Miracle-dz/UniFAR-FTUs huggingface dataset:
CD8+ T Cell-Mediated Mechanisms Contribute to the Progression of Neurocognitive Impairment in Both Multiple Sclerosis and Alzheimer's Disease?

Neurocognitive impairment (NCI) is one of the most relevant clinical manifestations of multiple sclerosis (MS). The profile of NCI and the structural and functional changes in the brain structures relevant for cognition in MS share some similarities to those in Alzheimer's disease (AD), the most common cause of neurocognitive disorders. Additionally, despite clear etiopathological differences between MS and AD, an accumulation of effector/memory CD8+ T cells and CD8+ tissue-resident memory T (Trm) cells in cognitively relevant brain structures of MS/AD patients, and higher frequency of effector/memory CD8+ T cells re-expressing CD45RA (TEMRA) with high capacity to secrete cytotoxic molecules and proinflammatory cytokines in their blood, were found. Thus, an active pathogenetic role of CD8+ T cells in the progression of MS and AD may be assumed. In this mini-review, findings supporting the putative role of CD8+ T cells in the pathogenesis of MS and AD are displayed, and putative mechanisms underlying their pathogenetic action are discussed. A special effort was made to identify the gaps in the current knowledge about the role of CD8+ T cells in the development of NCI to “catalyze” translational research leading to new feasible therapeutic interventions.
scikit-survival: A Library for Time-to-Event Analysis Built on Top of scikit-learn

scikit-survival is an open-source Python package for time-to-event analysis fully compatible with scikit-learn . It provides implementations of many popular machine learning techniques for time-to-event analysis, including penalized Cox model, Random Survival Forest, and Survival Support Vector Machine. In addition, the library includes tools to evaluate model performance on censored time-to-event data. The documentation contains installation instructions, interactive notebooks, and a full description of the API. scikit-survival is distributed under the GPL-3 license with the source code and detailed instructions available at https://github.com/sebp/scikit-survival
Translationese in Machine Translation Evaluation

The term translationese has been used to describe the presence of unusual features of translated text. In this paper, we provide a detailed analysis of the adverse effects of translationese on machine translation evaluation results. Our analysis shows evidence to support differences in text originally written in a given language relative to translated text and this can potentially negatively impact the accuracy of machine translation evaluations. For this reason we recommend that reverse-created test data be omitted from future machine translation test sets. In addition, we provide a re-evaluation of a past high-profile machine translation evaluation claiming human-parity of MT, as well as analysis of the since re-evaluations of it. We find potential ways of improving the reliability of all three past evaluations. One important issue not previously considered is the statistical power of significance tests applied in past evaluations that aim to investigate human-parity of MT. Since the very aim of such evaluations is to reveal legitimate ties between human and MT systems, power analysis is of particular importance, where low power could result in claims of human parity that in fact simply correspond to Type II error. We therefore provide a detailed power analysis of tests used in such evaluations to provide an indication of a suitable minimum sample size of translations for such studies. Subsequently, since no past evaluation that aimed to investigate claims of human parity ticks all boxes in terms of accuracy and reliability, we rerun the evaluation of the systems claiming human parity. Finally, we provide a comprehensive check-list for future machine translation evaluation.
Improving Soil Thickness Estimations Based on Multiple Environmental Variables with Stacking Ensemble Methods

Spatially continuous soil thickness data at large scales are usually not readily available and are often difficult and expensive to acquire. Various machine learning algorithms have become very popular in digital soil mapping to predict and map the spatial distribution of soil properties. Identifying the controlling environmental variables of soil thickness and selecting suitable machine learning algorithms are vitally important in modeling. In this study, 11 quantitative and four qualitative environmental variables were selected to explore the main variables that affect soil thickness. Four commonly used machine learning algorithms (multiple linear regression (MLR), support vector regression (SVR), random forest (RF), and extreme gradient boosting (XGBoost) were evaluated as individual models to separately predict and obtain a soil thickness distribution map in Henan Province, China. In addition, the two stacking ensemble models using least absolute shrinkage and selection operator (LASSO) and generalized boosted regression model (GBM) were tested and applied to build the most reliable and accurate estimation model. The results showed that variable selection was a very important part of soil thickness modeling. Topographic wetness index (TWI), slope, elevation, land use and enhanced vegetation index (EVI) were the most influential environmental variables in soil thickness modeling. Comparative results showed that the XGBoost model outperformed the MLR, RF and SVR models. Importantly, the two stacking models achieved higher performance than the single model, especially when using GBM. In terms of accuracy, the proposed stacking method explained 64.0% of the variation for soil thickness. The results of our study provide useful alternative approaches for mapping soil thickness, with potential for use with other soil properties.
Estimating Crop LAI Using Spectral Feature Extraction and the Hybrid Inversion Method

