Search

To efficiently train the billions of parameters in a giant model, sharing the parameter-fragments within the Federated Learning (FL) framework has become a popular pattern, where each client only trains and shares a fraction of parameters, extending the training of giant models to the broader resources-constrained scenarios. Compared with the previous works where the models are fully exchanged, the fragment-sharing pattern poses some new challenges for the backdoor attacks. In this paper, we investigate the backdoor attack on giant models when they are trained in an FL system. With the help of fine-tuning technique, a backdoor attack method is presented, by which the malicious clients can hide the backdoor in a designated fragment that is going to be shared with the benign clients. Apart from the individual backdoor attack method mentioned above, we additionally show a cooperative backdoor attack method, in which the fragment of a malicious client to be shared only contains a part of the backdoor while the backdoor is injected when the benign client receives all the fragments from the malicious clients. Obviously, the later one is more stealthy and harder to be detected. Extensive experiments have been conducted on the datasets of CIFAR-10 and CIFAR-100 with the ResNet-34 as the testing model. The numerical results show that our backdoor attack methods can achieve an attack success rate close to 100% in about 20 rounds of iterations.

Federated Learning (FL) is currently a widely used collaborative learning framework, and the distinguished feature of FL is that the clients involved in training do not need to share raw data, but only transfer the model parameters to share knowledge, and finally get a global model with improved performance. However, recent studies have found that sharing model parameters may still lead to privacy leakage. From the shared model parameters, local training data can be reconstructed and thus lead to a threat to individual privacy and security. We observed that most of the current attacks are aimed at client-specific data reconstruction, while limited attention is paid to the information leakage of the global model. In our work, we propose a novel FL attack based on shared model parameters that can deduce the data distribution of the global model. Different from other FL attacks that aim to infer individual clients’ raw data, the data distribution inference attack proposed in this work shows that the attackers can have the capability to deduce the data distribution information behind the global model. We argue that such information is valuable since the training data behind a well-trained global model indicates the common knowledge of a specific task, such as social networks and e-commerce applications. To implement such an attack, our key idea is to adopt a deep reinforcement learning approach to guide the attack process, where the RL agent adjusts the pseudo-data distribution automatically until it is similar to the ground truth data distribution. By a carefully designed Markov decision proces (MDP) process, our implementation ensures our attack can have stable performance and experimental results verify the effectiveness of our proposed inference attack.

Abstract COVID-19 has caused a worldwide pandemic, creating an urgent need for early detection methods. Breath analysis has shown great potential as a non-invasive and rapid means for COVID-19 detection. The objective of this study is to detect patients infected with SARS-CoV-2 and even the possibility to screen between different SARS-CoV-2 variants by analysis of carbonyl compounds in breath. Carbonyl compounds in exhaled breath are metabolites related to inflammation and oxidative stress induced by diseases. This study included a cohort of COVID-19 positive and negative subjects confirmed by reverse transcription polymerase chain reaction between March and December 2021. Carbonyl compounds in exhaled breath were captured using a microfabricated silicon microreactor and analyzed by ultra-high-performance liquid chromatography-mass spectrometry (UHPLC-MS). A total of 321 subjects were enrolled in this study. Of these, 141 (85 males, 60.3%) (mean ± SD age: 52 ± 15 years) were COVID-19 (55 during the alpha wave and 86 during the delta wave) positive and 180 (90 males, 50%) (mean ± SD age: 45 ± 15 years) were negative. Panels of a total of 34 ketones and aldehydes in all breath samples were identified for detection of COVID-19 positive patients. Logistic regression models indicated high accuracy/sensitivity/specificity for alpha wave (98.4%/96.4%/100%), for delta wave (88.3%/93.0%/84.6%) and for all COVID-19 positive patients (94.7%/90.1%/98.3%). The results indicate that COVID-19 positive patients can be detected by analysis of carbonyl compounds in exhaled breath. The technology for analysis of carbonyl compounds in exhaled breath has great potential for rapid screening and detection of COVID-19 and for other infectious respiratory diseases in future pandemics.

