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African swine fever virus (ASFV) recombinant strains pose new challenges for diagnosis and control. This study characterizes genotype I and II recombinant ASFV strains identified in northern Vietnam in 2023 through whole-genome sequencing and comparative genomic analysis. Seven ASFV-positive samples from six provinces were analyzed, with recombinant strains detected in Bac Giang, Phu Tho, and Vinh Phuc provinces. Isolates showed hemadsorption positivity despite having genotype I B646L, indicating their recombinant nature. Genome-wide analysis revealed 19 recombination breakpoints consistent with Chinese recombinant strains. Vietnamese isolates shared 99.86-99.98% nucleotide identity with Chinese recombinants, forming a distinct monophyletic group. Comparative analysis identified 50 SNPs and INDELs, with 39 variations found across Vietnamese strains, distinguishing them from Chinese isolates. Unique genetic markers in C962R, I329L, and MGF 505-11L genes distinguished Vietnamese recombinants from Chinese counterparts, while mutations in C122R and NP1450L differentiated all recombinants from parental genotypes. The central variable region (CVR) of the B602L gene showed diversity among Vietnamese isolates, while the I73R-I329L intergenic regions were recognized as in the IGR2 group. This study enhances understanding of recombinant ASFV evolution through homologous recombination and identifies new genetic markers for improved detection and characterization. The observed genetic diversity highlights challenges for existing diagnostic methods and vaccine development, emphasizing the need for continued surveillance and research into the functional implications of these genetic variations on ASFV pathogenicity and transmissibility.

African swine fever virus (ASFV) genotype II is endemic to Vietnam. We detected recombinant ASFV genotypes I and II (rASFV I/II) strains in domestic pigs from 6 northern provinces in Vietnam. The introduction of rASFV I/II strains could complicate ongoing ASFV control measures in the region.

Background: African swine fever virus (ASFV) continues to spread globally, causing severe economic losses to pig farmers. Vietnam licensed two live attenuated vaccines based on the ASFV strains ASFV-G-ΔI177L and ASFV-G-ΔMGF to control the ongoing ASF outbreaks. In 2023, newly emerging highly virulent recombinant ASF viruses (rASFV I/II) containing genetic elements from both p72 genotype I and II ASF viruses were reported from Northern Vietnam. Objective: This study evaluated whether the two vaccine strains were able to protect the pigs against the emerging rASFV I/II strain VNUA/rASFV/TN1/23. Results: Pigs vaccinated with ASFV-G-ΔMGF or ASFV-G-ΔI177L, when challenged with rASFV I/II, succumbed to the infection, or developed signs of chronic ASF. Conclusions: The findings from this study show that both vaccine strains that are licensed and used in Vietnam are unlikely to protect pigs from the emerging highly virulent rASFV I/II. This complicates the ongoing efforts to control ASF in Asia and globally and emphasizes the urgent need for a novel vaccine that can effectively protect pigs from the rASFV I/II.

Motivated by the increasing interest in clinical studies focused on infant movements and poses, this research addresses the limited emphasis on speed and efficiency in existing 2D and 3D pose estimation methods, particularly concerning infant datasets. The scarcity of publicly available infant data poses a significant challenge. In response, we aim to develop a lightweight pose estimation model tailored for edge devices and CPUs. Drawing inspiration from the OpenPose-2016 approach, we refine the algorithm’s architecture, focusing on 2D image training. The resulting model, with 4.09 million parameters, features a single-branch structure. During execution, it achieves an algorithmic complexity of 8.97 giga floating-point operations per second (GFLOPS), enabling operation at approximately 23 frames per second on a Core i5-10400f processor.Notably, this approach balances compact dimensions with superior performance on our self-collected infant dataset. We anticipate that this pragmatic methodology establishes a robust foundation, addressing the need for speed and efficiency in infant pose estimation and providing favorable conditions for future research in this application.

Flood-prone people and decision-makers are often unwilling to discuss and prepare for exceptional events, as such events are hard to perceive and out of experience for most people. Once an exceptional flood occurs, affected people and decision-makers are able to learn from this event. However, this learning is often focussed narrowly on the specific disaster experienced, thus missing an opportunity to explore and prepare for even more severe, or different, events. We propose spatial counterfactual floods as a means to motivate society to discuss exceptional events and suitable risk management strategies. We generate a set of extreme floods across Germany by shifting observed rainfall events in space and then propagating these shifted fields through a flood model. We argue that the storm tracks that caused past floods could have developed several tens of km away from the actual tracks. The set of spatial counterfactual floods generated contains events which are more than twice as severe as the most disastrous flood since 1950 in Germany. Moreover, regions that have been spared from havoc in the past should not feel safe, as they could have been badly hit as well. We propose spatial counterfactuals as a suitable approach to overcome society’s unwillingness to think about and prepare for exceptional floods expected to occur more frequently in a warmer world.

