Miracle-dz/UniFAR-FTUs huggingface dataset:
Transcription Factor Regulation of Gene Expression Network by ZNF385D and HAND2 in Carotid Atherosclerosis

Carotid intima-media thickness (CIMT) is a surrogate indicator for atherosclerosis and has been shown to predict cardiovascular risk in multiple large studies. Identification of molecular markers for carotid atheroma plaque formation can be critical for early intervention and prevention of atherosclerosis. This study performed transcription factor (TF) network analysis of global gene expression data focusing on two TF genes, ZNF385D and HAND2, whose polymorphisms have been recently reported to show association with CIMT. Genome-wide gene expression data were measured from pieces of carotid endarterectomy collected from 34 hypertensive patients (atheroma plaque of stages IV and above according to the Stary classification) each paired with one sample of distant macroscopically intact tissue (stages I and II). Transcriptional regulation networks or the regulons were reconstructed for ZNF385D (5644 target genes) and HAND2 (781 target genes) using network inference. Their association with the progression of carotid atheroma was examined using gene-set enrichment analysis with extremely high statistical significance for regulons of both ZNF385D and HAND2 (p < 6.95 × 10−7) suggesting the involvement of expression quantitative loci (eQTL). Functional annotation of the regulon genes found heavy involvement in the immune system’s response to inflammation and infection in the development of atherosclerosis. Detailed examination of the regulation and correlation patterns suggests that activities of the two TF genes could have high clinical and interventional impacts on impairing carotid atheroma plaque formation and preventing carotid atherosclerosis.
Characterization of OsPIN2 Mutants Reveal Novel Roles for Reactive Oxygen Species in Modulating Not Only Root Gravitropism but Also Hypoxia Tolerance in Rice Seedlings

Tolerance to submergence-induced hypoxia is an important agronomic trait especially for crops in lowland and flooding-affected areas. Although rice (Oryza sativa) is considered a flood-tolerant crop, only limited cultivars display strong tolerance to prolonged submergence and/or hypoxic stress. Therefore, characterization of hypoxic resistant genes and/or germplasms have important theoretical and practical significance for rice breeding and sustained improvements. Previous investigations have demonstrated that loss-of-function of OsPIN2, a gene encoding an auxin efflux transporter, results in the loss of root gravitropism due to disrupted auxin transport in the root tip. In this study, we revealed a novel connection between OsPIN2 and reactive oxygen species (ROS) in modulating root gravitropism and hypoxia tolerance in rice. It is shown that the OsPIN2 mutant had decreased accumulation of ROS in root tip, due to the downregulation of glycolate oxidase encoding gene OsGOX6, one of the main H2O2 sources. The morphological defects of root including waved rooting and agravitropism in OsPIN2 mutant may be rescued partly by exogenous application of H2O2. The OsPIN2 mutant exhibited increased resistance to ROS toxicity in roots due to treatment with H2O2. Furthermore, it is shown that the OsPIN2 mutant had increased tolerance to hypoxic stress accompanied by lower ROS accumulation in roots, because the hypoxia stress led to over production of ROS in the roots of the wild type but not in that of OsPIN2 mutant. Accordingly, the anoxic resistance-related gene SUB1B showed differential expression in the root of the WT and OsPIN2 mutant in response to hypoxic conditions. Notably, compared with the wild type, the OsPIN2 mutant displayed a different pattern of auxin distribution in the root under hypoxia stress. It was shown that hypoxia stress caused a significant increase in auxin distribution in the root tip of the WT but not in that of the war1 mutant. In summary, these results suggested that OsPIN2 may play a role in regulating ROS accumulation probably via mediating auxin transport and distribution in the root tip, affecting root gravitropism and hypoxic tolerance in rice seedlings. These findings may contribute to the genetic improvement and identification of potential hypoxic tolerant lines in rice.
Batch Fine Magnetic Pattern Transfer Method on Permanent Magnets Using Coercivity Change during Heating for Magnetic MEMS

