Miracle-dz/UniFAR-FTUs huggingface dataset:
Soluble Urokinase Plasminogen Activator Receptor: Genetic Variation and Cardiovascular Disease Risk in Black Adults

Supplemental Digital Content is available in the text. Background: suPAR (Soluble urokinase plasminogen activator receptor) has emerged as an important biomarker of coagulation, inflammation, and cardiovascular disease (CVD) risk. The contribution of suPAR to CVD risk and its genetic influence in Black populations have not been evaluated. Methods: We measured suPAR in 3492 Black adults from the prospective, community-based JHS (Jackson Heart Study). Cross-sectional associations of suPAR with lifestyle and CVD risk factors were assessed, whole-genome sequence data were used to evaluate genetic associations of suPAR, and relationships of suPAR with incident CVD outcomes and overall mortality were estimated over follow-up. Results: In Cox models adjusted for traditional CVD risk factors, estimated glomerular filtration rate, and CRP (C-reactive protein), each 1-SD higher suPAR was associated with a 21% to 31% increased risk of incident coronary heart disease, heart failure, stroke, and mortality. In the genome-wide association study, 2 missense (rs399145 encoding p.Thr86Ala, rs4760 encoding p.Phe272Leu) and 2 noncoding regulatory variants (rs73935023 within an enhancer element and rs4251805 within the promoter) of PLAUR on chromosome 19 were each independently associated with suPAR and together explained 14% of suPAR phenotypic variation. The allele frequencies of each of the four suPAR-associated genetic variants differ considerably across African and European populations. We further show that PLAUR rs73935023 can alter transcriptional activity in vitro. We did not find any association between genetically determined suPAR and CVD in JHS or a larger electronic medical record-based analyses of Blacks or Whites. Conclusions: Our results demonstrate the importance of ancestry-differentiated genetic variation on suPAR levels and indicate suPAR is a CVD biomarker in Black adults.
The pig as a model system for investigating the recruitment and contribution of myofibroblasts in skin healing

In the skin‐healing field, porcine models are regarded as a useful analogue for human skin due to their numerous anatomical and physiological similarities. Despite the widespread use of porcine models in skin healing studies, the initial origin, recruitment and transition of fibroblasts to matrix‐secreting contractile myofibroblasts are not well defined for this model. In this review, we discuss the merit of the pig as an animal for studying myofibroblast origin, as well as the challenges associated with assessing their contributions to skin healing. Although a variety of wound types (incisional, partial thickness, full thickness, burns) have been investigated in pigs in attempts to mimic diverse injuries in humans, direct comparison of human healing profiles with regards to myofibroblasts shows evident differences. Following injury in porcine models, which often employ juvenile animals, myofibroblasts are described in the developing granulation tissue at 4 days, peaking at Days 7–14, and persisting at 60 days post‐wounding, although variations are evident depending on the specific pig breed. In human wounds, the presence of myofibroblasts is variable and does not correlate with the age of the wound or clinical contraction. Our comparison of porcine myofibroblast‐mediated healing processes with those in humans suggests that further validation of the pig model is essential. Moreover, we identify several limitations evident in experimental design that need to be better controlled, and standardisation of methodologies would be beneficial for the comparison and interpretation of results. In particular, we discuss anatomical location of the wounds, their size and depth, as well as the healing microenvironment (wet vs. moist vs. dry) in pigs and how this could influence myofibroblast recruitment. In summary, although a widespread model used in the skin healing field, further research is required to validate pigs as a useful analogue for human healing with regards to myofibroblasts.
Current findings support the potential use of bioactive peptides in enhancing zinc absorption in humans

