Miracle-dz/UniFAR-FTUs huggingface dataset:
Heterologous expression, characterization and evolution prediction of a diaphorase from Geobacillus sp. Y4.1MC1

β-hydroxybutyric acid is the most sensitive indicator in ketoacidosis detection, and accounts for nearly 78% of the ketone bodies. Diaphorase is commonly used to detect the β-hydroxybutyric acid in clinical diagnosis. However, the extraction of diaphorase from animal myocardium is complex and low-yield, which is not convenient for large-scale production. In this study, a diaphorase from Geobacillus sp. Y4.1MC1 was efficiently heterologous expressed and purified in E . coli with a yield of 110 mg/L culture. The optimal temperature and pH of this recombinant diaphorase (rDIA) were 55 °C and 6.5, respectively. It was proved that rDIA was a dual acid- and thermo-stable enzyme, and which showed much more accurate detection of β-hydroxybutyric acid than the commercial enzyme. Additionally, we also investigated the molecular interaction of rDIA with the substrate, and the conformation transition in different pH values by using homology modeling and molecular dynamics simulation. The results showed that 141–161 domain of rDIA played important role in the structure changes and conformations transmission at different pH values. Moreover, it was predicted that F105W, F105R, and M186R mutants were able to improve the binding affinity of rDIA, and A2Y, P35F, Q36D, N210L, F211Y mutants were benefit for the stability of rDIA.
10 Year Follow-up of Lung Function, Respiratory Symptoms, and Functional Capacity in the COPDGene Study.

RATIONALE The course of lung function, respiratory symptoms, and functional status over time in people who smoke cigarettes is still incompletely understood. The Genetic Epidemiology of COPD (COPDGene) study provides a unique cohort to examine these trajectories, and now 10 year follow-up data are available. OBJECTIVES This study aims to provide insight into the progression of spirometric parameters, respiratory symptoms, and functional capacity over 10 years in current and former cigarette smokers. METHODS We analyzed available longitudinal data for COPDGene participants who did not change smoking status over 3 visits spanning approximately 10 years of follow-up. Change in post-bronchodilator forced expiratory volume in one second (FEV1), St. George's Respiratory Questionnaire (SGRQ), and six-minute walk distance (6MWD) from Phase 1 to Phase 3 were examined using linear mixed models. Terms were included in the models to estimate mean progression separately for current and former cigarette smokers. Models were stratified by baseline GOLD spirometry stages as well as by new 2019 COPDGene Classification. RESULTS Mean age at enrollment of the 9,103 participants in this analysis was 59.8 years (SD=9.2 years); 46.4% were women, and 32.6% were African American. In all GOLD COPD groups, including participants with normal spirometry, as well as all groups categorized by 2019 COPDGene Classification, FEV1 decreased, SGRQ increased (indicating higher symptom burden), and 6MWD decreased over the 10 year follow-up period. Current smokers exhibited a greater mean loss of FEV1 over the study period than former smokers for all groups except those with preserved ratio impaired spirometry (PRISm). For both SGRQ and 6MWD, rates of progression tended to be similar for former and current smokers except for 6MWD in the highest severity groups, where former smokers had greater progression. However, this could be impacted by some current smokers with faster progression that had quit smoking and were dropped from analyses. CONCLUSIONS Progression in FEV1, SGRQ, and 6MWD overall appears to be slow, and the change over time in groups traditionally characterized as not having disease closely mirrors that of the groups with COPD at all GOLD stages. Current cigarette smokers had greater loss of FEV1 than former smokers, while SGRQ and 6MWD changes were more similar between current and former cigarette smokers.
Dynamic and static mechanical properties of PP/EVA blend nanocomposites: Effects of type of masterbatch preparation technique and nature of compatibilizer

