Search

Recalibrating probabilistic classifiers is vital for enhancing the reliability and accuracy of predictive models. Despite the development of numerous recalibration algorithms, there is still a lack of a comprehensive theory that integrates calibration and sharpness (which is essential for maintaining predictive power). In this paper, we introduce the concept of minimum-risk recalibration within the framework of mean-squared-error (MSE) decomposition, offering a principled approach for evaluating and recalibrating probabilistic classifiers. Using this framework, we analyze the uniform-mass binning (UMB) recalibration method and establish a finite-sample risk upper bound of order $\tilde{O}(B/n + 1/B^2)$ where $B$ is the number of bins and $n$ is the sample size. By balancing calibration and sharpness, we further determine that the optimal number of bins for UMB scales with $n^{1/3}$, resulting in a risk bound of approximately $O(n^{-2/3})$. Additionally, we tackle the challenge of label shift by proposing a two-stage approach that adjusts the recalibration function using limited labeled data from the target domain. Our results show that transferring a calibrated classifier requires significantly fewer target samples compared to recalibrating from scratch. We validate our theoretical findings through numerical simulations, which confirm the tightness of the proposed bounds, the optimal number of bins, and the effectiveness of label shift adaptation.

We consider a federated learning (FL) system consisting of multiple clients and a server, where the clients aim to collaboratively learn a common decision model from their distributed data. Unlike the conventional FL framework that assumes the client's data is static, we consider scenarios where the clients' data distributions may be reshaped by the deployed decision model. In this work, we leverage the idea of distribution shift mappings in performative prediction to formalize this model-dependent data distribution shift and propose a performative federated learning framework. We first introduce necessary and sufficient conditions for the existence of a unique performative stable solution and characterize its distance to the performative optimal solution. Then we propose the performative FedAvg algorithm and show that it converges to the performative stable solution at a rate of O(1/T) under both full and partial participation schemes. In particular, we use novel proof techniques and show how the clients' heterogeneity influences the convergence. Numerical results validate our analysis and provide valuable insights into real-world applications.

Existing graph neural networks (GNNs) largely rely on node embeddings, which represent a node as a vector by its identity, type, or content. However, graphs with unattributed nodes widely exist in real-world applications (e.g., anonymized social networks). Previous GNNs either assign random labels to nodes (which introduces artefacts to the GNN) or assign one embedding to all nodes (which fails to explicitly distinguish one node from another). Further, when these GNNs are applied to unattributed node classification problems, they have an undesired equivariance property, which are fundamentally unable to address the data with multiple possible outputs. In this paper, we analyze the limitation of existing approaches to node classification problems. Inspired by our analysis, we propose a generalized equivariance property and a Preferential Labeling technique that satisfies the desired property asymptotically. Experimental results show that we achieve high performance in several unattributed node classification tasks.

In order to better improve the reliability of data transmission and extend the network lifetime, the paper proposes the Sensing Clustering Routing Algorithm Based on Collaborative Computing (SCR-CC). The proposed algorithm uses the characteristics of the perceptual radius, which obey the normal distribution, and gives the process of completing the expected value of the data transmission of any two nodes in the cluster. Secondly, the paper analysed the necessary conditions of the existence for the expected value of the number of neighbour nodes when the redundant nodes are closed and the working nodes meet arbitrary differences. Thirdly, the cluster angle formed by the base station and the cluster is used to optimize the clustering structure and complete the dynamic clustering process to achieve the energy balance of the entire network. Finally, the simulation experiments show that the proposed SCR-CC algorithm compared with the other three algorithms reduces the number of failed nodes by 11.37% on average and increases the network lifetime by 27.09% on average; therefore, the efficiency and effectiveness of the SCR-CC algorithm are verified.

