Miracle-dz/UniFAR-FTUs huggingface dataset:
Splenectomy outcomes in patients with autoimmune cytopenias and persistent antiphospholipid antibodies

Splenectomy is a therapy for patients with treatment‐refractory autoimmune cytopenias. Antiphospholipid antibodies (aPL) can be identified in 25%–85% of these patients. In this study, we sought to identify whether the presence of aPL was associated with worse outcomes in autoimmune cytopenia's patients who had undergone splenectomy.
An updated overview of experimental and computational approaches to identify non-canonical DNA/RNA structures with emphasis on G-quadruplexes and R-loops

Abstract Multiple types of non-canonical nucleic acid structures play essential roles in DNA recombination and replication, transcription, and genomic instability and have been associated with several human diseases. Thus, an increasing number of experimental and bioinformatics methods have been developed to identify these structures. To date, most reviews have focused on the features of non-canonical DNA/RNA structure formation, experimental approaches to mapping these structures, and the association of these structures with diseases. In addition, two reviews of computational algorithms for the prediction of non-canonical nucleic acid structures have been published. One of these reviews focused only on computational approaches for G4 detection until 2020. The other mainly summarized the computational tools for predicting cruciform, H-DNA and Z-DNA, in which the algorithms discussed were published before 2012. Since then, several experimental and computational methods have been developed. However, a systematic review including the conformation, sequencing mapping methods and computational prediction strategies for these structures has not yet been published. The purpose of this review is to provide an updated overview of conformation, current sequencing technologies and computational identification methods for non-canonical nucleic acid structures, as well as their strengths and weaknesses. We expect that this review will aid in understanding how these structures are characterised and how they contribute to related biological processes and diseases.
The VLT-FLAMES survey of massive stars: constraints on stellar evolution from the chemical compositions of rapidly rotating Galactic and Magellanic Cloud B-type stars

Aims. We have previously analysed the spectra of 135 early B-type stars in the Large Magellanic Cloud (LMC) and found several groups of stars that have chemical compositions that conflict with the theory of rotational mixing. Here we extend this study to Galactic and Small Magellanic Cloud (SMC) metallicities. Methods. We provide chemical compositions for ~50 Galactic and ~100 SMC early B-type stars and compare these to the LMC results. These samples cover a range of projected rotational velocities up to ~300 km s-1 and hence are well suited to testing rotational mixing models. The surface nitrogen abundances are utilised as a probe of the mixing process since nitrogen is synthesized in the core of the stars and mixed to the surface. Results. In the SMC, we find a population of slowly rotating nitrogen-rich stars amongst the early B type core-hydrogen burning stars, which is comparable to that found previously in the LMC. The identification of non-enriched rapid rotators in the SMC is not possible due to the relatively high upper limits on the nitrogen abundance for the fast rotators. In the Galactic sample we find no significant enrichment amongst the core hydrogen-burning stars, which appears to be in contrast with the expectation from both rotating single-star and close binary evolution models. However, only a small number of the rapidly rotating stars have evolved enough to produce a significant nitrogen enrichment, and these may be analogous to the non-enriched rapid rotators previously found in the LMC sample. Finally, in each metallicity regime, a population of highly enriched supergiants is observed, which cannot be the immediate descendants of core-hydrogen burning stars. Their abundances are, however, compatible with them having gone through a previous red supergiant phase. Together, these observations paint a complex picture of the nitrogen enrichment in massive main sequence and supergiant stellar atmospheres, where age and binarity cause crucial effects. Whether rotational mixing is required to understand our results remains an open question at this time, but could be answered by identifying the true binary fraction in those groups of stars that do not agree with single-star evolutionary models.
DomainMapper: Accurate domain structure annotation including those with non‐contiguous topologies

Automated domain annotation is an important tool for structural informatics. These pipelines typically involve searching query sequences against hidden Markov model (HMM) profiles, yielding matches to profiles for various domains. However, domain annotation can be ambiguous or inaccurate when proteins contain domains with non‐contiguous residue ranges, and especially when insertional domains are hosted within them. Here, we present DomainMapper, an algorithm that accurately assigns a unique domain structure annotation to a query sequence, including those with complex topologies. We validate our domain assignments using the AlphaFold database and confirm that non‐contiguity is pervasive (10.74% of all domains in yeast and 4.52% in human). Using this resource, we find that certain folds have strong propensities to be non‐contiguous or insertional across the Tree of Life. DomainMapper is freely available and can be ran as a single command‐line function.
Five‐day versus 7‐day treatment regimen with azacitidine in lower risk myelodysplastic syndrome: A phase 2, multicenter, randomized trial

