Miracle-dz/UniFAR-FTUs huggingface dataset:
Evolution of chest radiograph radiomics and association with respiratory and inflammatory parameters in COVID-19 patients undergoing prone ventilation: preliminary findings

The coronavirus disease 2019 (COVID-19) pandemic continues to spread internationally as deaths climb worldwide. Patients with severe disease may progress to acute respiratory distress syndrome (ARDS), often requiring mechanical ventilation. Proning may be employed for patients undergoing mechanical ventilation in order to improve oxygenation. Hospitalized COVID-19 patients are often monitored through the use of portable chest radiography (CXR). Computational modeling of CXRs has been explored in COVID-19, and radiomic features have been used to predict disease progression and severity. However, studies have not yet drawn strong connections between specific radiomic features and clinical manifestations of the disease. In this retrospective study we analyze 23 COVID-19 patients hospitalized between March and May 2020. Each of these patients underwent mechanical ventilation with proning. CXRs were taken before, during, and after proning. Clinical laboratory data including blood gas levels, severity assessment scores, serological markers for inflammation, etc. were also measured for patients before and after proning. In our analysis, 1) we identify CXR radiomic features that change significantly after proning and 2) we demonstrate correlations between radiomic features and the progression of several clinical parameters implicated in COVID-19 pathophysiology. We report statistically significant positive correlations between several radiomic features extracted from pre-proning CXRs with changes in clinical measurements of LDH, oxygen saturation, ferritin, tidal volume, arterial oxygen partial pressure (PaO2) to fraction of inspired oxygen (FIO2) ratio, and FIO2. These preliminary findings suggest that radiomic features from CXRs might reflect changes in measurable clinical variables, thus indicating the potential for portable radiography to monitor the efficacy of the proning procedure in COVID-19 patients undergoing mechanical ventilation.
Differentiation of Parkinson’s disease and non-Parkinsonian olfactory dysfunction with structural MRI data

Despite the increasingly refined protocols in the diagnosis of Parkinson’s disease (PD), detection of PD at an early stage remains challenging. As one of the most common non-motor symptoms of PD, olfactory dysfunction yields a high prevalence of < 90% and is observed years prior to the onset of motor symptoms. Thus, olfaction-based assessment may help premotor diagnosis of PD. Nevertheless, diagnosis of PD based on olfactory dysfunction is not yet realized in clinical settings due to low specificity, therefore, it would be of high clinical relevance to differentiate PD-induced olfactory dysfunction from other forms of non-Parkinsonian olfactory dysfunction (NPOD). In the present study, we assess the feasibility of machine learning approaches in the classification of PD and NPOD patients and show that PD patients could be differentiated from NPOD patients using (a) morphometric measurements including cortical thickness, cortical surface area and volumetric measures of parcellated cortical and subcortical brain regions, or (b) T1-weighted axial magnetic resonance imaging (MRI) scans. The proposed methods give rise to efficient classification of PD and NPOD, and may thus improve the specificity of olfaction-based diagnosis of PD.
A latent space exploration for microscopic skin lesion augmentations with VQ-VAE-2 and PixelSNAIL

Skin cancer affects more than 3 million people only in the US. Comprehensive microscopic databases include around 30 thousand samples, limiting the richness of patterns that can be presented to machine learning. To this end, generative models such as GANs have been proposed for creating realistic synthetic images but, despite their popularity, they are often difficult to train and control. Recently an autoregressive approach based on a quantized autoencoder showed state of the art performances while being simple to train and provide synthetic data generation opportunities. In the first part of this paper we evaluate the training of VQ-VAE-2 with different latent space configuration. In the second part, we show how to use a learned prior over the latent space with PixelSNAIL to generate and modify skin lesions. We show how this process can be used for powerful data augmentation and visualization for skin health, evaluating it on a downstream application that classifies malignant lesions
Ovarian assessment using deep learning-based 3D ultrasound super resolution

Ovarian volume assessment is the measurement of the size of ovaries during an Ultrasound (US) in order to estimate the ovarian reserve. Since the ovarian reserve is used in calculating a woman’s reproductive age and is also a diagnostic criterion for polycystic ovary syndrome (PCOS), it is imperative that it is measured accurately. Furthermore, ovarian rendering has clinical significance in terms of assessing ovarian anomalies (ovarian surface epithelial cells). Thus if the spacing in the US volume is high along one direction, reducing the spacing would greatly help in both the accurate measurement of the ovarian volume as well as surface assessment. In this paper, we aim to address this problem by developing a deep learning method for super-resolving 3D US data along the axial direction. On the collected dataset, our method has achieved high PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural Similarity Index) values, and has also resulted in a 54% improvement in ovarian volume computation accuracy. Furthermore, our solution has improved the quality of the 3D rendering of the ovary, and has also reduced the problem of fused follicles in segmentation. This proves the viability of our approach for clinical diagnostic assessment.
Physicochemical Properties of Canna edulis Ker Starch on Heat-Moisture Treatment

