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Abstract Purpose This study assesses the metastasis rate of the key distal lymph nodes (KDLN) that are not routinely dissected in proximal gastrectomy, aiming to explore the oncological safety of proximal gastrectomy for upper gastric cancer who underwent neoadjuvant chemotherapy. Methods We analyzed a cohort of 150 patients with proximal locally advanced gastric cancer (cT3/4 before chemotherapy) from two high-volume cancer centers in China who received preoperative neoadjuvant chemotherapy (NAC) and total gastrectomy with lymph node dissection. Metastasis rate of the KDLN (No.5/6/12a) and the risk factors were analyzed. Results Key distal lymph node metastasis was detected in 10% (15/150) of patients, with a metastasis rate of 6% (9/150) in No. 5 lymph nodes, 6.7% (10/150) in No. 6 lymph nodes, and 2.7% (2/75) in No. 12a lymph nodes. The therapeutic value index of KDLN as one entity is 5.8. Tumor length showed no correlation with KDLN metastasis, while tumor regression grade (TRG) emerged as an independent risk factor (OR: 1.47; p-value: 0.04). Of those with TRG3 (no response to NAC), 80% (12/15) was found with KDLN metastasis. Conclusion For cT3/4 proximal locally advanced gastric cancer patients, the risk of KDLN metastasis remains notably high even after NAC. Therefore, proximal gastrectomy is not recommended; instead, total gastrectomy with thorough distal lymphadenectomy is the preferred surgical approach.

Abstract Objective To compare the computed tomography (CT) images of patients with locally advanced gastric cancer (GC) before and after neoadjuvant chemotherapy (NAC) in order to identify CT features that could predict pathological response to NAC. Methods We included patients with locally advanced GC who underwent gastrectomy after NAC from September 2016 to September 2021. We retrieved and collected the patients’ clinicopathological characteristics and CT images before and after NAC. We analyzed CT features that could differentiate responders from non-responders and established a logistic regression equation based on these features. Results We included 97 patients (69 [71.1%] men; median [range] age, 60 [26–75] years) in this study, including 66 (68.0%) responders and 31 (32.0%) non-responders. No clinicopathological variable prior to treatment was significantly associated with pathological response. Out of 16 features, three features (ratio of tumor thickness reduction, ratio of reduction of primary tumor attenuation in arterial phase, and ratio of reduction of largest lymph node attenuation in venous phase) on logistic regression analysis were used to establish a regression equation that demonstrated good discrimination performance in predicting pathological response (area under receiver operating characteristic curve 0.955; 95% CI, 0.911–0.998). Conclusion Logistic regression equation based on three CT features can help predict the pathological response of patients with locally advanced GC to NAC.

Abstract Objective Adopting a healthy lifestyle, including regular physical activity, is widely believed to decrease cancer risk. This study aimed to quantitatively establish the dose‐response relationships between total physical activity and the risk of breast, colon, lung, gastric, and liver cancers. Methods A systematic review and dose‐response analysis were conducted using PubMed and Embase from January 1, 1980 to March 20, 2023. Prospective cohort studies that examined the association between physical activity and the risks of any of the 5 outcomes were included. The search was confined to publications in the English language with a specific focus on human studies. Physical activity is standardized by using the data from US National Health and Nutrition Examination Surveys (NHANES) and the Global Burden of Disease 2019 database. Results A total of 98 studies, involving a combined population of 16,418,361 individuals, were included in the analysis. Among the included studies, 57 focused on breast cancer, 17 on lung cancer, 23 on colon cancer, 5 on gastric cancer, and 7 on liver cancer. Overall, elevated levels of physical activity exhibited an inverse correlation with the risk of cancer. The dose‐response curve for lung cancer exhibited a non‐linear pattern, with the greatest benefit risk reduction observed at 13,200 MET‐minutes/week of physical activity, resulting in a 14.7% reduction in risk (relative risk 0.853, uncertainty interval 0.798 to 0.912) compared to the inactive population. In contrast, the dose‐response curves for colon, gastric, breast, and liver cancers showed linear associations, indicating that heightened levels of total physical activity were consistently associated with reduced cancer risks. However, the increase in physical activity yielded a smaller risk reduction for colon and gastric cancers compared to breast and liver cancers. Compared to individuals with insufficient activity (total activity level < 600 MET‐minutes/week), individuals with high levels of activity (≥ 8,000 MET‐minutes/week) experienced a 10.3% (0.897, 0.860 to 0.934) risk reduction for breast cancer; 5.9% (0.941, 0.884 to 1.001) for lung cancer; 7.1% (0.929, 0.909 to 0.949) for colon cancer; 5.1% (0.949, 0.908 to 0.992) for gastric cancer; 17.1% (0.829, 0.760 to 0.903) for liver cancer. Conclusions This study demonstrated a significant inverse relationship between total physical activity and the risk of breast, gastric, liver, colon, and lung cancers.