ObjectiveTo evaluate long-term impact of periconceptional folic acid supplementation on the intelligence quotient (IQ) of offspring, as well as potential modification effects of nuts consumption during pregnancy and breastfeeding duration on the association of maternal folic acid supplementation with offspring′s IQ.MethodsThe information on maternal folic acid supplementation and frequency of nuts consumption was collected through a structured questionnaire interview conducted at 12 – 16 gestational weeks among pregnant women being enrolled in the Shanghai-Minhang Birth Cohort Study (S-MBCS) during April – December, 2012. Information on duration of exclusive breastfeeding of the mothers was collected during postnatal follow-up visits. The mothers′ children were followed up and the children′s intellectual development at the age of 6 years was evaluated with Wechsler Intelligence Scale for Children, Fourth Edition (WISC-IV)-simplified Chinese Version at follow-ups by well-trained interviewers. Eventually, 530 mother-child pairs with complete information were included in this study. Multiple linear regression model was used to analyze the correlation between periconceptional folic acid supplementation and the children′s IQ at age of 6 years and to explore potential modification effects of frequency of maternal nuts consumption during pregnancy or duration of exclusive breastfeeding on the correlation. ResultsMultiple analysis showed that the children with maternal folic acid supplements regularly during periconceptional period had better IQ performance, with significantly increased scores of perceptual reasoning index (PRI) of 4.57 (95% confidence interval [95%CI]: 0.13 – 9.02) compared to the children without the maternal supplementation; such effect of maternal folic acid supplementation beginning before the pregnancy was similar to that of supplementation beginning after the pregnancy, with the increased PRI scores of 4.41 (95%CI : – 0.25 – 9.07) and 4.71 (95%CI: 0.11 – 9.31), respectively. In addition, the effect of maternal folic acid supplementation on the children′s PRI was stronger when combined by maternal nuts consumption of ≤ 2 days/week during pregnancy (increased PRI = 5.80, 95%CI: 0.52 – 11.08) or by exclusive breastfeeding of their children less than 6 months (increased PRI = 6.36, 95%CI: 0.89 – 11.83). ConclusionMaternal periconceptional folic acid supplementation is associated with the improvement of offspring’s PRI at 6 years, indicating a protective effect of the supplementation on long-term intelligence development of the children. The beneficial effect was more profound when the supplementation being combined by nuts consumption two days/ per week and less during pregnancy or by exclusive breastfeeding of their children less than 6 months.

Background/Objectives: With the increase of reactive oxygen species (ROS) production, cancer cells can avoid cell death and damage by up-regulating antioxidant programs. Therefore, it will be more effective to induce cell death by using targeted strategies to further improve ROS levels and drugs that inhibit antioxidant programs. Methods: Considering that dihydroartemisinin (DHA) can cause oxidative damage to protein, DNA, or lipids by producing excessive ROS, while, disulfiram (DSF) can inhibit glutathione (GSH) levels and achieve the therapeutic effect by inhibiting antioxidant system and amplifying oxidative stress, they were co-loaded onto the copper peroxide nanoparticles (CuO2) coated with copper tannic acid (Cu-TA), to build a drug delivery system of CuO2@Cu-TA@DSF/DHA nanoparticles (CCTDD NPs). In response to the tumor microenvironment, DHA interacts with copper ion (Cu2+) to produce ROS, and a double (diethylthiocarbamate)-copper (II) (CuET) is generated by the complexation of DSF and Cu2+, which consumes GSH and inhibits antioxidant system. Meanwhile, utilizing the Fenton-like effect induced by the multi-copper mode can achieve ROS storm, activate the MAPK pathway, and achieve chemotherapy (CT) and chemodynamic (CDT). Results: Taking pancreatic cancer cell lines PANC-1 and BxPC-3 as the research objects, cell line experiments in vitro proved that CCTDD NPs exhibit efficient cytotoxicity on cancer cells. Conclusions: The CCTDD NPs show great potential in resisting pancreatic cancer cells and provides a simple strategy for designing powerful metal matrix composites.

