Miracle-dz/UniFAR-FTUs huggingface dataset:
Establishment and Validation of a Predictive Model for Radiation-Associated Aspiration Pneumonia in Patients with Radiation-Induced Dysphagia after Nasopharyngeal Carcinoma

Introduction Radiotherapy for patients with head and neck cancers raises their risk of aspiration pneumonia-related death. We aimed to develop and validate a model to predict radiation-associated aspiration pneumonia (RAP) among patients with dysphagia after radiotherapy for nasopharyngeal carcinoma (NPC). Materials and Methods A total of 453 dysphagic patients with NPC were retrospectively recruited from Sun Yat-Sen Memorial Hospital from January 2012 to January 2018. Patients were randomly divided into training cohort (n = 302) and internal validation cohort (n = 151) at a ratio of 2 : 1. The concordance index (C-index) and calibration curve were used to evaluate the accuracy and discriminative ability of this model. Moreover, decision curve analysis was performed to evaluate the net clinical benefit. The results were externally validated in 203 dysphagic patients from the First People's Hospital of Foshan. Results Derived from multivariable analysis of the training cohort, four independent factors were introduced to predict RAP, including Kubota water drinking test grades, the maximum radiation dose of lymph node gross tumor volume (Dmax of the GTVnd), neutrophil count, and erythrocyte sedimentation rate (ESR). The nomogram showed favorable calibration and discrimination regarding the training cohort, with a C-index of 0.749 (95% confidence interval (CI), 0.681 to 0.817), which was confirmed by the internal validation cohort (C-index 0.743; 95% CI, 0.669 to 0.818) and the external validation cohort (C-index 0.722; 95% CI, 0.606 to 0.838). Conclusions Our study established and validated a simple nomogram for RAP among patients with dysphagia after radiotherapy for NPC.
Health Effects of Radiation Exposure to Human Sensitive Organs Across Some Selected

The interaction of ionizing radiation with human body, either from external sources (i.e. outside the body) or from internal contamination of the body by radioactive substances, leads to biological effects which may later show up as clinical symptoms. The nature and severity of these symptoms and the time at which they appear depend on the amount of radiation absorbed and the rate at which it is received. This study aimed at assessing the Health Effects of Radiation Exposure to Human Sensitive Organs across Some Selected Mining Sites of Plateau State Nigeria. Finding of this study have revealed that the mean Dorgan values for the lungs, ovaries, bone marrow, testes, kidney, liver and whole body for different mining points of Plateau State are 0.29, 0.26, 0.31, 0.36, 0.28, 0.21 and 0.30 mSv/y respectively. From the findings presented, it can be concluded that the background radiation in Plateau State is not an issue of health concern in regards to sensitive organs and may not course immediate health effect except when accumulated over long period of time which may cause cancer to the indoor members on approximately seventy years of exposure.
Gut Microbiota and Inflammatory Cytokine Changes in Patients with Ankylosing Spondylitis

Ankylosing spondylitis (AS) is a chronic inflammatory disease characterized by sacroiliac joint lesions and spinal ascending involvement. The aim of this work was at investigating the gut microbiota profile and proinflammatory cytokines in AS patients. Gut microbiota of AS patients was clearly different from that of healthy human controls. 16S rRNA sequencing analysis demonstrated a changed microbial diversity in the AS patients, and there was a significant increase in the abundance of Cyanobacteria, Deinococcota, Patescibacteria, Actinobacteriota, and Synergistota at a phyla level increased in AS, while the relative abundance of Acidobacteriota, Bdellovibrionota, Campylobacterota, Chloroflexi, Gemmatimonadota, Myxococcota, Nitrospirota, Proteobacteria, and Verrucomicrobiota declined in AS patients. ELISA results for the markers of inflammation in the AS patients revealed increased concentrations of proinflammatory cytokines such as IL-23, IL-17, and IFN-γ. Our findings support the fact that the intestinal microbiota are altered in AS with an inflammatory status, which indicates that gut microbiota should be a potential target for ankylosing spondylitis therapy.
Survival of Reinforced Concrete Flat Plate System against Column Loss

Column loss in a flat plate building, due to punching shear, explosion, impact load or any accidental event, can lead to what is termed progressive collapse. Progressive collapse is inherently a dynamic process, which makes it difficult to experimentally explore structures with real scale. Therefore, this paper aims to numerically investigate the behavior of flat plate systems due to column loss utilizing nonlinear finite element analysis with the aid of the computer software (ABAQUS). In this investigation, the nonlinear dynamic response of both an old flat plate building, designed according to the ACI 318-71, and a similar modern building, designed according the ACI 318-14, subjected to an instant removal of a column is examined. The obtained results clearly reveal that the old flat plate building without continuous slab bottom reinforcement at columns is highly vulnerable to progressive collapse and the efficiency of the continuous bottom reinforcement within the column strip, as recommended by the ACI 318-14, in preventing the disproportion or progressive collapse of a reinforced concrete flat plate building. Such reinforcement is able to produce alternate load path through the tensile membrane action, thus providing ductility and robustness in the system.
3D Common Corruptions for Object Recognition

