoKα1), resulting in the discovery of four new inhibitors. Our findings demonstrate a powerful tool for unraveling the complex chemical landscape of herbal extracts, considerably facilitating the search for new pharmaceutical candidates. This approach offers an efficient alternative to traditional methods in the quest for drug discovery from natural sources.

In Situ Raman Hyperspectral Analysis of Microbial Colonies for Secondary Metabolites Screening.

Since the discovery of penicillin, a vast array of microbial antibiotics has been identified and applied in the medical field. Globally, the search for drug candidates via microbial screening is ongoing. Traditional screening methods, however, are time-consuming and require labor-intensive sample processing, significantly reducing throughput. This research introduces a Raman spectroscopy-based screening system tailored to the in situ analysis of microbial colonies on solid culture media. Employing multivariate curve resolution-alternating least-squares (MCR-ALS) for spectral decomposition, our approach reveals the production of secondary metabolites at the single colony level. We enhanced the microbial culture method, enabling direct, high signal-to-noise (S/N) ratio Raman spectroscopic measurements of colonies of Escherichia coli and actinomycetes species. Through semisupervised MCR analysis using the known spectra of actinorhodin and undecylprodigiosin as references, we accurately assessed the production of these compounds by Streptomyces coelicolor A3(2). Furthermore, we herein successfully detected the production of amphotericin B by Streptomyces nodosus, even in the absence of prior spectral information. This demonstrates the potential of our technique in the discovery of secondary metabolites. In addition to enabling the detection of the above-mentioned compounds, this analysis revealed the heterogeneity of the spatial distribution of their production in each colony. Our technique makes a significant contribution to the advancement of microbial screening, offering a rapid, efficient alternative to conventional methods and opening avenues for secondary metabolites discovery.

GWSkyNet. II. A Refined Machine-learning Pipeline for Real-time Classification of Public Gravitational Wave Alerts

Electromagnetic follow-up observations of gravitational wave events offer critical insights and provide significant scientific gain from this new class of astrophysical transients. Accurate identification of gravitational wave candidates and rapid release of sky localization information are crucial for the success of these electromagnetic follow-up observations. However, searches for gravitational wave candidates in real time suffer from a nonnegligible false alarm rate. By leveraging the sky localization information and other metadata associated with gravitational wave candidates, GWSkyNet, a machine-learning classifier developed by Cabero et al., demonstrated promising accuracy for the identification of the origin of event candidates. We improve the performance of the classifier for LIGO–Virgo–KAGRA's (LVK) fourth observing run by reviewing and updating the architecture and features used as inputs by the algorithm. We also retrain and fine-tune the classifier with data from the third observing run. To improve the prospect of electromagnetic follow-up observations, we incorporate GWSkyNet into LVK's low-latency infrastructure as an automatic pipeline for the evaluation of gravitational wave alerts in real time. We test the readiness of the algorithm on an LVK mock data challenge campaign. The results show that by thresholding on the GWSkyNet score, noise masquerading as astrophysical sources can be rejected efficiently and the majority of true astrophysical signals can be correctly identified.

Effect of Apis mellifera syriaca Bee Venom on Glioblastoma Cancer: In Vitro and In Vivo Studies.

