Pcdhβ deficiency affects hippocampal CA1 ensemble activity and contextual fear discrimination

Clustered protocadherins (Pcdhs), a large group of adhesion molecules, are important for axonal projections and dendritic spread, but little is known about how they influence neuronal activity. The Pcdhβ cluster is strongly expressed in the hippocampus, and in vivo Ca2+ imaging in Pcdhβ-deficient mice revealed altered activity of neuronal ensembles but not of individual cells in this region in freely moving animals. Specifically, Pcdhβ deficiency increased the number of large-size neuronal ensembles and the proportion of cells shared between ensembles. Furthermore, Pcdhβ-deficient mice exhibited reduced repetitive neuronal population activity during exploration of a novel context and were less able to discriminate contexts in a contextual fear conditioning paradigm. These results suggest that one function of Pcdhβs is to modulate neural ensemble activity in the hippocampus to promote context discrimination

CD105 is a more appropriate marker for evaluating angiogenesis in urothelial cancer of the upper urinary tract than CD31 or CD34

Angiogenesis plays an important role in cancer progression in many types of cancer. Evaluation of angiogenesis is often performed, but the optimal methodology for human cancer has not been agreed upon. As adequate evaluation of angiogenesis in cancer tissues might be important for prediction of prognosis and treatment decisions, we evaluated angiogenesis semiquantitatively by assessing microvessel density (MVD) in urothelial cancer of the upper urinary tract (UC-UUT). We compared the performance of three endothelial cell markers (CD31, CD34, and CD105) on formalin-fixed tissues from 122 patients diagnosed with UC-UUT without metastasis. Vascular endothelial growth factor (VEGF)-A expression was also evaluated immunohistochemically. Correlations between MVD with each marker and pT stage, grade, survival, and VEGF-A expression were investigated. Mean (standard deviation) MVD as estimated by immunohistochemical staining with anti-CD31, anti-CD34, and anti-CD105 were 47.1 (17.9)/high-power field (HPF), 70.9 (19.5)/HPF, and 31.2 (16.7)/HPF, respectively. Although all MVDs were significantly associated with pT stage and grade, CD105-MVD showed the strongest association. Similarly, CD105-MVD showed the strongest correlation with VEGF-A expression (r = 0.530, p < 0.001). Although all MVDs were associated with metastasis-free survival and cause-specific survival on univariate analysis, only CD105-MVD was retained as an independent predictor in multivariate analysis including pT stage and grade. CD105-MVD may be the preferred marker for semiquantitative assessment of angiogenesis in patients with UC-UUT

Discovering Frequent Substructures In Large Unordered Trees

In this paper, we study a data mining problem of discovering frequent substructures in a large collection of semi-structured data, where both of the patterns and the data are modeled by labeled unordered trees. An unordered tree is a directed acyclic graph with a specified node called the root, and all nodes but the root have at most one parent. Each node is labeled by a symbol drawn from an alphabet. Such unordered trees can be seen as either a generalization of itemsets in relational databases or an efficient specialization of attributed graphs in graph mining. They are also useful in various applications such as analysis of chemical compounds and mining hyperlink structures in Web. Introducing novel definitions of the support and the canonical form for unordered trees, we present an efficient algorithm called Unot that computes all labeled unordered trees appearing in a collection of data trees with frequency above a user-specified threshold. We prove that the algorithm enumerates each frequent pattern T in O(kb n) per pattern, where k is the size of T , b is the branching factor of the data tree, and n is the total number of occurrences of T in the data trees. The keys of the algorithm are e#cient enumerating all unordered trees in canonical form and incrementally computation of the occurrences based on a powerful design technique known as the reverse searc

Groundwater Dynamics near the Saltwater–Freshwater Interface in an Island of Seto Inland Sea

