Miracle-dz/UniFAR-FTUs huggingface dataset:
The Role of UX Professionals in Agile Development: A Case Study From Industry

A highly debated topic within recent literature in HCI is the integration of UX activities into agile development. While we acknowledge the question of integration to be one of the main challenges for UX professionals, this seems to have received traction and is heading closer towards a resolution. We believe the time is right for gaining an in-depth understanding of the UX roles responsibilities in an agile case company that has moved beyond the question of UX integration. Through interviews with UX professionals, developers and managers we found that the UX professionals have a very broad set of responsibilities, of which some are unseen in relation to previous studies and not classically considered part of the UX role. Interestingly, these newly identified responsibilities are some of the most critical to the success of integrating UX into agile development. Author
Use of Intermittent Preventive Treatment among Pregnant Women in Sub-Saharan Africa: Evidence from Malaria Indicator Surveys

Uptake of intermittent preventive therapy in pregnancy (IPTp) with sulfadoxine-pyrimethamine (IPTp-SP) is a clinically-proven method to prevent the adverse outcomes of malaria in pregnancy (MiP) for the mother, her foetus, and the neonates. The majority of countries in sub-Saharan Africa have introduced IPTp policies for pregnant women during the past decade. Nonetheless, progress towards improving IPTp coverage remains dismal, with widespread regional and socioeconomic disparities in the utilisation of this highly cost-effective service. In the present study, our main objective was to measure the prevalence of IPTp uptake in selected malaria-endemic countries in sub-Saharan Africa, and to investigate the patterns of IPTp uptake among different educational and wealth categories adjusted for relevant sociodemographic factors. For this study, cross-sectional data on 18,603 women aged between 15 and 49 years were collected from the Malaria Indicator Surveys (MIS) conducted in Burkina Faso, Ghana, Mali, Malawi, Kenya, Nigeria, Sierra Leone, and Uganda. The outcome variable was taking three doses of IPTp-SP in the last pregnancy, defined as adequate by the WHO. According to the analysis, the overall prevalence of taking three doses of IPTp-SP in the latest pregnancy was 29.5% (95% CI = 28.2–30.5), with the prevalence being highest for Ghana (60%, 95% CI = 57.1–62.8), followed by Kenya (37%, 95% CI = 35.3–39.2) and Sierra Leone (31%, 95% CI = 29.2–33.4). Women from non-poor households (richer—20.7%, middle—21.2%, richest—18.1%) had a slightly higher proportion of taking three doses of IPTp-SP compared with those from poorest (19.0%) and poorer (21.1%) households. Regression analysis revealed an inverse association between uptake of IPTp-SP and educational level. With regard to wealth status, compared with women living in the richest households, those in the poorest, poorer, middle, and richer households had significantly higher odds of not taking at least three doses of IPTp-SP during their last pregnancy. The present study concludes that the prevalence of IPTp-SP is still alarmingly low and is significantly associated with individual education and household wealth gradient. Apart from the key finding of socioeconomic disparities within countries, were the between-country variations that should be regarded as a marker of inadequate policy and healthcare system performance in the respective countries. More in-depth and longitudinal studies are required to understand the barriers to, and preferences of, using IPTp-SP among women from different socioeconomic backgrounds.
Neural Educational Recommendation Engine (NERE)

Quizlet is the most popular online learning tool in the United States, and is used by over 2/3 of high school students, and 1/2 of college students. With more than 95% of Quizlet users reporting improved grades as a result, the platform has become the de-facto tool used in millions of classrooms. In this paper, we explore the task of recommending suitable content for a student to study, given their prior interests, as well as what their peers are studying. We propose a novel approach, i.e. Neural Educational Recommendation Engine (NERE), to recommend educational content by leveraging student behaviors rather than ratings. We have found that this approach better captures social factors that are more aligned with learning. NERE is based on a recurrent neural network that includes collaborative and content-based approaches for recommendation, and takes into account any particular student's speed, mastery, and experience to recommend the appropriate task. We train NERE by jointly learning the user embeddings and content embeddings, and attempt to predict the content embedding for the final timestamp. We also develop a confidence estimator for our neural network, which is a crucial requirement for productionizing this model. We apply NERE to Quizlet's proprietary dataset, and present our results. We achieved an R^2 score of 0.81 in the content embedding space, and a recall score of 54% on our 100 nearest neighbors. This vastly exceeds the recall@100 score of 12% that a standard matrix-factorization approach provides. We conclude with a discussion on how NERE will be deployed, and position our work as one of the first educational recommender systems for the K-12 space.
Ninety days repeated dose oral toxicity study of Makaradhwaja in Wistar rats

