Miracle-dz/UniFAR-FTUs huggingface dataset:
A Mixed Methods Study on the Barriers and Facilitators of Physical Activity Associated with Residential Relocation

Despite evidence suggesting that neighbourhood characteristics are associated with physical activity, very few mixed methods studies investigate how relocating neighbourhood, and subsequent changes in the built environment, influences physical activity. This sequential mixed methods study estimates associations between changes in overall physical activity and transportation walking and cycling and changes in objectively assessed neighbourhood walkability (quantitative phase) and describes perceived barriers and facilitators to physical activity following residential relocation (qualitative phase). During the quantitative phase, self-reported changes in transportation walking, transportation cycling, and overall physical activity following residential relocation were measured using a 5-point scale: (1) a lot less now, (2) a little less now, (3) about the same, (4) a little more now, and (5) a lot more now. Walkability improvers reported a slight increase in transportation walking (mean = 3.29, standard deviation (SD) = 0.87), while walkability decliners reported little or no perceived change in their transportation walking after relocation (mean = 2.96, SD = 1.12). This difference approached statistical significance (p=0.053). Furthermore, walkability decliners reported a slight decrease in transportation cycling (mean = 2.69, SD = 0.96), while walkability improvers reported little or no perceived change in their transportation cycling after relocation (mean = 3.02, SD = 0.84). This difference was statistically significant (p < 0.05). Change in walkability resulting from relocation was not significantly associated with perceived change in overall physical activity. Our qualitative findings suggest that moving to a neighbourhood with safe paths connecting to nearby destinations can facilitate transportation walking and cycling. Some participants describe adjusting their leisure physical activity to compensate for changes in transportation walking and cycling. Strong contributors to neighbourhood leisure physical activity included the presence of aesthetic features and availability of recreational opportunities that allow for the creation of social connections with community and family.
Prevalence, Antimicrobial Susceptibility Pattern, and Associated Factors of Urinary Tract Infections among Adult Diabetic Patients at Metu Karl Heinz Referral Hospital, Southwest Ethiopia

Urinary tract infection causes considerable morbidity in diabetic patients and if complicated, can cause severe renal damage and life-threatening infections. The escalating antimicrobial resistance rate among bacteria over the past years is another concern in the treatment of urinary tract infections. This study investigated the prevalence, antimicrobial susceptibility pattern of the isolates and associated factors of urinary tract infection among adult diabetic patients attending Metu Karl Heinz Referral Hospital, Southwest Ethiopia. An institutional-based cross-sectional study was conducted among 233 adult diabetic patients selected using simple random sampling technique. Data were collected using a structured questionnaire. Clean-catch midstream urine samples were investigated for the presence of pathogenic bacteria and their antimicrobial susceptibility pattern using recommended culture methods. Data were entered, cleaned, and analyzed using the Statistical Program for Social Sciences version 21.0. Statistical significance was set at a p-value < 0.05. The prevalence of urinary tract infection was 16.7% (95%, CI: 12.0, 21.5). The predominant isolates were Escherichia coli (25.6%) and Klebsiella spp. (20.5%). E. coli isolates showed higher sensitivity to ceftriaxone (80%), ciprofloxacin (70%), and gentamycin (70%), but resistant to tetracycline (60%). Staphylococcus aureus was sensitive to amoxicillin-clavulanic acid (85.7%), and gentamycin (57.1%), while resistant to tetracycline (85.7%), nitrofurantoin (85.7%), and ampicillin (71.4%). The odds of developing urinary tract infections were significantly higher in diabetic females (AOR: 3.56, 95% CI: 1.44, 8.76), those who were not able to read and write (AOR: 2.55, 95% CI: 1.19, 5.49) and those with a history of urinary tract infection (AOR: 2.31, 95% CI: 1.09, 4.90) compared with their counterparts. In this study, the prevalence of urinary tract infection among diabetic patients was relatively comparable with the previous studies conducted in Ethiopia. Management of urinary tract infection in diabetic patients should be supported with culture and antimicrobial susceptibility testing.
Monocular Total Capture: Posing Face, Body, and Hands in the Wild

We present the first method to capture the 3D total motion of a target person from a monocular view input. Given an image or a monocular video, our method reconstructs the motion from body, face, and fingers represented by a 3D deformable mesh model. We use an efficient representation called 3D Part Orientation Fields (POFs), to encode the 3D orientations of all body parts in the common 2D image space. POFs are predicted by a Fully Convolutional Network, along with the joint confidence maps. To train our network, we collect a new 3D human motion dataset capturing diverse total body motion of 40 subjects in a multiview system. We leverage a 3D deformable human model to reconstruct total body pose from the CNN outputs with the aid of the pose and shape prior in the model. We also present a texture-based tracking method to obtain temporally coherent motion capture output. We perform thorough quantitative evaluations including comparison with the existing body-specific and hand-specific methods, and performance analysis on camera viewpoint and human pose changes. Finally, we demonstrate the results of our total body motion capture on various challenging in-the-wild videos.
EENMF: An End-to-End Neural Matching Framework for E-Commerce Sponsored Search

