Tetraoctylammonium Bromide Interlayer between NiLiO and Perovskite for Light-Emitting Diodes.

Physical vapor deposition is a favorable technique for fabricating light-emitting diodes (LEDs) due to its scalability and reproducibility. However, the performances of LEDs fabricated via this method are worse than those prepared via solution processing owing to the generation of high defect densities. In this study, we introduce a layer of tetraoctylammonium bromide (TOABr), an interfacial-modification compound containing four long octyl chains that are symmetrically arranged around an N atom, to reduce nonradiative recombination and trap densities in CsPbBr.

Bridging Global Context Interactions for High-Fidelity Pluralistic Image Completion.

We introduce PICFormer, a novel framework for Pluralistic Image Completion using a transFormer based architecture, that achieves both high quality and diversity at a much faster inference speed. Our key contribution is to introduce a code-shared codebook learning using a restrictive CNN on small and non-overlapping receptive fields (RFs) for the local visible token representation. This results in a compact yet expressive discrete representation, facilitating efficient modeling of global visible context relations by the transformer.

Understanding the COVID-19 Infodemic: Analyzing User-Generated Online Information During a COVID-19 Outbreak in Vietnam.

Objectives: Online misinformation has reached unprecedented levels during the coronavirus disease 2019 (COVID-19) pandemic. This study analyzed the magnitude and sentiment dynamics of misinformation and unverified information about public health interventions during a COVID-19 outbreak in Da Nang, Vietnam, between July and September 2020.

Methods: We analyzed user-generated online information about five public health interventions during the Da Nang outbreak.

Correction: Le Dinh et al. Effect of Diet Composition on Excreta Composition and Ammonia Emissions from Growing-Finishing Pigs. 2022, , 229.

[...

Using 'infodemics' to understand public awareness and perception of SARS-CoV-2: A longitudinal analysis of online information about COVID-19 incidence and mortality during a major outbreak in Vietnam, July-September 2020.

Background: Trends in the public perception and awareness of COVID-19 over time are poorly understood. We conducted a longitudinal study to analyze characteristics and trends of online information during a major COVID-19 outbreak in Da Nang province, Vietnam in July-August 2020 to understand public awareness and perceptions during an epidemic.

Methods: We collected online information on COVID-19 incidence and mortality from online platforms in Vietnam between 1 July and 15 September, 2020, and assessed their trends over time against the epidemic curve.

Re-positive testing, clinical evolution and clearance of infection: results from COVID-19 cases in isolation in Viet Nam.

Objective: Asymptomatic infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and test re-positivity after a negative test have raised concerns about the ability to effectively control the coronavirus disease 2019 (COVID-19) pandemic. We aimed to investigate the prevalence of COVID-19 asymptomatic and pre-symptomatic infections during the second wave of COVID-19 in Viet Nam, and to better understand the duration of SARS-CoV-2 infection and the dynamics between the evolution of clinical symptoms and SARS-CoV-2 test positivity among confirmed COVID-19 cases.

Methods: We conducted a cohort analysis on the first 50 confirmed cases during the second COVID-19 wave in Viet Nam using clinical, laboratory and epidemiological data collected from 9 March to 30 April 2020.

Effect of Diet Composition on Excreta Composition and Ammonia Emissions from Growing-Finishing Pigs.

This study aimed to investigate the impact of decreased crude protein (CP) levels (by 2% units) or acidifying diets (by adding 10 g benzoic acid/kg diet in combination with replacing a part of CaCO by about 10 g Ca-formate/kg diet) on urine, feces and manure composition and ammonia emissions from growing and finishing pig houses. Yorkshire x F1(Landrace x Yorkshire) pigs (n = 576) with an initial body weight of 24.9 ± 3.

Transvenous retrograde embolization of ruptured brain arteriovenous malformations: A case report and review of the literature.

