Miracle-dz/UniFAR-FTUs huggingface dataset:
The unappreciated relevance of auxiliary assumptions for evaluating theory-based interventions in health psychology

The use of theory in health behavior change interventions has been recently questioned with mixed results found for theory-based intervention effectiveness. But theory testing in intervention depends on not only theoretical assumptions, but on auxiliary assumptions too. Specifically, auxiliary assumptions are required to traverse the distance from nonobservational terms in theories and observational terms at the level of the empirical hypotheses in interventions. We believe intervention failures are often due to flaws in auxiliary assumptions rather than assumptions at the theoretical level. We use the theory of planned behavior to illustrate how the consideration of these auxiliary assumptions is important to appraise the effectiveness of interventions informed by theory. We hope that bringing attention to the importance of auxiliary assumptions provides a more nuanced and accurate appraisal of theory utility.
Quantitative Analysis of Tozadenant Using Liquid Chromatography-Mass Spectrometric Method in Rat Plasma and Its Human Pharmacokinetics Prediction Using Physiologically Based Pharmacokinetic Modeling

Tozadenant is one of the selective adenosine A2a receptor antagonists with a potential to be a new Parkinson’s disease (PD) therapeutic drug. In this study, a liquid chromatography-mass spectrometry based bioanalytical method was qualified and applied for the quantitative analysis of tozadenant in rat plasma. A good calibration curve was observed in the range from 1.01 to 2200 ng/mL for tozadenant using a quadratic regression. In vitro and preclinical in vivo pharmacokinetic (PK) properties of tozadenant were studied through the developed bioanalytical methods, and human PK profiles were predicted using physiologically based pharmacokinetic (PBPK) modeling based on these values. The PBPK model was initially optimized using in vitro and in vivo PK data obtained by intravenous administration at a dose of 1 mg/kg in rats. Other in vivo PK data in rats were used to validate the PBPK model. The human PK of tozadenant after oral administration at a dose of 240 mg was simulated by using an optimized and validated PBPK model. The predicted human PK parameters and profiles were similar to the observed clinical data. As a result, optimized PBPK model could reasonably predict the PK in human.
Comparison of the AGN Baldwin effect with the modified Baldwin effect of the UV-optical emission lines in a single sample

The inverse relationship between equivalent width (EW) of broad emission lines and the underlying continuum luminosity, known as the Baldwin effect (BE), is an important and well-studied correlation in active galactic nuclei (AGN). However, the EW is also considered to be anti-correlated with the Eddington ratio, referred to as the modified Baldwin effect (MBE). Investigating whether the BE or MBE is the stronger correlation is important for studying the ionization mechanism and the broad-line region of AGN. Based on a sample of 85 bright quasars with quasi-simultaneous UV-optical spectra and a redshift range of 0.03–1.404 from Shang et al, we first present line fitting and measurements of the O vi-wavelength line that were not previously available and the Si iv+O iv]-wavelength line. We then investigate and compare the BE and MBE of multiple UV and optical emission lines from this single sample. We find that the BE of most of the broad emission lines is stronger than the MBE. Furthermore, we confirm the previous result that the slope of the BE steepens with the ionization energy of the line, and also find that the MBE slope shows a similar trend. In addition, compared with the C iv line, the O vi shows a broader line width and a larger peak velocity blueshift with respect to the systemic redshift. These results are also in agreement with previous studies and imply that, compared to the C iv gas, the O vi line-emitting gas is closer to the accretion disk and is more strongly influenced by the accretion disc wind.
Estimation of evaporation and transpiration rates under varying water availability for improving crop management of soybeans using oxygen isotope ratios of pore water

Knowledge of crop water requirements and the effects of management practices on the amounts of water used for crop transpiration and that lost through soil evaporation is essential for efficient agricultural water management. Therefore, this study investigated the temporal evolution of weekly evaporation and transpiration rates under varying soil water conditions in a conventionally managed soybean field by partitioning evap otranspiration based on a water and δ 18 O-stable isotope mass balance. The estimated rates were considered in combination with vertical soil water distribution, atmospheric demand (based on crop evapotranspiration), actual evapotranspiration, and the plant development stage. This allowed for the weekly rates to be compared to the current conditions resulting from dry periods, rain or irrigation events, and the extent of the canopy. The range of weekly transpiration/evapotranspiration, from blossom to maturation, was between 0.60 (±0.11) and 0.82 (±0.10). Within this range, transpiration/evapotranspiration shifted depending on the vertical soil water distribution and meteorological conditions. During dry soil surface periods, evaporation dropped to almost zero, whereas a wet surface layer substantially increased evaporation/evapotran-spiration, even under a closed canopy. Under given conditions, the application of a few intense irrigations before the drying of the soil surface is recommended.
Mitochondrial phylogeny within the Yellow Chat (Epthianura crocea) does not support subspecific designation of endangered Alligator Rivers population