Groundwater dynamics near the saltwater–freshwater interface were investigated in an island of the Seto Inland Sea, using multiple tracers (δD, δ18O, Cl−, SF6, and 14C) at two coastal groundwater monitoring wells at depths of 10–40 m. The groundwater recharge area and age were also estimated using these tracers. Additionally, bedrock groundwater at a depth of 40 m at the 2.7 m altitude was brackish and considered to be near the saltwater–freshwater interface, and a mixture of seawater (2–3.5%) and fresh groundwater (97–98%) was estimated by the Cl− concentration. Based on the δ18O of fresh groundwater estimated from the seawater mixing ratio, the recharge area was estimated to range from near to above the summit; however, this region is unlikely to be the actual recharge area, as the groundwater may be old freshwater that was recharged during a previously colder period. Groundwater dating using SF6 and 14C suggests that the fresh groundwater originated during the last glacial period (assumed 20,000 years ago) and that the 40 m deep bedrock groundwater is a mixture of old water (0–28%), 30 m deep groundwater (76–100%), and stagnant seawater (1–3%)

Efficient Tree Mining Using Reverse Search

In this paper, we review our data mining algorithms for discovering frequent substructures in a large collection of semi-structured data, where both of the patterns and the data are modeled by labeled trees. These algorithms, namely FREQT for mining frequent ordered trees and UNOT for mining frequent unordered trees, efficiently enumerate all frequent tree patterns without duplicates using reverse search, which is a general scheme for designing efficient algorithms for hard enumeration problems, and incrementally compute of the occurrences of a pattern. We also discuss classes of trees to which reverse search is applicable, such as itemsets, sequential episodes, path trees, and graphs

Carbohydrate intake is associated with time spent in the euglycemic range in patients with type 1 diabetes.

[Aims/Introduction]Greater glycemic variability and lack of predictability are important issues for patients with type 1 diabetes. Dietary factors are one of the contributors to this variability, but how closely diet is linked to glycemic fluctuation on a daily basis has not been investigated. We examined the association between carbohydrate intake and glycemic excursion in outpatients. [Materials and Methods]A total of 33 patients with type 1 diabetes were included in the analyses (age 44.5 ± 14.7 years, diabetes duration 15.1 ± 8.3 years, 64% female, 30% using insulin pump, glycated hemoglobin 8.1 ± 1.3%). Time spent in euglycemia (70–180 mg/dL), hyperglycemia (>180 mg/dL) and hypoglycemia (<70 mg/dL) of consecutive 48-h periods of continuous glucose monitoring data were collected together with simultaneous records of dietary intake, insulin dose and physical activity. Correlation analyses and multiple regression analyses were used to evaluate the contribution of carbohydrate intake to time spent in the target glycemic range. [Results]In multiple regression analyses, carbohydrate intake (β = 0.53, P = 0.001), basal insulin dose per kg per day (β = −0.31, P = 0.034) and diabetes duration (β = 0.30, P = 0.042) were independent predictors of time spent in euglycemia. Carbohydrate intake (β = −0.51, P = 0.001) and insulin pump use (β = −0.34, P = 0.024) were independent predictors of time spent in hyperglycemia. Insulin pump use (β = 0.52, P < 0.001) and bolus insulin dose per kg per day (β = 0.46, P = 0.001) were independent predictors of time spent in hypoglycemia. [Conclusions]Carbohydrate intake is associated with time spent in euglycemia in patients with type 1 diabetes

Root Response to Soil Water Status via Interaction of Crop Genotype and Environment

Flooding and drought are major causes of reductions in crop productivity. Root distribution indicates crop adaptation to water stress. Therefore, we aimed to identify crop roots response based on root distribution under various soil conditions. The root distribution of four crops—maize, millet, sorghum, and rice—was evaluated under continuous soil waterlogging (CSW), moderate soil moisture (MSM), and gradual soil drying (GSD) conditions. Roots extended largely to the shallow soil layer in CSW and grew longer to the deeper soil layer in GSD in maize and sorghum. GSD tended to promote the root and shoot biomass across soil moisture status regardless of the crop species. The change of specific root density in rice and millet was small compared with maize and sorghum between different soil moisture statuses. Crop response in shoot and root biomass to various soil moisture status was highest in maize and lowest in rice among the tested crops as per the regression coefficient. Thus, we describe different root distributions associated with crop plasticity, which signify root spread changes, depending on soil water conditions in different crop genotypes as well as root distributions that vary depending on crop adaptation from anaerobic to aerobic conditions