Glioblastoma multiforme (GBM) is a highly aggressive and fatal primary brain tumor. The resistance of GBM to conventional treatments is attributed to factors such as the blood-brain barrier, tumor heterogeneity, and treatment-resistant stem cells. Current therapeutic efforts show limited survival benefits, emphasizing the urgent need for novel treatments. In this context, natural anti-cancer extracts and especially animal venoms have garnered attention for their potential therapeutic benefits. Bee venom in general and that of the Middle Eastern bee, Apis mellifera syriaca in particular, has been shown to have cytotoxic effects on various cancer cell types, but not glioblastoma. Therefore, this study aimed to explore the potential of A. mellifera syriaca venom as a selective anti-cancer agent for glioblastoma through in vitro and in vivo studies. Our results revealed a strong cytotoxic effect of A. mellifera syriaca venom on U87 glioblastoma cells, with an IC50 of 14.32 µg/mL using the MTT test and an IC50 of 7.49 µg/mL using the LDH test. Cells treated with the bee venom became permeable to propidium iodide without showing any signs of early apoptosis, suggesting compromised membrane integrity but not early apoptosis. In these cells, poly (ADP-ribose) polymerase (PARP) underwent proteolytic cleavage similar to that seen in necrosis. Subsequent in vivo investigations demonstrated a significant reduction in the number of U87 cells in mice following bee venom injection, accompanied by a significant increase in cells expressing caspase-3, suggesting the occurrence of cellular apoptosis. These findings highlight the potential of A. mellifera syriaca venom as a therapeutically useful tool in the search for new drug candidates against glioblastoma and give insights into the molecular mechanism through which the venom acts on cancer cells.

A generic approach for reproducible model distillation

Model distillation has been a popular method for producing interpretable machine learning. It uses an interpretable “student” model to mimic the predictions made by the black box “teacher” model. However, when the student model is sensitive to the variability of the data sets used for training even when keeping the teacher fixed, the corresponded interpretation is not reliable. Existing strategies stabilize model distillation by checking whether a large enough sample of pseudo-data is generated to reliably reproduce student models, but methods to do so have so far been developed separately for each specific class of student model. In this paper, we develop a generic approach for stable model distillation based on central limit theorem for the estimated fidelity of the student to the teacher. We start with a collection of candidate student models and search for candidates that reasonably agree with the teacher. Then we construct a multiple testing framework to select a sample size such that the consistent student model would be selected under different pseudo samples. We demonstrate the application of our proposed approach on three commonly used intelligible models: decision trees, falling rule lists and symbolic regression. Finally, we conduct simulation experiments on Mammographic Mass and Breast Cancer datasets and illustrate the testing procedure throughout a theoretical analysis with Markov process. The code is publicly available at https://github.com/yunzhe-zhou/GenericDistillation.

Efficient generation of polarization-entangled photons in metal-organic framework waveguides

Parametric nonlinear optical processes are instrumental in optical quantum technology for generating entangled light. However, the range of materials conventionally used for producing entangled photons is limited. Metal-organic frameworks (MOFs) have emerged as a novel class of optical materials with customizable nonlinear properties and proven chemical and optical stability. The large number of combinations of metal atoms and organic ligand from which bulk MOF crystals are known to form, facilitates the search of promising candidates for nonlinear optics. To accelerate the discovery of next-generation quantum light sources, we employ a multi-scale modeling approach to study phase-matching conditions for collinear degenerate type-II spontaneous parametric down conversion (SPDC) with MOF-based one dimensional waveguides. Using periodic-density-functional theory calculations to compute the nonlinear optical properties of selected zinc-based MOF crystals, we predict polarization-entangled pair generation rates of order 104 − 107 s−1mW−1 at 1064 nm for 10 mm crystals, improving the brightness of industry materials such as PPKTP and BBO in some cases. This work underscores the great potential of MOF single crystals as entangled light sources for applications in quantum communication and sensing.

The sherlock pipeline: new exoplanet candidates in the WASP-16, HAT-P-27, HAT-P-26, and TOI-2411 systems

