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Identifying and statistically analyzing soybean pod types are crucial for seed evaluation and yield estimation. Traditional visual assessment by breeding personnel is time-consuming, labor-intensive, and prone to subjective bias, especially with large datasets. Automatic assessment methods usually struggle with the highly confusing pod types with two and three seeds, affecting the model’s identification accuracy. To address these issues, we propose to improve the standard YOLOv5s object detection model to enhance the differentiation between pod types and to boost the model’s efficiency in prediction. To reduce the number of parameters and the computational load, we propose to introduce the FasterNet Block module in the FasterNet model into the original C3 module, leading to improvements in both detection accuracy and speed. To strengthen the feature extraction and representation for specific targets, the Efficient Multi-Scale Attention (EMA) module is incorporated into the C3 module of the backbone network, improving the identification of similar pod types. The Inner-IoU is combined with the CIoU as the loss function to further enhance detection accuracy and generalization. Experiments comparing FEI-YOLO with the baseline YOLOv5s show that FEI-YOLO achieves an mAP@0.5 of 98.6% and an mAP@0.5:0.95 of 81.1%, with improvements of 1.5% and 1.4%, respectively. Meanwhile, the number of parameters is reduced by 13.2%, and FLOPs decreased by 10.8%, demonstrating the model’s effectiveness and efficiency, enabling rapid and accurate identification of soybean pod types from images.

Automatic collecting of phenotypic information from plants has become a trend in breeding and smart agriculture. Targeting mature soybean plants at the harvesting stage, which are dense and overlapping, we have proposed the SPP-extractor (soybean plant phenotype extractor) algorithm to acquire phenotypic traits. First, to address the mutual occultation of pods, we augmented the standard YOLOv5s model for target detection with an additional attention mechanism. The resulting model could accurately identify pods and stems and could count the entire pod set of a plant in a single scan. Second, considering that mature branches are usually bent and covered with pods, we designed a branch recognition and measurement module combining image processing, target detection, semantic segmentation, and heuristic search. Experimental results on real plants showed that SPP-extractor achieved respective R2 scores of 0.93–0.99 for four phenotypic traits, based on regression on manual measurements.

Abstract Background Climate change profoundly shapes the population health at the global scale. However, there was still insufficient and inconsistent evidence for the association between heat exposure and chronic kidney disease (CKD). Methods In the present study, we studied the association of heat exposure with hospitalizations for cause-specific CKD using a national inpatient database in China during the study period of hot season from 2015 to 2018. Standard time-series regression models and random-effects meta-analysis were developed to estimate the city-specific and national averaged associations at a 7 lag-day span, respectively. Results A total of 768,129 hospitalizations for CKD was recorded during the study period. The results showed that higher temperature was associated with elevated risk of hospitalizations for CKD, especially in sub-tropical cities. With a 1 °C increase in daily mean temperature, the cumulative relative risks (RR) over lag 0–7 d were 1.008 [95% confidence interval (CI) 1.003–1.012] for nationwide. The attributable fraction of CKD hospitalizations due to high temperatures was 5.50%. Stronger associations were observed among younger patients and those with obstructive nephropathy. Our study also found that exposure to heatwaves was associated with added risk of hospitalizations for CKD compared to non-heatwave days (RR = 1.116, 95% CI 1.069–1.166) above the effect of daily mean temperature. Conclusions Short-term heat exposure may increase the risk of hospitalization for CKD. Our findings provide insights into the health effects of climate change and suggest the necessity of guided protection strategies against the adverse effects of high temperatures.

Amyotrophic lateral sclerosis (ALS) is a progressive and fatal neurodegenerative disease that leads to the massive loss of motor neurons in cerebrum, brain stem and spinal cord. It affects not only motor neurons but also other neurons and glial cells, resulting in the progressive muscle atrophy, the severe disability and the eventual death due to the respiratory failure. The pathogenesis of ALS is not fully understood. Currently, several factors are considered to be involved in the pathogenesis of ALS, such as genetic factors, imbalances in protein homeostasis, RNA metabolism disorders, mitochondrial dysfunctions, glutamate-mediated excitatory toxicities and intra-neuronal material transport disorders in neurons. The study of genetic mutations related to ALS pathogenesis will link the molecular and cellular mechanisms of the disease, thus enhancing the understanding of its occurrence and progression, thereby providing new insights for the pathogenesis of ALS. This review summarizes the current insights in the molecular genetic pathogenesis of ALS.

