Vehicle-in-Virtual-Environment (VVE) Method for Autonomous Driving System Development, Evaluation and Demonstration.

The current approach to connected and autonomous driving function development and evaluation uses model-in-the-loop simulation, hardware-in-the-loop simulation and limited proving ground use, followed by public road deployment of the beta version of software and technology. The rest of the road users are involuntarily forced into taking part in the development and evaluation of these connected and autonomous driving functions in this approach. This is an unsafe, costly and inefficient method. Motivated by these shortcomings, this paper introduces the Vehicle-in-Virtual-Environment (VVE) method of safe, efficient and low-cost connected and autonomous driving function development, evaluation and demonstration. The VVE method is compared to the existing state-of-the-art. Its basic implementation for a path-following task is used to explain the method where the actual autonomous vehicle operates in a large empty area with its sensor feeds being replaced by realistic sensor feeds corresponding to its location and pose in the virtual environment. It is possible to easily change the development virtual environment and inject rare and difficult events which can be tested very safely. Vehicle-to-Pedestrian (V2P) communication-based pedestrian safety is chosen as the application use case for the VVE in this paper, and corresponding experimental results are presented and discussed. A no-line-of-sight pedestrian and vehicle moving towards each other on intersecting paths with different speeds are used in the experiments. Their time-to-collision risk zone values are compared for determining severity levels. The severity levels are used to slow down or brake the vehicle. The results show that V2P communication of pedestrian location and heading can be used successfully to avoid possible collisions. It is noted that actual pedestrians and other vulnerable road users can be used very safely in this approach.

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An 18-day-old male infant was admitted to the hospital due to recurrent hyperkalemia for more than 10 days. The neonate had milk refusal and dyspnea. The blood gas analysis revealed recurrent hyperkalemia, hyponatremia and metabolic acidosis. Adrenocortical hormone replacement therapy was ineffective. Additional tests showed a significant increase in aldosterone levels. Family whole exome sequencing revealed that the infant had compound heterozygous in the SCNNIA gene, inherited from both parents. The infant was diagnosed with neonatal systemic pseudohypoaldosteronism type I. The infant's electrolyte levels were stabilized through treatment with sodium polystyrene sulfonate and sodium supplement. The infant was discharged upon clinical recovery. This study provides a focused description of differential diagnosis of salt-losing syndrome in infants and introduces the multidisciplinary management of neonatal systemic pseudohypoaldosteronism type I.

First principles study on modulating electronic and optical properties with h-BN intercalation in AlN/MoS2heterostructure.

The van der Waals (vdW) heterostructures formed by stacking layered two-dimensional materials can improve the performance of materials and provide more applications. In our paper, six configurations of AlN/MoS2vdW heterostructures were constructed, the most stable structure was obtained by calculating the binding energy. On this basis, the effect of external vertical strain on AlN/MoS2heterostructure was analyzed, the calculated results show that the optimal interlayer distance was 3.593 Å and the band structure was modulated. Then the h-BN intercalation was inserted into the AlN/MoS2heterostructure, by fixing the distance between h-BN and AlN or MoS2, two kinds of models were obtained. Furthermore, the electronic properties of AlN/MoS2heterostructure can be regulated by adding h-BN intercalation layer and adjusting its position. Finally, the optical properties show that the absorption coefficient of AlN/MoS2heterostructure exhibits enhancement characteristic compared with that of the individual monolayers. Meantime, compared with AlN/MoS2, the AlN/h-BN/MoS2shows a redshift effect and the light absorption peak intensity increased, which indicated that h-BN intercalation layer can be used to regulate the electronic and optical properties of AlN/MoS2heterostructure.

Adjustable electronic and optical properties of BlueP/MoS2 van der Waals heterostructure by external strain: a first-principles study.

Blue phosphorene (BlueP) has been widely researched recently as a potential material for novel photocatalytic and electronic devices. In this letter, due to its similar in-plane hexagonal lattice structure to MoS2, BlueP/MoS2 van der Waals heterostructures were built in six configurations. The II-stacking configuration was the most stable due to the lowest binding energy obtained from the calculation results. Furthermore, by controlling the external vertical strain, the geometry structures were optimized and the electronic structures of the BlueP/MoS2 heterostructure were modulated. We found that when the interlayer distance was 3.71 Å, the structure was the most optimized. In addition, as the result of charge transfer at the interlayer, a built-in electric field was formed in the BlueP/MoS2 heterostructure, which explained the formation of the type-II band alignment structure. The optical properties results show that the BlueP/MoS2 heterostructure has a wide optical response range and good light absorption ability, which indicated significant potential for BlueP/MoS2 heterostructure use in the next generation of photovoltaic devices and water-splitting materials.