Brazing a SiO2f/SiO2 composite with metals is often faced with two problems: poor wettability with the brazing alloy and high residual stress in the joint. To overcome these problems, we report a combined method of selective etching and depositing reduced graphene oxide (rGO) on the surface of a SiO2f/SiO2 composite (3D-rGO-SiO2f/SiO2) to assist brazing with TC4. After the combined treatment, a “3D-rGO” buffer layer formed on the surface layer of the SiO2f/SiO2, and the contact angle was reduced from 130° to 38°, which meant the wettability of active brazing alloy on the surface of SiO2f/SiO2 was obviously improved. In addition, the “3D-rGO” buffer layer contributed to fully integrating the brazing alloy and SiO2f/SiO2; then, the infiltration of the brazing alloy into the surface layer of the SiO2f/SiO2 was enhanced and formed the reduced graphene oxide with a pinning structure in the three dimensional (“3D-pinning-rGO”) structure. Moreover, the joining area of the brazing alloy and SiO2f/SiO2 was expanded and the mismatch degree between the SiO2f/SiO2 and TC4 was reduced, which was achieved by the “3D-pinning-rGO” structure. Furthermore, the concentration of the residual stress in the SiO2f/SiO2-TC4 joints transferred from the SiO2f/SiO2 to the braided quartz fibers, and the residual stress reduced from 142 MPa to 85 MPa. Furthermore, the 3D-pinning-rGO layer facilitated the transfer of heat between the substrates during the brazing process. Finally, the shear strength of the SiO2f/SiO2-TC4 joints increased from 12.5 MPa to 43.7 MPa by the selective etching and depositing rGO method.

Abstract Background The prognosis of patients with gastric cancer (GC) with gastric outlet obstruction (GOO) after gastrectomy is highly variable. In this study, we aimed to develop a nomogram to predict the prognosis of these patients. Patients and Methods Data from 218 GC patients with GOO who underwent gastrectomy at Sun Yat-sen University Cancer Center were retrospectively collected as a training cohort. The data of 59 patients with the same diagnosis who underwent gastrectomy at the First Affiliated Hospital of Guangxi Medical University were collected as an external verification cohort. A nomogram for the overall survival (OS) was developed using the Cox regression model in the training cohort, which was validated in a verification cohort. Results Multivariate analysis showed that the surgical procedure (P

Introduction: GIST (gastrointestinal stromal tumor) is the most prominent mesenchymal neoplasms of the gastrointestinal tract, and liver is the most common metastasis site for GIST. The molecular mechanism leading to liver metastasis of GIST is currently unclear.Methods: With the goal of revealing the underlying mechanism, we performed whole-genome gene expression profiling on 18 pairs of RNA samples comprised of GIST tissues (with liver metastasis) and corresponding non-tumor tissues. After identifying differentially expressed gene, functional annotation and signal pathway analyses were conducted. GSE13861, datasets that compare GIST (without liver metastasis) with adjacent tissues, served as a comparison.Results: A total of 492 up-regulated genes and 629 down-regulated genes were identified as differentially expressed genes between liver metastasis tissues and non-tumor tissues. We characterized expression patterns of DEGs identified from our cohort and GSE13861 that show signatures of enrichment for functionality. In subsequent gene set enrichment analysis, differentially expressed genes were mainly enriched in Epithelial Mesenchymal Transition in both datasets. 493 genes were overlapped among our whole-genome gene expression profiling results and GSE13861, consisting 188 up-regulated genes and 305 down-regulated genes. By using CytoHubba plugin of Cytoscape, CDH1, CD34, KIT, PROM1, SOX9, FGF2, CD24, ALDH1A1, JAG1 and NES were identified as top ten hub genes in tumorigenesis and liver metastasis of GIST. higher expression levels of FGF2, JAG1, CD34, ALDH1A1 and the lower expression level of CDH1 were respectively associated with unfavorable overall survival. Meanwhile higher expression levels of CD34, FGF2, KIT, JAG1, ALDH1A were correlated with worse disease-free survival.Discussion: The present study may help to provide candidate pathways and targets for treatment of GIST and prevention methods to liver metastasis.

