Eosin Y Post-Decorated Metal-Organic Framework as a Selectivity Regulator for the Alcohols Oxidation.

The selective oxidation of alcohols into aldehydes is a basic and significant procedure, with great potential for scientific research and industrial applications. However, as an important factor in the C(sp3)-H activation process, high selectivity is generally difficult to achieve due to the fact that the more easily activated properties of aldehydes are compared to alcohols. Herein, by the ingenious decoration of eosin Y into a Zr-based metal-organic framework (MOF-808), EY@MOF-808 was prepared as a selectivity regulator for the aerobic oxidation of the benzyl alcohols into corresponding aldehydes, possessing applicability for the benzylic alcohols with various substituents. By anchoring eosin Y on Zr6O4(OH)4 clusters of MOF-808 and maintaining open metal nodes with selective binding effects, the benzyl alcohol substrates were selectively coordinated to the unsaturated metal clusters adjacent to eosin Y, which ensured that the excited eosin Y rapidly activated substrates to generate carbon radicals by the hydrogen atom transfer (HAT) process. The rapid electron transfer (ET) simultaneously produced reactive oxygen species (O2•-) and then a combination of both to further promote the generation of benzaldehydes. The weak interaction of benzaldehydes with the skeleton allowed it to dissociate rapidly, thus preventing overoxidation. Under the catalysis of EY@MOF-808, the selectivity of various benzaldehydes was more than 99%. In contrast, eosin Y gave only benzoic acid products under the same conditions, which demonstrated the superiority of regulatory selectivity of EY@MOF-808. Taking advantage of the heterogeneity of the MOF, EY@MOF-808 was recycled four times without a decrease in its selectivity and avoided the quenching effect of eosin Y. The organic functional units postdecorated MOF-based photocatalyst strategy exhibits a promising new perspective approach to sustainably regulating the selectivity of inert oxidation.

Prescribed-time containment control of multi-agent systems subject to collision avoidance and connectivity maintenance.

In this paper, the problem of prescribed-time containment control for a second-order multiple leader-follower systems (MLFSs) is studied, in where both collision avoidance and connectivity maintenance of the agents are considered. Firstly, an effective exponential potential field function (EAPF) with constraints based on the estimated distance is designed to achieve collision resistance and connectivity preservation of the agents at a prescribed-time. Secondly, an estimator-based distributed control protocol is proposed, which drives the agents to achieve containment control in a cooperative manner at a prescribed-time. Furthermore, a novel distributed control protocol containing a collision avoidance term and a containment control term is addressed as well, which enables all followers to complete collision avoidance and connectivity maintenance in any prescribed-time and enter the leaders' convex packet. Finally, the stability of the system is technically analyzed by using Lyapunov theory, and the effectiveness of the presented strategies is verified by several simulations.

Leader-Following Synchronization Control of Multiagent Systems Under Hybrid Cyber Attacks via Impulsive Control Based on Topology Switching.

This article investigates the leader-following synchronization problem of multiagent systems (MASs) under hybrid cyber attacks, which refers to deception attacks and multichannel independent denial-of-service (DoS) attacks in communication channels. In order to achieve the secure control of MASs under hybrid cyber attacks, a novel impulsive control method based on topology switching is proposed, and a new algorithm for determining impulsive instants is designed. In addition, the cooperative-competitive relationship between agents is also considered, which is more in line with reality. Sufficient conditions for ensuring secure control of MASs and a parametric upper bound on the error vector norm between the agents and the leader are obtained. Finally, the numerical simulation verified the effectiveness of the proposed algorithm.

Explanatory subgraph attacks against Graph Neural Networks.

Graph Neural Networks (GNNs) are often viewed as black boxes due to their lack of transparency, which hinders their application in critical fields. Many explanation methods have been proposed to address the interpretability issue of GNNs. These explanation methods reveal explanatory information about graphs from different perspectives. However, the explanatory information may also pose an attack risk to GNN models. In this work, we will explore this problem from the explanatory subgraph perspective. To this end, we utilize a powerful GNN explanation method called SubgraphX and deploy it locally to obtain explanatory subgraphs from given graphs. Then we propose methods for conducting evasion attacks and backdoor attacks based on the local explainer. In evasion attacks, the attacker gets explanatory subgraphs of test graphs from the local explainer and replace their explanatory subgraphs with an explanatory subgraph of other labels, making the target model misclassify test graphs as wrong labels. In backdoor attacks, the attacker employs the local explainer to select an explanatory trigger and locate suitable injection locations. We validate the effectiveness of our proposed attacks on state-of-art GNN models and different datasets. The results also demonstrate that our proposed backdoor attack is more efficient, adaptable, and concealed than previous backdoor attacks.