Ostrinia furnacalis Guenée is an important pest of maize. The current use of microbial agents to control O. furnacalis have been relatively successful. However, upon infestation with microorganisms, O. furnacalis initiates an innate immune response to defend itself against foreign invaders. Therefore, understanding the immune mechanisms in O. furnacalis is important to ensure a more efficient use of microbial agents for the control of this pest. In the present study, a gram-positive bacterium, Staphylococcus aureus, and a gram-negative bacterium, Escherichia coli, were used to induce an immune response in O. furnacalis. Transcriptomic sequencing was then used to screen for genes that were significantly differentially expressed after microbial induction and sequence comparison and phylogenetic analysis to identify immune-related genes the expression of which was significantly induced. The results show a large number of genes were differentially expressed in O. furnacalis after microbial induction, the majority of which were immune-related genes. The expression of some immune-related genes such as those encoding antimicrobial peptides, peptidoglycan recognition proteins and serine protease inhibitors were strongly induced. Overall, our study increases the understanding of the mechanism of the innate immune response in O. furnacalis and provides a theoretical basis for better biological control using microbial agents.

Background: Patients with diabetes are more likely to be predisposed to fractures compared to those without diabetes. In clinical practice, predicting fracture risk in diabetics is still difficult because of the limited availability and accessibility of existing fracture prediction tools in the diabetic population. The purpose of this study was to develop and validate models using machine learning (ML) algorithms to achieve high predictive power for fracture in patients with diabetes in China. Methods: In this study, the clinical data of 775 hospitalized patients with diabetes was analyzed by using Decision Tree (DT), Gradient Boosting Decision Tree (GBDT), Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), eXtreme Gradient Boosting (XGBoost) and Probabilistic Classification Vector Machines (PCVM) algorithms to construct risk prediction models for fractures. Moreover, the risk factors for diabetes-related fracture were identified by the feature selection algorithms. Results: The ML algorithms extracted 17 most relevant factors from raw clinical data to maximize the accuracy of the prediction results, including bone mineral density, age, sex, weight, high-density lipoprotein cholesterol, height, duration of diabetes, total cholesterol, osteocalcin, N-terminal propeptide of type I, diastolic blood pressure, and body mass index. The 7 ML models including LR, SVM, RF, DT, GBDT, XGBoost, and PCVM had f1 scores of 0.75, 0.83, 0.84, 0.85, 0.87, 0.88, and 0.97, respectively. Conclusions: This study identified 17 most relevant risk factors for diabetes-related fracture using ML algorithms. And the PCVM model proved to perform best in predicting the fracture risk in the diabetic population. This work proposes a cheap, safe, and extensible ML algorithm for the precise assessment of risk factors for diabetes-related fracture.

Abstract A low‐cost microfluidic platform integrated with a flexible heater was developed for in situ temperature‐dependent spectroscopic measurement at the point of care. After verifying the system by comparing on‐chip spectroscopic measurement of methylene blue with the conventional spectroscopy, we demonstrated its applications in temperature‐dependent absorption spectroscopy of a model biomolecule, curcumin. The system is portable, battery‐powered and requires ultra‐low volumes of analytes, which is highly suitable for point‐of‐care characterization.