The coverage of traditional wireless sensor networks (WSNs) is limited by node energy and data redundancy, forcing WSNs to be interrupted abnormally. To this end, a novel optimization cooperative coverage algorithm with controllable threshold-parameters (OCC-CT) is proposed. The algorithm first determines the location information of the focus target nodes (FTNs), and uses the genetic algorithm (GA) to give the node path planning. Second, the controllable threshold parameters and mutation parameters are used to control event domain nodes. Clustering is optimized to make the clustering of the nodes more uniform, so as to reduce the energy consumption of the nodes and improve the search ability of the global target nodes. Third, the adaptation function is used to cover the continuity of the covered target locations and the monitoring range of the nodes. Optimization is performed to achieve the goals of increasing network coverage and extending the network lifetime. Finally, simulation results show that the OCC-CT algorithm, compared with other three algorithms in this paper, improves 0.11 and 0.16 on average in terms of network coverage, network lifetime, and 0.14 in terms of network energy overhead, thereby further verifying OCC-CT algorithm has strong stability and effectiveness.

Objectives To develop and evaluate machine learning models using baseline and restaging computed tomography (CT) for predicting and early detecting pathological downstaging (pDS) with neoadjuvant chemotherapy in advanced gastric cancer (AGC). Methods We collected 292 AGC patients who received neoadjuvant chemotherapy. They were classified into (a) primary cohort (206 patients with 3–4 cycles chemotherapy) for model development and internal validation, (b) testing cohort I (46 patients with 3–4 cycles chemotherapy) for evaluating models’ predictive ability before and after the complete course, and (c) testing cohort II (n = 40) for model evaluation on its performance at early treatment. We extracted 1,231 radiomics features from venous phase CT at baseline and restaging. We selected radiomics models based on 28 cross-combination models and measured the areas under the curve (AUC). Our prediction radiomics (PR) model is designed to predict pDS outcomes using baseline CT. Detection radiomics (DR) model is applied to restaging CT for early pDS detection. Results PR model achieved promising outcomes in two testing cohorts (AUC 0.750, p = .009 and AUC 0.889, p = .000). DR model also showed a good predictive ability (AUC 0.922, p = .000 and AUC 0.850, p = .000), outperforming the commonly used RECIST method (NRI 39.5% and NRI 35.4%). Furthermore, the improved DR model with averaging outcome scores of PR and DR models showed boosted results in two testing cohorts (AUC 0.961, p = .000 and AUC 0.921, p = .000). Conclusions CT-based radiomics models perform well on prediction and early detection tasks of pDS and can potentially assist surgical decision-making in AGC patients. Key Points • Baseline contrast-enhanced computed tomography (CECT)-based radiomics features were predictive of pathological downstaging, allowing accurate identification of non-responders before therapy. • Restaging CECT-based radiomics features were predictive to achieve pDS after and even at an early stage of neoadjuvant chemotherapy. • Combination of baseline and restaging CECT-based radiomics features was promising for early detection and preoperative evaluation of pathological downstaging of AGC.

Although Deep Neural Network (DNN) has led to unprecedented progress in various natural language processing (NLP) tasks, research shows that deep models are extremely vulnerable to backdoor attacks. The existing backdoor attacks mainly inject a small number of poisoned samples into the training dataset with the labels changed to the target one. Such mislabeled samples would raise suspicion upon human inspection, potentially revealing the attack. To improve the stealthiness of textual backdoor attacks, we propose the first clean-label framework Kallima for synthesizing mimesis-style backdoor samples to develop insidious textual backdoor attacks. We modify inputs belonging to the target class with adversarial perturbations, making the model rely more on the backdoor trigger. Our framework is compatible with most existing backdoor triggers. The experimental results on three benchmark datasets demonstrate the effectiveness of the proposed method.