Context: Makaradhwaja is a Kupipakwa Rasayana. Since it contains two heavy metals, namely mercury and gold, it is essential to evaluate its safety. Hence, the present study was undertaken with an objective to evaluate toxicity and target organ of toxicity of Makaradhwaja if so. Aims: The objective was to evaluate toxicological profile, the target organ of toxicity and to find no observed effect level (NOEL) or no observed adverse effect level (NOAEL) in rats after oral administration for ninety consecutive days. Materials and Methods: Makaradhawaja preparation was administered to male and female Wistar rats for ninety consecutive days at 2.7, 13.5, and 27 mg/kg body weight. All relevant biochemical and hematological changes were observed. At termination, all the rats were sacrificed and necropsy was performed. Histopathological evaluation was also performed. Statistical Analysis Used: Dunnett's test followed by analysis of variance. Results: There was a significant increase in high-dose group kidney weight of both sexes which could not be correlated with histopathology findings and serum biochemistry. Therefore, the change was not considered as an adverse effect. Conclusions: The dose level 27 mg/kg of Makaradhwaja was found as NOAEL and dose level 13.5 mg/kg of Makaradhwaja was found as NOEL.
Fabrication and Characterization of a High-Performance Multi-Annular Backscattered Electron Detector for Desktop SEM

Scanning electron microscopy has been developed for topographic analysis at the nanometer scale. Herein, we present a silicon p-n diode with multi-annular configuration to detect backscattering electrons (BSE) in a homemade desktop scanning electron microscope (SEM). The multi-annular configuration enables the enhancement of the topography contrast of 82.11 nA/μm as compared with the commercial multi-fan-shaped BSE detector of 40.08 nA/μm. Additionally, we integrated it with lateral p-n junction processing and aluminum grid structure to increase the sensitivity and efficiency of the multi-annular BSE detector that gives higher sensitivity of atomic number contrast and better surface topography contrast of BSE images for low-energy detection. The responsivity data also shows that MA-AL and MA p-n detectors have higher gain value than the MA detector does. The standard deviation of measurements is no higher than 1%. These results verify that MA p-n and MA-AL detectors are stable and can function well in SEM for low-energy applications. It is demonstrated that the multi-annular (MA) detectors are well suited for imaging in SEM systems.
Joint Multitask Learning for Community Question Answering Using Task-Specific Embeddings

We address jointly two important tasks for Question Answering in community forums: given a new question, (i) find related existing questions, and (ii) find relevant answers to this new question. We further use an auxiliary task to complement the previous two, i.e., (iii) find good answers with respect to the thread question in a question-comment thread. We use deep neural networks (DNNs) to learn meaningful task-specific embeddings, which we then incorporate into a conditional random field (CRF) model for the multitask setting, performing joint learning over a complex graph structure. While DNNs alone achieve competitive results when trained to produce the embeddings, the CRF, which makes use of the embeddings and the dependencies between the tasks, improves the results significantly and consistently across a variety of evaluation metrics, thus showing the complementarity of DNNs and structured learning.
Constrained Exploration and Recovery from Experience Shaping

