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Abstract The utilization of high-risk test cases constitutes an effective approach to enhance the safety testing of autonomous vehicles (AVs) and enhance their efficiency. This research paper presents a derivation of 2052 high-hazard pre-crash scenarios for testing autonomous driving, which were based on 23 high-hazard cut-in accident scenarios from the National Automobile Accident In-Depth Investigation System (NAIS) through combining importance sampling and combined testing methods. Compared to the direct combination of the original distribution after sampling, the proposed method has a 2.92 times higher crash rate of 69.32% for the test case set in this paper. It also has a 5.8 times higher rate of triggering Automatic Emergency Braking (AEB), improving hazardous scenario coverage. Using the proposed method, the generated parameters of the cut-in accident scenario test set were compared with those of the cut-in test scenarios included in existing Chinese autonomous driving test protocols and standards. The velocity of the ego-vehicle obtained using the proposed method matched those in the existing protocols, whereas the velocity, time gap, and time to collision of the target vehicle were significantly lower than those existing protocols indicating scenarios obtained from accident data can enrich the selection of testing scenarios for autonomous driving.

PurposeThis study aims to develop an optimal machine learning model that uses lung equivalent uniform dose (lung EUD to predict radiation pneumonitis (RP) occurrence in lung cancer patients treated with volumetric modulated arc therapy (VMAT).MethodsWe analyzed a cohort of 77 patients diagnosed with locally advanced squamous cell lung cancer (LASCLC) receiving concurrent chemoradiotherapy with VMAT. Patients were categorized based on the onset of grade II or higher radiation pneumonitis (RP 2+). Dose volume histogram data, extracted from the treatment planning system, were used to compute the lung EUD values for both groups using a specialized numerical analysis code. We identified the parameter α, representing the most significant relative difference in lung EUD between the two groups. The predictive potential of variables for RP2+, including physical dose metrics, lung EUD, normal tissue complication probability (NTCP) from the Lyman-Kutcher-Burman (LKB) model, and lung EUD-calibrated NTCP for affected and whole lung, underwent both univariate and multivariate analyses. Relevant variables were then employed as inputs for machine learning models: multiple logistic regression (MLR), support vector machine (SVM), decision tree (DT), and K-nearest neighbor (KNN). Each model's performance was gauged using the area under the curve (AUC), determining the best-performing model.ResultsThe optimal α-value for lung EUD was 0.3, maximizing the relative lung EUD difference between the RP 2+ and non-RP 2+ groups. A strong correlation coefficient of 0.929 (P< 0.01) was observed between lung EUD (α = 0.3) and physical dose metrics. When examining predictive capabilities, lung EUD-based NTCP for the affected lung (AUC: 0.862) and whole lung (AUC: 0.815) surpassed LKB-based NTCP for the respective lungs. The decision tree (DT) model using lung EUD-based predictors emerged as the superior model, achieving an AUC of 0.98 in both training and validation datasets.DiscussionsThe likelihood of developing RP 2+ has shown a significant correlation with the advancements in RT technology. From traditional 3-D conformal RT, lung cancer treatment methodologies have transitioned to sophisticated techniques like static IMRT. Accurately deriving such a dose–effect relationship through NTCP modeling of RP incidence is statistically challenging due to the increased number of degrees-of-freedom. To the best of our knowledge, many studies have not clarified the rationale behind setting the α-value to 0.99 or 1, despite the closely aligned calculated lung EUD and lung mean dose MLD. Perfect independence among variables is rarely achievable in real-world scenarios. Four prominent machine learning algorithms were used to devise our prediction models. The inclusion of lung EUD-based factors substantially enhanced their predictive performance for RP 2+. Our results advocate for the decision tree model with lung EUD-based predictors as the optimal prediction tool for VMAT-treated lung cancer patients. Which could replace conventional dosimetric parameters, potentially simplifying complex neural network structures in prediction models.