Association of the dietary inflammation index (DII) with the prevalence of chronic kidney disease in patients with type-2 diabetes mellitus.

Chronic kidney disease (CKD) is a major complication of diabetes mellitus (DM). Inflammation is an essential component in the process of CKD progression in patients with DM. Diet is a significant determinant of systemic inflammation levels. However, the association between the dietary inflammatory index (DII) and CKD in individuals with DM remains largely unknown; therefore, the aim of this study was to explore whether the DII is linked to the prevalence of CKD in patients with DM. The research method was as follows: first, data from the National Health and Nutrition Examination Survey (NHANES) between 1999 and 2018 were obtained. There were 7,974 participants in our study. These individuals were then classified into three groups according to DII tertiles (T1-T3), with each group consisting of 2,658 participants. Logistic regression analysis was employed to examine whether there was a connection between the DII and CKD. We observed a significant association between the DII and the prevalence of CKD in individuals with DM. After full adjustment for age, sex, ethnicity, smoking, drinking, body mass index (BMI), triglyceride (TG), total cholesterol (TC), metabolic equivalents (METs), energy intake, hypoglycemic medications, hypertension, and cardiovascular disease (CVD), the group with a higher DII had a greater frequency of CKD (T2 group: OR: 1.40; 95% CI: 1.10-1.76; p = 0.006; T3 group: OR: 1.67; 95% CI: 1.29-2.17; p < 0.001). The implementation of an anti-inflammatory diet could serve as an intervention strategy for patients with DM to prevent the onset of CKD.

Influence of renal function on the ability of TyG Index to predict all-cause mortality.

BACKGROUND: The association between triglyceride-glucose (TyG) index and poor prognosis remains controversial. Whether renal function status affects the ability of the TyG index to predict poor prognosis has not yet been elucidated and merits further studies. METHODS: This retrospective cohort study included 22,031 participants from communities in the U.S. By juxtaposing the TyG categories with the estimated glomerular filtration rate (eGFR, either < 60 mL/min/1.73m2 or ≥ 60 mL/min/1.73m2), participants were categorized into four distinct groups: (1) TyG_L/eGFR_H; (2) TyG_H/eGFR_H; (3) TyG_L/eGFR_L; and (4) TyG_H/eGFR_L. The endpoint was the all-cause mortality rate. Standard Kaplan-Meier plots were constructed and multifactor Cox regression analyses were carried out and restricted cubic spline regression analysis was utilized to assess the association between death and the TyG index for different renal function statuses. RESULTS: No statistical differences were found in the TyG groups in participants with normal renal function after adjustment for all covariates (P = 0.070). However, in the high TyG index group with renal insufficiency, the risk of all-cause mortality rates was reduced by 18%. (HR, 0.82; CI, 0.69-0.98). The TyG index (high vs. low) and renal function (eGFR < 60 vs. eGFR ≥ 60) had statistically significant interactions with death (P < 0.001). When all covariates were adjusted, the risk of mortality for the TyG_L combined with eGFR_L group was 56% higher than that for the TyG_L combined with eGFR_H group (HR, 1.56; CI, 1.33-1.82). In the renal insufficiency population, a nonlinear relationship was observed between mortality and the TyG index, albeit with a differing pattern (P for nonlinearity < 0.001). CONCLUSIONS: While it has been known that TyG index was a prognosis marker of CVD, this research highlights that higher TyG index was associated with higher all-cause mortality rates for all participants. Furthermore, renal function status significantly moderates the effect of the TyG index on all-cause mortality in community-dwelling adults.

Epidemiological Investigation of Pediatric Fractures-A Retrospective Cohort Study of 1129 Patients.