Polypropylene (PP)/poly (ethylene-co-vinyl acetate) (EVA) nanocomposites were prepared with use of 3 wt% of organophilic halloysite nanotubes (Org-HNTs) and 3 types of compatibilizers in two steps. First, masterbatchs of EVA and the compatibilizers with the Org-HNT were prepared by two different preparation techniques; melt masterbatch (MM) and solution masterbatch (SM). The masterbatchs were then melt compounded with PP in the second step. Special attention was paid to effects of nature of compatibilizer and masterbatch preparation technique on morphology-mechanical property relationship for the composites. Poly(ethylene-vinyl acetate-carbon monoxide) (EVACO) and maleic anhydride grafted EVA (EVA-g-MA) were used as EVA-based compatibilizers which gave a homogeneous distribution of the nanotubes in the matrix and at the matrix-elastomer interphase as compared to maleic anhydride grafted PP (PP-g-MA). The both masterbatch techniques provided a core-shell morphology composed of nanotubes as core surrounded with elastomer phase as a shell, which led to higher toughness and impact resistance for the composites. Particularly, the EVACO compatibilizer provided the highest toughness, tensile modulus and impact resistance for 3% Org-HNT loaded nanocomposite produced with the SM technique. The same nanocomposite was found to act as an effective damper with an optimum modulus in a broad range of temperature and show a relatively higher creep resistance than the counterpart produced with the MMT technique. It also exhibited 66% higher scratch resistance compared to the PP/EVA blend, which makes it advantageous for the visible parts in automotive applications.
Exploring Deep Transfer Learning Techniques for Alzheimer’s Dementia Detection

Examination of speech datasets for detecting dementia, collected via various speech tasks, has revealed links between speech and cognitive abilities. However, the speech dataset available for this research is extremely limited because the collection process of speech and baseline data from patients with dementia in clinical settings is expensive. In this paper, we study the spontaneous speech dataset from a recent ADReSS challenge, a Cookie Theft Picture (CTP) dataset with balanced groups of participants in age, gender, and cognitive status. We explore state-of-the-art deep transfer learning techniques from image, audio, speech, and language domains. We envision that one advantage of transfer learning is to eliminate the design of handcrafted features based on the tasks and datasets. Transfer learning further mitigates the limited dementia-relevant speech data problem by inheriting knowledge from similar but much larger datasets. Specifically, we built a variety of transfer learning models using commonly employed MobileNet (image), YAMNet (audio), Mockingjay (speech), and BERT (text) models. Results indicated that the transfer learning models of text data showed significantly better performance than those of audio data. Performance gains of the text models may be due to the high similarity between the pre-training text dataset and the CTP text dataset. Our multi-modal transfer learning introduced a slight improvement in accuracy, demonstrating that audio and text data provide limited complementary information. Multi-task transfer learning resulted in limited improvements in classification and a negative impact in regression. By analyzing the meaning behind the Alzheimer's disease (AD)/non-AD labels and Mini-Mental State Examination (MMSE) scores, we observed that the inconsistency between labels and scores could limit the performance of the multi-task learning, especially when the outputs of the single-task models are highly consistent with the corresponding labels/scores. In sum, we conducted a large comparative analysis of varying transfer learning models focusing less on model customization but more on pre-trained models and pre-training datasets. We revealed insightful relations among models, data types, and data labels in this research area.
Soft Power Journalism: A Visual Framing Analysis of COVID-19 on Xinhua and VOA’s Instagram Pages

Abstract The coronavirus pandemic has been a battleground for China and the U.S. to score political points on international media. With outlets from both countries exemplifying distinct media systems, the coverage is very likely to differ between Chinese and U.S. media. Previous studies point to key differences across the two, with Chinese media being more pro-government, more focussed on the African community, and less reliant on reporting that emphasizes personal stories and negative economic impacts. Using visual framing as a theoretical lens, this study explores the intersection of soft power and journalism in Xinhua and Voice of America's (VOA) visual coverage of the coronavirus pandemic on Instagram. A quantitative and qualitative visual analyses of 1911 photographs reveals that Xinhua serves as a prototypical soft power media organization that projects pro-government narratives and downplays negative economic and social consequences of the crisis, while VOA operates as an aggregator of content from predominantly Western sources that viewers can consume elsewhere, hence making its coverage redundant and likely less effective and coherent. The study discusses the implications of both photojournalistic approaches on the global media market.
Visual Short-Term Memory for Coherent and Sequential Motion: A rTMS Investigation

