Miracle-dz/UniFAR-FTUs huggingface dataset:
The Swedish Version of the Multidimensional Inventory for Religious/Spiritual Well-Being: First Results From Swedish Students

Background: Studies investigating the relationship between religiosity/spirituality and mental health have suggested both positive and negative associations, highlighting the importance of multifaceted assessment of these rather broad constructs. The present study aims at contributing to this field of research by providing a validated Swedish version of the Multidimensional Inventory for Religious/Spiritual Well-Being (MI-RSWB-S) and further examining how this instrument relates to Big Five personality factors, Sense of Coherence (SOC), and religiosity. Methods: Data were collected from a total of 1,011 Swedish students (747 females; age range 18–40) via completion of an online survey, including a new Swedish Version of the MI-RSWB-S, the Ten Item Personality Inventory (TIPI), the Sense of Coherence Scale (SOC-13), and the Centrality of Religiosity Scale (CRS-5). Results: Results revealed adequate estimates of internal consistency and substantial evidence for the postulated six-dimensional structure. However, confirmatory factor analysis yielded poor fit indices, resulting in the development and validation of a revised measure of Religious/Spiritual Well-Being (RSWB), comprising the subscales General Religiosity and Connectedness. Most of the MI-RSWB-S dimensions were positively correlated with the personality domains Extraversion, Openness to Experience, Conscientiousness, and Agreeableness and negatively related to Neuroticism. SOC was positively linked to Hope Immanent, Forgiveness, Hope Transcendent, and Experiences of Sense of Meaning, whereas CRS exhibited positive correlations with all MI-RSWB-S subscales except Hope Transcendent. Conclusion: The findings of the current study support the validity and reliability of the Swedish adoption of the MI-RSWB and confirm previously reported associations with the Big Five personality traits, SOC, and CRS. More in general, our results underline the putative substantial link between RSWB dimensions and mental health. Further research especially in clinical surroundings as well as by employing more representative samples is now warranted.
Does Ethnicity Matter in Multiple Myeloma Risk Prediction in the Era of Genomics and Novel Agents? Evidence From Real-World Data

Introduction Current risk predictors of multiple myeloma do not integrate ethnicity-specific information. However, the impact of ethnicity on disease biology cannot be overlooked. In this study, we have investigated the impact of ethnicity in multiple myeloma risk prediction. In addition, an efficient and robust artificial intelligence (AI)-enabled risk-stratification system is developed for newly diagnosed multiple myeloma (NDMM) patients that utilizes ethnicity-specific cutoffs of key prognostic parameters. Methods K-adaptive partitioning is used to propose new cutoffs of parameters for two different datasets—the MMIn (MM Indian dataset) dataset and the MMRF (Multiple Myeloma Research Foundation) dataset belonging to two different ethnicities. The Consensus-based Risk-Stratification System (CRSS) is designed using the Gaussian mixture model (GMM) and agglomerative clustering. CRSS is validated via Cox hazard proportional methods, Kaplan–Meier analysis, and log-rank tests on progression-free survival (PFS) and overall survival (OS). SHAP (SHapley Additive exPlanations) is utilized to establish the biological relevance of the risk prediction by CRSS. Results There is a significant variation in the key prognostic parameters of the two datasets belonging to two different ethnicities. CRSS demonstrates superior performance as compared with the R-ISS in terms of C-index and hazard ratios on both the MMIn and MMRF datasets. An online calculator has been built that can predict the risk stage of a multiple myeloma (MM) patient based on the values of parameters and ethnicity. Conclusion Our methodology discovers changes in the cutoffs with ethnicities from the established cutoffs of prognostic features. The best predictor model for both cohorts was obtained with the new ethnicity-specific cutoffs of clinical parameters. Our study also revealed the efficacy of AI in building a deployable risk prediction system for MM. In the future, it is suggested to use the CRSS risk calculator on a large dataset as the cohort size of the present study is 25% of the cohort used in the R-ISS reported in 2015.
Key Role of Staphylococcal Fibronectin-Binding Proteins During the Initial Stage of Staphylococcus aureus Keratitis in Humans

