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Central nervous system (CNS) relapse in patients with diffuse large B cell lymphoma (DLBCL) is an uncommon event, and the outcome of patients with CNS relapse is poor. However, no reliable prediction models for CNS relapse have been developed. We retrospectively analyzed consecutive de novo DLBCL patients referred to our department between September 2004 and August 2015 and treated with R-CHOP or R-CHOP-like regimens. Of 413 patients analyzed in this study, a total of 27 patients (6.5 %) eventually developed CNS relapse. The 5-year probability of CNS relapse was 8.4 %. The median time from diagnosis of DLBCL to CNS relapse was 15 months, and the median survival after CNS relapse was 7 months. In univariate analysis, the risk factors significantly associated with CNS relapse were Ann Arbor stage 3 or 4, albumin level3.2 mg/L, number of extranodal sites1, and involvement of retroperitoneal lymph node. We developed a new prognostic model consisting of these four factors. The 5-year probability of CNS relapse was significantly higher in patients with at least three of these four factors than in those with two or fewer factors (26.4 vs. 3.0 %, P 0.001). Using this model, we evaluated the incidence and the risk factors of CNS relapse in DLBCL patients. The new risk model consisting of the four factors demonstrated good risk stratification for CNS relapse, and could help to identify high-risk patients for whom CNS prophylaxis is warranted.

In this paper, we report on our current work toward efficient and effective verification of web application's basic design. We use a UML class diagram with Object Constraint Language (OCL) to describe the application behaviors and data constraints. Then we generate test data from the formally represented specifications. We make the observation that key web application behaviors can be captured through table size constraints as well as data constraints like foreign key constraints. Based on the observation, we translate the OCL specification into the equivalent constraints using table size expressions. We present a scheme to generate test data from the translated constraints using a Satisfiability Modulo Theories solver. We employ two techniques to reduce constraints. The first is string handling and the other is decomposition of table structures. We also report on an experimental result of test data generation. The result indicates a potential that our scheme works well for real applications in reasonable times.

The Japan Clinical Oncology Group conducted two multicenter phase II trials in 200 patients with advanced Hodgkin lymphoma (HL) in the 1990s. Among 181 patients whose histopathological specimens were available and reviewed by 6 hematopathologists, 167 (92.3%) were diagnosed with HL. Five-year overall survival (OS) among these 167 patients was 88.3%, including 89.2% among nodular sclerosis and 82.2% among mixed cellularity cases. International prognostic score was not closely associated with OS. Seven unfavorable prognostic factors for OS on univariate analysis were male, B symptoms, clinical stage of III or IV, elevated serum LDH, elevated alkaline phosphatase, elevated beta2-microglobulin, and pathological subtype (mixed cellularity and lymphocyte depletion). On multivariate analysis, male [HR 3.30 (95% CI 1.15-9.52, p = 0.027)] and elevated serum LDH [HR 2.41 (95% CI 1.07-5.43, p = 0.034)] were independent factors for OS. Based on these prognostic factors, the 5-year OS was 95.7% in the low-risk group (no adverse factor), 87.9% in the intermediate-risk group (1 adverse factor) and 73.3% in the high-risk group (2 adverse factors). This simple prognostic model for HL warrants further validation studies.

We propose an artificial neural network model for autonomous agents, i.e., mobile robots, to learn maps of environments and acquire the ability to perform home-navigation autonomously. The networks consists of two subnetworks, each of which has a similar structure with hippocampal lamellar neuronal circuits. Hebbian learning procedures self-organize the first subnetwork to output the distributed sinusoidal activity of the cells by accumulating motor information generated during movement, and the second subnetwork to output localized activity by prototyping sensory information. These patterns represent a homing vector providing the relative coordinates of the agent from a starting point, and a place code corresponding uniquely to a point of the environment. By attaching homing vectors to the sensor map, the homing vector is associated with the sensory stimuli. Then the agents can perform home-navigation autonomously by this association.