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Abstract Purpose Previous studies have reported the potential impact of immune cells on kidney stone disease (KSD), but definitive causal relationships have yet to be established. The purpose of this paper is to elucidate the potential causal association between immune cells and KSD by Mendelian randomization (MR) analysis. Methods In our study, a thorough two-sample Mendelian randomization (MR) analysis was performed by us to determine the potential causal relationship between immune cell traits and kidney stone disease. We included a total of four immune traits (median fluorescence intensity (MFI), relative cellular (RC), absolute cellular (AC), and morphological parameters (MP)), which are publicly available data. GWAS summary data related to KSD (9713 cases and 366,693 controls) were obtained from the FinnGen consortium. The primary MR analysis method was Inverse variance weighted. Cochran’s Q test, MR Egger, and MR-Pleiotropy RESidual Sum and Outlier (MR-PRESSO) were used to assess the stability of the results. Results After FDR correction, the CD8 on HLA DR + CD8br (OR = 0.95, 95% CI = 0.93–0.98, p-value = 7.20 × 10− 4, q-value = 0.088) was determined to be distinctly associated with KSD, and we also found other 25 suggestive associations between immune cells and KSD, of which 13 associations were suggested as protective factors and 12 associations were suggested as risk factors. There was no horizontal pleiotropy or significant heterogeneity in our MR analysis, as determined by the p-value results of our Cochrane Q-test, MR Egger’s intercept test, and MR-PRESSO, which were all > 0.05. Conclusions Our study has explored the potential causal connection between immune cells and KSD by Mendelian randomization analysis, thus providing some insights for future clinical studies.

ObjectiveCollege students are currently grappling with severe mental health challenges, and research on artificial intelligence (AI) related to college students mental health, as a crucial catalyst for promoting psychological well-being, is rapidly advancing. Employing bibliometric methods, this study aim to analyze and discuss the research on AI in college student mental health.MethodsPublications pertaining to AI and college student mental health were retrieved from the Web of Science core database. The distribution of publications were analyzed to gage the predominant productivity. Data on countries, authors, journal, and keywords were analyzed using VOSViewer, exploring collaboration patterns, disciplinary composition, research hotspots and trends.ResultsSpanning 2003 to 2023, the study encompassed 1722 publications, revealing notable insights: (1) a gradual rise in annual publications, reaching its zenith in 2022; (2) Journal of Affective Disorders and Psychiatry Research emerged were the most productive and influential sources in this field, with significant contributions from China, the United States, and their affiliated higher education institutions; (3) the primary mental health issues were depression and anxiety, with machine learning and AI having the widest range of applications; (4) an imperative for enhanced international and interdisciplinary collaboration; (5) research hotspots exploring factors influencing college student mental health and AI applications.ConclusionThis study provides a succinct yet comprehensive overview of this field, facilitating a nuanced understanding of prospective applications of AI in college student mental health. Professionals can leverage this research to discern the advantages, risks, and potential impacts of AI in this critical field.

ObjectiveWith its high prevalence, depression's pathogenesis remains unclear. Recent attention has turned to the interplay between depression and epigenetic modifications. However, quantitative bibliometric analyses are lacking. This study aims to visually analyze depression epigenetics trends, utilizing bibliometric tools, while comprehensively reviewing its epigenetic mechanisms.MethodsUtilizing the Web of Science core dataset, we collected depression and epigenetics-related studies. Employing VOSViewer software, we visualized data on authors, countries, journals, and keywords. A ranking table highlighted field leaders.ResultsAnalysis encompassed 3,469 depression epigenetics studies published from January 2002 to June 2023. Key findings include: (1) Gradual publication growth, peaking in 2021; (2) The United States and its research institutions leading contributions; (3) Need for enhanced collaborations, spanning international and interdisciplinary efforts; (4) Keyword clustering revealed five main themes—early-life stress, microRNA, genetics, DNA methylation, and histone acetylation—highlighting research hotspots; (5) Limited focus on adolescent depression epigenetics, warranting increased attention.ConclusionTaken together, this study revealed trends and hotspots in depression epigenetics research, underscoring global collaboration, interdisciplinary fusion, and multi-omics data's importance. It discussed in detail the potential of epigenetic mechanisms in depression diagnosis and treatment, advocating increased focus on adolescent research in this field. Insights aid researchers in shaping their investigative paths toward understanding depression's epigenetic mechanisms and antidepressant interventions.

