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Accurate soil organic carbon storage (SOCS) estimation is crucial for sustaining ecosystem health and mitigating climate change impacts. This study investigated the accuracy and variability of SOCS predictions, focusing on the role of pedotransfer functions (PTFs) in estimating soil bulk density (BD). Utilizing a comprehensive dataset from the Korean Rural Development Administration (RDA database), which includes 516 soil horizons, we evaluated 36 widely-used BD PTFs, well-established formulas that estimate BD by considering soil properties, including soil organic carbon (SOC), soil organic matter (OM), sand, gravel, silt, and clay. These PTFs demonstrated varying levels of precision, with root mean squared errors (RMSE) ranging from 0.177 to 0.377 Mg m−3 and coefficients of determination (R2) from 0.176 to 0.658; hence, the PTFs have been classified into excellent, moderate, and poor-performing groups for predicting BD. Further, a novel PTF based on an exponential function of SOC was developed, showing superior predictive power (R2 = 0.73) compared to existing PTFs, using an independent validation dataset. Our findings reveal significant differences in SOCS predictions and observations among the PTFs, with a p-value

Summary: Objective: The aim of this study was to determine factors including biochemical markers, dietary habits, and social-anthropometric associated with uncontrolled hypertension among elderly hypertension in rural regions of Vietnam. Methods: This study included 272 hypertension patients aged over 60 years. Demographic data, anthropometric measurements, and risk factors associated with hypertension were from the cross-sectional study of 272 customers at the Medical Examination Department, Samson General Hospital, Thanh Hoa Province, Vietnam. Biochemical markers including total cholesterol, triglycerides, and fasting blood glucose were measured. Dietary habits were by using a frequency questionnaire. Uncontrolled hypertension was defined as systolic blood pressure of ≥140 mmHg and/or diastolic blood pressure of ≥90 mmHg in a patient taking anti-hypertensive medication.Generalized multiple linear regression was used to assess the impact of related factors on blood pressure, which were used as the dependent variables. Logistic regression analysis was used to calculate the odds ratios (ORs) and 95% confidence intervals (95% CIs) of related factors for hypertension status. Results: The study found that men had more uncontrolled hypertension than women among the elderly population. The prevalences of hyperglycemia, hypertriglyceridemia, and hypercholesterolemia were high in both controlled and uncontrolled groups. There was an inverse association between fruits (p=0.021) and vegetable consumption (p=0.038) and a positive correlation between salty habits (p=0.040) and processed food (p

Density of microalgae is critical information for production of algae in a closed cultivation system since it can be used to optimally control their growth rate, biomass concentration and quality of the products. Given advancement in image processing techniques and thanks to low-cost camera sensors, image based methods are increasingly widely utilized to indirectly estimate the density. Advantages of the image based techniques include being less invasive and more nondestructive and biosecured. However, most of the existing techniques rely on averaging all pixels of a microalgae image, which may eliminate crucial information of their spatial correlation. Therefore, in this work we propose to exploit a convolutional neural network (CNN) to efficiently extract information from the microalgae images, which are then employed to regress the density. Interestingly, the proposed deep CNN regression model accepts the whole color image as its input while the density is calculated in the output. The proposed CNN regression architecture was validated in real-world experiments where the microalgal strain Chlorella vulgaris was cultured and their images were captured by our low-cost camera sensor system. The obtained results demonstrate that the averaged estimation accuracy of the proposed model is 99.45% (± 0.68%) while the R2 value between the density predictions and the ground truths is 0.9997, which is highly accurate and practical.

Background: Cardiovascular disease is a leading cause of illness and death globally, primarily due to atherosclerosis. This disease reduces blood flow and oxygen delivery to organs, and when it affects the carotid arteries, it can lead to cognitive impairment and dementia. In a population of 104 individuals, comprising both healthy controls and individuals at elevated risk for developing cardiovascular diseases (CVD) due to identified risk factors, we used PET imaging with 18F-fluorodeoxyglucose (FDG) to assess cerebral glucose metabolism and 18F-sodium fluoride (NaF) to detect atherosclerotic calcification. Our statistical analysis revealed significant differences in metabolic activity between healthy and at-risk individuals in specific brain regions. 18F-FDG uptake in the brain varied inversely with respect to the clinical assessment of cardiovascular risk in regions such as the cuneus (β = −0.030, SE = 0.014, p = 0.035), middle occipital gyrus (β = −0.032, SE = 0.011, p = 0.005), and posterior cingulate gyrus (β = −0.032, SE = 0.015, p = 0.044). In contrast, areas including the basis pontis (β = 0.025, SE = 0.012, p = 0.038) and the pons (β = 0.034, SE = 0.013, p = 0.008) exhibited direct correlations. Notably, carotid 18F-NaF uptake had inverse associations with 18F-FDG uptake in the cerebellum (β = −0.825, SE = 0.354, p = 0.021), medulla (β = −0.888, SE = 0.405, p = 0.029), and posterior cingulate gyrus (β = −1.253, SE = 0.567, p = 0.028), while increased carotid calcification influenced metabolic activity in the fusiform gyrus (β = 1.660, SE = 0.498, p = 0.001) and globus pallidus (β = 1.505, SE = 0.571, p = 0.009). We observed that atherosclerotic plaque accumulation, especially in the carotid arteries, has potential implications for metabolic changes in brain regions governing cognition, emotion, sensory perception, and motor activities. Our findings underscore the possible early interventions that can be used to preempt or delay cognitive deterioration linked with cardiovascular ailments.

Abstract Under the influence of market rules, state intervention in development process is obviously necessary, especially in the context of emerging countries with an incomplete and non-mordern market economy toward the global race of sustainability. Applying 2590 enterprises covering 63 provinces and cities in the General Statistics Office, this study aims to evaluate the influence of state on the technical efficiency of enterprises. A fractional regression analysis model is employed purposefully since the technical efficiency of the dependent variable is an estimated randomly variable. The research findings unequivocally indicate a substantial influence of the state on the technical efficiency of livestock product processing companies. Moreover, when studying each specific aspect in isolation to gauge the role of the state, a discernible negative impact is evident on the technical efficiency of these enterprises. Consequently, this study implies that for tangilbe improvements in the technical efficiency of enterprises, the state should adopt a comprehensive approach, simultaneously implementing all aspects that demonstrate its role in the sector. By adopting such a holistic strategy, the state can create a more conducive environment toward development sustainability for improving the technical efficiency of livestock product processing enterprises.

The objective of this study is to investigate the final exam scheduling process at the Ho Chi Minh City Open University and develop an automated exam scheduling application. Our primary objectives are to prevent students from having conflicting exam schedules and to ensure that no student has to take more than two exams on the same day. This research focuses on applying graph coloring algorithms to the problem of automatic exam scheduling. Our research findings indicate that the graph coloring algorithm is highly effective for automated exam scheduling. This study has the potential to expand and support the development of an automatic exam scheduling and management system, in line with our overall goals. We conduct the experiments on the practical data at HCMCOU and obtain promising results.

