A nonlinear industrial soft sensor modeling method based on locality preserving stochastic configuration network with utilizing unlabeled samples.

Stochastic configuration network (SCN) is an emerging incremental randomized regression modeling technology with the advantages of adaptively determining the hidden layer parameters, and has been successfully applied to industrial soft sensor modeling field. However, the traditional SCN model is intrinsically a supervised learner, which has the underlying assumption that all the training samples are labeled. In fact, most of process samples are unlabeled and the labeled samples are relatively rare in real industrial scenarios. To handle this issue, this paper presents one modified SCN model, called locality preserving SCN (LPSCN), for semi-supervised industrial soft sensor modeling. In this method, all the training samples, including the labeled and the unlabeled, are fed into the soft sensor model, where the labeled samples are used to capture the modeling error, while the unlabeled samples help construct the local adjacency graph. Based on these two kinds of samples, the supervised optimization objective in the traditional SCN is improved to be a semi-supervised version by minimizing the modeling error and preserving the local data relationship simultaneously. Furthermore, the random parameter configuration mechanism is deduced under the modified semi-supervised optimization framework. A new inequality constraint condition with considering the unlabeled samples is obtained to generate the hidden layer nodes incrementally so that the LPSCN model structure is determined automatically. Experiments on two real industrial systems demonstrate that the proposed LPSCN method outperforms the SCN method in terms of the soft sensor prediction performance.

Human endoderm stem cells reverse inflammation-related acute liver failure through cystatin SN-mediated inhibition of interferon signaling.

Acute liver failure (ALF) is a life-threatening disease that occurs secondary to drug toxicity, infection or a devastating immune response. Orthotopic liver transplantation is an effective treatment but limited by the shortage of donor organs, the requirement for life-long immune suppression and surgical challenges. Stem cell transplantation is a promising alternative therapy for fulminant liver failure owing to the immunomodulatory abilities of stem cells. Here, we report that when transplanted into the liver, human endoderm stem cells (hEnSCs) that are germ layer-specific and nontumorigenic cells derived from pluripotent stem cells are able to effectively ameliorate hepatic injury in multiple rodent and swine drug-induced ALF models. We demonstrate that hEnSCs tune the local immune microenvironment by skewing macrophages/Kupffer cells towards an anti-inflammatory state and by reducing the infiltrating monocytes/macrophages and inflammatory T helper cells. Single-cell transcriptomic analyses of infiltrating and resident monocytes/macrophages isolated from animal livers revealed dramatic changes, including changes in gene expression that correlated with the change of activation states, and dynamic population heterogeneity among these cells after hEnSC transplantation. We further demonstrate that hEnSCs modulate the activation state of macrophages/Kupffer cells via cystatin SN (CST1)-mediated inhibition of interferon signaling and therefore highlight CST1 as a candidate therapeutic agent for diseases that involve over-activation of interferons. We propose that hEnSC transplantation represents a novel and powerful cell therapeutic treatment for ALF.

Feature-Weighted Echo State Network for Dynamic Frequency Offset Modeling of Quartz Crystal Resonators.

Quartz crystal resonators are widely used as reference frequency sources in modern electronic systems. However, their frequency often deviates from the nominal value due to the significant change in the ambient temperature. Therefore, it is of great value to develop an accurate dynamic frequency offset model regarding temperature changes. In this article, a novel feature-weighted echo state network (FWESN) method is presented to capture the dynamic frequency-temperature ( f - T ) characteristic of quartz crystal resonators. Different from the traditional echo state network (ESN), which simply takes the temperature measurement signal as a single model input variable, the proposed method mines the feature information hidden in the temperature measurement series to construct the model input vector. Specifically, five dynamic features are designed to substitute for the original temperature signal by investigating the influence mechanism of temperature versus frequency. Furthermore, considering the difference in these features' importance, two feature weighting strategies, including the Pearson's correlation coefficient (PCC)-based and particle swarm optimization (PSO)-based, are proposed to assign the different weights to the five features. Finally, the weighted features are fed into the ESN model to implement the dynamic frequency offset estimation. The application results on the real experiment datasets demonstrate that the presented FWESN method can estimate the frequency offset more precisely than the basic ESN method.