Abstract Molecular diagnostics for crop diseases can guide the precise application of pesticides, thereby reducing pesticide usage while improving crop yield, but tools are lacking. Here, we report an in-field molecular diagnostic tool that uses a cheap colorimetric paper and a smartphone, allowing multiplexed, low-cost, rapid detection of crop pathogens. Rapid nucleic acid amplification-free detection of pathogenic RNA is achieved by combining toehold-mediated strand displacement with a metal ion-mediated urease catalysis reaction. We demonstrate multiplexed detection of six wheat pathogenic fungi and an early detection of wheat stripe rust. When coupled with a microneedle for rapid nucleic acid extraction and a smartphone app for results analysis, the sample-to-result test can be completed in ~10 min in the field. Importantly, by detecting fungal RNA and mutations, the approach allows to distinguish viable and dead pathogens and to sensitively identify mutation-carrying fungicide-resistant isolates, providing fundamental information for precision crop disease management.

Abstract Glutamate dehydrogenase (Gdh), catalyzing the reversible conversion between 2-oxoglutarate and glutamate, plays an important role in the connection of nitrogen and carbon metabolism. Yet little is known about these enzymes in the amino acid-manufacturing Corynebacterium glutamicum. In the present study, we firstly identified the enzymatic characteristics of two Gdhs (GdhA and GdhB). The results showed that both GdhA and GdhB prefers NADPH as a coenzyme and have higher affinity for 2-OG than glutamate. The growth characteristics of gdhAΔ mutant and gdhBΔ mutant, gdhABΔ mutant showed GdhA serves as the main conduit for ammonium assimilation, and GdhB is the main glutamate- metabolizing enzyme in C. glutamicum. The full-genome transcriptomic analysis was used to investigate physiological response of C. glutamicum to the glutamate as nitrogen source, and gdh deletion. The results showed that the nitrogen starvation response was elicited when glutamine served as the sole nitrogen source. gdhAΔBΔ double deletion trigger a partially deregulated nitrogen starvation response, in which genes involved in nitrogen assimilation showed obviously upregulated in a certain extent. On the other hand, the genes of phosphotransferase system (PTS) and glycolysis pathway, most genes in pentose phosphate pathway were significantly upregulated, indicating that gdh deficiency initiated the enhancement of the absorption and metabolism of carbon sources. We believed that our results in this study will give new insights on the molecular mechanism of Gdh activity cross-talks with carbon and nitrogen metabolism, also setting a new background for further flux redistribution applied research of biotechnological interest.

Abstract We developed a physical model to fundamentally understand the conductive filament (CF) formation and growth behavior in the switching layer during electroforming process in the metal-oxide-based resistive random-access memories (RRAM). The effects of the electrode and oxide layer properties on the CF morphology evolution, current-voltage characteristic, local temperature, and electrical potential distribution have been systematically explored. It is found that choosing active electrodes with lower oxygen vacancy formation energy and oxides with small Lorenz number (ratio of thermal and electrical conductivity) enables CF formation at a smaller electroforming voltage and creates a CF with more homogeneous morphology. This work advances our understanding of the kinetic behaviors of the CF formation and growth during the electroforming process and could potentially guide the oxide and electrode materials selection to realize a more stable and functional RRAM.

Abstract Resin uptake plays a critical role in the stiffness‐to‐weight ratio of wind turbine blades in which sandwich composites are used extensively. This work examines the flexural properties of nominally half‐inch thick sandwich composites made with polyvinyl chloride (PVC) foam cores (H60 and H80; PSC and GPC) at several resin uptakes. We found that the specific flexural strength and modulus for the H80 GPC sandwich composites increase from 82.04 to 90.70 kN · m/kg and 6.03 to 7.13 MN · m/kg, respectively, with 11.0% resin uptake reduction, which stands out among the four core sandwich composites. Considering reaching a high stiffness‐to‐weight ratio while preventing resin starvation, 32% to 38% and 40% to 45% resin uptakes are adequate ranges for the H80 PSC and GPC sandwich composites, respectively. The H60 GPC sandwich composites have lower debonding toughness than H60 PSC due to stress concentration in the smooth side skin‐core interphase region. The ailure mode of the sandwich composites depends on the core stiffness and surface texture. The H60 GPC sandwich composites exhibit core shearing and bottom skin‐core debonding failure, while the H80 GPC and PSC sandwich composites show top skin cracking and core crushing failure. The findings indicate that an appropriate range of resin uptake exists for each type of core sandwich composite, and that within the range, a low‐resin uptake leads to lighter blades and thus lower cyclic gravitational loads, beneficial for long blades.

