Miracle-dz/UniFAR-FTUs huggingface dataset:
Drosophila melanogaster as a versatile model organism to study genetic epilepsies: An overview

Epilepsy is one of the most prevalent neurological disorders, affecting more than 45 million people worldwide. Recent advances in genetic techniques, such as next-generation sequencing, have driven genetic discovery and increased our understanding of the molecular and cellular mechanisms behind many epilepsy syndromes. These insights prompt the development of personalized therapies tailored to the genetic characteristics of an individual patient. However, the surging number of novel genetic variants renders the interpretation of pathogenetic consequences and of potential therapeutic implications ever more challenging. Model organisms can help explore these aspects in vivo. In the last decades, rodent models have significantly contributed to our understanding of genetic epilepsies but their establishment is laborious, expensive, and time-consuming. Additional model organisms to investigate disease variants on a large scale would be desirable. The fruit fly Drosophila melanogaster has been used as a model organism in epilepsy research since the discovery of “bang-sensitive” mutants more than half a century ago. These flies respond to mechanical stimulation, such as a brief vortex, with stereotypic seizures and paralysis. Furthermore, the identification of seizure-suppressor mutations allows to pinpoint novel therapeutic targets. Gene editing techniques, such as CRISPR/Cas9, are a convenient way to generate flies carrying disease-associated variants. These flies can be screened for phenotypic and behavioral abnormalities, shifting of seizure thresholds, and response to anti-seizure medications and other substances. Moreover, modification of neuronal activity and seizure induction can be achieved using optogenetic tools. In combination with calcium and fluorescent imaging, functional alterations caused by mutations in epilepsy genes can be traced. Here, we review Drosophila as a versatile model organism to study genetic epilepsies, especially as 81% of human epilepsy genes have an orthologous gene in Drosophila. Furthermore, we discuss newly established analysis techniques that might be used to further unravel the pathophysiological aspects of genetic epilepsies.
Kinematics of the 12 GHz Methanol Masers toward W3(OH)

In this paper, two epochs (1994 February and 1998 December) of Very Long Baseline Array observations of the 12 GHz CH3OH masers toward the ultracompact (UC) H II region W3(OH) are compared. The 12 GHz maser emission is observed in three separated clumps (<500 AU in size), located north, south, and at the center of the UC H II region. The general structure of the 12 GHz maser emission has remained remarkably constant, with the maser spots identified in the 1994 epoch maps having similar relative positions (within a few milliarcseconds), shapes, and line-of-sight velocities (within a few tenths of a kilometer per second) as in the 1998 epoch maps. Cross-correlating the intensity distribution of the corresponding spots between the two epochs, we measure relative proper motions of amplitude in the range 1-7 km s-1. The spots of the southern clump move relative to the reference feature (located in the northern clump) with high velocities (≈4 km s-1) and have similar directions of motion. This suggests that the whole southern clump moves relative to the northern clump. The amplitude of the proper motions of the 12 GHz masers agrees well with the values measured for the 1.665 GHz OH masers in W3(OH). The different spatial distribution of the 1.665 and 12 GHz maser spots prevents one from performing a detailed comparison of the gas motion traced by the two maser transitions. The 12 GHz CH3OH masers of the northern clump are clearly associated with both the 6.035 GHz OH and 6.7 GHz CH3OH masers. In particular, the same "linear structure" (≈200 mas in size at P.A. = 141°) is traced by the most intense spots of the three maser species, and they all show a common regular variation of the line-of-sight velocities along it. We fit the measured three-dimensional velocities and the sky-projected positions of the 12 GHz maser spots of the northern clump with an "expanding conical model," in which the spots lie on the surface of a bipolar cone and are accelerated radially outward, spiraling with a constant angular velocity around the cone axis. The model suggests that geometrical conditions may play an important selective role in the observation of the strong 12 GHz masers since the strongest spots are predicted at positions on the conical surface where the cone generators are approximately parallel (within a few degrees) to the line of sight.
Disparate Clinical Characteristics and Prognosis of HFpEF versus HFrEF Phenotype of Diabetic Cardiomyopathy