Abstract More than two billion people around the world are affected by zinc deficiency, mainly due to the inadequate intake and absorption of zinc. Based on recent research findings, the bioactive peptides could potentially be used to combat zinc deficiency particularly due to their Zinc chelating ability. The main aim of this review was to present current findings, supporting the potential use of bioactive peptides based on their ability to enhance zinc absorption. In-vivo, in-vitro, and ex-vivo studies have demonstrated that zinc chelating peptides can enhance the retention, transportation, and absorption of zinc. Comparative studies on zinc bioavailability from protein hydrolysates and zinc salts have demonstrated that the protein hydrolysates-zinc complexes are more bioavailable than the zinc salts. Data from the structure-function relationship of zinc chelating peptides suggest that the zinc chelating capacities of peptides increase in the following order; the position of zinc chelator > zinc chelator strength > abundance of zinc chelators > net charge > molecular weight. In addition, the transport mechanism of peptide-zinc complex is hypothesized, and the potential use of bioactive peptides based on their safety and taste and limitations to their commercialization are also discussed.
Multi-sensor data acquisition for assessing the condition of vegetation

The pressure on vegetation, whether forests, meadows or cultivated areas, is becoming increasingly greater. Climate change, extreme weather and ever higher yields taking place at the same time are creating enormous challenges for areas under cultivation. Drought stress, heavy rains and cultivation of monocultures stress both, the soil and the crops themselves. Regular monitoring of the crops or trees as well as soil condition is essential for a sustainable land use. The use of unmanned aerial vehicles (UAVs) for aerial structural surveys, the recording of soil parameters such as soil temperature, soil moisture and gas exchange have so far mostly been carried out independently of each other. Combining these measurement techniques, a holistic picture of the state of these ecosystems becomes possible. The Fraunhofer-Institute for Physical Measurement Techniques IPM presents a coherent process chain for the fully comprehensive recording of ecosystems. A recording by means of LiDAR systems from the ground, multispectral aerial images, terrestrial laser scans and the recording of nitrous oxide emission. Thus, we obtain a full structural image of the ecosystem enriched with metadata on plant condition and soil parameters. This forms the basis of an analysis of the overall condition of the full ecosystem. We present the results of the different sensors and the fused data of a first measurement campaign.
MODIS reflective solar bands calibration improvements for Collection 7

Calibration of Terra and Aqua MODIS reflective solar bands (RSB) has evolved significantly since the launch of the first MODIS instrument on the Terra satellite more than 21 years ago. In NASA’s current Collection 6 and 6.1 Level 1B products (C6/C6.1 L1B), the RSB calibration algorithm continues to rely primarily on the onboard solar diffuser to calibrate the instrument gain. Lunar observations are used to track on-orbit changes in the response versus scan angle (RVS), and data from pseudo-invariant desert sites are used to apply adjustments to the gain and RVS calibration for select bands. The resulting reflectance products have in general shown a very stable performance. In recent years, some performance degradation has been noted for a few bands and algorithm changes have been tested to further improve the calibration accuracy for the upcoming Collection 7 (C7) L1B reprocess. In this paper, we present the MODIS RSB calibration improvements that will be included in C7. Major improvements include: applying polarization correction to the desert data before using it to generate RVS for Terra bands 8, 9, 3, and 10; using ocean scene data and an interband calibration approach to correct for long-term drift of Terra bands 11 and 12; applying an updated crosstalk correction to Terra SWIR bands over the entire mission; and using data from deep convective clouds in Terra SWIR band calibration, including the addition of time-dependent RVS for bands 5 and 26. All other minor calibration changes are also covered. Overall, the reflectance differences at nadir between C6.1 and C7 are within a few percent, though the differences increase in some cases at large scan angles. The Terra visible (3, 8-12) and SWIR bands (5-7, 26) have the most significant improvements. For all other Terra bands and all Aqua bands, the C7-C6.1 differences are mostly within 1%.
The Taxonomy of Adult Females in the Genus Xenos (Strepsiptera: Xenidae) with a Re-description of the Females of Three North American Species

ABSTRACT: The taxonomy of neotenic female strepsipterans has previously relied upon characteristics of color, measurements of the cephalothorax, and identification of the host for species identification, but these characters have proven mostly uninformative in defining species. Taxonomically important characters of females of the genus Xenos (Strepsiptera: Xenidae) are presented, which should provide for species level identification using females in the genus Xenos, and these methods should be generally applicable to other strepsipterans. Xenos kifunei, X. nigrescens, and X. peckii are re-described using these characters.
State Antitrust Remedies: Lessons from the Laboratories