Drought–flood abrupt alternation (DFAA), which is defined as the rapid transition between drought and flood in a short period, amplifies the negative impacts of individual drought or flood. DFAA is divided into drought to flood (DTF) and flood to drought (FTD) according to the sequences of drought and flood. Previous studies of identifying DFAA events have mostly been conducted over a long timescale (e.g., over a month), leading to inaccurate identification or omissions of DFAA events. In addition, frequency analysis of DTF and FTD events, which is vital for the design of hydraulic structures and water supply systems, has rarely been studied. This study establishes an identification method for DFAA events based on a daily-scale standardized weighted average of the precipitation index (SWAP) and conducts frequency analysis of DTF and FTD events based on copula theory. The Han River Basin (HRB), China, a crucial area for water resource management, was selected as the study area. Our findings disclose that DFAA events occurred less frequently with larger intensities and durations between 1961 and 2020. The temporal trends of DTF and FTD events varied similarly, while the number of DTF events were less than that of FTD events, indicating that FTD is the main performance of DFAA in HRB. In addition, the identification processes of DFAA events at the Wuhan station were analyzed in detail and proved that SWAP is an effective index for capturing the change of precipitation and accurately depicting the occurrences of droughts and floods. Furthermore, drought intensity (DI) and flood intensity (FI) were selected for bivariate frequency analysis. An OR case was defined as DI ≥ di or FI ≥ fi, while an AND case was defined as DI ≥ di and FI ≥ fi. The results of frequency analysis showed that joint return periods (JRPs) of DFAA events under the OR case are basically equal in three sub-basins, meaning a similar occurrence of probability of drought–flood disaster. JRPs under the AND case increased from the upper and middle basins to the lower basin as a whole, indicating the decreasing risk of abrupt drought–flood transitions. Overall, this study may have potential value in the early warning and mitigation of DFAA disaster.

A variety of studies have demonstrated the role of lipocalin 2 (LCN2) in both diabetes and neurological disorders. Nevertheless, the relationship between LCN2 and diabetic peripheral neuropathy (DPN) needs to be elucidated in humans. Therefore, this study aimed to investigate the association of LCN2 with DPN in type 2 diabetes (T2D). A total of 207 participants with T2D and 40 participants with normal glucose tolerance (NGT) were included in this study. All participants were classified in to DPN group and non-DPN (NDPN) group based on the Toronto Clinical Neuropathy Scoring (TCNS). Demographic and biochemical parameters were measured. Serum LCN2 levels wer e determined using an ELISA technique. Serum LCN2 levels in NGT group were lower t han those in either DPN group (P = 0.000) or NDPN group (P = 0.043), while serum LCN2 levels in DPN group were higher than NDPN group (P = 0.001). Moreover, serum LCN2 levels positively correlated to TCNS scores, which reflects neuropathy severity ( r = 0.438, P = 0.000). Multivariate stepwise regression analysis showed that BMI, triglycerides, an d diastolic pressure were independently associated with serum LCN2 in DPN. Additionally, logistic regression analysis demonstrated that LCN2 (odds ratio (OR) = 1.009) and diabetes duration (OR = 1.058) were independently associated with the occurrence of DP N in T2D. Our report reveals the association of serum LCN2 with DPN in T2D. LCN2 mig ht be used to evaluate DPN severity and serve a role in the pathogenesis of DPN.

Background. Oxidative stress is one of the most critical factors that contribute to the pathogenesis of neuronal damage, including diabetic peripheral neuropathy (DPN). Uric acid is a kind of natural antioxidant that plays a major role in the antioxidant capacity against oxidative stress. Here, we aim to determine the role of serum uric acid (SUA) in the DPN of patients with type 2 diabetes mellitus (T2DM). Patients and Methods. 106 patients with T2DM were recruited and divided into the DPN group and the control group. Clinical parameters, especially for motor nerve fiber conduction velocity and sensory nerve fiber conduction velocity, were collected. Differences between T2DM patients with and without DPN were compared. Correlation and regression analyses were performed to explore the association between SUA and DPN. Results. Compare with 57 patients with DPN, 49 patients without DPN showed lower HbA1c and elevated SUA levels. Additionally, SUA levels are negatively associated with the motor conduction velocity of tibial nerve with or without adjusting for HbA1c. Besides, it is suggested that decreased SUA levels may influence the motor conduction speed of the tibial nerve by multiple linear regression analysis. Moreover, we demonstrated that decreased SUA level is a risk factor for DPN in patients with T2DM by binary logistic regression analysis. Conclusion. Lower SUA is a risk factor for DPN in patients with T2DM. Additionally, decreased SUA may influence the damage of peripheral neuropathy, especially for motor conduction velocity of the tibial nerve.

