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BackgroundAnesthesia information management systems (AIMSs) automatically import real-time vital signs from physiological monitors to anesthetic records, replacing part of anesthetists’ traditional manual record keeping. However, only a handful of studies have examined the effects of AIMSs on anesthetists’ monitoring performance. ObjectiveThis study aimed to compare the effects of AIMS use and manual record keeping on anesthetists’ monitoring performance, using a full-scale high-fidelity simulation. MethodsThis simulation study was a randomized controlled trial with a parallel group design that compared the effects of two record-keeping methods (AIMS vs manual) on anesthetists’ monitoring performance. Twenty anesthetists at a tertiary hospital in Hong Kong were randomly assigned to either the AIMS or manual condition, and they participated in a 45-minute scenario in a high-fidelity simulation environment. Participants took over a case involving general anesthesia for below-knee amputation surgery and performed record keeping. The three primary outcomes were participants’ (1) vigilance detection accuracy (%), (2) situation awareness accuracy (%), and (3) subjective mental workload (0-100). ResultsWith regard to the primary outcomes, there was no significant difference in participants’ vigilance detection accuracy (AIMS, 56.7% vs manual, 56.7%; P=.50), and subjective mental workload was significantly lower in the AIMS condition than in the manual condition (AIMS, 34.2 vs manual, 46.7; P=.02). However, the result for situation awareness accuracy was inconclusive as the study did not have enough power to detect a difference between the two conditions. ConclusionsOur findings suggest that it is promising for AIMS use to become a mainstay of anesthesia record keeping. AIMSs are effective in reducing anesthetists’ workload and improving the quality of their anesthetic record keeping, without compromising vigilance.

Abstract Timely, accurate, and reliable information is essential for decision-makers, emergency managers, and infrastructure operators during flood events. This study demonstrates that a proposed machine learning model, MaxFloodCast, trained on physics-based hydrodynamic simulations in Harris County, offers efficient and interpretable flood inundation depth predictions. Achieving an average $$R^2$$ R 2 of 0.949 and a Root Mean Square Error of 0.61 ft (0.19 m) on unseen data, it proves reliable in forecasting peak flood inundation depths. Validated against Hurricane Harvey and Tropical Storm Imelda, MaxFloodCast shows the potential in supporting near-time floodplain management and emergency operations. The model’s interpretability aids decision-makers in offering critical information to inform flood mitigation strategies, to prioritize areas with critical facilities and to examine how rainfall in other watersheds influences flood exposure in one area. The MaxFloodCast model enables accurate and interpretable inundation depth predictions while significantly reducing computational time, thereby supporting emergency response efforts and flood risk management more effectively.

Abstract Background Powdery mildew (PM) is one of the important soybean diseases, and host resistance could practically contribute to soybean PM management. To date, only the Rmd locus on chromosome (Chr) 16 was identified through traditional QTL mapping and GWAS, and it remains unclear if the bulk segregant RNA-Seq (BSR-Seq) methodology is feasible to explore additional PM resistance that might exist in other varieties. Results BSR-Seq was applied to contrast genotypes and gene expressions between the resistant bulk (R bulk) and the susceptible bulk (S bulk), as well as the parents. The ∆(SNP-index) and G’ value identified several QTL and significant SNPs/Indels on Chr06, Chr15, and Chr16. Differentially expressed genes (DEGs) located within these QTL were identified using HISAT2 and Kallisto, and allele-specific primers (AS-primers) were designed to validate the accuracy of phenotypic prediction. While the AS-primers on Chr06 or Chr15 cannot distinguish the resistant and susceptible phenotypes, AS-primers on Chr16 exhibited 82% accuracy prediction with an additive effect, similar to the SSR marker Satt431. Conclusions Evaluation of additional AS-primers in the linkage disequilibrium (LD) block on Chr16 further confirmed the resistant locus, derived from the resistant parental variety ‘Kaohsiung 11’ (‘KS11’), not only overlaps with the Rmd locus with unique up-regulated LRR genes (Glyma.16G213700 and Glyma.16G215100), but also harbors a down-regulated MLO gene (Glyma.16G145600). Accordingly, this study exemplified the feasibility of BSR-Seq in studying biotrophic disease resistance in soybean, and showed the genetic makeup of soybean variety ‘KS11’ comprising the Rmd locus and one MLO gene.

