Miracle-dz/UniFAR-FTUs huggingface dataset:
ENVISAT ASAR Wide Swath and SPOT‐VEGETATION Image Fusion for Wetland Mapping: Evaluation of Different Wavelet‐based Methods

Abstract Three wavelet‐based fusion methods (ARSIS, PSIMA and à trous method) are applied to combine ENVISAT ASAR Wide Swath and SPOT‐VEGETATION images. The objectives of the data fusion are feature enhancement and improvement of classification accuracy of a tropical wetland located in the Chad basin, Africa. The fusion results are compared to those obtained by the intensity hue saturation (IHS) method and the principal component (PC) method. Several quantitative tests and a visual inspection are performed to evaluate the different methods. The fused images are classified by means of a maximum likelihood classifier and the classification accuracies are calculated. The results show that the fusion methods based on the wavelet transform perform better then the IHS and PC method for both objectives. From all methods, the ARSIS and the à trous method preserve best the spectral contents of the SPOT‐VEGETATION image. However, the à trous method outperforms the ARSIS method in terms of spatial information preservation. When the PSIMA method is used, artefacts are minimized. The highest classification accuracies are obtained with the PSIMA and à trous methods.
Looking back and beyond the 2017 diagnostic criteria for hypermobile Ehlers‐Danlos syndrome: A retrospective cross‐sectional study from an Italian reference center

The most common conditions with symptomatic joint hypermobility are hypermobile Ehlers‐Danlos syndrome (hEDS) and hypermobility spectrum disorders (HSD). Diagnosing these overlapping connective tissue disorders remains challenging due to the lack of established causes and reliable diagnostic tests. hEDS is diagnosed applying the 2017 diagnostic criteria, and patients with symptomatic joint hypermobility but not fulfilling these criteria are labeled as HSD, which is not officially recognized by all healthcare systems. The 2017 criteria were introduced to improve diagnostic specificity but have faced criticism for being too stringent and failing to adequately capture the multisystemic involvement of hEDS. Herein, we retrospectively evaluated 327 patients from 213 families with a prior diagnosis of hypermobility type EDS or joint hypermobility syndrome based on Villefranche and Brighton criteria, to assess the effectiveness of the 2017 criteria in distinguishing between hEDS and HSD and document the frequencies of extra‐articular manifestations. Based on our findings, we propose that the 2017 criteria should be made less stringent to include a greater number of patients who are currently encompassed within the HSD category. This will lead to improved diagnostic accuracy and enhanced patient care by properly capturing the diverse range of symptoms and manifestations present within the hEDS/HSD spectrum.
Transitional zone prostate cancer: Performance of texture-based machine learning and image-based deep learning

This study is aimed to explore the performance of texture-based machine learning and image-based deep-learning for enhancing detection of Transitional-zone prostate cancer (TZPCa) in the background of benign prostatic hyperplasia (BPH), using a one-to-one correlation between prostatectomy-based pathologically proven lesion and MRI. Seventy patients confirmed as TZPCa and twenty-nine patients confirmed as BPH without TZPCa by radical prostatectomy. For texture analysis, a radiologist drew the region of interest (ROI) for the pathologically correlated TZPCa and the surrounding BPH on T2WI. Significant features were selected using Least Absolute Shrinkage and Selection Operator (LASSO), trained by 3 types of machine learning algorithms (logistic regression [LR], support vector machine [SVM], and random forest [RF]) and validated by the leave-one-out method. For image-based machine learning, both TZPCa and BPH without TZPCa images were trained using convolutional neural network (CNN) and underwent 10-fold cross validation. Sensitivity, specificity, positive and negative predictive values were presented for each method. The diagnostic performances presented and compared using an ROC curve and AUC value. All the 3 Texture-based machine learning algorithms showed similar AUC (0.854–0.861)among them with generally high specificity (0.710–0.775). The Image-based deep learning showed high sensitivity (0.946) with good AUC (0.802) and moderate specificity (0.643). Texture -based machine learning can be expected to serve as a support tool for diagnosis of human-suspected TZ lesions with high AUC values. Image-based deep learning could serve as a screening tool for detecting suspicious TZ lesions in the context of clinically suspected TZPCa, on the basis of the high sensitivity.
Cross-Domain Facial Expression Recognition via Contrastive Warm up and Complexity-Aware Self-Training

