Miracle-dz/UniFAR-FTUs huggingface dataset:
Perceptions and experiences of diabetic foot ulceration and foot care in people with diabetes: A qualitative meta‐synthesis

Diabetic foot ulceration (DFU) is a common and debilitating complication of diabetes that is preventable through active engagement in appropriate foot‐related behaviours, yet many individuals with diabetes do not adhere to foot care recommendations. The aim of this paper was to synthesise the findings of qualitative papers exploring diabetic people's perceptions and experiences of DFU in order to identify how they could be better supported to prevent ulceration or manage its impact. Five databases (MEDLINE, PsycINFO, CINAHL, EMBASE, Web of Science) were searched in May 2016 to identify eligible articles. Findings were synthesised using a meta‐ethnographic approach. Forty‐two articles were eligible for inclusion. Synthesis resulted in the development of five overarching themes: personal understandings of diabetic foot ulceration; preventing diabetic foot ulceration: knowledge, attitudes, and behaviours; views on health care experiences; development of diabetic foot ulceration and actions taken; and wide‐ranging impacts of diabetic foot ulceration. The findings highlight various barriers and facilitators of foot care experienced by people with diabetes and demonstrate the significant consequences of ulcers for their physical, social, and psychological well‐being. The insights provided could inform the development of interventions to promote foot care effectively and provide appropriate support to those living with ulceration.
Segmentation of Laterally Symmetric Overlapping Objects: Application to Images of Collective Animal Behavior

Video analysis is currently the main non-intrusive method for the study of collective behavior. However, 3D-to-2D projection leads to overlapping of observed objects. The situation is further complicated by the absence of stall shapes for the majority of living objects. Fortunately, living objects often possess a certain symmetry which was used as a basis for morphological fingerprinting. This technique allowed us to record forms of symmetrical objects in a pose-invariant way. When combined with image skeletonization, this gives a robust, nonlinear, optimization-free, and fast method for detection of overlapping objects, even without any rigid pattern. This novel method was verified on fish (European bass, Dicentrarchus labrax, and tiger barbs, Puntius tetrazona) swimming in a reasonably small tank, which forced them to exhibit a large variety of shapes. Compared with manual detection, the correct number of objects was determined for up to almost 90 % of overlaps, and the mean Dice-Sørensen coefficient was around 0.83 . This implies that this method is feasible in real-life applications such as toxicity testing.
The one comparing narrative social network extraction techniques

Analysing narratives through their social networks is an expanding field in quantitative literary studies. Manually extracting a social network from any narrative can be time consuming, so automatic extraction methods of varying complexity have been developed. However, the effect of different extraction methods on the resulting networks is unknown. Here we model and compare three extraction methods for social networks in narratives: manual extraction, co-occurrence automated extraction and automated extraction using machine learning. Although the manual extraction method produces more precise results in the network analysis, it is highly time consuming. The automatic extraction methods yield comparable results for density, centrality measures and edge weights. Our results provide evidence that automatically-extracted social networks are reliable for many analyses. We also describe which aspects of analysis are not reliable with such a social network. Our findings provide a framework to analyse narratives, which help us improve our understanding of how stories are written and evolve, and how people interact with each other.
Convolutional Neural Networks for Epileptic Seizure Prediction

Epilepsy is the most common neurological disorder and an accurate forecast of seizures would help to overcome the patient’s uncertainty and helplessness. In this contribution, we present and discuss a novel methodology for the classification of intracranial electroencephalography (iEEG) for seizure prediction. Contrary to previous approaches, we categorically refrain from an extraction of hand-crafted features and use a convolutional neural network (CNN) topology instead for both the determination of suitable signal characteristics and the binary classification of preictal and interictal segments. Three different models have been evaluated on public datasets with long-term recordings from four dogs and three patients. Overall, our findings demonstrate the general applicability. In this work we discuss the strengths and limitations of our methodology.
Cultural influences on the processing of social comparison feedback signals—an ERP study