In magnetic microelectromechanical systems (MEMSs), permanent magnets in the form of a thick film or thin plate are used for structural and manufacturing purposes. However, the geometric shape induces a strong self-demagnetization field during thickness–direction magnetization, limiting the surface magnetic flux density and output power. The magnets must be segmented or magnetized in a fine and multi-pole manner to weaken the self-demagnetization field. Few studies have been performed on fine multi-pole magnetization techniques that can generate a higher surface magnetic flux density than segmented magnets and are suitable for mass production. This paper proposes a batch fine multi-pole magnetic pattern transfer (MPT) method for the magnets of MEMS devices. The proposed method uses two master magnets with identical magnetic patterns to sandwich a target magnet. Subsequently, the coercivity of the target magnet is reduced via heating, and the master magnet’s magnetic pattern is transferred to the target magnet. Stripe, checkerboard, and concentric circle patterns with a pole pitch of 0.3 mm are magnetized on the NdFeB master magnets N38EH with high intrinsic coercivity via laser-assisted heating magnetization. The MPT yields the highest surface magnetic flux density at 160 °C, reaching 39.7–66.1% of the ideal magnetization pattern on the NdFeB target magnet N35.
The Application of Decision Making Models in Sustainable Management of Environmental Systems

Abstract Recent research has highlighted environmental sustainability as a key issue of concern affecting the future of human wellbeing. To pursue a sustainable future of a system, it is a critical challenge to make decisions at all levels of management. Decision making for controlling or steering a system requires models of the system that represent the determining characteristics of the system and have sufficient predictive power. Practitioners/researchers of environmental management use a large variety of (types of) models to support decision making, and it has become a complex task to select the appropriate model-type for a desired management goal. This paper considers the most common types of models and uses the mathematical properties of these model types to discuss the pros and cons of their use in environmental management / decision making. We explore how these mathematical properties influence the feasibility, viability and validity of the use of such models.
Statistical estimation of crop yields for the Midwestern United States using satellite images, climate datasets, and soil property maps

: In this paper, we described the statistical modeling of crop yields using satellite images, climatic datasets, soil property maps, and fertilizer data for the Midwestern United States during 2001-2012. Satellite images were obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS), and climatic datasets were provided by the Parameter-elevation Regressions on Independent Slopes Model (PRISM) Climate Group. Soil property maps were derived from the Harmonized World Soil Database (HWSD). Our multivariate regression models produced quite good prediction accuracies, with differences of approximately 8-15% from the governmental statistics of corn and soybean yields. The unfavorable conditions of climate and vegetation in 2012 could have resulted in a decrease in yields according to the regression models, but the actual yields were greater than predicted. It can be interpreted that factors other than climate, vegetation, soil, and fertilizer may be involved in the negative biases. Also, we found that soybean yield was more affected by minimum temperature conditions while corn yield was more associated with photosynthetic activities. These two crops can have different potential impacts regarding climate change, and it is important to quantify the degree of the crop sensitivities to climatic variations to help adaptation by humans. Considering the yield decreases during the drought event, we can assume that climatic effect may be stronger than human adaptive capacity. Thus, further studies are demanded particularly by enhancing the data regarding human activities such as tillage, fertilization, irrigation, and comprehensive agricultural technologies.
A unified model for cross-modal plasticity and skill acquisition

Historically, cross-modal plasticity following early blindness has been largely studied in the context of visual deprivation. However, more recently, there has been a shift in focus towards understanding cross-modal plasticity from the perspective of skill acquisition: the striking plasticity observed in early blind individuals reflects the extraordinary perceptual and cognitive challenges they solve. Here, inspired by two seminal papers on skill learning (the “cortical recycling” theory) and cross-modal plasticity (the “metamodal” hypothesis) respectively, we present a unified hypothesis of cortical specialization that describes how shared functional, algorithmic, and structural constraints might mediate both types of plasticity.
Platinum and the Greenwich System of Time-Signals in Britain