Unsupervised cross-domain Facial Expression Recognition (FER) aims to transfer the knowledge from a labeled source domain to an unlabeled target domain. Existing methods strive to reduce the discrepancy between source and target domain, but cannot effectively explore the abundant semantic information of the target domain due to the absence of target labels. To this end, we propose a novel framework via Contrastive Warm up and Complexity-aware Self-Training (namely CWCST), which facilitates source knowledge transfer and target semantic learning jointly. Specifically, we formulate a contrastive warm up strategy via features, momentum features, and learnable category centers to concurrently learn discriminative representations and narrow the domain gap, which benefits domain adaptation by generating more accurate target pseudo labels. Moreover, to deal with the inevitable noise in pseudo labels, we develop complexity-aware self-training with a label selection module based on prediction entropy, which iteratively generates pseudo labels and adaptively chooses the reliable ones for training, ultimately yielding effective target semantics exploration. Furthermore, by jointly using the two mentioned components, our framework enables to effectively utilize the source knowledge and target semantic information by source-target co- training. In addition, our framework can be easily incorporated into other baselines with consistent performance improvements. Extensive experimental results on seven databases show the superior performance of the proposed method against various baselines.
Test-Retest Reliability of Immersive Virtual Reality Measures of Perceptual-Motor Performance

The duration, accuracy, and consistency of responses to various types of stimuli are widely accepted as indirect indicators of the efficiency of brain information processing, but current clinical tests appear to lack sufficient sensitivity to detect subtle impairments. Immersive virtual reality (VR) offers a new means to acquire measures of perceptual-motor responses to moving visual stimuli that require rapid conflict resolution, but their test-retest reliability has not yet been demonstrated. Repeated measures. We analyzed data from 19 healthy young adults who performed a 40-trial VR test on three consecutive days. We focused on response time (RT) and perceptual latency (PL) for eye, neck, arm, and whole-body step displacements involved in executing a reaching/lunging movement in a right or left direction toward a peripherally located virtual target. Measures of RT and PL included a 40-trial mean, an intra-individual variability (IIV) value, and a rate correct score (RCS) that incorporated both response duration and accuracy. Most mean and IIV values for PL and RT demonstrated a positive distributional skew that was substantially reduced by natural logarithm transformation. While a learning effect was evident between sessions 1 and 2 for 7 of 8 mean PL and RT measures, 3-session intraclass correlation coefficient (ICC) values were moderate to excellent for 15 of 16 transformed PL and RT measures (range: .618 to .922). The composite RCS metric did not require transformation for either PL or RT, whose respective 3-session ICC values were . 877 and . 851. This moderate to excellent test-retest reliability for various VR measures of perceptual-motor function, combined with evidence of their validity from both past and future research, suggest that these measures can advance clinical detection of impaired brain processing and longitudinal assessments of potentially modifiable performance deficiencies.
FRACTURE ENERGY OF WEAK SNOWPACK LAYERS

Recent experimental and theoretical work has provided new insight into weak layer fracture in view of dry-snow slab avalanche release. The two main parameters which influence this process are slab stiffness (i.e. elastic modulus) and weak layer fracture energy. These parameters are therefore of importance for avalanche forecasting. So far, only few values of specific fracture energy exist, mainly because the stiffness of the slab cannot easily be determined. We performed about 150 propagation saw tests to calculate weak layer fracture energy. To estimate the stiffness of the slab that is required for the calculation, we applied two methods: (1) in-situ penetration resistance measurements, and (2) video im- aging of the fracture process. Both methods provided values of the average slab modulus on the order of a few MPa. The resulting weak layer fracture energies ranged between 0.28 and 2.2 J m -2 - considerably higher than previously published values. Many more measurements are needed for a comprehensive dataset of weak layer fracture energy.
Interpersonal Influences on the Choice to Treat Nausea during Pregnancy with Medication or Cannabis