Aims: Diabetic cardiomyopathy (DCM) is an ill-defined entity. This study aims to explore the clinical characteristics and prognosis of diabetic patients that disparately develop heart failure (HF) with preserved ejection fraction (HFpEF) other than HF with reduced ejection fraction (HFrEF). Patients and Methods: A total of 911 patients diagnosed with diabetes mellitus were identified in the ChiHFpEF cohort (NCT05278026). DCM was defined as diabetic patients diagnosed with HF, absent from flow obstructive coronary artery disease (CAD), uncontrolled refractory hypertension and hemodynamics significant heart valvular diseases, arrhythmia and congenital heart diseases. The primary endpoint was a composite of all-cause death and rehospitalization due to HF. Results: As compared to DCM-HFrEF patients, DCM-HFpEF patients had a longer duration of diabetes, were older and more noticeable in hypertension and non-obstructive CAD. After a median follow-up of 45.5 months, survival analysis showed that DCM-HFpEF patients had a better composite endpoint. Cox regression implicated that non-obstructive CAD was a negative (HR 0.101, 95% CI 0.028–0.373, p = 0.001) predictor for the composite endpoint of DCM-HFrEF patients. Age was a positive predictor for the composite endpoint of DCM-HFpEF patients (HR 1.044, 95% CI 1.007–1.082, p = 0.018). Conclusion: DCM-HFpEF is a disparate entity from DCM-HFrEF. Additional phenomic studies are needed to explore the molecular mechanisms and develop targeted therapies.
High-power phosphor-converted light-emitting diodes based on III-Nitrides

Phosphor conversion of light-emitting diode light for white light sources and some monochrome applications requires particular phosphor properties and has to take into account specific issues if aimed at high-power output. Limitations and solutions will be discussed, giving special considerations to drive and temperature dependencies. Efficiencies of 32 lm/W for white with good color rendering at 4600 K and 35 lm/W for green (535 nm) have been demonstrated.
Unsupervised Cross-Modal Hashing via Semantic Text Mining

Cross-modal hashing has been widely used in multimedia retrieval tasks due to its fast retrieval speed and low storage cost. Recently, many deep unsupervised cross-modal hashing methods have been proposed to deal the unlabeled datasets. These methods usually construct an instance similarity matrix by fusing the image and text modality-specific similarity matrices as the guiding information to train the hashing networks. However, most of them directly use cosine similarities between the bag-of-words (BoW) vectors of text datapoints to define the text modality-specific similarity matrix, which fails to mine the semantic similarity information contained in the text modal datapoints and leads to the poor quality of the instance similarity matrix. To tackle the aforementioned problem, in this paper, we propose a novel Unsupervised Cross-modal Hashing via Semantic Text Mining, called UCHSTM. Specifically, UCHSTM first mines the correlations between the words of text datapoints. Then, UCHSTM constructs the text modality-specific similarity matrix for the training instances based on the mined correlations between their words. Next, UCHSTM fuses the image and text modality-specific similarity matrices as the final instance similarity matrix to guide the training of hashing model. Furthermore, during the process of training the hashing networks, a novel self-redefined-similarity loss is proposed to further correct some wrong defined similarities in the constructed instance similarity matrix, thereby further enhancing the retrieval performance. Extensive experiments on two widely used datasets show that the proposed UCHSTM outperforms state-of-the-art baselines on cross-modal retrieval tasks.
Expression level of CD117 (KIT) on ovarian cancer extracellular vesicles correlates with tumor aggressiveness

Ovarian cancer is known to be the most lethal malignancy among all gynecological cancers affecting a large number of women worldwide. The treatment of ovarian cancer is challenging due to the high recurrence rate of the disease and is further complicated by acquired chemoresistance. Most ovarian cancer deaths are the result of the metastatic spread of drug-resistant cells. The theory of cancer stem cells (CSC) suggests that both tumor initiation and progression are driven by a population of undifferentiated capable of self-renewal, tumor initiation and development of chemoresistance. The CD117 mast/stem cell growth factor receptor (KIT) is the most commonly used marker for ovarian CSCs. Here, we analyze the correlation between CD117 expression and histological tumor type in ovarian cancer cell lines (SK-OV-3 and MES-OV) and in small/medium extracellular vesicles (EVs) isolated from the urine of ovarian cancer patients. We have demonstrated that the abundance of CD117 on cells and EVs is correlated with tumor grade and therapy resistance status. Moreover, using small EVs isolated from ovarian cancer ascites, it was shown that recurrent disease is characterized by a much higher abundance of CD117 on EVs than primary tumor.
Detection of COVID-19 Case from Chest CT Images Using Deformable Deep Convolutional Neural Network

The infectious coronavirus disease (COVID-19) has become a great threat to global human health. Timely and rapid detection of COVID-19 cases is very crucial to control its spreading through isolation measures as well as for proper treatment. Though the real-time reverse transcription-polymerase chain reaction (RT-PCR) test is a widely used technique for COVID-19 infection, recent researches suggest chest computed tomography (CT)-based screening as an effective substitute in cases of time and availability limitations of RT-PCR. In consequence, deep learning-based COVID-19 detection from chest CT images is gaining momentum. Furthermore, visual analysis of data has enhanced the opportunities of maximizing the prediction performance in this big data and deep learning realm. In this article, we have proposed two separate deformable deep networks converting from the conventional convolutional neural network (CNN) and the state-of-the-art ResNet-50, to detect COVID-19 cases from chest CT images. The impact of the deformable concept has been observed through performance comparative analysis among the designed deformable and normal models, and it is found that the deformable models show better prediction results than their normal form. Furthermore, the proposed deformable ResNet-50 model shows better performance than the proposed deformable CNN model. The gradient class activation mapping (Grad-CAM) technique has been used to visualize and check the targeted regions' localization effort at the final convolutional layer and has been found excellent. Total 2481 chest CT images have been used to evaluate the performance of the proposed models with a train-valid-test data splitting ratio of 80 : 10 : 10 in random fashion. The proposed deformable ResNet-50 model achieved training accuracy of 99.5% and test accuracy of 97.6% with specificity of 98.5% and sensitivity of 96.5% which are satisfactory compared with related works. The comprehensive discussion demonstrates that the proposed deformable ResNet-50 model-based COVID-19 detection technique can be useful for clinical applications.
Electrodermal activity and heart rate variability for detection of peripheral abnormalities in type 2 diabetes: A review