The leaf area index (LAI) is an essential indicator used in crop growth monitoring. In the study, a hybrid inversion method, which combined a physical model with a statistical method, was proposed to estimate the crop LAI. The simulated compact high-resolution imaging spectrometer (CHRIS) canopy spectral crop reflectance datasets were generated using the PROSAIL model (the coupling of PROSPECT leaf optical properties model and Scattering by Arbitrarily Inclined Leaves model) and the CHRIS band response function. Partial least squares (PLS) was then used to reduce the dimension of the simulated spectral data. Using the principal components (PCs) of PLS as the model inputs, the hybrid inversion models were built using various modeling algorithms, including the backpropagation artificial neural network (BP-ANN), least squares support vector regression (LS-SVR), and random forest regression (RFR). Finally, remote sensing mapping of the CHRIS data was achieved with the hybrid model to test the inversion accuracy of LAI estimates. The validation result yielded an accuracy of R2 = 0.939 and normalized root-mean-square error (NRMSE) = 6.474% for the PLS_RFR model, which indicated that the crops LAI could be estimated accurately by using spectral feature extraction and a hybrid inversion strategy. The results showed that the model based on principal components extracted by PLS had a good estimation accuracy and noise immunity and was the preferred method for LAI estimation. Furthermore, the comparative analysis results of various datasets showed that prior knowledge could improve the precision of the retrieved LAI, and using this information to constrain parameters (e.g., chlorophyll content or LAI), which make important contributions to the spectra, is the key to this improvement. In addition, among the PLS, BP-ANN, LS-SVR, and RFR methods, RFR was the optimal modeling algorithm in the paper, as indicated by the high R2 and low NRMSE in various datasets.
Hydroxychloroquine in COVID-19 Patients: Pros and Cons

The pandemic of COVID-19, caused by SARS-CoV-2, has recently overwhelmed medical centers and paralyzed economies. The unparalleled public distress caused by this pandemic mandated an urgent quest for an effective approach to manage or treat this disease. Due to their well-established anti-infectious and anti-inflammatory properties, quinine derivatives have been sought as potential therapies for COVID-19. Indeed, these molecules were originally employed in the treatment and prophylaxis of malaria, and later in the management of various autoimmune rheumatic and dermatologic diseases. Initially, some promising results for the use of hydroxychloroquine (HCQ) in treating COVID-19 patients were reported by a few in vitro and in vivo studies. However, current evidence is not yet sufficiently solid to warrant its use as a therapy for this disease. Additionally, the therapeutic effects of HCQ are not without many side effects, which range from mild gastrointestinal effects to life-threatening cardiovascular and neurological effects. In this review, we explore the controversy associated with the repurposing of HCQ to manage or treat COVID-19, and we discuss the cellular and molecular mechanisms of action of HCQ.
Meta-Analysis of Dyslipidemia Management for the Prevention of Ischemic Stroke Recurrence in China

Background: The benefit of blood cholesterol reduction for secondary prevention of ischemic stroke remains undetermined in Chinese patients. The purpose of this meta-analysis was to determine whether lipid-lowering agents including statins, fibrates, nicotinic acid, and ezetimibe reduced the risk of recurrent stroke in ischemic stroke patients in China and whether such findings could inform treatment decisions for blood lipid-lowering treatment in China. Methods: The English electronic databases PubMed, EMBASE, Cochrane Library and Chinese databases CNKI, Sino-Med, Wan Fang, and VIP were searched for studies published between January 1990 and April 2020. This meta-analysis included published data from trials that randomly assigned patients to groups treated with either blood lipid-lowering regimens or placebo. Effect comparisons were made using fixed effects model in meta-analysis and linear and spline regression were performed to identify the relative risk of stroke recurrence. The primary outcome was the reduction of total ischemic stroke events, and relative risk values were obtained using a risk prediction equation developed from the control groups of the included trials. Results: Five studies including 4,999 individuals with available data met the inclusion criteria. Relative to the control groups, the pooled estimated odds ratio (OR) for recurrent stroke among those who received lipid-lowering therapy was 0.79 (95% confidence interval [CI]: 0.63–1.00). A 50% or greater reduction in low-density lipoprotein cholesterol (LDL-C) significantly reduced the risk of ischemic stroke recurrence (OR: 0.15 [95% CI: 0.11–0.20]). The overall beneficial effect of statin therapy was confirmed to prevent ischemic stroke with an OR of 0.51 (95% CI: 0.36–0.72). Conclusions: Effective lipid-lowering therapy could decrease the blood LDL-C level, which had a protective effect against stroke recurrence. These results support the use of predicted baseline cerebrovascular disease risk equations to inform decisions regarding blood lipid-lowering treatment in ischemic stroke patients in China.
Tropical Cyclone Climatology from Satellite Passive Microwave Measurements