2 Arizona and North Dakota have adopted the Uniform State Antitrust Act [USAA). Michigan has adopted an antitrust law which is substantially similar to the USAA, but which includes criminal penalties. The USAA was drafted in 1963. See Arnold & Ford, Uniform State Antitrust Act: Toward Creation ofa National Antitrust Policy, 15 CASE W. RES. L. REV. 102 (1963). It was approved by the National Conference of Commissioners on Uniform State Laws in August of 1973. Several commentators have negatively assessed the USAA in part because it omits criminal sanctions and creates a discretionary treble damages remedy. See Rubin, Rethinking State Antitrust Enforcement, 26 FLA. L. REV. 653 (1974); Note, Reviving State Antitrust Enforcement: The Problems of Putting New Wine in Old Skins, 4 J. CORP. LAW 547 (1979).
Antitrust merger policy and the Reagan administration

1 The Department of Justice has issued merger guidelines on three occasions. In 1968, at the close of the Johnson Administration, the first guidelines were promulgated. TRADE REG. REP. (CCH) 14510. In 1982, the Reagan Administration replaced the by then obsolete 1968 guidelines with a completely new version. Id. at 14500. The 1982 guidelines were revised in 1984. Id. at 14490 [hereinafter 1984 DOJ Merger Guidelines]. Unless otherwise noted, all subsequent references are to the 1984 Justice Department guidelines. In 1982, the Federal Trade Commission issued a Statement on Horizontal Mergers. See T. BRUNNER, T. KRATTENMAKER, R. SKITOL & A. WEBSTER, MERGERS IN THE NEW ANTITRUST ERA 261-271 (1985). The FTC statement is much less instructive than the Justice Department guidelines and the FTC, in practice, now follows the Department's guidelines.
Automatic and Semi-automatic Augmentation of Migration Related Semantic Concepts for Visual Media Retrieval

Understanding the factors related to migration, such as perceptions about routes and target countries, is critical for border agencies and society altogether. A systematic analysis of communication and news channels, such as social media, can improve our understanding of such factors. Videos and images play a critical role in social media as they have significant impact on perception manipulation and misinformation campaigns. However, more research is needed in the identification of semantically relevant visual content for specific queried concepts. Furthermore, an important problem to overcome in this area is the lack of annotated datasets that could be used to create and test accurate models. A recent study proposed a novel video representation and retrieval approach that effectively bridges the gap between a substantiated domain understanding - encapsulated into textual descriptions of Migration Related Semantic Concepts (MRSCs) - and the expression of such concepts in a video. In this work, we build on this approach and propose an improved procedure for the crucial step of the concept labels' textual augmentation, which contributes towards the full automation of the pipeline. We assemble the first, to the best of our knowledge, migration-related videos and images dataset and we experimentally assess our method on it.
The Factors Affecting the IoT User Satisfaction in Sinyeong City, China

With the development of Internet and information technology as well as the birth of the concept of smart cities, paramount changes occur in people's life. People nowadays can use various high-end smart devices at home without going out. Therefore, the construction of Internet of Things (IoT) facilities is particularly suitable for modern smart cities, which is not only convenient and reliable, but also greatly facilitates people's lives. This research aimed to study and investigate the factors affecting the IOT user satisfaction of citizen in Sinyeong City from China through a quantitative survey. After studying, investigating, and researching, the problems had also been discovered in the construction and application of the Internet of Things in Sinyeong City, as well as the various needs and demands of the citizens. Among them, the influencing factors include information quality, system quality and service quality. The implication which expected, can help government departments to formulate better policies that benefit the people's livelihood to provide the IoT for further smart city, to comprehensively improve the speed of national smart city construction.
Loopback strategy for in-vehicle network processing in automotive gateway network on chip

