The gut microbiota, the body's richest microbial ecosystem, is essential for maintaining gut function and immune balance. Additionally, microbial-derived metabolites are linked to the onset and progression of various diseases. There is a potential bidirectional gut-lung axis by which the gut and lungs can communicate with each other mediated by microbiota, immune responses, and metabolic products, and thus affect lung cancer occurrence. As the pathological progression of lung cancer advances and treatment methods are optimized, there is a concurrent and continuous alteration in the gut microbiota and its metabolites in lung cancer patients. This review highlights that the composition and structure of the gut microbiota in lung cancer patients undergo dynamic alterations, which are intricately linked to the pathological progression of the disease and the implementation of therapeutic interventions. Longitudinal monitoring of this system may offer unprecedented insights into the early diagnosis, precise treatment, and prognostic evaluation of lung cancer.

The integration of Space-Air-Ground Networks with mobile edge computing represents a key 6G paradigm enabled by advances in low Earth orbit (LEO) satellites, unmanned aerial vehicles (UAVs), and terrestrial networks. However, several critical challenges emerge when handling delay-sensitive services in rescue missions and infrastructure-less scenarios, where terrestrial network layers are absent. In such scenarios, the limited battery capacity of UAVs and satellites can be exhausted quickly. These factors jointly increase the probability of task delay violations and degrade the quality of service. To address these challenges, our work first investigates the joint optimization of edge caching and computation offloading in the UAV-assisted LEO Satellite edge computing (UAV-LEC) system. The primary objective in our formulation is to minimize total energy consumption while maintaining strict service reliability requirements. Given the distributed and stochastic characteristics of the UAVLEC system, we utilize a partially observable Markov decision process (POMDP) to model the system. Furthermore, martingale theory is incorporated to analyze the service reliability bounds of the tandem queue in the proposed framework. Building on these theoretical foundations, we propose a novel asynchronous interactive multi-agent reinforcement learning algorithm to address the distributed caching and computation optimization problem in the UAV-LEC system. Finally, we conduct comprehensive simulations to demonstrate that the proposed algorithm achieves significant improvements compared to the baseline.

Circadian rhythm disturbances are known to impair ovarian reserve through endocrine and molecular pathways. However, the specific impact of daytime napping as a common compensatory sleep behavior remains poorly understood. We included 1250 women from the TREE cohort in Wuhan, China. The duration of daytime napping was collected through questionnaires at recruitment. Antral follicle count (AFC) and ovarian volume (OV) were obtained by transvaginal ultrasound. On day 2–5 of a menstrual cycle, blood samples were collected to determine reproductive hormone concentrations. Multivariate Poisson or linear regression models were performed to estimate the associations between daytime napping duration and ovarian reserve indicators. We also performed stratified analysis by nocturnal sleep duration and subjective sleep quality. A total of 624 women (49.9

BACKGROUND:How gut microbiota alterations may contribute to host inflammation and metabolomic profiles affecting atherosclerosis is not fully elucidated, especially in the context of HIV. METHODS:We examined associations between gut microbial features (measured by shotgun metagenomics) and subclinical carotid atherosclerosis, as assessed by high-resolution B-mode ultrasound, in 359 men from the MACS/WIHS Combined Cohort Study. We measured 822 plasma metabolites using LC-MS/MS, and up to 2866 circulating proteins by the Olink Explore 3072/384 platform (with a primary focus on 617 proteins related to inflammation and immune function). FINDINGS:Carotid artery plaque was detected in 115/359 men (32%). Adlercreutzia equolifaciens and Eubacterium sp3131 were associated with lower odds of plaque (OR [95% CI] = 0.57 [0.43, 0.77], 0.84 [0.76, 0.93], respectively), while Coprococcus sp13142 was associated with higher odds of plaque (OR [95% CI] = 1.14 [1.06, 1.23]). Results were consistent in men both with and without HIV. A. equolifaciens was positively correlated with HDL cholesterol and inversely correlated with systolic blood pressure. These plaque-associated microbial species were also associated with a range of circulating metabolites and inflammatory proteins. For example, A. equolifaciens positively correlated with the metabolites palmitoyl-EA and mesobilirubinogen, and inversely correlated with the pro-inflammatory chemokine CXCL9, the immune regulator CD160, and IL-24. INTERPRETATION:We identified gut microbial features associated with carotid artery atherosclerosis, consistent across HIV status; these associations were partially explained by specific microbiota-related metabolites and inflammatory markers. If validated, these findings suggest gut microbiota-related targets for CVD prevention. FUNDING:The study was funded by the National Heart, Lung, and Blood Institute (U01HL146204-04S1, K01HL169019).