Pathogen-specific alterations in intestinal microbiota precede urinary tract infections in preterm infants: a longitudinal case-control study.

Urinary tract infections (UTIs) are among the most common late-onset infections in preterm infants, characterized by nonspecific symptoms and a pathogenic spectrum that diverges from that of term infants and older children, which present unique diagnostic and therapeutic challenges. Existing data on the role of gut microbiota in UTI pathogenesis in this demographic are limited. This study aims to investigate alterations in gut microbiota and fecal calprotectin levels and their association with the development of UTIs in hospitalized preterm infants. A longitudinal case-control study was conducted involving preterm infants admitted between January 2018 and October 2020. Fecal samples were collected weekly and analyzed for microbial profiles and calprotectin levels. Propensity score matching, accounting for key perinatal factors including age and antibiotic use, was utilized to match samples from UTI-diagnosed infants to those from non-UTI counterparts. Among the 151 preterm infants studied, 53 were diagnosed with a UTI, predominantly caused by Enterobacteriaceae (79.3%) and Enterococcaceae (19.0%). Infants with UTIs showed a significantly higher abundance of these families compared to non-UTI infants, for both Gram-negative and positive pathogens, respectively. Notably, there was a significant pre-UTI increase in the abundance of pathogen-specific taxa in infants later diagnosed with UTIs, offering high predictive value for early detection. Shotgun metagenomic sequencing further confirmed the dominance of specific pathogenic species pre-UTI and revealed altered virulence factor profiles associated with Klebsiella aerogenes and Escherichia coli infections. Additionally, a decline in fecal calprotectin levels was observed preceding UTI onset, particularly in cases involving Enterobacteriaceae. The observed pathogen-specific alterations in the gut microbiota preceding UTI onset offer novel insight into the UTI pathogenesis and promising early biomarkers for UTIs in preterm infants, potentially enhancing the timely management of this common infection. However, further validation in larger cohorts is essential to confirm these findings.

Postnatal Dynamics and Clinical Associations of Fecal Calprotectin in Very Preterm Infants: Implications for Necrotizing Enterocolitis and Feeding Intolerance.

INTRODUCTION: To elucidate the postnatal dynamics and clinical associations of fecal calprotectin (FC) in very preterm infants, with a focus on necrotizing enterocolitis (NEC) and feeding intolerance (FI). METHODS: We performed a prospective observational cohort study in infants with a gestational age of <32 weeks or birth weight <1,500 g with weekly feces collection. The relationships between FC, NEC, and FI were investigated, adjusting for demographic and clinical factors. RESULTS: A total of 1,086 fecal samples were collected from 194 preterm infants. Postnatal FC levels of non-NEC infants were highly variable and followed an age-dependent patterned progression. FC levels were elevated in patients with NEC before and at NEC onset, distinguishing them from non-NEC infants and those at sepsis onset. Among infants without NEC or sepsis, those with FI exhibited lower FC concentrations throughout hospitalization and displayed a significant delay in reaching high FC levels after meconium compared with non-FI infants. The age to reach the first high nonmeconial FC levels was positively associated with the time to achieve full enteral feeding. DISCUSSION: Postnatal FC dynamics among premature infants followed a patterned progression but were disturbed in patients with NEC and FI. Because of the high variations, the use of FC levels in NEC diagnosis should be implemented with caution in clinical practice. FC may help understand FI and feeding progression in very preterm infants. Further research is needed to validate these findings and explore the potential clinical applications of FC in this population.

Dynamics and Crosstalk between Gut Microbiota, Metabolome, and Fecal Calprotectin in Very Preterm Infants: Insights into Feeding Intolerance.

BACKGROUND: Feeding intolerance (FI) is a significant concern in the care of preterm infants, impacting their growth and development. We previously reported that FI is linked to lower fecal calprotectin (FC) levels. This study aims to explore the postnatal dynamics and interplay between microbiota, metabolic profiles, and host immunity in preterm infants with and without FI. METHODS: Infants with gestational age <32 weeks or birth weight <1500 g were enrolled at the Children's Hospital of Fudan University between January 2018 and October 2020. Weekly fecal samples were analyzed for bacterial profiling, metabolome, and calprotectin levels, exploring their longitudinal development and interrelationships. RESULTS: Of the 118 very preterm infants studied, 48 showed FI. These infants experienced an interrupted microbial-immune trajectory, particularly at 3-4 weeks of age, marked by a reduced bacterial abundance, alpha diversity, and FC levels. Metabolic changes in FI were pronounced between 3 and 6 weeks. Pantothenic acid and two polyamine metabolites were closely associated with bacterial abundance and FC levels and negatively correlated with the duration to attain full enteral feeding. CONCLUSIONS: FI infants demonstrated compromised microbiome-immune interactions, potentially influenced by specific metabolites. This research underscored the importance of early microbial and metabolic development in the pathogenesis of FI in very preterm infants.