Jiatong Chai, Zeyu Sun, Jiancheng Xu Department of Laboratory Medicine, The First Hospital of Jilin University, Changchun, People’s Republic of ChinaCorrespondence: Jiancheng Xu, Department of Laboratory Medicine, The First Bethune Hospital of Jilin University, 71 Xinmin Street, Changchun, 130021, People’s Republic of China, Tel +86-431-8878-2595, Fax +86-431-8878-6169, Email xjc@jlu.edu.cnAbstract: High-throughput omics has been widely applied in metabolic disease, type 1 diabetes (T1D) was one of the most typical diseases. Effective prevention and early diagnosis are very important because of infancy and persistent characteristics of T1D. The occurrence and development of T1D is a chronic and continuous process, in which the production of autoantibodies (ie serum transformation) occupies the central position. Metabolomics can evaluate the metabolic characteristics of serum before seroconversion, the changes with age and T1D complications. And the addition of natural drug metabolomics is more conducive to the systematic and comprehensive diagnosis and treatment of T1D. This paper reviewed the metabolic changes and main pathogenesis from pre-diagnosis to treatment in T1D. The metabolic spectrum of significant abnormal energy and glucose-related metabolic pathway, down-regulation of lipid metabolism and up-regulation of some antioxidant pathways has appeared before seroconversion, indicating that the body has been in the dual state of disease progression and disease resistance before T1D onset. Some metabolites (such as methionine) are closely related to age, and the types of autoantibodies produced are age-specific. Some metabolites may jointly predict DN with eGFR, and metabolomics can further contribute to the pathogenesis based on the correlation between DN and DR. Many natural drug components have been proved to act on abnormal metabolic pathways of T1D and have a positive impact on some metabolic levels, which is very important for further finding therapeutic targets and developing new drugs with small side effects. Metabolomics can provide auxiliary value for the diagnosis of T1D and provide a new direction to reveal the pathogenesis of T1D and find new therapeutic targets. The development of T1D metabolomics shows that high-throughput research methods are expected to be introduced into clinical practice.Keywords: type 1 diabetes, metabolomics, diabetic complications, metabolites, drug targets

In recent years, lakes’ water quality and quantity have been affected and damaged by agricultural activities. The sensitivity of phytoplankton to the hydrological environment can effectively indicate the health of the aquatic ecosystem and the change in water quality. Understanding the changes of phytoplankton communities in lakes contaminated by agriculture may contribute to determining the directions of protection of these water bodies and provide reference cases for wider research. It is found that there are 146 species, 63 genera, and 8 phyla of phytoplankton, including 57 species of Bacillariophyta, 46 species of Chlorophyta, 17 species of Cyanobacteria and Euglenophyta. The total abundance was changed successively with Bacillariophyta (75% in June), Cyanobacteria (50% in July–August), and Chlorophyta (75% in September–October). The total biomass decreased continuously over time. The biomass of Chlorophyta is highest in October (67.4%), and diatoms contribute the most biomass in other months (76.5%). The Redundancy analysis indicated that the main environmental factors affecting phytoplankton’s dynamic change are total salt, water temperature, total phosphorus, and 5-day biochemical oxygen demand. The phytoplankton can be divided into 21 functional groups. The MP group has the highest frequency, mainly distributed in the frequently stirred and turbid shallow water. Representative functional groups indicate the high degree of eutrophication and nutrient-rich conditions and the good associated environment for phytoplankton and slow water flow rate. With the seasonal change of agricultural irrigation and drainage, the water quality of Xinmiao Lake began to deteriorate from medium pollution in July and reached serious pollution in October finally. Furthermore, this research discovered that the risk of cyanobacteria bloom is high in summer, primarily when thermal stratification occurs. This study provides necessary information for understanding and predicting the changes of the phytoplankton community caused by the increase of nutrients, human disturbance, and temperature conditions in eutrophic lakes in agricultural areas.