Endocrine-disrupting chemicals are recognized for their impact on fallopian tube function; however, epidemiological evidence linking phthalates to fallopian tube disorders is still limited. In this cross-sectional study within the Tongji Reproductive and Environmental (TREE) Cohort, we collected urine and follicular fluid (FF) samples from 291 infertile women aged 20 to 45 years with no history of sexually transmitted infections (STIs) or pelvic inflammatory disease (PID), and measured eight phthalate metabolites. Salpingitis cases (n = 62) were identified based on clinical diagnosis documented in medical records. Notably, residual confounding by STI/PID-related factors may still exist despite this exclusion. We evaluated associations with individual metabolites and mixtures using logistic regression, Bayesian kernel machine regression (BKMR), and weighted quantile sum (WQS) regression models. In both crude and adjusted models, higher tertiles of monobenzyl phthalate (MBzP) in FF were positively associated with salpingitis (P for trend = 0.04). Upon adjusting for covariates, a per logarithm (ln)-unit rise in mono(2-ethyl-5-hydroxyhexyl) phthalate (MEHHP) levels in FF was linked to a 1.63-fold elevation (95% CI: 1.01, 2.64) in salpingitis odds. Furthermore, concentrations of monoethyl phthalate (MEP) in urine samples were positively correlated with salpingitis [adjusted model: 1.38 (1.06, 1.80)]. Additionally, the mixtures of phthalate metabolites in both urine and FF generally did not show significant associations with salpingitis. Stratified analyses revealed an increased odds of salpingitis associated with urinary mono(2-ethyl-5-carboxypentyl) phthalate (MECPP) in women under 30 years of age (P for interaction = 0.03), with urinary MBzP in women with body mass index (BMI) ≥ 24 kg/m2 and non-female factor infertility (P for interaction = 0.01), and with FF mono(2-ethyl-5-oxohexyl) phthalate (MEOHP) in the female-factor infertility group (P for interaction = 0.02). These results indicate that certain phthalate metabolites in FF and urine show a positive correlation with salpingitis, underscoring the necessity for further research.

CONTEXT:Renal function may play a crucial role in the development of gestational diabetes mellitus (GDM). However, prospective studies on this topic are scarce and the mechanisms remain unclear. OBJECTIVE:This work aimed to assess the associations of early-pregnancy renal function with GDM and the mediating role of carnitine metabolites. METHODS:The study was based on the Tongji-Huaxi-Shuangliu Birth Cohort. Renal function was routinely assessed before 15 gestational weeks. Plasma carnitine metabolites in early pregnancy were quantified using ultrahigh-performance liquid chromatography-tandem mass spectrometry. GDM was diagnosed at 24 to 28 gestational weeks by a 2-hour oral glucose tolerance test. Multivariable logistic regression was used to examine the associations of renal function indicators with GDM. Mediation analyses were applied to assess the mediating effects of carnitines. RESULTS:The mean age of 6770 participants was 26.6 ± 3.7 years. Serum uric acid, uric acid to creatinine ratio, and estimated glomerular filtration rate (eGFR) were positively associated with GDM, and the odds ratios (95% CIs) were 1.67 (95% CI, 1.25-2.23), 1.94 (1.47-2.57), and 1.53 (1.17-2.01) for the extreme-quartile comparison. Increased creatinine, cystatin C, and creatinine to weight ratio were associated with lower GDM risk, with ORs of 0.62 (0.47-0.82), 0.72 (0.52-0.99), and 0.69 (0.53-0.91) for the extreme-quartile comparison. Serum creatinine-, creatinine to weight ratio-, and eGFR-related carnitine scores played positive mediating roles, and the mediation proportions were 43.1%, 81.9%, and 56.7%, respectively. CONCLUSION:Renal function should be monitored for GDM, and the potential roles of carnitine metabolites require further evaluation and validation.