We introduce a set of image transformations that can be used as corruptions to evaluate the robustness of models. The primary distinction of the proposed transformations is that, unlike existing approaches such as Common Corrup-tions (Hendrycks & Dietterich, 2019), the geometry of the scene is incorporated in the transformations – thus leading to corruptions that are more likely to occur in the real world. We apply these corruptions to the ImageNet validation set to create 3D Common Corruptions (ImageNet-3DCC) benchmark. The evaluations on recent ImageNet models with robustness mechanisms show that ImageNet-3DCC is a challenging benchmark for object recognition task. Furthermore, it exposes vulnerabilities that are not captured by Common Corruptions, which can be informative during model development.
Exact and Mid-p Confidence Intervals for the Odds Ratio

The odds ratio is a frequently used effect measure for two independent binomial proportions. Unfortunately, the confidence intervals that are available for it in Stata and other standard software packages are generally wider than necessary, particularly for small-sample and exact estimation. The performance of the Cornfield exact interval—the only widely available exact interval for the odds ratio—may be improved by incorporating a small modification attributed to Baptista and Pike (1977, Journal of the Royal Statistical Society, Series C 26: 214–220). A further improvement is achieved when the Baptista–Pike method is combined with the mid-p approach. In this article, I present the command merci (mid-p and exact odds-ratio confidence intervals) and its immediate version mercii, which calculate the Cornfield exact, Cornfield mid-p, Baptista–Pike exact, and Baptista–Pike mid-p confidence intervals for the odds ratio. I compare these intervals with three well-known logit intervals. I strongly recommend the Baptista– Pike mid-p interval.
Factors of Purchase Intentions toward Foreign Products: Empirical Evidence from Vietnamese Consumers’ Perspective

This study empirically explores factors affecting consumers ‘purchase intentions toward foreign products, and their impact magnitudes in six major cities in Vietnam (including Ha Noi, Hai Phong, Da Nang, Ho Chi Minh, Binh Duong, and Can Tho). Our results illustrate that Vietnamese consumers ‘purchase intentions toward foreign products are positively affected by Perceived quality, Perceived prestige, Perceived value, and Influence of others. Notably, Perceived p restige has the strongest impact on consumers’ purchase intentions . The findings of this study enrich the international marketing literature on the consumer evaluation of foreign products in developing countries like Vietnam as well as assist practitioners to build more appropriate marketing strategies for targeting emerging markets.
CER-ETH – Center of Economic Research at ETH Zurich The Elasticity of Substitution between Clean and Dirty Energy with Technological Bias

The elasticity of substitution between clean and dirty energy and the direction of technological change are central parameters in discussing one of the most challenging questions today, climate change. Despite their importance, there are few studies that empirically estimate these key parameters. In this paper, I estimate the elasticity of substitution between clean and dirty energy from micro data, jointly with technological parameters that reflect the direction of technological change within the energy aggregate. I find estimates of the elasticity of substitution ranging between 2 and 3. The largely dirty-energybiased technological change observed in the data validates the framework of directed technological change, given the historical movement of relative energy prices and the estimated elasticity of substitution above unity. However, I also find suggestive evidence that clean-energy-augmenting technology is growing faster than dirty-energy-augmenting technology in recent years with changes in relative energy prices and higher subsidies for clean energy.
Flares on active M-type stars observed with XMM-Newton and Chandra

M-type red dwarfs are among the most active stars. Their light curves display random variability of rapid increase and gradual decrease in emission. It is believed that these large energy events, or flares, are the manifestation of the permanently reforming magnetic field of the stellar atmosphere. Stellar coronal flares are observed in the radio, optical, ultraviolet and X-rays. With the new generation of X-ray telescopes, XMM-Newton and Chandra , it has become possible to study these flares in much greater detail than ever before. This thesis focuses on three core issues about flares: (i) how their X-ray emission is correlated with the ultraviolet, (ii) using an oscillation to determine the loop length and the magnetic field strength of a particular flare, and (iii) investigating the change of density sensitive lines during flares using high-resolution X-ray spectra. (i) It is known that flare emission in different wavebands often correlate in time. However, here is the first time where data is presented which shows a correlation between emission from two different wavebands (soft X-rays and ultraviolet) over various sized flares and from five stars, which supports that the flare process is governed by common physical parameters scaling over a large range. (ii) As it is impossible to spatially resolve any but a very few giant stars, the only information on spatial dimensions as well as the magnetic field strength of stellar coronae has to come from indirect measurements. Using wavelet analysis, I isolated the first stellar X-ray flare oscillation. Interpreting it as a standing coronal flare loop oscillation, I derived a flare loop length as well as the magnetic field strength for this X-ray flare.
InfoMixup : An Intuitive and Information-Driven Approach to Robust Generalization