We consider the problem of reinforcement learning under safety requirements, in which an agent is trained to complete a given task, typically formalized as the maximization of a reward signal over time, while concurrently avoiding undesirable actions or states, associated to lower rewards, or penalties. The construction and balancing of different reward components can be difficult in the presence of multiple objectives, yet is crucial for producing a satisfying policy. For example, in reaching a target while avoiding obstacles, low collision penalties can lead to reckless movements while high penalties can discourage exploration. To circumvent this limitation, we examine the effect of past actions in terms of safety to estimate which are acceptable or should be avoided in the future. We then actively reshape the action space of the agent during reinforcement learning, so that reward-driven exploration is constrained within safety limits. We propose an algorithm enabling the learning of such safety constraints in parallel with reinforcement learning and demonstrate its effectiveness in terms of both task completion and training time.
Monitoring local delivery of vancomycin from gelatin nanospheres in zebrafish larvae

Background Infections such as biomaterial-associated infection and osteomyelitis are often associated with intracellular survival of bacteria (eg, Staphylococcus aureus). Treatment of these infections remains a major challenge due to the low intracellular efficacy of many antibiotics. Therefore, local delivery systems are urgently required to improve the therapeutic efficacy of antibiotics by enabling their intracellular delivery. Purpose To assess the potential of gelatin nanospheres as carriers for local delivery of vancomycin into macrophages of zebrafish larvae in vivo and into THP-1-derived macrophages in vitro using fluorescence microscopy. Materials and methods Fluorescently labeled gelatin nanospheres were prepared and injected into transgenic zebrafish larvae with fluorescent macrophages. Both the biodistribution of gelatin nanospheres in zebrafish larvae and the co-localization of vancomycin-loaded gelatin nanospheres with zebrafish macrophages in vivo and uptake by THP-1-derived macrophages in vitro were studied. In addition, the effect of treatment with vancomycin-loaded gelatin nanospheres on survival of S. aureus-infected zebrafish larvae was investigated. Results Internalization of vancomycin-loaded gelatin nanospheres by macrophages was observed qualitatively both in vivo and in vitro. Systemically delivered vancomycin, on the other hand, was hardly internalized by macrophages without the use of gelatin nanospheres. Treatment with a single dose of vancomycin-loaded gelatin nanospheres delayed the mortality of S. aureus-infected zebrafish larvae, indicating the improved therapeutic efficacy of vancomycin against (intracellular) S. aureus infection in vivo. Conclusion The present study demonstrates that gelatin nanospheres can be used to facilitate local and intracellular delivery of vancomycin.
Phrase Position, but not Lexical Status, Affects the Prosody of Noun/Verb Homophones

Words that can occur in more than one lexical category produce regions of ambiguity that could confound language learning and processing. However, previous findings suggest that pronunciation of noun/verb homophones may, in fact, differ as a function of category of use, potentially mitigating that ambiguity. Whether these differences are part of the lexical representation of such words or mere by-products of sentence-level prosodic processes remains an open question, the answer to which is critical to resolving questions about the structure of the lexicon. In three studies, adult native speakers of English read aloud passages containing noun/verb homophones or nonce words used in both noun and verb contexts. Acoustic measurements of the target words indicated that, while sentence position influences the acoustic properties of noun/verb homophones, including duration and pitch, there are not significant effects of lexical category when other factors are controlled. Furthermore, the lexical status of a word (real or nonce) does not produce consistent prosodic effects. These findings suggest that previously reported prosodic differences in noun/verb homophones may result from the syntactic positions in which those categories tend to occur.
IS ORDERED WEIGHTED ℓ1 REGULARIZED REGRESSION ROBUST TO ADVERSARIAL PERTURBATION? A CASE STUDY ON OSCAR

Many state-of-the-art machine learning models such as deep neural networks have recently shown to be vulnerable to adversarial perturbations, especially in classification tasks. Motivated by adversarial machine learning, in this paper we investigate the robustness of sparse regression models with strongly correlated covariates to adversarially designed measurement noises. Specifically, we consider the family of ordered weighted ℓ1 (OWL) regularized regression methods and study the case of OSCAR (octagonal shrinkage clustering algorithm for regression) in the adversarial setting. Under a norm-bounded threat model, we formulate the process of finding a maximally disruptive noise for OWL-regularized regression as an optimization problem and illustrate the steps towards finding such a noise in the case of OSCAR. Experimental results demonstrate that the regression performance of grouping strongly correlated features can be severely degraded under our adversarial setting, even when the noise budget is significantly smaller than the ground-truth signals.
Customer journey heatmaps: a wake-up call