Abstract People living in coastal areas are frequently affected by natural disasters, such as floods and storms. This study aimed to assess the quality of life (QoL) of people living in disadvantaged coastal communes (subdivision of Vietnam) and identify their associated factors by using the World Health Organization’s quality of life instrument (WHOQOL-BREF). To achieve this, a cross-sectional descriptive study was conducted on 595 individuals aged 18 years and above living in the coastal communes in Thua Thien Hue province, Vietnam, from October 2022 to February 2023. The results showed that the mean overall QoL (mean ± SD) was 61.1 ± 10.8. Among the four domains of QoL, the physical health (57.2 ± 12.3) domain had a lower score than the psychological health (61.9 ± 13.0), social relations (63.4 ± 13.4), and environment (61.9 ± 13.3) domains. The QoL score of the domains for participants affected by flooding was significantly lower than that of those not affected, except for social relations. Multivariable logistic regression showed that subjects with not good QoL had the educational background with no formal education (Odds ratio (OR) = 2.63, 95% CI 1.19–5.83), fairly poor/poor households (OR = 2.75, 95% CI 1.48–5.12), suffered Musculoskeletal diseases (OR = 1.61, 95% CI 1.02–2.56), unsatisfaction with health status (OR = 5.27, 95% CI 2.44–11.37), family conflicts (OR = 4.51, 95%CI 2.10–9.69), and low levels of social support (OR = 2.62; 95% CI 1.14–6.02). The analysis also revealed that workers (OR = 0.17, 95% CI 0.04–0.66) had a better QoL than farmer-fisherman. QoL in disadvantaged coastal communes was low, with the lowest scores in the physical health domain. Based on the socioeconomic factors associated with not good QoL identified here, it is recommended that local authorities take more appropriate and practical measures to increase support, including measures for all aspects of physical health, psychological health, social relations, and the living environment, especially for people affected by floods.

The innate lymphoid cell (ILC) family is composed of heterogeneous innate effector and helper immune cells that preferentially reside in tissues where they promote tissue homeostasis. In cancer, they have been implicated in driving both pro- and anti-tumor responses. This apparent dichotomy highlights the need to better understand differences in the ILC composition and phenotype within different tumor types that could drive seemingly opposite anti-tumor responses. Here, we characterized the frequency and phenotype of various ILC subsets in melanoma metastases and primary epithelial ovarian tumors. We observed high PD-1 expression on ILC subsets isolated from epithelial ovarian tumor samples, while ILC populations in melanoma samples express higher levels of LAG-3. In addition, we found that the frequency of cytotoxic ILCs and NKp46+ILC3 in tumors positively correlates with monocytic cells and conventional type 2 dendritic cells, revealing potentially new interconnected immune cell subsets in the tumor microenvironment. Consequently, these observations may have direct relevance to tumor microenvironment composition and how ILC subset may influence anti-tumor immunity.

The quickly developing drone technology can be used efficiently in the field of pipeline leak detection. The aim of this article is to provide drone mission concepts for detecting releases from pipelines. It provides an overview of the current applications of natural gas pipeline surveys, it considers environmental conditions by plume modelling, it discusses suitable commercially available sensors, and develops concepts for routine monitoring of pipelines and short term missions for localising and identifying a known leakage. Suitable platforms depend on the particular mission and requirements concerning sensors and legislation. As an illustration, a feasibility study during a release experiment is introduced. The main challenge of this study was the variability of wind direction on a time scale of minutes, which produces considerable differences to the plume simulations. Nevertheless, the leakage rates derived from the observations are in the same order of magnitude as the emission rates. Finally the results from the modeling, the release experiment and possible drone scenarios are combined and requirements for future application derived.

Abstract Sensors stand as pivotal cornerstones of technology, driving progress across a spectrum of industries through their ability to precisely capture and interpret an extensive array of physical phenomena. Among these advancements, microwave photonic (MWP) sensing has emerged as a new sensing technique, elevating sensing speed and resolution for practical applications. Integrated MWP sensors exhibit unparalleled capabilities in ultra‐sensitive, label‐free nanoscale detection, offering the potential to synergize with advanced integration techniques for a compact footprint and versatile designs. This paper reviews and summarizes the development and recent advances in integrated MWP sensing, focusing on the schemes based on microresonators. The diverse array of existing schemes is systematically categorized, elucidating their operational principles and performance demonstration. Furthermore, the assistance of machine learning and deep learning in integrated MWP sensors is explored, highlighting the potential of intelligent sensing paradigms. Finally, current challenges and opportunities aimed at further advancing MWP sensors are discussed.

Background:. Enhanced recovery after surgery (ERAS) pathways represent a comprehensive approach to optimizing perioperative management and reducing hospital stay and cost. In living donor kidney transplantation, key impediments to postoperative discharge include pain, and opioid associated complications such as nausea, vomiting, and the return of gastrointestinal function. Methods:. In this randomized controlled trial, living kidney transplantation donors were assigned to either the ERAS or control group. The ERAS group patients received 15 preoperative, 17 intraoperative, 19 postoperative element intervention. The control group received standard care. The ERAS group received a multimodal opioid sparing pain management including an intraoperative transverse abdominis plane block. Our primary outcome measure was postoperative opioid consumption. The secondary outcome measures were postoperative pain scores, first oral intake, and hospital length of stay. Results:. There were no significant differences in demographics between the 2 groups. The ERAS group had a statistically significant reduction in total postoperative opioid consumption calculated in intravenous morphine equivalents (24.2 ± 20.2 versus 71 ± 39.5 mg, P

Background: The burden of chronic liver disease (CLD) deaths attributable to the hepatitis B virus (HBV) and hepatitis C virus (HCV) remains unknown. Further research is required to elucidate the extent of this burden in the eventual elimination of these diseases. Methods: Data on liver cancer, cirrhosis, and other CLD among 204 countries and territories between 1990 and 2019 was extracted from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) published in 2019. The Bayesian age-period-cohort model was used to analyze the temporal trend and predict the disease burden by 2030. Results: The number of HCV-related CLD deaths surpassed that of CLD deaths caused by HBV in 2019 (536833 deaths versus 523003 deaths) and is expected to be maintained until 2030 (689124 deaths versus 628824 deaths). East Asia had the highest burden of chronic HBV and HCV infections during the study period. In 2019, the largest age-standardized death rates (ASDR) of CLD deaths caused by HBV and HCV were mainly observed in Western Sub-Saharan Africa (18.75%) and Eastern Sub-Saharan Africa (16.42%), respectively. South Asia and East Asia are predicted to have the highest number of CLD deaths related to HCV and HBV by 2030. Eastern Europe and South Asia show the largest expected increase in disease burden caused by HCV or HBV between 2019 and 2030. No GBD region is projected to achieve the WHO target of a 65% reduction in mortality from chronic HBV and HCV infections by 2030. Conclusions: Although the mortality of CLD caused by HBV and HCV decreased in the last three decades (from 1990 to 2019), the number of deaths will continue to increase until 2030. Therefore, governments and international organizations need to strengthen the effectiveness of vaccines, screening, and treatment, especially in potential emerging hotspot regions.

Glycosylation of the SARS-CoV-2 spike (S) protein represents a key target for viral evolution because it affects both viral evasion and fitness. Successful variations in the glycan shield are difficult to achieve though, as protein glycosylation is also critical to folding and structural stability. Within this framework, the identification of glycosylation sites that are structurally dispensable can provide insight into the evolutionary mechanisms of the shield and inform immune surveillance. In this work, we show through over 45 μs of cumulative sampling from conventional and enhanced molecular dynamics (MD) simulations, how the structure of the immunodominant S receptor binding domain (RBD) is regulated by N-glycosylation at N343 and how this glycan’s structural role changes from WHu-1, alpha (B.1.1.7), and beta (B.1.351), to the delta (B.1.617.2), and omicron (BA.1 and BA.2.86) variants. More specifically, we find that the amphipathic nature of the N-glycan is instrumental to preserve the structural integrity of the RBD hydrophobic core and that loss of glycosylation at N343 triggers a specific and consistent conformational change. We show how this change allosterically regulates the conformation of the receptor binding motif (RBM) in the WHu-1, alpha, and beta RBDs, but not in the delta and omicron variants, due to mutations that reinforce the RBD architecture. In support of these findings, we show that the binding of the RBD to monosialylated ganglioside co-receptors is highly dependent on N343 glycosylation in the WHu-1, but not in the delta RBD, and that affinity changes significantly across VoCs. Ultimately, the molecular and functional insight we provide in this work reinforces our understanding of the role of glycosylation in protein structure and function and it also allows us to identify the structural constraints within which the glycosylation site at N343 can become a hotspot for mutations in the SARS-CoV-2 S glycan shield.