This work proposes a novel fluid-thermal-electric multiphysics numerical model to predict the performance of thermoelectric generator systems applied to fluid waste heat recovery, with the consideration of multiphysics coupling effects of fluid, thermal, and electric fields. The comprehensive numerical simulations of the thermoelectric generator system are performed via COMSOL coupled solver. Besides, the effect of the neglect of parasitic heat on the output performance is investigated through the comparison with numerical results predicted by ANSYS and COMSOL separate solver, wherein the fluid-thermal field is computed first, then the thermal-electric field. The results show that the output power predicted by COMSOL separate solver is 8.52% lower than that predicted by COMSOL coupled solver at the inlet air temperature of 550 K and inlet air velocity of 30 m/s due to the neglect of parasitic heat. The output performance of the TEG system predicted by ANSYS is less affected by inlet air boundary conditions than that predicted by COMSOL. Finally, the experimental results show that the fluid-thermal-electric multiphysics model solved by the COMSOL coupled solver shows the lowest output power deviation of 2.81%. The proposed model can guide the numerical modeling of the thermoelectric generator system applied to fluid waste heat recovery.

Recently, neural techniques have been used to generate source code automatically. While promising for declarative languages, these approaches achieve much poorer performance on datasets for imperative languages. Since a declarative language is typically embedded in an imperative language (i.e., the turducken-style programming) in real-world software development, the promising results on declarative languages can hardly lead to significant reduction of manual software development efforts. In this paper, we define a new code generation task: given a natural language comment, this task aims to generate a program in a base imperative language with an embedded declarative language. To our knowledge, this is the first turducken-style code generation task. For this task, we present Lyra: a dataset in Python with embedded SQL. This dataset contains 2,000 carefully annotated database manipulation programs from real-world projects. Each program is paired with both a Chinese comment and an English comment. In our experiment, we adopted Transformer, BERT-style, and GPT-style models as baselines. In the best setting, the generation performance of GPT-style models is better than others, where the AST exact matching accuracy is 24% and 25.5% when using Chinese and English comments, respectively. Therefore, we believe that Lyra provides a new challenge for code generation. Yet, overcoming this challenge may significantly boost the applicability of code generation techniques for real-world software development.

Order flow imbalance can explain short-term changes in stock price. This paper considers the change of non-minimum quotation units in real transactions, and proposes a generalized order flow imbalance construction method to improve Order Flow Imbalance (OFI) and Stationarized Order Flow Imbalance (log-OFI). Based on the high-frequency order book snapshot data, we conducted an empirical analysis of the CSI 500 constituent stocks. In order to facilitate the presentation, we selected 10 stocks for comparison. The two indicators after the improvement of the generalized order flow imbalance construction method both show a better ability to explain changes in stock prices. Especially Generalized Stationarized Order Flow Imbalance (log-GOFI), using a linear regression model, on the time scales of 30 seconds, 1 minute, and 5 minutes, the average R-squared out of sample compared with Order Flow Imbalance (OFI) 32.89%, 38.13% and 42.57%, respectively increased to 83.57%, 85.37% and 86.01%. In addition, we found that the interpretability of Generalized Stationarized Order Flow Imbalance (log-GOFI) showed stronger stability on all three time scales., Comment: 9 pages,5 figures

