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\"Battling both inner and external demons, Stephanie must learn to balance school and crime-fighting or face the wrath of Barbara Gordon! With guest appearences from Batman and Robin and villains like Man-Bat and Clayface, Batgirl must step up to the mantle! Batgirl must battle the Calculator and stop his plan to unleash a nanovirus upon the citizens of Gotham City that will turn them into mindless techno-zombies, enter the FLOOD!\"-- Provided by publisher.

Most of the existing chest X-ray datasets include labels from a list of findings without specifying their locations on the radiographs. This limits the development of machine learning algorithms for the detection and localization of chest abnormalities. In this work, we describe a dataset of more than 100,000 chest X-ray scans that were retrospectively collected from two major hospitals in Vietnam. Out of this raw data, we release 18,000 images that were manually annotated by a total of 17 experienced radiologists with 22 local labels of rectangles surrounding abnormalities and 6 global labels of suspected diseases. The released dataset is divided into a training set of 15,000 and a test set of 3,000. Each scan in the training set was independently labeled by 3 radiologists, while each scan in the test set was labeled by the consensus of 5 radiologists. We designed and built a labeling platform for DICOM images to facilitate these annotation procedures. All images are made publicly available in DICOM format along with the labels of both the training set and the test set. Measurement(s) diseases and abnormal findings from chest X-ray scans Technology Type(s) AI is used to detect diseases and abnormal findings Sample Characteristic - Location Vietnam

This paper studies whether distance shapes credit allocation by estimating the impact of bank branch closings during the 2000s on local access to credit. To generate plausibly exogenous variation in the incidence of closings, I use an instrument based on within-county, tract-level variation in exposure to post- merger branch consolidation. Closings lead to a persistent decline in local small business lending. Annual originations fall by $453,000 after a closing, off a baseline of $4.7 million, and remain depressed for up to six years. The effects are very localized, dissipating within six miles, and are especially severe during the financial crisis.

Tuberculous meningitis remains highly lethal. In this trial, an intensified regimen of levofloxacin and higher-dose rifampin added to standard therapy was compared with standard antituberculosis therapy alone. The intensified regimen did not result in a higher survival rate. Early treatment with antituberculosis chemotherapy and adjunctive treatment with glucocorticoids reduce the rate of death and disability from tuberculous meningitis, but the disease still kills or disables almost half the patients with the condition. 1 , 2 The current guidelines recommend treatment with four antituberculosis drugs for at least the first 2 months of therapy, followed by treatment with two drugs (rifampin and isoniazid) for an additional 7 to 10 months. 3 , 4 However, these recommendations are based on data from pulmonary tuberculosis and do not take into account the differential ability of antituberculosis drugs to penetrate the brain. Rifampin is considered to . . .

