Miracle-dz/UniFAR-FTUs huggingface dataset:
Use and Misuse of Emergency Room for Children: Features of Walk-In Consultations and Parental Motivations in a Hospital in Southern Italy

Objective: Inappropriate use of the emergency department (ED) represents a major worldwide issue both in pediatric and adult age. Herein, we aim to describe features of pediatric visits to the ED of Salerno University Hospital and to evaluate parental reasons behind the decision to walk in. Materials and Methods: We performed a retrospective observational study evaluating ED encounters for children from January 2014 to December 2019. The appropriateness of visits was measured with a national tool assessing every ED encounter, namely, “the Mattoni method,” which consists of the combination of the triage code assigned, the diagnostic resources adopted, and the consultation outcomes. Moreover, 64 questionnaires were collected from a sample of parents in the waiting rooms in January 2020. Results: A total number of 42,507 visits were recorded during the study period (19,126 females; mean age ± SD: 4.3 ± 3.8 years), the majority of whom were inappropriate (75.8% over the considered period; 73.6% in 2014; 74.6% in 2015; 76.3% in 2016; 76.7% in 2017; 77.9% in 2018; 75.5% in 2019). Most of the inappropriate consultations arrived at the ED by their own vehicle (94.4%), following an independent decision of the parents (97.2%), especially in the evening and at night on Saturdays/Sundays/holidays (69.7%). A multivariate analysis revealed the following: patients of younger age (OR: 1.11, 95% C.I. 1.06–1.16; p < 0.0019), night visits (OR 1.39; 95% C.I.: 1.32–1.47; p < 0.001), patients living in the municipality of Salerno (OR 1.28; 95% C.I.: 1.22–1.34; p < 0.001), weekend day visits (OR 1.48; 95% C.I.: 1.41–1.56; p < 0.001), and independent parental decision without previous contact with primary care pediatrician (OR 3.01; 95% C.I.: 2.64–3.44; p < 0.001) were all significant independent predictors of inappropriate consultation. The most frequent trigger of ED encounters was fever (51.4%). Hospital admission made up 17.6% of all consultations. The questionnaire showed that most parents were aware of the lack of urgency (20.3%) or minor urgency (53.1%) of the visit. The reasons for walking in were the impossibility to receive a home consultation (70%), the difficulty of contacting their family pediatrician during weekends and holidays (54.4%), as well as the search for a quick, effective, diagnosis and therapy (48.4%). Conclusions: The study suggests a highly inappropriate use of ED for children in our region. This issue deserves considerable attention by health care system leaders in order to optimally integrate hospitals and primary care.
Exposure to Literary Fiction Is Associated With Lower Psychological Essentialism

We investigated the impact of exposure to literary and popular fiction on psychological essentialism. Exposure to fiction was measured by using the Author Recognition Test, which allows us to separate exposure to authors of literary and popular fiction. Psychological essentialism was assessed by the discreteness subscale of the psychological essentialism scale in Study 1, and by the three subscales of the same scale (such as discreteness, informativeness, and biological basis) in Study 2 that was pre-registered. Results showed that exposure to literary fiction negatively predicts the three subscales. The results emerged controlling for political ideology, a variable that is commonly associated with psychological essentialism, and level of education.
AMPK and Pulmonary Hypertension: Crossroads Between Vasoconstriction and Vascular Remodeling

Pulmonary hypertension (PH) is a debilitating and life-threatening disease characterized by increased blood pressure within the pulmonary arteries. Adenosine monophosphate-activated protein kinase (AMPK) is a heterotrimeric serine-threonine kinase that contributes to the regulation of metabolic and redox signaling pathways. It has key roles in the regulation of cell survival and proliferation. The role of AMPK in PH is controversial because both inhibition and activation of AMPK are preventive against PH development. Some clinical studies found that metformin, the first-line antidiabetic drug and the canonical AMPK activator, has therapeutic efficacy during treatment of early-stage PH. Other study findings suggest the use of metformin is preferentially beneficial for treatment of PH associated with heart failure with preserved ejection fraction (PH-HFpEF). In this review, we discuss the “AMPK paradox” and highlight the differential effects of AMPK on pulmonary vasoconstriction and pulmonary vascular remodeling. We also review the effects of AMPK activators and inhibitors on rescue of preexisting PH in animals and include a discussion of gender differences in the response to metformin in PH.
A Case Series of Metastatic Metaplastic Breast Carcinoma Treated With Anti-PD-1 Therapy

