Miracle-dz/UniFAR-FTUs huggingface dataset:
The Impact of Digital Technology on Self-Management in Cancer: Systematic Review

Background Self-management (SM) plays an important role in supporting patients’ adaptation to and management of the symptoms of chronic diseases. Cancer is a chronic disease that requires patients to have responsibility in management. Digital technology has the potential to enhance SM support, but there is little data on what SM skills are most commonly supported by digital technology. Objective This review aimed to examine the SM core skills that were enabled and supported by digital interventions in people with cancer and identify any predictors of the effect of digital health intervention on SM core skills. Methods Three electronic databases (MEDLINE, Scopus, and CINAHL) were searched for papers, published from January 2010 to February 2022, that reported randomized controlled trials (RCTs) involving patients with cancer or survivors of cancer where a digital technology intervention was evaluated and change in 1 or more SM core skills was a measured outcome. Results This systematic review resulted in 12 studies that were eligible to identify which SM core skills were enabled and supported by digital intervention. The total number of participants in the 12 studies was 2627. The most common SM core skills targeted by interventions were decision-making, goal setting, and partnering with health professionals. A total of 8 (67%) out of 12 RCTs demonstrated statistically significant improvement in outcomes including self-efficacy, survivorship care knowledge and attitude, quality of life, increased knowledge of treatment, and emotional and social functioning. A total of 5 (62%) out of 8 positive RCTs used theoretical considerations in their study design; whereas in 1 (25%) out of 4 negative RCTs, theoretical considerations were used. In 3 studies, some factors were identified that were associated with the development of SM core skills, which included younger age (regression coefficient [RC]=–0.06, 95% CI –0.10 to –0.02; P=.002), computer literacy (RC=–0.20, 95% CI –0.37 to –0.03; P=.02), completing cancer treatment (Cohen d=0.31), male sex (SD 0.34 in social functioning; P=.009), higher education (SD 0.19 in social functioning; P=.04), and being a recipient of chemotherapy (SD 0.36 in depression; P=.008). In all 3 studies, there were no shared identical factors that supported the development of SM core skills, whereby each study had a unique set of factors that supported the development of SM core skills. Conclusions Digital technology for patients with cancer appears to improve SM core skills including decision-making, goal setting, and partnering with health care partners. This effect is greater in people who are younger, male, educated, highly computer literate, completing cancer treatment, or a recipient of chemotherapy. Future research should focus on targeting multiple SM core skills and identifying predictors of the effect of digital technology intervention. Trial Registration PROSPERO CRD42021221922; https://tinyurl.com/mrx3pfax
Joint Label/Example Hyperbolic Representation for Extreme Classification

Extreme multi-label classiﬁcation task deals with nowadays problems involving up to millions of labels. The extremely large number of labels requires eﬃcient methods not only in terms of prediction performances but also in terms of time processing and memory management. Most recent works focus on representation learning approaches: embeddings allow to infer a semantic structured space with interesting generalization properties; the label prediction is often performed by using approximated nearest neighbors search. Recent works in Language Modeling and especially in Question Answering have shown increasing performances using the hyperbolic space to learn the representation. The Poincar´e ball model is indeed more relevant to represent ontology than the euclidean space. In this work, we propose to explore representation learning in hyperbolic space in the context of extreme classiﬁcation. The proposed model performs a joint embedding of examples and labels. Most of the embedding methods for extreme classiﬁcation only learn example embedding. Thus in order to structure the space, they require to precompute examples neighborhood. In our approach, learning a joint embedding allows structuring the space without any expensive preprocessing. Experiments conducted on real-world datasets show the performances of our model compared to state of the art.
Cross-lingual Text Clustering in a Large System