As smart cities rapidly evolve, the Vehicle-toEverything (V2X) network faces significant challenges in data security and communication efficiency. This paper introduces an innovative algorithm based on a reputation mechanism, named the Dynamic Vehicle Reputation Consensus (DVRC), which focuses on improving the data security and communication efficiency of vehicular networks using blockchain technology. The DVRC algorithm comprehensively assesses vehicle behaviors and the consensus contribution within the blockchain network, utilizing a reputation scoring system to evaluate the reliability within the network. Furthermore, this study delves into multimodal communication strategies in vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) scenarios. Notably, when vehicles leave the range of basic infrastructure, those with high reputation scores (as determined by the DVRC's reputation values) relay the communication service. Additionally, the introduction of a reputation incentive mechanism and dynamic consensus threshold adjustments aim to improve consensus efficiency and encourage honest behavior. Experimental results demonstrate that the DVRC algorithm significantly improves communication efficiency in blockchain-based vehicular networks, such as increased throughput, reduced latency, and improved network scalability. These advances have substantial theoretical and practical significance for the development of vehicular networks in smart cities

Our previous research revealed aberrant serum N-glycan profiles in non-small-cell lung cancer (NSCLC), but the specific protein sources remain unclear. While immunoglobulin G (IgG) N-glycosylation has been implicated in cancer, its alterations in NSCLC are not well defined. Herein, we profiled the serum IgG N-glycome of 314 NSCLC patients and 364 healthy controls using a high-throughput MALDI-TOF-MS platform. Lectin-based enzyme-linked immunosorbent assay (ELISA) was applied for orthogonal validation. Machine learning was employed to construct a glycan-based diagnostic model. Two-sample Mendelian randomization (MR) analysis was performed to access potential causal relationships. The findings suggested positive correlations between matched IgG and whole-serum N-glycans. Compared with controls, NSCLC patients exhibited distinct IgG glycosylation patterns, including decreased galactosylation, monosialylation, and bisecting N-acetylglucosamine, alongside increased agalactosylation. The lectin-based assay confirmed the reductions in IgG galactosylation and sialylation. An eight-glycan panel demonstrated robust capability for NSCLC discrimination. MR analysis further revealed an inverse association between the IgG FS1/FS2 ratio and NSCLC risk. In conclusion, this study identified dysregulated IgG N-glycan signatures in NSCLC and proposed a pathogenic role for specific glycosylation traits. The findings unveil the potential of IgG glycans as non-invasive biomarkers and provide novel insights into the pathogenesis and therapeutic strategies for NSCLC.

Network performance monitoring and troubleshooting are crucial yet challenging tasks in datacenter management. Despite the numerous solutions that have been proposed in recent years, their efforts are often hindered by high costs and unreliable failure localization, making it difficult to deploy them in real-world environments. In this paper, we present LMon, a highly reliable and efficient system for monitoring and troubleshooting in datacenter networks. LMon utilizes the characteristic of ECMP hashing linearity to control probe packet routing, enabling the monitoring of targeted paths without any modification of underlying protocols and devices. Additionally, LMon leverages a lightweight probing technique to reduce monitoring overhead, as well as integrates the improved LASSO regression and hypothesis testing for higher accuracy and faster processing in link failure localization. We evaluate the performance of LMon in our testing environment. Compared to the monitoring system Pingmesh, LMon generates only one-third probes while maintaining 99% accuracy and 1% false negatives.