Metaplastic breast cancer is a rare and often chemo-refractory subtype of breast cancer with poor prognosis and limited treatment options. Recent studies have reported overexpression of programmed death ligand 1 (PD-L1) in metaplastic breast cancers, and there are several reports of anti-PD-1/L1 being potentially active in this disease. In this case series, we present 5 patients with metastatic metaplastic breast cancer treated with anti-PD-1-based therapy at a single center, with 3 of 5 cases demonstrating a response to therapy, and one of the responding cases being a metaplastic lobular carcinoma with low-level hormone receptor expression. Cases were evaluated for PD-L1 expression, tumor infiltrating lymphocytes (TILs), DNA mutations, RNA sequencing, and T-cell receptor sequencing. Duration of the response in these cases was limited, in contrast to the more durable responses noted in other recently published reports.
The Status and Prospects of Epstein–Barr Virus Prophylactic Vaccine Development

Epstein–Barr virus (EBV) is a human herpesvirus that is common among the global population, causing an enormous disease burden. EBV can directly cause infectious mononucleosis and is also associated with various malignancies and autoimmune diseases. In order to prevent primary infection and subsequent chronic disease, efforts have been made to develop a prophylactic vaccine against EBV in recent years, but there is still no vaccine in clinical use. The outbreak of the COVID-19 pandemic and the global cooperation in vaccine development against SARS-CoV-2 provide insights for next-generation antiviral vaccine design and opportunities for developing an effective prophylactic EBV vaccine. With improvements in antigen selection, vaccine platforms, formulation and evaluation systems, novel vaccines against EBV are expected to elicit dual protection against infection of both B lymphocytes and epithelial cells. This would provide sustainable immunity against EBV-associated malignancies, finally enabling the control of worldwide EBV infection and management of EBV-associated diseases.
Can Smoking Cause Differences in Urine Microbiome in Male Patients With Bladder Cancer? A Retrospective Study

Background Tobacco smoking is a carcinogen for many cancers including bladder cancer. The microbiota is involved in the occurrence, development, and treatment of tumors. We explored the composition of male urinary microbiome and the correlation between tobacco smoking and microbiome in this study. Methods Alpha diversity, principal component analysis (PCA) and Adonis analysis, linear discriminant analysis (LDA) coupled with effect size measurement, and PICRUSt function predictive analysis were used to compare different microbiome between smokers and non-smokers in men. Results There were 26 qualified samples included in the study. Eleven of them are healthy controls, and the others are from men with bladder cancer. Simpson index and the result of PCA analysis between smokers and non-smokers were not different (P > 0.05) in healthy men. However, the abundance of Bacteroidaceae, Erysipelotrichales, Lachnospiraceae, Bacteroides, and so on in the urinary tract of smokers is much higher than that of non-smokers. Compared to non-smokers, the alpha diversity in smokers was elevated in patients with bladder cancer (P < 0.05). PCA analysis showed a significant difference between smokers and non-smokers (P < 0.001), indicating that tobacco smoking plays a vital role in urinary tract microbial composition. Conclusion The composition of microbiome in the urinary tract is closely related to tobacco smoking. This phenomenon is more significant in patients with bladder cancer. This indicates tobacco smoking may promote the occurrence and development of bladder cancer by changing urinary tract microbiome.
Map2k5-Deficient Mice Manifest Phenotypes and Pathological Changes of Dopamine Deficiency in the Central Nervous System

MAP2K5, a member of the MAPK family, is associated with central nervous system disorders. However, neural functional of Map2k5 from animal models were not well examined so far. Here, we established a Map2k5-targeted knockout mouse model to investigate the behavior phenotypes and its underlying molecular mechanism. Our results showed that female Map2k5 mutant mice manifested decreased circadian-dependent ambulatory locomotion, coordination, and fatigue. Male Map2k5 mutant mice displayed impairment in open field exploration and prepulse inhibition of acoustic startle response (ASR) when compared with wild-type controls. Furthermore, Map2k5 mutant mice showed a decreased dopaminergic cell survival and tyrosine hydroxylase levels in nigrostriatal pathway, indicating a crucial role of MAP2K5 in regulating dopamine system in the central nervous system. In conclusion, this is the first study demonstrating that Map2k5 mutant mice displayed phenotypes by disturbing the dopamine system in the central nervous system, implicating Map2k5 mutant mouse as a promising model for many dopamine related disorders.
E-Learning and Covid-19 - the Nigerian Experience: Challenges of Teaching Technical Courses in Tertiary Institutions