Abstract This study investigated cultural differences regarding social connectedness in association with social vs non-social comparison feedback. We performed electroencephalography in 54 Chinese and 49 Western adults while they performed a time estimation task in which response–accuracy feedback was either delivered pertaining to participants’ own performance (non-social reference frame) or to the performance of a reference group (social reference frame). Trait interdependence and independence were assessed using a cultural orientations questionnaire. Applying a principal component approach, we observed divergent effects for the two cultural groups during feedback processing. In particular, Feedback-Related Negativity results indicated that non-social (vs social) reference feedback was more salient/motivating for Chinese participants, while Westerners showed the opposite pattern. The results suggest that Chinese individuals perceive a non-social context as more salient than a social comparison context, possibly due to their extensive experience of social comparisons in daily life. The reverse pattern was found in Western participants, for whom a social comparison context is less common and presumably more salient. The cultural differences in neural responses to social vs non-social feedback might be caused by culturally diverse cognitive traits, as well as by exposure to culturally defined behaviour on a systemic level—such as the education system.
Identification of a novel therapeutic target in driver-negative non-small cell lung cancer.

Molecular targeted therapy against driver gene aberrations has a significantly high positive response rate compared with traditional cytotoxic chemotherapies, and has thus dramatically changed the treatment strategies for malignant tumors, including non-small cell lung cancer (NSCLC) (1). Lung adenocarcinoma (LADC) can be treated with targeted therapy against driver gene aberrations using tyrosine kinase inhibitors (TKIs).
Wearable Respiration Sensor Platform Using Ultrasound Transducer

In this paper, a novel wearable respiration sensor using ultrasound transducer is proposed. Respiration is one of interesting physiological information which are affected by voluntary and in-voluntary motions. Hence, respiration reflects the consciousness and unconsciousness of person's state such as sleep, speaking, etc. The ultrasound transducer installed into the sensor can detect small abdominal movement as the impedance variation by pressure. In addition, the wide dynamic range allows to measure respiration under various situations in daily living. Moreover, our wearable sensor has generic devices such as accelerometer and the software development kit enables users to handle data obtained from these sensor. We demonstrate that the proposed sensor can measure respiration information precisely in various conditions through an experiment.
A Unified Framework of DNN Weight Pruning and Weight Clustering/Quantization Using ADMM

Many model compression techniques of Deep Neural Networks (DNNs) have been investigated, including weight pruning, weight clustering and quantization, etc. Weight pruning leverages the redundancy in the number of weights in DNNs, while weight clustering/quantization leverages the redundancy in the number of bit representations of weights. They can be effectively combined in order to exploit the maximum degree of redundancy. However, there lacks a systematic investigation in literature towards this direction. In this paper, we fill this void and develop a unified, systematic framework of DNN weight pruning and clustering/quantization using Alternating Direction Method of Multipliers (ADMM), a powerful technique in optimization theory to deal with non-convex optimization problems. Both DNN weight pruning and clustering/quantization, as well as their combinations, can be solved in a unified manner. For further performance improvement in this framework, we adopt multiple techniques including iterative weight quantization and retraining, joint weight clustering training and centroid updating, weight clustering retraining, etc. The proposed framework achieves significant improvements both in individual weight pruning and clustering/quantization problems, as well as their combinations. For weight pruning alone, we achieve 167x weight reduction in LeNet-5, 24.7x in AlexNet, and 23.4x in VGGNet, without any accuracy loss. For the combination of DNN weight pruning and clustering/quantization, we achieve 1,910x and 210x storage reduction of weight data on LeNet-5 and AlexNet, respectively, without accuracy loss. Our codes and models are released at the link this http URL
Towards a Zero-One Law for Entrywise Low Rank Approximation

