Miracle-dz/UniFAR-FTUs huggingface dataset:
Sex Differences in Mitral Annular Calcification and the Clinical Implications

Background: Heterogeneous mechanisms may contribute to the occurrence of mitral annular calcification (MAC), however, little is known about the sex differences in MAC and the clinical implications of these differences. This study aimed to investigate clinical and imaging differences of MAC according to sex. Methods: In total, 537 patients (221 men) with MAC were identified by transthoracic echocardiography at a single center from January 2012 to June 2016. Moderate-to-severe MAC was defined as calcification extent ≥120° of the mitral annulus. Significant functional mitral stenosis (MS) was defined as a transmitral mean diastolic pressure gradient ≥5 mmHg. Results: Women more frequently had moderate-to-severe MAC and concomitant mitral regurgitation than men; however, significant functional MS was comparable between sexes. In the logistic regression analysis, old age, uncontrolled hypertension, end-stage renal disease (ESRD), and obstructive hypertrophic cardiomyopathy were significantly associated with moderate-to-severe MAC in women, whereas ESRD and moderate-to-severe aortic stenosis were in men. In the Cox regression analysis, significant functional MS was associated with all-cause death in both sexes, although an independent association was found in only women. Conclusion: Women had more extended MAC than men. Significant functional MS was independently associated with unfavorable clinical outcomes in patients with MAC, which was more pronounced in women than in men.
Efficient Ventricular Parameter Estimation Using AI-Surrogate Models

The onset and progression of pathological heart conditions, such as cardiomyopathy or heart failure, affect its mechanical behaviour due to the remodelling of the myocardial tissues to preserve its functional response. Identification of the constitutive properties of heart tissues could provide useful biomarkers to diagnose and assess the progression of disease. We have previously demonstrated the utility of efficient AI-surrogate models to simulate passive cardiac mechanics. Here, we propose the use of this surrogate model for the identification of myocardial mechanical properties and intra-ventricular pressure by solving an inverse problem with two novel AI-based approaches. Our analysis concluded that: (i) both approaches were robust toward Gaussian noise when the ventricle data for multiple loading conditions were combined; and (ii) estimates of one and two parameters could be obtained in less than 9 and 18 s, respectively. The proposed technique yields a viable option for the translation of cardiac mechanics simulations and biophysical parameter identification methods into the clinic to improve the diagnosis and treatment of heart pathologies. In addition, the proposed estimation techniques are general and can be straightforwardly translated to other applications involving different anatomical structures.
Mediating Role of Rumination and Negative Affect in the Effect of Mind-Wandering on Symptoms in Patients With Obsessive-Compulsive Disorder

To explore the relationship between negative affect, mind-wandering, rumination and obsessive-compulsive symptoms, 100 patients with obsessive-compulsive disorder and 100 healthy controls were assessed using the Obsessive-Compulsive Inventory, the Beck Anxiety Inventory, the Beck Depression Inventory, the Mind Wandering Scale and the Ruminative Response Scale. The results show that (i) patients diagnosed with obsessive-compulsive disorder displayed higher obsessive-compulsive symptoms, negative affect, mind-wandering and rumination compared with healthy controls; (ii) negative affect, mind-wandering and rumination were positively correlated with the severity of obsessive-compulsive symptoms; (iii) mind-wandering predicted the severity of obsessive-compulsive symptoms (both directly and indirectly); (iv) rumination and negative affect mediated the relationship between mind-wandering and obsessive-compulsive symptoms. The results preliminarily reveal the relationship between mind-wandering and psychopathological obsessive-compulsive symptoms, providing a reference for exploring novel psychological treatments for obsessive-compulsive disorder.
Deep Learning for Classification and Selection of Cine CMR Images to Achieve Fully Automated Quality-Controlled CMR Analysis From Scanner to Report