This study leverages machine learning to enhance the diagnostic accuracy of COVID-19 using chest X-rays. The study evaluates various architectures, including efficient neural networks (EfficientNet), multiscale vision transformers (MViT), efficient vision transformers (EfficientViT), and vision transformers (ViT), against a comprehensive open-source dataset comprising 3616 COVID-19, 6012 lung opacity, 10192 normal, and 1345 viral pneumonia images. The analysis, focusing on loss functions and evaluation metrics, demonstrates distinct performance variations among these models. Notably, multiscale models like MViT and EfficientNet tend towards overfitting. Conversely, our vision transformer model, innovatively fine-tuned (FT) on the encoder blocks, exhibits superior accuracy: 95.79% in four-class, 99.57% in three-class, and similarly high performance in binary classifications, along with a recall of 98.58%, precision of 98.87%, F1 score of 98.73%, specificity of 99.76%, and area under the receiver operating characteristic (ROC) curve (AUC) of 0.9993. The study confirms the vision transformer model’s efficacy through rigorous validation using quantitative metrics and visualization techniques and illustrates its superiority over conventional models. The innovative fine-tuning method applied to vision transformers presents a significant advancement in medical image analysis, offering a promising avenue for improving the accuracy and reliability of COVID-19 diagnosis from chest X-ray images.

This paper presents an approach based on the gamma-ray transmission technique and artificial neural network for accurately measuring the thickness of various materials in flat sheet form. The gamma-ray transmission system comprises a NaI(Tl) scintillation detector coupled with a 137Cs radioactive source. The artificial neural network model predicts the sample thickness through three input features: mass density, linear attenuation coefficient, and ln(R) - where R represents the ratio of areas under the 662 keV peak in spectra acquired from measurements with and without the sample. The artificial neural network model was trained using simulation data generated by MCNP6 code, facilitating the creation of comprehensive datasets covering diverse material types and thickness variations at a low cost. Hyperparameters of the artificial neural network model were defined by several optimization methods, such as hyperband-bayesian, tree-structured Parzen estimator, and random search, to establish an optimal artificial neural network architecture. Subsequently, the optimal artificial neural network model was deployed to predict the thickness of graphite, aluminum, copper, steel, and polymethyl methacrylate sheets, using input data obtained from the experiments. The results showed a good agreement between predicted and reference thicknesses, with a maximum relative deviation of 1.94 % and an average relative deviation of 0.52%.

The development of fifth-generation (5G) technology marks a significant milestone for digital communication systems, providing substantial improvements in data transmission speeds and enabling enhanced connectivity across a wider range of devices. However, this rapid increase in data volume also introduces new challenges related to transmission latency, reliability, and security. This paper introduces KyMLP-LDPC, a novel approach that integrates a multi-layer parallel LDPC (MLP-LDPC) algorithm with Kyber, a post-quantum cryptography scheme, to accelerate and enable reliable and secure transmission. MLP-LDPC partitions the LDPC parity check matrix into processing groups to streamline parallel decoding and minimize message collisions during transmission, thereby accelerating error correction operations. Kyber encrypts data preemptively to safeguard against potential attacks. The effectiveness of our proposed method is evaluated using both image data and signals transmitted through an additive white Gaussian noise communication channel. Evaluation results demonstrate that the proposed method achieves superior performance in terms of error correction capabilities and data security compared to existing approaches.

The research was conducted based on the theory of planned behavior (TPB), with the research model built on 06 influencing factors to the digital transformation process of businesses, with the intermediate variable “Decision on digital transformation”. There were 456 small and medium-sized enterprises in Hanoi surveyed for this research from March to June 2023. Research results found that the digital transformation process of small and medium-sized businesses in Hanoi are strongly affected by the “digital transformation decision”; while the technology platform and employees' capabilities largely determine these businesses’ decision to digital transformation and its process. On that basis, three policy implications are proposed to promote the digital transformation process at small and medium-sized enterprises in Hanoi.

Wildfires increasingly threaten ecosystems and infrastructure, making accurate burn severity mapping (BSM) essential for effective disaster response and environmental management. Machine learning (ML) models utilizing satellite-derived vegetation indices are crucial for assessing wildfire damage; however, incorporating many indices can lead to multicollinearity, reducing classification accuracy. While principal component analysis (PCA) is commonly used to address this issue, its effectiveness relative to other feature extraction (FE) methods in BSM remains underexplored. This study aims to enhance ML classifier accuracy in BSM by evaluating various FE techniques that mitigate multicollinearity among vegetation indices. Using composite burn index (CBI) data from the 2014 Carlton Complex fire in the United States as a case study, we extracted 118 vegetation indices from seven Landsat-8 spectral bands. We applied and compared 13 different FE techniques—including linear and nonlinear methods such as PCA, t-distributed stochastic neighbor embedding (t-SNE), linear discriminant analysis (LDA), Isomap, uniform manifold approximation and projection (UMAP), factor analysis (FA), independent component analysis (ICA), multidimensional scaling (MDS), truncated singular value decomposition (TSVD), non-negative matrix factorization (NMF), locally linear embedding (LLE), spectral embedding (SE), and neighborhood components analysis (NCA). The performance of these techniques was benchmarked against six ML classifiers to determine their effectiveness in improving BSM accuracy. Our results show that alternative FE techniques can outperform PCA, improving classification accuracy and computational efficiency. Techniques like LDA and NCA effectively capture nonlinear relationships critical for accurate BSM. The study contributes to the existing literature by providing a comprehensive comparison of FE methods, highlighting the potential benefits of underutilized techniques in BSM.

BackgroundThe electronic National Immunization Information System (NIIS) was introduced nationwide in Vietnam in 2017. Health workers were expected to use the NIIS alongside the legacy paper-based system. Starting in 2018, Hanoi and Son La provinces transitioned to paperless reporting. Interventions to support this transition included data guidelines and training, internet-based data review meetings, and additional supportive supervision visits. ObjectiveThis study aims to assess (1) changes in NIIS data quality and use, (2) changes in immunization program outcomes, and (3) the economic costs of using the NIIS versus the traditional paper system. MethodsThis mixed methods study took place in Hanoi and Son La provinces. It aimed to analyses pre- and postintervention data from various sources including the NIIS; household and health facility surveys; and interviews to measure NIIS data quality, data use, and immunization program outcomes. Financial data were collected at the national, provincial, district, and health facility levels through record review and interviews. An activity-based costing approach was conducted from a health system perspective. ResultsNIIS data timeliness significantly improved from pre- to postintervention in both provinces. For example, the mean number of days from birth date to NIIS registration before and after intervention dropped from 18.6 (SD 65.5) to 5.7 (SD 31.4) days in Hanoi (P

Abstract Social capital, embedded in the networking relationship, is critical for firm performance. SMEs, especially in emerging economies, should effectively use their limited resource and network relationships to deal with increased uncertainties in the business environment. Accordingly, this paper investigates the effect of social capital on innovation, marketing communication expenditure, and entrepreneurial orientation (EO). The research employs a binary logistic model based on data collected from a survey of 645 small and medium enterprises (SMEs) in Vietnam. The survey focused on SMEs in Ho Chi Minh City, which is the biggest city in Vietnam and accounts for one-third of the country’s gross domestic product. The research found significant evidence of the benefits of social capital on innovation, marketing communication expenditure, and EO. Specifically, we found significant relationships between personal network and marketing communication expenditure, between a business network with innovation and EO, and between network range and marketing communication expenditure. This research has important implications for managers and government agents. SMEs managers should effectively use what they receive from network relationships to improve the performance of firms. The findings clarify the characteristics of networks constituting social capital. Moreover, we respond to the call for more empirical study in the emerging economy.