There are a number of approximation algorithms for NP-hard versions of low rank approximation, such as finding a rank-$k$ matrix $B$ minimizing the sum of absolute values of differences to a given matrix $A$, $\min_{\textrm{rank-}k~B}\|A-B\|_1$, or more generally finding a rank-$k$ matrix $B$ which minimizes the sum of $p$-th powers of absolute values of differences, $\min_{\textrm{rank-}k~B}\|A-B\|_p^p$. Many of these algorithms are linear time columns subset selection algorithms, returning a subset of ${\rm{poly}}(k\log n)$ columns whose cost is no more than a ${\rm{poly}}(k)$ factor larger than the cost of the best rank-$k$ matrix. The above error measures are special cases of the following general entrywise low rank approximation problem: given an arbitrary function $g:\mathbb{R}\rightarrow\mathbb{R}_{\geq0}$, find a rank-$k$ matrix $B$ which minimizes $\|A-B\|_g=\sum_{i,j}g(A_{i,j}-B_{i,j})$. A natural question is which functions $g$ admit efficient approximation algorithms? Indeed, this is a central question of recent work studying generalized low rank models. We give approximation algorithms for every function $g$ which is approximately monotone and satisfies an approximate triangle inequality, and we show both of these conditions are necessary. Further, our algorithm is efficient if the function $g$ admits an efficient approximate regression algorithm. Our algorithm handles functions which are not even scale-invariant, such as the Huber loss function. Our algorithms have ${\rm{poly}}(k)$-approximation ratio in general. We further improve the approximation ratio to (1+$\epsilon$) for $\ell_1$ loss when the entries of the error matrix are i.i. d. random variables drawn from a distribution $\mu$ of which (1+$\gamma$) moment exists, for an arbitrary small constant $\gamma>0$. We also show our moment assumption is necessary.
Qualitative Investigation of Coaches’ Perspectives on Moral Education in Sport

Background. Moral behaviour and moral education of young athletes still remain an important issue in sports context. This study was designed to examine coaches’ perspectives on moral education in sport. Methods. In this study, aiming at establishing the perspectives of coaches on the moral education of athletes in sports activities, qualitative research was selected. Primary data were collected via semi-structured interviews with nine basketball and football coaches of diﬀerent experience and age. The data were analysed applying thematic analysis. Results. Thematic analysis indicated that coaches identifed goals of athletes’ education as the development of athletes’ personality, motivation, the development of sports excellence and the encouragement to achieve victory. Coaches defned moral education as the development of the authority of an athlete, fair play, compliance with rules, and integrity. The most common means coaches used for moral education were explanation, discussion, lectures, meetings, personal examples, case analyses. Such means are important for athlete’s personal development, pursuit of results, and career planning. The qualities of good coach were professional knowledge, authority, competences of creating motivational climate, and also moral competences. Conclusion. A central fnding of the study is that coaches defne moral education in sport through the education of moral values and the goals set by coaches related not only to the sports results, but also to the development of the personality of athletes. Personal role of coaches in moral education encompassing professional knowledge and moral competences of athletes is of great importance. Keywords: morality in sport, moral values, coaches’ role, athletes’ education.
Elastic CRFs for Open-ontology Slot Filling

Slot filling is a crucial component in task-oriented dialog systems that is used to parse (user) utterances into semantic concepts called slots. An ontology is defined by the collection of slots and the values that each slot can take. The most widely used practice of treating slot filling as a sequence labeling task suffers from two main drawbacks. First, the ontology is usually pre-defined and fixed and therefore is not able to detect new labels for unseen slots. Second, the one-hot encoding of slot labels ignores the correlations between slots with similar semantics, which makes it difficult to share knowledge learned across different domains. To address these problems, we propose a new model called elastic conditional random field (eCRF), where each slot is represented by the embedding of its natural language description and modeled by a CRF layer. New slot values can be detected by eCRF whenever a language description is available for the slot. In our experiment, we show that eCRFs outperform existing models in both in-domain and cross-domain tasks, especially in predicting unseen slots and values.
Transdermal Measurement of Glomerular Filtration Rate in Mice