Objective: Pneumonia, both in the community and the hospital setting, represents a significant cause of morbidity and mortality in the cardiothoracic patient population. Diagnosis of pneumonia can be masked by other disease processes and is often diagnosed after the patient is already experiencing the disease. A noninvasive, sensitive test for pneumonia could decrease hospitalizations and length of stay for patients. We have developed a porcine model of pneumonia and evaluated the exhaled breath of infected pigs for biomarkers of infection. Methods: Anesthetized 60-kg adult pigs were intubated, and a bronchoscope was used to instill a solution containing 12 × 108 cfu of methicillin-sensitive Staphylococcus aureus or a control solution without bacteria (Sham) into the distal airways. The pigs were then reintubated on postoperative days 3, 6, and 9, with bronchoscopic bronchial lavages taken at each time point. At each time point, a 500-mL breath was captured from each pig. The breath was evacuated over a silicon microchip, with the volatile carbonyl compounds from the breath captured via oximation reaction, and the results of this capture were analyzed by ultra-high performance liquid chromatography mass spectrometry. Results: A total of 64% of the pigs inoculated with methicillin-sensitive S. aureus demonstrated consolidation on chest radiography and increasing counts of methicillin-sensitive S. aureus in the bronchial lavages over the span of the experiment, consistent with development of pneumonia. Analysis of the exhaled breath demonstrated 1 carbonyl compound (2-pentenal) that increased 10-fold over the span of the experiment, from an average of 0.0294 nmol/L before infection to an average of 0.3836 nmol/L on postoperative day 9. The amount of 2-pentenal present was greater in the breath of infected pigs than in the noninfected pigs or the sham inoculated pigs at postoperative days 6 and 9. Using an elevated concentration of 2-pentenal as a marker of infection yielded a sensitivity of 88% and specificity of 92% at postoperative day 6, and a sensitivity and specificity of 100% at postoperative day 9. Conclusions: We were able to successfully develop a clinical pneumonia in adult 60-kg pigs. The concentration of 2-pentenal correlated with the presence of pneumonia, demonstrating the potential for this compound to function as a biomarker for methicillin-sensitive S. aureus infection in pigs.

Wireless networks have become integral to modern communication systems, enabling the seamless exchange of information across a myriad of applications. However, the inherent characteristics of wireless channels, such as fading, interference, and openness, pose significant challenges to achieving fault-tolerant consensus within these networks. Fault-tolerant consensus, a critical aspect of distributed systems, ensures that network nodes collectively agree on a consistent value even in the presence of faulty or compromised components. This survey paper provides a comprehensive overview of fault-tolerant consensus mechanisms specifically tailored for wireless networks. We explore the diverse range of consensus protocols and techniques that have been developed to address the unique challenges of wireless environments. The paper systematically categorizes these consensus mechanisms based on their underlying principles, communication models, and fault models. It investigates how these mechanisms handle various types of faults, including communication errors, node failures, and malicious attacks. It highlights key use cases, such as sensor networks, Internet of Things (IoT) applications, wireless blockchain, and vehicular networks, where fault-tolerant consensus plays a pivotal role in ensuring reliable and accurate data dissemination.

Background Knowledge about whether, how much, and why individuals are willing to pay for health apps is limited. Objectives This study aimed to examine (1) the proportion of individuals willing to pay for health apps, (2) their willingness to pay (WTP; i.e. the maximum price the individual is willing to pay) for health apps, (3) the sociodemographic correlates determining whether individuals are willing to pay for these apps, (4) the sociodemographic correlates of their WTP, and (5) reasons for being unwilling to pay. Methods Six hundred adults were invited to participate in a questionnaire survey examining their sociodemographic characteristics, WTP for health apps, and reasons for being unwilling to pay. Sociodemographic characteristics and WTP for health apps were analyzed using descriptive statistics. Logistic regression was applied to examine the sociodemographic variables correlated with whether individuals were willing to pay for health apps. Among those who were willing to pay, log-linear regression was conducted to examine the sociodemographic correlates of their WTP. The reasons for unwillingness to pay were descriptively analyzed. Results A total of 577 individuals completed the questionnaire. Of them, 58.9% were willing to pay for health apps. Their median WTP was HK$50 (HK$1 ≈ US$0.13). Participants with a bachelor's degree or higher and those who had previously installed health apps were more inclined to pay for health apps. WTP was positively associated with the maximum price previously paid for a health app. The most frequently cited reasons for being unwilling to pay were the belief that the government should provide free health apps, distrust in health apps, and a lack of awareness of health apps and their benefits. Conclusions This study provides insights that can inform strategies to enhance the marketability, affordability, and accessibility of health apps.