Abstract In this paper, a novel co‐operative mean field type game based task offloading strategy is proposed for multi‐access edge computing networks. The objective is to optimally determine the tasks offloaded to each edge computing node (ECN) in the network so that to balance the overall system performance and individual revenue of each ECN. To do so, the utility function of each ECN is first formulated, which is the payback of his time and energy cost for processing the tasks, tailed with the penalty from the network for not processing the tasks on time. A mean field type game is then formulated, where each ECN can either cooperate or compete with each other to maximize their own utility. With respect to the proposed game, the direct method is applied to find the final solutions. Simulation results reveal that the proposed strategy can well balance the utility of each ECN and the overall efficiency of the system, leading to a better performance compared with its cooperative‐ and competitive‐counterparts.

BackgroundDepression is a serious psychological disorder that college students are experiencing. College students’ depression problems, which can be caused by various factors, have been easily ignored and untreated. In recent years, exercise, as a low-cost and easily accessible method for treating depression, has attracted widespread attention. The purpose of this study is to use bibliometrics to explore the hotspots and trends in the field of exercise therapy of college students in depression from 2002 to 2022.MethodsWe retrieved relevant literature from the Web of Science (WoS), PubMed, and Scopus databases, and generated a ranking table to describe the core productivity in the field. We used VOSViewer software to generate network maps of authors, countries, co-cited journals, and co-occurring keywords to help us better understand the scientific collaboration patterns, potential disciplinary foundations, as well as research hotspots and trends in this field.ResultsFrom 2002 to 2022, a total of 1,397 articles related to exercise therapy of college students in depression were selected. The key findings of this study are as follows: (1) the number of publications has gradually increased, especially after 2019; (2) United States and its affiliated higher education institutions have made significant contributions to the development of this field; (3) there are multiple research groups in this field, but their connections are relatively limited; (4) the field is relatively interdisciplinary, primarily a convergence of behavioral science, public health, and psychology; (5) based on co-occurring keyword analysis, six main themes were summarized: health-promoting factors, body image, negative behaviors, increased stress, depression coping strategies, and diet.ConclusionOur study illustrates the research hotspots and trends for the research of exercise therapy of college students in depression, presents some challenges and new insights, and provides valuable information for further research.

Abstract Background Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal cell carcinoma and accounts for cancer-related deaths. Survival rates are very low when the tumor is discovered in the late-stage. Thus, developing an efficient strategy to stratify patients by the stage of the cancer and inner mechanisms that drive the development and progression of cancers is critical in early prevention and treatment. Results In this study, we developed new strategies to extract important gene features and trained machine learning-based classifiers to predict stages of ccRCC samples. The novelty of our approach is that (i) We improved the feature preprocessing procedure by binning and coding, and increased the stability of data and robustness of the classification model. (ii) We proposed a joint gene selection algorithm by combining the Fast-Correlation-Based Filter (FCBF) search with the information value, the linear correlation coefficient, and variance inflation factor, and removed irrelevant/redundant features. Then the logistic regression-based feature selection method was used to determine influencing factors. (iii) Classification models were developed using machine learning algorithms. This method is evaluated on RNA expression value of clear cell renal cell carcinoma derived from The Cancer Genome Atlas (TCGA). The results showed that the result on the testing set (accuracy of 81.15% and AUC 0.86) outperformed state-of-the-art models (accuracy of 72.64% and AUC 0.81) and a gene set FJL-set was developed, which contained 23 genes, far less than 64. Furthermore, a gene function analysis was used to explore molecular mechanisms that might affect cancer development. Conclusions The results suggested that our model can extract more prognostic information, and is worthy of further investigation and validation in order to understand the progression mechanism.

