Miracle-dz/UniFAR-FTUs huggingface dataset:
Mother as a Teacher at Home : Challenges and Opportunities for Parental Involvement in Online Mathematics Learning for Elementary School Students

Apart from changing learning and teaching habits, the COVID-19 pandemic has also affected the way parents involve themselves in learning from home. This study explores the challenges parents face when participating in online learning during the COVID-19 pandemic and what ways can be done to encourage students to learn mathematics online. We use multiple case studies to achieve research objectives. This study involved eight mothers of children in elementary school. Six of them are housewives, and the rest are government employees. Semi-structured interviews were used to gather data. Aside from technical constraints such as the availability of internet networks and infrastructure, the findings of this study show that technological literacy and parental involvement in cognitive, emotional, social, and pedagogical aspects are still lacking. We discussed four key findings, including issues with network availability and technological literacy, issues with parents’ routine work and how to accompany their children, parents’ beliefs about mathematics, and parental involvement in cognitive, affective, social, and pedagogical aspects. Finally, the parents’ learning community must be accommodated, developed, and assisted for their involvement to be more effective.
Local Window Attention Transformer for Polarimetric SAR Image Classification

Convolutional neural networks (CNNs) have recently found great attention in image classification since deep CNNs have exhibited excellent performance in computer vision. Owing to their immense success, of late, scientists are exploring the functionality of transformers in Earth observation applications. Nevertheless, the primary issue with transformers is that they demand significantly more training data than CNN classifiers. Thus, the use of these transformers in remote sensing is considered challenging, notably in utilizing polarimetric synthetic aperture radar (PolSAR) data, due to the insufficient number of existing labeled data. In this letter, we develop and propose a vision transformer (ViT)-based framework that utilizes 3-D and 2-D CNNs as feature extractors and, in addition, local window attention (LWA) for the effective classification of PolSAR data. Extensive experimental results demonstrated that the developed model PolSARFormer obtained better classification accuracy than the state-of-the-art vision Swin Transformer and FNet algorithms. The PolSARFormer outperformed the Swin Transformer and FNet by the margin of 5.86% and 17.63%, in terms of average accuracy (AA) in the San Francisco data benchmark. Moreover, the results over the Flevoland dataset illustrated that the PolSARFormer exceeds several other algorithms, including the ResNet (97.49%), Swin Transformer (96.54%), FNet (95.28%), 2-D CNN (94.57%), and AlexNet (91.83%), with a kappa index (KI) of 99.30%. The code will be made available publicly at https://github.com/aj1365/PolSARFormer.
Body composition in woman with rheumatoid arthritis

Rheumatoid arthritis is a common rheumatic disease, characterized by erosive joint damage and destruction of internal organs. Chronic inflammation, malnutrition, physical disability, comorbid conditions, the use of steroids and biologics may also affect the body composition in patients with RA and lead to rheumatoid cachexia.Aim of the study: The aim of this study was to evaluate body composition in patients with rheumatoid arthritis.Materials and methods: Forty five women over 18 years of age with RA verified based on the 2010 ACR/EULAR criteria were included. The median age of patients was 54 years [45; 61] with a disease duration of 11 years [7; 20]. Patients with moderate disease activity prevailed (DAS28 4.40 [4.07; 5.03]). Results: The median AMI was 6.4 [5.5; 7.3]. Sarcopenia, assessed based on AMI, was detected in 16 (35.6%) patients. Patients with sarcopenia were younger (p = 0.044). They had a predominantly normal BMI (p = 0.001) and had a decrease in the percentage of androgynous adipose tissue (p=0,007). After excluding the total body mass, an inverse relationship was revealed between the AMM and CRP (rs=-0,524; p=0.009), By calculating partial correlations, excluding the total body mass, an inverse relationship was revealed between the AMM and CRP (rs=-0,524; p=0.009), TG (rs=-0.523; p=0.009) and LDL (rs=-0.421; p=0.01). Sarcopenia was more common in patients receiving sulfasalazine (p=0.29) and prednisolone (p=0.42). Conclusions: Sarcopenia was more common revealed in young patients with rheumatoid arthritis with normal body weight. SP was associated with taken of prednisolone and sulfasalazine.
The Associations between Results in Different Domains of Cognitive and Psychomotor Abilities Measured in Medical Students