The COVID-19 pandemic has imposed a heavy burden on health care systems and governments. Health literacy (HL) and eHealth literacy (as measured by the eHealth Literacy Scale [eHEALS]) are recognized as strategic public health elements but they have been underestimated during the pandemic. HL, eHEALS score, practices, lifestyles, and the health status of health care workers (HCWs) play crucial roles in containing the COVID-19 pandemic. The aim of this study is to evaluate the psychometric properties of the eHEALS and examine associations of HL and eHEALS scores with adherence to infection prevention and control (IPC) procedures, lifestyle changes, and suspected COVID-19 symptoms among HCWs during lockdown. We conducted an online survey of 5209 HCWs from 15 hospitals and health centers across Vietnam from April 6 to April 19, 2020. Participants answered questions related to sociodemographics, HL, eHEALS, adherence to IPC procedures, behavior changes in eating, smoking, drinking, and physical activity, and suspected COVID-19 symptoms. Principal component analysis, correlation analysis, and bivariate and multivariate linear and logistic regression models were used to validate the eHEALS and examine associations. The eHEALS had a satisfactory construct validity with 8 items highly loaded on one component, with factor loadings ranked from 0.78 to 0.92 explaining 76.34% of variance; satisfactory criterion validity as correlated with HL (ρ=0.42); satisfactory convergent validity with high item-scale correlations (ρ=0.80-0.84); and high internal consistency (Cronbach α=.95). HL and eHEALS scores were significantly higher in men (unstandardized coefficient [B]=1.01, 95% CI 0.57-1.45, P<.001; B=0.72, 95% CI 0.43-1.00, P<.001), those with a better ability to pay for medication (B=1.65, 95% CI 1.25-2.05, P<.001; B=0.60, 95% CI 0.34-0.86, P<.001), doctors (B=1.29, 95% CI 0.73-1.84, P<.001; B 0.56, 95% CI 0.20-0.93, P=.003), and those with epidemic containment experience (B=1.96, 95% CI 1.56-2.37, P<.001; B=0.64, 95% CI 0.38-0.91, P<.001), as compared to their counterparts, respectively. HCWs with higher HL or eHEALS scores had better adherence to IPC procedures (B=0.13, 95% CI 0.10-0.15, P<.001; B=0.22, 95% CI 0.19-0.26, P<.001), had a higher likelihood of healthy eating (odds ratio [OR] 1.04, 95% CI 1.01-1.06, P=.001; OR 1.04, 95% CI 1.02-1.07, P=.002), were more physically active (OR 1.03, 95% CI 1.02-1.03, P<.001; OR 1.04, 95% CI 1.03-1.05, P<.001), and had a lower likelihood of suspected COVID-19 symptoms (OR 0.97, 95% CI 0.96-0.98, P<.001; OR 0.96, 95% CI 0.95-0.98, P<.001), respectively. The eHEALS is a valid and reliable survey tool. Gender, ability to pay for medication, profession, and epidemic containment experience were independent predictors of HL and eHEALS scores. HCWs with higher HL or eHEALS scores had better adherence to IPC procedures, healthier lifestyles, and a lower likelihood of suspected COVID-19 symptoms. Efforts to improve HCWs' HL and eHEALS scores can help to contain the COVID-19 pandemic and minimize its consequences.

Catharanthus roseus (Vinca) is a perennial herbaceous plant that is renowned for its abundance of vincristine (VCR) and vinblastine (VBL). These vinca alkaloids possess valuable anticancer properties and have been extensively used in chemotherapy treatment for various type of cancers. However, the current supply of these vinca alkaloids is reliant on plant material with low productivity and high costs. Endophytic fungi, a category of symbiotic mycota that are capable of synthesizing their host plant-specific bioactive compounds, have gained significant attention as a bioreactor for large-scale production of vinca alkaloids. In this study, a total of 34 endophytic fungal strains were isolated from stem and root tissues of C. roseus . The isolated endophytic fungi were taxonomically characterized as Alternaria sp., Talaromyces sp., and Cladosporium sp. by morphological observation and sequence analysis of the ITS region of rDNA. Three endophytic fungal strains were identified to be capable of synthesizing VCR and VBL by UPLC/MRM-MS analysis. The fungal strain Alternaria DC1 was determined to be the most prolific producer, producing VCR and VBL at concentrations of 177.6 and 114.8 µg/L, respectively. The Talaromyces DC2 strain followed with VCR and VBL yields of 44.0 and 111.6 µg/L, respectively. While the fungal strain Cladosporium DC3 was identified as a producer of VCR (36.9 µg/L) and VBL (99.6 µg/L) for the first time. These endophytic fungi exhibit the potential to serve as viable sources for the production of vinca alkaloids on a larger scale.

Mammography, or breast X-ray imaging, is the most widely used imaging modality to detect cancer and other breast diseases. Recent studies have shown that deep learning-based computer-assisted detection and diagnosis (CADe/x) tools have been developed to support physicians and improve the accuracy of interpreting mammography. A number of large-scale mammography datasets from different populations with various associated annotations and clinical data have been introduced to study the potential of learning-based methods in the field of breast radiology. With the aim to develop more robust and more interpretable support systems in breast imaging, we introduce VinDr-Mammo, a Vietnamese dataset of digital mammography with breast-level assessment and extensive lesion-level annotations, enhancing the diversity of the publicly available mammography data. The dataset consists of 5,000 mammography exams, each of which has four standard views and is double read with disagreement (if any) being resolved by arbitration. The purpose of this dataset is to assess Breast Imaging Reporting and Data System (BI-RADS) and breast density at the individual breast level. In addition, the dataset also provides the category, location, and BI-RADS assessment of non-benign findings. We make VinDr-Mammo publicly available as a new imaging resource to promote advances in developing CADe/x tools for mammography interpretation.