Abstract The delineation of subspecies is important in the evaluation and protection of biodiversity. Subspecies delineation is hampered by inconsistently applied criteria and a lack of agreement and shifting standards on how a subspecies should be defined. The Australian endemic Yellow Chat (Epthianura crocea) is split into three subspecies (E. c. crocea, E. c. tunneyi, and E. c. macgregori) based on minor plumage differences and geographical isolation. Both E. c. tunneyi (Endangered) and E. c. macgregori (Critically Endangered) are recognized under Australian legislation as threatened and are the subject of significant conservation effort. We used mitochondrial DNA to evaluate the phylogeny of the Yellow Chat and determine how much genetic variation is present in each of the three subspecies. We found no significant difference in the cytochrome b sequences (833 base pairs) of E. c. crocea and E. c. tunneyi, but approximately 0.70% or 5.83 bp difference between E. c macgregori and both E. c. crocea and E. c. tunneyi. This analysis supports the delineation of E. c. macgregori as a valid subspecies but does not support separation of E. c. crocea from E. c. tunneyi. We also found very low levels of genetic variation within the Yellow Chat, suggesting it may be vulnerable to environmental change. Our results cast doubt upon the geographic isolation of E. c. crocea from E. c. tunneyi, but more advanced genetic sequencing and a robust comparison of plumage are needed to fully resolve taxonomy.
Privacy‐preserving federated learning cyber‐threat detection for intelligent transport systems with blockchain‐based security

Artificial intelligence (AI) techniques implemented at a large scale in intelligent transport systems (ITS), have considerably enhanced the vehicles' autonomous behaviour in making independent decisions about cyber threats, attacks, and faults. While, AI techniques are based on data sharing among the vehicles, it is important to note that sensitive data cannot be shared. Thus, federated learning (FL) has been implemented to protect privacy in vehicles. On the other hand, the integrity of data and the safety of aggregation are ensured by using blockchain technology. This paper applied classification approaches to VANET and ITS cyber‐threats detection at the vehicle. Subsequently, by using blockchain and by applying an aggregation strategy to different models, models from the previous step were uploaded in a smart contract. Lastly, we returned the updated models to the vehicles. Furthermore, we conducted an experimental study to measure the effectiveness of the proposed prototype. In this paper, the VeReMi data set was distributed in a balanced manner into five parts in the experimental study. Thus, classification techniques were executed by each vehicle separately, and models were generated. Upon the aggregation of the models in blockchain, they were returned to the vehicles. Lastly, the vehicles updated their decision functions and accessed the precision and accuracy of cyber‐threat detection. The results indicated that the precision and accuracy decreased by 7.1% on average with comparable F1‐score and recall. Our solution ensures the privacy preservation of vehicles whereas blockchain guarantees the safety of aggregation technique and low gas consumption.
A general test for univariate seasonality

We propose a general test for univariate seasonality. Starting from a multivariate model for the seasons. some constraints must hold both, on the covariance matrix of the innovations, as well as among coefficients across equations, for a univariate representation of seasonality to be appropriate. Appied to a set of 23 U.K. macroeconomic variables, our test shows that a multivariate representation of seasonality should be preferred in at least 8 cases.
Two-stage control for transfer synchronisation and regularity of subsequent bus line service

Transfer synchronisation aims at reducing the waiting time of transferring passengers to achieve a seamless transfer. Taking control at the operation level is widely regarded as an effective method to make real-time response to transfer synchronisation. However, the irregularity of bus line service after transfer may be caused due to the real-time control before the transfer behaviour. In this paper, we propose a two-stage speed control strategy to improve the transfer efficiency and the regularity of bus service after transfer simultaneously. The influence of speed control is estimated by constructing dynamic rolling horizon. Considering the service performances of both the transfer node and the next stop, we develop a multi-objective optimisation model. Experimental results show that compared with the one-stage control, the two-stage control can significantly reduce the deviation of headway after transfer while reducing the transfer time.
Transposon mutagenesis in oral streptococcus

ABSTRACT Oral streptococci are gram-positive facultative anaerobic bacteria that are normal inhabitants of the human oral cavity and play an important role in maintaining oral microecological balance and pathogenesis. Transposon mutagenesis is an effective genetic manipulation strategy for studying the function of genomic features. In order to study cariogenic related genes and crucial biological element genes of oral Streptococcus, transposon mutagenesis was widely used to identify functional genes. With the advent of next-generation sequencing (NGS) technology and the development of transposon random mutation library construction methods, transposon insertion sequencing (TIS) came into being. Benefiting from high-throughput advances in NGS, TIS was able to evaluate the fitness contribution and essentiality of genetic features in the bacterial genome. The application of transposon mutagenesis, including TIS, to oral streptococci provided a massive amount of valuable detailed linkage data between genetic fitness and genetic backgrounds, further clarify the processes of colonization, virulence, and persistence and provides a more reliable basis for investigating relationships with host ecology and disease status. This review focuses on transposon mutagenesis, including TIS, and its applicability in oral streptococci.
Quantitative Cytologic Descriptors to Differentiate CLL, Sézary, Granular, and Villous Lymphocytes Through Image Analysis.