Background and Objectives: Fractures are common in pediatric trauma, and they are caused by a broad spectrum of factors. Only a few studies have discussed the mechanisms of injury and their relationships to different types of fractures. The most frequent type of fractures in different age groups remains unclear. Therefore, we aim to summarize the epidemiological characteristics of pediatric fractures in a medical center in Zhuhai, China from 2006 to 2021 and analyze the causes of fractures with the highest frequency in different age groups. Materials and Methods: We extracted the information from the Zhuhai Center for Maternal and Child Health Care of those under 14 years old who had fractures from 2006 to 2021. Results: We reviewed the information of 1145 children. The number of patients increased during the 15 years (p < 0.0001). The number of patients was significantly different between genders after Y2 (p = 0.014). In addition, more than two-thirds of patients (71.3%) had upper limb fractures, and all types of falls were the most common cause of fractures (83.6%). The incidence demonstrated an insignificant difference in age groups except for the fractures of humerus and radius. Moreover, we discovered that the prevalence of fall-related injuries decreased with age, while that of sports-related injuries increased with age. Conclusions: Our study demonstrates that the prevalence of fall-related injuries decreases with age, and that of sports-related injuries increases with age. Most patients have upper limb fractures, and all types of falls are the most common cause of fractures. Fracture types with the highest frequency differ in each age group. These findings might supplement current epidemiological knowledge of childhood fracture and provide references for decision-making in children's health policies.

Exhaustive drainage versus fixed-time drainage for chronic subdural hematoma after one-burr hole craniostomy (ECHO): study protocol for a multicenter randomized controlled trial.

BACKGROUND: Chronic subdural hematomas (CSDHs) are one of the most common neurosurgical conditions. The standard surgical technique includes burr-hole craniostomy, followed by intraoperative irrigation and placement of subdural closed-system drainage. The drainage is generally removed after 48 h, which can be described as fixed-time drainage strategy. According to literature, the recurrence rate is 5-33% with this strategy. In our retrospective study, postoperative hematoma volume was found to significantly increase the risk of recurrence. Based on these results, an exhaustive drainage strategy is conducted to minimize postoperative hematoma volume and achieve a low recurrence rate and good outcomes. METHODS: This is a prospective, multicenter, open-label, blinded endpoint randomized controlled trial designed to include 304 participants over the age of 18-90 years presenting with a symptomatic CSDH verified on cranial computed tomography or magnetic resonance imaging. Participants will be randomly allocated to perform exhaustive drainage (treatment group) or fixed-time drainage (control group) after a one-burr hole craniostomy. The primary endpoint will be recurrence indicating a reoperation within 6 months. DISCUSSION: This study will validate the effect and safety of exhaustive drainage after one-burr hole craniostomy in reducing recurrence rates and provide critical information to improve CSDH surgical management. TRIAL REGISTRATION: Clinicaltrials.gov, NCT04573387. Registered on October 5, 2020.

Primal-Dual Fixed Point Algorithms Based on Adapted Metric for Distributed Optimization.

This article considers distributed optimization by a group of agents over an undirected network. The objective is to minimize the sum of a twice differentiable convex function and two possibly nonsmooth convex functions, one of which is composed of a bounded linear operator. A novel distributed primal-dual fixed point algorithm is proposed based on an adapted metric method, which exploits the second-order information of the differentiable convex function. Furthermore, by incorporating a randomized coordinate activation mechanism, we propose a randomized asynchronous iterative distributed algorithm that allows each agent to randomly and independently decide whether to perform an update or remain unchanged at each iteration, and thus alleviates the communication cost. Moreover, the proposed algorithms adopt nonidentical stepsizes to endow each agent with more independence. Numerical simulation results substantiate the feasibility of the proposed algorithms and the correctness of the theoretical results.

Decentralized Primal-Dual Proximal Operator Algorithm for Constrained Nonsmooth Composite Optimization Problems over Networks.

In this paper, we focus on the nonsmooth composite optimization problems over networks, which consist of a smooth term and a nonsmooth term. Both equality constraints and box constraints for the decision variables are also considered. Based on the multi-agent networks, the objective problems are split into a series of agents on which the problems can be solved in a decentralized manner. By establishing the Lagrange function of the problems, the first-order optimal condition is obtained in the primal-dual domain. Then, we propose a decentralized algorithm with the proximal operators. The proposed algorithm has uncoordinated stepsizes with respect to agents or edges, where no global parameters are involved. By constructing the compact form of the algorithm with operators, we complete the convergence analysis with the fixed-point theory. With the constrained quadratic programming problem, simulations verify the effectiveness of the proposed algorithm.