Gross Tumor Volume Predicts Survival and Pathological Complete Response of Locally Advanced Esophageal Cancer After Neoadjuvant Chemoradiotherapy.

Background: Neoadjuvant chemoradiotherapy (neo-CRT) plus surgery has greatly improved the prognosis of locally advanced esophageal cancer (EC) patients. But which factors may influence the pathological tumor response and long-term survival remains unclear. The purpose of this study was to identify the prognostic biomarkers of locally advanced EC patients receiving neo-CRT. Methods: We reviewed the data of 72 patients with cT2-4N0-3M0 EC who underwent neo-CRT at our hospital. The patients received intensity-modulated radiation therapy with a total radiation dose of 41.4-60.0 Gy. Most patients received platinum + paclitaxel-based combination regimens every three weeks for 2-4 cycles. The recorded data included age, sex, smoking history, alcohol use, histology, tumor location, clinical TNM stage, tumor length, gross tumor volume (GTV), GTV of primary tumor (GTVp), GTV of lymph nodes (GTVn), radiation dose, and number of chemotherapy cycles. Overall survival (OS), progression-free survival (PFS), and pathological complete response (pCR) were analyzed. Results: The 3-year OS and PFS rates of these patients who underwent neo-CRT were 51.14% and 43.28%, respectively. In the univariate analyses, smoking history, clinical stage, GTV, GTVp, and GTVn were significantly associated with OS, whereas alcohol use, GTV, GTVp, and GTVn were significantly associated with PFS. Furthermore, in the multivariate analysis, GTV was an independent prognostic predictor of OS (hazard ratio (HR): 14.14, 95% confidence interval (CI): 3.747-53.33, P < 0.0001) and PFS (HR: 6.090, 95% CI: 2.398-15.47, P < 0.0001). In addition, GTV < 60.50 cm3 compared to > 60.50 cm3 was significantly associated with higher pCR rate (59.3% and 27.8%, respectively, P = 0.038). High dose (> 50 Gy) and increased number of chemotherapy cycles (≥ 3) didn't improve the OS or PFS in patients with GTV > 60.50 cm3. Conclusion: GTV was an independent prognostic factor of long-term survival in EC patients, which may be because GTV is associated with histological response to neo-CRT. Additionally, patients with GTV > 60.50 cm3 didn't benefit from increased radiation dose or increased number of chemotherapy cycles.

Semi-supervised echo state network with temporal-spatial graph regularization for dynamic soft sensor modeling of industrial processes.

Echo state network (ESN) has been successfully applied to industrial soft sensor field because of its strong nonlinear and dynamic modeling capability. Nevertheless, the traditional ESN is intrinsically a supervised learning technique, which only depends on labeled samples, but omits a large number of unlabeled samples. In order to eliminate this limitation, this work proposes a semi-supervised ESN method assisted by a temporal-spatial graph regularization (TSG-SSESN) for constructing soft sensor model with all the available samples. Firstly, the traditional supervised ESN is enhanced to construct the semi-supervised ESN (SSESN) model by integrating both unlabeled and labeled samples in the reservoir computing procedure. The SSESN computes the reservoir states under high sampling rate for better process dynamic information mining. Furthermore, the SSESN's output optimization objective is modified by applying the local adjacency graph of all training samples as a regularization term. Especially, in view of the dynamic data characteristic, a temporal-spatial graph is constructed by considering both the temporal relationship and the spatial distances. The applications to a debutanizer column process and a wastewater treatment plant demonstrate that the TSG-SSESN model can build much smoother model and has better generalization capability than the basic ESN models in terms of soft sensor prediction results.