Abstract Difficulty falling asleep is one of the typical insomnia symptoms. However, intervention therapies available nowadays, ranging from pharmaceutical to hi-tech tailored solutions, remain ineffective due to their lack of precise real-time sleep tracking, in-time feedback on the therapies, and an ability to keep people asleep during the night. This paper aims to enhance the efficacy of such an intervention by proposing a novel sleep aid system that can sense multiple physiological signals continuously and simultaneously control auditory stimulation to evoke appropriate brain responses for fast sleep promotion. The system, a lightweight, comfortable, and user-friendly headband, employs a comprehensive set of algorithms and dedicated own-designed audio stimuli. Compared to the gold-standard device in 883 sleep studies on 377 subjects, the proposed system achieves (1) a strong correlation (0.89 ± 0.03) between the physiological signals acquired by ours and those from the gold-standard PSG, (2) an 87.8% agreement on automatic sleep scoring with the consensus scored by sleep technicians, and (3) a successful non-pharmacological real-time stimulation to shorten the duration of sleep falling by 24.1 min. Conclusively, our solution exceeds existing ones in promoting fast falling asleep, tracking sleep state accurately, and achieving high social acceptance through a reliable large-scale evaluation.

Red blood cell (RBC) storage solutions have evolved significantly over the past decades to optimize the preservation of cell viability and functionality during hypothermic storage. This comprehensive review provides an in-depth analysis of the effects of various storage solutions and conditions on critical RBC parameters during refrigerated preservation. A wide range of solutions, from basic formulations such as phosphate-buffered saline (PBS), to advanced additive solutions (ASs), like AS-7 and phosphate, adenine, glucose, guanosine, saline, and mannitol (PAGGSM), are systematically compared in terms of their ability to maintain key indicators of RBC integrity, including adenosine triphosphate (ATP) levels, morphology, and hemolysis. Optimal RBC storage requires a delicate balance of pH buffering, metabolic support, oxidative damage prevention, and osmotic regulation. While the latest alkaline solutions enable up to 8 weeks of storage, some degree of metabolic and morphological deterioration remains inevitable. The impacts of critical storage conditions, such as the holding temperature, oxygenation, anticoagulants, irradiation, and processing methods, on the accumulation of storage lesions are also thoroughly investigated. Personalized RBC storage solutions, tailored to individual donor characteristics, represent a promising avenue for minimizing storage lesions and enhancing transfusion outcomes. Further research integrating omics profiling with customized preservation media is necessary to maximize post-transfusion RBC survival and functions. The continued optimization of RBC storage practices will not only enhance transfusion efficacy but also enable blood banking to better meet evolving clinical needs.

The construction industry is undergoing a digital transformation, with the digital twin serving as a core system for project information. This digital twin provides an opportunity to utilize AR technology for real-time verification of on-site project information. Although many AR developments for construction sites have been attempted, they have been limited to accessing information on stationary components via Building Information Models. There have been no attempts to access information on dynamically changing resources, such as personnel and equipment. This paper addresses this gap by presenting an AR framework that enables site managers to verify real-time information on specific personnel or equipment. It introduces a matching algorithm for retrieving the necessary information from the digital twin. This algorithm is pivotal in identifying and retrieving the specific information needed from the vast dataset within the digital twin. The matching process integrates object detection and tracking algorithms applied to video frames from AR devices, along with GPS and IMU sensor data. Experimental results demonstrate the potential of this matching algorithm to streamline on-site management and reduce the effort required to interact with digital twin information. This paper highlights the transformative potential of AR and digital twin technologies in revolutionizing construction site operations.

Abstract Focal segmental glomerulosclerosis (FSGS) is a common glomerular injury leading to end-stage renal disease. Monogenic FSGS is primarily ascribed to decreased podocyte integrity. Variants between residues 184 and 245 of INF2, an actin assembly factor, produce the monogenic FSGS phenotype. Meanwhile, variants between residues 57 and 184 cause a dual-faceted disease involving peripheral neurons and podocytes (Charcot–Marie–Tooth CMT/FSGS). To understand the molecular basis for INF2 disorders, we compared structural and cytoskeletal effects of INF2 variants classified into two subgroups: One (G73D, V108D) causes the CMT/FSGS phenotype, and the other (T161N, N202S) produces monogenic FSGS. Molecular dynamics analysis revealed that all INF2 variants show distinct flexibility compared to the wild-type INF2 and could affect stability of an intramolecular interaction between their N- and C-terminal segments. Immunocytochemistry of cells expressing INF2 variants showed fewer actin stress fibers, and disorganization of cytoplasmic microtubule arrays. Notably, CMT/FSGS variants caused more prominent changes in mitochondrial distribution and fragmentation than FSGS variants and these changes correlated with the severity of cytoskeletal disruption. Our results indicate that CMT/FSGS variants are associated with more severe global cellular defects caused by disrupted cytoskeleton-organelle interactions than are FSGS variants. Further study is needed to clarify tissue-specific pathways and/or cellular functions implicated in FSGS and CMT phenotypes

Abstract We propose the concept of Riemann diamond-alpha integrals for time scales interval-valued functions. We first give the definition and some properties of the interval Riemann diamond-alpha integral that are naturally investigated as an extension of interval Riemann nabla and delta integrals. With the help of the interval Riemann diamond-alpha integral, we present interval variants of Jensen inequalities for convex and concave interval-valued functions on an arbitrary time scale. Moreover, diamond-alpha Hölder’s and Minkowski’s interval inequalities are proved. Also, several numerical examples are provided in order to illustrate our main results.

The automotive industry plays a crucial role in national and global economic development. Vietnam and the ASEAN-5 countries are commented to be active in the automotive international production network but mainly participate in the low-value-added segments of the global value chains (GVC). This study aims to identify Vietnam’s position and level of participation in the global automotive value chain and compare them to the other ASEAN-5 nations. The paper uses the orthodox and modern trade-related GVC indicators built by Koopman et al. (2010) to accomplish the research objective. The results show that Vietnam and the ASEAN-5 countries located downstream of the global automotive value chain often have less potential to gain more value-added than the upstream position, despite their high level of participation. Compared to ASEAN-5 countries, Vietnam still needs to catch up. Although Vietnam’s auto industry has made many positive changes, it still mainly produces simple products and currently participates in the least added value segment in the global automotive value chain. The underdeveloped support industry in Vietnam is the main cause of this situation. The study makes several practical policy recommendations for Vietnam based on the research findings to improve its position in the production network for the automotive sector in the following years. AcknowledgmentThe authors would like to thank the VNU University of Economics and Business, Hanoi, for financing the Research Project, number BBQT.04.2022.

Objectives: Cervical cancer has markedly declined due to widespread use of screening, but Hispanic women continue to bear a disproportionate amount of the cervical cancer burden due to under-screening. Previous studies have explored barriers to screening but have failed to identify targetable facilitators in this group. We aimed to assess facilitators to cervical cancer screening among a predominantly urban, Hispanic population who presented to a no-cost, community-based clinic. Methods: Patients completed demographic and health information, a validated social determinants of health (SDOH) screen, and a self-reported facilitators survey on factors which enabled them to present to clinic. Descriptive statistics were conducted to assess patients’ sociodemographic characteristics, SDOH, and perceived facilitators. Results: 124 patients were included. 98 % were Hispanic, 90 % identified Spanish as their preferred language, and 94 % had no insurance. Median age was 41. 31 % of patients reported a history of abnormal screening. On SDOH, over 80 % of patients screened positive in at least one domain, with the most common being food insecurity (53 %) and stress (46 %). The most frequently reported facilitator was encouragement from a family member/friend (30 %). 26 % of patients reported time off from work and 25 % reported availability of child/elder care as facilitators. Conclusions: Identifying facilitators among patients who present for cervical cancer screening is critical to designing care plans to reach all populations. Our survey showed that the single greatest facilitator to patients presenting for cervical cancer screening was encouragement from a family member/friend. These findings suggest that increasing community involvement and awareness may help to improve cervical cancer screening in a minority, urban, underserved population.