E-commerce sponsored search contributes an important part of revenue for the e-commerce company. In consideration of effectiveness and efficiency, a large-scale sponsored search system commonly adopts a multi-stage architecture. We name these stages as ad retrieval, ad pre-ranking and ad ranking. Ad retrieval and ad pre-ranking are collectively referred to as ad matching in this paper. We propose an end-to-end neural matching framework (EENMF) to model two tasks---vector-based ad retrieval and neural networks based ad pre-ranking. Under the deep matching framework, vector-based ad retrieval harnesses user recent behavior sequence to retrieve relevant ad candidates without the constraint of keyword bidding. Simultaneously, the deep model is employed to perform the global pre-ranking of ad candidates from multiple retrieval paths effectively and efficiently. Besides, the proposed model tries to optimize the pointwise cross-entropy loss which is consistent with the objective of predict models in the ranking stage. We conduct extensive evaluation to validate the performance of the proposed framework. In the real traffic of a large-scale e-commerce sponsored search, the proposed approach significantly outperforms the baseline.
Suspicion, Cognition, and Automaticity Model of Phishing Susceptibility

Social-psychological research on phishing has implicated ineffective cognitive processing as the key reason for individual victimization. Interventions have consequently focused on training individuals to better detect deceptive emails. Evidence, however, points to individuals sinking into patterns of email usage that within a short period of time results in an attenuation of the training effects. Thus, individual email habits appear to be another predictor of their phishing susceptibility. To comprehensively account for all these influences, we built a model that accounts for the cognitive, preconscious, and automatic processes that potentially leads to phishing-based deception. The resultant suspicion, cognition, and automaticity model (SCAM) was tested using two experimental studies in which participants were subjected to different types of email-based phishing attacks.
Zoom-In-To-Check: Boosting Video Interpolation via Instance-Level Discrimination

We propose a light-weight video frame interpolation algorithm. Our key innovation is an instance-level supervision that allows information to be learned from the high-resolution version of similar objects. Our experiment shows that the proposed method can generate state-of-the-art results across different datasets, with fractional computation resources (time and memory) of competing methods. Given two image frames, a cascade network creates an intermediate frame with 1) a flow-warping module that computes coarse bi-directional optical flow and creates an interpolated image via flow-based warping, followed by 2) an image synthesis module to make fine-scale corrections. In the learning stage, object detection proposals are generated on the interpolated image. Lower resolution objects are zoomed into, and the learning algorithms using an adversarial loss trained on high-resolution objects to guide the system towards the instance-level refinement corrects details of object shape and boundaries.
An Exploration of Deception Detection: Are Groups More Effective Than Individuals?

Deception is a pervasive problem often found in human behavior. This study investigates why past deception studies have found groups perform no better than individuals in detection using time-interaction-performance theory which suggests teams are not immediately effective. Only after establishing relational links is their potential reached. Established groups spend less time building relational links and instead focus on task-oriented activities more effectively. We sought to determine whether groups with prior history of interaction outperform individuals in deception detection. First, participants were randomly assigned to an individual or ad hoc group role. Later, additional preexisting work groups were recruited. Participants were instructed to identify deception in online video interviews. The experiment tested theoretical explanations regarding cohesion, interaction, and satisfaction as components of relational links and relationships to deception detection. Results indicated that groups which exhibited higher levels of relational links, that is, established groups, were more accurate in deception detection than ad hoc groups.
Modelling Identity Rules with Neural Networks

In this paper, we show that standard feed-forward and recurrent neural networks fail to learn abstract patterns based on identity rules. We propose Relation Based Pattern (RBP) extensions to neural network structures that solve this problem and answer, as well as raise, questions about integrating structures for inductive bias into neural networks. Examples of abstract patterns are the sequence patterns ABA and ABB where A or B can be any object. These were introduced by Marcus et al (1999) who also found that 7 month old infants recognise these patterns in sequences that use an unfamiliar vocabulary while simple recurrent neural networks do not.This result has been contested in the literature but it is confirmed by our experiments. We also show that the inability to generalise extends to different, previously untested, settings. We propose a new approach to modify standard neural network architectures, called Relation Based Patterns (RBP) with different variants for classification and prediction. Our experiments show that neural networks with the appropriate RBP structure achieve perfect classification and prediction performance on synthetic data, including mixed concrete and abstract patterns. RBP also improves neural network performance in experiments with real-world sequence prediction tasks. We discuss these finding in terms of challenges for neural network models and identify consequences from this result in terms of developing inductive biases for neural network learning.
Tuberculosis control from the perspective of health professionals working in street clinics