Introduction: Deep learning demonstrates great promise for automated analysis of CMR. However, existing limitations, such as insufficient quality control and selection of target acquisitions from the full CMR exam, are holding back the introduction of deep learning tools in the clinical environment. This study aimed to develop a framework for automated detection and quality-controlled selection of standard cine sequences images from clinical CMR exams, prior to analysis of cardiac function. Materials and Methods: Retrospective study of 3,827 subjects that underwent CMR imaging. We used a total of 119,285 CMR acquisitions, acquired with scanners of different magnetic field strengths and from different vendors (1.5T Siemens and 1.5T and 3.0T Phillips). We developed a framework to select one good acquisition for each conventional cine class. The framework consisted of a first pre-processing step to exclude still acquisitions; two sequential convolutional neural networks (CNN), the first (CNNclass) to classify acquisitions in standard cine views (2/3/4-chamber and short axis), the second (CNNQC) to classify acquisitions according to image quality and orientation; a final algorithm to select one good acquisition of each class. For each CNN component, 7 state-of-the-art architectures were trained for 200 epochs, with cross entropy loss and data augmentation. Data were divided into 80% for training, 10% for validation, and 10% for testing. Results: CNNclass selected cine CMR acquisitions with accuracy ranging from 0.989 to 0.998. Accuracy of CNNQC reached 0.861 for 2-chamber, 0.806 for 3-chamber, and 0.859 for 4-chamber. The complete framework was presented with 379 new full CMR studies, not used for CNN training/validation/testing, and selected one good 2-, 3-, and 4-chamber acquisition from each study with sensitivity to detect erroneous cases of 89.7, 93.2, and 93.9%, respectively. Conclusions: We developed an accurate quality-controlled framework for automated selection of cine acquisitions prior to image analysis. This framework is robust and generalizable as it was developed on multivendor data and could be used at the beginning of a pipeline for automated cine CMR analysis to obtain full automatization from scanner to report.
Possible Interactions of Extracellular Loop IVP2-S6 With Voltage-Sensing Domain III in Cardiac Sodium Channel

Motion transmission from voltage sensors to inactivation gates is an important problem in the general physiology of ion channels. In a cryo-EM structure of channel hNav1.5, residues N1736 and R1739 in the extracellular loop IVP2-S6 approach glutamates E1225 and E1295, respectively, in the voltage-sensing domain III (VSD-III). ClinVar-reported variants E1230K, E1295K, and R1739W/Q and other variants in loops IVP2-S6, IIIS1-S2, and IIIS3-S4 are associated with cardiac arrhythmias, highlighting the interface between IVP2-S6 and VSD-III as a hot spot of disease mutations. Atomic mechanisms of the channel dysfunction caused by these mutations are unknown. Here, we generated mutants E1295R, R1739E, E1295R/R1739E, and N1736R, expressed them in HEK-293T cells, and explored biophysical properties. Mutation E1295R reduced steady-state fast inactivation and enhanced steady-state slow inactivation. In contrast, mutation R1739E slightly enhanced fast inactivation and attenuated slow inactivation. Characteristics of the double mutant E1295R/R1739E were rather similar to those of the wild-type channel. Mutation N1736R attenuated slow inactivation. Molecular modeling predicted salt bridging of R1739E with the outermost lysine in the activated voltage-sensing helix IIIS4. In contrast, the loss-of-function substitution E1295R repelled R1739, thus destabilizing the activated VSD-III in agreement with our data that E1295R caused a depolarizing shift of the G-V curve. In silico deactivation of VSD-III with constraint-maintained salt bridge E1295-R1739 resulted in the following changes: 1) contacts between IIIS4 and IVS5 were switched; 2) contacts of the linker-helix IIIS4-S5 with IVS5, IVS6, and fast inactivation tripeptide IFM were modified; 3) contacts of the IFM tripeptide with helices IVS5 and IVS6 were altered; 4) mobile loop IVP2-S6 shifted helix IVP2 that contributes to the slow inactivation gate and helix IVS6 that contributes to the fast inactivation gate. The likelihood of salt bridge E1295-R1739 in deactivated VSD-III is supported by Poisson–Boltzmann calculations and state-dependent energetics of loop IVP2-S6. Taken together, our results suggest that loop IVP2-S6 is involved in motion transmission from VSD-III to the inactivation gates.
The Changes in Concentration of Cerebral Oxygenated Hemoglobin During Single Event-Related Japanese Shiritori Task in Patients With Major Depression Disorder: Comparison With Healthy Subjects