This study investigated the current practices and challenges for the sustainable fashion of luxury boutique fashion brands (LBFBs) in Vietnam. A series of in-depth interviews with 20 founders and managers of LBFBs in Vietnam was conducted. Findings show that sustainable practices improve ethnic cultures, strengthen the usage of local resources, promote sustainable lifestyle, and thereby contributing to sustainable development of the boutique fashion brands. However, the brands face some challenges while dealing with their stakeholders such as shortage of available internal resources, bias in consumer perception and purchase behaviors, and legal barriers to achieve accredited environment certification that, in turn, weaken the sustainable practices in the local context. Results also provide some insightful information for small & medium sized enterprises (SMEs) to adjust their sustainability practices in order to improve their competitive advantages in the marketplace.

From the end of 2019, one of the most serious and largest spread pandemics occurred in Wuhan (China) named Coronavirus (COVID-19). As reported by the World Health Organization, there are currently more than 100 million infectious cases with an average mortality rate of about five percent all over the world. To avoid serious consequences on people’s lives and the economy, policies and actions need to be suitably made in time. To do that, the authorities need to know the future trend in the development process of this pandemic. This is the reason why forecasting models play an important role in controlling the pandemic situation. However, the behavior of this pandemic is extremely complicated and difficult to be analyzed, so that an effective model is not only considered on accurate forecasting results but also the explainable capability for human experts to take action pro-actively. With the recent advancement of Artificial Intelligence (AI) techniques, the emerging Deep Learning (DL) models have been proving highly effective when forecasting this pandemic future from the huge historical data. However, the main weakness of DL models is lacking the explanation capabilities. To overcome this limitation, we introduce a novel combination of the Susceptible-Infectious-Recovered-Deceased (SIRD) compartmental model and Variational Autoencoder (VAE) neural network known as BeCaked. With pandemic data provided by the Johns Hopkins University Center for Systems Science and Engineering, our model achieves 0.98 R 2 and 0.012 MAPE at world level with 31-step forecast and up to 0.99 R 2 and 0.0026 MAPE at country level with 15-step forecast on predicting daily infectious cases. Not only enjoying high accuracy, but BeCaked also offers useful justifications for its results based on the parameters of the SIRD model. Therefore, BeCaked can be used as a reference for authorities or medical experts to make on time right decisions.

Urinary tract infections (UTIs) are one of the most common infections in older people and are associated with increased morbidity and mortality. UTIs are also associated with increased risk of antimicrobial resistance (AR). This study examined changes in AR among older inpatients with a primary diagnosis of UTIs in the United States over an 8-year period and the impact of AR on clinical outcomes and hospital costs. Data were obtained from the longitudinal hospital HCUP-NIS database from 2009 to 2016 for inpatient episodes that involved those aged 65+ years. The ICD-9 and ICD-10 codes were used to identify episodes with a primary diagnosis of UTIs, comorbidities, AR status and age-adjusted Deyo-Charlson comorbidity index (ACCI) for the patient concerned. Weighted multivariable regression was used to examine the impact of AR on all-cause inpatient mortality, discharge destination, length of stay and hospital expenditures, adjusted for socio-demographic and clinical covariates. The proportion of admissions with AR increased, from 3.64% in 2009 to 6.88% in 2016 (p<0.001), with distinct patterns for different types of resistance. The likelihood of AR was higher in admissions with high ACCI scores and admissions to hospitals in urban areas. Admissions with AR were more likely to be discharged to healthcare facilities (e.g. care homes) compared to routine discharge (OR 1.81; 95%CI, 1.75-1.86), had increased length of stay (1.12 days; 95%CI, 1.06-1.18) and hospital costs (1259 USD; 95%CI, 1178-1340). Resistance due to MRSA was specifically associated with increased hospital mortality (OR 1.33; 95%CI, 1.15-1.53). Our findings suggest that the prevalence of AR has increased among older inpatients with UTIs in the USA. The study highlights the impact of AR among older inpatients with a primary diagnosis of UTIs on clinical outcomes and hospital costs. These relationships and their implications for the care homes to which patients are frequently discharged warrant further research.