Background Aging-related comorbidities are more common in people with human immunodeficiency virus (HIV) compared to people without HIV. The gut microbiome may play a role in healthy aging; however, this relationship remains unexplored in the context of HIV. Methods 16S rRNA gene sequencing was conducted on stool from 1409 women (69% with HIV; 2304 samples) and 990 men (54% with HIV; 1008 samples) in the MACS/WIHS Combined Cohort Study. Associations of age with gut microbiome diversity, uniqueness, and genus-level abundance were examined in women and men separately, followed by examining relationships of aging-related genera with frailty (Fried frailty phenotype) and mortality risk (Veterans Aging Cohort Study [VACS] index). Results Older age was associated with greater microbiome diversity and uniqueness, greater abundance of Akkermansia and Streptococcus, and lower abundance of Prevotella and Faecalibacterium, among others; findings were generally consistent by sex and HIV status. An aging-related microbiome score, generated via combination of 18 age-related genera, significantly increased with age in both women and men independently of demographic, behavioral, and cardiometabolic factors. In general, age was more strongly related to microbiome features (eg, diversity, microbiome score) in men without compared to with HIV, but age-microbiome associations were similar in women with and without HIV. Some age-related genera associated with healthy/unhealthy aging, such as Faecalibacterium (related to reduced frailty) and Streptococcus (related to higher VACS index). Conclusions Age is associated with consistent changes in the gut microbiome in both women and men with or without HIV. Some aging-related microbiota are associated with aging-related declines in health.

With the rapid development of Internet of Things (IoT) technology, interconnectivity between devices has become increasingly widespread. However, traditional IoT security measures struggle to cope with increasingly complex security threats and cannot fully exploit the advantages of interconnectivity due to the limited computational resources of the devices. To address this, we propose a blockchain-based IoT security framework comprising wallet component, smart contract component, multilayer security component, and common component. This framework, designed for resource-constrained environments and embedded into cellular communication modules, enables multiend offloading of computational tasks and secure transmission for IoT devices. Experimental results show that the data processing capability of the decentralized network architecture based on this framework is improved by 115.06% compared to traditional methods, enhances the security and autonomy of IoT devices, and significantly strengthens the degree of IoT decentralization. This provides a valuable reference for designing next-generation IoT security architectures.

AimsThe utilization of targeted metabolomics technology promises to facilitate the identification of novel metabolic markers in women with gestational diabetes mellitus (GDM), which may in turn facilitate a more comprehensive investigation of the underlying mechanisms of gestational diabetes GDM.Materials and MethodsIn this study, we used targeted metabolomics to identify serum metabolites from women with or without GDM. The differential metabolites were categorized and analysed using pathway analyses, correlated with maternal glucose level, and assessed as predictors of GDM by receiver operating characteristics analysis.ResultsNotably, we detected 46 differential metabolites (24 upregulated and 22 downregulated) between GDM and normal pregnancy, which were catalogued into amino acids, peptides and analogues, and organic acids and derivatives, and others. Pathway analysis showed that amino acid metabolites were abnormally active. In addition, most of the metabolites were closely related to maternal glucose level. Of these, two metabolites were associated with fasting blood glucose, 22 correlated with 1-h postprandial plasma glucose and 13 were related to 2-h postprandial plasma glucose. Next, we identified metabolites that could better diagnose GDM with the area under the receiver operating characteristics above 0.75, including 2-hydroxybutyric acid, itaconic acid, O-acetylcarnitine, glutathione disulfide, P-cresolsulfate, 2-furoic acid, l-asparagine, d-biotin, choline and homovanillic acid.ConclusionWe identified abnormal serum metabolites caused by GDM, which may contribute to our understanding of the pathomechanisms of GDM.

Multipath enhances the reliability and bandwidth of datacenter networks, but it also necessitates effective load balancing. The dynamic nature of traffic and diverse flow characteristics present significant challenges in achieving optimal load distribution. Existing schemes either result in mediocre performance or rely on hard-acquired global information, leading to poor FCT or high implementation costs. This paper introduces DeFlow, a simple yet efficient flowlet-based load balancing scheme implementable on programmable switches with a low cost. DeFlow distinguishes flowlets of large and small flows based on packet size and interval. It employs distinct scheduling strategies for throughout-sensitive large flows and latency-sensitive small flows under congestion by prioritizing the performance of small flows. Extensive experiments on NS-3 demonstrate that DeFlow consistently outperforms competing schemes across various topologies and workloads, improving the FCT of small flows and throughput of large flows simultaneously.