Patients with major depressive disorder (MDD) have been reported to show cognitive impairments in attention, cognition control, and motivation. The purpose of this study is to compare and examine the characteristics of frontal and temporal cortical activity in outpatients with MDD during the word production task (Shiritori) using a single event-related Near-Infrared Spectroscopy (NIRS) measurement method that was originally devised. The subjects were 29 MDD patients and 29 age matched healthy controls. In this task, one session consisted of two contrasting conditions (word production task, control condition), and all subjects alternated between these conditions. Each word was visually presented by a monitor for 0.3 s as an activation task and a fixed circle was presented for 12 s. In the activation task, subjects had to immediately generate a noun that starts with the last syllable of the presented word and they were required to say only creatures. From the data obtained at each measurement point during the 20 trials, and averaged waveform during activation task (20 trials) was calculated for each channel. During the word production task, the MDD patients showed significantly smaller activation than the controls in the prefrontal cortex area and inferior parietal area, especially in the left area. In addition, there was a significant negative correlation between Δoxy-Hb at the bilateral temporal lobe area and HAM-D total score in the MDD patients. These findings suggest that a single event-related NIRS measurement during Japanese shiritori tasks may be useful tool for evaluating psychophysiological indices in MDD patients, that relationship between activation and symptom may be of help in predicting functional outcome in patients.
Widely Targeted Metabolomics Analysis to Reveal Transformation Mechanism of Cistanche Deserticola Active Compounds During Steaming and Drying Processes

Cistanche deserticola is one of the most precious plants, traditionally as Chinese medicine, and has recently been used in pharmaceutical and healthy food industries. Steaming and drying are two important steps in the processing of Cistanche deserticola. Unfortunately, a comprehensive understanding of the chemical composition changes of Cistanche deserticola during thermal processing is limited. In this study, ultra-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS)-based widely targeted metabolomics analysis was used to investigate the transformation mechanism of Cistanche deserticola active compounds during steaming and drying processes. A total of 776 metabolites were identified in Cistanche deserticola during thermal processing, among which, 77 metabolites were differentially regulated (p < 0.05) wherein 39 were upregulated (UR) and 38 were downregulated (DR). Forty-seven (17 UR, 30 DR) and 30 (22 UR, 8 DR) differential metabolites were identified during steaming and drying, respectively. The most variation of the chemicals was observed during the process of steaming. Metabolic pathway analysis indicated that phenylpropanoid, flavonoid biosynthesis, and alanine metabolism were observed during steaming, while glycine, serine, and threonine metabolism, thiamine metabolism, and unsaturated fatty acid biosynthesis were observed during drying. The possible mechanisms of the chemical alterations during thermal processing were also provided by the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Furthermore, the blackening of the appearance of Cistanche deserticola mainly occurred in the steaming stage rather than the drying stage, which is associated with the metabolism of the amino acids. All results indicated that the formation of active compounds during the processing of Cistanche deserticola mainly occurred in the steaming stage.
An In-Silico, In-Vitro and In-Vivo Combined Approach to Identify NMNATs as Potential Protein Targets of ProEGCG for Treatment of Endometriosis