Dynamic Frequency-Temperature Characteristic Modeling for Quartz Crystal Resonator Based on Improved Echo State Network.

Quartz crystal resonators are the key component of various kinds of electronic systems because they provide the reference frequency source of the system running clocks. However, the frequency stability is often affected by the temperature. Therefore, the frequency-temperature ( f-T ) characteristic modeling has been an important research topic in the frequency control field. The classic f-T modeling method omits the system dynamics and may lead to a large frequency compensation error in the case of rapid temperature changing. To deal with this issue, this article proposes a dynamic f-T modeling method based on improved echo state network (ESN), called residual scaled ESN (RSESN). In the proposed method, the residual modeling framework is designed for purposes of good physical understandability and high prediction precision. This framework uses the static polynomial f-T model to depict the approximated data relationship and applies the complicated network model to compensate the detailed dynamic error. To estimate the dynamic errors, one effective dynamic modeling tool, ESN, is introduced to build the dynamic compensation model for f-T characteristic of quartz crystal resonators. For a better fitting performance, the ESN activation limitations are analyzed and the scaled echo states are constructed in the improved ESN model. The modeling and testing results on the real experiment data show that the proposed method can capture the dynamic information effectively and provide better frequency deviation predictions.

Evolutionary optimization assisted delayed deep cycle reservoir modeling method with its application to ship heave motion prediction.

As one emerging reservoir modeling method, cycle reservoir with regular jumps (CRJ) provides one effective tool for many time series analysis tasks such as ship heave motion prediction. However, the shallow learning structure of single CRJ model limits its memory capacity and leads to unsatisfactory prediction performance. In order to pursue the stronger dynamic characteristic description of time series data, a delayed deep CRJ model is presented in this paper by integrating the deep learning framework with delay links and the evolutionary optimization for mixed-integer problem. Different from the basic CRJ model with only one reservoir, delayed deep CRJ builds multiple serial reservoirs with inserting the delay links between adjacent reservoirs. Due to the design of dynamic deep learning structure, the memory capacity is enlarged to improve ship heave motion prediction. Aiming at the mix-integer optimization problem in delayed deep CRJ model, a heuristic evolutionary optimization scheme based on the stepwise differential evolution algorithm is applied to determine the delayed deep CRJ parameters automatically. The stepwise differential evolution assisted delayed deep CRJ model can avoid the non-optimal solution resulted from the manual parameter setting effectively. Finally, one numerical example and the real experiment data are utilized to validate the methods and the results demonstrate that delayed deep CRJ model has better prediction performance in contrast to the basic CRJ method.

Deep Subdomain Learning Adaptation Network: A Sensor Fault-Tolerant Soft Sensor for Industrial Processes.

Sensor faults are non-negligible issues for soft sensor modeling. However, existing deep learning-based soft sensors are fragile and sensitive when considering sensor faults. To improve the robustness against sensor faults, this article proposes a deep subdomain learning adaptation network (DSLAN) to develop a sensor fault-tolerant soft sensor, which is capable of handling both sensor degradation and sensor failure simultaneously. Primarily, domain adaptation works for process data with sensor degradation in industrial processes. Being founded on the basic structure of deep domain adaptation, a novel subdomain learner is added to automatically learn the subdomain division, enabling DSLAN adaptable to multimode industrial processes. Notably, the subdomain structure of each sample follows a categorical distribution parameterized by output of the subdomain learner. Based on the designed subdomain learner, a new probabilistic local maximum mean discrepancy (PLMMD) is presented to measure the difference in distribution between source and target features. In addition, a generator for failure data imputation is integrated in the framework, making DSLAN handle sensor failure simultaneously. Finally, the Tennessee Eastman (TE) benchmark process and two real industrial processes are used to verify the effectiveness of the proposed method. With the fault tolerance ability, soft sensing technology will take a step toward practical applications.

Modified Modeling Method of Quartz Crystal Resonator Frequency-Temperature Characteristic With Considering Thermal Hysteresis.