This study investigates the expression and prognostic value of TRIM6 in gliomas, the most prevalent primary brain and spinal cord tumors. Our results show that TRIM6 is predominantly overexpressed in glioma tissues and is associated with reduced overall survival, disease-specific survival, and progression-free interval. Furthermore, TRIM6 expression is correlated with WHO grade and primary treatment outcomes. Functional analysis indicates that interactions between cytokines and their receptors play a critical role in the prognosis of glioma patients. A protein-protein interaction network reveals 10 hub genes closely linked to cytokine-cytokine receptor interaction. In vitro experiments demonstrate that silencing TRIM6 impairs the proliferation, invasion, and migration of glioma cells, while overexpressing TRIM6 enhances these abilities. Additionally, TRIM6 expression is positively associated with the abundance of innate immune cells and negatively associated with the abundance of adaptive immune cells. In summary, TRIM6 is significantly upregulated in gliomas and linked to poor prognosis, making it a potential diagnostic and prognostic biomarker. TRIM6 plays a crucial role in promoting cell viability, clonogenic potential, migration, and invasion in glioma cells. It may regulate glioma progression by modulating cytokine-cytokine receptor interaction, leading to an inflammatory response and an imbalance in immunomodulation, thereby representing a potential therapeutic target.

Background and objectivePCOS is a common metabolic disorder in women of reproductive age, which pathogenesis is very complex. The role of ferroptosis in PCOS is a novel finding, and the mechanistic studies are not clear. Metformin is a commonly used drug of PCOS but few studies on whether metformin can improve the follicle development and ovarian function in PCOS. We aims to use PCOS mouse model to study the effect of metformin on PCOS based on the ovarian function and explored the regulation of metformin in PCOS mice by intervening in ferroptosis pathway.Materials and methodsC57 BL/6J female mice aged 4-5 weeks were purchased and gavaged with letrozole (1 mg/kg/day) combined with high-fat diet for 21days to establish PCOS model, and control group was set up. After modeling, the mice were divided into PCOS model group and metformin treatment group (Met) (n=6).The Met group were gavaged metformin (200 mg/kg/day) for 28 days. The body weight, estrous cycle, glucose tolerance test (OGTT)and insulin resistance test (ITT) were monitored. Then, The mice were euthanized to collect serum and ovaries. Elisa was used to detect changes in related serum hormones (E2, LH, FSH, TP). Ovaries used for molecular biology experiments to detect changes in GPX4, SIRT3, AMPK/p-AMPK, and mTOR/p-mTOR by Western blot and qPCR.ResultsCompared with the model group mice, body weight was significantly reduced, and their estrous cycle was restored in Met group. The results of OGTT and ITT showed an improvment of glucose tolerance and insulin resistance. Morphological results showed that after metformin treatment, polycystic lesions in ovaries were reduced, the ovarian function was restored, and the expressions of SIRT3 and GPX4 were elevated. WB results demonstrated that the expressions of p-mTOR and p-AMPK in ovaries were significantly reduced in Model group, but reversed in MET group.ConclusionOur study confirmed metformin could not only improve body weight and metabolism disorders, but also improve ovarian dysfunction in PCOS mice.In addition, we explored metformin could regulate ferroptosis to improve PCOS via the SIRT3/AMPK/mTOR pathway. Our study complements the mechanisms by which metformin improves PCOS.

Summary: The difficulty of obtaining reliable individual identification of animals has limited researcher's ability to obtain quantitative data to address important ecological, behavioral, and conservation questions. Traditional marking methods placed animals at undue risk. Machine learning approaches for identifying species through analysis of animal images has been proved to be successful. But for many questions, there needs a tool to identify not only species but also individuals. Here, we introduce a system developed specifically for automated face detection and individual identification with deep learning methods using both videos and still-framed images that can be reliably used for multiple species. The system was trained and tested with a dataset containing 102,399 images of 1,040 individuals across 41 primate species whose individual identity was known and 6,562 images of 91 individuals across four carnivore species. For primates, the system correctly identified individuals 94.1% of the time and could process 31 facial images per second.

Activity recognition is important for taking care of patients and old men especially in e-Health. The activity recognition system without carrying any wearable devices is widely used in our daily life. Current methods employing uneconomical equipment or even dedicated devices lead to cost-inefficiency for large-scale deployments. This paper introduces R&P, a device-free activity recognition system only using cheap radio frequency identification devices (RFID) tags. Based on the analysis of RFID signals, we extract received signal strength fingerprints and phase fingerprints for each activity and synthesize these two kinds of fingerprints to accurately recognize activities. Moreover, we also modify the dynamic time warping (DTW) algorithm and propose T-DTW method to improve the recognition efficiency. We use commercial passive RFID hardware and verify R&Pin three different environments with different targets and six activities. The results demonstrate that our solution can recognize activities with an average accuracy of 87.9%.