Abstract Large‐scale flood risk assessments are crucial for decision making, especially with respect to new flood defense schemes, adaptation planning and estimating insurance premiums. We apply the process‐based Regional Flood Model (RFM) to simulate a 5000‐year flood event catalog for all major catchments in Germany and derive risk curves based on the losses per economic sector. The RFM uses a continuous process simulation including a multisite, multivariate weather generator, a hydrological model considering heterogeneous catchment processes, a coupled 1D–2D hydrodynamic model considering dike overtopping and hinterland storage, spatially explicit sector‐wise exposure data and empirical multi‐variable loss models calibrated for Germany. For all components, uncertainties in the data and models are estimated. We estimate the median Expected Annual Damage (EAD) and Value at Risk at 99.5% confidence for Germany to be €0.529 bn and €8.865 bn, respectively. The commercial sector dominates by making about 60% of the total risk, followed by the residential sector. The agriculture sector gets affected by small return period floods and only contributes to less than 3% to the total risk. The overall EAD is comparable to other large‐scale estimates. However, the estimation of losses for specific return periods is substantially improved. The spatial consistency of the risk estimates avoids the large overestimation of losses for rare events that is common in other large‐scale assessments with homogeneous return periods. Thus, the process‐based, spatially consistent flood risk assessment by RFM is an important step forward and will serve as a benchmark for future German‐wide flood risk assessments.

Today, the rapid development of industrial zones leads to an increased incidence of skin diseases because of polluted air. According to a report by the American Cancer Society, it is estimated that in 2022 there will be about 100,000 people suffering from skin cancer and more than 7600 of these people will not survive. In the context that doctors at provincial hospitals and health facilities are overloaded, doctors at lower levels lack experience, and having a tool to support doctors in the process of diagnosing skin diseases quickly and accurately is essential. Along with the strong development of artificial intelligence technologies, many solutions to support the diagnosis of skin diseases have been researched and developed. In this paper, a combination of one Deep Learning model (DenseNet, InceptionNet, ResNet, etc) with Soft-Attention, which unsupervisedly extract a heat map of main skin lesions. Furthermore, personal information including age and gender are also used. It is worth noting that a new loss function that takes into account the data imbalance is also proposed. Experimental results on data set HAM10000 show that using InceptionResNetV2 with Soft-Attention and the new loss function gives 90 percent accuracy, mean of precision, F1-score, recall, and AUC of 0.81, 0.81, 0.82, and 0.99, respectively. Besides, using MobileNetV3Large combined with Soft-Attention and the new loss function, even though the number of parameters is 11 times less and the number of hidden layers is 4 times less, it achieves an accuracy of 0.86 and 30 times faster diagnosis than InceptionResNetV2.

This work aimed to prepare the spinel phase Li4Ti5O12 by a combination of the low-temperature precipitation technique and assisted calcination step. X-ray diffraction (XRD) revealed that the intermediated phase was Li2TiO3, and the spinel phase could be evidently formed at 700°C for 12 to 20 hours. The morphology of spinel powder, determined by SEM and TEM, exhibited a good distribution at the submicrometric scale that promoted a fast kinetic of Li migration and an excellent performance at the high-rate cycling test. The stable performances were achieved in the charge-discharge test at different current densities: 80 mA/g (165 mAh/g), 320 mA (160 mAh/g), and 1600 mA (145 mAh/g) upon 100 cycles. Moreover, we observe a capacity retention of 48% (corresponding 80 mA/g) at a high rate of 5000 mAh/g. The cyclic voltammetry measurement displayed a reversible system and revealed the lithium diffusion coefficient of 1.15 × 10−11 cm2/s.

After a flood disaster, the question often arises: "What could have happened if the event had gone differently?" For example, what would be the effects of a flood if the path of a pressure system and the precipitation field had taken a different trajectory? In this paper, we use alternative scenarios of precipitation footprints shifted in space, the so-called "spatial counterfactuals" to generate plausible but unprecedented events. We explore the spatial counterfactuals of the deadly July 2021 flood in the Ahr Valley, Germany. We drive a hydrological model of the Ahr catchment with precipitation fields of this event systematically shifted in space. The resulting discharge is used as a boundary condition for a high-resolution two-dimensional hydrodynamic model. We simulate changes in peak flows, hydrograph volumes, maximum inundation extent and depths and affected assets and compare them to the simulations of the actual event. We show that even a slight shift of the precipitation field by 15–25 km eastwards, which does not seem implausible due to orographic conditions, causes an increase in peak flows at the gauge Altenahr of about 32 % and of up to 160 % at the individual tributaries. Also, significantly larger flood volumes of more than 25 % can be expected due to this precipitation shift. This results in significantly larger inundation extents and maximum depths at a number of analyzed focus areas. For example, in the focus area around Altenahr, the increase of mean and maximum depth of up to 1.25 m and 1.75 m, respectively, is simulated. The presented results should encourage flood risk managers as well as the general public to meet precautionary measures for extreme and unprecedented events. [ABSTRACT FROM AUTHOR]