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Polycystic Ovary Syndrome (PCOS) is a common endocrine disorder with a worldwide prevalence of 6–10 % of women of reproductive age. PCOS is a risk factor for cardiometabolic disorders such as type 2 diabetes, myocardial infarction, and stroke in addition to exhibiting signs of hyperandrogenism and anovulation. However, there is no known cure for PCOS, and medications have only ever been used symptomatically, with a variety of adverse effects. Drugs made from natural plant products may help treat PCOS because several plant extracts have been widely recognized to lessen the symptoms of PCOS. In light of this, 72 current studies on natural products with the potential to control PCOS were examined. By controlling the PI3K/AKT signaling pathway and decreasing NF-κB and cytokines such as tumor necrosis factor (TNF), interleukin-1 (IL-1), and interleukin-6 (IL-6), certain plant-derived chemicals might reduce inflammation. Other substances altered the HPO axis, which normalized hormones. Additionally, other plant components increased glutathione (GSH), superoxide dismutase (SOD), catalase (CAT), and glutathione peroxidase (GPx) levels to reduce radiation-induced oxidative stress. The other substances prevented autophagy by impairing beclin 1, autophagy-related 5 (ATG5), and microtubule-associated protein 1A/1B-light chain 3 - II (LC3- II). The main focus of this comprehensive review is the possibility of plant extracts as natural bio-resources of PCOS treatment by regulating inflammation, hormones, reactive oxygen species (ROS), or autophagy.

Abstract Various electrophysiological and imaging techniques have been studied for the diagnosis and treatment of epilepsy. In particular, electrocorticography (ECoG) provides valuable information that can guide clinical treatment of patients with epilepsy. Currently, it is necessary to define the clinical benefits of ECoG in free-moving animals for the treatment of epilepsy. Here, we present the results of simultaneous recordings of multiple cortical sites and responsive neurostimulations for epilepsy treatment carried out in free-moving rats. In this study, we developed a high-density, flexible electrode array comprising graphene/Au/graphene that stably wraps onto the cortex surface of a living rat brain, exhibiting a superior signal-to-noise ratio. The hybrid graphene multichannel electrode successfully detected brain signals with high-throughput spatiotemporal resolution and substantially suppressed pilocarpine-induced epileptic discharges and behavior. Simultaneous recording and neurostimulation in awake animals can lead to a fundamental change in the treatment of medically intractable epilepsy.

The role of malware classification is crucial in addressing the explosive increase in malware variants. By classifying malware instances into malware families, malware analysts can apply appropriate techniques and tools to handle malware variants in each family. Using high-level representations of malware, such as disassembled codes, yields meaningful classification performance. However, malware classification based on disassembled codes depends on the practically implausible assumption that every malware is correctly reversed by disassemblers. Unfortunately, sophisticated malware, which has anti-disassembly capabilities, seeks to confuse disassemblers, yielding incorrectly disassembled codes. In this study, we focus on malware family classification, which requires no disassembly, and propose a new CNN-based malware classification model using non-disassembled malware files (i.e., binary files). Our model associates two modalities: “malware images” and “structural entropies,” which are converted and extracted from binary files. Both modalities have different granularities of bytes and chunks that complement each other. The model adopts a cross-modal attention mechanism to combine the features of the two modalities by moderating their expressive limitations. We validate our model using three popular datasets from the Kaggle Microsoft Malware Classification, Malimg, and BODMAS datasets. The experimental results show that our model identifies malware families with a higher degree of accuracy than previous methods and does not require the burden of disassembling.

We demonstrate that double-wall or multi-wall WS2 nanotubes can exhibit unexpectedly efficient bulk photovoltaic effect owing to its unique inter-wall charge-shifting excitations.