Performance of a collaborative task is mostly dependent on the collective effort from participants. To accomplish a participatory task effectively and efficiently, the team formation problem (TFP) outweighs all other considerations. It is even more complicated when social connections among candidates is taken into account. As we can imagine, a large number of tasks require members of the team to be socially close. On the contrary, a portion of tasks, e.g., proposal review, pay more attention to a multidimensional view, and team members should be selected from a variety of cliques. Due to the nature of tasks, it is challenging to find a subset that meets the skill requirement of the task as well as socially diversity demand of team members from a pool of candidates. In this paper, we explore the TFP in a social network. Based on different task objectives, we first formulate the TFP as TFP with strong ties (TFP-ST) and TFP with weak ties (TFP-WT), respectively. Both TFP-ST and TFP-WT are proven to be NP-hard, and we then design corresponding heuristic algorithms to solve the two problems. Through extensive simulations, we show that the solution to TFP-ST can achieve significant improvement in terms of collaboration cost, team size, as well as running time, and the solution to TFP-WT can provide better performance than existing approaches at the same time.

A two-motor power-split dedicated hybrid transmission (DHT) with two planetary gears is proposed for the applications of a hybrid electric vehicle (HEV) and plug-in HEV (PHEV). The proposed DHT can provide electronically controlled continuous variable transmission (eCVT) with two different gear ratios. One of two electric motors is employed to act as a speeder for splitting the input power of internal combustion engine (ICE) and the other acts as a torquer to assist ICE for boosting. Assisted by an electric motor, ICE can always be enhanced to operate at its efficient area for the benefits of fuel economy improvement. The maximum ICE torque is viable to be mechanically transmitted to vehicle wheels from standstill with two different gear ratios. This feature can help reduce the traction motor torque and power sizing significantly. The paper presents detailed theoretical analyses of the proposed eCVT. Comprehensive simulation demonstrations for a pickup truck HEV application are given to address that the vehicle fuel consumption can be considerably reduced without compromising acceleration performance.

Range-free localization algorithms have caused widespread attention due to their low cost and low power consumption. However, such schemes heavily depend on the assumption that the hop count distance between two nodes correlates well with their Euclidean distance, which will be satisfied only in isotropic networks. When the network is anisotropic, holes or obstacles will lead to the estimated distance between nodes deviating from their Euclidean distance, causing a serious decline in localization accuracy. This paper develops HCD-DV-Hop for node localization in anisotropic sensor networks. HCD-DV-Hop consists of two steps. Firstly, an anisotropic network is decomposed into several different isotropic subnetworks, by using the proposed Hop Count Based Decomposition (HCD) scheme. Secondly, DV-Hop algorithm is carried out in each subnetwork for node localization. HCD first uses concave/convex node recognition algorithm and cleansing criterion to obtain the optimal concave and convex nodes based on boundary recognition, followed by segmentation of the network's boundary. Finally, the neighboring boundary nodes of the optimal concave nodes flood the network with decomposition messages; thus, an anisotropic network is decomposed. Extensive simulations demonstrated that, compared with range-free DV-Hop algorithm, HCD-DV-Hop can effectively reduce localization error in anisotropic networks without increasing the complexity of the algorithm.

Self-localization is one of the key technologies in the wireless sensor networks (WSN). Some traditional self-localization algorithms can provide a reasonable positioning accuracy only in a uniform and dense network, while for a nonuniform network the performance is not acceptable. In this paper, we presented a novel grid-based linear least squares (LLS) self-localization algorithm. The proposed algorithm uses the grid method to screen the anchors based on the distribution characteristic of a nonuniform network. Furthermore, by taking into consideration the quasi-uniform distribution of anchors in the area, we select suitable anchors to assist the localization. Simulation results demonstrate that the proposed algorithm can greatly enhance the localization accuracy of the anonymous nodes and impose less computation burden compared to traditional Trilateration and Multilateration.

Localization is emerging as a fundamental component in wireless sensor network and is widely used in the field of environmental monitoring, national and military defense, transportation monitoring, and so on. Current localization methods, however, focus on how to improve accuracy without considering the robustness. Thus, the error will increase rapidly when nodes density and SNR (signal to noise ratio) have changed dramatically. This paper introduces CTLL, Cell-Based Transfer Learning Method for Localization in WSNs, a new way for localization which is robust to the variances of nodes density and SNR. The method combines samples transfer learning and SVR (Support Vector Regression) regression model to get a better performance of localization. Unlike past work, which considers that the nodes density and SNR are invariable, our design applies regional division and transfer learning to adapt to the variances of nodes density and SNR. We evaluate the performance of our method both on simulation and realistic deployment. The results show that our method increases accuracy and provides high robustness under a low cost.