Transdermal analysis of glomerular filtration rate (GFR) is an established technique that is used to assess renal function in mouse and rat models of acute kidney injury and chronic kidney disease. The measurement system consists of a miniaturized fluorescence detector that is directly attached to the skin on the back of conscious, freely moving animals, and measures the excretion kinetics of the exogenous GFR tracer, fluorescein-isothiocyanate (FITC) conjugated sinistrin (an inulin analog). This system has been described in detail in rats. However, because of their smaller size, measurement of transcutaneous GFR in mice presents additional technical challenges. In this paper we therefore provide the first detailed practical guide to the use of transdermal GFR monitors in mice based on the combined experience of three different investigators who have been performing this assay in mice over a number of years.
The Nomenclature of Human White Matter Association Pathways: Proposal for a Systematic Taxonomic Anatomical Classification

The heterogeneity and complexity of white matter (WM) pathways of the human brain were discretely described by pioneers such as Willis, Stenon, Malpighi, Vieussens and Vicq d’Azyr up to the beginning of the 19th century. Subsequently, novel approaches to the gross dissection of brain internal structures have led to a new understanding of WM organization, notably due to the works of Reil, Gall and Burdach highlighting the fascicular organization of WM. Meynert then proposed a definitive tripartite organization in association, commissural and projection WM pathways. The enduring anatomical work of Dejerine at the turn of the 20th century describing WM pathways in detail has been the paramount authority on this topic (including its terminology) for over a century, enriched sporadically by studies based on blunt Klingler dissection. Currently, diffusion-weighted magnetic resonance imaging (DWI) is used to reveal the WM fiber tracts of the human brain in vivo by measuring the diffusion of water molecules, especially along axons. It is then possible by tractography to reconstitute the WM pathways of the human brain step by step at an unprecedented level of precision in large cohorts. However, tractography algorithms, although powerful, still face the complexity of the organization of WM pathways, and there is a crucial need to benefit from the exact definitions of the trajectories and endings of all WM fascicles. Beyond such definitions, the emergence of DWI-based tractography has mostly revealed strong heterogeneity in naming the different bundles, especially the long-range association pathways. This review addresses the various terminologies known for the WM association bundles, aiming to describe the rules of arrangements followed by these bundles and to propose a new nomenclature based on the structural wiring diagram of the human brain.
Cytokine Induced Killer cells are effective against sarcoma cancer stem cells spared by chemotherapy and target therapy.

ABSTRACT Metastatic bone and soft tissue sarcomas often relapse after chemotherapy (CHT) and molecular targeted therapy (mTT), maintaining a severe prognosis. A subset of sarcoma cancer stem cells (sCSC) is hypothesized to resist conventional drugs and sustain disease relapses. We investigated the immunotherapy activity of cytokine induced killer cells (CIK) against autologous sCSC that survived CHT and mTT. The experimental platform included two aggressive bone and soft tissue sarcoma models: osteosarcoma (OS) and undifferentiated-pleomorphic sarcoma (UPS). To visualize putative sCSC we engineered patient-derived sarcoma cultures (2 OS and 3 UPS) with a lentiviral sCSC-detector wherein the promoter of stem-gene Oct4 controls the expression of eGFP. We visualized a fraction of sCSC (mean 24.2 ± 5.2%) and confirmed their tumorigenicity in vivo. sCSC resulted relatively resistant to both CHT and mTT in vitro. Therapeutic doses of doxorubicin significantly enriched viable eGFP+sCSC in both OS (2.6 fold, n = 16) and UPS (2.3 fold, n = 29) compared to untreated controls. Treatment with sorafenib (for OS) and pazopanib (for UPS) also determined enrichment (1.3 fold) of viable eGFP+sCSC, even if less intense than what observed after CHT. Sarcoma cells surviving CHT and mTT were efficiently killed in vitro by autologous CIK even at minimal effector/target ratios (40:1 = 82%, 1:4 = 29%, n = 13). CIK immunotherapy did not spare sCSC that were killed as efficiently as whole sarcoma cell population. The relative chemo-resistance of sCSC and sensitivity to CIK immunotherapy was confirmed in vivo. Our findings support CIK as an innovative, clinically explorable, approach to eradicate chemo-resistant sCSC implicated in tumor relapse.
A Reconfigurable 2-D Wireless Charging System