The multilingual world needs systems that can cluster text written in multiple languages into the same thread or topic. Clustering multilingual text can be accomplished by translating and then clustering text in a canonical language, using multilingual embed-dings to cluster articles in a shared embedding space, and via other language-independent methods. The performance and pitfalls of these various methods have not been well studied in the context of real-time clustering across documents written in many languages. We address this problem by generating a large dataset of news articles using a reference architecture that continuously indexed and clustered articles spanning 17 languages over the last 15 years. Through the analysis of these documents and their clusters, the clustering quality is shown to be dependent on the normalization of proper nouns, the types of georeferences, and the overall geographic focus of the document.
UvA-DARE (Digital Academic Repository) A Maxwell-Stefan-Glueckauf description of transient mixture uptake in microporous adsorbents

Based on the assumptions of uncoupled di ﬀ usion ﬂ uxes and loading-independent Fick di ﬀ usivities, the linear driving force (LDF) model developed by Glueckauf ﬁ nds widespread usage in the modelling of transient mixture uptake in microporous adsorbents. A number of experimental investigations report overshoots in intra-crystal-line loadings of the more mobile species during transient binary mixture uptake in microporous adsorbents; these overshoots are not anticipated in the classic Glueckauf approach. The origins of the overshoots are traceable to strong coupling between species transfers engendered by mixture adsorption equilibrium thermo-dynamics; such coupling e ﬀ ects are most conveniently described by the Maxwell-Stefan (M-S) di ﬀ usion formulation. In this article, an explicit analytic model is developed to calculate transient mixture uptakes by combining the Maxwell-Stefan formulation with the linearization procedure of Glueckauf. The Maxwell-Stefan-Glueckauf model is validated by comparison with six di ﬀ erent experimental data sets. In all six cases, the overshoots in the uptake of the more mobile partner species are properly captured; the incorporation of this approach in practical design procedures for adsorbers is expected to result in signi ﬁ cant reduction in model complexity and computational times.
Perception of ATR in Dàgáárè

This paper reports on two related perception studies about the property Advanced Tongue Root (ATR) in Dàgáárè (Mabia; Ghana). We examine how well native speakers are able to distinguish ATR contrasts as well as the effects of harmony and disharmony on perception, thereby testing hypotheses that have been made in the literature about the perceptual motivations of harmony systems. We find that, as expected, ATR mid vowels and Retracted Tongue Root (RTR) high vowels are the hardest to distinguish in Dàgáárè, but contrary to expectations, harmony does not improve accuracy in discriminating ATR contrasts. Nonetheless, we find the accuracy on disharmonic disyllabic forms is significantly worse than the accuracy in monosyllabic forms, which may indicate that disharmony hurts perception. We examine the implications for our understanding of the motivations of harmony systems and discuss how this paper contributes to the very minimal existing literature on perception in African languages.
Normal Pressure Hydrocephalus CSF Flow in the Brain in the Context of

SUMMARY: CSF normally ﬂows back and forth through the aqueduct during the cardiac cycle. During systole, the brain and intracranial vasculature expand and compress the lateral and third ventricles, forcing CSF craniocaudad. During diastole, they contract and ﬂow through the aqueduct reverses. Hyperdynamic CSF ﬂow through the aqueduct is seen when there is ventricular enlargement without cerebral atrophy. Therefore, patients presenting with clinical normal pressure hydrocephalus who have hyperdynamic CSF ﬂow have been found to respond better to ventriculoperitoneal shunting than those with normal or decreased CSF ﬂow. Patients with normal pressure hydrocephalus have also been found to have larger intracranial volumes than sex-matched controls, suggesting that they may have had benign external hydrocephalus as infants. While their arachnoidal granulations clearly have decreased CSF resorptive capacity, it now appears that this is ﬁxed and that the arachnoidal granulations are not merely immature. Such patients appear to develop a parallel pathway for CSF to exit the ventricles through the extracellular space of the brain and the venous side of the glymphatic system. This pathway remains functional until late adulthood when the patient develops deep white matter ischemia, which is characterized histologically by myelin pallor (ie, loss of lipid). The attraction between the bare myelin protein and the CSF increases resistance to the extracellular outﬂow of CSF, causing it to back up, resulting in hydrocephalus. Thus idiopathic normal pressure hydrocephalus appears to be a “2 hit” disease: benign external hydrocephalus in infancy followed by deep white matter ischemia in late adulthood.
A New Approach to Precision Airborne GPS Positioning for Photogrammetry