We aimed to investigate the associations between intelligence quotient test scores obtained using the Raven’s Advanced Progressive Matrices (APM) and psychomotor testing using the Complex Reactionmeter Drenovac (CRD) test battery, while taking into account previous theoretical approaches recognizing intelligent behavior as the cumulative result of a general biological speed factor reflected in the reaction time for perceptual detections and motor decisions. A total of 224 medical students at the University of Split School of Medicine were recruited. Their IQ scores were assessed using Raven’s APM, while the computerized tests of CRD-series were used for testing the reaction time of perception to visual stimulus (CRD311), psychomotor limbs coordination task (CRD411), and solving simple arithmetic operations (CRD11). The total test-solving (TTST) and the minimum single-task-solving (MinT) times were analyzed. On the CRD11 test, task-solving times were shorter in students with higher APM scores (r = −0.48 for TTST and r = −0.44 for MinT; p < 0.001 for both). Negative associations between task-solving times and APM scores were reported on CRD311 (r = −0.30 for TTST and r = −0.33 for MinT, p < 0.001 for both). Negative associations between task-solving times in CRD411 and APM scores (r = −0.40 for TTST and r = −0.30 for MinT, p < 0.001 for both) were found. Faster reaction time in psychomotor limbs coordination tasks, the reaction time of perception to visual stimulus, and the reaction time of solving simple arithmetic operations were associated with a higher APM score in medical students, indicating the importance of mental speed in intelligence test performance. However, executive system functions, such as attention, planning, and goal weighting, might also impact cognitive abilities and should be considered in future research.
The Influence of VAR Dimensions on Estimator Biases

Vector AutoRegressions (VARs) have now become the most popular tool of Time Series analysis amongst econometricians. Unfortunately, little is known about the analytic finite-sample properties of parameter estimators for such systems. The asymptotic analysis of VARs published to date does not address questions regarding the influence of the number and nature of the system's variates on parameter estimates. Clearly, both questions will have repercussions on the way VARs are used, and we intend to address them here.We consider the implications of varying the dimensions of VARs on the biases of Maximum Likelihood and Least Squares Estimators (MLE and LSE, respectively). In the purely nonstationary case (k-dimensional random walk), estimator biases are approximately equal to the dimension of the system (k) times the univariate bias, even when the variates are generated independently of each other. We show that the variance too increases with the dimension of the system, hence also raising the Mean Squared Error (MSE) of the estimator. When some stable linear combinations exist, the biases are generally smaller and are asymptotically proportional to the sum of the characteristic roots of the VAR. One source of such combinations is meaningful economic relations that are represented by the cointegration of some of the components of the VAR. Adding economically-irrelevant variables to a VAR is thus shown to have more serious negative consequences in integrated time series than in classical ergodic or cross section analyses. The findings strengthen the case for parsimonious modelling and for the reduction step of the general-to-specific marginalization method. They also support the use of seasonally unadjusted data whenever possible.
Proteins Found in the Triple-Negative Breast Cancer Secretome and Their Therapeutic Potential

The cancer secretome comprises factors secreted by tumors, including cytokines, growth factors, proteins from the extracellular matrix (ECM), proteases and protease inhibitors, membrane and extracellular vesicle proteins, peptide hormones, and metabolic proteins. Secreted proteins provide an avenue for communication with other tumor cells and stromal cells, and these in turn promote tumor growth and progression. Breast cancer is the most commonly diagnosed cancer in women in the US and worldwide. Triple-negative breast cancer (TNBC) is characterized by its aggressiveness and its lack of expression of the estrogen receptor (ER), progesterone receptor (PR), and HER2, making it unable to be treated with therapies targeting these protein markers, and leaving patients to rely on standard chemotherapy. In order to develop more effective therapies against TNBC, researchers are searching for targetable molecules specific to TNBC. Proteins in the TNBC secretome are involved in wide-ranging cancer-promoting processes, including tumor growth, angiogenesis, inflammation, the EMT, drug resistance, invasion, and development of the premetastatic niche. In this review, we catalog the currently known proteins in the secretome of TNBC tumors and correlate these secreted molecules with potential therapeutic opportunities to facilitate translational research.
Gaussian Processes for High-Dimensional, Large Data Sets: A Review

Gaussian processes, known to have versatile uses in several fields across engineering, science, economics, show important advantages to several alternative approaches while controlling model complexity. However, the use of this family of models is hindered for inputs that are high dimensional as well as large sample sizes due to the intractability of the likelihood function, and the growth of the variance covariance matrix. This article investigates state-of-art solutions to these challenges according classifying them into categories. The goal is to select several algorithms covering each category and perform empirical experiments to compare their performances on the same set of test functions. Our preliminary results focus on deterministic implementations of a set of selected approaches. The results of the experiments may serve as a guidance to future readers who want to study and use Gaussian process in problems with high dimensions and big data sets.
Robust Statistical Modeling of Heterogeneity for Repairable Systems Using Multivariate Gaussian Convolution Processes