ABSTRACT The launches of NASA Kepler and Transiting Exoplanet Survey Satellite (TESS) missions have significantly enhanced the interest in the exoplanet field during the last 15 yr, providing a vast amount of public data that are being exploited by the community thanks to the continuous development of new analysis tools. However, using these tools is not straightforward, and users must dive into different codes, input–output formats, and methodologies, hindering an efficient and robust exploration of the available data. We present the sherlock pipeline, an end-to-end public software that allows the users to easily explore observations from space-based missions such as TESS or Kepler to recover known planets and candidates issued by the official pipelines and search for new planetary candidates that remained unnoticed. The pipeline incorporates all the steps to search for transit-like features, vet potential candidates, provide statistical validation, conduct a Bayesian fitting, and compute observational windows from ground-based observatories. Its performance is tested against a catalogue of known and confirmed planets from the TESS mission, trying to recover the official TESS Objects of Interest (TOIs), explore the existence of companions that have been missed, and release them as new planetary candidates. sherlock demonstrated an excellent performance, recovering 98 per cent of the TOIs and confirmed planets in our test sample and finding new candidates. Specifically, we release four new planetary candidates around the systems WASP-16 (with P $\sim$ 10.46 d and R $\sim$ 2.20 $\mathrm{ R}_{\oplus }$), HAT-P-27 (with P $\sim$ 1.20 d and R $\sim$ 4.33 $\mathrm{ R}_{\oplus }$), HAT-P-26 (with P $\sim$ 6.59 d and R $\sim$ 1.97 $\mathrm{ R}_{\oplus }$), and TOI-2411 (with P $\sim$ 18.75 d and R $\sim$ 2.88 $\mathrm{ R}_{\oplus }$).

Sport as a Pathway to Political Office and its Gendered Effects

ABSTRACT This paper finds that a background in competitive, organised sports is common among those active in electoral politics. Unique data collected through surveys of federal election candidates and local party association presidents together with the examination of candidate and MP biographies establish that many have a sporting background. This sporting experience offers the opportunity for individuals to acquire skills beneficial to political candidacy and makes them more attractive to party gatekeepers. This paper then connects this, and the significantly lower sports participation rates among women/girls, to their consistent under-representation in the pool of election candidates presented by Canada’s political parties and at every level of our elected legislatures. Women/girls participate in sports at significantly lower levels than men/boys. Men thus benefit disproportionately from both the personal skills developed through sports that lead to formal political participation and the local notoriety desired by party candidate search committees that sports participation offers.

Modulating the biosynthesis and TLR4-interaction of lipopolysaccharide as an approach to counter gut dysbiosis and Parkinson's disease: Role of phyto-compounds

The prevalence of the world's second leading neurodegenerative disorder Parkinson's disease (PD) is well known while its pathogenesis is still a topical issue to explore. Clinical and experimental reports suggest the prevalence of disturbed gut microflora in PD subjects, with an abundance of especially Gram-negative bacteria. The endotoxin lipopolysaccharide (LPS) released from the outer cell layer of these bacteria interacts with the toll-like receptor 4 (TLR4) present on the macrophages and it stimulates the downstream inflammatory cascade in both the gut and brain. Recent research also suggests a positive correlation between LPS, alpha-synuclein, and TLR4 levels, which indicates the contribution of a parallel LPS-alpha-synuclein-TLR4 axis in stimulating inflammation and neurodegeneration in the gut and brain, establishing a body-first type of PD. However, owing to the novelty of this paradigm, further investigation is mandatory. Modulating LPS biosynthesis and LPS-TLR4 interaction can ameliorate gut dysbiosis and PD. Several synthetic LpxC (UDP-3-O-(R-3-hydroxymyristoyl)-N-acetylglucosamine deacetylase; LPS-synthesizing enzyme) inhibitors and TLR4 antagonists are reported to show beneficial effects including neuroprotection in PD models, however, are not devoid of side effects. Plant-derived compounds have been long documented for their benefits as nutraceuticals and thus to search for effective, safer, and multitarget therapeutics, the present study focused on summarizing the evidence reporting the potential of phyto-compounds as LpxC inhibitors and TLR4 antagonists. Studies demonstrating the dual potential of phyto-compounds as the modulators of LpxC and TLR4 have not yet been reported. Also, very few preliminary studies have reported LpxC inhibition by phyto-compounds. Nevertheless, remarkable neuroprotection along with TLR4 antagonism has been shown by curcumin and juglanin in PD models. The present review thus provides a wide look at the research progressed to date in discovering phyto-compounds that can serve as LpxC inhibitors and TLR4 antagonists. The study further recommends the need for expanding the search for potential candidates that can render dual protection by inhibiting both the biosynthesis and TLR4 interaction of LPS. Such multitarget therapeutic intervention is believed to bring fruitful yields in countering gut dysbiosis, neuroinflammation, and dopaminergic neuron damage in PD patients through a single treatment paradigm.