Modern medicine exhibits an upward trend towards non-invasive methods for the early detection of disease and long-term monitoring of patients’ health. Diabetes mellitus and its complications are a promising area for implementation of new medical diagnostic devices. One of the most serious complications of diabetes is diabetic foot ulcer. The main causes responsible for diabetic foot ulcer are ischemia caused by peripheral artery disease and diabetic neuropathy caused by polyol pathway-induced oxidative stress. Autonomic neuropathy impairs function of sweat glands, which can be measured by electrodermal activity. On the other hand, autonomic neuropathy leads to changes in heart rate variability, which is used to assess autonomic regulation of the sinoatrial node. Both methods are enough sensitive to detect pathological changes caused by autonomic neuropathy and are promising screening methods for early diagnosis of diabetic neuropathy, which could prevent the onset of diabetic ulcer.
Skin-Based Vaccination: A Systematic Mapping Review of the Types of Vaccines and Methods Used and Immunity and Protection Elicited in Pigs

The advantages of skin-based vaccination include induction of strong immunity, dose-sparing, and ease of administration. Several technologies for skin-based immunisation in humans are being developed to maximise these key advantages. This route is more conventionally used in veterinary medicine. Skin-based vaccination of pigs is of high relevance due to their anatomical, physiological, and immunological similarities to humans, as well as being a source of zoonotic diseases and their livestock value. We conducted a systematic mapping review, focusing on vaccine-induced immunity and safety after the skin immunisation of pigs. Veterinary vaccines, specifically anti-viral vaccines, predominated in the literature. The safe and potent skin administration to pigs of adjuvanted vaccines, particularly emulsions, are frequently documented. Multiple methods of skin immunisation exist; however, there is a lack of consistent terminology and accurate descriptions of the route and device. Antibody responses, compared to other immune correlates, are most frequently reported. There is a lack of research on the underlying mechanisms of action and breadth of responses. Nevertheless, encouraging results, both in safety and immunogenicity, were observed after skin vaccination that were often comparable to or superior the intramuscular route. Further research in this area will underlie the development of enhanced skin vaccine strategies for pigs, other animals and humans.
Offline Measurement of Stator Resistance and Inverter Voltage Drop Using Least Squares

Modern industrial and commercial electrical drives are typically designed to control motors of different types, operating under scalar, field-oriented or direct torque control. These drives execute various algorithm such as sensorless control, temperature monitoring, etc., which require the knowledge of motor drive parameters. In order to do this, the inverters allow data to be input manually or execute self-commissioning routines before running the motor. The parameters necessary for proper operation include motor phase resistance and inverter voltage drop, which are especially important in low-speed range. This paper presents an offline technique for estimation of mentioned parameters, which can be obtained with enough precision for the overwhelming majority of applications. The proposed algorithm consequently injects DC-current of several levels into the stator of a motor, measuring the corresponding voltages. Each pair of current and voltage is a point in the voltage current characteristic of motor drive, thus set of these points maybe approximated with a first order polynomial using least squares method. The parameters of polynomial are desired inverter voltage drop and phase resistance. The experimental Section analyzes estimation errors and their dependence on the number of injected levels, their values and filtering capability of measuring algorithm. After that, the authors give suggestions on algorithm parameters selection, depending on the demanded precision. Finally, the authors demonstrate a mass-producing dishwasher motor drive, which adopted this technique.
The Gender Gap in Earnings

The gender income gap is a much debated subject both at an analytical and economic level. This article considers both, but emphasizes the different ways the data can be analyzed. The authors show that a hierarchical linear model is the best way to evaluate male-female wage differentials. Both interindustry and intraindustry wage disparities between men and women are measured by using a technique that assumes that observations within the same industry have correlated error terms. By simultaneously testing human capital factors and environmental factors, the analysis model serves as a link between theory and empirical analysis. The results show that the wage differences are larger in some industries than in others, so that it can be assumed that a gender income gap is not only a function of individual differences in qualification, but also differences between industries. The between-industry differences in gender income gaps contradict the hypothesis that gender income differential is largely due to female work preferences and the resulting segregation.
Geochemistry: Sedimentary geochemistry: environments and processes dominated by involvement of an aqueous phase