Abstract Diterpene synthase VenA is responsible for assembling venezuelaene A with a unique 5-5-6-7 tetracyclic skeleton from geranylgeranyl pyrophosphate. VenA also demonstrates substrate promiscuity by accepting geranyl pyrophosphate and farnesyl pyrophosphate as alternative substrates. Herein, we report the crystal structures of VenA in both apo form and holo form in complex with a trinuclear magnesium cluster and pyrophosphate group. Functional and structural investigations on the atypical 115DSFVSD120 motif of VenA, versus the canonical Asp-rich motif of DDXX(X)D/E, reveal that the absent second Asp of canonical motif is functionally replaced by Ser116 and Gln83, together with bioinformatics analysis identifying a hidden subclass of type I microbial terpene synthases. Further structural analysis, multiscale computational simulations, and structure-directed mutagenesis provide significant mechanistic insights into the substrate selectivity and catalytic promiscuity of VenA. Finally, VenA is semi-rationally engineered into a sesterterpene synthase to recognize the larger substrate geranylfarnesyl pyrophosphate.

There is growing evidence that prenatal exposure to Per- and polyfluoroalkyl substances (PFAS) was associated with childhood obesity, but evidence on multiple adiposity measures including arm circumference (AC), and waist circumference (WC) among Chinese children is limited. We investigated the associations of prenatal exposure to PFAS with adiposity measures of children at 4 and 6 years of age in the Shanghai-Minhang Birth Cohort Study. A total of 573 mother-child pairs with maternal PFAS concentrations and at least one measurement of adiposity measures of children were included in the present study. Eleven PFAS were assessed in maternal fasting blood samples. Information on children's weight, height, AC, and WC was collected at follow-ups. Weight for age Z score (WAZ), body mass index for age Z score (BMIz), and children overweight were calculated based on the World Health Organization Child Growth Standards. Multivariate linear regression, Poisson regression with robust error variance, and Bayesian Kernel Machine Regression (BKMR) models were used to examine the associations of prenatal exposure to PFAS with children's adiposity measures. Eight PFAS with detection rates above 85 % were included in the analyses. In the multivariate linear regression models, maternal PFNA concentrations were associated with a greater AC (β = 0.29, 95 % Confidence Interval (CI): 0.04–0.55) in 4-year-old children and with an increase in WAZ (β = 0.26, 95 % CI: 0.06–0.46), BMIz (β = 0.31, 95 % CI: 0.09–0.53), AC (β = 0.49, 95 % CI: 0.08–0.90), and WC (β = 1.47, 95 % CI: 0.41–2.52) in 6-year-old children. We also observed the associations of maternal concentrations of PFOS, PFNA, PFUdA, and PFTrDA with the increased risk of children overweight in 6-year-old children. BKMR models further supported the findings from multivariate linear regression and Poisson regression models, and identified PFNA as the most important contributor. Moreover, the associations described above were generally more pronounced in girls. In conclusion, prenatal exposure to PFAS was associated with an increased risk of children's adiposity with a sex-specific manner, and PFNA contributed most to the associations after controlling for the effect of co-exposure to other PFAS compounds, especially among girls at 6 years of age.

Abstract BackgroundThere is growing interest in mobile health apps; however, not all of them have been successful. The most common issue has been users’ nonadoption or abandonment of health apps because the app designs do not meet their preferences. Therefore, to facilitate design-preference fit, understanding consumers’ preferences for health apps is necessary, which can be accomplished by using a discrete choice experiment. ObjectiveThis study aims to examine consumer preferences for health apps and how these preferences differ across individuals with different sociodemographic characteristics and health app usage and purchase experiences. MethodsA cross-sectional discrete choice experiment questionnaire survey was conducted with 593 adults living in Hong Kong. A total of 7 health app attributes that might affect consumers’ preferences for health apps were examined, including usefulness, ease of use, security and privacy, health care professionals’ attitudes, smartphone storage consumption, mobile data consumption, and cost. Mixed-effect logit regressions were used to examine how these attributes affected consumer preferences for health apps. Fixed effects (coefficient β) of the attributes and random effects of individual differences were modeled. Subgroup analyses of consumer preferences by sex, age, household income, education level, and health app usage and purchase experiences were conducted. ResultsCost was the attribute that had the greatest effect on consumers’ choice of health apps (compared to HK $10 [US $1.27]—HK $50 [US $6.37]: β=−1.064; PPno security insurancesome security policiesPcomplete security systemPslightly usefulmoderately usefulPvery usefulPdata-consuminga bit data-consumingPdata-savingPPPneutralmoderately supportivePvery supportivePnot easy to usemoderately easy to usePvery easy to useP ConclusionsIt is recommended that future health apps keep their mobile data and phone storage consumption low, include a complete security system to protect personal health information, provide useful content and features, adopt user-friendly interfaces, and involve health care professionals. In addition, health app developers should identify the characteristics of their intended users and design and develop health apps to fit the preferences of the intended users.