This paper examines the challenges of teaching technical courses through e-learning in Nigerian tertiary institutions during the COVID-19 pandemic lockdown. The COVID-19 pandemic has widespread aftereffect on education systems all over the world, with Nigeria, not an exception. The lack of the requirements needed for remote education during the worldwide lockdown caused by the COVID-19 pandemic has impeded teaching and learning. In the underdeveloped world, home education worked well for a few students who had adequate resources accessible to them and were adaptable to remote learning. This has not just affected teaching and learning but has posed a big problem in teaching technical courses. Problems associated with teaching technical courses in Nigeria during the pandemic lockdown especially the difficulties in handling practical and technical processes online are discussed. Students and educators at different programmes at the Federal Polytechnic Nekede Owerri, Nigeria will be interviewed to ascertain the most pressing problems. The paper will also proffer possible solutions through its recommendations at the different levels of the concerned stakeholders in the Nigerian education sector. © 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
Deep3D reconstruction: methods, data, and challenges

Three-dimensional (3D) reconstruction of shapes is an important research topic in the fields of computer vision, computer graphics, pattern recognition, and virtual reality. Existing 3D reconstruction methods usually suffer from two bottlenecks: (1) they involve multiple manually designed states which can lead to cumulative errors, but can hardly learn semantic features of 3D shapes automatically; (2) they depend heavily on the content and quality of images, as well as precisely calibrated cameras. As a result, it is difficult to improve the reconstruction accuracy of those methods. 3D reconstruction methods based on deep learning overcome both of these bottlenecks by automatically learning semantic features of 3D shapes from low-quality images using deep networks. However, while these methods have various architectures, in-depth analysis and comparisons of them are unavailable so far. We present a comprehensive survey of 3D reconstruction methods based on deep learning. First, based on different deep learning model architectures, we divide 3D reconstruction methods based on deep learning into four types, recurrent neural network, deep autoencoder, generative adversarial network, and convolutional neural network based methods, and analyze the corresponding methodologies carefully. Second, we investigate four representative databases that are commonly used by the above methods in detail. Third, we give a comprehensive comparison of 3D reconstruction methods based on deep learning, which consists of the results of different methods with respect to the same database, the results of each method with respect to different databases, and the robustness of each method with respect to the number of views. Finally, we discuss future development of 3D reconstruction methods based on deep learning.
On the Fracture of Pharmaceutical Needle-Shaped Crystals during Pressure Filtration: Case Studies and Mechanistic Understanding

Pharmaceutical compounds often crystallise as particles with high aspect ratio, typically as needle-shaped particles. These particles must be isolated from the crystallisation liquors before further secondary processing to form the drug product. A common isolation method in the fine chemical and pharmaceutical industries is pressure filtration. Previous experience has shown that, on scale-up from lab to pilot plant, the particle size distribution can change quite significantly. This has typically been associated with agitation of the filter cake. To test this assumption a number of industrial case studies were conducted. The particle size distribution of various needle-shaped particles of different pharmaceutical and model compounds have been tracked through the filtration and drying process. The work here shows that, for the needle-shaped particles tested, particle breakage occurs during both pressure filtration and agitated drying. This is a previously unreported observation and is contrary to the previ.
TaLNet: Voice Reconstruction from Tongue and Lip Articulation with Transfer Learning from Text-to-Speech Synthesis

This paper presents TaLNet, a model for voice reconstruction with ultrasound tongue and optical lip videos as inputs. TaLNet is based on an encoder-decoder architecture. Separate encoders are dedicated to processing the tongue and lip data streams respectively. The decoder predicts acoustic features conditioned on encoder outputs and speaker codes.To mitigate for having only relatively small amounts of dual articulatory-acoustic data available for training, and since our task here shares with text-to-speech (TTS) the common goal of speech generation, we propose a novel transfer learning strategy to exploit the much larger amounts of acoustic-only data available to train TTS models. For this, a Tacotron 2 TTS model is first trained, and then the parameters of its decoder are transferred to the TaLNet decoder. We have evaluated our approach on an unconstrained multi-speaker voice recovery task. Our results show the effectiveness of both the proposed model and the transfer learning strategy. Speech reconstructed using our proposed method significantly outperformed all baselines (DNN, BLSTM and without transfer learning) in terms of both naturalness and intelligibility. When using an ASR model decoding the recovery speech, the WER of our proposed method is relatively reduced over 30% compared to baselines.
Span-Based Event Coreference Resolution

Motivated by the recent successful application of span-based models to entity-based information extraction tasks, we investigate span-based models for event coreference resolution, focusing on determining (1) whether the successes of span-based models of entity coreference can be extended to event coreference; (2) whether exploiting the dependency between event coreference and the related subtask of trigger detection; and (3) whether automatically computed entity coreference information can benefit span-based event coreference resolution. Empirical results on the standard evaluation dataset provide affirmative answers to all three questions.
User Acceptance of Smart Watch for Medical Purposes: An Empirical Study