Objectives Staphylococcus aureus is one of the main causes of bacterial keratitis in humans. This study was aimed at investigating the mechanisms of S. aureus adhesion to the human corneal epithelium involved during the initial stage of infectious keratitis. Methods Human corneas stored in a specific active storage machine that restores a normal pluristratified epithelium were used to assess S. aureus adhesion level to intact and injured tissues using immunostaining. S. aureus adhesion to immobilized fibronectin was measured in microtiter plate. Internalization of S. aureus clinical isolates recovered from keratitis was assessed on human corneal epithelial HCE-2 cells. Results Superficial corneal injury unmasked fibronectin molecules expressed within the human corneal epithelium. S. aureus adhesion level was increased by 117-fold in the area of injured epithelium (p < 0.0001). The deletion of staphylococcal fnbA/B genes decreased by 71% the adhesion level to immobilized fibronectin (p < 0.001). The deletion of fnbA/B genes and the incubation of the corneas with anti-fibronectin blocking antibodies prior to the infection significantly reduced the S. aureus adhesion level to injured corneal epithelium (p < 0.001). Finally, S. aureus clinical isolates triggered its internalization in human corneal epithelial cells as efficiently as the 8325-4 wt. Conclusion S. aureus was almost unable to bind the intact corneal epithelium, whereas a superficial epithelial injury of the corneal epithelium strongly increased S. aureus adhesion, which is mainly driven by the interaction between staphylococcal fibronectin-binding proteins and unmasked fibronectin molecules located underneath the most superficial layer of the corneal epithelium.
Added Value of a New Strain Elastography Technique in Conventional Ultrasound for the Diagnosis of Breast Masses: A Prospective Multicenter Study

Objective This study aimed to explore the value of elasticity score (ES) and strain ratio (SR) combined with conventional ultrasound in distinguishing benign and malignant breast masses and reducing biopsy of BI-RADS (Breast Imaging Reporting and Data System) 4a lesions. Methods This prospective, multicenter study included 910 patients from nine different hospitals. The acquisition and analysis of conventional ultrasound and strain elastography (SE) were obtained by radiologists with more than 5 years of experience in breast ultrasound imaging. The diagnostic sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under curve (AUC) of conventional ultrasound alone and combined tests with ES and/or SR were calculated and compared. Results The optimal cutoff value of SR for differentiating benign from malignant masses was 2.27, with a sensitivity of 60.2% and a specificity of 84.8%. When combined with ES and SR, the AUC of the new BI-RADS classification increased from 0.733 to 0.824 (p < 0.001); the specificity increased from 48.1% to 68.5% (p < 0.001) without a decrease in the sensitivity (98.5% vs. 96.4%, p = 0.065); and the PPV increased from 52.2% to 63.7% (p < 0.001) without a loss in the NPV (98.2% vs. 97.1%, p = 0.327). All three combinations of conventional ultrasound, ES, and SR could reduce the biopsy rate of category 4a lesions without reducing the malignant rate of biopsy (from 100% to 68.3%, 34.9%, and 50.4%, respectively, all p < 0.001). Conclusions SE can be used as a useful and non-invasive additional method to improve the diagnostic performance of conventional ultrasound by increasing AUC and specificity and reducing the unnecessary biopsy of BI-RADS 4a lesions.
Increased Circulating Levels of Ectodysplasin A in Newly Diagnosed Type 2 Diabetic Patients