ABSTRACT Purpose: to present the opinion of professionals about street dwellers undergoing treatment of tuberculosis and identify strategies of control of tuberculosis in this population. Method: an exploratory and descriptive study involving 17 health professionals working in street clinics. A semi-structured study composed of closed questions and a guiding question. The statements were analyzed using the discourse analysis technique, resulting in the identification of two analytical categories: 1. Meanings attributed to street dwellers with tuberculosis, and 2. Control of tuberculosis in homeless people. Results: the analysis identified situations that limited adherence to tuberculosis treatment, including the reasons for staying in the streets, living conditions, and risk factors (dependence on alcohol and other drugs, short-sightedness, constant relocations, and lack of perspectives). Street dwellers were knowledgeable about the disease. Furthermore, there were difficulties in solving several problems of people living in the streets, including living conditions and lifestyle, social stigma, relocations, drug abuse, and lack of life project. Conclusion: coping with the complexity of situations related to living in the streets limits to the work of health professionals because these situations go beyond health care and require intersectoral actions.
Exploring the Association between Parental Factors and Age of Onset of Alcohol and Tobacco’s Using

Background: There are few reports about relationship between parents’ age of onset of substance use and their children’s age of onset. The aim of this study was to explore the association between parents’ age of onset of substance use and that of their children, the influence of parents’ factor on their children’s age of onset of substance use. Methods: Overall, 2036 Chinese college students from ten universities in five cities were assessed for their alcohol and tobacco’s use, and they reported on their parents’ alcohol and tobacco’s use, their parents’ attitudes towards their substance use and their parental education level. Among current substance users, multiple regression analyses were used to test whether their parents’ age of onset of substance use and their parents’ attitudes towards their substance use predict their age of onset of substance use. Results: Parents of children who used substances earlier showed significantly more tolerance towards their children’s substance use than parents of children with a later onset of substance use. Among current substance users, their parents’ age of onset of substance significantly predicted children’s age of onset. The parents’ attitudes had a significant impact on their children’s age of onset of substance use. Conclusion: Parental factors (attitudes towards their children’s substance use and parents’ age of onset of substance use) are related to age of onset and substance use in children. Prevention program for parents is necessary, especially for those parents who have a tolerant attitude towards their children’s substance use.
Learning Dynamic Embeddings from Temporal Interactions

Modeling a sequence of interactions between users and items (e.g., products, posts, or courses) is crucial in domains such as e-commerce, social networking, and education to predict future interactions. Representation learning presents an attractive solution to model the dynamic evolution of user and item properties, where each user/item can be embedded in a euclidean space and its evolution can be modeled by dynamic changes in embedding. However, existing embedding methods either generate static embeddings, treat users and items independently, or are not scalable. Here we present JODIE, a coupled recurrent model to jointly learn the dynamic embeddings of users and items from a sequence of user-item interactions. JODIE has three components. First, the update component updates the user and item embedding from each interaction using their previous embeddings with the two mutually-recursive Recurrent Neural Networks. Second, a novel projection component is trained to forecast the embedding of users at any future time. Finally, the prediction component directly predicts the embedding of the item in a future interaction. For models that learn from a sequence of interactions, traditional training data batching cannot be done due to complex user-user dependencies. Therefore, we present a novel batching algorithm called t-Batch that generates time-consistent batches of training data that can run in parallel, giving massive speed-up. We conduct six experiments on two prediction tasks---future interaction prediction and state change prediction---using four real-world datasets. We show that JODIE outperforms six state-of-the-art algorithms in these tasks by up to 22.4%. Moreover, we show that JODIE is highly scalable and up to 9.2x faster than comparable models. As an additional experiment, we illustrate that JODIE can predict student drop-out from courses five interactions in advance.
Unexpected Fat Distribution in Adolescents With Narcolepsy