ObjectiveThis study aims to investigate the effect of single urine C peptide/creatinine (UCPCR) in assessing the islet β Cell function of type 2 diabetes mellitus (T2DM) patients with different renal function.MethodsA total of 85 T2DM patients were recruited in this study, all the patients were assigned to one group with estimated glomerular filtration rate (eGFR)≤60 ml·min-1·1.73 m-2 and another group complicated with eGFR>60 ml·min-1·1.73 m-2. Serum creatinine, urine creatinine, serum fasting C-peptide (FCP), fasting blood glucose (FBG), glycosylated hemoglobin (HbA1C) and 24-hour urinary C-peptide (24hUCP) were measured. The modified homeostasis model assessment-islet β cell function [HOMA-islet (CP-DM)], the modified homeostasis model assessment-insulin resistance [HOMA-IR(CP)] and UCPCR were calculated.ResultsWhen compared with group eGFR ≤60 ml·min-1·1.73 m-2, the levels of UCPCR, FCP, the modified HOMA-IR(CP) and HOMA-islet (CP-DM) were promoted and the concentrations of HbA1C, FPG, creatinine were decreased in the patients of eGFR>60 ml·min-1·1.73 m-2 (P

Macamides are a class of amide alkaloids that are only found in maca and are widely considered to be its bioactive marker compounds. More than thirty macamide monomers have been identified in recent years; however, it is difficult to obtain a single macamide monomer from the maca plant because of their similar structures and characteristics. We used the carbodiimide condensation method (CCM) to efficiently synthesize five typical macamides, including N-benzyl-hexadecanamide (NBH), N-benzyl-9Z,12Z,15Z-octadecenamide, N-(3-methoxybenzyl)-9Z,12Z-octadecenamide, N-benzyl-9Z,12Z-octadecenamide, and N-(3-methoxybenzyl)-9Z,12Z,15Z-octadecadienamide. All the synthesized macamides were purified by a one-step HPLC with a purity of more than 95%. NBH is the most abundant macamide monomer in natural maca, and it was selected to evaluate the anti-fatigue effects of macamides. The results indicated that NBH could enhance the endurance capacity of mice by increasing liver glycogen levels and decreasing blood urea nitrogen, lactate dehydrogenase, blood ammonia, and blood lactic acid levels. Macamides might be the active substances that give maca its anti-fatigue active function.

Titanium (Ti) implants are widely used in orthopedic and dental applications due to their excellent biocompatibility and mechanical properties. However, bacterial adhesion and subsequent biofilm formation on implant surfaces pose a significant risk of postoperative infections and complications. Conventional surface modifications often lack long-lasting antibacterial efficacy, necessitating the development of novel coatings with enhanced antimicrobial properties. This study aims to develop a novel Ag-TiO2-OTS (Silver-Titanium dioxide-Octadecyltrichlorosilane, ATO) nanocomposite coating, through a chemical plating method. By employing a 'resist-killing-disintegrate' approach, the coating is designed to inhibit bacterial adhesion effectively, and facilitate pollutant removal with lasting effects. Characterization of the coatings was performed using spectroscopy, electron microscopy, and contact angle analysis. Antibacterial efficacy, quantitatively evaluated against E. coli and S. aureus over 168 h, showed a significant reduction in bacterial adhesion by 76.6% and 66.5% respectively, and bacterial removal rates were up to 83.8% and 73.3% in comparison to uncoated Ti-base material. Additionally, antibacterial assays indicated that the ratio of the Lifshitz-van der Waals apolar component to electron donor surface energy components significantly influences bacterial adhesion and removal, underscoring a tunable parameter for optimizing antibacterial surfaces. Biocompatibility assessments with the L929 cell line revealed that the ATO coatings exhibited excellent biocompatibility, with minimal cytotoxicity and no significant impact on cell proliferation or apoptosis. The ATO coatings provided a multi-functionality surface that not only resists bacterial colonization but also possesses self-cleaning capabilities, thereby marking a substantial advancement in the development of antibacterial coatings for medical implants. [ABSTRACT FROM AUTHOR]