Objective Ectodysplasin A (EDA), a newly discovered hepatokine, has recently been considered to be closely related to glycolipid metabolism disorders, but the pathophysiological effects of EDA are still poorly understood. This study was the first time to determine the level of serum EDA in newly diagnosed type 2 diabetes mellitus (T2DM) patients, and to explore the relationships between serum EDA levels and various metabolic indexes. Methods A total of 184 subjects were enrolled in the study, including 92 subjects with newly diagnosed T2DM and 92 subjects with age- and sex-matched normal glucose tolerance (NGT). Serum EDA levels were determined using enzyme-linked immunosorbent assay (ELISA). Oral glucose tolerance test, glycosylated hemoglobin c (HbA1c), and insulin were also measured. Results Serum EDA levels were significantly increased in the T2DM group than in the NGT group (359.91 ± 117.99 vs. 265.82 ± 86.51 pg/ml, p < 0.001). Serum EDA levels were positively correlated with body mass index (BMI), waist-to-hip ratio (WHR), fasting plasma glucose (FPG), HbA1c, 2-hour postprandial plasma glucose (2hPG), fasting plasma insulin (FIns), fasting C peptide (FCP), triglyceride (TG), HOMA-IR, and negatively correlated with high-density lipoprotein cholesterol (HDL-c) and HOMA-β (p < 0.05). Multiple stepwise regression analysis demonstrated that 2hPG and FIns were independent influencing factors of serum EDA level (p < 0.05). Logistic regression analysis showed that serum EDA level was significantly independently correlated with T2DM (p < 0.05). Conclusions Serum EDA levels are significantly higher in T2DM patients, suggesting that EDA may play a role in the occurrence and development of T2DM.
Application of coarse-grid computational fluid dynamics on indoor environment modeling: Optimizing the trade-off between grid resolution and simulation accuracy

Computational fluid dynamics has been playing an important role in building design and indoor environment study for decades. However, the computing cost of grid-independent computational fluid dynamics prevents its application from real-time simulation in many areas, such as building emergency evacuation and hourly-based energy simulation. Although coarse-grid computational fluid dynamics has the potential of being as fast as or faster than real time and can provide valuable information for decision making, the credibility of coarse-grid computational fluid dynamics results is questioned due to the unknown error scale it brings. This study investigates the grid-induced error, evaluates the potential computing cost saving by using a coarse grid, and provides a guideline for optimizing the trade-off between grid resolution and computing cost. The numerical error caused by coarse grid can be minimized by appropriately adapting the distribution of grid size. Following the guideline of coarse-grid specifications, coarse-grid computational fluid dynamics can provide informative prediction that is comparable to a grid-independent result on building environment modeling. The computing cost of computational fluid dynamics with an optimized coarse grid is usually orders of magnitude less than that with uniform fine grid.
Implications of Engineered Nanomaterials in Drinking Water Sources

Nanotechnology has grown rapidly over the last two decades, leading to advances in science and engineering and to releases of engineered nanomaterials (ENMs) to drinking water sources. Conventional drinking water treatment processes remove sub‐micron particles, including natural nanomaterials (e.g., viruses); however, the novel shapes, configurations, and properties of ENMs may enable their persistence. This review of the state of knowledge of ENM production and release, environmental fate and transport, detection in aquatic environments, and removal in drinking water treatment shows that detection methods and source water data are limited. In the absence of adequate methods for detecting ENMs in the environment, models have been developed and predict that ENM concentrations in surface water are likely to be very low (<<1 μg/L), and research of physicochemical transformations of ENMs in the environment suggests that they will be transformed into forms that are more likely to be removed during drinking water treatment.
Compact dual‐band antenna for 2.4/5.2/5.8 GHz WLAN service for laptop computer applications

In this study, a compact dual‐band antenna for wireless universal serial bus dongle applications is proposed. The proposed structure consists of a conventional folded monopole antenna (driven element) and a strip pattern that extends from the ground (GND) plane (shorten parasitic element). The proposed antenna exhibits triple‐resonance (f1, f2, and f3). The first two resonance frequencies (f1 and f2) are generated by adding the shorted parasitic element. The third resonance frequency (f3) is generated by the folded monopole antenna. The first resonance frequency (f1) covers 2.4‐GHz wireless local area network (WLAN) service, whereas the second and third resonance frequencies (f2 and f3) cover 5.2/5.8‐GHz WLAN service. Thus, dual‐band operation for 2.4/5.2/5.8‐GHz WLAN service can be easily performed. The antenna occupies a small area of 7 × 20 mm2. It can be easily connected to the edge of the system GND plane or the supporting metal frame of a laptop computer. Details of the proposed antenna are presented and studied. © 2015 Wiley Periodicals, Inc. Microwave Opt Technol Lett 57:2207–2213, 2015
Patent Pending – Why Faster Isn’t Always Better