The satellite passive microwave (PMW) sensor brightness temperatures (TBs) of all tropical cyclones (TCs) from 1987–2012 have been carefully calibrated for inter-sensor frequency differences, center position fixing using the Automated Rotational Center Hurricane Eye Retrieval (ARCHER) scheme, and application of the Backus–Gilbert interpolation scheme for better presentation of the TC horizontal structure. With additional storm motion direction and the 200–850 hPa wind shear direction, a unique and comprehensive TC database is created for this study. A reliable and detailed climatology for each TC category is analyzed and discussed. There is significant annual variability of the number of storms at hurricane intensity, but the annual number of all storms is relatively stable. Results based on the analysis of the 89 GHz horizontal polarization TBs over oceans are presented in this study. An eyewall contraction is clearly displayed with an increase in TC intensity. Three composition schemes are applied to present a reliable and detailed TC climatology at each intensity category and its geographic characteristics. The global composition relative to the North direction is not able to lead a realistic structure for an individual TC. Enhanced convection in the down-motion quadrants relative to direction of TC motion is obvious for Cat 1–3 TCs, while Cat 4–5 TCs still have a concentric pattern of convection within 200 km radius. Regional differences are evident for weak storms. Results indicate the direction of TC movement has more impact on weak storms than on Cat 4–5 TCs. A striking feature is that all TCs have a consistent pattern of minimum TBs at 89 GHz in the downshear left quadrant (DSLQ) for the northern hemisphere basins and in the downshear right quadrant (DSRQ) for the southern hemisphere basin, regarding the direction of the 200–850 hPa wind shear. Tropical depression and tropical storm have the minimum TBs in the downshear quadrants. The axis of the minimum TBs is slightly shifted toward the vertical shear direction. There is no geographic variation of storm structure relative to the vertical wind shear direction except over the southern hemisphere which shows a mirror image of the storm structure over the northern hemisphere. This study indicates that regional variation of storm structure relative to storm motion direction is mainly due to differences of the vertical wind shear direction among these basins. Results demonstrate the direction of the 200–850 hPa wind shear plays a critical role in TC structure.
Ambiguous Impact of Wheat Gluten Proteins on the Colloidal Haze of Wheat Beers

Laboratory-scale brews were prepared from barley malt with various addition levels of commercial wheat gluten. These were calculated to range from 0 to 150% of the amount of gluten in a typical wheat beer mash (60% barley malt and 40% unmalted wheat). Nephelometric permanent haze readings of the experimental beers showed an increase of the haze intensity with the increasing gluten levels, reaching a maximum of 50 EBC haze units at the gluten level corresponding to the wheat gluten level in a mash based on 88% barley malt and 12% wheat. At higher gluten levels, the haze intensity declined to values lower than 1 EBC haze unit. In contrast, the polyphenol levels of centrifuged beers decreased continuously with increasing gluten additions. Total polyphenol, total flavanoid, and simple flavanoid levels decreased by as much as 17, 23, and 21%, respectively. Haze intensities of laboratory-scale beers brewed with variable levels of two wheat varieties with different protein levels were higher for 5% wheat inclusion than for the reference all-malt beer. However, at higher wheat levels (from 10% onward), the haze intensity decreased continuously to 1 EBC haze unit at 40% wheat. Beer brewed with low-protein (12.6%) wheat contained systematically more permanent haze than did beer brewed with high-protein (14.7%) wheat. It was concluded that wheat gluten proteins interact with polyphenols, resulting in the formation of a haze or precipitate depending on the gluten concentration.
Preliminary Estimation and Validation of Polar Motion Excitation from Different Types of the GRACE and GRACE Follow-On Missions Data