A certain company experienced an increasing level of dissatisfaction among its customers with the service it provided, and disagreed internally about the root causes of the negative trend. This case study describes a formalised methodology for customer journeys and a procedure to identify customer pain points and unwanted deviations. We present a three-step procedure to improve the company's service experience: 1) identifying the key planned journeys through workshops and world café methodology; 2) researching actual customer journeys through interviews; 3) establishing customer journey heatmaps as an internal call-for-action and prioritisation tool in the effort of service improvement.
An architecture for recycling intermediates in a column-store

Automatic recycling of intermediate results to improve both query response time and throughput is a grand challenge for state-of-the-art databases. Tuples are loaded and streamed through a tuple-at-a-time processing pipeline, avoiding materialization of intermediates as much as possible. This limits the opportunities for reuse of overlapping computations to DBA-defined materialized views and function/result cache tuning. In contrast, the operator-at-a-time execution paradigm produces fully materialized results in each step of the query plan. To avoid resource contention, these intermediates are evicted as soon as possible. In this article we study an architecture that harvests the byproducts of the operator-at-a-time paradigm in a column-store system using a lightweight mechanism, the recycler. The key challenge then becomes the selection of the policies to admit intermediates to the resource pool, to determine their retention period, and devise the eviction strategy when facing resource limitations. The proposed recycling architecture has been implemented in an open-source system. An experimental analysis against the TPC-H ad-hoc decision support benchmark and a complex, real-world application (SkyServer) demonstrates its effectiveness in terms of self-organizing behavior and its significant performance gains. The results indicate the potentials of recycling intermediates and charts a route for further development of database kernels.
COPD-Type lung inflammation promotes K-ras mutant lung cancer through epithelial HIF-1α mediated tumor angiogenesis and proliferation

Chronic obstructive pulmonary disease (COPD), an inflammatory disease of the lung, is an independent risk factor for lung cancer. Lung tissues obtained from human smokers with COPD and lung cancer demonstrate hypoxia and up-regulated hypoxia inducible factor-1 (HIF-1). HIF-1 activation is the central mechanism for controlling the cellular response to hypoxia during inflammation and tumor development. These facts suggest a link between COPD-related airway inflammation, HIF-1, and lung cancer. We have previously established a mouse model of COPD-like airway inflammation that promotes lung cancer in a K-ras mutant mouse model (CC-LR). Here we show that tumors in the CC-LR model have significantly elevated levels of HIF-1α and HIF-1 activity. To determine the tumor-promoting functions of HIF-1 in CC-LR mice, the gene Hif1a which encodes HIF-1α and is required for HIF-1 activity, was disrupted in the lung epithelium of CC-LR animals. Airway epithelial specific HIF-1α deficient mice demonstrated significant reductions in lung surface tumor numbers, tumor angiogenesis, and tumor cell proliferation in the absence or presence of COPD-like airway inflammation. In addition, when CC-LR mice were bred with transgenic animals that overexpress a constitutively active mutant form of human HIF-1α in the airway epithelium, both COPD- and adenocarcinoma-like phenotypes were observed. HIF-1α overexpressing CC-LR mice had significant emphysema, and they also showed potentiated tumorigenesis, angiogenesis, and cell proliferation accompanied by an invasive metastatic phenotype. Our gain and loss of function studies support a key role for HIF-1α in the promotion of lung cancer by COPD-like inflammation.
Understanding grassroots sports gamification in the wild