Edge intelligence is an emerging technology that enables artificial intelligence on connected systems and devices in close proximity to the data sources. decentralized collaborative learning (DCL) is a novel edge intelligence technique that allows distributed clients to cooperatively train a global learning model without revealing their data. DCL has a wide range of applications in various domains, such as smart city and autonomous driving. However, DCL faces significant challenges in ensuring its trustworthiness, as data isolation and privacy issues make DCL systems vulnerable to adversarial attacks that aim to breach system confidentiality, undermine learning reliability or violate data privacy. Therefore, it is crucial to design DCL in a trustworthy manner, with a focus on security, robustness, and privacy. In this survey, we present a comprehensive review of existing efforts for designing trustworthy DCL systems from the three key aformentioned aspects: security, robustness, and privacy. We analyze the threats that affect the trustworthiness of DCL across different scenarios and assess specific technical solutions for achieving each aspect of trustworthy DCL (TDCL). Finally, we highlight open challenges and future directions for advancing TDCL research and practice.

A public-private-graph (pp-graph) is developed to model social networks with hidden relationships, and it consists of one public graph in which edges are visible to all users, and multiple private graphs in which edges are only visible to its endpoint users. In contrast with conventional graphs where the edges can be visible to all users, it lacks accurate indexes to evaluate the importance of a vertex in a pp-graph. In this paper, we first propose a novel concept, public-private-core (pp-core) number based on the k-core number, which integrally considers both the public graph and private graphs of vertices, to measure how critical a user is. We then give an efficient algorithm for the pp-core number computation, which takes only linear time and space. Considering that the graphs can be always evolving over time, we also present effective algorithms for pp-core maintenance after the graph changes, avoiding redundant re-computation of pp-core number. Extension experiments conducted on real-world social networks show that our algorithms achieve good efficiency and stability. Compared to recalculating the pp-core numbers of all vertices, our maintenance algorithms can reduce the computation time by about 6–8 orders of magnitude.

BackgroundDespite the great potential of eHealth, substantial costs are involved in its implementation, and it is essential to know whether these costs can be justified by its benefits. Such needs have led to an increased interest in measuring the benefits of eHealth, especially using the willingness to pay (WTP) metric as an accurate proxy for consumers’ perceived benefits of eHealth. This offered us an opportunity to systematically review and synthesize evidence from the literature to better understand WTP for eHealth and its influencing factors. ObjectiveThis study aimed to provide a systematic review of WTP for eHealth and its influencing factors. MethodsThis study was performed and reported as per the Cochrane Collaboration and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. PubMed, CINAHL Plus, Cochrane Library, EconLit, and PsycINFO databases were searched from their inception to April 19, 2022. We conducted random-effects meta-analyses to calculate WTP values for eHealth (at 2021 US dollar rates) and meta-regression analyses to examine the factors affecting WTP. ResultsA total of 30 articles representing 35 studies were included in the review. We found that WTP for eHealth varied across studies; when expressed as a 1-time payment, it ranged from US $0.88 to US $191.84, and when expressed as a monthly payment, it ranged from US $5.25 to US $45.64. Meta-regression analyses showed that WTP for eHealth was negatively associated with the percentages of women (β=−.76; P

Analysis of large-scale networks generally requires mapping high-dimensional network data to a low-dimensional space. We thus need to represent the node and connections accurate and effectively, and representation learning could be a promising method. In this paper, we investigate a novel social similarity-based method for learning network representations. We first introduce neighborhood structural features for representing node identities based on higher-order structural parameters. Then the node representations are learned by a random walk approach that based on the structural features. Our proposed truss2vec is able to maintain both structural similarity of nodes and domain similarity. Extensive experiments have shown that our model outperforms the state-of-the-art solutions.