The rotary vector reducer is the core component of industrial robots, and the transmission efficiency is undoubtedly an important indicator of transmission performance. In addition, the assembly dimensional chain leads to clearance between parts, which can have a certain impact on the transmission efficiency. During previous studies, this effect was often ignored. Firstly, the cycloid tooth profile is a relatively large collection of points. Therefore, a more efficient tooth profile model is employed as the basis for the calculation. Secondly, the contact between the cycloid and the pinion is determined by experimental observation to be a point contact rather than a theoretical line contact. Moreover, the dynamics and friction loss models of the cycloid–pinion system are constructed. Finally, the assembly dimensional chain is introduced into the different structural designs. The clearance results were obtained with the extreme value method. The results show that the addition of the pinion sleeve allows the system to have a smoother drive process and a more efficient transmission; this was verified with ADAMS.

Automatic fault detection, localization, and repairing have always been a research hot spot. Because software faults can appear anywhere in a software, it is impossible to automatically repair all types of faults. One possible solution is to detect, localize, and fix a specific fault each time according to the fault signature pattern. Along this direction, we propose an approach that detects pairwise-constraint software faults using algebra graph representation. The approach takes a program and a pairwise-constraint as inputs and generates a fault report for programmers. It firstly generates a constraint control flow graph (CFG) that is labeled using pairwise constraint and then translates the constraint CFG as a path expression using algebra graph representation, which is an abstract path model for the program. Finally, it employs a detection algorithm to detect whether the program contains pairwise-constraint faults. We perform case studies to validate the effectiveness of our approach. The preliminary results show that the approach can detect pairwise-constraint software faults before software testing.

Peptidoglycan recognition proteins (PGRPs) are important components of insect immune systems, in which they play key roles. We cloned and sequenced two full-length PGRP, named OfPGRP-A and OfPGRP-B, from the Asian corn borer, Ostrinia furnacalis. These two genes comprise open reading frames of 658 and 759 bp, encoding proteins of 192 and 218 amino acids, respectively. qPCR showed that OfPGRP-A and OfPGRP-B are prominently expressed in the midgut of O. furnacalis fourth instar larvae. After inoculation with Staphylococcus aureus and Bacillus thuringiensis, the expression of OfPGRP-A was significantly upregulated, whereas the expression of OfPGRP-B was enhanced after inoculation with Escherichia coli. This suggests that OfPGRP-A mainly recognizes Gram-positive bacteria and may participate in the Toll signaling pathways, while OfPGRP-B identifies Gram-negative bacteria and may participate in Imd signaling pathways. Our results provide insights into the roles of PGRPs in O. furnacalis immune function and a foundation for using pathogens for the biological control of O. furnacalis.

Device fingerprinting has lately received great attention due to its effectiveness in fraud detection, secure authentication, and user tracking. Whereas fingerprinting performs well on labeled device data using classification methods, there are several researches concentrated on unlabeled mobile device data and existing methods often lack in precision and efficiency. To overcome this challenge, we focus on the use of the mobile device’s configuration-related characteristics as a mean to build a device fingerprint, which allows to distinctively and reliably characterize each device. In addition, an incremental clustering approach is proposed to classify unlabeled device data into clusters on the basis of their similarity. Moreover, we customize individual distance threshold for each user according to their device configurations’ modifying frequency, in order to construct a precise authentication mechanism between users and devices. The proposed clustering model and device fingerprinting system are evaluated on 8220 fingerprints from 815 different devices. The experimental results demonstrate the effectiveness and efficiency of our algorithms.