This paper proposes an improved neutral voltage modulation (NVM) method for symmetrical and asymmetrical three-phase multilevel cascaded inverters (MLCIs). The maximum synthesizable space vector region is analyzed using the number of healthy inverter modules and the amplitudes of the dc-link voltages in each phase. After that, the NVM strategy is proposed to maximize the balanced three-phase output voltages in the given space vector region. The proposed method matches the polarities of the neutral and the phase voltage references as well as minimizing the neutral voltage. By using the proposed method, the linear modulation region of the three-phase MLCI are fully utilized allowing no overmodulations even under severe dc-link voltage imbalances. Moreover, the proposed NVM can synthesizes rotating voltage vectors even all modules of one phase are not available. To verify the effectiveness of the proposed method, a two-by-three MLCI prototype is built and tested. The experimental results show that the proposed method reduces the total harmonic distortion (THD) and the ripple component of the phase current by 28% and 32.8%, respectively, compared to traditional modulation strategies under severe dc-link imbalance conditions.

This study focuses on the reentry target classification and fuses target features based on the generalized evidence theory. The features are extensively investigated, and the ballistic factor and length of the high-resolution range profile are selected. The evidence theory is advantageous for solving feature fusion, representing uncertainty, and is widely used in defense applications. However, determining the generalized basic probability assignment (GBPA) and dealing with uncertainty is a matter that requires further improvement. In this paper, we propose a new method to determine GBPA using uncertainty with time-series radar data. First, the samples of each known class are encoded as a generalized fuzzy number (GFN), and the power set comprising the frame of discernment (FOD) is calculated from the GFN and each intersection area. Subsequently, the test samples with uncertainty are encoded as triangular fuzzy numbers, reflecting the mean and standard deviation of a Kalman filter. Finally, the firing strength between the model and the input is calculated as the degree of support for the class hypothesis, which is used to determine the GBPA. The proposed algorithm is compared with the existing methods and exhibits high classification accuracy and a short classification time without leakage. In experiments with various input uncertainties, the results demonstrate that our method can effectively reflect the input uncertainty and determine the GBPA.

Materials exhibiting a Berry curvature (BC) show potential for optoelectronics, but, finite BC dipoles have so far only been shown in small-gap materials. Here in semiconducting tin telluride monolayers a ferroelectrically driven BC dipole is shown to be used to control charge and spin photocurrents.

Classifying space targets from debris is critical for radar resource management as well as rapid response during the mid-course phase of space target flight. Due to advances in deep learning techniques, various approaches have been studied to classify space targets by using micro-Doppler signatures. Previous studies have only used micro-Doppler signatures such as spectrogram and cadence velocity diagram (CVD), but in this paper, we propose a method to generate micro-Doppler signatures taking into account the relative incident angle that a radar can obtain during the target tracking process. The AlexNet and ResNet-18 networks, which are representative convolutional neural network architectures, are transfer-learned using two types of datasets constructed using the proposed and conventional signatures to classify six classes of space targets and a debris–cone, rounded cone, cone with empennages, cylinder, curved plate, and square plate. Among the proposed signatures, the spectrogram had lower classification accuracy than the conventional spectrogram, but the classification accuracy increased from 88.97% to 92.11% for CVD. Furthermore, when recalculated not with six classes but simply with only two classes of precessing space targets and tumbling debris, the proposed spectrogram and CVD show the classification accuracy of over 99.82% for both AlexNet and ResNet-18. Specially, for two classes, CVD provided results with higher accuracy than the spectrogram.

Two-dimensional electron gases that form in some complex oxide heterostructures may have useful functional behavior due to the interaction of the parent materials. Here the authors show that PZT/STO interfaces can host a spin-polarized electron gas, even though the bulk materials are nonmagnetic.