While gamification is an often used tool in building interactive experiences for sports, little work has addressed systems designed by users for users and deeply embedded in the social setting of physical exercise. Consequently, a better understanding of sports gamification in the wild is needed to build systems that reflect the users' pre-existing social context. This paper presents a qualitative study of a gamification system, the Boar Board, designed by a sports coach to support users participating in regular exercises. Through surveys, interviews and observations over eight months, we built an understanding of the user adoption of the system and how the Boar Board supported the goals of the group. Based on this, we endeavour to understand the social aspects of the system, including trust, and posit a number of design considerations for future inquiry into gamification systems for sports.
Photodynamic therapy reduces the inhibitory effect of osteosarcoma cells on dendritic cells by upregulating HSP70

Osteosarcoma is the most common primary bone tumor and predominantly affects children and adolescents. The prognosis for patients with osteosarcoma is poor. Therefore, the development of novel treatments for osteosarcoma is required. Photodynamic therapy (PDT) is a disease site-specific treatment that utilizes a photosensitizing agent along with light to kill cancer cells. This agent only works following activation by certain wavelengths of light. After the agent is absorbed by the cancer cells, light is then applied to the area to be treated. The light causes the drug to react with oxygen, which produces radical and reactive oxygen species that kill the cells. However, the immune reaction that occurs following PDT remains unknown. The present study demonstrated that the necrosis of osteosarcoma cells inhibited the function of dendritic cells. However, treatment of osteosarcoma cells with PDT restored the function of dendritic cells by upregulating heat shock protein 70. Taken together, the results of the present study provided insight into the subsequent molecular reaction following PDT treatment of osteosarcoma at the molecular level.
Inhibitory Effect of Methotrexate on Rheumatoid Arthritis Inflammation and Comprehensive Metabolomics Analysis Using Ultra-Performance Liquid Chromatography-Quadrupole Time of Flight-Mass Spectrometry (UPLC-Q/TOF-MS)

Rheumatoid arthritis (RA) is a common autoimmune disease. The inflammation in joint tissue and system endanger the human health seriously. Methotrexate have exhibited a satisfactory therapeutic effect in clinical practice. The aim of this research was to establish the pharmacological mechanism of methotrexate on RA therapy. Collagen induced arthritic rats were used to identify how methotrexate alleviates inflammation in vivo. Lipopolysaccharide-induced inflammatory proliferation in macrophages was also be detected in vitro. The activation level of Nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) and Nucleotide binding domain and leucine-rich repeat pyrin 3 domain (NLRP3)/Caspase-1 and related cytokines were examined by real-time PCR and western blotting or quantified with the enzyme-linked immunosorbent assay. Comprehensive metabolomics analysis was performed to identify the alteration of metabolites. Results showed that treating with methotrexate could alleviate the inflammatory condition, downregulate the activation of NF-κB and NLRP3/Caspase-1 inflammatory pathways and reduce the level of related cytokines. Docking interaction between methotrexate and caspase-1 was visualized as six H-bonds indicating a potential inhibitory effect. Metabolomics analysis reported three perturbed metabolic inflammation related pathways including arachidonic acid, linoleic acid and sphingolipid metabolism. These findings indicated that methotrexate could inhibit the onset of inflammation in joint tissue by suppressing the activation of NF-κB and NLRP3/Caspase-1 pathways and regulating the inflammation related metabolic networks.
A Framework to Automate the generation of movies' trailers using only subtitles

With the rapidly increasing rate of user-generated videos over the World Wide Web, it becoming a high necessity for users to navigate through them efficiently. Video summarization is considered to be one of the promising and effective approach for efficacious realization of video content by means of identifying and selecting descriptive frames of the video. In this paper, a proposed adaptive framework called Smart-Trailer (S-Trailer) is introduced to automatize the process of creating a movie trailer for any movie through its associated subtitles only. The proposed framework utilizes only English subtitles to be the language of usage. S-Trailer resolves the subtitle file to extract meaningful textual features that used to classify the movie into its corresponding genre(s). Experimentations on real movies showed that the proposed framework returns a considerable classification accuracy rate (0.89) to classify movies into their associated genre(s). The introduced framework generates an automated trailer that contains on average about (43%) accuracy in terms of recalling same scenes issued on the original movie trailer.