PurposeRadiation-induced dermatitis is one of the most common side effects for breast cancer patients treated with radiation therapy (RT). Acute complications can have a considerable impact on tumor control and quality of life for breast cancer patients. In this study, we aimed to develop a novel quantitative high-accuracy machine learning tool for prediction of radiation-induced dermatitis (grade ≥ 2) (RD 2+) before RT by using data encapsulation screening and multi-region dose-gradient-based radiomics techniques, based on the pre-treatment planning computed tomography (CT) images, clinical and dosimetric information of breast cancer patients.Methods and Materials214 patients with breast cancer who underwent RT between 2018 and 2021 were retrospectively collected from 3 cancer centers in China. The CT images, as well as the clinical and dosimetric information of patients were retrieved from the medical records. 3 PTV dose related ROIs, including irradiation volume covered by 100%, 105%, and 108% of prescribed dose, combined with 3 skin dose-related ROIs, including irradiation volume covered by 20-Gy, 30-Gy, 40-Gy isodose lines within skin, were contoured for radiomics feature extraction. A total of 4280 radiomics features were extracted from all 6 ROIs. Meanwhile, 29 clinical and dosimetric characteristics were included in the data analysis. A data encapsulation screening algorithm was applied for data cleaning. Multiple-variable logistic regression and 5-fold-cross-validation gradient boosting decision tree (GBDT) were employed for modeling training and validation, which was evaluated by using receiver operating characteristic analysis.ResultsThe best predictors for symptomatic RD 2+ were the combination of 20 radiomics features, 8 clinical and dosimetric variables, achieving an area under the curve (AUC) of 0.998 [95% CI: 0.996-1.0] and an AUC of 0.911 [95% CI: 0.838-0.983] in the training and validation dataset, respectively, in the 5-fold-cross-validation GBDT model. Meanwhile, the top 12 most important characteristics as well as their corresponding importance measures for RD 2+ prediction in the GBDT machine learning process were identified and calculated.ConclusionsA novel multi-region dose-gradient-based GBDT machine learning framework with a random forest based data encapsulation screening method integrated can achieve a high-accuracy prediction of acute RD 2+ in breast cancer patients.

The scale and dimensionality of imaging data means information is commonly overlooked. Here, using recurrent neural networks we understand temporal dependencies in hyperspectral imagery, enabling the observation of differences in ferroelectric switching mechanisms in PbZr0.2Ti0.8O3 thin films due to formation of charged domain walls.

Cytochrome P450 reductase (CPR) is an integral component of the P450 oxidoreductase system (P450s). It serves as the electron donor for most cytochromes in P450s, which are involved in the metabolism of foreign compounds and the synthesis of endocrine hormones and have tremendous biotechnological potential for the synthesis of pharmaceuticals and fine chemicals. However, commercially available CPR is very expensive, and heterologous expression in Escherichia coli is a more affordable way to obtain enough CPR. In the present study, a full-length cDNA encoding a CPR was isolated from the liver of the Chinese white rabbit using reverse transcription-polymerase chain reaction (RT-PCR). The cDNA contains a 2,040-bp open reading frame, which is predicted to encode an enzyme of 679 amino acids. The deduced peptide shares 99.5% amino acid similarity with CPR of Oryctolagus cuniculus, showing that the Chinese white rabbit is a close genetic relative of the European rabbit. The cloned CPR has the typical hallmarks, including an N-terminal membrane anchor and flavin adenine dinucleotide (FAD)-, flavin mononucleotide (FMN)- and nicotinamide adenine dinucleotide phosphate (NADPH)-binding domains. An N-terminally truncated protein was heterologously expressed in E. coli BL21 (DE3) cells and purified, and the specific activity of the recombinant enzyme was determined. The enzyme activity analysis indicated that electrons were passed from NADPH to Cyt C at a rate of 2.3174 μmol/(min/mg protein). The present study provides an efficient procedure for preparing large amounts of recombinant CPR, which would facilitate the synthesis of pharmaceuticals and fine chemicals with P450s.