Estimating a predictive model from a dataset is best initiated with an unbiased estimator. However, since the unbiased estimator is unknown in general, the problem of the bias-variance tradeoff is raised. Aside from searching for an unbiased estimator, the convenient approach to the problem of the bias-variance tradeoff may be to use the clustering method. Within a cluster whose size is smaller than the whole sample, we would expect the simple form of the estimator for prediction to avoid the overfitting problem. In this paper, we propose a new method to find the optimal cluster for prediction. Based on the previous literature, this cluster is considered to exist somewhere between the whole dataset and the typical cluster determined by partitioning data. To obtain a reliable cluster size, we use the bootstrap method in this paper. Additionally, through experiments with simulated and real-world data, we show that the prediction error can be reduced by applying this new method. We believe that our proposed method will be useful in many applications using a clustering algorithm for a stable prediction performance.

Inflammatory bowel disease (IBD) is a chronic inflammatory disease, in which the intestinal epithelium loses its barrier function. Given the existence of the oxygen gradient in the intestinal epithelium and that inflammation further contributes to the tissue hypoxia, we investigated the role of hypoxia-inducible factor (HIF), a transcription factor activated under hypoxic conditions in myeloid cells, in the progression of IBD. To do this, we utilized myeloid-specific knockout (KO) mice targeting HIF pathways, created by a Cre-loxP system with human MRP8 (hMRP8), an intracellular calcium-binding protein, as the myeloid promoter. By feeding 5% dextran sodium sulfate (DSS) to hMRP8 von Hippel Lindau (Vhl) KO mice, in which HIF-1α and HIF-2α are constitutively activated in myeloid cells, we found that these mice were highly susceptible to DSS-induced colitis, demonstrating greater body weight loss, increased mortality, faster onset of rectal bleeding, shortened colon length, and increased CD11b- or Gr-1-positive myeloid cells in the colon compared with wild-type (WT) mice. These parameters were restored to, if not better than, the WT levels when we examined hMRP8 Hif-1a KO mice upon 5% DSS feeding. hMRP8 Hif-2a KO mice, on the other hand, exhibited a similar degree of DSS-induced colitis to that of WT mice. Lastly, when DSS was given together with azoxymethane to induce tumorigenesis in the colon, we found that hMRP8 Hif-1a KO mice exhibited comparable levels of colorectal tumors to those of WT mice, indicating that HIF-1α in myeloid cells is dispensable for tumorigenesis. Collectively, our results suggest that HIF-1α activation in myeloid cells critically regulates IBD progression.

The identification of disease-related genes and disease mechanisms is an important research goal; many studies have approached this problem by analysing genetic networks based on gene expression profiles and interaction datasets. To construct a gene network, correlations or associations among pairs of genes must be obtained. However, when gene expression data are heterogeneous with high levels of noise for samples assigned to the same condition, it is difficult to accurately determine whether a gene pair represents a significant gene-gene interaction (GGI). In order to solve this problem, we proposed a random forest-based method to classify significant GGIs from gene expression data. To train the model, we defined novel feature sets and utilised various high-confidence interactome datasets to deduce the correct answer set from known disease-specific genes. Using Alzheimer's disease data, the proposed method showed remarkable accuracy, and the GGIs established in the analysis can be used to build a meaningful genetic network that can explain the mechanisms underlying Alzheimer's disease.

As the size of networks increases, it is becoming important to analyze large-scale network data. A network clustering algorithm is useful for analysis of network data. Conventional network clustering algorithms in a single machine environment rather than a parallel machine environment are actively being researched. However, these algorithms cannot analyze large-scale network data because of memory size issues. As a solution, we propose a network clustering algorithm for large-scale network data analysis using Apache Spark by changing the paradigm of the conventional clustering algorithm to improve its efficiency in the Apache Spark environment. We also apply optimization approaches such as Bloom filter and shuffle selection to reduce memory usage and execution time. By evaluating our proposed algorithm based on an average normalized cut, we confirmed that the algorithm can analyze diverse large-scale network datasets such as biological, co-authorship, internet topology and social networks. Experimental results show that the proposed algorithm can develop more accurate clusters than comparative algorithms with less memory usage. Furthermore, we confirm the proposed optimization approaches and the scalability of the proposed algorithm. In addition, we validate that clusters found from the proposed algorithm can represent biologically meaningful functions.