In this study we analyze why firms intentionally delay the patenting process. We analyze 443,988 applications at the German Patent Office, which — due to its comparatively long, seven-year examination deferral system — provides its applicants with a very strong leverage for increasing pendency of their applications. More than 50 percent of all filings show applicant-induced delays, more than 20 percent even for the maximum duration of seven years. A mail survey that was answered by 445 inventors (response rate 34.6 percent) of such patent applications reveals the motives behind the delays. The more importance is attributed to “create insecurity” and “gain time for evaluation” as filing motives, the more likely applicants delay the patent process. Implications from our study are twofold. Whereas patent applications that are withdrawn after a re-evaluation by the applicant disburden the patent office, patents that are filed solely in order to create insecurity give cause for concern. Such patent applications reduce transparency of the patent system. Inefficient relocation of resources from innovative activities to patent screening and the development of invent-around solutions by third parties may result. This would ultimately hamper innovation. Our results suggest that a reduction of the maximum examination deferral period from seven to three years would mitigate that problem.
Calibration and Development of Safety Performance Functions for Alabama

One critical component of the Highway Safety Manual (HSM) statistical methods is the safety performance function (SPF). SPFs are essentially regression models that correlate quantitatively the expected number of crashes with traffic exposure and geometric characteristics of the road. As part of a project performed by the University of Alabama to facilitate implementation of the new HSM procedures in the state, this study aims to evaluate the applicability of HSM predictive methods to Alabama data and to develop state-specific statistical models for two facility types: two-lane, two-way rural roads and four-lane divided highways. This study first calibrates HSM base SPFs by using two approaches: the method recommended by the HSM and a newly proposed approach that treats the estimation of calibration factors as a special case of a negative binomial regression. In addition, new forms of state-specific SPFs are further investigated by using Poisson-gamma regression techniques. Four new functional forms are studied in this project. The prediction capabilities of the two calibrated models and the four newly developed state-specific SPFs are evaluated with a validation data set. Five performance measures are considered for model evaluation. The study is able to identify a particular state-specific SPF that fits the Alabama data well and outperforms other models, including the calibrated SPFs. The best model describes the mean crash frequency as a function of annual average daily traffic, segment length, lane width, year, and speed limit. The study finds that the HSM-recommended method for calibration factor estimation also performs well.
State Party Organizations, Independent Expenditures, and Spending Strategies

Recent changes in the campaign finance legal landscape have led to a proliferation of new types of organizations eager to participate financially in American elections. The influx of new groups, many of whom share similar goals (and donors) of state and local party organizations, have to varying degrees become part of what scholars have called the Extended Party Network (EPN). In this paper, we use a novel dataset of campaign donations and independent spending undertaken by three groups in particular: party organizations; party-affiliated groups; and partisan coalition groups. We address the differences between these groups in their makeup, their motivations, and their financial behavior in state elections from 2006-2016. Specifically, we demonstrate that groups in this “inner-circle” of the EPN are all motivated by variance in two crucial contexts: campaign finance laws at the national and state level; and partisan electoral competition at the race and state legislative chamber levels. However, these groups often react in different ways, suggesting a coordinated approach to campaign finance strategy in some cases, and a more parallel, independent approach in others. If you wish to cite this paper, please note that it is a working draft subject to change. Paper prepared for the State of the Parties Conference November 4-5, 2021
EEG Emotion Recognition via Graph-based Spatio-Temporal Attention Neural Networks