Precise determination of GPS phase ambiguity integers on the fly is a key requirement for accurate kinematic positioning. A fully automatic approach is developed to resolve phase integers and perform airborne GPS positioning for aerial triangulation using dual-frequency GPS data. Although always helpful, static initialization is not required; the problem of signal interruptions (losses of lock) while an airplane is maneuvering distant from a base station has also been addressed. Test results show that positions using precise phase data are reliably determined with this approach.
Evaluating power to detect recurrent selective sweeps under increasingly realistic evolutionary null models.

The detection of selective sweeps from population genomic data often relies on the premise that the beneficial mutations in question have fixed very near the sampling time. As it has been previously shown that the power to detect a selective sweep is strongly dependent on the time since fixation as well as the strength of selection, it is naturally the case that strong, recent sweeps leave the strongest signatures. However, the biological reality is that beneficial mutations enter populations at a rate, one that partially determines the mean wait time between sweep events and hence their age distribution. An important question thus remains about the power to detect recurrent selective sweeps when they are modelled by a realistic mutation rate and as part of a realistic distribution of fitness effects (DFE), as opposed to a single, recent, isolated event on a purely neutral background as is more commonly modelled. Here we use forward-in-time simulations to study the performance of commonly used sweep statistics, within the context of more realistic evolutionary baseline models incorporating purifying and background selection, population size change, and mutation and recombination rate heterogeneity. Results demonstrate the important interplay of these processes, necessitating caution when interpreting selection scans; specifically, false positive rates are in excess of true positive across much of the evaluated parameter space, and selective sweeps are often undetectable unless the strength of selection is exceptionally strong.
Inclusive Education and T eachers’ Perceptions of Lesson Planning and Lesson Work from a Student Inclusive Perspective

This article discusses the multifaceted concept of inclusion referring to a study (Kotte, 2017). It aims to contribute with deeper knowledge of teachers’ perceptions of inclusive education regarding students’ diverse learning prerequisites. The methodology is a combination of a quantitative and qualitative data analysis related to teachers participating in a Swedish national research and school improvement program. The analysis is performed through Biesta’s dimensions of the aim of education, Uljens’ model for analyzing didactic action; Hedegaard-Sørensen’s situated professionalism and Tomlinson’s differentiation of teaching. Some main results show that teachers have a positive attitude to inclusion but they also understand it as a difficult task to carry through during lessons. They experience a dilemma between single students’ needs and the interests of the class as a whole. Further results indicate that collegial collaboration is regarded as a valuable aspect for the inclusive didactic lesson planning and lesson work to be successful.
Understanding investors behaviors during the COVID-19 outbreak using Twitter sentiment analysis

during the first two of the three periods. This suggests that Twitter sentiment could serve as an early warning signal for market turbulence. The practical takeaway from our study is that both institutional and individual investors can benefit from monitoring social media sentiment, like Twitter, as part of their market analysis toolkit to make more informed investment decisions during times of crisis. Additionally, policymakers could leverage social media sentiment for gauging market sentiment and stability during disruptive events.
NFAT5 represses canonical Wnt signaling via inhibition of NFAT5 represses canonical Wnt signaling via inhibition of β -catenin acetylation and participates in regulating intestinal cell -catenin acetylation and participates in regulating intestinal cell differentiation differentiation