Endometriosis is defined as endometrial tissues found outside the uterine cavity. ProEGCG is a prodrug of Epigallocatechin gallate (EGCG), a potent polyphenol found in green tea. It inhibits the development of endometriotic lesions of mouse model in vivo, with higher efficacy and more remarkable anti-oxidative ability than EGCG. Our study aims to identify the molecular binding targets and pharmacological actions of ProEGCG in treating endometriosis. Protein target interaction study is essential to fully characterize the mechanism of actions, related therapeutic effects, and side effects. We employed a combined approach, starting with an in silico reverse screening of protein targets and molecular docking, followed by in vitro cellular thermal shift assay (CESTA) to assess the stability of protein-small molecule complexes. Then microarray and immunostaining of endometriotic lesions in mice in vivo confirmed the molecular interaction of the selected targets after treatment. Our study identified enzymes nicotinamide nucleotide adenylyltransferase (NMNAT)1 and NMNAT3 as protein targets of ProEGCG in silico and in vitro and were overexpressed after ProEGCG treatment in vivo. These findings suggested that participation in nicotinate and nicotinamide metabolism potentially regulated the redox status of endometriosis via its antioxidative capacities through binding to the potential therapeutic targets of ProEGCG.
The Concept of Steady State, Cyclicity and Debris Unloading of Debris-Covered Glaciers

It can easily be expected that debris-covered glaciers show a different response on external forcing compared to clean-surface glaciers. The supra-glacial debris cover acts as an additional transfer layer for the energy exchange between atmosphere and ice. The related glacier reaction is the integral of local effects, which changes strongly between enhanced melt for thin debris layers and considerably reduced melt for thicker debris. Therefore, a realistic feedback study can only be performed, if both the ice flow and the debris-influenced melt is treated with a high degree of detail. We couple a full Stokes representation of ice dynamics and the most complete description of energy transfer through the debris layer, in order to describe the long-term glacier reaction in the coupled system. With this setup, we can show that steady-state conditions are highly unlikely for glaciers, in case debris is not unloaded from the surface. For continuous and complete debris removal from the lowermost glacier tongue, however, a balance of the debris budget and the glacier conditions are possible. Depending on displacement and removal processes, our results demonstrate that debris-covered glaciers have an inherent tendency to switch to an oscillating state. Then, glacier mass balance and debris balance are out of phase, such that glacier advance periods end with the separation of the heavily debris-loaded lowermost glacier tongue, at time scales of decades to centuries. As these oscillations are inherent and happen without any variations in climatic forcing, it is difficult to interpret modern observations on the fluctuation of debris-covered glaciers on the basis of a changing climate only.
Artificial Intelligence Combined With Big Data to Predict Lymph Node Involvement in Prostate Cancer: A Population-Based Study

Background A more accurate preoperative prediction of lymph node involvement (LNI) in prostate cancer (PCa) would improve clinical treatment and follow-up strategies of this disease. We developed a predictive model based on machine learning (ML) combined with big data to achieve this. Methods Clinicopathological characteristics of 2,884 PCa patients who underwent extended pelvic lymph node dissection (ePLND) were collected from the U.S. National Cancer Institute’s Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2015. Eight variables were included to establish an ML model. Model performance was evaluated by the receiver operating characteristic (ROC) curves and calibration plots for predictive accuracy. Decision curve analysis (DCA) and cutoff values were obtained to estimate its clinical utility. Results Three hundred and forty-four (11.9%) patients were identified with LNI. The five most important factors were the Gleason score, T stage of disease, percentage of positive cores, tumor size, and prostate-specific antigen levels with 158, 137, 128, 113, and 88 points, respectively. The XGBoost (XGB) model showed the best predictive performance and had the highest net benefit when compared with the other algorithms, achieving an area under the curve of 0.883. With a 5%~20% cutoff value, the XGB model performed best in reducing omissions and avoiding overtreatment of patients when dealing with LNI. This model also had a lower false-negative rate and a higher percentage of ePLND was avoided. In addition, DCA showed it has the highest net benefit across the whole range of threshold probabilities. Conclusions We established an ML model based on big data for predicting LNI in PCa, and it could lead to a reduction of approximately 50% of ePLND cases. In addition, only ≤3% of patients were misdiagnosed with a cutoff value ranging from 5% to 20%. This promising study warrants further validation by using a larger prospective dataset.
Pumpkin (Cucurbita moschata) HSP20 Gene Family Identification and Expression Under Heat Stress