Abstract Objective This study aimed to better understand the interpersonal influences on a pregnant individual's decision of how to treat nausea and vomiting during pregnancy using a qualitative approach. Study Design A semistructured interview guide was developed to assess pregnancy symptoms, decision-making regarding treating nausea, and interpersonal influences on treatment decisions. Interviews were conducted with 17 individuals enrolled in a neuroimaging and behavioral study of prenatal exposure to cannabis who used medication and/or cannabis to treat symptoms associated with pregnancy. Results Interviews revealed four groups of stakeholders who influenced participant decision-making: medical providers, partners, family, and friends. Influence was categorized as either positive, negative, neutral, or absent (if not discussed or participant chose not to disclose). Those in the medication group reported only positive or neutral feedback from friends, family, partners, and providers. In contrast, the cannabis group participants reported positive feedback from friends, mixed feedback from family and partners, and negative feedback from providers, which was often felt to be stigmatizing. Many in the cannabis group also reported varying feedback from different medical providers. While the cannabis group frequently reported eliciting feedback from friends, family, and partners, the medication group often did not. Conclusion Medication group participants reported entirely positive feedback from providers and often did not mention any feedback at all from partners, family, and friends. Cannabis group participants reported much more varied feedback, both positive and negative, from a variety of interpersonal contacts and sometimes decided to conceal their treatment choice after receiving or fearing negative feedback. We recommend further research into the health outcomes of pregnant patients who chose not to discuss their treatment decisions with providers, family, partners, or friends. We also suggest further study of possible reasons behind a lack of disclosure, including fear of stigma and/or legal consequences. Key Points Providers, partners, family, friends gave feedback. Medication group got positive feedback. Cannabis group stigmatized by providers. Cannabis group got mixed feedback.
Simulating progressive motor neuron degeneration and collateral reinnervation in motor neuron diseases using a dynamic muscle model based on human single motor unit recordings

Objective. To simulate progressive motor neuron loss and collateral reinnervation in motor neuron diseases (MNDs) by developing a dynamic muscle model based on human single motor unit (MU) surface-electromyography (EMG) recordings. Approach. Single MU potentials recorded with high-density surface-EMG from thenar muscles formed the basic building blocks of the model. From the baseline MU pool innervating a muscle, progressive MU loss was simulated by removal of MUs, one-by-one. These removed MUs underwent collateral reinnervation with scenarios varying from 0% to 100%. These scenarios were based on a geometric variable, reflecting the overlap in MU territories using the spatiotemporal profiles of single MUs and a variable reflecting the efficacy of the reinnervation process. For validation, we tailored the model to generate compound muscle action potential (CMAP) scans, which is a promising surface-EMG method for monitoring MND patients. Selected scenarios for reinnervation that matched observed MU enlargements were used to validate the model by comparing markers (including the maximum CMAP and a motor unit number estimate (MUNE)) derived from simulated and recorded CMAP scans in a cohort of 49 MND patients and 22 age-matched healthy controls. Main results. The maximum CMAP at baseline was 8.3 mV (5th–95th percentile: 4.6 mV–11.8 mV). Phase cancellation caused an amplitude drop of 38.9% (5th–95th percentile, 33.0%–45.7%). To match observations, the geometric variable had to be set at 40% and the efficacy variable at 60%–70%. The Δ maximum CMAP between recorded and simulated CMAP scans as a function of fitted MUNE was −0.4 mV (5th–95th percentile = −4.0 – +2.4 mV). Significance. The dynamic muscle model could be used as a platform to train personnel in applying surface-EMG methods prior to their use in clinical care and trials. Moreover, the model may pave the way to compare biomarkers more efficiently, without directly posing unnecessary burden on patients.
Hurst Analysis of Hydrologic and Water Quality Time Series