The intestinal mucosa undergoes a continual process of proliferation, differentiation, and apoptosis, which is regulated by multiple signaling pathways. The Wnt/ b -catenin pathway has a critical role in this process. Previously, we have shown that the calcineurin-dependent nuclear factor of activated T cell (NFAT) is involved in the regulation of intestinal cell differentiation, as noted by the alteration of brush-border enzyme intestinal alkaline phosphatase (IAP) activity. Here, we show that calcineurin-independent NFAT5 interacts with b -catenin to repress Wnt signaling. We found that overexpression of NFAT5 inhibits, whereas knockdown of NFAT5 increases, TOPﬂash reporter activity and the expression of Wnt/ b -catenin target genes, suggesting that NFAT5 inhibits Wnt signaling. In addition, we demonstrated that NFAT5 directly interacts with the C-terminal transactivation domain (TAD) of b -catenin, inhibits CBP interaction with b -catenin, and inhibits CBP-mediated b -catenin acetylation. Moreover, NFAT5 is expressed in the mucosa of human intestine, with the most pronounced staining in the most differentiated region near the epithelial surface. Knockdown of NFAT5 attenuated sodium butyrate (NaBT)-mediated induction of IAP and sucrase activities; overexpression of NFAT5 induced IAP promoter activity. In summary, we provide evidence showing that NFAT5 is a regulator of Wnt signaling. Importantly, our results suggest that NFAT5 regulation of intestinal cell differentiation may be through inhibition of Wnt/ b -catenin signaling.
Incorporating Social Welfare in Program-Evaluation and Treatment Choice ∗

We introduce a notion of money-metric social welfare for discrete choice, under unrestricted heterogeneity and income-effects. It is the maximized indirect utility under normalization of the outside option. It also equals the amount of income necessary to achieve a given level of utility, while certain choices are prohibited. We show that the distribution of this quantity is non-parametrically identified as a closed-form functional of average structural demand for the outside option, making it useful for cost-benefit analysis and optimal targeting. An illustration with private tuition subsidies in India shows that the income-path of usage-maximizing subsidies differs significantly from welfare-maximizing ones.
Two-Step Bayesian PINNs for Uncertainty Estimation

We use a two-step procedure to train Bayesian neural networks that provide uncertainties over the solutions to differential equation systems provided by Physics-Informed Neural Networks (PINNs). We take advantage of available error bounds over PINNs to formulate a heteroscedastic variance that improves the uncertainty estimation. Furthermore, we solve forward problems and utilize the uncertainties obtained to improve parameter estimation in inverse problems in the fields of cosmology and fermentation
Computers and Electronics in Agriculture

Precision agriculture is a growing field in the agricultural industry and it holds great potential in fruit and vegetable harvesting. In this work, we present a robust accurate method for the detection and localization of the peduncle of table grapes, with direct implementation in automatic grape harvesting with robots. The bunch and peduncle detection methods presented in this work rely on a combination of instance segmentation and monocular depth estimation using Convolutional Neural Networks (CNN). Regarding depth estimation, we propose a combination of different depth techniques that allow precise localization of the peduncle using traditional stereo cameras, even with the particular complexity of grape peduncles. The methods proposed in this work have been tested on the WGISD (Embrapa Wine Grape Instance Segmentation) dataset, improving the results of state-of-the-art techniques. Furthermore, within the context of the EU project CANOPIES, the methods have also been tested on a dataset of 1,326 RGB-D images of table grapes, recorded at the Corsira Agricultural Cooperative Society (Aprilia, Italy), using a Realsense D435i camera located at the arm of a CANOPIES two-manipulator robot developed in the project. The detection results on the WGISD dataset show that the use of RGB-D information ( 𝑚𝐴𝑃 = 0 . 949 ) leads to superior performance compared to the use of RGB data alone ( 𝑚𝐴𝑃 = 0 . 891 ). This trend is also evident in the CANOPIES Grape Bunch and Peduncle dataset, where the mAP for RGB-D images ( 𝑚𝐴𝑃 = 0 . 767 ) outperforms that of RGB data ( 𝑚𝐴𝑃 = 0 . 725 ). Regarding depth estimation, our method achieves a mean squared error of 2.66 cm within a distance of 1 m in the CANOPIES dataset.
Predicting the Onset of Dynamic Stall on Large Wind Turbines