IntroductionMaternal disorders are the third leading cause of sepsis globally, accounting for 5.7 million (12%) cases in 2017. There are increasing concerns about the emergence of antimicrobial resistance (AMR) in bacteria commonly causing maternal sepsis. Our aim is to describe the protocol for a clinical and microbiology laboratory study to understand risk factors for and the bacterial etiology of maternal sepsis in a tertiary Obstetrics and Gynaecology Hospital.MethodsThis case-control study aims to recruit 100 cases and 200 controls at Tu Du Hospital in Ho Chi Minh City, Vietnam, which had approximately 55,000 births in 2022. Women aged ≥ 18 years and ≥ 28 weeks gestation having a singleton birth will be eligible for inclusion as cases or controls, unless they have an uncomplicated localised or chronic infection, or an infection with SARS-CoV-2. Cases will include pregnant or recently pregnant women with sepsis recognised between the onset of labour and/or time of delivery/cessation of pregnancy for up to 42 days post-partum. Sepsis will be defined as suspected or confirmed infection with an obstetrically modified Sequential Organ Failure Assessment score of ≥ 2, treatment with intravenous antimicrobials and requested cultures of any bodily fluid. Controls will be matched by age, location, parity, mode of delivery and gestational age. Primary and secondary outcomes are risk factors associated with the development of maternal sepsis, the frequency of adverse outcomes due to maternal sepsis, bacterial etiology and AMR profiles of cases and controls.DiscussionThis study will improve understanding of the epidemiology and clinical implications of maternal sepsis management including the presence of AMR in women giving birth in Vietnam. It will help us to determine whether women in this setting are receiving optimal care and to identify opportunities for improvement.

Electronic waste generation is increasing dramatically throughout the world. Consequently, this increase in E-waste harms the environment, health, and other aspects of human life. Moreover, hazardous substances and the informal disposal of E-waste severely threaten human health and the environment. Saudi Arabia is the largest Arab country in terms of electronic waste generation and is the Arab country that generates the most E-waste. Over the past few decades, several initiatives and policy implementations have been undertaken in the country. However, the management of E-waste is still a source of distress and an unresolved issue. Sustainable development requires much more effort, primarily efficient E-waste management, which can only be achieved by establishing a formal collection system, early forecasting, and accurate estimations. The purpose of this study is to provide an overview of the fundamental and emerging trends in E-waste production worldwide and in Saudi Arabia. This paper aims to summarize the hazardous elements present in E-waste, their dangerous effects, and the economic potential for recycling E-waste as a means of developing sustainable communities. This study explores the practices regarding efficient E-waste management and E-waste estimation and prediction globally, specifically in Saudi Arabia and other Arab countries. This study suggests that more than the use of a single management probe will be needed to achieve significant results. Instead, a complementary approach can be used to achieve the most effective results. Additionally, people should be aware of the importance of handling and recycling E-waste. This study emphasizes the importance of formal collection and documentation to ensure effective monitoring and sustainable development in any developed country.

Introduction: Osteogenic and angiogenic properties of synthetic bone grafts play a crucial role in the restoration of bone defects. Angiogenesis is recognised for its support in bone regeneration, particularly in larger defects. The objective of this study is to evaluate the new bone formation and neovascularisation of a 3D-printed isosorbide-based novel CSMA-2 polymer in biomimetic gyroid structures. Methods: The gyroid scaffolds were fabricated by 3D printing CSMA-2 polymers with different hydroxyapatite (HA) filler concentrations using the digital light processing (DLP) method. A small animal subcutaneous model and a rat calvaria critical-size defect model were performed to analyse tissue compatibility, angiogenesis, and new bone formation. Results: The in vivo results showed good biocompatibility of the 3D-printed gyroid scaffolds with no visible prolonged inflammatory reaction. Blood vessels were found to infiltrate the pores from day 7 of the implantation. New bone formation was confirmed with positive MT staining and BMP-2 expression, particularly on scaffolds with 10% HA. Bone volume was significantly higher in the CSMA-2 10HA group compared to the sham control group. Discussion and Conclusions: The results of the subcutaneous model demonstrated a favourable tissue response, including angiogenesis and fibrous tissue, indicative of the early wound healing process. The results from the critical-size defect model showcased new bone formation, as confirmed by micro-CT imaging and immunohistochemistry. The combination of CSMA-2 as the 3D printing material and the gyroid as the 3D structure was found to support essential events in bone healing, specifically angiogenesis and osteogenesis.

Most of the cashew nuts in the world are produced in the developing countries. Hence, there is a need to have a low-cost system to automatically grade cashew nuts, especially in small-scale farms, to improve mechanization and automation in agriculture, helping reduce the price of the products. To address this issue, in this work we first propose a low-cost grading system for cashew nuts by using the off-the-shelf equipment. The most important but complicated part of the system is its “eye”, which is required to detect and classify the nuts into different grades. To this end, we propose to exploit advantages of both the YOLOv8 and Transformer models and combine them in one single model. More specifically, we develop a module called SC3T that can be employed to integrate into the backbone of the YOLOv8 architecture. In the SC3T module, a Transformer block is dexterously integrated into along with the C3TR module. More importantly, the classifier is not only efficient but also compact, which can be implemented in an embedded device of our developed cashew nut grading system. The proposed classifier, called the YOLOv8–Transformer model, can enable our developed grading system, through a low-cost camera, to correctly detect and accurately classify the cashew nuts into four quality grades. In our grading system, we also developed an actuation mechanism to efficiently sort the nuts according to the classification results, getting the products ready for packaging. To verify the effectiveness of the proposed classifier, we collected a dataset from our sorting system, and trained and tested the model. The obtained results demonstrate that our proposed approach outperforms all the baseline methods given the collected image data.

This study aims to develop and test the hypothesis of the relationship between green perceived value, green perceived risk, green trust, and green purchase intentions especially the moderator effect of gender on relationship of green trust and green intention to clarify their correlation. This study focuses on the interdependence between the four factors mentioned (green perceived value, green perceived risk, green trust, and green purchase intentions) in order to increase green purchase intentions in modern society, which is an important factor to be concerned about in this environmental era. The technique of research applied in this study is nonprobability sampling with snowball sampling method. Structural Equation Modeling (PLS-SEM) was used to test the cause and effect relationships in the research. A survey conducted by 214 respondents who concerned in buying eco-friendly product were asked to fill the questionnaire to analyze the hypothesis. The analyzed results show that the moderator effect of gender on relationship of green trust and green intention, green perceived value has a positive effect on both green trust and green purchase intentions. Then, green perceived risk positively affects green trust but has no impact on green purchase intentions. In comparison, green trust is an important factor that leads to green purchase intentions.