A continued important area of research in hydrologic modeling is the issue of spatial and temporal scaling of biogeochemical properties and processes. Hurst analysis, which is a fractal-based scale invariant approach for analyzing long-term time series data, can provide insight into this issue as a quantitative approach for evaluating temporal scale in time series. The objectives of this paper were to compute the Hurst coefficient (H) for hydrologic and water quality variables, to study the effects of seasonality on H, and to determine how the H for the water quality indicators are related to that of the hydrologic parameters (e.g., discharge and rainfall). Two sites were investigated, Little River and Walker Branch, both located in east Tennessee. The water quality indicators include total coliform for Little River data and nitrate, chloride, sulfate, and calcium concentrations for Walker Branch data. H was estimated using spectral analysis. It was found that H for water quality indicators were significa.
The UK Grey‘ Market Overseas Package Holiday Experience: a critical evaluation of consumer and management perspectives

Purpose The UK package holiday market is valued at over £9.4 billion. Although over 14.4 million package holidays are expected to be consumed by UK travellers in 2010, the sector has been subject to very little academic research. A gap exists in the experience literature on the package holiday, a high value, unique and complex service that provides a fragmented experience which can take place over several months or more. The research sits within a marketing management and consumer behaviour theoretical framework and focuses on the 'grey' market segment, of particular interest to industry. The aim is to critically evaluate these holiday makers‘ experiences. Design/methodological/approach A Literature review discussed the nature of the package holiday and the experience as it relates to travel. The methodology takes a social constructionist epistemological orientation with an interpretive phenomenological philosophy. A qualitative, inductive approach ensued. Interviews in Tenerife were conducted with Thomson Holidays consumers, part of TUI Plc, and the largest international providers of package holidays. Template analysis was applied to the transcriptions. An interview took place with the Corporate Director of TUI, the objective being to evaluate management‘s perspectives of the provision of the experience. These findings will be analysed to add further depth and insights to the research. Limitations and ethical procedures are discussed. Findings Preliminary findings identified the importance of security, familiarity, value for money, local airport departures and socialising. Brand loyalty and repeat visits are exceptionally high. Originality/value The research provides new insights into the 'grey' market‘s package holiday experience which is of value to tour operators and academics specialising in this area.
Atmospheric boundary layer rolls observed by the synthetic aperture radar aboard the ERS-1 satellite

Two synthetic aperture radar (SAR) images acquired by the European Remote Sensing Satellite ERS-1 over the Jade-Weser estuary in the German Bight of the North Sea on January 2 and 20, 1992, are analyzed. The images show sea surface manifestations of atmospheric boundary layer rolls. This is inferred from the orientation of the quasi-periodic sea surface patterns which are aligned approximately with the wind direction, from the ratio of the wavelength of the patterns to the height of the boundary layer, and from the conditions encountered in the atmospheric boundary layer as measured quasi-simultaneously by radiosondes. The atmospheric boundary layer rolls were generated by a dynamic instability on January 2 and by a thermal instability on January 20. For the first time, quantitative estimates of variations of the wind velocity at the sea surface associated with the atmospheric rolls are extracted from a spaceborne radar SAR image. It is shown that wind velocities derived from SAR image intensity variations are in agreement with theoretical estimates.
Single Nucleotide Polymorphisms in DNA Repair Genes (XRCC1, XRCC2, XRCC3) and Their Association with Radiotherapy Toxicity among Head and Neck Cancer Patients:A Study from South-Western Maharashtra