The establishment of regular time-signals and their distribution throughout Britain by means of galvanic telegraphy was largely the outcome of collaboration between G. B. Airy and C. V. Walker, with some assistance from the brothers E. and L. Clark. The early history of this development, and the role which platinum occupied in its successful operation, is traced largely from records preserved in the archives of the Royal Greenwich Observatory at Herstmonceux Castle.
Mechanisms of Resistance and Therapeutic Perspectives in Immunotherapy for Advanced Head and Neck Cancers

Simple Summary The programmed death-1 receptor monoclonal antibody treatments pembrolizumab and nivolumab have been successful in the treatment of recurrent or metastatic head and neck cancer. However, about 60% of patients will experience recurrence after this immunotherapeutic treatment. This is believed to be due to a combination of innate and adaptive tumor characteristics that suppress the body’s immune response to recurrent or metastatic head and neck cancer. This review details the mechanisms of these innate and adaptive characteristics and describes potential treatments that could be used to target and overcome these immunotherapy resistance mechanisms. These include the combinations of novel and existing therapies aimed to overcome current challenges with immunotherapy resistance and hopefully leading to improved patient outcomes. Abstract Immunotherapy is emerging as an effective treatment for advanced head and neck cancers and interest in this treatment modality has led to rapid expansion of this research. Pembrolizumab and nivolumab, monoclonal antibodies directed against the programmed cell death-1 (PD-1) receptor, are US Food and Drug Administration (FDA)- and European Medical Agency (EMA)-approved immunotherapies for head and neck squamous cell carcinoma (HNSCC). Resistance to immunotherapy is common, with about 60% of patients with recurrent or metastatic HNSCC not responding to immunotherapy and only 20–30% of patients without disease progression in the long term. Overcoming resistance to immunotherapy is therefore essential for augmenting the effectiveness of immunotherapy in HNSCC. This review details the innate and adaptive mechanisms by which head and neck cancers can become resistant to immunotherapeutic agents, biomarkers that can be used for immunotherapy patient selection, as well as other factors of the tumor microenvironment correlated with therapeutic response and prognosis. Numerous combinations and novel immunotherapies are currently being trialed, based on better understood immune evasion mechanisms. These potential treatments hold the promise of overcoming resistance to immunotherapy in head and neck cancers.
Detection Of Cataract, Diabetic Retinopathy and Glaucoma Eye Diseases with Deep Learning Approach

Eye diseases are one of the serious health problems affecting human life. Cataract, diabetic retinopathy and glaucoma eye diseases cause visual impairment and cause irreversible eye defects. Throughout human life; genetic, age and environmental factors affect people's eye health. Detection of the disease plays a critical role in order to apply the right diagnosis and therefore increase the quality of life of the patient. With the developing technology, artificial intelligence can detect eye defects and therefore whether there is a disease or not. This study aims to develop solutions for detecting an important health problem such as eye health by using deep learning models. In the related study, Convolutional Neural Networks models, one of the deep learning types are used. The data set used for disease detection includes a total of 2748 Retinal Fundus images taken from 1374 normal individuals and 1374 different disease groups. In order to compare the classification performances and to achieve better performance, a solution to the disease detection problem was sought by using a total of 5 different Convolutional Neural Networks architectures. These are DenseNet, EfficientNet, Xception, VGG, Resnet. For the validity of the approach, it was tested using the 10-fold cross-validation technique. Accuracy, Recall, Precision, F1-Score, and Matthews’s coefficient correlation metrics were used as performance evaluation criteria. When the classification performances were examined, the results obtained with the EfficientNet architecture were measured as 94.88%, 94.88%, 95.02%, 94.88%, 89.89% for Accuracy, Recall, Precision, F1-Score, and Matthews’s coefficient correlation metrics. In this context, the best classification performance was obtained with the EfficientNet architecture.
Towards a replacement therapy for stimulant betel quid dependence: A proof of concept study