Study region: South Korea is situated in the northeastern region of Asia Study focus:: Recent technological developments have enabled multi-source precipitation products (MSPs), including satellite-based and model-based, to be useful data sources for quantifying the spatiotemporal variations in precipitation. Unfortunately, the main limitation of MSPs in potential applications is inheritance errors with high uncertainty. To tackle this problem, the capabilities of six machine learning algorithms (Ridge Linear Regression, k-Nearest Neighbors, Support Vector Regression, Gradient Boosting Decision Tree, Light Gradient Boosting Machine, and Random Forest) to produce new precipitation product by merging MSPs with ground-based data have investigated. Ground-based data from 2003 to 2017 were utilized for train and valid process. The robustness of the ML algorithms was highlighted using several evaluation metrics such as continuous indices (modified Kling-Gupta Efficiency and root mean square error) and categorical indices (probability of detection, false alarm rate, and critical success index). New hydrological insights for the region: The results indicate that (1) the ML approaches can merge MSPs with observed data for accurately estimate rainfall, particularly in basins with sparsely distributed rain gauge stations. (2) The merged precipitation products generated from the six ML approaches showed significant agreement and high accuracy with observation data considering rainfall intensity estimation and improved the capability of detecting rain and non-rain events over South Korea.

This paper presents a new approach to constructing multistep combination forecasts in a nonstationary framework with stochastic and deterministic trends. Existing forecast combination approaches in the stationary setup typically target the in-sample asymptotic mean squared error (AMSE), relying on its approximate equivalence with the asymptotic forecast risk (AFR). Such equivalence, however, breaks down in a nonstationary setup. This paper develops combination forecasts based on minimizing an accumulated prediction errors (APE) criterion that directly targets the AFR and remains valid whether the time series is stationary or not. We show that the performance of APE-weighted forecasts is close to that of the optimal, infeasible combination forecasts. Simulation experiments are used to demonstrate the finite sample efficacy of the proposed procedure relative to Mallows/Cross-Validation weighting that target the AMSE as well as underscore the importance of accounting for both persistence and lag order uncertainty. An application to forecasting US macroeconomic time series confirms the simulation findings and illustrates the benefits of employing the APE criterion for real as well as nominal variables at both short and long horizons. A practical implication of our analysis is that the degree of persistence can play an important role in the choice of combination weights.

Background and Aims: Time-lapse monitoring of embryo development may represent a superior way to culture and select embryos in vitro. The EEVA Test records the development of each embryo with a cell-tracking system and predicts the potential (High, Medium or Low) that an embryo will form a blastocyst based on automated detection and analysis of time-lapse imaging information of early cell division stage. The blastocyst grade will help clinicians in selecting the best embryos for transfer to increase pregnancy rates and reduce multiple pregnancies. The aim of this study is to determine the correlation between EEVA score and some blastocyst morphological parameters. Methods: Retrospective analysis study on 2522 embryos with indication for prolonging embryo culture to the blastocyst stage in time-lapse GERI incubator from January 2020 to June 2022. Results: Embryos with E1 and E2 score had significant higher morphological ratios of ICM A, ICM B, TE A, TE B, blastocoele expansion ratios of BL5,6 and lower morphological ratios of ICM D, TE D,compared with embryos having E3, E4, E5 score, p

Abstract Background Burkholderia pseudomallei is a causative agent of melioidosis, a fatal infectious disease highly prevalent in the tropics where traditional medicinal plants are widely used for the treatment of various human ailments. In this study, we aimed to evaluate the in vitro antibacterial activity of common eligible herbs and medicinal plants against B. pseudomallei. Thermal and gastric stability, antibacterial spectrum, bactericidal activity, and cell cytotoxicity were also tested to verify the possible usage of these plants in the treatment of melioidosis. Results Eighteen eligible herbs and twenty-one medicinal plants were collected. Herb juices and aqueous plant samples extracted at different temperatures were prepared for antibacterial testing. A higher proportion of aqueous plant extracts (17/21; 80.9%) against B. pseudomallei was observed, in comparison with that of herb juices (8/18; 44.5%). Two herb juices and twelve aqueous plant extracts were selected for further tests. The juices of A. sativum and A. tuberosum decreased their antimicrobial activity when treated at higher temperatures whereas the aqueous plant extracts increased their antimicrobial activity when prepared at 70 and 100 °C. The herb juices showed a broader spectrum of antimicrobial activity than the aqueous plant extracts. All samples showed less cytotoxicity on the HT29, HepG2, and HEK293 cell lines. At the 2× minimal inhibitory concentration (MIC), aqueous extracts of Blechnum orientale, Breynia fruticose, Psidium guajava, Rhodomyrtus tomentosa, Rosa odorata, and Schima wallichii showed similar bactericidal activity to that of amoxicillin clavulanic acid. The antimicrobial activity of Mangifera indica, Punica granatum, and R. tomentosa remained under the stimulated gastric conditions. Conclusion Our data indicate that traditional medicinal plants prepared by decoction could be effectively used to treat melioidosis via the oral route. Further in vivo investigations are needed to explore other alternative therapies for the prevention and treatment of tested pathogenic bacterial species.

Study region: The Ca River basin is located in the North Central Coast area of Vietnam Study focus: This study aims to develop a deep learning framework that is both effective and straightforward in order to forecast water levels in the Ca River basin in advance of multiple time steps for the event scales. We have thoroughly studied and assessed two deep learning models (DLMs), long-short term memory (LSTM) and gated recurrent unit (GRU), for their capacity to forecast water levels, focusing on various aspects such as the influence of sequence length or the impact of hyperparameter selection. Besides, two data scenarios were established using hydrological data from eight severe floods between 2007 and 2019 to examine the effect of input variables on model performance. Water level data was employed for both the scenarios (S1 and S2), whereas precipitation data was used only in S2. The cross-validation technique was used dynamically to address the issue of limited data. The inputs were reformatted as tensors and were then randomly divided into subsets. This flexible tuning preserved the sequential nature of the hydrological data while enabling the DLMs to be trained efficiently. New hydrological insights for the region: The findings revealed that both the models exhibited equally excellent performances. The NSE of the LSTM model varies from 0.999–0.971 compared to 0.998–0.974 of the GRU model, corresponding to forecast cases from one to four-time steps ahead. This indicated that the use of multiple-input data types (S2) contrary to only one date type (S1) does not necessarily improve the forecasting performance. LSTM/GRU models with one hidden layer are adequate for delivering high performance while minimizing the data processing time.

Summary: Background: Improvements in the early diagnosis of dengue are urgently needed, especially in resource-limited settings where the distinction between dengue and other febrile illnesses is crucial for patient management. Methods: In this prospective, observational study (IDAMS), we included patients aged 5 years and older with undifferentiated fever at presentation from 26 outpatient facilities in eight countries (Bangladesh, Brazil, Cambodia, El Salvador, Indonesia, Malaysia, Venezuela, and Viet Nam). We used multivariable logistic regression to investigate the association between clinical symptoms and laboratory tests with dengue versus other febrile illnesses between day 2 and day 5 after onset of fever (ie, illness days). We built a set of candidate regression models including clinical and laboratory variables to reflect the need of a comprehensive versus parsimonious approach. We assessed performance of these models via standard measures of diagnostic values. Findings: Between Oct 18, 2011, and Aug 4, 2016, we recruited 7428 patients, of whom 2694 (36%) were diagnosed with laboratory-confirmed dengue and 2495 (34%) with (non-dengue) other febrile illnesses and met inclusion criteria, and were included in the analysis. 2703 (52%) of 5189 included patients were younger than 15 years, 2486 (48%) were aged 15 years or older, 2179 (42%) were female and 3010 (58%) were male. Platelet count, white blood cell count, and the change in these variables from the previous day of illness had a strong association with dengue. Cough and rhinitis had strong associations with other febrile illnesses, whereas bleeding, anorexia, and skin flush were generally associated with dengue. Model performance increased between day 2 and 5 of illness. The comprehensive model (18 clinical and laboratory predictors) had sensitivities of 0·80 to 0·87 and specificities of 0·80 to 0·91, whereas the parsimonious model (eight clinical and laboratory predictors) had sensitivities of 0·80 to 0·88 and specificities of 0·81 to 0·89. A model that includes laboratory markers that are easy to measure (eg, platelet count or white blood cell count) outperformed the models based on clinical variables only. Interpretation: Our results confirm the important role of platelet and white blood cell counts in diagnosing dengue, and the importance of serial measurements over subsequent days. We successfully quantified the performance of clinical and laboratory markers covering the early period of dengue. Resulting algorithms performed better than published schemes for distinction of dengue from other febrile illnesses, and take into account the dynamic changes over time. Our results provide crucial information needed for the update of guidelines, including the Integrated Management of Childhood Illness handbook. Funding: EU's Seventh Framework Programme. Translations: For the Bangla, Bahasa Indonesia, Portuguese, Khmer, Spanish and Vietnamese translations of the abstract see Supplementary Materials section.