Low‐dose azacitidine (AZA) regimens, primarily 5‐day AZA, have been used in lower risk myelodysplastic syndrome (LrMDS) but they have yet to be directly compared to the standard 7‐day, uninterrupted dosing schedule.
Introduction: Quotas, Parties and Electoral Design – Mechanisms and Effects of Ethnic Representation in Diverse Societies

ABSTRACT This article introduces the special issue and places the eight contributions in context. It offers a discussion of the key terms and highlights the contributions of the articles for forging new research agendas on ethnic inclusion and political representation. Drawing from cases from the Asia Pacific, Americas and Europe, this special collection of papers investigates the effects and practices of electoral designs on the descriptive and substantive effects of ethnic minority representation based on both qualitative and quantitative methods. Together, the findings seek to engender debate and advance contemporary scholarship on comparative democratisation, electoral and party politics and conflict resolution studies on indigenous and ethnic minority representation.
Direct Imaging Search for Extrasolar Planets in the Pleiades

We carried out an imaging survey for extrasolar planets around stars in the Pleiades (125 Myr, 135 pc) in the H and KS bands using HiCIAO combined with adaptive optics, AO188, on the Subaru telescope. We found 13 companion candidates fainter than 14.5 mag in the H band around 9 stars. Five of these 13 were confirmed to be background stars by measurement of their proper motion. One was not found in the second epoch observation, and thus was not a background or companion object. One had multi-epoch images, but the precision of its proper motion was not sufficient to conclude whether it was a background object. Four other candidates are waiting for second-epoch observations to determine their proper motion. Finally, the remaining two were confirmed to be 60 MJ brown dwarf companions orbiting around HD 23514 (G0) and HII 1348 (K5), respectively, as had been reported in previous studies. In our observations, the average detection limit for a point source was 20.3 mag in the H band beyond 1. ′′ 5 from the central star. On the basis of this detection limit, we calculated the detection efficiency to be 90% for a planet with 6 to 12 Jovian masses and a semi-major axis of 50-1000 AU. For this reason we extrapolated the distribution of the planet mass and the semi-major axis derived from radial velocity observations, and adopted the planet evolution model Baraffe et al. (2003, A&A, 402, 701). Since there was no detection of a planet, we estimated the frequency of such planets to be less than 17.9% (2 σ) around one star of the Pleiades cluster.
Bird sound recognition based on novel classifier

With the rapid development of the Internet, voice recognition has become one of the core technologies on information era. Bird monitoring through sound recognition can be used as an effective indicator of wetland environmental quality. In this paper, we use Python to classify birds based on the features of Mel frequency cepstrum coefficient via K-Nearest Neighbor, support vector machine and multi-layer perceptron. Further, we carry out the comparisons of these algorithms and propose a novel classifier on the base of them. The experimental results show that the new classifier absorbs the fast prediction speed of the Multi-Layer Perception, the high accuracy and strong noise immunity of the K-Nearest Neighbor.
Inhomogeneity-induced variance of cosmological parameters

Modern cosmology relies on the assumption of large-scale isotropy and homogeneity of the Universe. However, locally the Universe is inhomogeneous and anisotropic. So, how can local measurements (at the 100 Mpc scale) be used to determine global cosmological parameters (defined at the 10 Gpc scale)? We use Buchert's averaging formalism and determine a set of locally averaged cosmological parameters in the context of the flat Lambda cold dark matter model. We calculate their ensemble means (i.e. their global values) and variances (i.e. their cosmic variances). We apply our results to typical survey geometries and focus on the study of the effects of local fluctuations of the curvature parameter. By this means we show, that in the linear regime cosmological backreaction and averaging can be reformulated as the issue of cosmic variance. The cosmic variance is found largest for the curvature parameter and discuss some of its consequences. We further propose to use the observed variance of cosmological parameters to measure the growth factor. [abbreviated]
Hybrid Feature Selection for Efficient Detection of DDoS Attacks in IoT

The increasing Distributed Denial of Service (DDoS) attacks on the Internet of Things (IoT) is leading to the need for an efficient detection approach. Although much research has been conducted to detect DDoS attacks on traditional networks, such as machine learning (ML) based approaches that have improved accuracy and confidence, the limited bandwidth and computation resources in IoT networks restrict the application of ML, especially deep learning (DL) based solutions that require extensive input data. In order to appropriately address the security issues in the resources-constrained IoT network, this paper is aimed to reduce the input data dimensions by extracting a subset of the most relevant features from the original features and using this subset to detect DDoS attacks on IoT without degrading the detection performance. A cost-effective model is developed to clean and prepare raw data before dimensionality reduction. A hybrid feature selection that uses Mutual Information (MI), Analysis of Variance (ANOVA), Chi-Squared, L1-based feature selection, and Tree-based feature selection algorithms is designed to identify important data features and reduce the data inputs needed for detection. Simulation results show that detection accuracy is improved with the combination of features chosen by the proposed hybrid feature selection approach. The training time is much less than the combination of each individual feature selection method.
An Image-based Transfer Learning Framework for Classification of E-Commerce Products