In wireless sensor networks (WSNs), Radio Signal Strength Indicator (RSSI)-based localization techniques have been widely used in various applications, such as intrusion detection, battlefield surveillance, and animal monitoring. One fundamental performance measure in those applications is the sensing coverage of WSNs. Insufficient coverage will significantly reduce the effectiveness of the applications. However, most existing studies on coverage assume that the sensing range of a sensor node is a disk, and the disk coverage model is too simplistic for many localization techniques. Moreover, there are some localization techniques of WSNs whose coverage model is non-disk, such as RSSI-based localization techniques. In this paper, we focus on detecting and recovering coverage holes of WSNs to enhance RSSI-based localization techniques whose coverage model is an ellipse. We propose an algorithm inspired by Voronoi tessellation and Delaunay triangulation to detect and recover coverage holes. Simulation results show that our algorithm can recover all holes and can reach any set coverage rate, up to 100% coverage.

The passive object localization (POL) problem in wireless sensor networks aims to determine the location of a target without any device attached for receiving or transmitting signal. This problem is challenging as there is very limited information available for deriving the target location. By combining the diffraction and scattering models, we propose a link sensing adaptive approach to POL, which first decides the target position attribute based on the signal strength and then localizes the target in different modes. We conduct rigorous localizability analyses and design a unit localization area scheme to achieve a higher level of localization accuracy. The efficacy of the proposed method is evaluated through comprehensive experiments in real life network environments.

An understanding of the changes of the rammed earth temperature of earthen ruins is important for protection of such ruins. To predict the rammed earth temperature pattern using the air temperature pattern of the monitoring data of earthen ruins, a pattern prediction method based on interesting pattern mining and correlation, called PPER, is proposed in this paper. PPER first finds the interesting patterns in the air temperature sequence and the rammed earth temperature sequence. To reduce the processing time, two pruning rules and a new data structure based on an R-tree are also proposed. Correlation rules between the air temperature patterns and the rammed earth temperature patterns are then mined. The correlation rules are merged into predictive rules for the rammed earth temperature pattern. Experiments were conducted to show the accuracy of the presented method and the power of the pruning rules. Moreover, the Ming Dynasty Great Wall dataset was used to examine the algorithm, and six predictive rules from the air temperature to rammed earth temperature based on the interesting patterns were obtained, with the average hit rate reaching 89.8%. The PPER and predictive rules will be useful for rammed earth temperature prediction in protection of earthen ruins.

The low-cost crystal oscillators in wireless sensor networks are prone to be affected by their working conditions such as voltage, temperature, and humidity. Such effect is often ignored by existing time synchronization solutions that typically assume the frequency error of a given node to be constant and hence adopt frequent timestamp exchanges, resulting in high energy consumptions. We propose a novel voltage-aware time synchronization (VATS) scheme that is inspired by the fact that the clock skew is highly correlated to voltage supplies. VATS features a two-phase process: (i) it first estimates the clock skew and updates the frequency error autonomously based on the local voltage level; (ii) it then adjusts the resynchronization intervals dynamically according to a given synchronization error controlling factor and the synchronization error accumulating rate to balance the calibration accuracy and cost. Since VATS leverages voltage measurements to assist clock skew estimation, it does not require frequent timestamp exchanges as in traditional schemes. Extensive simulation results illustrate the superior performance of the proposed method in terms of calculation accuracy, robustness, and reduced timestamp exchanges for energy saving.

When we encounter a malicious rumor or an infectious disease outbreak, immunizing k nodes of the relevant network with limited resources is always treated as an extremely effective method. The key challenge is how we can insulate limited nodes to minimize the propagation of those contagious things. In previous works, the best k immunised nodes are selected by learning the initial status of nodes and their strategies even if there is no feedback in the propagation process, which eventually leads to ineffective performance of their solutions. In this paper, we design a novel vaccines placement strategy for protecting much more healthy nodes from being infected by infectious nodes. The main idea of our solution is that we are not only utilizing the status of changing nodes as auxiliary knowledge to adjust our scheme, but also comparing the performance of vaccines in various transmission slots. Thus, our solution has a better chance to get more benefit from these limited vaccines. Extensive experiments have been conducted on several real-world data sets and the results have shown that our algorithm has a better performance than previous works.