OBJECTIVES We aimed to find descriptors to identify chronic lymphocytic leukemia (CLL), Sézary, granular, and villous lymphocytes among normal and abnormal lymphocytes in peripheral blood. METHODS Image analysis was applied to 768 images from 15 different types of lymphoid cells and monocytes to determine four discriminant descriptors. For each descriptor, numerical scales were obtained using 627 images from 79 patients. An assessment of the four descriptors was performed using smears from 209 new patients. RESULTS Cyan correlation of the nucleus identified clumped chromatin, and standard deviation of the granulometric curve of the cyan of the nucleus was specific for cerebriform chromatin. Skewness of the histogram of the u component of the cytoplasm identified cytoplasmic granulation. Hairiness showed specificity for cytoplasmic villi. In the assessment, 96% of the smears were correctly classified. CONCLUSIONS The quantitative descriptors obtained through image analysis may contribute to the morphologic identification of the abnormal lymphoid cells considered in this article.
Detecting Significant Events in Arabic Microblogs using Soft Frequent Pattern Mining

nowadays, people use microblogs as a main platform to write about events that occur in their environment. Many researches have been conducted for event detection on the English language, but, Arabic context has not received much research. Furthermore, existing approaches rely on platform dependent features such as hashtags, mentions, or retweets, which make their approaches less efficient when these features are not presented. Further, some approaches which depend only on bursty or frequently used words, detect general viral topics instead of event related topics. In this work, we present a new approach for detecting events written in Arabic using frequent event triggers. The approach first identifies the part of speech tags of a sentence and then analyze them to extract event triggers. A soft frequent pattern mining method is applied to find co-occuring event triggers. The approach has been evaluated using a subset of the Evetar dataset. We divided the data into timely constrained windows to mimic the data stream behavior. Two experiments of different time intervals were conducted, 6-hours and one-day time intervals. We achieved an average F-meaure of 0.644 and 0.717. The results show that our approach outperformed some widely known approaches and it was comparable with others.
Cancer Clinical Trials beyond Pandemic: Report of an American Statistical Association Biopharmaceutical Section Open Forum Discussion

Abstract This article provides a summary of discussions from the American Statistical Association (ASA) Biopharmaceutical (BIOP) Section Open Forum organized by the ASA BIOP Statistical Methods in Oncology Scientific Working Group in coordination with the US FDA Oncology Center of Excellence and LUNGevity Foundation on January 14, 2021, and February 8, 2021. Diverse stakeholders including oncologists, patient advocates, experts from international regulatory agencies, academicians, and representatives of the pharmaceutical industry engaged in a discussion on how best to incorporate lessons learned during the COVID-19 pandemic into the design of future oncology trials. While recognizing that decentralized or hybrid cancer trials may increase variability associated with measurement error and potentially increase bias in treatment effect estimation, panel discussions highlighted the importance of flexibility for decreasing patient burden, which has the potential to increase access to and retention in cancer clinical trials and may broaden the representation of real-world patients in the trial setting.
Gratitude to God Predicts Religious Well-Being over Time

The authors used a prospective design to investigate how gratitude to God predicts religious well-being over time. Gratitude to God is a central aspect of monotheistic religions, and thus may be particularly important to the religious/spiritual well-being of believers. Participants completed online measures of trait and state gratitude to God, along with spiritual well-being, nearness to God, and religious commitment scales over a one-to-two-month period. General well-being, trait gratitude, and the Big Five personality traits were also assessed. After controlling baseline levels, trait gratitude and the Big Five personality traits, dispositional gratitude to God at Time 1 predicted increased religious well-being, nearness to God, and religious commitment at Time 2. Although gratitude to God was significantly related to general well-being variables in cross-sectional analyses, it did not predict these variables over time. Validity data for the gratitude to God measures are also presented. The results suggest that gratitude to God is important to religious/spiritual well-being, and gratitude to God may be a critical variable for research on positive psychology and the psychology of religion/spirituality.
COVID-19 mortality: positive correlation with cloudiness but no correlation with sunlight and latitude in Europe

