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Research has shown that climate change creates warmer temperatures and drier conditions, leading to longer wildfire seasons and increased wildfire risks in the United States. These factors have, in turn, led to increases in the frequency, extent, and severity of wildfires in recent years. Given the danger posed by wildland fires to people, property, wildlife, and the environment, there is an urgent need to provide tools for effective wildfire management. Early detection of wildfires is essential to minimizing potentially catastrophic destruction. To that end, in this paper, we present our work on integrating multiple data sources into SmokeyNet, a deep learning model using spatiotemporal information to detect smoke from wildland fires. We present Multimodal SmokeyNet and SmokeyNet Ensemble for multimodal wildland fire smoke detection using satellite-based fire detections, weather sensor measurements, and optical camera images. An analysis is provided to compare these multimodal approaches to the baseline SmokeyNet in terms of accuracy metrics, as well as time-to-detect, which is important for the early detection of wildfires. Our results show that incorporating weather data in SmokeyNet improves performance numerically in terms of both F1 and time-to-detect over the baseline with a single data source. With a time-to-detect of only a few minutes, SmokeyNet can be used for automated early notification of wildfires, providing a useful tool in the fight against destructive wildfires. [ABSTRACT FROM AUTHOR]

The size and frequency of wildland fires in the western United States have dramatically increased in recent years. On high-fire-risk days, a small fire ignition can rapidly grow and become out of control. Early detection of fire ignitions from initial smoke can assist the response to such fires before they become difficult to manage. Past deep learning approaches for wildfire smoke detection have suffered from small or unreliable datasets that make it difficult to extrapolate performance to real-world scenarios. In this work, we present the Fire Ignition Library (FIgLib), a publicly available dataset of nearly 25,000 labeled wildfire smoke images as seen from fixed-view cameras deployed in Southern California. We also introduce SmokeyNet, a novel deep learning architecture using spatiotemporal information from camera imagery for real-time wildfire smoke detection. When trained on the FIgLib dataset, SmokeyNet outperforms comparable baselines and rivals human performance. We hope that the availability of the FIgLib dataset and the SmokeyNet architecture will inspire further research into deep learning methods for wildfire smoke detection, leading to automated notification systems that reduce the time to wildfire response. [ABSTRACT FROM AUTHOR]

Recent thrust-sense deformation in the vicinity of the Main Central Thrust (MCT) zone in the Himalaya of central Nepal can be attributed to tectonics, erosion, or a combination of both. In the same area, contrasting cooling-age patterns in medium- and low-temperature thermochronometers have been interpreted to imply a recent (2.0-0.9 Ma) significant increase in erosional exhumation rates, likely attributable to recent climate changes. No comparable evidence exists for changes in local plate convergence rates. We use numerical models with constant convergence velocity but contrasting erosion rates to show that increased erosion and recent thrusting may be directly connected. In the models, increasing erosivity by a factor of 3 over 3 Ma fundamentally changes the style of deformation, reactivating the dormant model MCT system in the region corresponding to observed thrust faults. The high-erosion model also reproduces the observed cooling-age patterns, whereas the equivalent low-erosion model does not reproduce either observation. Other model predictions, and their implications for the effects of increased erosion on the southern flank of the Himalaya, include: 1) no associated reactivation of normal faulting on the South Tibetan Detachment (STD) system; 2) enhanced upper-crustal extension in the vicinity of the north Himalayan gneiss domes (NHGD); 3) reinvigorated mid-crustal channel flow beneath the NHGD; 4) possible destabilization and wholesale southward flow of the upper crust between the MCT and NHGD, with the potential for catastrophic earthquakes. The first three questions are testable and address the persistent question of the existence and current location of the low-viscosity channel. In particular, the model predicts that it has been stagnant beneath the Tibetan plateau under a relatively low-erosion regime, but has been, or could be, reactivated by more aggressive erosion driven by climate change. The potential for destabilization of the south flank of the orogen has important geological and societal implications. [ABSTRACT FROM AUTHOR]

Since facing outbreaks of severe acute respiratory syndrome and avian influenza A in 2003, Vietnam has increasingly applied a One Health approach to address emerging infectious diseases of animal origin. Here, we reflect on the challenges and opportunities of One Health in the context of zoonoses, food safety, and antimicrobial resistance, drawing on a stocktake of One Health training, policy, and research in Vietnam. We also report on the results of a virtual consultation workshop held on July 2021 with representatives from 32 institutions in Vietnam to explore future One Health directions. As Vietnam approaches nearly two decades of disease preparedness and response, we hope our experiences can provide practical insights to support countries in developing coordination mechanisms and moving the One Health agenda forward toward better public health outcomes., Competing Interests: No conflict of interest is stated., (© 2021 The Authors.)