We investigated the role of the human medio-temporal complex (hMT+) in the memory encoding and storage of a sequence of four coherently moving random dot kinematograms (RDKs), by applying repetitive transcranial magnetic stimulation (rTMS) during an early or late phase of the retention interval. Moreover, in a second experiment, we also tested whether disrupting the functional integrity of hMT+ during the early phase impaired the precision of the encoded motion directions. Overall, results showed that both recognition accuracy and precision were worse in middle serial positions, suggesting the occurrence of primacy and recency effects. We found that rTMS delivered during the early (but not the late) phase of the retention interval was able to impair not only recognition of RDKs, but also the precision of the retained motion direction. However, such impairment occurred only for RDKs presented in middle positions along the presented sequence, where performance was already closer to chance level. Altogether these findings suggest an involvement of hMT+ in the memory encoding of visual motion direction. Given that both position sequence and rTMS modulated not only recognition but also the precision of the stored information, these findings are in support of a model of visual short-term memory with a variable resolution of each stored item, consistent with the assigned amount of memory resources, and that such item-specific memory resolution is supported by the functional integrity of area hMT+.
The Trauma Recovery Actions Checklist: Applying Mixed Methods to a Holistic Gender-Based Violence Recovery Actions Measure

Gender-Based Violence (GBV) trauma recovery models have evolved in such a way that survivors are viewed as actively engaging in a multitude of strategies. In addition to seeking help and coping, survivors engage in diverse lifestyle, social, spiritual, and practical strategies to promote their health and wellbeing. This exploratory sequential mixed-methods study develops an instrument to measure the holistic recovery actions used by GBV survivors. The qualitative phase combined recovery action codes from interviews with 50 GBV survivors in three different survivor samples to create an initial six-concept 41-item Trauma Recovery Actions Checklist (TRAC). The quantitative psychometrics phase used data from 289 American GBV survivors. Results revealed a five-factor 35-item final version (sharing/connecting; building positive emotions; reflecting and creating healing spaces; establishing security; and planning the future). There were positive significant correlations between sharing/connecting and depression scores, and between sharing/connecting, reflecting, and building security with PTSD scores. No correlations were found between any recovery action type and the barriers to help-seeking subscales of Problem Management Beliefs, Discrimination, or Unavailability. However, there were significant negative correlations between Shame and Financial barriers and Sharing/Connecting, and between Feeling Frozen, Constraints, and Establishing Security. Implications for research, clinical practice and ways of understanding survivorship recovery are suggested.
Characterization of genomic, physiological, and probiotic features of Lactiplantibacillus plantarum DY46 strain isolated from Turkish fermented turnip juice (Shalgam)

A new Lb. plantarum strain DY46 was isolated from a traditionally fermented non-alcoholic beverage called shalgam from the Southern region of Anatolia following incubation on MRS agar at 30°C for 5 days. DY46 is gram-positive, short rod and catalase-negative. This bacterium fermented 22 of the 49 substrates tested on API CH50 fermentation panels. Whole-genome sequencing was performed using the Illumina Miseq platform to learn more about the metabolic capabilities of DY46. The sequences were assembled into a 3.32 Mb draft genome using PATRIC 3.6. 8. consisting of 153 contigs, and preliminary genome annotation was performed using the RAST algorithm. The DY46 genome consists of a single circular chromosome of 3,332,827 bp that is predicted to carry 3219 genes, including 61 tRNA genes, 2 rRNA operons. The genome has a GC content of 44.3% includes 98 predicted pseudogenes, 25 complete or partial transposases and 3 intact prophages. DY46 genome also predicted to carry genes of Plantaricin-E, Plantaricin-F and Plantaricin-K showing the antimicrobial potential of this bacterium which can be linked-to in vitro antagonism tests that DY46 can inhibit Salmonella Typimirium ATCC14028, Klebsiella pneumonie ATCC13883, and Proteus vulgaris ATCC8427. The acid and bile tolerance of DY46 revealed this strain could potentially pass through the stomach and reach into the gut to provide probiotic therapeutic affects on health.
Impacts of Background Removal on Convolutional Neural Networks for Plant Disease Classification In-Situ