Feng Liang,1,* Jianli Wang,1,* Xiangyu Zhu,2,* Zhen Wang,1 Jingwei Zheng,1 Zeyu Sun,1 Shenbin Xu,1 Jianmin Zhang,1,3,4 Jingyi Zhou,1 Ligen Shi1 1Department of Neurosurgery, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, People’s Republic of China; 2Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan, People’s Republic of China; 3Brain Research Institute, Zhejiang University, Hangzhou, Zhejiang, People’s Republic of China; 4Collaborative Innovation Center for Brain Science, Zhejiang University, Hangzhou, Zhejiang, People’s Republic of China*These authors contributed equally to this workCorrespondence: Ligen Shi 88 Jiefang Road, Hangzhou City, Zhejiang Province 310009, People’s Republic of ChinaTel +86-571-87784715Fax +86-571-87784755Email slg0904@zju.edu.cnJianmin Zhang 88 Jiefang Road, Hangzhou City, Zhejiang Province 310009, People’s Republic of ChinaTel +86-571-87784715Fax +86-571-87784755Email zjm135@zju.edu.cnBackground: This study sought to investigate a novel effect of melatonin in reducing brain injury in an in vivo hyperglycemic intracerebral hemorrhage (ICH) model and further explore the mechanisms of protection.Methods: Hyperglycemia ICH was induced in Sprague-Dawley rats by streptozocin injection followed by autologous blood injection into the striatum. A combined approach including RNA-specific depletion, electron microscopy, magnetic resonance, Western blots, and immunohistological staining was applied to quantify the brain injuries after ICH.Results: Hyperglycemia resulted in enlarged hematoma volume, deteriorated brain edema, and aggravated neuronal mitochondria damage 3 days after ICH. Post-treatment with melatonin 2 hours after ICH dose-dependently improved neurological behavioral performance lasting out to 14 days after ICH. This improved neurological function was associated with enhanced structural and functional integrity of mitochondria. Mechanistic studies revealed that melatonin alleviated mitochondria damage in neurons via activating the PPARδ/PGC-1α pathway. Promisingly, melatonin treatment delayed until 6 hours after ICH still reduced brain edema and improved neurological functions. Melatonin supplementation reduces neuronal damage after hyperglycemic ICH by alleviating mitochondria damage in a PPARδ/PGC-1α-dependent manner.Conclusion: Melatonin may represent a therapeutic strategy with a wide therapeutic window to reduce brain damage and improve long-term recovery after ICH.Keywords: melatonin, intracerebral hemorrhage, hyperglycemia, mitochondria, apoptosis

The performance of radar automatic target recognition (ATR) highly depends on the quality of training database, the extracted features and classification algorithm. Radar target is detected by the Doppler effect in radar echo signal. Through processing the echo signals in different domains, the distinctive characteristic can be obtained intuitively. Furthermore, we can utilize the extracted features to complete radar target classification. This paper proposes a novel target recognition method based on 1D-convolution neural network (CNN) aiming at the ATR of low-resolution ground surveillance radar. The proposed approach uses 1D-CNN as feature extractor and softmax layer as classifier. We tested our method on actual collected database to classify human and car, which reached an accuracy of 98%. Compared with conventional artificial feature extraction approaches, our model shows better performance and adaptability.

As an effective method in signal reconstruction model, compressed sensing (CS) has achieved excellent performance in sparse array reconstruction. However, it is necessary to set the penalization factor before iterative calculation, which will increase the difficulty to convergence the result to the global optimal solution. In this paper, we remove the process of choosing penalization factor and reconstruction error by modifying the iterative expression as well as alternating direction method of multipliers (ADMM) algorithm respectively. In addition, the improved model is shown to be convex and thus can be solved using the CVX toolbox. Simulation result shows that the reference pattern could be reconstructed with minimum number of antenna elements by the proposed algorithms. Moreover, the proposed methods have significant performance improvement in main sidelobe level (MSL).

Constant false alarm rate (CFAR) detectors are widely used in modern radar system to declare the presence of targets. Due to the serious masking effects under the multiple targets situation and the clutter edge, the detection probability of CFAR detectors decrease sharply and the alarm rates increase significantly. To solve these problems, a robust adaptive amplitude iteration CFAR (AAI-CFAR) algorithm is proposed in this paper and obtains good performance. By combining the 2nd-order statistic, variability index, and the 4th-order statistic, kurtosis, a variable scaling factor is designed in the amplitude iteration to adapt different environment. Plenty of Monte Carlo simulations are applied to evaluate the performance of the proposed method under different clutter scenarios compared with existing CFAR detectors, which illustrate the superiority and robustness of AAI-CFAR.