A main challenge in reliability analysis of repairable systems is to model the heterogeneity in their failure behavior, which can be reflected by the corresponding recurrent failure-time data. To capture the system heterogeneity for data analysis, a system-specific random effect is typically introduced in most existing statistical models. In practice, the random effect of repairable systems tends to be time varying; for example, each repair action could change system's physical properties. Prior studies, however, generally do not take account of this time-varying nature and few of them circumvent the risk of model misspecification on the parametric distribution of frailty. In this article, we propose a semiparametric model that uses multivariate Gaussian convolution processes (MGCPs) to meet the above challenges. First, we use the trend RP to model the baseline intensity function of each repairable system. Based on the baseline intensity function, we then introduce MGCPs to simultaneously factor in heterogeneity and infer commonalities across multiple systems. A Bayesian framework is used for parameter estimation and time-to-failure prediction. Simulation studies show the advantages of our model in terms of robustness and estimation accuracy. A group of oil and gas well systems are used to illustrate the application of the proposed model.
3D FE implementation of an incompressible quadriphasic mixture model

A quadriphasic mixture model, intended to apply to biological tissues, consists of a deforming solid saturated with a fluid, cations and anions. Governing equations of the model are briefly given. The coupled set of differential equations are solved in a finite element method using a weighted residual approach (updated Lagrange). A Newton–Raphson iteration scheme is applied for linearization of the non‐linear set of equations. Interpolation functions are chosen equal to the weighting functions (Galerkin) in discretization. The FE model is then validated for small deformations using the analytical solutions for 1D confined consolidation and swelling. The 3D effects are shown in a swelling cylindrical hydrogel sample. Copyright © 2003 John Wiley & Sons, Ltd.
The Relative Merit of White and Monochromatic Light for the Determination of Visibility by Backscattering Measurements

The response of a simulated single-ended transmissometer, based on the Mie theory of the scattering of light, has been computed for monochromatic light similar to that of a ruby laser (λ = 0.7 μ) and for white light (0.4 μ ≤ λ ≤ 0.7 μ) for many fog and haze models. The results for white light are compared with data obtained from actual field measurements. The strong dependence of backscatter on the size distribution of the scatterers and on the spectral energy distribution of the source is illustrated, and the limitations of the single-ended transmissometer as a device for determining the visibility in haze and fog are discussed. A table of Mie extinction efficiency (or scattering coefficient) and intensity function (for the backscatter direction) is presented for integral values of size parameter up to 100.
Evaluating Permafrost Degradation in the Tuotuo River Basin by MT-InSAR and LSTM Methods

Permafrost degradation can significantly affect vegetation, infrastructure, and sustainable development on the Qinghai-Tibet Plateau (QTP). The permafrost on the QTP faces a risk of widespread degradation due to climate change and ecosystem disturbances; thus, monitoring its changes is critical. In this study, we conducted a permafrost surface deformation prediction over the Tuotuo River tributary watershed in the southwestern part of the QTP using the Long Short-Term Memory model (LSTM). The LSTM model was applied to the deformation information derived from a time series of Multi-Temporal Interferometry Synthetic Aperture Radar (MT-InSAR). First, we designed a quadtree segmentation-based Small BAseline Subset (SBAS) to monitor the seasonal permafrost deformation from March 2017 to April 2022. Then, the types of frozen soil were classified using the spatio-temporal deformation information and the temperature at the top of the permafrost. Finally, the time-series deformation trends of different types of permafrost were predicted using the LSTM model. The results showed that the deformation rates in the Tuotuo River Basin ranged between −80 to 60 mm/yr. Permafrost, seasonally frozen ground, and potentially degraded permafrost covered 7572.23, 900.87, and 921.70 km2, respectively. The LSTM model achieved high precision for frozen soil deformation prediction at the point scale, with a root mean square error of 4.457 mm and mean absolute error of 3.421 mm. The results demonstrated that deformation monitoring and prediction using MT-InSAR technology integrated with the LSTM model can be used to accurately identify types of permafrost over a large region and quantitatively evaluate its degradation trends.
The Dynamic and Crucial Role of the Arginine Methylproteome in Myoblast Cell Differentiation

Protein arginine methylation is an extensive and functionally significant post-translational modification. However, little is known about its role in differentiation at the systems level. Using stable isotope labeling by amino acids in cell culture (SILAC) proteomics of whole proteome analysis in proliferating or five-day differentiated mouse C2C12 myoblasts, followed by high-resolution mass spectrometry, biochemical assays, and specific immunoprecipitation of mono- or dimethylated arginine peptides, we identified several protein families that were differentially methylated on arginine. Our study is the first to reveal global changes in the arginine mono- or dimethylation of proteins in proliferating myoblasts and differentiated myocytes and to identify enriched protein domains and novel short linear motifs (SLiMs). Our data may be crucial for dissecting the links between differentiation and cancer growth.
Rootstocks Comparison in Grafted Watermelon under Water Deficit: Effects on the Fruit Quality and Yield