Convolutional neural networks have an immense impact on computer vision tasks. However, the accuracy of convolutional neural networks on a dataset is tremendously affected when images within the dataset highly vary. Test images of plant leaves are usually taken in situ. These images, apart from the region of interest, contain unwanted parts of plants, soil, rocks, and/or human body parts. Segmentation helps isolate the target region and a deep convolutional neural network classifies images precisely. Therefore, we combined edge and morphological based segmentation, background subtraction, and the convolutional neural network to help improve accuracy on image sets with images containing clean and cluttered backgrounds. In the proposed system, segmentation was applied to first extract leaf images in the foreground. Several images contained a leaf of interest interposed between unfavorable foregrounds and backgrounds. Background subtraction was implemented to remove the foreground image followed by segmentation to obtain the region of interest. Finally, the images were classified by a pre-trained classification network. The experimental results on two, four, and eight classes of datasets show that the proposed method achieves 98.7%, 96.7%, and 93.57% accuracy by fine-tuned DenseNet121, InceptionV3, and DenseNet121 models, respectively, on a clean dataset. For two class datasets, the accuracy obtained was about 12% higher for a dataset with images taken in the homogeneous background compared to that of a dataset with testing images with a cluttered background. Results also suggest that image sets with clean backgrounds tend to start training with higher accuracy and converge faster.
Neuropathy in COVID-19 associated with dysbiosis-related inflammation

Although COVID-19 affects mainly lungs with a hyperactive and imbalanced immune response, gastrointestinal and neurological symptoms such as diarrhea and neuropathic pains have been described as well in patients with COVID-19. Studies indicate that gut–lung axis maintains host homeostasis and disease development with the association of immune system, and gut microbiota is involved in the COVID-19 severity in patients with extrapulmonary conditions. Gut microbiota dysbiosis impairs the gut permeability resulting in translocation of gut microbes and their metabolites into the circulatory system and induce systemic inflammation which, in turn, can affect distal organs such as the brain. Moreover, gut microbiota maintains the availability of tryptophan for kynurenine pathway, which is important for both central nervous and gastrointestinal system in regulating inflammation. SARS-CoV-2 infection disturbs the gut microbiota and leads to immune dysfunction with generalized inflammation. It has been known that cytokines and microbial products crossing the blood-brain barrier induce the neuroinflammation, which contributes to the pathophysiology of neurodegenerative diseases including neuropathies. Therefore, we believe that both gut–lung and gut–brain axes are involved in COVID-19 severity and extrapulmonary complications. Furthermore, gut microbial dysbiosis could be the reason of the neurologic complications seen in severe COVID-19 patients with the association of dysbiosis-related neuroinflammation. This review will provide valuable insights into the role of gut microbiota dysbiosis and dysbiosis-related inflammation on the neuropathy in COVID-19 patients and the disease severity.
Cross-Validation, Information Theory, or Maximum Likelihood? A Comparison of Tuning Methods for Penalized Splines