Canna edulis Ker starch was modified by heat-moisture treatment at moisture levels ranging from 18 to 27 g/100 g starch and its physicochemical properties were investigated. Amylose content, swelling power, solubility as well as water and oil absorption capacity in native starch were higher than in all treated starches. However, alkaline water retention and acid susceptibility of native starch were lower, along with different extent of amylose leaching. The result in the X-ray diffraction measurement revealed that the crystalline type of the starch gradually changed from B-type to A-type, and the degree of crystallinity changed. Investigation on thermal properties showed that the gelatinization enthalpy decreased, whereas the onset temperature, peak temperature, concluding temperature and transition temperature range increased in modified starch than in native starch. In addition, all modified starches exhibited remarkably low values of peak viscosity, hot pasting viscosity and final viscosity, compared to those of native starch.
Can Social Media Participation Enhance LGBTQ+ Youth Well-Being? Development of the Social Media Benefits Scale

Social media sites offer critical opportunities for lesbian, gay, bisexual, trans, queer, and other sexual and/or gender minority (LGBTQ+) youth to enhance well-being through exploring their identities, accessing resources, and connecting with peers. Yet extant measures of youth social media use disproportionately focus on the detrimental impacts of online participation, such as overuse and cyberbullying. This study developed a Social Media Benefits Scale (SMBS) through an online survey with a diverse sample (n = 6,178) of LGBTQ+ youth aged 14–29. Over three-quarters of the sample endorsed non-monosexual and/or and gender fluid identities (e.g., gender non-conforming, non-binary, pansexual, bisexual). Participants specified their five most used social media sites and then indicated whether they derived any of 17 beneficial items (e.g., feeling connected, gaining information) with the potential to enhance well-being from each site. An exploratory factor analysis determined the scale’s factor structure. Analysis of variance (ANOVA) and Sheffe post hoc tests examined age group differences. A four-factor solution emerged that measures participants’ use of social media for: (1) emotional support and development, (2) general educational purposes, (3) entertainment, and (4) acquiring LGBTQ+-specific information. Bartlett’s test of sphericity was significant (χ2 = 40,828, p < .0005) and the scale had an alpha of . 889. There were age group differences for all four factors (F = 3.79–75.88, p < .05). Younger adolescents were generally more likely to use social media for beneficial factors than older youth. This article discusses the scale’s development, exploratory properties, and implications for research and professional practice.
Development of dielectric coated alternating current electrothermal micropumps for biomicrofluidic applications

Microfluidic systems are growing increasingly prevalent as the future for modern medicine. With the transition to miniaturized systems, comes a growing need for equally miniaturized fluid delivery and control mechanisms. Electrokinetic pumping systems are uniquely suited to this task due to low power requirements and ease of scalability. Electrothermal micropumps in particular are efficient at manipulating high conductivity fluids, such as biofluids. This work describes methods by which electrothermal pumps are effectively simulated, fabricated, and tested with unique improvements designed to improve efficiency and adoptability in microfluidic systems.
Another Voice in the Room: Negotiating Authority in Multidisciplinary Writing Groups

Scholarship has shown that writing groups are important sites of authority negotiation for student writers, yet little empirical research has examined how groups negotiate authority through conversation or how these negotiations influence students’ developing expertise. Drawing on observations and interviews of an undergraduate thesis and a graduate dissertation writing group, I use the concept of “presentification” to analyze conversational moments in which group members referenced advisors, “making present” advisor authority to influence group collaborations. Specifically, I analyze these moments to show how writing groups can serve as low-stakes communities in which students negotiate their emerging sense of authority. I found that whereas less experienced writers looked to advisors to solve writing problems and used advisor authority to stand in for disciplinary expertise, more experienced writers voiced advisor guidance to help pose writing problems and negotiate their own stance as disciplinary experts. This study thus theorizes one process through which student writers negotiate emerging authority across sites of literate practice and in collaboration with others who may not themselves be members of the same disciplinary community.
Identifying Unethical Academic Behaviors of Students Studying Food Service, Hospitality, Tourism and Culinary Arts