Wireless charging pads such as Qi are rapidly gaining ground, but its limited power supply range still requires precise placement on a specific point. 2-D wireless power transfer (WPT) sheets consisting of coil arrays are one well-known solution for this issue [3]. However, these approaches require custom-made designs by experts; what we need is a system that can be reconfigured by simply placing ready-made modules on the intended surface. In this demo, we present "Alvus", a reconfigurable 2-D wireless charging system which enables such simple construction of wireless charging surfaces. Alvus is based on multihop WPT, which constructs "virtual power codes" and consists of three types of ready-made resonator modules: transmitter, relay, and receiver. Our system instantly and interactively forms wireless charging surface on everyday objects (e.g. floor, wall, table, etc.) only by placing the resonator modules.
Revealing Inaccessible Information on-the-go, by Augmenting Humans

People with visual impairments (PVI) experience difficulties with daily tasks that require visual queues, making PVI dependent on sighted people. Emergent assistive technology has proven to enable PVI with performing complex tasks, such as item identification by using phone applications or stand-alone assistive devices. Existing technologies currently fail to provide seamless interactions, where users can easily access information, at any location and time, without requiring the performance of multiple steps. In this research, we focus on augmenting PVI through wearable technology to provide seamless support in a mobile environment, such as the supermarket.
Kernel Distillation for Fast Gaussian Processes Prediction

Gaussian processes (GPs) are flexible models that can capture complex structure in large-scale dataset due to their non-parametric nature. However, the usage of GPs in real-world application is limited due to their high computational cost at inference time. In this paper, we introduce a new framework, \textit{kernel distillation}, to approximate a fully trained teacher GP model with kernel matrix of size $n\times n$ for $n$ training points. We combine inducing points method with sparse low-rank approximation in the distillation procedure. The distilled student GP model only costs $O(m^2)$ storage for $m$ inducing points where $m \ll n$ and improves the inference time complexity. We demonstrate empirically that kernel distillation provides better trade-off between the prediction time and the test performance compared to the alternatives.
The Use of Ultrasonography for Verifying Gastric Tube Placement in Newborns

Supplemental Digital Content is Available in the Text. Background: The use of gastric tubes in newborns admitted to a neonatal intensive care unit is fairly high, and there is a risk of serious complications related to this procedure. Purpose: Considering the need to find a method that does not involve the patient's exposure to radiation, this study aimed to evaluate the diagnostic accuracy of ultrasonography for verifying gastric tube placement in newborns. Methods: This was a prospective, double-blind, observational study performed in a neonatal intensive care unit, in which 159 infants had gastric intubation using ultrasound examination and radiological imaging, to verify positioning. Results were analyzed in terms of diagnostic accuracy. Results: The tubes were correctly positioned in 157 cases (98.7%), according to radiological images, and in 156 cases (98.1%), according to ultrasound. The sensitivity analysis was 0.98 and the positive predictive value was 0.99. It was not possible to perform a specificity analysis, as there were not enough negative cases in the sample. Implications for Practice: The use of ultrasonography to identify correct positioning of gastric tubes in infants and newborns shows good sensitivity. Implications for Research: It was not possible to evaluate the ultrasonography specificity; further studies with greater samples are probably necessary, so that this objective can be achieved. Video Abstract available at https://journals.lww. com/advancesinneonatalcare/Pages/videogallery.aspx? videoId=29&autoPlay=true.
Affective Haptic Furniture: Directional Vibration Pattern to Regulate Emotion

Emotion regulation is an important part of humans' life and it is not only negative emotion that is harmful but excessive positive emotion could also prevent someone from achieving their goals. For example, high excitement that prevents someone from falling asleep. In our research, we are exploring the use of directional vibration patterns and embedding them onto a furniture to provide more accessibility to useful emotions. We have designed three vibration patterns and implemented them onto a chair-like cushion to evaluate the generated emotions in a user study.