Functional data analysis techniques, such as penalized splines, have become common tools used in a variety of applied research settings. Penalized spline estimators are frequently used in applied research to estimate unknown functions from noisy data. The success of these estimators depends on choosing a tuning parameter that provides the correct balance between fitting and smoothing the data. Several different smoothing parameter selection methods have been proposed for choosing a reasonable tuning parameter. The proposed methods generally fall into one of three categories: cross-validation methods, information theoretic methods, or maximum likelihood methods. Despite the well-known importance of selecting an ideal smoothing parameter, there is little agreement in the literature regarding which method(s) should be considered when analyzing real data. In this paper, we address this issue by exploring the practical performance of six popular tuning methods under a variety of simulated and real data situations. Our results reveal that maximum likelihood methods outperform the popular cross-validation methods in most situations—especially in the presence of correlated errors. Furthermore, our results reveal that the maximum likelihood methods perform well even when the errors are non-Gaussian and/or heteroscedastic. For real data applications, we recommend comparing results using cross-validation and maximum likelihood tuning methods, given that these methods tend to perform similarly (differently) when the model is correctly (incorrectly) specified.
Understanding policy integration of the Sustainable Development Goals: A Network Theory approach

The Sustainable Development Goals (SDGs) have been much debated but there is no question that their goal is to achieve a more balanced and sustainable planet. This study systematically reviewed literature in order to determine the key criteria needed to implement such goals. A search of Scopus indexed journals as well as grey literature published up to March 2020 yielded 114 papers for examination. Using a Network Theory approach, the study found 14 common criteria for successful policy integration of the SDGs. The findings from this paper support that the achievement of the SDGs needs a comprehensive and holistic approach to achieve them. Through an assessment of many different fields of study, commonalities or networks occurred demonstrating how one goal can support and be a means to an end to achieve another. Conclusions consider that using Network Theory to assess tourism policies may assist in the success of policies for the achievement of the SDGs due to its interrelated nature.
Beyond “Feeling Blessed”: Using International Documentary Film to Foster Global Awareness in an Online College Course

As US colleges and universities incorporate an international requirement for undergraduate students, this study assesses the value of an online international documentary course. The effect of documentary viewing was evaluated using students’ reflective essays, noting possible shifts in perspectives on international issues after film viewing. Findings show that students gained new knowledge, displayed empathy, felt “blessed” for their privileges, and were inspired to help others. Findings also reflected an ignorance about inequitable power relationships between the United States and other countries, producing an “othering” effect. Recommendations are provided for engaging students in more critical research and reflection about local-global connections.
Evaluation of IMERG satellite precipitation over the land-coast-ocean continuum – Part I: Detection

As a fundamental water flux, quantitative understanding of precipitation is important to understand and manage water systems under a changing climate, especially in transition regions such as the coastal interface between land and ocean. This work aims to assess the uncertainty in precipitation detection over the land-coast-ocean continuum in the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) V06B. It is examined over three coastal regions of the U.S., i.e. the West Coast, the Gulf of Mexico, and the East Coast, each of which are characterized by different topographies and precipitation climatologies. Detection capabilities are contrasted over different surfaces (land, coast, ocean). A novel and integrated approach traces the IMERG detection performance back to its components (passive microwave, infrared, and morphing-based estimates). The analysis is performed by using high-resolution, high-quality Ground Validation Multi-Radar/Multi-Sensor (GV-MRMS) rainfall estimates as ground reference. The best detection performances are reported with PMW estimates (hit rates in the range of [25-39]%), followed by morphing ([20-34]%), morphing+IR ([17-27]%) and IR ([11-16]%) estimates. Precipitation formation mechanisms play an important role, especially in the West Coast where orographic processes challenge detection. Further, precipitation typology is shown to be a strong driver of IMERG detection. Over the ocean, IMERG detection is generally better but suffers from false alarms ([10-53]%). Overall, IMERG displays nonhomogeneous precipitation detection capabilities tracing back to its components. Results point toward a similar behavior across various land-coast-ocean continuum regions of the CONUS, which suggests that results can be potentially transferred to other coastal regions of the world.