MicroRNA-342-3p loaded by human umbilical cord mesenchymal stem cells-derived exosomes attenuates deep vein thrombosis by downregulating EDNRA.

Exosomes (exos) exert biological functions to maintain the dynamic balance of cells and tissues by transferring biological cargo to recipient cells. Thus, this study explored human umbilical cord mesenchymal stem cells (hucMSCs)-derived exo transfer of microRNA (miR)-342-3p in deep vein thrombosis (DVT). DVT rat models were established via inferior vena cava (IVC) ligation. HucMSCs-exos were extracted and injected into rats with DVT to observe whether they could influences thrombus formation in vivo. HucMSCs-exos were co-cultured with human umbilical vein endothelial cells (HUVECs) in vitro to observe angiogenesis. miR-342-3p and endothelin A receptor (EDNRA) expression in rats with DVT, as well as their interaction was analyzed. miR-342-3p was downregulated and EDNRA was upregulated in rats with DVT. HucMSCs-exos inhibited the formation of thrombus in rats with DVT, as well as promoted angiogenesis of HUVECs. Upregulated miR-342-3p delivery by hucMSCs-exos alleviated DVT in rats and improved angiogenesis of HUVECs. miR-342-3p targeted EDNRA, and the effect of hucMSCs-exos transfer of upregulated miR-342-3p was rescued by overexpressing EDNRA. Briefly, miR-342-3p loaded by hucMSCs-exos attenuates DVT by downregulating EDNRA, offering a novel direction to treat DVT.

Robust wide-range chirp rate measurement based on a flexible photonic fractional Fourier transformer.

We propose a photonic-assisted approach to measure the chirp rate of a linear frequency modulation waveform (LFMW) with a long duration, based on tunable photonic fractional Fourier transform (FrFT). Since the FrFT order can be continuously tuned by varying the frequency shift in an optical frequency-shifting loop (FSL), a specific FrFT order leads the fundamental frequency component arising in the output electrical spectrum to reach its maximum value, after the photonic-to-electrical conversion. Based on the designated FrFT order and the corresponding fundamental frequency in the output electrical spectrum, the chirp rate measurement over a wide range can be accessed, even the signal-to-noise ratio (SNR) of the input LFMW is substantially low. Simulation results indicate that the chirp rate of a 0.16-ms LFMW over a frequency range from 20 GHz to 26 GHz can be precisely characterized, with a relative measurement error of less than 0.13%, under the SNR condition of 0 dB. Moreover, an unambiguous chirp-rate measurement within the range of -5200 MHz/µs to 550 MHz/µs can be achieved. Hence, the proposed chirp rate measurement is featured with broadband operation, robust noise tolerance, low-frequency detection, and long-duration LFMW characterization.

Therapy-resistant and -sensitive lncRNAs, SNHG1 and UBL7-AS1 promote glioblastoma cell proliferation.

The current treatment options for glioblastoma (GBM) can result in median survival of 15-16 months only, suggesting the existence of therapy-resistant factors. Emerging evidence suggests that long non-coding RNAs (lncRNAs) play an essential role in the development of various brain tumors, including GBM. This study aimed to identify therapy-resistant and therapy-sensitive GBM associated lncRNAs and their role in GBM. We conducted a genome-wide transcriptional survey to explore the lncRNA landscape in 195 GBM brain tissues. Cell proliferation was evaluated by CyQuant assay and Ki67 immunostaining. Expression of MAD2L1 and CCNB2 was analyzed by western blotting. We identified 51 lncRNAs aberrantly expressed in GBM specimens compared with either normal brain samples or epilepsy non-tumor brain samples. Among them, 27 lncRNAs were identified as therapy-resistant lncRNAs that remained dysregulated after both radiotherapy and chemoradiotherapy; while 21 lncRNAs were identified as therapy-sensitive lncRNAs whose expressions were reversed by both radiotherapy and chemoradiotherapy. We further investigated the potential functions of the therapy-resistant and therapy-sensitive lncRNAs and demonstrated their relevance to cell proliferation. We also found that the expressions of several lncRNAs, including SNHG1 and UBL7-AS1, were positively correlated with cell-cycle genes' expressions. Finally, we experimentally confirmed the function of a therapy-resistant lncRNA, SNHG1, and a therapy-sensitive lncRNA, UBL7-AS1, in promoting cell proliferation in GBM U138MG cells. Our in vitro results demonstrated that knockdown of SNHG1 and UBL7-AS1 showed an additive effect in reducing cell proliferation in U138MG cells.