Media-Based Modulation (MBM) is regarded as a promising technique for future massive machine-type communications (mMTC) due to its high energy/spectral efficiency, good error performance and low-complexity radio frequency hardware implementation. In this paper, we consider both sparsity nature of user activity and sparsity nature of MBM signals in the uplink MBM-enabled mMTC system. According to the static user activation or the dynamic user activation in a coherent time, we classify the transmission schemes into two types and propose corresponding improved compressive sensing (CS)-based joint user identification and data detection with/without prior information of channel state information (CSI). The simulation results demonstrate the performance advantages of our proposed algorithms over the state-of-the-art CS-based user detection methods or CS-based symbol detection methods and evaluate the performance with different system parameters.

The bed separation backfill grouting (BSBG) is commonly used to mitigate the surface subsidence caused by coal mining. The distribution characteristics of bed separation and its dynamic evolving process are crucial for BSBG design. This paper utilizes the continuum-discontinuum element method (CDEM) to study the distribution characteristics of bed separation for a longwall top coal caving (LTCC) panel of Longquan coal mine. Numerical results indicate that in addition to the bed separation below the primary key stratum, several small bed separations may also occurred in the strata between the primary key stratum and the subordinate key stratum. The bed separations in the overburden could be classified into three classes: the upper bed separation, the middle bed separation, and the lower bed separation. The upper bed separation has the longest duration time, and the middle bed separation has the shortest duration time. And the BSBG should be started before the closure of the middle bed separation. Based on the actual geological information, the BSBG scheme for 4203 LTCC panel is proposed to mitigate the surface subsidence by taken the results of numerical simulation into consideration. In addition, the case study of the BSBG is introduced in detail. By using gangue power slurry, BSBG could not only effectively mitigate the surface subsidence but also solve the problems of environmental pollution and land occupation caused by traditional gangue stacking. The present study could provide technical support for surface subsidence mitigation and coal gangue disposal for LTCC mining with similar conditions.

BackgroundThere is no standard neoadjuvant therapy for locally advanced esophageal cancer in China. The role of neoadjuvant chemotherapy plus immunotherapy for locally advanced esophageal cancer is still being explored.MethodsThis open-label, randomized phase II study was conducted at a single center between July 2019 and September 2020; 30 patients with locally advanced esophageal squamous cell carcinoma (ESCC) (T3, T4, or lymph-node positive) were enrolled. Patients were randomized according to the enrollment order at a 1:1 ratio to receive chemotherapy on day 1 and toripalimab on day 3 (experimental group) or chemotherapy and toripalimab on day 1 (control group). The chemotherapeutic regimen was paclitaxel and cisplatin. Surgery was performed 4 to 6 weeks after the second cycle of chemoimmunotherapy. The primary endpoint was pathological complete response (pCR) rate, and the secondary endpoint was safety and disease-free survival.ResultsThirty patients completed at least one cycle of chemoimmunotherapy; 11 in the experimental group and 13 in the control group received surgery. R0 resection was performed in all these 24 patients. Four patients (36%) in the experimental group and one (7%) in the control group achieved pCR. The experimental group showed a statistically non-significant higher pCR rate (p = 0.079). PD-L1 combined positive score (CPS) examination was performed in 14 patients; one in the control group had a PD-L1 CPS of 10, and pCR was achieved; the remaining 13 all had ≤1, and 11 of the 13 patients received surgery in which two (in the experimental group) achieved pCR. Two patients endured ≥grade 3 adverse events, and one suffered from grade 3 immune-related enteritis after one cycle of chemoimmunotherapy and dropped off the study. Another patient died from severe pulmonary infection and troponin elevation after surgery.ConclusionsAlthough the primary endpoint was not met, the initial results of this study showed that delaying toripalimab to day 3 in chemoimmunotherapy might achieve a higher pCR rate than that on the same day, and further large-sample clinical trials are needed to verify this.Clinical Trial RegistrationClinicalTrials.gov, identifier NCT 03985670.