Classification of e-commerce products involves identifying the products and placing those products into the correct category. For example, men’s Nike Air Max will be in the men’s category shoes on an e-Commerce platform. Identifying the correct classification of a product from hundreds of categories is time-consuming for businesses. This research proposes an Image-based Transfer Learning Framework to classify the images into the correct category in the shortest time. The framework combines Image-based algorithms with Transfer Learning. This research compares the time to predict the category and accuracy of traditional CNN and transfer learning models such as VGG19, InceptionV3, ResNet50, and MobileNet. A visual classifier is trained CNN and transfer learning models such as VGG19, InceptionV3, ResNet50, and MobileNet. The models are trained on an e-commerce product dataset that combines the ImageNet dataset with pre-trained weights. The dataset consists of 15000 images scraped from the web. Results demonstrate that Inception V3 outperforms all other models based on a TIMING of 0.10 seconds and an accuracy of 85%.
Smart Grid Cyber-Physical Situational Awareness of Complex Operational Technology Attacks: A Review

The smart grid (SG), regarded as the complex cyber-physical ecosystem of infrastructures, orchestrates advanced communication, computation, and control technologies to interact with the physical environment. Due to the high rewards that threats to the grid can realize, adversaries can mount complex cyber-attacks such as advanced persistent threats-based and coordinated attacks to cause operational malfunctions and power outages in the worst scenarios: The latter of which was reflected in the Ukrainian power grid attack. Despite widespread research on smart grid security, the impact of targeted attacks on control and power systems is anecdotal. This article reviews the smart grid security from collaborative factors, emphasizing the situational awareness (SA). Specifically, we propose a threat modeling framework and review the nature of cyber-physical attacks to understand their characteristics and impacts on the smart grid’s control and physical systems. We examine the existing threats detection and defense capabilities, such as intrusion detection systems (IDSs), moving target defense (MTD), and co-simulation techniques, along with discussing the impact of attacks through situational awareness and power system metrics. We discuss the human factor aspects for power system operators in analyzing the impacts of cyber-attacks. Finally, we investigate the research challenges with key research gaps to shed light on future research directions.
Determinants of the Trade Balance in India. Evidence from a Post-Liberalisation Period

Abstract The present study investigates trade balance determinants in post-liberalization India from 1991 to 2020. The main aim of this study is to test the validity of the J-curve and Marshal-Lerner condition and examine the impact of other related macroeconomic variables, including foreign income, exchange rate, domestic prices, and domestic demand on the country’s trade balance. To achieve this objective, bounds tests and error correction model within asymmetric and symmetrical framework was used for estimation. In addition, variance decomposition analysis was applied to examine the dynamic interaction of selected variables. The results indicate the absence of the ‘J-curve’ effect and ‘Marshall-Lerner’ condition in India. Further, the results indicate that domestic prices, domestic demand, and money supply have negative signs, whereas exchange rate and world demand have positive signs in the long and short-run. The results from the variance decomposition indicate that as compared to other variables, the exchange rate highly contributes to forecasting error variance of trade balance in the case of India
Solid-state laser refrigeration of core-shell polystyrene microspheres

Microlaser designs based on the coupling of whispering gallery modes (WGMs) with the upconversion processes which take place within lanthanide-doped nanoparticles (UCNPs) have been demonstrated and shown to have many valuable qualities, such as high Q factors and low lasing thresholds. One obstacle that these microlaser designs still face is the challenges caused by photothermal heating of the gain medium, which could be solved through the design of a radiation balanced microlaser. In this work, WGM microresonators composed of 5 μm diameter polystyrene spheres are fabricated with a layer of Yb3+-doped NaYF4 UCNPs in order to test if the anti-Stokes cooling properties of the UCNPs can cool the microresonator and its environment under laser irradiation. We find via calibrated mean fluorescence spectroscopy that the UCNPs can cool their local environment by as much as 23 °C and significantly reduce the heating of the aqueous environment surrounding the microresonator, showing promise for inclusion in a design for a radiation balanced microlaser.
A THERMAL INFRARED IMAGING STUDY OF VERY LOW MASS, WIDE-SEPARATION BROWN DWARF COMPANIONS TO UPPER SCORPIUS STARS: CONSTRAINING CIRCUMSTELLAR ENVIRONMENTS