Peptide is a promising source of safe hypoglycaemics in nutraceuticals. A biocatalytic approach, isolation and purification procedures, amino acid analysis, and identification of peptides were implemented successively. Results indicated that the hydrolysates with high α-glucosidase inhibitory activity (α-GIA) were obtained from hemp seed protein (HSP) by Alcalase treatment at the degree of hydrolysis (DH) of 27.24 ± 0.88%. Interestingly, the hydrolysates at DH ≤ 9.68 ± 0.45% could be an α-glucosidase activator instead. Two novel α-glucosidase inhibitory peptides with their sequences of Leu-Arg (287.2 Da) and Pro-Leu-Met-Leu-Pro (568.4 Da) were identified. Hydrophobic amino acids in the peptides, particularly Pro and Leu, contribute greatly to the α-GIA, while rich essential amino acids, branched chain amino acids, Pro and Met enlarged markedly the application in functional foods. The present research provided a green route for production of novel α-glucosidase inhibitory peptides as nutrient and functional food ingredients in the management of hyperglycaemia.

A comprehensive knowledge of the types and ratios of microbes that inhabit the healthy human gut is necessary before any kind of pre-clinical or clinical study can be performed that attempts to alter the microbiome to treat a condition or improve therapy outcome. To address this need we present an innovative scalable comprehensive analysis workflow, a healthy human reference microbiome list and abundance profile (GutFeelingKB), and a novel Fecal Biome Population Report (FecalBiome) with clinical applicability. GutFeelingKB provides a list of 157 organisms (8 phyla, 18 classes, 23 orders, 38 families, 59 genera and 109 species) that forms the baseline biome and therefore can be used as healthy controls for studies related to dysbiosis. This list can be expanded to 863 organisms if closely related proteomes are considered. The incorporation of microbiome science into routine clinical practice necessitates a standard report for comparison of an individual's microbiome to the growing knowledgebase of "normal" microbiome data. The FecalBiome and the underlying technology of GutFeelingKB address this need. The knowledgebase can be useful to regulatory agencies for the assessment of fecal transplant and other microbiome products, as it contains a list of organisms from healthy individuals. In addition to the list of organisms and their abundances, this study also generated a collection of assembled contiguous sequences (contigs) of metagenomics dark matter. In this study, metagenomic dark matter represents sequences that cannot be mapped to any known sequence but can be assembled into contigs of 10,000 nucleotides or higher. These sequences can be used to create primers to study potential novel organisms. All data is freely available from https://hive.biochemistry.gwu.edu/gfkb and NCBI's Short Read Archive.

The prevalence of Staphylococcus aureus in 2160 bulk ready-to-eat foods from the Sichuan province of China during 2013–2016 was investigated. The antibiotic resistance and the associated genes, as well as biofilm formation capacity of the S. aureus isolates were measured. Furthermore, the relationship between the antibiotic resistance and the resistant genes was discussed. It was found that 54 S. aureus isolates were recovered, and their prevalence in meat products, dairy, fruit and vegetables, and desserts were 31 (2.6%), six (3.0%), nine (2.2%) and eight (2.3%), respectively. Most strains (52/54) were resistant to at least one of the antibiotics, and 21 isolates were identified as multidrug-resistant (MDR) S. aureus. Three isolates were found to be methicillin-resistant S. aureus. Penicillin, erythromycin, clindamycin, tetracycline and inducible clindamycin resistance were determined as the predominant antibiotics, and the isolates with the phenotypic resistance on these five antibiotics were all determined positive for the resistant gene associated. In total, 33 of 54 S. aureus isolates showed biofilm formation capacity, including two strong biofilm producers, one moderate and 30 weak ones. Two S. aureus isolates with strong biofilm formation abilities showed multi-drug resistance, and one moderate biofilm producer was resistant to two categories of antibiotics.

β-glucan extract (GE) was obtained from highland barley bran using alkaline–acid–alcohol extraction method. The stability, solubility, foaming ability, and prebiotics characteristics of GE were assessed consecutively. GE demonstrated excellent heat stability (hardly degraded at 220 °C) and pH stability, especially at neutral or alkaline condition, and its solubility was significantly influenced by temperature instead of pH or NaCl, achieving 0.91 g/100 g at 100 °C. Good foaming ability and foam stability of GE were observed during low temperatures (≤40 °C), neutral or alkaline condition. GE indicated a strong anti-digestibility capacity of resisting the hydrolysis of α-amylase and simulated human gastric acid. Interestingly, GE could effectively promote the growth of Lactobacillus bulgaricus and Bifidobacterium adolescentis, which was close to fructooligosaccharide. The results of this study could offer valuable information for the application of β-glucan from highland barley as prebiotics in promoting human intestinal health metabolism.