Drought is widely recognized as one of the most significant agricultural constraints worldwide. A strategy to avoid the adverse effects of drought on crops is to cultivate high-yielding varieties by grafting them onto drought-tolerant rootstocks with a differentiated root system. Thus, the objective of this study was to evaluate fruit yield and quality, root system architecture, and water productivity of watermelon grafted onto Lagenaria siceraria rootstocks. To do so, a commercial watermelon cultivar “Santa Amelia” [Citrullus lanatus (Thunb.)] was grafted onto five L. siceraria rootstocks: ‘Illapel’, ‘Osorno’, ‘BG-48’, ‘GC’, and ‘Philippines’, which were grown under three irrigation treatments (100%, 75%, and 50% of evapotranspiration). The comparison of the L. siceraria rootstocks in the irrigation treatments demonstrated no significant effect on watermelon fruit quality parameters. The rootstocks ‘Illapel’, ‘Osorno’, and ‘GC’ significantly improved the fruit number and yield (total fruit weight) under water deficit. Similarly, ‘Illapel’, ‘Osorno’, and ‘GC’ consistently showed statistical differences for root system architecture traits compared to ‘BG-48’ and ‘Philippines’. Based on these results, we concluded that the used L. siceraria rootstocks did not affect the fruit yield and quality of grafted watermelon under water deficit. This study may help adjust the amount of applied water for watermelon production where L. siceraria rootstocks are utilized.
Maternal Methyl-Enriched Diet Increases DNMT1, HCN1, and TH Gene Expression and Suppresses Absence Seizures and Comorbid Depression in Offspring of WAG/Rij Rats

The reduced expression of the HCN1 ion channel in the somatosensory cortex (SSC) and mesolimbic dopamine deficiency are thought to be associated with the genesis of spike-wave discharges (SWDs) and comorbid depression in the WAG/Rij rat model of absence epilepsy. This study aimed to investigate whether the maternal methyl-enriched diet (MED), which affects DNA methylation, can alter DNMT1, HCN1, and TH gene expression and modify absence seizures and comorbid depression in WAG/Rij offspring. WAG/Rij mothers were fed MED (choline, betaine, folic acid, vitamin B12, L-methionine, zinc) or a control diet for a week before mating, during pregnancy, and for a week after parturition. MED caused sustained suppression of SWDs and symptoms of comorbid depression in the offspring. Disease-modifying effects of MED were associated with increased expression of the DNMT1 and HCN1 genes in the SSC and hippocampus, as well as DNMT1, HCN1, and TH genes in the nucleus accumbens. No changes in gene expression were detected in the hypothalamus. The results indicate that maternal MED can suppress the genetic absence epilepsy and comorbid depression in offspring. Increased expression of the DNMT1, HCN1, and TH genes is suggested to be a molecular mechanism of this beneficial phenotypic effect.
Prospective evaluation of multidimensional health‐related quality of life after endoscopic endonasal surgery for pituitary adenomas using the endoscopic endonasal sinus and skull base surgery questionnaire

Social functioning is an important factor in the evaluation of postoperative health‐related quality of life (HRQoL) for pituitary adenoma patients. In a prospective cohort study multidimensional HRQoL of non‐functioning (NFA) and functioning (FA) pituitary adenoma patients were evaluated following endoscopic endonasal surgery using the endoscopic endonasal sinus and skull base surgery questionnaire (EES‐Q).
Systematic Item Content and Overlap Analysis of Self-Reported Multiple Sleep Disorder Screening Questionnaires in Adults

Sleep disorders are defined on the basis of diagnostic criteria presented in medical classifications. However, no consensus has emerged on the exact list of operational symptoms that should be systematically investigated in the field of sleep medicine. We propose a systematic analysis of sleep symptoms that figure in a set of self-reported multiple sleep disorder screening questionnaires for adult populations, to identify the content overlap of symptoms that probe the presence of central sleep symptoms, and to highlight the potential level of heterogeneity among sleep disorder questionnaires. The method comprises three steps: (i) the selection of self-reported multiple sleep disorder screening questionnaires; (ii) item extraction and selection; (iii) the extraction of symptoms from items. Frequency of sleep symptoms and content overlap (Jaccard Index) are analyzed. We extracted 469 items that provide 60 different symptoms from 12 questionnaires. Insomnia, somnolence, and sleep-related breathing symptoms were found in all the questionnaires. The mean overlap among all questionnaires evaluated with the Jaccard Index is 0.44, i.e., moderate similarity. Despite limitations related to the selection of questionnaires and the symptom extraction and harmonization, this study underlines the need to standardize sleep symptom contents for sleep medicine in order to enhance the practicability, reliability, and validity of sleep disorder diagnoses.