We present a 3–5 μm LBT/MMT adaptive optics imaging study of three Upper Scorpius stars with brown dwarf (BD) companions with very low masses/mass ratios (MBD <25 MJup; MBD/M⋆ ≈ 1%–2%) and wide separations (300–700 AU): GSC 06214, 1RXS 1609, and HIP 78530. We combine these new thermal IR data with existing 1–4 μm and 24 μm photometry to constrain the properties of the BDs and identify evidence for circumprimary/circumsecondary disks in these unusual systems. We confirm that GSC 06214B is surrounded by a disk, further showing that this disk produces a broadband IR excess due to small dust near the dust sublimation radius. An unresolved 24 μm excess in the system may be explained by the contribution from this disk. 1RXS 1609B exhibits no 3–4 μm excess, nor does its primary; however, the system as a whole has a modest 24 μm excess, which may come from warm dust around the primary and/or BD. Neither object in the HIP 78530 system exhibits near- to mid-IR excesses. We additionally find that the 1–4 μm colors of HIP 78530B match a spectral type of M3 ± 2, inconsistent with the M8 spectral type assigned based on its near-IR spectrum, indicating that it may be a low-mass star rather than a BD. We present new upper limits on additional low-mass companions in the system (<5 MJup beyond 175 AU). Finally, we examine the utility of circumsecondary disks as probes of the formation histories of wide BD companions, finding that the presence of a disk may disfavor BD formation near the primary with subsequent outward scattering.
Design of a Chimeric Multi-Epitope Vaccine (CMEV) against Both Leishmania martiniquensis and Leishmania orientalis Parasites Using Immunoinformatic Approaches

Simple Summary Leishmaniasis, caused by parasites from the genus Leishmania, is one of the neglected tropical diseases that is particularly problematic in tropical regions. Vaccine development is anticipated, but the diversity of pathogens poses a challenge. In this study, we designed a vaccine that is expected to be effective against both Leishmania martiniquensis and Leishmania orientalis, two different species isolated in Thailand. Predicted antigenic proteins and their epitopes were extracted from the draft genomes of these two species, and a chimeric multi-epitope vaccine was constructed in silico. The immunogenicity, chemical, and structural properties of the designed protein molecules suggest that this molecule could be properly synthesized in a heterologous expression system and induce responses in the inoculated host immune system. Based on these results, further experiments are required to determine the practical application of this vaccine candidate. Abstract Leishmaniasis is a parasitic disease caused by protozoan flagellates of the genus Leishmania. Recently, Leishmania martiniquensis and Leishmania orientalis, emerging species of Leishmania, were isolated from patients in Thailand. Development of the vaccine is demanded; however, genetic differences between the two species make it difficult to design a vaccine that is effective for both species. In this study, we applied immuno-informatic approaches to design a chimeric multi-epitope vaccine (CMEV) against both L. martiniquensis and L. orientalis. We identified seven helper T lymphocyte (HTL) epitopes, sixteen cytotoxic T lymphocyte (CTL) epitopes, and eleven B-cell epitopes from sixteen conserved antigenic proteins found in both species. All these epitopes were joined together, and to further enhance immunogenicity, protein and peptides adjuvant were also added at the N-terminal of the molecule by using specific linkers. The candidate CMEV was subsequently analyzed from the perspectives of the antigenicity, allergenicity, and physiochemical properties. The interaction of the designed multi-epitope vaccine and immune receptor (TLR4) of the host were evaluated based on molecular dockings of the predicted 3D structures. Finally, in silico cloning was performed to construct the expression vaccine vector. Docking analysis showed that the vaccine/TLR4 complex took a stable form. Based on the predicted immunogenicity, physicochemical, and structural properties in silico, the vaccine candidate was expected to be appropriately expressed in bacterial expression systems and show the potential to induce a host immune response. This study proposes the experimental validation of the efficacy of the candidate vaccine construct against the two Leishmania.
Determinants of hedging strategy in foreign exchange risk management by exporting small and medium-sized enterprises: The mediating role of resources

Foreign exchange risk management is well researched in the context of multinational enterprises, but how small and medium sized exporting firms manage their forex risk is still largely unexplored, especially through an international business lens. This study investigates how New Zealand and Australian exporting small and medium-sized enterprises (SMEs) manage foreign exchange (forex) risk, and the impact of increasing internationalisation on this. The study draws on two theoretical perspectives to assist the investigation: the resource-based view and internationalisation theory. The surveys were distributed to New Zealand and Australian exporting firms across a full range of industry sectors represented in the business database provided by Kompass. Statistical analyses, including exploratory factor analysis, and confirmatory factor analysis were conducted to test the measurement model, structural model and research hypotheses. The study identifies four determinants of forex risk strategy of exporting SMEs: degree of internationalisation, forex exposure, perceived forex risk and resources. Organisational and human resources are shown to have a key mediation role in the model. The study provides insights into key determinants of forex risk management and their interrelationships in the little examined context of exporting SMEs.