Aim. The slow coronary flow (SCF) phenomenon was characterized by delayed perfusion of epicardial arteries, and no obvious coronary artery lesion in coronary angiography. The prognosis of patients with slow coronary flow was poor. However, there is lack of rapid, simple, and accurate method for SCF diagnosis. This study aimed to explore the utility of plasma choline as a diagnostic biomarker for SCF. Methods. Patients with coronary artery stenosis

People with diabetes have higher risks of various infections. Therefore, these diabetic patients might be at increased risk of COVID-19 and have a poorer prognosis. Up until now, little is known about critical role in the pathogenesis. This study aims to investigate the clinical characteristics of COVID-19 patients with diabetes and secondary hyperglycemia, as well as to explore the purported mechanisms. 80 confirmed COVID-19 subjects were classified into the euglycemia group, secondary hyperglycemia group, and diabetes group. Severity of COVID-19 was defined based on the diagnostic and treatment guideline for SARS-CoV-2 issued by Chinese National Health Committee. According to the severity of the disease, patients of the mild type and common type were registered as mild cases (patients with minimal symptoms and negative CT findings), while patients of the severe type and critical type were enrolled as severe cases (patients with positive CT findings and different extent of clinical manifestations). Patients in the diabetes group were older than those in the euglycemia group, and most of them were male. In the diabetes group, the proportion of severe cases was 57.14%, which was significantly higher than those in the other two groups, and 32% of the COVID-19 patients diagnosed as severe cases were with diabetes. The CD4+ cell counts in the diabetes group were lower than those in the other two groups, while the levels of LDH and hs-CRP were higher. Compared with the euglycemia group, the CD3+ cell counts and the CD4+/CD8+ ratio were decreased, whereas the levels of IL-6 were increased in the secondary hyperglycemia group and diabetes group, with the diversities in the diabetes group being especially more significant. The Spearman correlation analysis revealed that the presence of diabetes was positively correlated with age, hs-CRP, LDH, IL-6, CD8+ cells, and severity of COVID-19 and negatively correlated with CD3+ cell counts, CD4+ cell counts, and CD4+/CD8+ ratio. Compared with the other two groups, the diabetes group exhibited more diverse and multifocal features in CT imagings. Diabetes is a risk factor for influence of the progression and prognosis of COVID-19 due to ongoing inflammation and impaired immune response.

Maca (Lepidium meyenii) has emerged as a popular functional plant food because of its medicinal properties and nutritional value. Macamides, as the exclusively active ingredients found in maca, are a unique series of non-polar, long-chain fatty acid N-benzylamides with multiple bioactivities such as antifatigue characteristics and improving reproductive health. In this study, a new kind of macamide, N-benzyl eicosapentaenamide (NB-EPA), was identified from maca. We further explore its potential neuroprotective role in hypoxic–ischemic brain injury. Our findings indicated that treatment with biosynthesized NB-EPA significantly alleviates the size of cerebral infarction and improves neurobehavioral disorders after hypoxic–ischemic brain damage in neonatal mice. NB-EPA inhibited the apoptosis of neuronal cells after ischemic challenge. NB-EPA improved neuronal cell survival and proliferation through the activation of phosphorylated AKT signaling. Of note, the protective property of NB-EPA against ischemic neuronal damage was dependent on suppression of the p53–PUMA pathway. Taken together, these findings suggest that NB-EPA may represent a new neuroprotectant for newborns with hypoxic–ischemic encephalopathy.

Endothelial progenitor cells (EPCs) contribute to the endogenous endothelial repair program during hypercholesterolemia. EPC count and migratory and proliferative capacities remain unchanged in the premenopausal female with hypercholesterolemia. However, the changes of count and activity of circulating EPCs in the hypercholesterolemic postmenopausal females are unknown. Here, we find that the migratory and proliferative capacities of circulating EPCs were decreased in patients with hypercholesterolemia versus normocholesterolemia. No significant differences were found between postmenopausal females and age-matched males. NO production showed positive correlation with the activity and count of circulating EPCs in patients with hypercholesterolemia. Flow-mediated dilatation (FMD) is directly interrelated with EPC counts and function. Our findings reveal that decreased EPC count and endothelial dysfunction lead to less NO production in hypercholesterolemic postmenopausal females. Maintaining the EPC numbers and activity might be emerging as a potential therapeutic strategy to reduce the risk of cardiovascular injury in elder women.