Findings from epidemiological studies on the associations between prenatal perfluoroalkyl substances (PFASs) exposure and children's neurodevelopment were inconclusive, and most studies did not account for the co-exposure to multiple PFASs with strong inter-correlations. The present study aimed to assess the effects of prenatal multiple PFAS exposure on children’s neurobehavioral development based on 614 mother-infant pairs in the Shanghai-Minhang Birth Cohort Study. Eight PFAS concentrations were measured in maternal plasma at 12–16 weeks of gestation. Children’s neurobehavioral development at 2 and 4 years of age was assessed by the Child Behavior Checklist for Ages 1.5–5. In Bayesian kernel machine regression (BKMR) analyses that could address the inter-correlations between multiple PFASs, PFAS mixture appeared to be associated with fewer Somatic Complaints and more Externalizing Problems in boys, but more Somatic Complaints and Sleep Problems in girls. There were suggestive associations of PFNA and PFOS with decreased risk of Somatic Complaints and of PFUdA and PFTrDA with increased risk of Externalizing Problems in boys; trends of increased risk in girls were observed between PFUdA and Somatic Complaints and between PFTrDA and Sleep Problems. Overall, we found no clear evidence that prenatal exposure to PFASs had negative effects on neurobehavioral development in children. However, the modest associations still suggested the potential developmental neurotoxicity of prenatal PFAS exposure.

Abstract Background Patients with coexisting type 2 diabetes and hypertension generally exhibit poor adherence to self-management, which adversely affects their disease control. Therefore, identification of the factors related to patient adherence is warranted. In this study, we aimed to examine (i) the socio-demographic correlates of patient adherence to a set of self-management behaviors relevant to type 2 diabetes and hypertension, namely, medication therapy, diet therapy, exercise, tobacco and alcohol avoidance, stress reduction, and self-monitoring/self-care, and (ii) whether health attitudes and self-efficacy in performing self-management mediated the associations between socio-demographic characteristics and adherence. Methods We performed a secondary analysis of data collected in a randomized controlled trial. The sample comprised 148 patients with coexisting type 2 diabetes mellitus and hypertension. Data were collected by a questionnaire and analyzed using logistic regression. Results Female patients were found to be less likely to exercise regularly (odds ratio [OR] = 0.49, P = 0.03) and more likely to avoid tobacco and alcohol (OR = 9.87, P

Self-sufficient cytochrome P450 monooxygenases, which contain all redox partners in a single polypeptide chain, are of interest for biotechnological applications. Here, the authors present the crystal structure of full-length Thermobispora bispora CYP116B46 and discuss the potential electron transfer pathway.

Vinegar is an acid condiment shared all over the world. According to the raw materials, vinegar can be mainly divided into fruit and cereal ones, both of which possess unique aroma and flavor characteristics and corresponding volatile organic compounds (VOCs). Many studies about vinegar VOCs' (VVOCs) sorts, analytical methods, and forming mechanisms have been done. In this review, the main categories of vinegar and their distribution in the world are briefly introduced, then VVOCs' analytical and identified methods, types, and forming processes are summarized. Additionally, the VVOCs' research directions are discussed and prospected. According to the searched literatures, this study is the first to systematically review the analytical methods, sorts, and formation mechanisms of VVOCs, which will make the readers better understand the vinegar's aromas and flavors and their producing mechanisms.

Acetic acid bacteria (AAB) are a group of Gram-negative, strictly aerobic bacteria, including 19 reported genera until 2021, which are widely found on the surface of flowers and fruits, or in traditionally fermented products. Many AAB strains have the great abilities to incompletely oxidize a large variety of carbohydrates, alcohols and related compounds to the corresponding products mainly including acetic acid, gluconic acid, gulonic acid, galactonic acid, sorbose, dihydroxyacetone and miglitol via the membrane-binding dehydrogenases, which is termed as AAB oxidative fermentation (AOF). Up to now, at least 86 AOF products have been reported in the literatures, but no any monograph or review of them has been published. In this review, at first, we briefly introduce the classification progress of AAB due to the rapid changes of AAB classification in recent years, then systematically describe the enzymes involved in AOF and classify the AOF products. Finally, we summarize the application of molecular biology technologies in AOF researches.

The tomato hind, Cephalopholis sonnerati, is a bottom-dwelling coral reef fish, which is widely distributed in the Indo-Pacific and Red Sea. C. sonnerati also features complex social structures and behaviour mechanisms. Here, we present a high-quality, chromosome-level genome assembly for C. sonnerati that was derived using PacBio sequencing and Hi-C technologies. A 1043.66 Mb genome with an N50 length of 2.49 Mb was assembled, produced containing 795 contigs assembled into 24 chromosomes. Overall, 97.2% of the complete BUSCOs were identified in the genome. A total of 26,130 protein-coding genes were predicted, of which 94.26% were functionally annotated. Evolutionary analysis revealed that C. sonnerati diverged from its common ancestor with E. lanceolatus and E. akaara approximately 41.7 million years ago. In addition, comparative genome analyses indicated that the expanded gene families were highly enriched in the sensory system. Finally, we found the tissue-specific expression of 8108 genes. We found that these tissue-specific genes were highly enriched in the brain. In brief, the high-quality, chromosome-level reference genome will provide a valuable genome resource for studies of the genetic conservation, resistance breeding, and evolution of C. sonnerati.