Supplemental Digital Content is available in the text, The differential diagnosis of Crohn disease (CD) from intestinal tuberculosis (ITB) and primary intestinal lymphoma (PIL) is challenging in patients who exhibit atypical clinical characteristics. The aim of the present study was to explore the serum proteome profiles of CD, PIL and ITB and to identify their differentiations. Treatment-naïve patients with CD (n = 10), PIL (n = 10) and ITB (n = 10) were enrolled in the present study. Differentially expressed proteins (DEPs) in patient serum samples were compared between groups using tandem mass tag labeled proteomic technology. A principal component analysis (PCA) plot and volcano maps were also visualized. Functional pathway analysis was performed using Reactome. The Area under the Curve (AUC) was calculated for each DEP. A total of 818 proteins were identified through proteomic quantification. Among them, 108 DEPs were identified to be differentiated between CD and ITB, 105 proteins between CD and PIL and 55 proteins between ITB and PIL. The proteome from the three groups was distinguishable in the PCA plot. The results revealed that 19, 12, and 10 proteins (AUC ≥ 0.95) were differentially expressed between CD and PIL, CD and ITB, and PIL and ITB, respectively. Among these DEPs, tumor necrosis factor ligand superfamily member 13 was higher in CD than in ITB and PIL. Peroxiredoxin-5, T-complex protein 1 subunit Gamma, CutA, and Fibulin-5 were increased in CD and PIL when compared with ITB. The levels of fibrinogen chains were also significantly higher in patients with PIL compared with CD. The current study demonstrated that serum proteome was distinguishable among patients with CD, PIL, and ITB. The identified proteins may assist in the clinical differentiation among them.

In order to prepare Polyethersulfone (PES) melt-spun fibers, epoxy (EP) oligomer was adopted as a novel plasticizer. The plasticization of EP on PES was investigated by differential scanning calorimetry (DSC), fourier transform infrared (FTIR), and rheological techniques. The results revealed that the glass transition temperature ( T g ), the complex viscosity ( η ∗ ), and melt processing temperature ( T mp ) of PES/EP blends decreased significantly with the increase of EP content. FTIR spectroscopy and rheological behavior demonstrated no signs of crosslinking reactions occurring between PES and EP molecules. Finally, PES melt-spun fibers were successfully obtained by using EP as a plasticizer, showing the highest tensile strength at 2.71 cN/dtex. The morphologies of the fibers observed by scanning electron microscopy (SEM) presented smooth and uniform surface, and compact fracture.

Steering vector (SV) mismatch and the covariance matrix (CM) mismatch are two main factors which affect the performance of minimum variance distortionless response (MVDR). To enhance the robustness when encountering a mismatch problem, we use reconstructed interference-plus-noise CM and corrected SV to improve MVDR. In addition, inequality constraints are added to obtain low sidelobe level. Simulation results show our proposed method can accurately control the sidelobe level and suppress interference, while achieves good robustness to CM mismatch and SV mismatch.

A new algorithm of robust adaptive beamforming (RAB) with low sidelobe is proposed in this paper. To control the sidelobe, an extra sidelobe power constraint function is added on the objective function in minimum variance distortionless response (MVDR) algorithm. Meanwhile, we constrain the steering vector error to enhance the robustness. Simulation results show that the proposed algorithm has better performance in robustness and the controlled sidelobe compared with conventional RAB algorithms.

In this paper, a convex optimization method has been proposed for synthesizing a cosecant squared pattern(CSP). By taking the autocorrelation function of weight vector and the maximum ripple level as the optimization variables, and changing the non-convex optimization to convex optimization problem through variable substitution, the optimal autocorrelation function can be achieved, and then we can obtain the array weight vector. Some detailed simulations are presented and results show that compared to particle swarm optimization (PSO) method, the proposed method has fewer ripples in the shaped region and lower sidelobe (SLL).