Hereditary sensory and autonomic neuropathy type 4 (HSAN4), also known as congenital insensitivity to pain with anhidrosis (CIPA), is a rare genetic disorder caused by NTRK1 gene mutations, affecting nerve growth factor signaling. This study investigates the central nervous system’s (CNS) involvement and its relation to pain insensitivity in HSAN4. We present a 15-year-old girl with HSAN4, displaying clinical signs suggestive of CNS impact, including spasticity and a positive Babinski’s sign. Using Technetium-99m ethyl cysteinate dimer single-photon emission computed tomography (Tc−99m ECD SPECT) imaging, we discovered perfusion deficits in key brain regions, notably the cerebellum, thalamus, and postcentral gyrus. These regions process pain signals, providing insights into HSAN4’s pain insensitivity. This study represents the first visualization of CNS perfusion abnormality in an HSAN4 patient. It highlights the intricate relationship between the peripheral and central nervous systems in HSAN4. The complexity of HSAN4 diagnosis, involving potential unidentified genes, underscores the need for continued research to refine diagnostic approaches and develop comprehensive treatments.

This paper is based on the VAR model, a regression analysis of the correlation between labor education and ideological and political education. Firstly, the LDA algorithm is used to speculate about the document to obtain the probability distribution of the subject, and this is used as the current subject feature. Then use the gray correlation algorithm to measure the distribution characteristics of each different indicator from the object dimension, and measure the correlation size and positive and negative by extracting the relative difference between indicators. Finally, the TF-IDF algorithm is used to determine the word weights, and text clustering is invoked to merge texts with the same theme. After the teaching of the VAR model, the significance of the learning motivation of the students in the experimental group is 0.009, the passing rate of the final grades is around 76%, and the significance of the dimensions of the learning effect is less than 0.05. It shows that the integration of labor education with ideological and political education can lead students to strengthen their concepts of labor so as to realize students’ comprehensive development.

Purpose: Children with autism are found to have delayed and heterogeneous gesture abilities. It is important to understand the growth of gesture abilities and the underlying factors affecting its growth. Addressing these issues can help to design effective intervention programs. Method: Thirty-five Chinese-speaking preschoolers with autism spectrum disorder (M[subscript age] = 4.89 years, SD = 0.91; four girls) participated in four play sessions with their parents over 9 months. Their child-based factors including autism severity, intellectual functioning, and expressive language abilities were assessed. The gestures (deictic, iconic, and conventional) of the children and their parents were coded. Growth curve analyses were conducted to examine individual growth trajectories and the roles of child-based factors and parental input in shaping the children's gesture development. Results: Child-based factors and parental input predicted gesture development differently. Parents' gestures positively predicted their children's gestures of the same type. Autism severity negatively predicted iconic and conventional gestures. Overall growth was found in deictic rather than iconic and conventional gestures. Subgroup variation was also found. Specifically, children with better expressive language ability showed a decrease in deictic gestures. An increase in iconic and conventional gestures was found in children with more severe autism and those with poorer expressive language ability and intellectual functioning, respectively. Conclusions: Different types of gestures may have different growth trajectories and be predicted by different child-based factors. Particular attention should be given to children who never produced iconic gestures, which is more challenging and may not develop over a short period, and hence require direct instruction.

Abstract Many previous studies showed that some nodes should be in a sleeping state while the traffic is high. These nodes can wake up periodically to transmit data while they have data to transmit. The system throughput will be increased while each node can change between wake-up and sleeping states periodically. If a node can estimate the active rate and wake up at the optimal time, the collision probability decreases. This study proposes a Q-learning-based distributed queuing medium access control (QL-based DQMAC) protocol for Internet-of-Things (IoT) networks. In the proposed QL-based DQMAC, we derive the optimal number of contention IoT nodes. Each node calculates the active rate by itself through the Q-learning algorithm. Then, each node determines whether it will be active or in sleeping mode in the next contention period according to the active rate. Finding the optimal IoT nodes in each contention period decreases the probability of collision. The energy consumption due to the contention and delay for MAC contention is reduced owing to the lower number of contentions. Protocol comparison with other DQMAC protocols shows that the proposed QL-based DQMAC protocol achieves higher performance in IoT networks.