ABSTRACTS Cheating by students is a problem in academic institutions. The purpose of this study was to assess how likely or unlikely senior students studying: food service, hospitality, tourism and culinary arts would participate in certain types of academic dishonesty behaviors. Although the problem is well known, few studies have looked at students perceptions of individual cheating behaviors. College senior students studying food service, hospitality, tourism and culinary arts at three colleges were contacted via their professors. Statistical analysis was done to test for significant differences between gender and academic institute of the survey population. Significant differences were found. Results of this pilot study may help to better understand the cheating behaviors of food service, hospitality, tourism and culinary arts students. Further research into how these behaviors impact the careers of these students after they graduate and take jobs in the fields of culinary, hotel, restaurant and tourism.
Triplet network for classification of benign and pre-malignant polyps

Colorectal polyps are critical indicators of colorectal cancer (CRC). Classification of polyps during colonoscopy is still a challenge for which many medical experts have come up with visual models, albeit with limited success. An early detection of CRC prevents further complications in the colon, which makes identification of abnormal tissue a crucial step during routinary colonoscopy. In this paper, a classification approach is proposed to differentiate between benign and pre-malignant polyps using features learned from a Triplet Network architecture. The study includes a total of 154 patients, with 203 different polyps. For each polyp an image is acquired with White Light (WL), and additionally with two recent endoscopic modalities:Blue Laser Imaging (BLI) and Linked Color Imaging (LCI). The network is trained with the associated triplet loss, allowing the learning of non-linear features, which prove to be a highly discriminative embedding, leading to excellent results with simple linear classifiers. Additionally, the acquisition of multiple polyps with WL, BLI and LCI, enables the combination of the posterior probabilities, yielding a more robust classification result. Threefold cross-validation is employed as validation method and accuracy, sensitivity, specificity and area under the curve (AUC) are computed as evaluation metrics. While our approach achieves a similar classification performance compared to state-of-the-art methods, it has a much lower inference time (from hours to seconds, on a single GPU). The increased robustness and much faster execution facilitates future advances towards patient safety and may avoid time-consuming and costly histhological assessment.
Online Engagement Between Opposing Political Protest Groups via Social Media is Linked to Physical Violence of Offline Encounters

The rise of the Internet and social media has allowed individuals with different backgrounds, experiences, and opinions to communicate with one another in an open and largely unstructured way. One important question is whether the nature of online engagements between groups relates to the nature of encounters between these groups in the real world. We analyzed online conversations that occurred between members of protest groups from opposite sides of the political spectrum, obtained from Facebook event pages used to organize upcoming political protests and rallies in the United States and the United Kingdom and the occurrence of violence during these protests and rallies. Using natural language processing and text analysis, we show that increased engagement between groups online is associated with increased violence when these groups met in the real world. The level of engagement between groups taking place online is substantial, and can be characterized as negative, brief, and low in integrative complexity. These findings suggest that opposing groups may use unstructured online environments to engage with one another in hostile ways. This may reflect a worsening of relationships, in turn explaining the observed increases in physical violence offline. These findings raise questions as to whether unstructured online communication is compatible with positive intergroup contact, and highlights the role that the Internet might play in wider issues of extremism and radicalization.
YouTube as Alternative Television in Russia: Political Videos During the Presidential Election Campaign 2018

Previous studies on “youtubification” of political communication (May, 2010) have largely focused on democratic contexts. This study aims at exploring the role of the global video-sharing platform in non-democratic political communication, using the example of the Russian presidential election of 2018. It draws on the qualitative content analysis of 169 political videos collected from the “Popular” section of Russian YouTube during the last 2 months of the presidential campaign. The results show that oppositional discourse dominated the most popular political videos of Russian YouTube and that pro-state actors tried to co-opt the platform, publishing videos made in amateur and semi-professional styles that imitated user-generated content. Drawing on the findings, I discuss the risks and benefits of YouTube publics for the Russian authoritarian regime and the role of social media platforms in consultative authoritarianism.
The Role of Error Type and Working Memory in Written Corrective Feedback Effectiveness on First-Language Self Error-Correction

This study examined the role of error-type and working memory (WM) in the effectiveness of direct-metalinguistic and indirect written corrective feedback (WCF) on self error-correction in first-language writing. Fifty-one French first-year psychology students volunteered to participate in the experiment. They carried out a first-language error-correction task after receiving WCF on typographical, orthographic, grammatical, and semantic errors. Results indicated that error-type affected the efficacy of WCF. In both groups, typographical error-correction was performed better than the others; orthographic and grammatical error-correction were not different, but both were corrected more frequently than semantic errors. Between-group comparisons showed no difference between the two groups in correcting typographical, orthographic, and grammatical errors, while semantic error-correction was performed significantly better for the direct group. Results revealed that WM was not involved in correcting typographical, orthographic, and grammatical errors in both groups. It did, however, predict semantic error-correction only in response to direct-metalinguistic WCF. In addition, the processing component of WM was predictive of semantic error-correction in the direct WCF group. These findings suggest that error-type mediates the effectiveness of WCF on written error-correction at the monitoring stage of writing, while WM does not associate with all WCF types efficacy at this stage.
Information seeking behavior about obesity among South Koreans: applying the risk information seeking and processing model