Hemorrhagic stroke due to ruptured brain arteriovenous malformations (AVMs) is a common cause in young stroke patients. When the ruptured AVMs are in deep location, the choice of endovascular intervention with the arterial approach to AVM embolization is routine but in many cases, it is not feasible due to the inability to access because of the small and tortuous arterial branch, however, the intravenous approach also results in high complete obliteration rates but also carries a higher risk of stroke than the intra-arterial route. We describe a 36-year-old female patient diagnosed with intracranial and intraventricular hemorrhage who underwent complete transvenous embolization of the ruptured AVMs, and achieved near-complete clinical recovery after 1 month with the modified Rankin scale 1.

Application of Machine Learning Techniques to Identify Data Reliability and Factors Affecting Outcome After Stroke Using Electronic Administrative Records.

To use available electronic administrative records to identify data reliability, predict discharge destination, and identify risk factors associated with specific outcomes following hospital admission with stroke, compared to stroke specific clinical factors, using machine learning techniques. The study included 2,531 patients having at least one admission with a confirmed diagnosis of stroke, collected from a regional hospital in Australia within 2009-2013. Using machine learning (penalized regression with Lasso) techniques, patients having their index admission between June 2009 and July 2012 were used to derive predictive models, and patients having their index admission between July 2012 and June 2013 were used for validation.

The Monash Autism-ADHD genetics and neurodevelopment (MAGNET) project design and methodologies: a dimensional approach to understanding neurobiological and genetic aetiology.

Background: ASD and ADHD are prevalent neurodevelopmental disorders that frequently co-occur and have strong evidence for a degree of shared genetic aetiology. Behavioural and neurocognitive heterogeneity in ASD and ADHD has hampered attempts to map the underlying genetics and neurobiology, predict intervention response, and improve diagnostic accuracy. Moving away from categorical conceptualisations of psychopathology to a dimensional approach is anticipated to facilitate discovery of data-driven clusters and enhance our understanding of the neurobiological and genetic aetiology of these conditions.

Association of public health interventions and COVID-19 incidence in Vietnam, January to December 2020.

Background: Vietnam implemented various public health interventions such as contact tracing and testing, mandatory quarantine, and lockdowns in response to coronavirus disease 2019 (COVID-19). However, the effects of these measures on the epidemic remain unclear.

Methods: This article describes the public health interventions in relation to COVID-19 incidence.

Timeliness of contact tracing among flight passengers during the COVID-19 epidemic in Vietnam.

Background: International air travel plays an important role in the global spread of SARS-CoV-2, and tracing of close contacts is an integral part of the public health response to COVID-19. We aimed to assess the timeliness of contact tracing among airline passengers arriving in Vietnam on flights containing COVID-19 cases and investigated factors associated with timeliness of contact tracing.

Methods: We included data from 2228 passengers on 22 incoming flights between 2 and 19 March 2020.

Transmission of SARS-CoV 2 During Long-Haul Flight.

To assess the role of in-flight transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), we investigated a cluster of cases among passengers on a 10-hour commercial flight. Affected persons were passengers, crew, and their close contacts. We traced 217 passengers and crew to their final destinations and interviewed, tested, and quarantined them.

The First 100 Days of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Control in Vietnam.

Background: One hundred days after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was first reported in Vietnam on 23 January, 270 cases were confirmed, with no deaths. We describe the control measures used by the government and their relationship with imported and domestically acquired case numbers, with the aim of identifying the measures associated with successful SARS-CoV-2 control.

Methods: Clinical and demographic data on the first 270 SARS-CoV-2 infected cases and the timing and nature of government control measures, including numbers of tests and quarantined individuals, were analyzed.

Combating the COVID-19 Epidemic: Experiences from Vietnam.

The COVID-19 pandemic is spreading fast globally. Vietnam's strict containment measures have significantly reduced the spread of the epidemic in the country. This was achieved through the use of emergency control measures in the epidemic areas and integration of resources from multiple sectors including health, mass media, transportation, education, public affairs, and defense.

Usefulness of Wearable Cameras as a Tool to Enhance Chronic Disease Self-Management: Scoping Review.

Background: Self-management is a critical component of chronic disease management and can include a host of activities, such as adhering to prescribed medications, undertaking daily care activities, managing dietary intake and body weight, and proactively contacting medical practitioners. The rise of technologies (mobile phones, wearable cameras) for health care use offers potential support for people to better manage their disease in collaboration with their treating health professionals. Wearable cameras can be used to provide rich contextual data and insight into everyday activities and aid in recall.