This study aims to investigate the most effective and interesting variables that urge use of the smartwatch (SW) in a medical environment. To achieve this aim, the study was framed using an innovative and integrated research model, which is based on combining constructs from a well-established theoretical model’s TAM and other features that are critical to the effectiveness of SW which are content richness and personal innovativeness. The Technology Acceptance Model (TAM) is used to detect the determinants affecting the adoption of SW. The current study depends on an online questionnaire that is composed of (20) items. The questionnaire is distributed among a group of doctors, nurses, and administration staff in medical centers within the UAE. The total number of respondents is (325). The collected data were implemented to test the study model and the proposed constructs and hypotheses depending on the Smart PLS Software. The results of the current study show that the main constructs in the model contribute differently to the acceptance of SW. Based on the previous assumption, content richness and innovativeness are critical factors that enrich the user’s perceived usefulness. In addition, perceived ease of use was significantly predictive of either perceived usefulness or behavioral intention. Overall findings suggest that SW is in high demand in the medical field and is used as a common channel among doctors and their patients and it facilitates the role of transmitting information among its users. The outcomes of the current study indicate the importance of certain external factors for the acceptance of the technology. The genuine value of this study lies in the fact that it is based on a conceptual framework that emphasizes the close relationship between the TAM constructs of perceived usefulness and perceived ease of use to the construct of content richness, and innovativeness. Finally, this study helps us recognize the embedded motives for using SW in a medical environment, where the main motive is to enhance and facilitate the effective roles of doctors and patients.
Reviewing Stranger on the Internet: The Role of Identifiability through "Reputation" in Online Decision Making

The stranger on the Internet effect has been studied in relation to self-disclosure. Nonetheless, quantitative evidence about how people mentally represent and perceive strangers online is still missing. Given the dynamic development of web technologies, quantifying how much strangers can be considered suitable for pro-social acts such as self-disclosure appears fundamental for a whole series of phenomena ranging from privacy protection to fake news spreading. Using a modified and online version of the Ultimatum Game (UG), we quantified the mental representation of the stranger on the Internet effect and tested if people modify their behaviors according to the interactors’ identifiability (i.e., reputation). A total of 444 adolescents took part in a 2 × 2 design experiment where reputation was set active or not for the two traditional UG tasks. We discovered that, when matched with strangers, people donate the same amount of money as if the other has a good reputation. Moreover, reputation significantly affected the donation size, the acceptance rate and the feedback decision making as well.
Prioritizing Test Inputs for Deep Neural Networks via Mutation Analysis

Deep Neural Network (DNN) testing is one of the most widely-used ways to guarantee the quality of DNNs. However, labeling test inputs to check the correctness of DNN prediction is very costly, which could largely affect the efficiency of DNN testing, even the whole process of DNN development. To relieve the labeling-cost problem, we propose a novel test input prioritization approach (called PRIMA) for DNNs via intelligent mutation analysis in order to label more bug-revealing test inputs earlier for a limited time, which facilitates to improve the efficiency of DNN testing. PRIMA is based on the key insight: a test input that is able to kill many mutated models and produce different prediction results with many mutated inputs, is more likely to reveal DNN bugs, and thus it should be prioritized higher. After obtaining a number of mutation results from a series of our designed model and input mutation rules for each test input, PRIMA further incorporates learning-to-rank (a kind of supervised machine learning to solve ranking problems) to intelligently combine these mutation results for effective test input prioritization. We conducted an extensive study based on 36 popular subjects by carefully considering their diversity from five dimensions (i.e., different domains of test inputs, different DNN tasks, different network structures, different types of test inputs, and different training scenarios). Our experimental results demonstrate the effectiveness of PRIMA, significantly outperforming the state-of-the-art approaches (with the average improvement of 8.50%~131.01% in terms of prioritization effectiveness). In particular, we have applied PRIMA to the practical autonomous-vehicle testing in a large motor company, and the results on 4 real-world scene-recognition models in autonomous vehicles further confirm the practicability of PRIMA.
An Unfair Affinity Toward Fairness: Characterizing 70 Years of Social Biases in BHollywood (Student Abstract)

Bollywood, aka the Mumbai film industry, is one of the biggest movie industries in the world with a current movie market share of worth 2.1 billion dollars and a target audience base of 1.2 billion people. While the entertainment impact in terms of lives that Bollywood can potentially touch is mammoth, no NLP study on social biases in Bollywood content exists. We thus seek to understand social biases in a developing country through the lens of popular movies. Our argument is simple -- popular movie content reflects social norms and beliefs in some form or shape. We present our preliminary findings on a longitudinal corpus of English subtitles of popular Bollywood movies focusing on (1) social bias toward a fair skin color (2) gender biases, and (3) gender representation. We contrast our findings with a similar corpus of Hollywood movies. Surprisingly, we observe that much of the biases we report in our preliminary experiments on the Bollywood corpus, also gets reflected in the Hollywood corpus.