The Gravity Recovery and Climate Experiment (GRACE) mission has provided global observations of temporal variations in the gravity field resulting from mass redistribution at the surface and within the Earth for the period 2002–2017. Although GRACE satellites are not able to realistically detect the second zonal parameter (ΔC20) of geopotential associated with the flattening of the Earth, they can accurately determine variations in degree-2 order-1 (ΔC21, ΔS21) coefficients that are proportional to variations in polar motion. Therefore, GRACE measurements are commonly exploited to interpret polar motion changes due to variations in the global mass redistribution, especially in the continental hydrosphere and cryosphere. Such impacts are usually examined by computing the so-called hydrological polar motion excitation (HAM) and cryospheric polar motion excitation (CAM), often analyzed together as HAM/CAM. The great success of the GRACE mission and the scientific robustness of its data contributed to the launch of its successor, GRACE Follow-On (GRACE-FO), which began in May 2018 and continues to the present. This study presents the first estimates of HAM/CAM computed from GRACE-FO data provided by three data centers: Center for Space Research (CSR), Jet Propulsion Laboratory (JPL), and GeoForschungsZentrum (GFZ). In this paper, the data series is computed using different types of GRACE/GRACE-FO data: ΔC21, ΔS21 coefficients of geopotential, gridded terrestrial water storage anomalies, and mascon solutions. We compare and evaluate different methods of HAM/CAM estimation and examine the compatibility between CSR, JPL, and GFZ data. We also validate different HAM/CAM estimations using precise geodetic measurements and geophysical models. Analysis of data from the first 19 months of GRACE-FO shows that the consistency between GRACE-FO-based HAM/CAM and observed hydrological/cryospheric signals in polar motion is similar to the consistency obtained for the initial period of the GRACE mission, worse than the consistency received for the best GRACE period, and higher than the consistency obtained for the terminal phase of the GRACE mission. In general, the current quality of HAM/CAM from GRACE Follow-On meets expectations. In the following months, after full calibration of the instruments, this accuracy is expected to increase.
Mining Topological Structure in Graphs through Forest Representations

We consider the problem of inferring simplified topological substructures—which we term backbones—in metric and non-metric graphs. Intuitively, these are subgraphs with ‘few’ nodes, multifurcations, and cycles, that model the topology of the original graph well. We present a multistep procedure for inferring these backbones. First, we encode local (geometric) information of each vertex in the original graph by means of the boundary coefficient (BC) to identify ‘core’ nodes in the graph. Next, we construct a forest representation of the graph, termed an f -pine, that connects every node of the graph to a local ‘core’ node. The final backbone is then inferred from the f -pine through CLOF (Constrained Leaves Optimal subForest), a novel graph optimization problem we introduce in this paper. On a theoretical level, we show that CLOF is NP-hard for general graphs. However, we prove that CLOF can be efficiently solved for forest graphs, a surprising fact given that CLOF induces a nontrivial monotone submodular set function maximization problem on tree graphs. This result is the basis of our method for mining backbones in graphs through forest representation. We qualitatively and quantitatively confirm the applicability, effectiveness, and scalability of our method for discovering backbones in a variety of graph-structured data, such as social networks, earthquake locations scattered across the Earth, and high-dimensional cell trajectory data.
Digital Image Correlation of Google Earth Images for Earth's Surface Displacement Estimation

An increasing number of satellite platforms provide daily images of the Earth’s surface that can be used in quantitative monitoring applications. However, their cost and the need for specific processing software make such products not often suitable for rapid mapping and deformation tracking. Google Earth images have been used in a number of mapping applications and, due to their free and rapid accessibility, they have contributed to partially overcome this issue. However, their potential in Earth’s surface displacement tracking has not yet been explored. In this paper, that aspect is analyzed providing a specific procedure and related MATLAB™ code to derive displacement field maps using digital image correlation of successive Google Earth images. The suitability of the procedure and the potential of such images are demonstrated here through their application to two relevant case histories, namely the Slumgullion landslide in Colorado and the Miage debris-covered glacier in Italy. Result validation suggests the effectiveness of the proposed procedure in deriving Earth’s surface displacement data from Google Earth images.
A Novel Ambiguity Parameter Estimation and Elimination Strategy for GNSS/INS Tightly Coupled Integration

The estimation of ambiguity in the global navigation satellite system/inertial navigation system (GNSS/INS) tightly coupled system is a key issue for GNSS/INS precise navigation positioning. Only when the ambiguity is solved correctly can the integrated navigation system obtain high-precision positioning results. Aiming at the problem of ambiguity parameter estimation in GNSS/INS tightly coupled system, a new strategy for ambiguity parameter estimation and elimination is proposed in this paper. Here, the ambiguity parameter is first added to the state equations of GNSS/INS in the estimation process. Then, the strategy of eliminating the parameter is used to improve efficiency. A residual test is carried out based on introducing the ambiguity parameter, thereby reducing or avoiding its influence on the filtering estimation process. Two groups of experiments were carried out and compared with GNSS positioning results. The results showed that, in the open sky observation environment, the positioning accuracy of the GNSS/INS tightly coupled method proposed in this paper was within 5 cm, and the ambiguity fixed rate was more than 97%, which is basically consistent. In a GNSS-denied environment, the positioning accuracy of the GNSS/INS tightly coupled method proposed in this paper was obviously better than that of GNSS, and the positioning accuracy in X, Y, and Z directions was improved by 82.46%, 78.87%, and 79.67%, respectively. The fixed rate of ambiguity increased from 73% to 78.57%. Therefore, in a GNSS-challenged environment, the novel strategy of the GNSS/INS tightly coupled system has higher ambiguity fixed rate and significantly improves positioning accuracy and continuity.