The recent integration of citizen science with modern technology has greatly increased its applications and has allowed more people than ever to contribute to research across all areas of science. In particular, citizen science has been instrumental in the detection and monitoring of novel introduced species across the globe. This study provides the first records of Miomantis caffra Saussure, 1871, the South African Mantis, from the Australian mainland and uses records from four different citizen science and social media platforms in conjunction with museum records to track the spread of the species through the country. A total of 153 wild mantises and oothecae were observed across four states and territories (New South Wales, Norfolk Island, Victoria, and Western Australia) between 2009 and 2021. The large number of observations of the species in Victoria and the more recent isolated observations in other states and territories suggest that the species initially arrived in Geelong via oothecae attached to plants or equipment, likely from the invasive population in New Zealand. From there it established and spread outwards to Melbourne and eventually to other states and territories, both naturally and with the aid of human transport. We also provide a comparison of M. caffra to similar native mantises, specifically Pseudomantis albofimbriata (Stål, 1860), and comment on the potential impact and further spread of the species within Australia. Finally, we reiterate the many benefits of engaging directly with citizen scientists in biodiversity research and comment on the decision to include them in all levels of this research investigation.

This paper presents a novel approach, using multi-step predictions, to the adaptive sampling problem for efficient monitoring of environmental spatial phenomena in a mobile sensor network. We employ a Gaussian process to represent the spatial field of interest, which is then used to predict the field at unmeasured locations. The adaptive sampling problem aims to drive the mobile sensors to optimally navigate the environment while the sensors adaptively take measurements of the spatial phenomena at each sampling step. To this end, an optimal sampling criterion based on conditional entropy is proposed, which minimizes the prediction uncertainty of the Gaussian process model. By predicting the measurements the mobile sensors potentially take in a finite horizon of multiple future sampling steps and exploiting the chain rule of the conditional entropy, a multi-step-ahead adaptive sampling optimization problem is formulated. Its objective is to find the optimal sampling paths for the mobile sensors in multiple sampling steps ahead. Robot-robot and robot-obstacle collision avoidance is formulated as mixed-integer constraints. Compared with the single-step-ahead approach typically adopted in the literature, our approach provides better navigation, deployment, and data collection with more informative sensor readings. However, the resulting mixed-integer nonlinear program is highly complex and intractable. We propose to employ the proximal alternating direction method of multipliers to efficiently solve this problem. More importantly, the solution obtained by the proposed algorithm is theoretically guaranteed to converge to a stationary value. The effectiveness of our proposed approach was extensively validated by simulation using a real-world dataset, which showed highly promising results.

Despite satellite-based precipitation products (SPPs) providing a worldwide span with a high spatial and temporal resolution, their efficiency in disaster risk forecasting, hydrological, and watershed management remains a challenge due to the significant dependence of rainfall on the spatiotemporal pattern and geographical features of each area. This research proposes an effective deep learning-based solution that combines the convolutional neural network and the benefit of encoder-decoder architecture to eliminate pixel-by-pixel bias to enhance the accuracy of daily SPPs. This work uses five gridded precipitation products, four of which are satellite-based (TRMM, CMORPH, CHIRPS, and PERSIANN-CDR) and one of which is gauge-based (APHRODITE). The Lancang-Mekong River Basin (LMRB), an international basin, was chosen as the research region because of its diverse climate and geographical spread spanning six countries. According to the results of the analyses, the TRMM product exhibits better performance than the other three SPPs. The deep learning model proved its efficacy by successfully reducing the spatial–temporal gap between the four SPPs and APHRODITE. In addition, the ADJ-TRMM product performed the best of the four corrected items, followed by the ADJ-CDR and ADJ-CHIRPS products. This study's findings indicate that each SPP has advantages and disadvantages across LMRB. In the aftermath of the discontinuation of the APHRODITE product in 2015, we believe that the deep learning framework will be a solution for generating a more up-to-date and dependable dataset for LMRB research.

Background and Aim. Soft and hard periodontal tissues contain limited numbers of stem cells. This makes regeneration of the periodontium in patients with periodontitis challenging and unpredictable. Granulation tissue is traditionally removed during periodontal corrective therapy, but it is believed to contain multipotent stem cells which could be used for regenerative therapy. The aim of this research was to critically appraise the evidence on cellular components within granulation tissue in patients with periodontitis and its regenerative potential when compared to control healthy periodontal tissue. Methods. Electronic searches were conducted in five databases including CENTRAL, MEDLINE, EMBASE, Web of Science, and Dentistry & Oral Sciences Source using a combination of MeSH terms and keywords up to 21 June 2022. Human studies including patients aged over 18 years with all forms of periodontitis were included. Following the risk of bias assessment, both qualitative and quantitative analyses were performed. Results. A total of twelve studies were included in qualitative analysis and six of them in quantitative analyses. The evidence suggested that cells derived from periodontitis granulation tissue have osteogenic, adipogenic, chondrogenic, neurogenic, and angiogenic differentiation abilities as well as immunoregulatory properties. In particular, CD44+, CD73+, CD90+, CD105+, and CD146+ cells were found widely in granulation tissue whilst the only meta-analysis confirmed that CD90+ cells were present in lower numbers within the granulation tissue when compared with healthy periodontal tissue (WMD=−23.43%, 95% CI -30.43 to -16.44, p

Abstract Abdominal injury is a frequent cause of death for trauma patients, and early recognition is essential to limit fatalities. There is a need for a wearable sensor system for prehospital settings that can detect and monitor bleeding in the abdomen (hemoperitoneum). This study evaluates the potential for microwave technology to fill that gap. A simple prototype of a wearable microwave sensor was constructed using eight antennas. A realistic porcine model of hemoperitoneum was developed using anesthetized pigs. Ten animals were measured at healthy state and at two sizes of bleeding. Statistical tests and a machine learning method were used to evaluate blood detection sensitivity. All subjects presented similar changes due to accumulation of blood, which dampened the microwave signal ( $$p < 0.05$$ p < 0.05 ). The machine learning analysis yielded an area under the receiver operating characteristic (ROC) curve (AUC) of 0.93, showing 100% sensitivity at 90% specificity. Large inter-individual variability of the healthy state signal complicated differentiation of bleedings from healthy state. A wearable microwave instrument has potential for accurate detection and monitoring of hemoperitoneum, with automated analysis making the instrument easy-to-use. Future hardware development is necessary to suppress measurement system variability and enable detection of smaller bleedings.