The discovery of Adversarial Examples — data points which are easily recognized by humans, but which fool artificial classifiers with ease, is relatively new in the world of machine learning. Corruptions imperceptible to the human eye are often sufficient to fool state of the art classifiers. The resolution of this problem has been the subject of a great deal of research in recent years as the prevalence of Deep Neural Networks grows in everyday systems. To this end, we propose InfoMixup , a novel method to improve the robustness of Deep Neural Networks without significantly affecting performance on clean samples. Our work is focused in the domain of image classification, a popular target in contemporary literature due to the proliferation of Deep Neural Networks in modern products. We show that our method achieves state of the art improvements in robustness against a variety of attacks under several measures.
Mind-body exercise and cognitive function: potential approaches to manage cognitive impairment- a meta-analysis

Cognitive impairment is prevalent among older adults population. It brings restriction to older adults’ lives and bring huge burden to the society. Mind-body exercise has characteristics from both physical exercise and intellectual experience. Moreover, it has potential cognitive benefits to reduce the incidence even reverse cognitive impairment. Using meta-analysis to analyze findings form published research on mind-body exercise, this study will explore whether practicing mind-body exercise is beneficial for the management of cognitive impairment. INDEX WORDS: Mind-body exercise, Tai Chi, Yoga, Older adults, Cognitive function MIND-BODY EXERCISE AND COGNITIVE FUNCTION: POTENTIAL APPROACHES TO MANAGE COGNITIVE IMPAIRMENT-A META-ANALYSIS
Agricultural Liming is a Solution for Olive Tree Growth Deficits in Southern Italy

Over the years, changes in the Mediterranean climate have caused olive farmers to struggle in keeping up with a growing demand for the fruit’s oil. The situation is only expected to worsen, and farmers need to better control the soil of their orchards in order to maximize olive output. This study tests how soil chemistry affects olive tree growth and canopy health. The collected data suggest that acidic soil, being generally deﬁcient in calcium, yields shorter trees with narrower trunks. Furthermore, there is no correlation between soil iron content (measured through soil redness) and tree canopy health (measured through NDVI). As a result of these observations, agricultural liming, which increases soil pH and calcium content, emerges as the most promising strategy for increasing tree size and olive output.
Bounds on Generalization Error of Variational Lower Bound Estimates of Mutual Information and Applications of Mutual Information

Machine learning techniques have found widespread usage in critical systems in the recent past. This rise of ML was mainly fueled by Neural Networks, which enabled end-to-end systems to use. This is possible because of the feature learning and universal approximator properties of NNs. Recent research has extended the application of NNs to the estimation of mutual information (MI) from samples. They construct a critic using a NN and maximize a variation lower bound of MI. These methods have alleviated many problems faced by previous MI estimation methods like the inability to scale and incompatibility with mini-batch-based optimization. While these estimation methods are reliable when the true MI is low, they tend to produce high variance estimates when the true MI is high. We argue that the high variance characteristics are due to the uncontrolled complexity of the critic’s hypothesis space. In support of this argument, we use the data-driven Rademacher complexity of the hypothesis space associated with the critic’s architecture to derive upper bounds on the generalization error of variational lower bound based estimates of MI. Using the derived bounds, we show that Rademacher complexity of the critic’s hypothesis is a major contributor to the generalization error. Inspired by this we propose to negate the high variance characteristics of these estimators by constraining the critic’s hypothesis space to a Reproducing Kernel Hilbert Space (RKHS), which corresponds to a kernel learned using Automated Spectral Kernel Learning (ASKL). Further, we propose two regularisation terms by examining the upper bound on the Rademacher complexity of RKHS learned by ASKL. The weights of these regularization terms can be varied for a bias-variance tradeoff. We empirically demonstrate the efficacy of this regularization in enforcing proper bias-variance tradeoff on four different variational lower bounds of MI, namely NWJ, MINE, JS and SMILE. We also propose a novel application of MI estimation using variational lower bounds and NNs in learning disentangled representations of videos. The learned representations contain two sets of features, content and pose representations which disjointly represent the content and movement in the video. This is achieved by minimizing the mutual information between pose representation of different frames from
Cranium morphometry for distinguishing male and female Muscovy duck ( Cairina moschata ) before sexual maturity