The purposes of this study were to evaluate the expression of retinol-binding protein 4 (RBP4) in diabetic rats with atherosclerosis and to investigate the role of vitamin D intervention. Male Wistar rats were randomly divided into 4 groups, including the control group (NC), the diabetic rats (DM1), the untreated diabetic atherosclerosis rats (DM2), and the vitamin D-treated diabetic atherosclerosis rats (DM3). The levels of serum and adipose RBP4, fasting insulin (FINS), fasting plasma glucose (FPG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-c), triglycerides (TG), low-density lipoprotein cholesterol (LDL-c), 25-hydroxyvitamin D [25(OH)D], C-reactive protein (CRP), and systolic blood pressure (SBP) were measured. Homeostasis model assessment of insulin resistance (HOMA-IR), homeostasis model assessment β-cell function index (HOMA-β), and atherogenic indexes (AI) were calculated. Compared with group NC, the levels of RBP4, TG, LDL-c, FPG, FINS, CRP, AI1, AI2, SBP, and HOMA-IR increased, while the levels of HDL-c, 25(OH)D, and HOMA-β decreased in groups DM1 and DM2. After 8 weeks of vitamin D supplementation in group DM3, the levels of 25(OH)D and HOMA-β increased and the levels of LDL-c, TC, HOMA-IR, FINS, CRP, RBP4, AI1, AI2, and SBP decreased significantly when compared with group DM2 (P

Quantum error mitigation (QEM) is crucial for obtaining reliable results on quantum computers by suppressing quantum noise with moderate resources. It is a key factor for successful and practical quantum algorithm implementations in the noisy intermediate scale quantum (NISQ) era. Since quantum‐classical hybrid algorithms can be executed with moderate and noisy quantum resources, combining QEM with quantum‐classical hybrid schemes is one of the most promising directions toward practical quantum advantages. This work shows how the variational quantum‐neural hybrid eigensolver (VQNHE) algorithm, which seamlessly combines the expressive power of a parameterized quantum circuit with a neural network, is inherently noise resilient with a unique QEM capacity, which is absent in vanilla variational quantum eigensolvers (VQE). The study carefully analyzes and elucidates the asymptotic scaling of this unique QEM capacity in VQNHE from both theoretical and experimental perspectives. Finally, a variational basis transformation is proposed for the Hamiltonian to be measured under the VQNHE framework, yielding a powerful tri‐optimization setup, dubbed as VQNHE++. VQNHE++ can further enhance the quantum‐neural hybrid expressive power and error mitigation capacity. [ABSTRACT FROM AUTHOR]

In order to develop efficient lures for soybean pod borer Leguminivora glycinivorella (Matsumura) in China, (E,E)-8,10-dodecadienyl acetate (EE-8,10-12:Ac), the main component of the pheromone of L. glycinivorella, and 12 structurally-related compounds were synthesised in good overall yields, regiospecificities, and stereo-selectivities via coupling reactions catalysed by Li2CuCl4. The effect of different synthetic compounds, alone or in combination with EE-8,10-12:Ac, on numbers of captured L. glycinivorella males was evaluated. EE-8,10-12:Ac, (E)-10-dodecenyl acetate (E-10-12:Ac), (E)-8-dodecenol (E-8-12:OH), tetradecyl acetate (14:Ac), and (Z)-9-tetradecenyl acetate (Z-9-14:Ac) alone displayed different attractiveness to L. glycinivorella males. 14:Ac, E-8-12:OH, E-10-12:Ac, (E,E)-8,10-dodecadienal (EE-8,10-12:Ald), (E)-8-dodecenal (E-8-12:Ald), (E)-10-dodecenal (E-10-12:Ald) and Z-9-14:Ac all showed a synergistic effect to EE-8,10-12:Ac at certain dosages. The binary mixtures of EE-8,10-12:Ac and E-10-12:Ald, Z-9-14:Ac,14:Ac, E-8-12:Ald, EE-8,10-12:Ald, E-8-12:OH, or E-10-12:Ac in suitable ratios give 17.00-, 10.98-, 10.67-, 6.73-, 5.54-, 4.30- and 4.50-fold increases in trap catch, respectively, over the standard pheromone lure, and as novel pheromone blends, demonstrated potential use in pheromone traps to monitor or control L. glycinivorella populations in China.