In this work, authors introduce an innovative loopback strategy for In-Vehicle Network (IVN) processing in automotive gateway (GW) Network on Chip. The new proposed architecture is fully HW centric, and allows performing any IVN processing algorithms without intervention from the CPU. In essence, the loopback strategy allows for adapting the number of stages in the pipeline of the processing stage by betting on the centralization of the processing resources and recirculating frames from output to input when further stages are needed. It permits even to select to which stage to send them depending on the processing required, optimizing thus the number of stages traversed by frames and consequently reducing latency. The processing unit is built as a stack of parallel tasks with the required interconnection resources that allow performing any processing over any frame, and to handle several frames in parallel. With this architecture, the GW data path is fully adaptable per frame, optimizing latency and Quality of Service, allowing for fulfilling the high demanding requirements of future IVNs.
Drug Repositioning Prediction Based on Multi-source Data and Multi-tasking Models

(1) Objectives: In the process of drug repositioning, there are two important problems need to be resolved. One is how to choose the drug candidates and the other is how to select the drug which has fewer side effects than other candidates; (2) Methods: In the paper, a new workflow was created which is combined by integrating the data about drug information to form a dataset and using multitask learning model to process the datasets. In the experiment, we integrated a dataset, in which each drug has 881 features and two labels, and then two machine learning models are used as a screening strategy to predict drug repositioning; (3) Results: In the two methods, MNN(Multitask Neural Networks) and MDBN(Multitask Deep Belief Networks) were both applied for multitasks. After optimization, the MDBN model has the best performance with the mean AUC value of 0.608. It is higher than MNN by 6%. (4) Conclusions: Through the integration of data, the screening range for a certain type of disease can be achieved. The multitask deep model MDBN can be used as a screening method on these datasets. This new workflow can be used as a new idea for drug repositioning to provide more drug candidates. However, the workflow can't fit for some classification which only have several drugs. So the next optimization direction is to mine as much data on these diseases as possible from all perspectives.
Experimental Analysis of Received Signals Strength in Bluetooth Low Energy in Crowd Environments

The Bluetooth Low Energy sensor detects smartphones by sending notifications to users when their smartphones enter the range. Indeed, the crowded environment influences detection because of the human body, which is 80% water. The main challenge here is the sensitivity of the Received Signal Strength Indicator (RSSI) values to environment changes, particularly in the crowded environment, which results in signal propagation or radio map changes. Therefore, distance estimation's accuracy and efficiency depend heavily on the accuracy of the measured RSSI values and radio propagation exponent. This paper aims to study the RSSI variations at different transmission distances and develop a radio waves propagation model to minimize the distance estimation error. Moreover, we analyzed the differences between measured values and estimated values of distance. The experimental result, acquired through practical experiments in a real-world environment, shows the relative error (RE) of estimation distance is 0.06 with the presence of 16.5 dBm RSSI maximum variation between crowded and uncrowded environment. Furthermore, we have also perceived that environmental specific radio waves propagation exponent significantly affects distance estimation accuracy in BLE modules. This indicates that the logarithmic distance propagation model is suitable for path-loss modeling in a crowded environment. The validity of the propagation model was further verified by comparisons between the measured and predicted distance. To explore the findings, experiments were conducted in Al-Madinah Mosque. This study's outcomes will further facilitate the development and design of several applications for the crowding environment.
Lung Cancer Detection using a Dilated CNN with VGG16