Abstract Background Non-small cell lung cancer (NSCLC) is the second most prevalent cause of cancer-related fatality. Long non-coding RNAs (lncRNAs) have been observed to exercise functions in NSCLC. Here, the current study aimed to explore the potential mechanism of lncRNA MBNL1-AS1 in NSCLC. Methods Microarray analysis was performed to screen the differentially expressed lncRNA associated with NSCLC and its potential mechanism. The lncRNA MBNL1-AS1 expression was quantified in 56 paired NSCLC and adjacent normal tissue samples. In an attempt to outline the function of lncRNA MBNL1-AS1 in NSCLC and to identify the interaction among lncRNA MBNL1-AS1, microRNA-301b-3p (miR-301b-3p) and TGFBR2, ectopic expression, depletion, and reporter assay experiments were conducted to detect CSC proliferation, migration, invasion, drug resistance, and sphere formation in NSCLC. Results Initially, the intersection among lncRNA MBNL1-AS1, miR-301b-3p, and TGFBR2 was observed in NSCLC. While a poor expression of lncRNA MBNL1-AS1 and TGFBR2, along with a high expression of miR-301b-3p was observed in NSCLC tissues. A demonstration of lncRNA MBNL1-AS1 restoration significantly decreased CSC proliferation, migration, invasion, drug resistance, and sphere formation in NSCLC. LncRNA MBNL1-AS1 functioned as a sponge of miR-301b-3p, which inverted the inhibitory role of lncRNA MBNL1-AS1 in CSC proliferation, migration, invasion, drug resistance, and sphere formation in NSCLC. LncRNA MBNL1-AS1 positively regulated TGFBR2 which was a target gene of miR-301b-3p. At last, upregulated lncRNA MBNL1-AS1 or depleted miR-301b-3p suppressed the xenograft tumor formation in vivo. Conclusion Collectively, the present study suggests an inhibitory role of lncRNA MBNL1-AS1 in CSC drug resistance of NSCLC by upregulating miR-301b-3p-targeted TGFBR2.

This paper investigates the downlink transmission of massive multiple-input multiple-output-enabled simultaneous wireless information and power transfer systems in Rician fading channels. In the system, the base station is equipped with massive antennas to concurrently deliver information and energy to the users. While a portion of the harvested energy is utilized by the users to support the channel estimation, and the remaining is reserved as the supplement to the user battery for signal processing. The asymptotic expressions of the harvested energy and achievable rate are, respectively, derived, in the context of downlink scenario and line-of-sight propagation. With respect to the proposed system, two optimization problems are formulated, with the purposes of maximizing the system sum rate and the minimum rate among individual users, respectively. As both original problems are nonconvex and hard to solve, we propose a general iterative optimization framework to first decompose each of them into three subproblems, where the power allocation profiles, the channel estimation duration, and the power split ratios are optimized independently. The iteration is then carried out to update the solutions to each subproblem and finally approach the optimum of the original ones. The convergence and the effectiveness of the proposed algorithms are proved and evaluated through both theoretical analysis and simulations. Results demonstrate that the proposed algorithms can achieve the optimal transmission performance of the system with moderate complexity. Moreover, it can be further concluded that the maximum rate can be achieved if the harvested energy is fully devoted to pilot transmission.

This paper investigates resource modeling and management for a base station (BS) providing mobile edge computing (MEC) service. In the proposed modeling, BS is recognized as a queueing network consisting of multiple multi-type servers. The uplink transmission users, downlink transmission users, and MEC users with different priority levels are jointly considered. It is assumed that their service-requests arrive dynamically and are also served dynamically. With such a general resource modeling, the interaction among these users can be analyzed based on the queueing network theory. The average delay of each service-type with different priority levels is derived. Based on the derived results, two resource management optimization problems are formulated and solved from the perspective of a service provider. The revenue brought by MEC services is first maximized by doing user admission control while provisioning the quality-of-service (QoS) of all admitted users with the given amount of communication and computation resources. Then, the capital expenditure of resource deployment is minimized by satisfying the QoS of all users. It is formulated as an integer programming problem. An algorithm is developed to solve it, which can help service providers to determine the optimal amount of communication and computation resources to be placed in a BS to guarantee QoS for all users at a minimal total capital expenditure. Computer simulations are done to validate all analysis and comparisons are made with BS serving multi-type users of single priority level. Through comparison, an insight is gained that service providers can obtain more revenue or spare less capital expenditure by differentiating the user priority levels.