Effective Identification of Similar Patients Through Sequential Matching over ICD Code Embedding.

Evidence-based medicine often involves the identification of patients with similar conditions, which are often captured in ICD (International Classification of Diseases (World Health Organization 2013)) code sequences. With no satisfying prior solutions for matching ICD-10 code sequences, this paper presents a method which effectively captures the clinical similarity among routine patients who have multiple comorbidities and complex care needs. Our method leverages the recent progress in representation learning of individual ICD-10 codes, and it explicitly uses the sequential order of codes for matching.

Estimation of the prevalence of adverse drug reactions from social media.

This work aims to estimate the degree of adverse drug reactions (ADR) for psychiatric medications from social media, including Twitter, Reddit, and LiveJournal. Advances in lightning-fast cluster computing was employed to process large scale data, consisting of 6.4 terabytes of data containing 3.

Predicting healthcare trajectories from medical records: A deep learning approach.

Personalized predictive medicine necessitates the modeling of patient illness and care processes, which inherently have long-term temporal dependencies. Healthcare observations, stored in electronic medical records are episodic and irregular in time. We introduce DeepCare, an end-to-end deep dynamic neural network that reads medical records, stores previous illness history, infers current illness states and predicts future medical outcomes.

Estimation of methane emissions from local and crossbreed beef cattle in Daklak province of Vietnam.

Objective: This study was aimed at evaluating effects of cattle breed resources and alternative mixed-feeding practices on meat productivity and emission intensities from household farming systems (HFS) in Daklak Province, Vietnam.

Methods: Records from Local Yellow×Red Sindhi (; Lai Sind) and 1/2 Limousin, 1/2 Drought Master, and 1/2 Red Angus cattle during the growth (0 to 21 months) and fattening (22 to 25 months) periods were used to better understand variations on meat productivity and enteric methane emissions. Parameters were determined by the ruminant model.

A Framework for Mixed-Type Multioutcome Prediction With Applications in Healthcare.

Health analysis often involves prediction of multiple outcomes of mixed type. The existing work is restrictive to either a limited number or specific outcome types. We propose a framework for mixed-type multioutcome prediction.

Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A Multidisciplinary View.

Background: As more and more researchers are turning to big data for new opportunities of biomedical discoveries, machine learning models, as the backbone of big data analysis, are mentioned more often in biomedical journals. However, owing to the inherent complexity of machine learning methods, they are prone to misuse. Because of the flexibility in specifying machine learning models, the results are often insufficiently reported in research articles, hindering reliable assessment of model validity and consistent interpretation of model outputs.

Forecasting Daily Patient Outflow From a Ward Having No Real-Time Clinical Data.

Objective: Our study investigates different models to forecast the total number of next-day discharges from an open ward having no real-time clinical data.

Methods: We compared 5 popular regression algorithms to model total next-day discharges: (1) autoregressive integrated moving average (ARIMA), (2) the autoregressive moving average with exogenous variables (ARMAX), (3) k-nearest neighbor regression, (4) random forest regression, and (5) support vector regression. Although the autoregressive integrated moving average model relied on past 3-month discharges, nearest neighbor forecasting used median of similar discharges in the past in estimating next-day discharge.

Screening for post 32-week preterm birth risk: how helpful is routine perinatal data collection?

Background: Preterm birth is a clinical event significant but difficult to predict. Biomarkers such as fetal fibronectin and cervical length are effective, but the often are used only for women with clinically suspected preterm risk. It is unknown whether routinely collected data can be used in early pregnancy to stratify preterm birth risk by identifying asymptomatic women.

Consistency of the Health of the Nation Outcome Scales (HoNOS) at inpatient-to-community transition.

Objectives: The Health of the Nation Outcome Scales (HoNOS) are mandated outcome-measures in many mental-health jurisdictions. When HoNOS are used in different care settings, it is important to assess if setting specific bias exists. This article examines the consistency of HoNOS in a sample of psychiatric patients transitioned from acute inpatient care and community centres.