Revealing potential diagnostic gene biomarkers of septic shock based on machine learning analysis.

BACKGROUND: Sepsis is an inflammatory response caused by infection with pathogenic microorganisms. The body shock caused by it is called septic shock. In view of this, we aimed to identify potential diagnostic gene biomarkers of the disease. MATERIAL AND METHODS: Firstly, mRNAs expression data sets of septic shock were retrieved and downloaded from the GEO (Gene Expression Omnibus) database for differential expression analysis. Functional enrichment analysis was then used to identify the biological function of DEmRNAs (differentially expressed mRNAs). Machine learning analysis was used to determine the diagnostic gene biomarkers for septic shock. Thirdly, RT-PCR (real-time polymerase chain reaction) verification was performed. Lastly, GSE65682 data set was utilized to further perform diagnostic and prognostic analysis of identified superlative diagnostic gene biomarkers. RESULTS: A total of 843 DEmRNAs, including 458 up-regulated and 385 down-regulated DEmRNAs were obtained in septic shock. 15 superlative diagnostic gene biomarkers (such as RAB13, KIF1B, CLEC5A, FCER1A, CACNA2D3, DUSP3, HMGN3, MGST1 and ARHGEF18) for septic shock were identified by machine learning analysis. RF (random forests), SVM (support vector machine) and DT (decision tree) models were used to construct classification models. The accuracy of the DT, SVM and RF models were very high. Interestingly, the RF model had the highest accuracy. It is worth mentioning that ARHGEF18 and FCER1A were related to survival. CACNA2D3 and DUSP3 participated in MAPK signaling pathway to regulate septic shock. CONCLUSION: Identified diagnostic gene biomarkers may be helpful in the diagnosis and therapy of patients with septic shock.

GPBAR1 promotes proliferation and is related to poor prognosis of high-grade glioma via inducing MAFB expression.

BACKGROUND: Glioma is the most prevalent brain tumors with extremely poor prognosis, but the prognostic biomarkers of high-grade (grade III and IV) gliomas (HGG) are still insufficient. MATERIALS AND METHODS: In our study, we investigated the expression of GPBAR1 in HGG by qRT-PCR and immunohistochemistry (IHC), and evaluated the clinical significance of GPBAR1 with univariate and multivariate analyses. By retrieving the data from TCGA, we screened the genes significantly associated with GPBAR1, and identified the correlation between GPBAR1 and MAFB. By experiments in vitro, we showed the pivotal role of MAFB in GPBAR1-induced proliferation of HGG. RESULTS: GPBAR1 expression in HGGs was significantly higher than that in normal brain tissues. GPBAR1 was an independent prognostic biomarker of HGG. GPBAR1 promoted the proliferation of HGG by inducing MAFB expression. MAFB was also a prognostic biomarker of HGG, and patients with co-expression of MAFB and GPBAR1 had worse prognosis. CONCLUSIONS: GPBAR1 promoted the proliferation of HGG by inducing MAFB expression. Both GPBAR1 and MAFB were prognostic biomarkers of HGG, and patients with co-expression of MAFB and GPBAR1 had worse prognosis than those with only GPBAR1 or MAFB expression.

Retrospective analysis: The application of human menopausal gonadotropin combined with letrozole for IUI in patients undergoing artificial insemination by husband due to unexplained or mild male factors.