This study addresses the challenge of predicting dynamic stall on wind turbine airfoils, focusing on the development of a reduced-order model applicable to thick airfoils (t/c > 0.21). Utilizing a Delayed Detached-Eddy simulation of a pitching FFA-W3-211 airfoil at Re = 15 M, our analysis identifies the transition from the primary instability phase to the vortex formation stage as a critical aspect of dynamic stall. By examining the dynamic time scales, 5 we observed a ten-fold increase in the growth rate of the shear layer height during the transition of these stages. The stall delays attributed to these stages are substantially dependent on the airfoil’s camber distribution and the location of the maximum thickness. We discovered that the Leading-Edge Suction-Parameter (LESP) proposed by Ramesh et al. (2014) for thin airfoils is also helpful in predicting the onset of the vortex formation stage for thick airfoils. Based on this finding, we propose a Mid-Chord Suction-Parameter (MCSP), that is more effective for wind 10 turbine airfoils. The MCSP exhibits a breakdown in magnitude at the onset of the vortex formation stage and deep stall.
Alteration of immune function in women collegiate soccer players Alteration of immune function in women collegiate soccer players and college students and college students

The purpose of this study was to monitor the stress-induced alteration in concentrations of salivary immunoglobulin (S-IgA) and cortisol and the incidence of upper respiratory tract infections (URTI) over the course of a 9-week competitive season in college student-athletes and college students. The subjects consisted of 14 NCAA Division III collegiate female soccer athletes (19.8 ± 1.0 years, mean ± SD) and 14 female college students (22.5 ± 2.6 years). Salivary samples were collected for 9 weeks during a competitive soccer season. S-IgA and cortisol concentrations were determined by enzyme linked immunosorbent assay (ELISA). A training and performance questionnaire was given to the subjects every week, to record the subjects’ session rating of perceived exertion (RPE) for all the training, load, monotony and strain, as well as any injuries or illnesses experienced. The between groups ANOVA procedure for repeated measures showed no changes in salivary concentrations of IgA and cortisol. Chi-square analysis showed that during the 9-week training season injury and illness occurred at a higher rate among the soccer players. There was a significant difference at baseline between soccer and control S-IgA levels (p ≤ 0.05). Decreased levels of S-IgA and increases in the indices of training (load, strain and monotony) were associated with an increase in the incidence of illness during the 9-week competitive soccer season.
Diversity-Guided Distillation With Modality-Center Regularization for Robust Multimodal Remote Sensing Image Classification

Multimodal learning has shown great potential in remote sensing image classification and attracted increasing interest in the community. Although it is preferable to collect multiple modalities for training, not all of them are available in practical scenarios. To this end, we propose a general diversity-guided distillation network (DGDNet) with modality-center regularization (MCR) to facilitate accurate model inference when modalities are missing. Compared with existing modality reconstruction methods, DGDNet does not need prior knowledge of the missing modality and can handle various missing modalities via only one model. Specifically, DGDNet consists of two components: the deployment network extracting the modality-invariant representation for robust inference and the teacher network transferring comprehensive multimodal information to the deployment network. This enables the deployment network to learn the modality invariant and specific information simultaneously while maintaining robustness for incomplete modality input. In particular, we design a novel diversity-guided distillation (DGD) method that transfers knowledge by matching the feature diversity. This helps overcome the representation heterogeneity when encouraging the deployment network to learn modality-specific information. Besides, an MCR strategy is proposed to address the unbalanced training of teacher and deployment networks by constraining the intra-class inter-modality variations. This helps alleviate the underfitting for weak modality, improving the model performance. Finally, extensive experiments demonstrate that the proposed DGDNet can address the problem of missing modalities effectively and achieves state-of-the-art performance. The code is available at https://github.com/shicaiwei123/TGRS-DGDNet.