Abstract Nucleic acid test (NAT), most typically quantitative PCR, is one of the standard methods for species specific flavivirus diagnosis. Semi-comprehensive NATs such as pan-flavivirus PCR which covers genus Flavivirus are also available; however, further specification by sequencing is required for species level differentiation. In this study, a semi-comprehensive detection system that allows species differentiation of flaviviruses was developed by integration of the pan-flavivirus PCR and Nanopore sequencing. In addition, a multiplexing method was established by adding index sequences through the PCR with a streamlined bioinformatics pipeline. This enables defining cut-off values for observed read counts. In the laboratory setting, this approach allowed the detection of up to nine different flaviviruses. Using clinical samples collected in Vietnam and Brazil, seven different flaviviruses were also detected. When compared to a commercial NAT, the sensitivity and specificity of our system were 66.7% and 95.4%, respectively. Conversely, when compared to our system, the sensitivity and specificity of the commercial NAT were 57.1% and 96.9%, respectively. In addition, Nanopore sequencing detected more positive samples (n = 8) compared to the commercial NAT (n = 6). Collectively, our study has established a semi-comprehensive sequencing-based diagnostic system for the detection of flaviviruses at extremely affordable costs, considerable sensitivity, and only requires simple experimental methods.

When raindrops collide with the topsoil surface, they cause soil detachment, which can be estimated by measuring the kinetic energy (KE) of the raindrops. Considering their direct measurements on terrestrial surfaces are challenging, empirical equations are commonly utilized for estimating the KE from rainfall intensity (Ir), which has a great influence on soil loss and can be easily obtained. However, establishing the optimal relationship between KE and Ir is difficult. In this study, we used a laser-based instrument (OTT Parsivel2 Optical disdrometer) to collect datasets in Sangju City (South Korea) between June 2020 and December 2021 to examine the characteristics of KE–Ir relationships. We derived two different expressions for KE–Ir: KE expenditure (KEexp; J m−2h−1) and KE content (KEcon; J m−2mm−1), using 37 rainfall events. Subsequently, the 37 rainfall events were categorized into three groups based on the magnitude of the mean rainfall intensity of each event. Overall, the KE values estimated through the equations derived based on 37 events were higher than those estimated by the equations derived based on the three rainfall event groups. Our findings should facilitate the development of more suitable physics-based soil erosion models at event scales.

ABSTRACT Metagenomic next-generation sequencing (mNGS) offers a hypothesis-free approach for pathogen detection, but its applicability in clinical diagnosis, in addition to other factors, remains limited due to complicated library construction. The present study describes a PCR-free isothermal workflow for mNGS targeting RNA, based on a multiple displacement amplification, termed circular whole-transcriptome amplification (cWTA), as the template is circularized before amplification. The cWTA approach was validated with clinical samples and nanopore sequencing. Reads homologous to dengue virus 2 and chikungunya virus were detected in clinical samples from Bangladesh and Brazil, respectively. In addition, the practicality of a high-throughput detection system that combines mNGS and a group testing algorithm termed mNGS screening enhanced by a group testing algorithm (mEGA) was established. This approach enabled significant library size reduction while permitting trackability between samples and diagnostic results. Serum samples of patients with undifferentiated febrile illnesses from Vietnam (n = 43) were also amplified with cWTA, divided into 11 pools, processed for library construction, and sequenced. Dengue virus 2, hepatitis B virus, and parvovirus B19 were successfully detected without prior knowledge of their existence. Collectively, cWTA with the nanopore platform opens the possibility of hypothesis-free on-site comprehensive pathogen diagnosis, while mEGA contributes to the scaling up of sample throughput. IMPORTANCE Given the breadth of pathogens that cause infections, a single approach that can detect a wide range of pathogens is ideal but is impractical due to the available tests being highly specific to a certain pathogen. Recent developments in sequencing technology have introduced mNGS as an alternative that provides detection of a wide-range of pathogens by detecting the presence of their nucleic acids in the sample. However, sequencing library preparation is still a bottleneck, as it is complicated, costly, and time-consuming. In our studies, alternative approaches to optimize library construction for mNGS were developed. This included isothermal nucleic acid amplification and expansion of sample throughput with a group testing algorithm. These methods can improve the utilization of mNGS as a diagnostic tool and can serve as a high-throughput screening system aiding infectious disease surveillance.

Summary: The determination of magnetic structure poses a long-standing challenge in condensed matter physics and materials science. Experimental techniques such as neutron diffraction are resource-limited and require complex structure refinement protocols, while computational approaches such as first-principles density functional theory (DFT) need additional semi-empirical correction, and reliable prediction is still largely limited to collinear magnetism. Here, we present a machine learning model that aims to classify the magnetic structure by inputting atomic coordinates containing transition metal and rare earth elements. By building a Euclidean equivariant neural network that preserves the crystallographic symmetry, the magnetic structure (ferromagnetic, antiferromagnetic, and non-magnetic) and magnetic propagation vector (zero or non-zero) can be predicted with an average accuracy of 77.8% and 73.6%. In particular, a 91% accuracy is reached when predicting no magnetic ordering even if the structure contains magnetic element(s). Our work represents one step forward to solving the grand challenge of full magnetic structure determination.

In a continuous and closed system of culturing microalgae, constantly monitoring and controlling pH, dissolved oxygen (DO) and microalgal density in the cultivation environment are paramount, which ultimately influence on the growth rate and quality of the microalgae products. Apart from the pH and DO parameters, the density of microalgae can be used to contemplate what light condition in the culture chamber is or when nutrients should be supplemented, which both also decide productivity of the cultivation. Moreover, the microalgal density is considered as an indicator indicating when the microalgae can be harvested. Therefore, this work proposes a low-cost monitoring equipment that can be employed to observe pH, DO and microalgal density over time in a culture environment. The measurements obtained by the proposed monitoring device can be utilized for not only real-time observations but also controlling other sub-systems in a continuous culture model including stirring, ventilating, nutrient supplying and harvesting, which leads to more efficiency in the microalgal production. More importantly, it is proposed to utilize the off-the-shelf materials to fabricate the equipment with a total cost of about 513 EUR, which makes it practical as well as widespread. The proposed monitoring apparatus was validated in a real-world closed system of cultivating a microalgae strain of Chlorella vulgaris. The obtained results indicate that the measurement accuracies are 0.3%, 3.8% and 8.6% for pH, DO and microalgae density quantities, respectively.

This paper presents a depth-based hybrid method to generate safe flight corridors for a memoryless local navigation planner. It is first proposed to use raw depth images as inputs in the learning-based object-detection engine with no requirement for map fusion. We then employ an object-detection network to directly predict the base of polyhedral safe corridors in a new raw depth image. Furthermore, we apply a verification procedure to eliminate any false predictions so that the resulting collision-free corridors are guaranteed. More importantly, the proposed mechanism helps produce separate safe corridors with minimal overlap that are suitable to be used as space boundaries for path planning. The average intersection of union (IoU) of corridors obtained by the proposed algorithm is less than 2%. To evaluate the effectiveness of our method, we incorporated it into a memoryless planner with a straight-line path-planning algorithm. We then tested the entire system in both synthetic and real-world obstacle-dense environments. The obtained results with very high success rates demonstrate that the proposed approach is highly capable of producing safe corridors for memoryless local planning.

The synthesis and spectroscopic data of (E)-2-{4-[3-(thiophen-3-yl)acryloyl]phenoxy}acetic acid are described. Crystallization from an ethanol–water mixture resulted in the title compound, C30H23KO8S2 or [K(C15H11O4S)(C15H12O4S)]n, containing one molecule of the acid and one molecule of the potassium salt in the asymmetric unit. Both molecules share the H atom between their carboxyl groups and a potassium ion. The C=C bonds display an E configuration. The thiophene and phenyl rings in the two molecules are inclined by 43.3 (2) and 22.7 (2)°. The potassium ion is octahedrally coordinated by six O atoms. This distorted octahedron shares on opposite sides two oxygen atoms with inversion-related octahedra, resulting in chains of octahedra running in the [010] direction, which form ladder-like chains by C—H...π interactions. A Hirshfeld surface analysis indicates that the highest contributions to the surface contacts arise from interactions in which H atoms are involved, with the most important contribution being from H...H (31.6 and 31.9% for the two molecules) interactions.