Emotion recognition based on electroencephalography (EEG) signals has been receiving significant attention in the domains of affective computing and brain-computer interfaces (BCI). Although several deep learning methods have been proposed dealing with the emotion recognition task, developing methods that effectively extract and use discriminative features is still a challenge. In this work, we propose the novel spatio-temporal attention neural network (STANN) to extract discriminative spatial and temporal features of EEG signals by a parallel structure of multi-column convolutional neural network and attention-based bidirectional long-short term memory. Moreover, we explore the inter-channel relationships of EEG signals via graph signal processing (GSP) tools. Our experimental analysis demonstrates that the proposed network improves the state-of-the-art results in subject-wise, binary classification of valence and arousal levels as well as four-class classification in the valence-arousal emotion space when raw EEG signals or their graph representations, in an architecture coined as GFT-STANN, are used as model inputs.
REASONING IN AN ABSURD WORLD: DIFFICULTIES WITH PROOF BY CONTRADICTION

The study presented in this report is part of a wide research project ♣ concerning proofs by contradiction. Starting from the notion of mathematical theorem as the unity of statement, proof and theory, a structural analysis of proofs by contradiction has been carried out, producing a model to be used in the observation, analysis, and interpretation of cognitive and didactical issues related to this particular type of proof. In particular, the model highlights the complex relationship between the original statement to be proved and a new statement (the secondary statement) that is actually proved. Through the analysis of an exemplar protocol, this paper discusses on cognitive difficulties concerning the relationship between the reference theory and the proof of the secondary statement.
Effect of petrol and spent oil on the growth of Guinea Corn (Sorghum bicolor L.)

This study assessed the effect of petrol and spent lubricating oil on the major growth traits (such as root length, stem length, leaf area, and biomass), and the changes in epidermal layer of leaf and its mitotic index in Guinea Corn ( Sorghum bicolor L.) exposed to 0% (control), 5%, 10%, 15% and 20% concentrations of petrol and spent lubricating oil. Each concentration was mixed with 3 kg of soil in a plastic pot and each treatment was carried out in three replicates. Forty days after planting, the leaf areas of guinea corn plant were 95.83 cm2, 89.67 cm2, 89.47 cm2, and 77.80 cm2 in control, 5%, 10%, 15%, and 20% respectively of petrol pollutant. The means of stem length were 32.50±0.5 cm, 22.60±0.65 cm, 21.27±0.75 cm, 20.83±0.28 cm and 20.33±0.28 cm in control, 5%, 10%, 15% and 20% respectively. Both leaf area and stem length of treated seedlings reduced with increased concentration of the pollutants. Additionally, reduction in the dry weight of the seedlings increased with increasing concentration of both petrol and spent oil. The micrograph of the internal anatomy of the upper epidermal layers of the leaf revealed broken and scattered epidermal cells and smaller sizes of the stomata, and were increased with the increasing concentration of the treatment. Statistical analysis of the treatment shows that there was a significant reduction (P<0.05) in the stem length and leaf area of the seedlings. This study revealed that petroleum pollutant adversely affected germination, growth and development of guinea corn but petroleum products like spent oil can provide nutrition necessary for growth and yield of plant at low concentration.
Environmental Drivers of Agricultural Productivity Growth: CO2 Fertilization of US Field Crops

We assess the CO2 fertilization effect on US agriculture using spatially-varying CO2 data from NASA’s Orbiting Carbon Observatory-2 (OCO-2) satellite covering the majority of US cropland under actual growing conditions. This study complements the many CO2 enrichment experiments that have found important interactions between CO2 and local environmental conditions in controlled settings. We use three empirical strategies: (i) a panel of CO2 anomalies and county yields, (ii) a panel of spatial first-differences between neighboring counties, and (iii) a cross-sectional spatial firstdifference. We find consistently high fertilization effects: a 1 ppm increase in CO2 equates to a 0.5%, 0.6%, and 0.8% yield increase for corn, soybeans, and wheat, respectively. Viewed retrospectively, 10%, 30%, and 40% of each crop’s yield improvements since 1940 are attributable to rising CO2. Acknowledgments: This work was supported by the U.S. Department of Energy, Office of Science, Biological and Environmental Research Program, Earth and Environmental Systems Modeling, MultiSector Dynamics, Contract No. DE-SC0016162. 1 School of International and Public Affairs, Columbia University, 420 West 118th St., New York, NY 10027. 2 National Bureau of Economic Research (NBER), 1050 Massachusetts Ave., Cambridge, MA 02138.