The frequency-temperature ( f - T ) characteristic of quartz crystal resonators is an important topic closely related to the frequency-deviation compensation in the design of cost-effective oscillators. Traditional studies depict the f - T characteristic as a polynomial function (usually a cubic function). However, this omits the thermal hysteresis phenomenon and cannot provide a very accurate frequency compensation. To handle this issue, this article is to propose two modified f - T characteristic modeling methods with considering the thermal hysteresis. First, to reflect the thermal hysteresis property of quartz crystal resonators, a two-directional f - T model is designed by introducing two individual submodels for describing the increasing and decreasing temperature stages, respectively. Furthermore, to integrate the submodels and provide the more accurate frequency-deviation estimation, a holistic f - T characteristic model based on double-hidden layer extreme learning machine (DHL-ELM) is presented. Different from the basic ELM model, two hidden layers, including one deterministic nonlinear mapping and one random nonlinear activation layer, are constructed for a better description of f - T characteristic. To validate our studies, an experiment system is applied to obtain the testing data of the real crystal resonators, and the applications demonstrate the effectiveness of the proposed methods.

Large-scale Generation of Functional and Transplantable Hepatocytes and Cholangiocytes from Human Endoderm Stem Cells.

The ever-increasing therapeutic and pharmaceutical demand for liver cells calls for systems that enable mass production of hepatic cells. Here we describe a large-scale suspension system that uses human endoderm stem cells (hEnSCs) as precursors to generate functional and transplantable hepatocytes (E-heps) or cholangiocytes (E-chos). hEnSC-derived hepatic populations are characterized by single-cell transcriptomic analyses and compared with hESC-derived counterparts, in-vitro-maintained or -expanded primary hepatocytes and adult cells, which reveals that hepatic differentiation of hEnSCs recapitulates in vivo development and that the heterogeneities of the resultant populations can be manipulated by regulating the EGF and MAPK signaling pathways. Functional assessments demonstrate that E-heps and E-chos possess properties comparable with adult counterparts and that, when transplanted intraperitoneally, encapsulated E-heps were able to rescue rats with acute liver failure. Our study lays the foundation for cell-based therapeutic agents and in vitro applications for liver diseases.

Nonlinear Chemical Process Fault Diagnosis Using Ensemble Deep Support Vector Data Description.

As one classical anomaly detection technology, support vector data description (SVDD) has been successfully applied to nonlinear chemical process monitoring. However, the basic SVDD model cannot achieve a satisfactory fault detection performance in the complicated cases because of its intrinsic shallow learning structure. Motivated by the deep learning theory, one improved SVDD method, called ensemble deep SVDD (EDeSVDD), is proposed in order to monitor the process faults more effectively. In the proposed method, a deep support vector data description (DeSVDD) framework is firstly constructed by introducing the deep feature extraction procedure. Different to the traditional SVDD with only one feature extraction layer, DeSVDD is designed with multi-layer feature extraction structure and optimized by minimizing the data-enclosing hypersphere with the regularization of the deep network weights. Further considering the problem that DeSVDD monitoring performance is easily affected by the model structure and the initial weight parameters, an ensemble DeSVDD (EDeSVDD) is presented by applying the ensemble learning strategy based on Bayesian inference. A series of DeSVDD sub-models are generated at the parameter level and the structure level, respectively. These two levels of sub-models are integrated for a holistic monitoring model. To identify the cause variables for the detected faults, a fault isolation scheme is designed by applying the distance correlation coefficients to measure the nonlinear dependency between the original variables and the holistic monitoring index. The applications to the Tennessee Eastman process demonstrate that the proposed EDeSVDD model outperforms the traditional SVDD model and the DeSVDD model in terms of fault detection performance and can identify the fault cause variables effectively.

Incipient fault detection for nonlinear processes based on dynamic multi-block probability related kernel principal component analysis.