Pumpkin (Cucurbita moschata) is an important cucurbit vegetable crop that has strong resistance to abiotic stress. While heat shock protein 20 (HSP20) has been implicated in vegetable response to heat stress, little is known regarding activity of HSP20 family proteins in C. moschata. Here, we performed a comprehensive genome-wide analysis to identify and characterize the functional dynamics of the Cucurbita moschata HSP20 (CmoHSP20) gene family. A total of 33 HSP20 genes distributed across 13 chromosomes were identified from the pumpkin genome. Our phylogenetic analysis determined that the CmoHSP20 proteins fell into nine distinct subfamilies, a division supported by the conserved motif composition and gene structure analyses. Segmental duplication events were shown to play a key role in expansion of the CmoHSP20 gene family. Synteny analysis revealed that 19 and 18 CmoHSP20 genes were collinear with those in the cucumber and melon genomes, respectively. Furthermore, the expression levels of pumpkin HSP20 genes were differentially induced by heat stress. The transcript level of CmoHSP20-16, 24 and 25 were down-regulated by heat stress, while CmoHSP20-7, 13, 18, 22, 26 and 32 were up-regulated by heat stress, which could be used as heat tolerance candidate genes. Overall, these findings contribute to our understanding of vegetable HSP20 family genes and provide valuable information that can be used to breed heat stress resistance in cucurbit vegetable crops.
Assessing InSAR Coherence for Quantification of Agriculture Area Affected by Rainfall Events in Gujrat, India

In the present study, InSAR coherence products extracted from Sentinel-1 were used for the quantification of the agricultural region affected by normal to large excess rainfall events that occurred in the districts of Gujrat, India in July and August 2020. In this analysis, the coherence values retrieved during co-event (i.e. during rainfall) were comparatively found lower than the pre-event (i.e. before rainfall) for all the sub-divisions of the districts. Here, the pre-event and co-event coherence histograms were drawn and their point of intersection was used to determine an optimal threshold value for coherence below which agriculture area was considered as affected due to rainfall. More than 80 % of the total study area was found affected due to rainfall events. The obtained outcomes were crossexamined with the Landsat-8 images obtained for the study duration and the results were found encouraging.
Assessment of the Capability to Monitor Oil Inventories During the COVID-19 Pandemic by Using Sentinel-1 Data

Oil stock estimation has a direct impact on the oil price and is a critical asset in the global economy. The COVID-19 pandemic and the subsequent lockdowns in multiple countries had a big impact on the oil inventories. In this paper, a methodology to monitor oil inventories using Sentinel-1 data is presented. It exploits the differences in the backscatter response with respect to the roof level in floating roof tanks. The methodology doesn't imply advanced processing techniques (such as interferogram or coherence estimation) and it can be complemented with other data sources (e.g. Sentinel-2, VHR) for a more comprehensive foundation of the oil stock estimation.
Analysis of the Seasonal Variation of Horizontal Delay Gradient for the Tropical Island Singapore

In recent years, the research on atmospheric gradient for weather forecasting as well as GPS positioning has significantly increased as it contains real atmospheric information. In this paper, the seasonal dependence of the atmospheric gradient orientation consistent with wind direction has been presented vividly. We have illustrated the gradient time series of four different monsoon seasons for the tropical station Singapore. The tropospheric gradient alters its direction from season to season and mostly remains oriented along the wind flow. Also, the cumulative distribution plot between the abrupt change in gradient and precipitation gives rise to an indicative feature for rainfall forecasting. The results substantiate the fact that gradient can be contemplated as an imperative atmospheric parameter for predicting a weather event for a larger region.
An Improved InSAR Baseline Estimation Based on Interferometric Fringe Frequency

Baseline is an essential parameter in Interferometric SAR (InSAR). It's directly related to the estimation accuracy of elevation, so precise baseline estimation is required. Aiming at the existing baseline estimation methods based on interferometric fringe frequency, this paper proposes an improved baseline estimation method based on least square. Compared with the existing methods, the proposed method can fully exploit the potential of the interferogram data by applying the least square method to solve the formula between fringe frequency and baseline parameter to achieve baseline estimation. The simulated results show that the proposed method has improved performance in both accuracy and robustness for InSAR baseline estimation.