Cataclysmic variables around the period-bounce: An eROSITA-enhanced multiwavelength catalog

Context. Cataclysmic variables (CVs) with degenerate donors that have evolved past the period minimum are predicted to make up a great portion of the CV population, namely, between 40% and 80%. However, either due to shortcomings in the models or the intrinsic faintness of these strongly evolved systems, only a few of these so-called “period-bouncers” have been confidently identified thus far. Aims. We compiled a multiwavelength catalog of period-bouncers and CVs around the period minimum from the literature to provide an in-depth characterization of the elusive subclass of period-bounce CVs that will support the identification of new candidates. Methods. We combined recently published or archival multiwavelength data with new X-ray observations from the all-sky surveys carried out with the extended ROentgen Survey with an Imaging Telescope Array (eROSITA) on board the Spektrum-Roentgen-Gamma spacecraft (SRG). Our catalog comprises 192 CVs around the period minimum, chosen as likely period-bounce candidates based on reported short orbital periods and low donor mass. This sample helped us establish specific selection parameters, which were used to compile a “scorecard” that rates the likelihood that a particular system is a period-bouncer. Results. Our “scorecard” correctly assigns high scores to the already confirmed period-bouncers in our literature catalog. It has also identified 103 additional strong period-bounce candidates in the literature that had not previously been classified as such. We established two selection cuts based on the X-ray-to-optical flux ratio (−1.21 ≤ log(Fx/Fopt) ≤ 0) and the typical X-ray luminosity (log(Lx,bol) ≤ 30.4 [erg s−1]) observed from the eight period-bouncers that have already been confirmed with eROSITA data. These X-ray selection cuts led to the updated categorization of seven systems as new period-bouncers, increasing their known population to 24 systems in total. Conclusions. Our multiwavelength catalog of CVs around the period minimum drawn from the literature, together with X-ray data from eROSITA, has resulted in a ~40% increase in the population of period-bouncers. Both the catalog and “scorecard” we constructed will aid in future searches for new period-bounce candidates. These tools will contribute to the goal of resolving the discrepancy between the predicted high number of period-bouncers and the low number of these systems successfully observed to date.

Online Job Portal

ABSTRACT___ The Online Job Portal is a web application developed using the Python Django framework. This project aims to provide a platform for job seekers and companies to interact and fulfill their employment needs. The application allows job seekers to create profiles, search and apply for jobs, and receive updates on their application status. Companies can post job vacancies, search for suitable candidates, and manage the hiring process. The evolution of technology has revolutionized the job search and recruitment process, giving rise to online job portals as essential tools for job seekers and employers. This abstract presents the concept and development of a comprehensive online job portal designed to bridge the gap between job seekers and employers efficiently. INDEX TEARMS – HTML, CSS , JavaScript, Python, Django Framework, SQLlite.

MDF-DTA: A Multi-Dimensional Fusion Approach for Drug-Target Binding Affinity Prediction.

Drug-target affinity (DTA) prediction is an important task in the early stages of drug discovery. Traditional biological approaches are time-consuming, effort-consuming, and resource-consuming due to the large size of genomic and chemical spaces. Computational approaches using machine learning have emerged to narrow down the drug candidate search space. However, most of these prediction models focus on single feature encoding of drugs and targets, ignoring the importance of integrating different dimensions of these features. We propose a deep learning-based approach called Multi-Dimensional Fusion for Drug Target Affinity Prediction (MDF-DTA) incorporating different dimensional features. Our model fuses 1D, 2D, and 3D representations obtained from different pretrained models for both drugs and targets. We evaluated MDF-DTA on two standard benchmark data sets: DAVIS and KIBA. Experimental results show that MDF-DTA outperforms many state-of-the-art techniques in the DTA task across both data sets. Through ablation studies and performance evaluation metrics, we evaluate the importance of individual representations and the impact of each representation on MDF-DTA.