In order to detect the incipient faults of nonlinear industrial processes effectively, this paper proposes an enhanced kernel principal component analysis (KPCA) method, called multi-block probability related KPCA method (DMPRKPCA). First of all, one probability related nonlinear statistical monitoring framework is constructed by combining KPCA with Kullback Leibler divergence (KLD), which measures the probability distribution changes caused by small shifts. Second, in view of the problem that the traditional KLD ignores the dynamic characteristic of process data, the dynamic KLD component is designed by applying the exponentially weighted moving average approach, which highlights the temporal data changes in the moving window. Third, considering that the holistic KLD component may submerge the local statistical changes, a multi-block modeling strategy is designed by dividing the whole KLD components into two sub-blocks corresponding to the mean and variance information, respectively. Case studies on one numerical system and the simulated chemical reactor demonstrate the superiority of the DMPRKPCA method over the conventional KPCA method.

Postoperative glioma segmentation in CT image using deep feature fusion model guided by multi-sequence MRIs.

OBJECTIVES: Computed tomography (CT) and magnetic resonance imaging (MRI) are the most commonly selected methods for imaging gliomas. Clinically, radiotherapists always delineate the CT glioma region with reference to multi-modal MR image information. On this basis, we develop a deep feature fusion model (DFFM) guided by multi-sequence MRIs for postoperative glioma segmentation in CT images. METHODS: DFFM is a multi-sequence MRI-guided convolutional neural network (CNN) that iteratively learns the deep features from CT images and multi-sequence MR images simultaneously by utilizing a multi-channel CNN architecture, and then combines these two deep features together to produce the segmentation result. The whole network is optimized together via a standard back-propagation. A total of 59 CT and MRI datasets (T1/T2-weighted FLAIR, T1-weighted contrast-enhanced, T2-weighted) of postoperative gliomas as tumor grade II (n = 24), grade III (n = 18), or grade IV (n = 17) were included. Dice coefficient (DSC), precision, and recall were used to measure the overlap between automated segmentation results and manual segmentation. The Wilcoxon signed-rank test was used for statistical analysis. RESULTS: DFFM showed a significantly (p < 0.01) higher DSC of 0.836 than U-Net trained by single CT images and U-Net trained by stacking the CT and multi-sequence MR images, which yielded 0.713 DSC and 0.818 DSC, respectively. The precision values showed similar behavior as DSC. Moreover, DSC and precision values have no significant statistical difference (p > 0.01) with difference grades. CONCLUSIONS: DFFM enables the accurate automated segmentation of CT postoperative gliomas of profit guided by multi-sequence MR images and may thus improve and facilitate radiotherapy planning. KEY POINTS: • A fully automated deep learning method was developed to segment postoperative gliomas on CT images guided by multi-sequence MRIs. • CT and multi-sequence MR image integration allows for improvements in deep learning postoperative glioma segmentation method. • This deep feature fusion model produces reliable segmentation results and could be useful in delineating GTV in postoperative glioma radiotherapy planning.

Fifth-order finite-difference scheme for Fokker-Planck equations with drift-admitting jumps.

Recently a useful finite-difference scheme was proposed in [Phys. Rev. E 98, 033302 (2018)2470-004510.1103/PhysRevE.98.033302] to solve Fokker-Planck equations with drift-admitting jumps. However, while the scheme is fifth order for the case with smooth drifts, it is only second order for the case with discontinuous drifts. To rectify this, we propose in this paper an improved scheme that achieves a fifth-order convergence rate for the case with drift-admitting jumps. Numerical experiments are also employed to verify the validity of the scheme.

Boosting-based Cascaded Convolutional Neural Networks for the Segmentation of CT Organs-at-risk in Nasopharyngeal Carcinoma.

