Bagging GLM: Improved generalized linear model for the analysis of zero-inflated data

Species-occurrence data sets tend to contain a large proportion of zero values, i.e., absence values (zero-inflated). Statistical inference using such data sets is likely to be inefficient or lead to incorrect conclusions unless the data are treated carefully. In this study, we propose a new modeling method to overcome the problems caused by zero-inflated data sets that involves a regression model and a machine-learning technique. We combined a generalized liner model (GLM), which is widely used in ecology, and bootstrap aggregation (bagging), a machine-learning technique. We established distribution models of Vincetoxicum pycnostelma (a vascular plant) and Ninox scutulata (an owl), both of which are endangered and have zero-inflated distribution patterns, using our new method and traditional GLM and compared model performances. At the same time we modeled four theoretical data sets that contained different ratios of presence/absence values using new and traditional methods and also compared model performances. For distribution models, our new method showed good performance compared to traditional GLMs. After bagging, area under the curve (AUC) values were almost the same as with traditional methods, but sensitivity values were higher. Additionally, our new method showed high sensitivity values compared to the traditional GLM when modeling a theoretical data set containing a large proportion of zero values. These results indicate that our new method has high predictive ability with presence data when analyzing zero-inflated data sets. Generally, predicting presence data is more difficult than predicting absence data. Our new modeling method has potential for advancing species distribution modeling.     

An overview of the effects of urbanization on the quantity and quality of groundwater in South Asian megacities

One of the challenges facing megacities in South Asian developing countries in their attempts to realize so-called urban rejuvenation is groundwater conditioned by canal seepage and immense sewage loads. To understand the connection between groundwater pollutants and urbanization, numerous studies were reviewed, and data obtained from a variety of national and international organizations were analyzed. In Delhi and Dhaka, urbanization has lowered the groundwater level as a result of severe overextraction, and its dense population makes Delhi particularly vulnerable to groundwater pollution. In the coastal areas of Karachi and Mumbai, such pollution is magnified by the proximity of seawater as well as the increasing populations of these areas. Among the groundwater pollutants associated with urbanization, chlorides and nitrates are the chief anthropogenic toxins; thus, they are examined in this study. This paper concludes with policy recommendations for minimizing the impact of urbanization on groundwater.     

Instability of parasympathetic nerve function evaluated by instantaneous time–frequency analysis in patients with obstructive sleep apnea

Sleep and Biological Rhythms - The purpose was to determine whether the instability of parasympathetic nerve (PN) function is associated with fragmentation of sleep and the instability can be...     

Characterization of antisense DNA derivatives having stereoisomeric linkages.

The isomer separation of oligonucleoside phosphoramidates (OPA) by RPLC was studied. All stereoisomers of OPA-tetramer (dATCG) modified with isopropylamine could be separated and they showed different CD spectra each other. OPA-decamer (dGGGCATCGTC) modified with 3-amino-1-propanol could be separated into five fractions. Each fraction was found to have different ability to form hybrids with its complementary oligonucleotide, indicating that it is possible to exclude stereoisomers which can hardly bind to target nucleic acids.