ABSTRACT Using national online panel survey data (N = 1,000) in South Korea, this study applied the Risk Information Seeking and Processing (RISP) model to examine how people are motivated to seek information about obesity. We added autonomous motivation and health consciousness to the RISP model to investigate their association with health information seeking behavior about obesity. We proposed that information seeking is influenced by information insufficiency, autonomous motivation that is associated with health consciousness, and negative affective response that is related to subjective norm and risk perception. Using structural equation modeling analysis, this study largely supported our hypotheses. The results revealed associations among the socio-psychological variables that predict information seeking behavior on obesity. We found that information insufficiency, autonomous motivation, and negative affective responses appeared to be strong predictors in seeking obesity-related information. These findings have practical implications for health communication efforts in an emerging obesity risk country.
Semantic Segmentation of High Resolution Satellite Imagery using Generative Adversarial Networks with Progressive Growing

ABSTRACT With increase in urbanization and Earth Sciences research into urban areas, the need to quickly and accurately segment urban rooftop maps has never been greater. Current machine learning techniques struggle to produce high accuracy maps in dense urban zones where there is high image noise and foot print overlap. In this paper, we evaluate a training methodology for pixel-wise segmentation for high-resolution satellite imagery using progressive growing of generative adversarial networks as a solution. We apply our model to segmenting building rooftops and compare these results to conventional methods for rooftop segmentation. We evaluate our approach using the SpaceNet version 2 and xView datasets. Our experiments show that for SpaceNet, progressive Generative Adversarial Network (GAN) training achieved a test accuracy of 93% compared to 89% for traditional GAN training and 87% for U-Net architecture, while for xView, we achieved 71% accuracy using progressive GAN training compared to 69% through traditional GAN training and 65% using U-Net.
Comparing the effectiveness of two remedial mathematics courses using modern regression discontinuity techniques

ABSTRACT Studying the effectiveness of remedial courses in higher education has attracted a lot of interest from educational researchers and practitioners. Remediation is associated with significant economic and social costs while the results are usually dubious. In this paper, we apply a widely used method called Regression Discontinuity Design (RDD) to measure the effectiveness of two differently designed remedial mathematics courses at the Budapest University of Technology and Economics. Our large-scale study is based on data of almost 20,000 undergraduate students enrolled between 2010 and 2018. Using modern RDD tools in various settings, we study both the direct and longer-term effects of remediation; and find that the design of the remedial course matters a lot. We measured a statistically significant positive effect on subsequent academic achievement for both course designs; however, the magnitude of the effect differs substantially. We measured a higher effect for the remedial course that serves as an extra practice class for the university level calculus course than for traditional remediation. As a methodological novelty, we propose a novel alternative method to handle discrete running variable in the RDD setting. We also provide some suggestions on how to improve mathematical remediation using personalized e-learning systems. Abbreviations: BME: Budapest University of Technology and Economics; STEM: science; technology; engineering; and mathematics; IntroMathHS: the general introductory mathematics course; IntroMathCalc: the introductory mathematics course at the VBK; Calc1: the first-year calculus course; AdvCalc: the advanced calculus course at the VBK; ACWA-C: adjusted credit weighted average without the result of the Calc1 course; RDD: regression discontinuity design; IK: Imbens–Kalyanaraman; VBK: Faculty of Chemical Technology and Biotechnology; ÉMK: Faculty of Civil Engineering; GPK: Faculty of Mechanical Engineering; GTK: Faculty of Economic and Social Sciences; KJK: Faculty of Transportation Engineering and Vehicle Engineering; VIK: Faculty of Electricalengineering and Informatics
The Roles of IT Strategies and Security Investments in Reducing Organizational Security Breaches

ABSTRACT This research examines the joint effects of information technology (IT) strategies and security investments on organizational security breaches. We focus on two forms of IT strategies: digitalization and embeddedness in IT outsourcing networks. Our longitudinal analysis of U.S. hospitals demonstrates that IT security investments reduce security breaches in less digitalized organizations but increase security breaches for highly digitalized organizations. Investing in technical network control security systems such as anti-virus and intrusion detection systems reduces external breaches. Implementing identity and access management security systems such as biometric scanning and user authentication decreases internal breaches but increases external breaches. However, organizations’ embeddedness in IT outsourcing networks weakens the impacts of these technologies investments on external breaches but amplifies the negative relationship between identity and access management security systems and internal breaches. Our results offer an alternative understanding of organizational IT security investments and explain contrary results found in prior studies. Practical guidelines on organizational IT security strategies are discussed.