PURPOSE: Accurately segmenting organs-at-risk (OARs) is a key step in the effective planning of radiation therapy for nasopharyngeal carcinoma (NPC) treatment. In OAR segmentation of the head and neck CT, the low contrast and surrounding adhesion tissues of the parotids, thyroids, and optic nerves result in the difficulty in segmentation and lower accuracy of automatic segmentation for these organs than the other organs. In this paper, we propose a cascaded network structure to delineate these three OARs for NPC radiotherapy by combining deep learning and Boosting algorithm. MATERIALS AND METHODS: The CT images of 140 NPC patients treated with radiotherapy were collected, and each of the three OAR annotations was respectively delineated by an experienced rater and reviewed by a professional radiologist (with 10 years of experience). The datasets (140 patients) were divided into a training set (100 patients), a validation set (20 patients), and a test set (20 patients). From the Boosting method for combining multiple classifiers, three cascaded CNNs for segmentation were combined. The first network was trained with the traditional approach. The second one was trained on patterns (pixels) filtered by the first net. That is, the second machine recognized a mix of patterns (pixels), 50% of which was accurately identified by the first net. Finally, the third net was trained on the new patterns (pixels) screened jointly by the first and second networks. During the test, the outputs of the three nets were considered to obtain the final output. Dice similarity coefficient (DSC), 95th percentile of the Hausdorff distance (95% HD), and volume overlap error (VOE) were used to assess the method performance. RESULTS: The mean DSC (%) values were above 92.26 for the parotids, above 92.29 for the thyroids, and above 89.37 for the optic nerves. The mean 95% HDs (mm) were approximately 3.08 for the parotids, 2.64 for the thyroids, and 2.03 for the optic nerves. The mean VOE (%) values were approximately 14.16 for the parotids, 14.94 for the thyroids, and 19.07 for the optic nerves. CONCLUSION: The proposed cascaded deep learning structure could achieve high performance compared with existing single-network or other segmentation algorithms.

Batch process fault detection and identification based on discriminant global preserving kernel slow feature analysis.

As an attractive nonlinear dynamic data analysis tool, global preserving kernel slow feature analysis (GKSFA) has achieved great success in extracting the high nonlinearity and inherently time-varying dynamics of batch process. However, GKSFA is an unsupervised feature extraction method and lacks the ability to utilize batch process class label information, which may not offer the most effective means for dealing with batch process monitoring. To overcome this problem, we propose a novel batch process monitoring method based on the modified GKSFA, referred to as discriminant global preserving kernel slow feature analysis (DGKSFA), by closely integrating discriminant analysis and GKSFA. The proposed DGKSFA method can extract discriminant feature of batch process as well as preserve global and local geometrical structure information of observed data. For the purpose of fault detection, a monitoring statistic is constructed based on the distance between the optimal kernel feature vectors of test data and normal data. To tackle the challenging issue of nonlinear fault variable identification, a new nonlinear contribution plot method is also developed to help identifying the fault variable after a fault is detected, which is derived from the idea of variable pseudo-sample trajectory projection in DGKSFA nonlinear biplot. Simulation results conducted on a numerical nonlinear dynamic system and the benchmark fed-batch penicillin fermentation process demonstrate that the proposed process monitoring and fault diagnosis approach can effectively detect fault and distinguish fault variables from normal variables.

Modified kernel principal component analysis using double-weighted local outlier factor and its application to nonlinear process monitoring.

Traditional kernel principal component analysis (KPCA) based nonlinear process monitoring method may not perform well because its Gaussian distribution assumption is often violated in the real industrial processes. To overcome this deficiency, this paper proposes a modified KPCA method based on double-weighted local outlier factor (DWLOF-KPCA). In order to avoid the assumption of specific data distribution, local outlier factor (LOF) is introduced to construct two LOF-based monitoring statistics, which are used to substitute for the traditional T2 and SPE statistics, respectively. To provide better online monitoring performance, a double-weighted LOF method is further designed, which assigns the weights for each component to highlight the key components with significant fault information, and uses the moving window to weight the historical statistics for reducing the drastic fluctuations in the monitoring results. Finally, simulations on a numerical example and the Tennessee Eastman (TE) benchmark process are used to demonstrate the superiority of the proposed DWLOF-KPCA method.