No population-based studies examined gut microbiota and related metabolites associated with sugar-sweetened beverage (SSB) intake among US adults. In this cohort of US Hispanic/Latino adults, higher SSB intake was associated with nine gut bacterial species, including lower abundances of several short-chain-fatty-acid producers, previously shown to be altered by fructose and glucose in animal studies, and higher abundances of fructose- and glucose-utilizing Clostridium bolteae and Anaerostipes caccae. Fifty-six serum metabolites were correlated with SSB intake and a gut microbiota score based on these SSB-related species in consistent directions. These metabolites were clustered into several modules, including a glycerophospholipid module, two modules comprising branched-chain amino acid (BCAA) and aromatic amino acid (AAA) derivatives from microbial metabolism, etc. Higher glycerophospholipid and BCAA derivative levels and lower AAA derivative levels were associated with higher incident diabetes risk during follow-up. These findings suggest a potential role of gut microbiota in the association between SSB intake and diabetes.

Introduction and Objective: Dietary carbohydrates (CHOs) and circadian rhythms impact gut microbiome and metabolism. We tested associations of CHO intake timing with gut microbiome and cardiovascular disease (CVD) and diabetes risks. Methods: CHO intake timing was assessed by two 24-h recalls (n=12401, Fig A): CHO intake (% daily energy intake) and proportion of CHO intake per period. CHO quality was assessed by food sources (high-/low-quality; Fig B), glycemic index (GI), and glycemic load (GL). Incident CVD (myocardial infarction, stroke, heart failure) and diabetes were followed for medians of 9.7 and 11.3 y. Gut microbiome was measured by fecal metagenomics (n=2700). Results: Higher early morning CHO intake was linked to lower CVD risk (HR per 5% CHOs: 0.87, 95% CI 0.79-0.97; Fig B). Higher afternoon CHO intake was linked to higher gut microbial α-diversity and lower diabetes risk. Higher evening/night CHO intake was linked to lower α-diversity and higher CVD risk. Low-quality CHOs, GI, and GL, rather than high-quality CHOs, had similar patterns. 20 gut bacteria were linked to CHO intake timing (Fig C); those linked to higher early-morning and afternoon CHO intake were linked to better cardiometabolic traits (Fig D). Conclusion: Higher early morning and afternoon CHO intake and lower CHO intake after evening were linked to beneficial gut microbial features and lower cardiometabolic risk. Disclosure Y. Zhang: None. S.K. Alver: None. B. Peters: None. K. Luo: None. Y. Wang: None. Y. Mossavar-Rahmani: None. X. Xue: None. B. Yu: None. B. Zhao: None. M.L. Daviglus: None. L. Van Horn: None. C. Cordero: None. E. Romaker: None. R. Burk: None. R. Kaplan: None. Q. Qi: None. Funding National Institute of Diabetes and Digestive and Kidney Diseases (R01DK119268, R01DK126698); National Institute on Minority Health and Health (R01MD011389).

Circulating linoleic acid (LA) levels have been reported to be associated with various metabolic outcomes. However, the role of LA and its interplay with gut microbiota in gestational diabetes mellitus (GDM) remains unclear. This study aimed to investigate the longitudinal association between circulating LA levels during pregnancy and the risk of GDM, and the potential role of gut microbiota. A nested case–control study was conducted within the ongoing Tongji-Huaxi-Shuangliu Birth Cohort in Chengdu, China. Blood and fecal samples were collected during early and middle pregnancy from 807 participants. GDM was diagnosed in middle pregnancy using the International Association of Diabetes and Pregnancy Study Groups criteria. Plasma LA levels were measured using gas chromatography-mass spectrometry, and gut microbiota was analyzed through 16S rRNA gene sequencing and shotgun metagenomic sequencing. A two-sample Mendelian randomization study was conducted using data from the IEU OpenGWAS database and the FinnGen consortium. Elevated plasma LA levels were associated with a lower risk of GDM in both early (P for trend = 0.002) and middle pregnancy (P for trend = 0.02). Consistently, Mendelian randomization analysis revealed that each unit increase in LA was associated with a 16