In this paper, a scalable and cooperative medium access control (MAC) protocol (SCMAC) is proposed to support the periodic beaconing over the control channel in vehicular ad hoc networks. The main concerned aspects in SCMAC are communication reliability and protocol scalability. The communication reliability is guaranteed by cooperative beaconing. When broadcasting a beacon, the node embeds the slot state information into the beacon so that surrounding nodes can determine the availability of each slot. Together with the contention window, collisions can be alleviated. The protocol scalability is achieved by the slot access method and the proactive slot reservation. On one hand, the slot access method reduces the average delay by using the smaller beaconing period as the node density is decreasing. On the other hand, the proactive slot reservation always keeps enough idle slots so that more nodes can quickly join the network. Correspondingly, the beaconing period will increase and all nodes can gain chances to broadcast beacons. The simulation results show that SCMAC can achieve the reliable and scalable periodic beaconing in vehicular environments.

Targeting the medical monitoring applications of wireless body area networks (WBANs), a hybrid medium access control protocol using an interrupt mechanism (I-MAC) is proposed to improve the energy and time slot utilization efficiency and to meet the data delivery delay requirement at the same time. Unlike existing hybrid MAC protocols, a superframe structure with a longer length is adopted to avoid unnecessary beacons. The time slots are mostly allocated to nodes with periodic data sources. Short interruption slots are inserted into the superframe to convey the urgent data and to guarantee the real-time requirements of these data. During these interruption slots, the coordinator can break the running superframe and start a new superframe. A contention access period (CAP) is only activated when there are more data that need to be delivered. Experimental results show the effectiveness of the proposed MAC protocol in WBANs with low urgent traffic.

This paper considers power splitting (PS)-based simultaneous wireless information and power transfer (SWIPT) for multiple-input multiple-output (MIMO) interference channel networks where multiple transceiver pairs share the same frequency spectrum. As the PS model is adopted, an individual receiver splits the received signal into two parts for information decoding (ID) and energy harvesting (EH), respectively. Aiming to minimize the total transmit power, transmit precoders, receive filters and PS ratios are jointly designed under a predefined signal-to-interference-plus-noise ratio (SINR) and EH constraints. The formulated joint transceiver design and power splitting problem is non-convex and thus difficult to solve directly. In order to effectively obtain its solution, the feasibility conditions of the formulated non-convex problem are first analyzed. Based on the analysis, an iterative algorithm is proposed by alternatively optimizing the transmitters together with the power splitting factors and the receivers based on semidefinite programming (SDP) relaxation. Moreover, considering the prohibitive computational cost of the SDP for practical applications, a low-complexity suboptimal scheme is proposed by separately designing interference-suppressing transceivers based on interference alignment (IA) and optimizing the transmit power allocation together with splitting factors. The transmit power allocation and receive power splitting problem is then recast as a convex optimization problem and solved efficiently. To further reduce the computational complexity, a low-complexity scheme is proposed by calculating the transmit power allocation and receive PS ratios in closed-form. Simulation results show the effectiveness of the proposed schemes in achieving SWIPT for MIMO interference channel (IC) networks.