Early Antibiotic Use and Neonatal Outcomes Among Preterm Infants Without Infections.

OBJECTIVES: To determine whether use, duration, and types of early antibiotics were associated with neonatal outcomes and late antibiotic use in preterm infants without infection-related diseases. METHODS: This cohort study enrolled infants admitted to 25 tertiary NICUs in China within 24 hours of birth during 2015-2018. Death, discharge, or infection-related morbidities within 7 days of birth; major congenital anomalies; and error data on antibiotic use were excluded. The composite outcome was death or adverse morbidities. Late antibiotic use indicated antibiotics used after 7 days of age. Late antibiotic use rate was total antibiotic use days divided by the days of hospital stay after the first 7 days of life. RESULTS: Among 21 540 infants, 18 302 (85.0%) received early antibiotics. Early antibiotics was related to increased bronchopulmonary dysplasia (BPD) (adjusted odds ratio [aOR], 1.28; 95% confidence interval [CI], 1.05-1.56), late antibiotic use (aOR, 4.64; 95% CI, 4.19-5.14), and late antibiotic use rate (adjusted mean difference, 130 days/1000 patient-days; 95% CI, 112-147). Each additional day of early antibiotics was associated with increased BPD (aOR, 1.07; 95% CI, 1.04-1.10) and late antibiotic use (aOR, 1.41; 95% CI, 1.39-1.43). Broad-spectrum antibiotics showed larger effect size on neonatal outcomes than narrow-spectrum antibiotics. The correlation between early antibiotics and outcomes was significant among noncritical infants but disappeared for critical infants. CONCLUSIONS: Among infants without infection, early antibiotic use was associated with increased risk of BPD and late antibiotic use. Judicious early antibiotic use, especially avoiding prolonged duration and broad-spectrum antibiotics among noncritical infants, may improve neonatal outcomes and overall antibiotic use in NICUs.

Polygalasaponin F protects hippocampal neurons against glutamate-induced cytotoxicity.

Excess extracellular glutamate leads to excitotoxicity, which induces neuronal death through the overactivation of N-methyl-D-aspartate receptors (NMDARs). Excitotoxicity is thought to be closely related to various acute and chronic neurological disorders, such as stroke and Alzheimer's disease. Polygalasaponin F (PGSF) is a triterpenoid saponin monomer that can be isolated from Polygala japonica, and has been reported to protect cells against apoptosis. To investigate the mechanisms underlying the neuroprotective effects of PGSF against glutamate-induced cytotoxicity, PGSF-pretreated hippocampal neurons were exposed to glutamate for 24 hours. The results demonstrated that PGSF inhibited glutamate-induced hippocampal neuron death in a concentration-dependent manner and reduced glutamate-induced Ca2+ overload in the cultured neurons. In addition, PGSF partially blocked the excess activity of NMDARs, inhibited both the downregulation of NMDAR subunit NR2A expression and the upregulation of NMDAR subunit NR2B expression, and upregulated the expression of phosphorylated cyclic adenosine monophosphate-responsive element-binding protein and brain-derived neurotrophic factor. These findings suggest that PGSF protects cultured hippocampal neurons against glutamate-induced cytotoxicity by regulating NMDARs. The study was approved by the Institutional Animal Care Committee of Nanchang University (approval No. 2017-0006) on December 29, 2017.

Epidemiology of necrotizing enterocolitis in preterm infants in China: A multicenter cohort study from 2015 to 2018.