Background: The genetic polymorphisms in DNA repair genes and their correlation with normal tissue toxicity in response to radiation therapy has not been consistently proven in many of the studies done in head and neck cancers (HNC). This study was intended to investigate the association of most common single nucleotide polymorphisms of DNA repair genes with acute radiation induced toxicities such as skin reactions and oral mucositis in normal tissue from HNC patients receiving radiotherapy from South-Western Maharashtra. Methods: Two hundred HNC patients receiving radiotherapy were enrolled in this study and the radiation injuries in the form of skin reactions and oral mucositis were recorded. Three single nucleotide polymorphisms (SNPs) rs1799782, rs25489) rs25487 of XRCC1 gene, rs3218536in XRCC2 gene and rs861539 SNP of XRCC3 gene were studied by PCR-RFLP and direct DNA sequencing. Results: The univariate analysis of SNPs of XRCC1, XRCC2 and XRCC3, the obtained results verified that XRCC1 polymorphism at 194Trp of exon 6 (OR=0.69, 95% CI: 0.28-1.71; p=0.433), codon 280 at exon 9 ((OR=1.05, 95% CI: 0.42-2.63; p=0.911) and codon 399 of at exon 10(OR=1.06, 95% CI: 0.52-2.15; p=0.867) and XRCC2 polymorphism at codon 188 at exon 3 (OR=1.07, 95% CI: 0.46-2.47; p=0.866) and 241Met variant genotype of XRCC3 (OR=2.63 95% CI: 0.42-16.30; p=0.298) showed no association with degree of radiotherapy associated dermatitis or mucositis in HNC patients. Conclusion: The findings from this study postulated that none of rs1799782, rs25489, rs25487 SNPs of XRCC1, rs3218536 SNP of XRCC2 nor rs861539 SNP of XRCC3 were associated with increased toxicity of radiotherapy in HNC patients of south-western Maharashtra.
The Association of P53, CK29, and FGFR3 Overexpression with the Characteristics of Urothelial Cell Carcinoma of the Bladder

Objectives: The aim of this study was to investigate the association between the overexpression of tumor protein (P53), cytokeratin 20 (CK20), fibroblast growth factor receptor 3 (FGFR3), biomarkers and the grading, prognosis, heterogeneity, and relapse tendency of urothelial cell carcinomas (UCCs) of the bladder. Methods: A cross-sectional study was conducted using 413 samples of Iranian patients diagnosed with UCC of the bladder. The tissue microarray technique was used to evaluate the patterns of tumor tissue. Two pathologists scored tissue staining using a semi-quantitative scoring system. Results: The results showed that P53 was a predictor of a high-grade pattern (the area under the curve (AUC)=0.620) with a best cut-off value of 95.0 using the receiver operating characteristic (ROC) curve. CK20 was another predictor of a high-grade pattern (AUC=0.745) with a best cut-off value of 15. However, the overexpression of both biomarkers was not associated with a heterogeneous pattern and could not predict tumor-associated death or relapse. The heterogeneous (odds ratio (OR)=4.535, p-value=0.001) and non-papillary (OR= 6.363, p-value= 0.001) patterns were effective predictors of tumor recurrence among all baseline variables, including patient and tumor characteristics. FGFR3 was positive in all specimens and was not a valuable biomarker for differentiating patterns. None of the variables predicted tumor prognosis. Conclusion: The study findings indicate that the intensity and percentage of cell staining for P53 and CK20 in the UCC of the bladder can aid in differentiating the grading patterns. The tendency of tumor relapse can be predicted by demonstrating heterogeneous and non-papillary patterns.
The impact of coping behaviors on perceived competence and social anxiety in the everyday social engagement of autistic adolescents.

LAY ABSTRACT Individuals who cope well with challenges may engage in social situations more successfully. We examined how well autistic adolescents coped, depending on how competent they felt and how much anxiety they experienced during social activities. We included 133 individuals (82 autistic, 51 neurotypical) between the ages of 10 and 16 years. Participants carried a mobile device that prompted them seven times a day for 7 days to record what they were doing, how competent they felt and how much anxiety they experienced. We used the Coping Inventory to understand how well participants coped with environmental challenges and met their needs for growth. Autistic adolescents were more likely than neurotypical peers to feel anxious while doing activities with adults. Autistic adolescents who had more difficulty coping with challenges were more likely to feel anxious when doing leisure activities with peers. Interestingly, autistic adolescents who coped better with challenges tended to feel less competent in social situations. However, those better able to meet their needs for growth tended to perceive their social competence positively. These findings can help practitioners develop strategies and programs to reduce the negative social experiences of autistic adolescents by helping them cope better.