In this paper, we propose to combine Orthogonal Frequency Division Multiplexing-Interleave Division Multiple Access (OFDM-IDMA) with Simultaneous Wireless Information and Power Transfer (SWIPT), resulting in SWIPT aided OFDM-IDMA scheme for power-limited sensor networks. In the proposed system, the Receive Node (RN) applies Power Splitting (PS) to coordinate the Energy Harvesting (EH) and Information Decoding (ID) process, where the harvested energy is utilized to guarantee the iterative Multi-User Detection (MUD) of IDMA to work under sufficient number of iterations. Our objective is to minimize the total transmit power of Source Node (SN), while satisfying the requirements of both minimum harvested energy and Bit Error Rate (BER) performance from individual receive nodes. We formulate such a problem as a joint power allocation and splitting one, where the iteration number of MUD is also taken into consideration as the key parameter to affect both EH and ID constraints. To solve it, a sub-optimal algorithm is proposed to determine the power profile, PS ratio and iteration number of MUD in an iterative manner. Simulation results verify that the proposed algorithm can provide significant performance improvement.

This paper addresses the problem of frequency synchronization in multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) systems with periodic preambles. Two Maximum Likelihood Estimators (MLEs) and four low complexity Best Linear Unbiased Estimators (BLUEs) are proposed. When cyclic shift delay is applied to periodic preambles, MLEs can adopt a unique structure which employs diversity and correlation calculation in the estimator. We first propose a multiple-antenna estimator named as MLE-MA, which has an estimation range of the entire signal bandwidth. MLE-MAS is a simplified version of MLE-MA, which reduces the computational load at the cost of a narrower estimation range. Moreover, phases or differential phases obtained from the autocorrelation of periodic sequences with different delays can be used to estimate carrier frequency offset (CFO). The weighting coefficients of all BLUEs are derived in closed form and shown to be independent from the signal-to-noise ratio (SNR). BLUEs have asymptotically consistent variances. Theoretical and simulation results demonstrate that all estimators can asymptotically approach the theoretical Cramer Rao Lower bound (CRLB) at different SNRs.

This paper presents an optimal and fair strategy for multiuser multimedia radio resource allocation (RRA) based on coopetition, which suggests a judicious mixture of competition and cooperation. We formulate the co-opetition strategy as sum utility maximization at constraints from both Physical (PHY) and Application (APP) layers. We show that the maximization can be solved efficiently employing the well-defined Layering as Optimization Decomposition (LOD) method. Moreover, the coopetition strategy is applied to power allocation among multiple video users, and evaluated through comparing with existing- competition based strategy. Numerical results indicate that, the co-opetition strategy adapts the best to the changes of network conditions, participating users, and so forth. It is also shown that the coopetition can lead to an improved number of satisfied users, and in the meanwhile provide more flexible tradeoff between system efficiency and fairness among users.

Cyberattack Detection plays a vital role in network security and is an important means to maintain network security. In order to enhance the security and improve the detection ability of malicious intrusion behavior in the network, this paper proposes a multi-layer Dense Attention (Denseformer) model. The model is composed of multiple transformer like structures, and each layer is stacked of multiple encoder and decoder sub-layers. The encoder and decoder include self-attention and cross-attention mechanisms, and their features are obtained by cross-fusion of multi-branch structures. By sharing information among multiple encoder-decoder layers, Denseformer can use the attention mechanism to process unserialized input source samples. On the whole, Denseformer is like an attention network embedded on the dense layer, making it easier to handle correlations between features. By stacking the encoding and decoding modules with attention, Denseformer has better generalization performance than other models, thereby improving its cyberattack detection accuracy. The experimental results show that, without other complex training techniques, the proposed method achieves 85.65% on the NSL-KDD dataset. [ABSTRACT FROM AUTHOR]

The IEEE 802.15.6 standard is introduced to satisfy all the requirements for monitoring systems operating in, on, or around the human body. In this paper, analytical models are developed for evaluating the performance of the IEEE 802.15.6 CSMA/CA-based medium access control protocol for wireless body area networks (WBAN) under unsaturation condition. We employ a three-dimensional Markov chain to model the backoff procedure, and an M/G/1/K queuing system to describe the packet queues in the buffer. The throughput and delay performances of WBAN operating in the beacon mode are analyzed in heterogeneous network comprised of different user priorities. Simulation results are included to demonstrate the accuracy of the proposed analytical model. [ABSTRACT FROM AUTHOR]