Distal posterior inferior cerebellar artery (PICA) aneurysm is a kind of rare intracranial aneurysm with controversial evidence in managements. The main purposes of this research are to study the safety and effectiveness of parent artery occlusion (PAO) compared with selective aneurysm coiling (SAC) as well as figure out factors that affect the outcomes. All characteristics of patients that diagnosed with ruptured distal PICA aneurysm and treated with endovascular management in our hospital from July 2009 to February 2016 were retrospectively collected. Details include complete occlusion rate, procedure-related complications and Modified Rankin Scale (mRS) of 31 months (mean) follow-up. Total 36 patients finally met the criterions and were included in the present study. New imaging infarction was observed in 12 (33.33%) patients. And 10 of them showed no apparent neurological dysfunctions after 34 months (mean) follow-up; 1 remained coma status since the symptom onset; and 1 patient died. 27 (75%) patients got a mRS ≤ 2 at the discharge while 20 (86.96%) patients with 3-year follow-up after the operation. There is no significant difference between patients treated with SAC and PAO in both complications and functional recovery in 3-years follow-up period. Hunt&Hess classification (H&H), age, and hydrocephalus are risk factors for functional recovery at discharge. PAO is safe and efficient in the management of distal PICA aneurysms while complications and outcomes are similar with SAC. H&H, age, and hydrocephalus are predicting factors for the mRS at discharge. Further prospective study is still needed to confirm the results of present study.

High-grade prostatic intraepithelial neoplasia (HGPIN) is the most likely precursor of prostate cancer, but the frequency and timing of epigenetic changes found in prostate carcinogenesis has not been extensively documented.Here, we performed a differential proteomic profiling study on the serum of HGPIN and prostate cancer patients. Eleven HGPIN patients were enrolled, their serum protein patterns (2D-electrophoresis and mass spectroscopy) were compared with fifteen prostate cancer patients, and the follow-up study was further performed in the HGPIN patients.We described eleven altered protein spots in these two groups, in which pigment epithelium-derived factor (PEDF) was found to be significantly down-regulated in prostate cancer patients, which was further confirmed by ELISA method. In addition, 18.2% (2/11) of the HGPIN revealed strong expression for PEDF, 27.3%(3/11) showed a moderate expression and 54.5% (6/11) a weak PEDF expression in immunohistochemistry study, while in prostatic cancerous cells, 100% patients (15/15) revealed a weak expression for PEDF. The ten months' follow-up study demonstrated that 2 of 6 HGPIN patients with weakly expressed PEDF were found to have subsequent prostate cancer.our data identified PEDF in HGPIN as a significant predictor of subsequent cancer, suggesting that PEDF implies in prostatic tumorigenesis and may be used to identify the patients with isolated HGPIN who are at high risk for cancer onset in the disease process.

How to cover a monitored area with the minimum number of sensor nodes is a fundamental in wireless sensor network (WSN). To address this problem, this paper proposes ECAPM, that is, Enhanced Coverage Algorithm Based on Probability Model. Given an area to monitor, ECAPM can calculate the minimum number of sensor nodes to cover that area. Firstly, ECAPM introduces the method to compute the expectation of probability that a sensor node is covered. Then ECAPM presents the procedure of expected value calculation when target node is covered by one sensor node and N sensor nodes. Finally ECAPM verifies the relation of proportional function when random variables are not independent. The simulation results show that ECAPM can cover the monitored area effectively with less sensor nodes and improve the quality of coverage.

Constrained by extraneous factors and power consumption of nodes, the traditional localization algorithms have some disadvantages in localization process, such as low localization accuracy, large amount of computation, etc. For this reason, the WSN node localization system based on RSSI technology is proposed. This algorithm improves node localization accuracy by conducting iteration refinement to the solutions, through quantification of the measurement of target parameters and geometric approach computation. This method can effectively reduce the communication overhead among network nodes, save network power and extend the life cycle of the network. The experimental results show that this method can complete complex computation process with fewer parametric variables, thus strengthening the robustness and stability of the entire network.