Crust and mantle processes yield mineral assemblages which are not generally stable in Earth surface environments. Uplift to the zone of weathering therefore initiates chemical reactions which produce quite different assemblages. The precise nature of the resulting minerals depends much upon the composition of the aqueous phase present. Erosion, sedimentation and diagenesis move weathering products through a succession of chemical environments. Further solid-solution reactions occur at each stage. Until recently, the least-well documented part of this cycle was burial diagenesis. New information provided by oil companies from their submarine exploration programmes has done much to rectify the situation. It is now possible to present a fairly complete account of the important chemical reactions occurring at each stage of the surface cycle. The major conclusion to be drawn from this exercise is that reactions involving an aqueous phase play a very important part in geological evolution as a whole.
Thermodynamic modeling of a phase transformation in protein filaments with mechanical function

Protective egg capsules from whelks (intertidal marine gastropods) were recently demonstrated to derive their impressive mechanical behavior—reminiscent of the pseudoelastic behavior in some alloy systems from a reversible phase transition of component protein building blocks from a compact α-helical conformation to a more extended softer conformation, called β*. This behavior was previously modeled under equilibrium conditions, demonstrating that the transition from the α- to β*-phase could account for the pronounced yield plateau and reversibility; however, a theoretical understanding of the non-equilibrium behaviors of the egg capsule (e.g. strain rate dependence and hysteresis) requires a new approach. Here, we modify the previously proposed model in order to address the time-dependent behaviors of the whelk egg capsule biopolymer. Our results indicate that hysteresis during cyclic loading originates from a mismatch between the speed of the mechanical driving force and the rate at which the phase transition occurs. Furthermore, the characteristic curved shape of the stress–strain plot arises from a nonlinear relationship between the transformation rate and the amount of applied load. These results have important implications for our understanding of the mechanics of biological polymers and may have implications for the design of biomimetic pseudoelastic polymers.
A Dual Triggering Scheme to Adaptive Fuzzy Resilient Control of Switched Nonlinear Systems Against Mixed Attacks

A dual event-triggered adaptive fuzzy resilient control scheme for a class of switched nonlinear systems with vanishing control gains under mixed attacks is proposed in this article. The scheme proposed achieves dual triggering in the channels of sensor-to-controller and controller-to-actuator by designing two new switching dynamic event-triggering mechanisms (ETMs). An adjustable positive lower bound of interevent times for each ETM is found to preclude Zeno behavior. Meanwhile, mixed attacks, that is, deception attacks on sampled state and controller data and dual random denial-of-service attacks on sampled switching signal data, are handled by constructing event-triggered adaptive fuzzy resilient controllers of subsystems. Compared with the existing works for switched systems with only single triggering, more complex asynchronous switching caused by dual triggering and mixed attacks and subsystem switching is addressed. Further, the obstacle caused by vanishing control gains at some points is eliminated by proposing an event-triggered state-dependent switching law and introducing vanishing control gains into a switching dynamic ETM. Finally, a mass–spring–damper system and a switched RLC circuit system are applied to verify the obtained result.
What’s on the Other Side of the Gate: A Structural Perspective on DNA Gate Opening of Type IA and IIA DNA Topoisomerases

DNA topoisomerases have an essential role in resolving topological problems that arise due to the double-helical structure of DNA. They can recognise DNA topology and catalyse diverse topological reactions by cutting and re-joining DNA ends. Type IA and IIA topoisomerases, which work by strand passage mechanisms, share catalytic domains for DNA binding and cleavage. Structural information has accumulated over the past decades, shedding light on the mechanisms of DNA cleavage and re-ligation. However, the structural rearrangements required for DNA-gate opening and strand transfer remain elusive, in particular for the type IA topoisomerases. In this review, we compare the structural similarities between the type IIA and type IA topoisomerases. The conformational changes that lead to the opening of the DNA-gate and strand passage, as well as allosteric regulation, are discussed, with a focus on the remaining questions about the mechanism of type IA topoisomerases.
Type Ia Supernovae: Bolometric properties and new tools for photometric techniques

Using a novel fitting technique, I have constructed bolometric (or UVOIR) light curves from individual filter photometry for a large sample of type Ia supernovea. The UVOIR light curve provides a sorely needed tool that allows us to link observations with results obtained from modern explosion models. With this sample of UVOIR light curves I have been able to directly investigate the explosion mechanism and subsequent radiation transport. I place new constraints on the ejected mass, the explosion mechanism and on the Hubble constant. Finally, I provide an atlas of spectrophotometric standard stars useful for calibrating future observations of type Ia supernovae.