Abstract Stimulant betel quid (SBQ) containing Piper betle leaf (L), green unripe Areca catechu nut (AN) and the alkalizing agent, slaked lime, is an addictive, carcinogenic stimulant, with no pharmacotherapy, chewed by millions of people in the Asia/Pacific region. We compared the in vivo physiological profile of chewing (1) non‐stimulant P. betle leaf+AN (LAN), (2) SBQ utilizing slaked lime and (3) a novel SBQ utilizing Mg(OH)2, as an alkalizing agent, by measuring physiological parameters of intoxication and these were correlated with in vitro levels of alkaloids measured by UHPLC–MS/MS. Chewing LAN, which contains high levels of arecoline, had no stimulatory physiological effect. Chewing SBQ containing slaked lime or novel SBQ containing Mg(OH)2, induced equivalent stimulatory physiological responses. In vitro, slaked lime hydrolyzed muscarinic esters in LAN while Mg(OH)2 did not. The physiological stimulation induced by chewing both SBQ and the lack of physiology to chewing LAN can be explained by changes in lipid solubility of phytochemicals induced by mouth pH during chewing of basic SBQ or acidic LAN. Since antiquity people have added slaked lime to SBQ to enhance absorption of phyto‐chemicals across oral membranes to stimulate physiology. The same physiological changes can be induced by substituting slaked lime for less physically and chemically destructive bases. If attitudes regarding SBQ dependence can advance towards the more progressive attitudes already used to help smokers quit tobacco, modern chemistry has the potential to make chewing SBQ safer and quitting programs may become more accessible and efficacious.
Cardiovascular Biomarkers in Cardio-Oncology: Antineoplastic Drug Cardiotoxicity and Beyond

Serum biomarkers represent a reproducible, sensitive, minimally invasive and inexpensive method to explore possible adverse cardiovascular effects of antineoplastic treatments. They are useful tools in risk stratification, the early detection of cardiotoxicity and the follow-up and prognostic assessment of cancer patients. In this literature review, we aim at describing the current state of knowledge on the meaning and the usefulness of cardiovascular biomarkers in patients with cancer; analyzing the intricate relationship between cancer and cardiovascular disease (especially HF) and how this affects cardiovascular and tumor biomarkers; exploring the role of cardiovascular biomarkers in the risk stratification and in the identification of chemotherapy-induced cardiotoxicity; and providing a summary of the novel potential biomarkers in this clinical setting.
Electroacupuncture Reduces Fibromyalgia Pain via Neuronal/Microglial Inactivation and Toll-like Receptor 4 in the Mouse Brain: Precise Interpretation of Chemogenetics

Fibromyalgia (FM) is a complex, chronic, widespread pain syndrome that can cause significant health and economic burden. Emerging evidence has shown that neuroinflammation is an underlying pathological mechanism in FM. Toll-like receptors (TLRs) are key mediators of the immune system. TLR4 is expressed primarily in microglia and regulates downstream signaling pathways, such as MyD88/NF-κB and TRIF/IRF3. It remains unknown whether electroacupuncture (EA) has therapeutic benefit in attenuating FM pain and what role the TLR4 pathway may play in this effect. We compared EA with sham EA to eliminate the placebo effect due to acupuncture. We demonstrated that intermittent cold stress significantly induced an increase in mechanical and thermal FM pain in mice (mechanical: 2.48 ± 0.53 g; thermal: 5.64 ± 0.32 s). EA but not sham EA has an analgesic effect on FM mice. TLR4 and inflammatory mediator-related molecules were increased in the thalamus, medial prefrontal cortex, somatosensory cortex (SSC), and amygdala of FM mice, indicating neuroinflammation and microglial activation. These molecules were reduced by EA but not sham EA. Furthermore, a new chemogenetics method was used to precisely inhibit SSC activity that displayed an anti-nociceptive effect through the TLR4 pathway. Our results imply that the analgesic effect of EA is associated with TLR4 downregulation. We provide novel evidence that EA modulates the TLR4 signaling pathway, revealing potential therapeutic targets for FM pain.
Simultaneous Detection of Exosomal microRNAs Isolated from Cancer Cells Using Surface Acoustic Wave Sensor Array with High Sensitivity and Reproducibility