Using Hidden Markov Models (HMMs) as a recognition framework for automatic classification of animal vocalizations has a number of benefits, including the ability to handle duration variability through nonlinear time alignment, the ability to incorporate complex language or recognition constraints, and easy extendibility to continuous recognition and detection domains. In this work, we apply HMMs to several different species and bioacoustic tasks using generalized spectral features that can be easily adjusted across species and HMM network topologies suited to each task. This experimental work includes a simple call type classification task using one HMM per vocalization for repertoire analysis of Asian elephants, a language-constrained song recognition task using syllable models as base units for ortolan bunting vocalizations, and a stress stimulus differentiation task in poultry vocalizations using a non-sequential model via a one-state HMM with Gaussian mixtures. Results show strong performance across all tasks and illustrate the flexibility of the HMM framework for a variety of species, vocalization types, and analysis tasks.

Nuclear receptor coactive 4 (NCOA4), which functions as a selective cargo receptor, is a critical regulator of the particularly autophagic degradation of ferritin, a process known as ferritinophagy. Mechanistically, NCOA4‐mediated ferritinophagy performs an increasingly vital role in the maintenance of intracellular iron homeostasis by promoting ferritin transport and iron release as needed. Ferritinophagy is not only involved in iron‐dependent responses but also in the pathogenesis and progression of various human diseases, including metabolism‐related, neurodegenerative, cardiovascular and infectious diseases. Therefore, ferritinophagy is of great importance in maintaining cell viability and function and represents a potential therapeutic target. Recent studies indicated that ferritinophagy regulates the signalling pathway associated with ferroptosis, a newly discovered type of cell death characterised by iron‐dependent lipid peroxidation. Although accumulating evidence clearly demonstrates the importance of the interplay between dysfunction in iron metabolism and ferroptosis, a deeper understanding of the double‐edged sword effect of ferritinophagy in ferroptosis has remained elusive. Details of the mechanisms underlying the ferritinophagy–ferroptosis axis in regulating relevant human diseases remain to be elucidated. In this review, we discuss the latest research findings regarding the mechanisms that regulate the biological function of NCOA4‐mediated ferritinophagy and its contribution to the pathophysiology of ferroptosis. The important role of the ferritinophagy–ferroptosis axis in human diseases will be discussed in detail, highlighting the great potential of targeting ferritinophagy in the treatment of diseases. [ABSTRACT FROM AUTHOR]

The rise of antimicrobial resistance in ESKAPEE pathogens poses significant clinical challenges, especially in polymicrobial infections. Bacteriophage-derived endolysins offer promise in combating this crisis, but face practical hurdles. Our study focuses on engineering endolysins from a Klebsiella pneumoniae phage, fusing them with ApoE23 and COG133 peptides. We assessed the resulting chimeric proteins' bactericidal activity against ESKAPEE pathogens in vitro. ApoE23-Kp84B (CHU-1) reduced over 3 log units of CFU for A. baumannii, E. faecalis, K. pneumoniae within 1 h, while COG133-Kp84B (CHU-2) showed significant efficacy against S. aureus. COG133-L1-Kp84B, with a GS linker insertion in CHU-2, exhibited outstanding bactericidal activity against E. cloacae and P. aeruginosa. Scanning electron microscopy revealed alterations in bacterial morphology after treatment with engineered endolysins. Notably, CHU-1 demonstrated promising anti-biofilm and anti-persister cell activity against A. baumannii and E. faecalis but had limited efficacy in a bacteremia mouse model of their coinfection. Our findings advance the field of endolysin engineering, facilitating the customization of these proteins to target specific bacterial pathogens. This approach holds promise for the development of personalized therapies tailored to combat ESKAPEE infections effectively. [ABSTRACT FROM AUTHOR]

Author(s): Di Ren [sup.1] [sup.2], Chao Ren [sup.3], Ren-qi Yao [sup.3], Yong-wen Feng [sup.1], Yong-ming Yao [sup.1] [sup.3] Author Affiliations: (1) grid.452847.8, Department of Critical Care Medicine, The Second People's [...]