The peroxidation of unsaturated fatty acids is a widely recognized metabolic process that creates a complex mixture of volatile organic compounds including aldehydes. Elevated levels of reactive oxygen species in cancer cells promote random lipid peroxidation, which leads to a variety of aldehydes. In the case of lung cancer, many of these volatile aldehydes are exhaled and are of interest as potential markers of the disease. Relevant studies reporting aldehydes in the exhaled breath of lung cancer patients were collected for this review by searching the PubMed and SciFindern databases until 25 May 2022. Information on breath test results, including the biomarker collection, preconcentration, and quantification methods, was extracted and tabulated. Overall, 44 studies were included spanning a period of 34 years. The data show that, as a class, aldehydes are significantly elevated in the breath of lung cancer patients at all stages of the disease relative to healthy control subjects. The type of aldehyde detected and/or deemed to be a biomarker is highly dependent on the method of exhaled breath sampling and analysis. Unsaturated aldehydes, detected primarily when derivatized during preconcentration, are underrepresented as biomarkers given that they are also likely products of lipid peroxidation. Pentanal, hexanal, and heptanal were the most reported aldehydes in studies of exhaled breath from lung cancer patients.

The use of machine learning (ML) algorithms to conduct prediction or analysis tasks in a data center networking (DCN) environment is gaining increasing attention today. Recent research in traffic prediction, abnormal traffic monitoring, and routing selection has led to significant progress by making full use of historical data and improved ML models. However, such approaches face challenges when dealing with graphical data. These approaches have limited capabilities to explore the information that is hiding in the network's topological structure. To solve these challenges, we study the problem of representation learning in DCN topologies. To serve as a bridge, we proposed a novel method, “Topology2Vec,”to learn the network topology and represent the nodes using low-dimensional vectors, which is useful in many topology-related tasks. Both network structure and performance are considered in our method to ensure that the representation can adapt to different requirements. To evaluate the effectiveness, we demonstrate this method in a controller placement problem as a typical use case using topological data from real-world data centers. The experiments show that the use of “Topology2Vec”as a premise has produced better results in terms of network latency.

In recent years, the Internet has become an indispensable part of people's lives, and it offers increasingly comprehensive information tailored to people's personal preferences as well as commodity attribute information. Consequently, many researchers have used external information to improve recommendation technology. However, most previous studies consider only adding single relationship types, such as social networking friend-relationships. In the real world, considering multiple types of external relations can more accurately determine the reason why a user selected an item. To address this problem, in this paper, we propose a hybrid method called the semantic preference-based personalized recommendation on heterogeneous information networks (SPR), which combines user feedback scores with heterogeneous information networks. This method can improve recommendation problems by considering multiple types of external relationships. To apply the method, we first introduce a similarity measure between users based on a user's potential preferences in the meta-path and design the recommended model at the global and individual level. Finally, we perform experiments on two real-world data sets, finding that the SPR method achieves better results compared with the several widely employed and the state-of-the-art recommendation methods.

Increasing both the sensitivity and selectivity of thiol-functionalized gold nanoparticle chemiresistors remains a challenging issue in the quest to develop real-time gas sensors. The effects of thiol molecular structure on such sensor properties are not well understood. This study investigates the effects of steric as well as electronic effects in a panel of substituted thiol-urea compounds on the sensing properties of thiolate monolayer-protected gold nanoparticle chemiresistors. Three series of urea-substituted thiols with different peripheral end groups were synthesized for the study and used to prepare gold nanoparticle-based chemiresistors. The responses of the prepared sensors to trace volatile analytes were significantly affected by the urea functional motifs. The largest response for sensing acetone among the three series was observed for the thiol-urea sensor featuring a tert-butyl end group. Furthermore, the ligands fitted with N, N’-dialkyl urea moieties exhibit a much larger response to carbonyl analytes than the more acidic urea series containing N-alkoxy-N’-alkyl urea and N, N’-dialkoxy urea groups with the same peripheral end groups. The results show that the peripheral molecular structure of thiolate-coated gold nanoparticles plays a critical role in sensing target analytes.