Narcolepsy type 1 is a chronic sleep disorder with significantly higher BMI reported in more than 50% of adolescent patients, putting them at a higher risk for metabolic syndrome in adulthood. Although well-documented, the body fat distribution and mechanisms behind weight gain in narcolepsy are still not fully understood but may be related to the loss of orexin associated with the disease. Orexin has been linked to the regulation of brown adipose tissue (BAT), a metabolically active fat involved in energy homeostasis. Previous studies have used BMI and waist circumference to characterize adipose tissue increases in narcolepsy but none have investigated its specific distribution. Here, we examine adipose tissue distribution in 19 adolescent patients with narcolepsy type 1 and compare them to 17 of their healthy peers using full body magnetic resonance imaging (MRI). In line with previous findings we saw that the narcolepsy patients had more overall fat than the healthy controls, but contrary to our expectations there were no group differences in supraclavicular BAT, suggesting that orexin may have no effect at all on BAT, at least under thermoneutral conditions. Also, in line with previous reports, we observed that patients had more total abdominal adipose tissue (TAAT), however, we found that they had a lower ratio between visceral adipose tissue (VAT) and TAAT indicating a relative increase of subcutaneous abdominal adipose tissue (ASAT). This relationship between VAT and ASAT has been associated with a lower risk for metabolic disease. We conclude that while weight gain in adolescents with narcolepsy matches that of central obesity, the lower VAT ratio may suggest a lower risk of developing metabolic disease.
Longitudinal Assessment of Retinal Structure in Achromatopsia Patients With Long-Term Follow-up

Purpose To longitudinally characterize structural retinal changes in achromatopsia (ACHM) over extended follow-up. Methods Fifty molecularly confirmed ACHM subjects underwent serial spectral-domain optical coherence tomography (SD-OCT) and fundus autofluorescence (FAF) imaging. Foveal structure on SD-OCT was graded and compared for evidence of progression, and foveal total retinal thickness (FTRT) and outer nuclear layer (ONL) thickness were serially measured. FAF patterns were characterized and compared over time. Results Mean SD-OCT follow-up was 61.6 months (age range at baseline, 6–52 years). Forty-five of the subjects had serial FAF (mean follow-up: 48.5 months). Only 6 (12%) of the subjects demonstrated qualitative change on serial foveal SD-OCT scans. Among the entire cohort, there was no statistically significant change over time in FTRT (P = 0.2459) or hyporeflective zone (HRZ) diameter (P = 0.3737). There was a small—but statistically significant—increase in ONL thickness (P = 0.0084). Three different FAF patterns were observed: centrally increased FAF (13/45), normal FAF (14/45), and well-demarcated reduced FAF (18/45), with the latter group displaying a small gradual increase in the area of reduced FAF of 0.055 mm2 over 43.4 months (P = 0.0011). Conclusions This longitudinal study of retinal structure in ACHM represents the largest cohort and longest follow-up period to date. Our findings support the presiding notion that ACHM is essentially a stationary condition regarding retinal structure, and any change over time is likely to be small, slow, and variable across patients. This may potentially afford a wider window for therapeutic intervention.
Facial nerve hemangioma in the middle ear

ABSTRACT Facial nerve hemangioma is a rare and benign vascular tumor, and accounts for 0.7% of intratemporal tumors. We report the second case described in the literature of a facial nerve hemangioma in its tympanic segment. A 14-year-old male patient presented with a history of progressive right ear hearing loss with preserved facial mimicry. Pure tone audiometry showed a right ear moderate conductive hearing loss. Magnetic resonance imaging demonstrated an expansive lesion involving the tympanic segment of the right facial nerve, suggestive of hemangioma. Watchful waiting was chosen as management. In the first case of middle ear facial hemangioma described in the literature, facial palsy was the symptom that led the patient to seek medical care. In the present case, it can be inferred that the first symptom was conductive hearing loss ipsilateral to the lesion. Facial palsy may not be present and the clinical presentation may resemble otosclerosis, ossicular chain disruption, and third window abnormalities, among other differential diagnoses of conductive hearing loss. The second case of tympanic portion facial nerve hemangioma is reported, describing the specificity of conductive hearing loss as its only clinical manifestation.
Applying ecological resistance and resilience to dissect bacterial antibiotic responses

Applying an ecological framework to bacterial antibiotic responses improves understanding of population recovery. An essential property of microbial communities is the ability to survive a disturbance. Survival can be achieved through resistance, the ability to absorb effects of a disturbance without a notable change, or resilience, the ability to recover after being perturbed by a disturbance. These concepts have long been applied to the analysis of ecological systems, although their interpretations are often subject to debate. Here, we show that this framework readily lends itself to the dissection of the bacterial response to antibiotic treatment, where both terms can be unambiguously defined. The ability to tolerate the antibiotic treatment in the short term corresponds to resistance, which primarily depends on traits associated with individual cells. In contrast, the ability to recover after being perturbed by an antibiotic corresponds to resilience, which primarily depends on traits associated with the population. This framework effectively reveals the phenotypic signatures of bacterial pathogens expressing extended-spectrum β-lactamases (ESBLs) when treated by a β-lactam antibiotic. Our analysis has implications for optimizing treatment of these pathogens using a combination of a β-lactam and a β-lactamase (Bla) inhibitor. In particular, our results underscore the need to dynamically optimize combination treatments based on the quantitative features of the bacterial response to the antibiotic or the Bla inhibitor.