In the domain of Time-Sensitive Networking (TSN), the quest for ultra-reliable low-latency communication is paramount. Current scheduling strategies, which hinge on strict isolation to ensure low latency and jitter, confront the challenges of high overhead in worst-case latency evaluation and consequent limitations in network flow capacity. This paper introduces an innovative framework that transcends traditional isolation constraints, thereby expanding the solution space and augmenting network schedulability. At the heart of this framework lies a novel latency jitter analysis method that assesses the viability of non-isolation scenarios with constant time complexity. This method underpins a heuristic scheduling algorithm that not only boasts the smallest time complexity among existing heuristics but also significantly increases the number of scheduled flows. Complementing this, we integrate a discrete time reference approach to hasten time-intensive scheduling operations, achieving an optimal balance between schedulability and runtime efficiency. The framework further incorporates a workload-shifting technique to enhance online scheduling responsiveness. It adeptly manages the variability in scheduling times caused by disharmonious flow periods, further bolstering the framework’s robustness. Experimental validations demonstrate that our framework can increase the scheduled flows up to 269%. It reduces scheduling runtime by up to 98.44% for medium-scale networks while maintaining a flat runtime growth curve, ensuring predictable performance in online scheduling scenarios.

Background Ovarian cancer (OC) is one of the most common gynecological tumors with high morbidity and mortality. Altered serum N -glycome has been observed in many diseases, while the association between serum protein N -glycosylation and OC progression remains unclear, particularly for the onset of carcinogenesis from benign neoplasms to cancer. Methods Herein, a mass spectrometry based high-throughput technique was applied to characterize serum N -glycome profile in individuals with healthy controls, benign neoplasms and different stages of OC. To elucidate the alterations of glycan features in OC progression, an orthogonal strategy with lectin-based ELISA was performed. Results It was observed that the initiation and development of OC was associated with increased high-mannosylationand agalactosylation, concurrently with decreased total sialylation of serum, each of which gained at least moderately accurate merits. The most important individual N -glycans in each glycan group was H7N2, H3N5 and H5N4S2F1, respectively. Notably, serum N -glycome could be used to accurately discriminate OC patients from benign cohorts, with a comparable or even higher diagnostic score compared to CA125 and HE4. Furthermore, bioinformatics analysis based discriminative model verified the diagnostic performance of serum N -glycome for OC in two independent sets. Conclusions These findings demonstrated the great potential of serum N -glycome for OC diagnosis and precancerous lesion prediction, paving a new way for OC screening and monitoring.

Time Sensitive Networks (TSN), as an important representative of deterministic networks, provide low-latency and highly reliable communication services for the growing network applications that have strict requirements. Cyclic Queuing and Forwarding (CQF) is a well-known mechanism proposed by IEEE 802.1Qch for low-latency flow control of time-sensitive networks. It achieves bounded end-to-end delay and jitter transmission through a set of queues without complicated queue gating. However, most of the current work overlooks the widespread existence of multi-link rate networks in LANs and WANs, and the single-cycle CQF is unable to adjust different link rates, resulting in low bandwidth utilization and high latency. In this paper, we propose a novel scheduling approach named Multi-Cycle CQF (MCCQF) to solve the transmission problem in multi-link rate networks, aiming to reduce deterministic end-to-end delay and improve link bandwidth utilization. In addition, we formulate the scheduling constraints, being of guiding significance for designing the transmission of multi-link-rate networks, and we design an online scheduling algorithm based on it. We compare the proposed scheme with the single-cycle CQF online scheduling algorithm in hierarchical multi-link-rate networking scenarios, and the evaluation shows that our algorithm achieves better end-to-end ultra-low latency (38.9% reduction) with a smaller schedulability gap compared with single-cycle CQF. And we also improved the schedulability based on MCCQF by utilizing internal offset.