We systematically investigated an ongoing debate about the possible correlation between SARS-CoV-2 (COVID-19) epidemiological outcomes and solar exposure in European countries, in the period of March – December 2020. For each country, we correlated its mortality data with: i) objective sky cloudiness (as cloud fraction) derived from satellite weather data, ii) solar insolation (watt/square metre at ground level), iii) latitude. We found a positive correlation between the monthly mortality rate and the overall cloudiness in that month (Pearson's r(35)=. 68, P<. 001; averaged linear model fitting the data, adjusted R2 =0.45). In an additional month-by-month analysis, 17% to 59% of the variance in COVID-19 mortality/million appears to be predicted by the cloudiness fraction of the sky, except in the last two months of 2020. We did not find any statistical significant correlation with insolation, nor with latitude of the countries, when the “latitude of a country” was precisely defined as the average landmass location (country centroid). The unexpected correlation found between cloudiness and mortality could perhaps be explained by the following: 1) heavy cloudiness is linked with colder outdoor surfaces, which might aid virus survival; 2) reduced evaporation rate; 3) moderate pollution may be linked to both cloudiness and mortality; and 4) large-scale The results show a difference in the relationships of COVID-19 deaths to cloud fraction versus solar radiation. This could perhaps be explained by a higher net amount of infrared radiation that reaches the ground surface when higher clouds are present compared with the presence of lower clouds Our study could not distinguish between the types of clouds constituting the cloud fraction.
Decomposition of structured large-scale optimization problems and parallel optimization

In this dissertation, we present serial and parallel algorithms to solve efficiently the problem: inf $\{\sum\sbsp{i=1}{N} f\sb{i}(x\sb{i})\mid$ $\sum\sbsp{i=1}{N} A\sb{i}x\sb{i}$ = $a\}. $ Here, a $\in$ ${\rm I\! R}\sp{m},$ and for i = 1, dots, N, $A\sb{i}$ $\in$ ${\rm I\! R}\sp{m\times n\sb{i}},$ $x\sb{i}$ $\in$ ${\rm I\! R}\sp{n\sb{i}},$ and, $f\sb{i}$'s are closed proper convex functions (not necessarily differentiable) taking values in the extended real line $(-\infty, \infty\rbrack. $ For example, block-angular linear programming problems and linear multi-commodity network optimization problems can be cast into the above form. In our approach, we take the Rockafellar dual of the problem to arrive at an unconstrained nonsmooth maximization problem. The difficulty arises from the nonsmoothness of the dual objective. Traditional subgradient methods are not good enough as they do not have implementable stopping criterion and are reported to have slow convergence. One also may not obtain a primal solution at the end. Instead, we apply a modified bundle algorithm, which has an implementable stopping criterion, and more importantly, one can recover an approximate primal solution. We also obtain some theoretical a posteriori error information on the approximate solution. We have implemented this algorithm on randomly generated block-angular linear programming problems of size up to 4,000 equality constraints and 10,000 variables. Our implementation ran up to seventy times faster than MINOS version 5.0, and did substantially better than an advanced implementation of the Dantzig-Wolfe decomposition method. Thus we think that for this type of problem, our algorithm is very promising. A nice feature of the dual problem is that is breaks up the original problem into smaller independent subproblems. Exploiting this fact, we present parallel algorithms implemented on the CRYSTAL multicomputer. We considered two groups of test problems for these algorithms, one in which the subproblems required approximately equal amounts of time to solve, and another in which the solution times varied. In the first group, we obtained 70%-80% efficiency with up to eleven processors. In the second group, we obtained 60% or more efficiency with relatively small problems and with up to five processors.
Toward understanding deep learning classification of anatomic sites: lessons from the development of a CBCT projection classifier

Abstract. Purpose: Deep learning (DL) applications strongly depend on the training dataset and convolutional neural network architecture; however, it is unclear how to objectively select such parameters. We investigate the classification performance of different DL models and training schemes for the anatomic classification of cone-beam computed tomography (CBCT) projections. Approach: CBCT scans from 1055 patients were collected and manually classified into five anatomic classes and used to develop DL models to predict the anatomic class from single x-ray projections. VGG-16, Xception, and Inception v3 architectures were trained with 75% of the data, and the remaining 25% was used for testing and evaluation. To study the dependence of the classification performance on dataset size, training data was downsampled to various dataset sizes. Gradient-weighted class activation maps (grad-CAM) were generated using the model with highest classification performance, to identify regions with strong influence on CNN decisions. Results: The highest precision and recall values were achieved with VGG-16. One of the best performing combinations was the VGG-16 trained with 90 deg projections (mean class precision = 0.87). The training dataset size could be reduced to ∼50 % of its initial size, without compromising the classification performance. For correctly classified cases, Grad-CAM were more heavily weighted for anatomically relevant regions. Conclusions: It was possible to determine those dependencies with a higher influence on the classification performance of DL models for the studied task. Grad-CAM enabled the identification of possible sources of class confusion.