Lung nodules are an early manifestation of lung cancer. Early detection of lung nodules plays a vital role in improving the survival rate of patients. Computed tomography (CT) has fast scanning speed, high image size, and can capture tiny areas. The application of CT for clinical diagnosis is an effective method. We use convolutional neural network (CNN) to study the algorithm of lung nodule detection and diagnosis based on CT images. This paper is based on a lung nodule segmentation network combining VGG-16 and dilated convolution. And compared with traditional lung nodule segmentation methods and Inception v2, XOR evaluation coefficient, Hausdorff distance, Jaccard similarity coefficient, accuracy, sensitivity and specificity were used as segmentation evaluation indexes. VGG-16 is superior to traditional image processing methods in all indicators. The accuracy of VGG-16 is as high as 0.971, the false detection rate is only 0.101, and the missed detection rate is only 0.074. The segmented image result is closest to Ground Truth, and there is no problem of incorrect segmentation of lung parenchyma and lung nodules. The VGG-16 proposed in this paper improves the accuracy of segmenting lung nodules, and is superior to traditional image processing methods in various performance indicators, which can effectively help experts diagnose lung nodules.
Joe Biden: A Bearer of Hope

Abstract Expectations on Biden’s Presidency are immense. The international community hopes for a paradigm shift away from US unilateral policies and towards more international cooperation. International economic experts expect positive growth effects far beyond the United States. President Biden’s early actions point towards reversing some of President Trump’s core economic policies. President Biden’s reform proposals include large-scale fiscal expansions, a substantial increase of the corporate income tax rate and increased international cooperation in the upcoming years.
Clinical features of atypical tuberculosis mimicking bacterial pneumonia

Abstract Objectives The aim of this study is to investigate clinical features of atypical pulmonary tuberculosis (aPTB) mimicking bacterial pneumonia, determine imaging features with the highest degree of correlation, and identify predictors for acid-fast bacilli (AFB) positivity. Methods The clinical data of 259 patients considered as aPTB were retrospectively analyzed. The correlation of CT patterns was evaluated with Spearman analysis, and the predictors for AFB positivity were assessed with the multivariate analysis. Results The most common symptom of aPTB was cough (84.6%), followed by fever and anorexia (47.1 and 41.7%, respectively). Infiltrated patchy was the most common radiological pattern (84.9%), followed by nodules (3–10 mm), micronodules (<3 mm), and consolidation (79.2, 78.8, and 66.0%, respectively). Nodules (3–10 mm) and micronodules (r = 0.988, p < 0.001), consolidation and air bronchogram (r = 0.590, p < 0.001), and pulmonary atelectasis and consolidation (r = 0.323, p < 0.001) showed high correlation. In the multivariate analysis, hyperpyrexia (OR, 2.29; 95% CI, 1.22–4.29) and bronchiectasis (OR, 2.06; 95% CI, 1.04–4.06) were the predictors of AFB-smear positivity, while bulla (OR, 0.22; 95% CI, 0.05–0.97) was the predictor of AFB-smear negativity. Conclusion This study demonstrated the clinical and radiological features of aPTB mimicking pneumonia. Several paired radiological findings may guide us to the diagnosis of aPTB. Hyperpyrexia and bronchiectasis may be helpful for predicting AFB positivity, and bulla may be a predictive sign of AFB negativity.
Impact of data accuracy on the evaluation of COVID-19 mitigation policies

Abstract Evaluating the effectiveness of nonpharmaceutical interventions (NPIs) to mitigate the COVID-19 pandemic is crucial to maximize the epidemic containment while minimizing the social and economic impact of these measures. However, this endeavor crucially relies on surveillance data publicly released by health authorities that can hide several limitations. In this article, we quantify the impact of inaccurate data on the estimation of the time-varying reproduction number $ R(t) $ , a pivotal quantity to gauge the variation of the transmissibility originated by the implementation of different NPIs. We focus on Italy and Spain, two European countries among the most severely hit by the COVID-19 pandemic. For these two countries, we highlight several biases of case-based surveillance data and temporal and spatial limitations in the data regarding the implementation of NPIs. We also demonstrate that a nonbiased estimation of $ R(t) $ could have had direct consequences on the decisions taken by the Spanish and Italian governments during the first wave of the pandemic. Our study shows that extreme care should be taken when evaluating intervention policies through publicly available epidemiological data and call for an improvement in the process of COVID-19 data collection, management, storage, and release. Better data policies will allow a more precise evaluation of the effects of containment measures, empowering public health authorities to take more informed decisions.