The issue of long patient waits has attracted increasing public attention due to the negative effects of waiting on patients’ satisfaction with health care. The present study examined the associations between actual waiting time, perceived acceptability of waiting time, actual service time, perceived acceptability of service time, actual visit duration, and the level of patient satisfaction with care. We conducted a cross-sectional time study and questionnaire survey of endocrinology outpatients visiting a major teaching hospital in China. Our results show that actual waiting time was negatively associated with patient satisfaction regarding several aspects of the care they received. Also, patients who were less satisfied with the sociocultural atmosphere and the identity-oriented approach to their care tended to perceive the amounts of time they spent waiting and receiving care as less acceptable. It is not always possible to prevent dissatisfaction with waiting, or to actually reduce waiting times by increasing resources such as increased staffing. However, several improvements in care services can be considered. Our suggestions include providing clearer, more transparent information to keep patients informed about the health care services that they may receive, and the health care professionals who are responsible for those services. We also suggest that care providers are encouraged to continue to show empathy and respect for patients, that patients are provided with private areas where they can talk with health professionals and no one can overhear, and that hospital staff treat the family members or friends who accompany patients in a courteous and friendly way.

The complete mitochondrial genome of the Sparus aurata was presented in this study. The mitochondrial genome is 16,652 bp long and consists of 13 protein-coding genes, 2 rRNA genes, 22 tRNA genes, and a control region. The gene order and composition of S. aurata mitochondrial genome were similar to that of most other vertebrates. The nucleotide compositions of the light strand in descending order is 28.84% of G, 27.26% of A, 26.77% of T and 17.13% of C. With the exception of the NADH dehydrogenase subunit 6 (ND6) and eight tRNA genes, all other mitochondrial genes are encoded on the heavy strand. The phylogenetic analysis by maximum-likelihood (ML) method showed that the Sparus aurata has the closer relationship to the Rhabdosargus sarba in the phylogenetic relationship.

The fast development of a secondary aerosol layer was observed over megacities in eastern Asia during summertime. Within three hours, from midday to early afternoon, the contribution of secondary aerosols above the planetary boundary layer (PBL) increased by a factor of three to five, and the coatings on black carbon (BC) also increased and enhanced its absorption efficiency by 50%. This tended to result from the intensive actinic flux received above the PBL which promoted photochemical reactions. The absorption of BC could be further amplified by the strong reflection of solar radiation over the cloud top across the PBL. This enhanced heating effect of BC introduced by combined processes (intensive solar radiation, secondary formation and cloud reflection) may considerably increase the temperature inversion above the PBL. This mechanism should be considered when evaluating the radiative impact of BC, especially for polluted regions receiving strong solar radiation.

A novel series of 2-substituted-4,6-diarylpyrimidines 6a–6t has been synthesized, characterized by 1H-NMR, 13C-NMR and HRMS, and screened for in vitro α-glucosidase inhibitory activity. The majority of the screened compounds possessed significant α-glucosidase inhibitory activity with IC50 values ranging from 19.6 ± 0.21 to 38.9 ± 0.35 μM, which is more potent than the positive control α-glucosidase inhibitor acarbose (IC50 = 817.38 ± 6.27 μM). Among them, 6j was found to be the most active compound against α-glucosidase with an IC50 of 19.6 ± 0.21 μM. In addition, molecular docking studies were carried out to explore the binding interactions of 2-substituted-4,6-diarylpyrimidine derivatives with α-glucosidase.

A series of novel isatin-thiazole derivatives were synthesized and screened for their in vitro α-glucosidase inhibitory activity. These compounds displayed a varying degree of α-glucosidase inhibitory activity with IC50 ranging from 5.36 ± 0.13 to 35.76 ± 0.31 μm as compared to the standard drug acarbose (IC50 = 817.38 ± 6.27 μm). Among the series, compound 6p bearing a hydroxyl group at the 4-position of the right phenyl and 2-fluorobenzyl substituent at the N1-positions of the 5-methylisatin displayed the highest inhibitory activity with an IC50 value of 5.36 ± 0.13 μm. Molecular docking studies revealed the existence of hydrophobic interaction, CH-π interaction, arene-anion interaction, arene-cation interaction, and hydrogen bond between these compounds and α-glucosidase enzyme.