We present a surface acoustic wave (SAW) sensor array for microRNA (miRNA) detection that utilizes photocatalytic silver staining on titanium dioxide (TiO2) nanoparticles as a signal enhancement technique for high sensitivity with an internal reference sensor for high reproducibility. A sandwich hybridization was performed on working sensors of the SAW sensor array that could simultaneously capture and detect three miRNAs (miRNA-21, miRNA-106b, and miRNA-155) known to be upregulated in cancer. Sensor responses due to signal amplification varied depending on the concentration of synthetic miRNAs. It was confirmed that normalization (a ratio of working sensor response to reference sensor response) screened out background interferences by manipulating data and minimized non-uniformity in the photocatalytic silver staining step by suppressing disturbances to both working sensor signal and reference sensor signal. Finally, we were able to successfully detect target miRNAs in cancer cell-derived exosomal miRNAs with performance comparable to the detection of synthetic miRNAs.
Detection of Adulterated Oregano Samples Using Untargeted Headspace–Gas Chromatography–Ion Mobility Spectrometry Analysis

Oregano is often adulterated for economic reasons. This fraud mainly consists of adding other species with lower commercial value, such as olive leaves. To ensure the authenticity of oregano, an analytical method based on the analysis of the volatile organic compound (VOC) profile obtained by headspace gas chromatography coupled to ion mobility spectrometry (HS-GC-IMS) was developed and validated. Samples of ecological Mediterranean oregano adulterated with different percentages of two types of olive leaves (cornicabra and manzanilla) were studied using a non-targeted analysis. Moreover, a total of 30 VOCs were identified in the analyzed samples, and 24 compounds could be quantified using calibration curves based on Boltzmann’s equation. A chemometric model based on orthogonal partial least squares discriminant analysis (OPLS-DA) was used to detect the adulterated oregano samples, obtaining a 100% validation success rate, and partial least squares (PLS) analysis was used to quantify the percentage of adulterant. Finally, the proposed methodology was applied to 15 commercial oregano samples, resulting in two of them being classified as adulterated with 31 and 43% of olive leaves, respectively.
Study on the Fluid-Solid Coupling Heat Transfer in the Stator of Large Synchronous Condenser Considering Complex Transposition Structure

To solve the problem in which it is difficult to consider the complex transposition structure of the stator winding in the stator fluid-solid coupling analysis of a large synchronous condenser, a three-dimensional stator fluid-solid coupling model considering the transposition structure of the stator winding is established in this paper. Based on the heat transfer characteristics of the transposition insulation in the slot portion and involute insulation in the end portion, a non-uniform grid division method is proposed to solve the three-dimensional fluid-solid coupling model. Then, taking a 300 Mvar synchronous condenser as an example, the stator fluid distribution and temperature distribution were calculated and analyzed, and the results were compared with the non-transposition model and short-circuit type test data. The results show that the transposition structure affects the temperature distribution trend, temperature value and location of the highest temperature of the stator winding. The calculated results obtained using the transposition model were closer to the test data. Finally, the stator temperatures under no-load, over-excited, and no-excited operating conditions were calculated and compared. The results show that the stator temperature distribution trends and values are significantly affected by operating conditions. The stator temperature under the over-excited condition was the highest, and that under the no-load condition was the lowest.
Individual Building Rooftop Segmentation from High-resolution Urban Single Multispectral Image Using Superpixels

This paper proposes an algorithm to segment individual building rooftops from optical photographs based on superpixels. A novel method based on image complexity is proposed to calculate the number of superpixels and refine their boundaries. In order to differentiate buildings from ground and roads, the salient feature vectors are built by using features extracted from refined superpixels. In particular, those feature vectors are used to classify refined superpixels as object or non-object. Our segmentation algorithm is compared with two other state-of-the-art segmentation algorithms in terms of the recall and precision. Experiments show that the proposed method can segment building rooftops appropriately, yielding higher precision and better recall rates.