The Synthetic Biology Knowledge System (SBKS) is an instance of the SynBioHub repository that includes text and data information that has been mined from papers published in ACS Synthetic Biology. This paper describes the SBKS curation framework that is being developed to construct the knowledge stored in this repository. The text mining pipeline performs automatic annotation of the articles using natural language processing techniques to identify salient content such as key terms, relationships between terms, and main topics. The data mining pipeline performs automatic annotation of the sequences extracted from the supplemental documents with the genetic parts used in them. Together these two pipelines link genetic parts to papers describing the context in which they are used. Ultimately, SBKS will reduce the time necessary for synthetic biologists to find the information necessary to complete their designs.

Different signal-transforming algorithms were applied for UV spectrophotometric analysis of paracetamol, ibuprofen, and caffeine in ternary mixtures. Phosphate buffer pH 7.2 was used as the spectrophotometric solvent. Severe overlapping spectra could be resolved into individual bands in the range of wavelengths 200-300 nm by using Savitzky-Golay smoothing and differentiation, trigonometric Fourier series, and mother wavelet functions (i.e., sym6, haar, coif3, and mexh). To optimize spectral recoveries, the concentration of various types of divisors (single, double, and successive) was tested. The developed spectrophotometric methods showed linearity over the ranges 20-40 mg/L for paracetamol, 12-32 mg/L for ibuprofen, and 1-3.5 mg/L for caffeine ( R 2  > 0.990). They could be successfully applied to the assay and dissolution test of paracetamol, ibuprofen, and caffeine in their combined tablets and capsules, with accuracy (99.1-101.5% recovery) and precision (RSD < 2%). For comparison, an isocratic RP-HPLC analysis was also developed and validated on an Agilent ZORBAX Eclipse XDB-C18 (150 × 4.6 mm, 5  µ m) at an ambient temperature. A mixture of methanol : phosphate buffer 0.01 M pH 3 (30 : 70 v/v) was used as the mobile phase delivered at 2 mL/min, and the effluent was monitored at 225 nm. It was shown that spectrophotometric data were statistically comparable to HPLC ( p > 0.05), suggesting possible interchange between UV spectrophotometric and HPLC methods for routine analysis of paracetamol, ibuprofen, and caffeine in their solid pharmaceutical dosage forms., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2020 Hoang Vu Dang et al.)

Objectives: The aim of this paper was to assess the diarrhea risks caused by various pathogens among those exposed to biogas wastewater through different activities., Methods: Application of quantitative microbial risk assessment (QMRA) of biogas wastewater was conducted in Hanam Province, Vietnam. A total of 451 representatives from households that use biogas were interviewed about their practices of handling biogas plant and reuse of biogas effluent for irrigation. In addition, 150 samples of biogas wastewater were analyzed for Escherichia coli, Cryptosporidium parvum, and Giardia lamblia. Risk characterization was calculated using Monte Carlo simulation., Results: The annual diarrhea risk caused by exposure to biogas effluent through irrigation activities ranged from 17.4 to 21.1% (E. coli O157:H7), 1.0 to 2.3% (G. lamblia), and 0.2 to 0.5% (C. parvum), while those caused through unblocking drains connected to biogas effluent tanks were 22.0% (E. coli), 0.7% (G. lamblia), and 0.5% (C. parvum)., Conclusions: Further measures are needed to reduce the risk by either improving the microbial quality of biogas effluent or by ensuring the use of personal protective measures when exposed to biogas wastewater.

The use of wastewater and excreta in agriculture is a common practice in Southeast Asia; however, concerns remain about the potential public health risks of this practice. We undertook a scoping review to examine the extent, range, and nature of literature, as well as synthesize the evidence for associations between wastewater and excreta management practices and public health risks in Southeast Asia. Three electronic databases (PubMed, CAB Direct, and Web of Science) were searched and a total of 27 relevant studies were included and evaluated. The available evidence suggested that possible occupational health risks of wastewater and excreta management practices include diarrhea, skin infection, parasitic infection, bacterial infection, and epilepsy. Community members can be at risk for adverse health outcomes through consuming contaminated fish, vegetables, or fruits. Results suggested that practices including handling, treatment, and use of waste may be harmful to human health, particularly farmer's health. Many studies in this review, however, had limitations including lack of gender analyses, exposure assessment, and longitudinal study designs. These findings suggest that more studies on identifying, quantitatively assessing, and mitigating health risks are needed if sustainable benefits are to be obtained from wastewater and excreta reuse in agriculture in Southeast Asia.