PURPOSE: To describe the current incidence, case-fatality rate and surgical treatment proportion of necrotizing enterocolitis (NEC) among preterm infants in China. METHODS: The study included all live neonates with <34 weeks gestational age (GA) admitted to 25 tertiary hospitals within 7 days of birth from 19 provinces in China between May 2015 and April 2018. NEC was defined as ≥stage II according to Bell's criteria. RESULTS: A total of 24,731 infants were included. The overall incidence of NEC was 3.3% and decreased with increasing GA and birth weight. The incidence of NEC was 4.8% in very preterm infants and 1.8% in infants born ≥32 weeks GA, respectively. The overall case-fatality rate of NEC was 9.5%, and the case-fatality rate was 7.0% among infants born ≥32 weeks GA. A total of 214 (27.9%) infants underwent surgery, and their overall case-fatality rate was 13.6%. Significant variation in the incidence of NEC existed among different centers (0.6-11.1%). CONCLUSIONS: The incidence and case-fatality rate of NEC are high in China, especially among infants with GA ≥32 weeks, and varies significantly among sites. A high proportion of NEC infants required surgical management, with an even higher case-fatality rate.

Automatic Seizure Classification Based on Domain-Invariant Deep Representation of EEG.

Accurate identification of the type of seizure is very important for the treatment plan and drug prescription of epileptic patients. Artificial intelligence has shown considerable potential in the fields of automated EEG analysis and seizure classification. However, the highly personalized representation of epileptic seizures in EEG has led to many research results that are not satisfactory in clinical applications. In order to improve the clinical adaptability of the algorithm, this paper proposes an adversarial learning-driven domain-invariant deep feature representation method, which enables the hybrid deep networks (HDN) to reliably identify seizure types. In the train phase, we first use the labeled multi-lead EEG short samples to train squeeze-and-excitation networks (SENet) to extract short-term features, and then use the compressed samples to train the long short-term memory networks (LSTM) to extract long-time features and construct a classifier. In the inference phase, we first adjust the feature mapping of LSTM through the adversarial learning between LSTM and clustering subnet so that the EEG of the target patient and the EEG in the database obey the same distribution in the deep feature space. Finally, we use the adjusted classifier to identify the type of seizure. Experiments were carried out based on the TUH EEG Seizure Corpus and CHB-MIT seizure database. The experimental results show that the proposed domain adaptive deep feature representation improves the classification accuracy of the hybrid deep model in the target set by 5%. It is of great significance for the clinical application of EEG automatic analysis equipment.

Production of ethylene glycol from direct catalytic conversion of cellulose over a binary catalyst of metal-loaded modified SBA-15 and phosphotungstic acid.

This study presents the utilization of a binary catalyst composed of metal-loaded modified SBA-15 (M/SBA-15) and phosphotungstic acid (H3PW12O40) for ethylene glycol (EG) production from direct catalytic conversion of cellulose. M/SBA-15 (M = Ru, Au, Pd, Pt, Rh and Ni) catalysts were prepared using the impregnation method and characterized by means of XRD, N2 physisorption, TEM and H2-temperature-programmed reduction (H2-TPR) techniques. Their catalytic performance was then studied in detail on the basis of cellulose conversion and the selectivity of polyols and EG. The results showed that the mesoporous structure of the SBA-15 sample was well maintained after the metal-loaded modification, and almost all of the selected catalysts gave about 100% conversion of cellulose. However, the selectivity for EG was greatly different. Among the various binary catalysts, the combination of Rh/SBA-15 and H3PW12O40 gave the best selectivity to EG (55.5%), whereas the worst selectivity of EG (11%) was obtained over the Au/SBA-15 and H3PW12O40 system under identical conditions. In addition to phosphotungstic acid, other W compounds were also studied in combination with the Ru/SBA-15 catalyst. The results showed that the EG selectivity depended on the W compounds as follows: H4SiW12O40 < H2WO4 < H3PW12O40. Therefore, the binary catalyst of Rh/SBA-15 and H3PW12O40 showed the greatest potential for EG production from direct catalytic conversion of cellulose.

An Intelligent Gear Fault Diagnosis Methodology Using a Complex Wavelet Enhanced Convolutional Neural Network.

As a typical example of large and complex mechanical systems, rotating machinery is prone to diversified sorts of mechanical faults. Among these faults, one of the prominent causes of malfunction is generated in gear transmission chains. Although they can be collected via vibration signals, the fault signatures are always submerged in overwhelming interfering contents. Therefore, identifying the critical fault's characteristic signal is far from an easy task. In order to improve the recognition accuracy of a fault's characteristic signal, a novel intelligent fault diagnosis method is presented. In this method, a dual-tree complex wavelet transform (DTCWT) is employed to acquire the multiscale signal's features. In addition, a convolutional neural network (CNN) approach is utilized to automatically recognise a fault feature from the multiscale signal features. The experiment results of the recognition for gear faults show the feasibility and effectiveness of the proposed method, especially in the gear's weak fault features.