Objective: To compare the effects of human menopausal gonadotropin (HMG) combined with letrozole (LE) to HMG only for ovarian stimulation on pregnancy outcome of infertile patients undergoing artificial insemination by husband (AIH) due to unexplained or mild male factors. Materials and methods: Infertile patients with unexplained or mild male factors treated from July 2015 to December 2021 were selected as subjects. The patients were divided into two groups according to the ovarian stimulation schemes they received, namely HMG combined with LE or HMG only. We analyzed the laboratory examination results before drug treatment (baseline) and during ovarian stimulation and compared the pregnancy outcomes of the two groups using univariable analysis and multivariable logistic regression analysis. Results: In total, 526 cycles of 372 couples were included. The univariate analysis showed that the clinical pregnancy rate of the HMG combined with LE group was 24.8%, significantly higher than that of the HMG group (14.8%, P = 0.007). The live birth rate (19.9%) of the HMG combined with LE group were also significantly higher than those of the HMG group (11.2%, respectively). In multivariate logistic analysis, the age of males was negatively associated with the clinical pregnancy rate (OR 0.874, 95% CI 0.793~0.963, P=0.006) and live birth (OR0.875, 95% CI 0.783~0.977, P=0.018). Moreover, ovarian stimulation with HMG+LE was the only beneficial factor significantly associated with clinical pregnancy (OR 1.929, 95% CI 1.068~3.485, P=0.029) and live birth (OR 2.255, 95% CI 1.188~4.282, P=0.013). Conclusion: Ovarian stimulation using HMG combined with LE can increase the clinical outcomes (live birth and clinical pregnancy) among infertile patients undergoing AIH due to explained or mild male factors.

Event-Triggering Interaction Scheme for Discrete-Time Decentralized Optimization With Nonuniform Step Sizes.

In this article, we study the discrete-time decentralized optimization problems of multiagent systems by an event-triggering interaction scheme, in which each agent privately knows its local convex cost function, and collectively minimizes the total cost functions. The underlying interaction and the corresponding weight matrix are required to be undirected connected and doubly stochastic, respectively. To resolve this optimization problem collaboratively, we propose a decentralized event-triggering algorithm (DETA) that is based on the consensus theory and inexact gradient tracking technique. DETA involves each agent interacting with its neighboring agents only at some independent event-triggering sampling time instants. Under the assumptions that the global convex cost function is coercive and has Lipschitz continuous gradient, we prove that DETA steers all agents' states to an optimal solution even with nonuniform constant step sizes. Moreover, our analysis also shows that DETA converges at a rate of O(1/√t) if the step sizes are uniform and do not exceed some upper bounds. We illustrate the effectiveness of DETA on a canonical simple decentralized parameter estimation problem.

Pattern Formation in a Reaction-Diffusion BAM Neural Network With Time Delay: (k1, k2) Mode Hopf-Zero Bifurcation Case.

This article investigates the joint effects of connection weight and time delay on pattern formation for a delayed reaction-diffusion BAM neural network (RDBAMNN) with Neumann boundary conditions by using the (k1,k2) mode Hopf-zero bifurcation. First, the conditions for k1 mode zero bifurcation are obtained by choosing connection weight as the bifurcation parameter. It is found that the connection weight has a great impact on the properties of steady state. With connection weight increasing, the homogeneous steady state becomes inhomogeneous, which means that the connection weight can affect the spatial stability of steady state. Then, the specified conditions for the k2 mode Hopf bifurcation and the (k1,k2) mode Hopf-zero bifurcation are established. By using the center manifold, the third-order normal form of the Hopf-zero bifurcation is obtained. Through the analysis of the normal form, the bifurcation diagrams on two parameters' planes (connection weight and time delay) are obtained, which contains six areas. Some interesting spatial patterns are found in these areas: a homogeneous periodic solution, a homogeneous steady state, two inhomogeneous steady state, and two inhomogeneous periodic solutions.

Couple-Group Consensus of Cooperative-Competitive Heterogeneous Multiagent Systems: A Fully Distributed Event-Triggered and Pinning Control Method.

This article discusses the couple-group consensus for heterogeneous multiagent systems via event-triggered and pinning control methods. Considering cooperative-competitive interaction among the agents, a novel group consensus protocol is designed. As inducing the time-correlation threshold function, a class of fully distributed event-triggered conditions without depending on any global information is proposed. Utilizing the Lyapunov stability theory, some sufficient conditions are obtained. Under hybrid event triggered and pinning control, pinning control strategies are first introduced. It is shown that under the proposed strategies, all agents can asymptotically achieve pinning couple-group consensus with discontinuous communication in a fully distributed way. Furthermore, the Zeno behavior for each agent is overcome. Finally, the reduction of the systems' controller update frequency and the correctness of our conclusions are illustrated by some simulations.