Gender determination of Muscovy duck as early as possible is essential for the efficiency and effectiveness of duck production. This study aimed to determine the morphometric differences in prepubertal male and female Muscovy duck ( Cairina moschata ) as predictors of gender. This study used a split-plot design, with the observed variables cranium length, width and height, mandibular length, and rostrum length and width. This study used a total of 80 samples of duck heads consisting of 40 male and 40 female Muscovy duck heads, with ten heads each in the age group of 2, 4, 6, and 8 weeks, respectively. Results showed that the length, width, and height of the cranium, mandibular length, and rostrum lengthand width were greater in males (p <0.05) than those of the females in all age groups. It could be concluded that the morphometric size of prepubertal male Muscovy ducks head was greater than those of the female ones at the same age; thereby, it could be used to distinguish gender. Based on the morphometrics, further study can also be conducted to measure other body parts of the Muscovy duck apart from the cranium. In addition, it is necessary to study the morphometric measurements of the cranium in other poultry as a basis for identifying sex.
Detecting and mitigating issues in image-based COVID-19 diagnosis

As urgency over the coronavirus disease 2019 (COVID-19) increased, many datasets with chest radiography (CXR) and chest computed tomography (CT) images emerged aiming at the detection and prognosis of COVID-19. Over the last two years, thousands of studies have been published, reporting promising results. However, a deeper analysis of the datasets and the methods employed reveals issues that may hamper conclusions and practical applicability. We investigate three major datasets commonly used in these studies, detect problems related to the existence of duplicates, address the specificity of classes within those datasets, and propose a way to perform ex-ternal validation via cross-dataset evaluation. Our guidelines and findings contribute towards a trust-worthy application of Machine Learning in the context of image-based diagnosis, as well as offer a more accurate assessment of models applied to the prognostication of diseases using image datasets and pave the way towards models that can be relied upon in the real world.
Having siblings is associated with better mentalizing abilities in adults

Although typically-developing children all acquire the foundation for social cognition — a theory- of-mind (ToM) — at around age 6, there exists meaningful variability in social cognitive abilities among adults. Given that social cognition underpins our ability to relate and successfully collaborate with others, it is important to investigate potential contributors to this variability in adults. What factors, in particular developmental factors, help determine whether adults are better or worse at understanding other people? A likely factor to consider is siblinghood, as children with siblings tend to exhibit better ToM ability than only children. But does this influ- ence extend into adulthood? In a pre-registered study, we examined whether various aspects of siblinghood predict mentalizing ability in a large and diverse young adult sample (N = 1792, M age = 24.12, Number of Siblings ranging from 0 to 9), using the Reading-the-Mind-in-the-Eyes Test (Baron-Cohen et al., 2001), an age-appropriate mentalizing task. Those who had more older siblings exhibited better mentalizing performance and the effect of siblinghood differed by gender. Men tended to have better mentalizing abilities when they had siblings, but this advantage was greatly attenuated for women. The results were robust, persisting even after controlling for age, race, and language ability.
It’s Not All Black and White: Portrayals of Mass Shooters Before and After the 2020 Black Lives Matter Movement

Mental health and mass shootings are two topics that are increasingly prevalent in the media. This study analyzes the framing of online news coverage of Black and White mass shooters and whether the portrayal of these perpetrators changed after the 2020 Black Lives Matter protests. Qualitative content analysis of 36 articles and 12 perpetrators showed that perpetrators are grouped and framed based on personality and background. The data suggests that articles about Black perpetrators were less likely to include mental health discussions and more likely to include behavioral details about the shooter that were irrelevant to the crime. Results showed that the 2020 movement sparked an increase in mental health conversations among articles about Black perpetrators only in cases that could be deemed more newsworthy. This data suggests that there are implicit biases disseminated in these reports, as articles are framed differently depending on the perpetrators background, personality, or race. The researcher began by randomizing a list of White mass perpetrators who committed mass shootings between 2017 and May of 2020. In this study, a mass shooting consists of four or more people injured or killed, not including the shooter (Gun Violence Archive, 2021). Each White mass perpetrator during this period was given a number. The same process of listing and numbering was then done for Black mass shooters during the same time frame. This process was then repeated for Black and White mass shooters who committed acts after May of 2020. Three perpetrators of each race and time frame were randomly selected using a randomizer and selector tool. This resulted in a list of twelve randomly selected mass shooters: three White perpetrators before May 2020, three Black perpetrators before May 2020, three White perpetrators after May 2020, and three Black perpetrators after May 2020.