Nonlinear Process Fault Diagnosis Based on Serial Principal Component Analysis.

Many industrial processes contain both linear and nonlinear parts, and kernel principal component analysis (KPCA), widely used in nonlinear process monitoring, may not offer the most effective means for dealing with these nonlinear processes. This paper proposes a new hybrid linear-nonlinear statistical modeling approach for nonlinear process monitoring by closely integrating linear principal component analysis (PCA) and nonlinear KPCA using a serial model structure, which we refer to as serial PCA (SPCA). Specifically, PCA is first applied to extract PCs as linear features, and to decompose the data into the PC subspace and residual subspace (RS). Then, KPCA is performed in the RS to extract the nonlinear PCs as nonlinear features. Two monitoring statistics are constructed for fault detection, based on both the linear and nonlinear features extracted by the proposed SPCA. To effectively perform fault identification after a fault is detected, an SPCA similarity factor method is built for fault recognition, which fuses both the linear and nonlinear features. Unlike PCA and KPCA, the proposed method takes into account both linear and nonlinear PCs simultaneously, and therefore, it can better exploit the underlying process's structure to enhance fault diagnosis performance. Two case studies involving a simulated nonlinear process and the benchmark Tennessee Eastman process demonstrate that the proposed SPCA approach is more effective than the existing state-of-the-art approach based on KPCA alone, in terms of nonlinear process fault detection and identification.

Multi-phase simultaneous segmentation of tumor in lung 4D-CT data with context information.

Lung 4D computed tomography (4D-CT) plays an important role in high-precision radiotherapy because it characterizes respiratory motion, which is crucial for accurate target definition. However, the manual segmentation of a lung tumor is a heavy workload for doctors because of the large number of lung 4D-CT data slices. Meanwhile, tumor segmentation is still a notoriously challenging problem in computer-aided diagnosis. In this paper, we propose a new method based on an improved graph cut algorithm with context information constraint to find a convenient and robust approach of lung 4D-CT tumor segmentation. We combine all phases of the lung 4D-CT into a global graph, and construct a global energy function accordingly. The sub-graph is first constructed for each phase. A context cost term is enforced to achieve segmentation results in every phase by adding a context constraint between neighboring phases. A global energy function is finally constructed by combining all cost terms. The optimization is achieved by solving a max-flow/min-cut problem, which leads to simultaneous and robust segmentation of the tumor in all the lung 4D-CT phases. The effectiveness of our approach is validated through experiments on 10 different lung 4D-CT cases. The comparison with the graph cut without context constraint, the level set method and the graph cut with star shape prior demonstrates that the proposed method obtains more accurate and robust segmentation results.

Intensity of Offending Following State Prison Release Among Persons Treated for Mental Health Problems While Incarcerated.

OBJECTIVE: This study examined a range of demographic, clinical, and criminal history factors as they relate to the intensity of offending for up to two years postrelease. METHODS: This study drew on data from 1,438 individuals released from Massachusetts state prisons between 2007 and 2009 who, while incarcerated, received treatment from the prisons' mental health services and were followed for 24 months postrelease. These data were used to explore predictive factors related to the intensity of criminal justice involvement, defined as number of arrests in the two-year follow-up period. RESULTS: Predictors of subsequent arrests included number of previous incarcerations and black race. Protective factors included older age, supervision by parole, and a drug-related or person-related governing offense on previous arrest. Clinical symptoms were not related to incidence of postrelease arrests. CONCLUSIONS: This study identified factors related to criminal history, such as type of charge, that were associated with the intensity of subsequent criminal justice involvement. These findings have not been reported in previous studies, perhaps because intensity of offending as opposed to a different dependent variable was used to measure criminal justice involvement. Further investigation should focus on whether the type of previous offense is related to postrelease risk factors for recidivism.