Stemming the tide: Does climate risk affect M&A performance?

We examine the effect of climate change risks (CCR) on firms' decision of engaging in mergers and acquisitions (M&A) and M&A performance. In this study we use the responses by firms on ‘climate change-related risks and opportunities’ of the CDP survey and 1,372 deals of listed US firms during 2010-2020. Consistent with risk vulnerability theory, our evidence indicates that firms with higher CCR have a lower probability of engaging in M&As. After controlling for possible endogeneity, our results also indicate that if acquirers with higher climate change risks choose to engage in M&A, it significantly reduces the announcement returns. These findings suggest that extant measures of climate change risks should be rethought when evaluating M&A efficiency. More broadly, our paper provides causal evidence that managers need to integrate CCR into their formal risk management systems to avoid unsuccessful M&As

Factors influencing verbal intelligence and spoken language in children with phenylketonuria

Objectives: To determine verbal intelligence and spoken language of children with phenylketonuria and to study the effect of age at diagnosis and phenylalanine plasma level on these abilities. Design: Cross-sectional. Setting: Children with phenylketonuria were recruited from pediatric hospitals in 2012. Normal control subjects were recruited from kindergartens in Tehran. Participants: 30 phenylketonuria and 42 control subjects aged 4- 6.5 years. Skills were compared between 3 phenylketonuria groups categorized by age at diagnosis/treatment, and between the phenylketonuria and control groups. Main outcome measures: Scores on Wechsler Preschool and Primary Scale of Intelligence for verbal and total intelligence, and Test of Language Development-Primary, third edition for spoken language, listening, speaking, semantics, syntax, and organization. Results: The performance of control subjects was significantly better than that of early-treated subjects for all composite quotients from Test of Language Development and verbal intelligence (P >0.001). Early-treated subjects scored significantly higher than the two groups of late-treated subjects for spoken language (P =0.01), speaking (P =0.04), syntax (P =0.02), and verbal intelligence (P =0.019). There was a negative correlation between phenylalanine level and verbal intelligence (r= �0.79) in early-treated subjects and between phenylalanine level and spoken language (r= �0.71), organization (r= �0.82) and semantics (r= �0.82) for late-treated subjects diagnosed before the age one year. Conclusion: The study confirmed that diagnosis of newborns and control of blood phenylalanine concentration improves verbal intelligence and spoken language scores in phenylketonuria subjects. © 2015, Indian Academy of Pediatrics

The Work TIME Spent by Female Rabbit Breeders and Its' Influential Factors

The aim of this research was to find out the work time spent by female rabbit breeders and factors influencing the work time spent to raise rabbits in Soppeng, South Sulawesi. The research used survey method with female rabbit breeders as analysis unit. Population of 356 female rabbit breeders in Soppeng was used and a sample of 78 female rabbit breeders were selected by region. The result of the research showed that average work time of female rabbit breeders was 3.044 hours per day, which was spent cleaning cages and equipment, preparing feedstuffs and feeding on rabbits, assisting bunnies breastfeed to their mother and health care. The F-test results reveal that the most significantly influential factors affecting female rabbit breeders work time was household income, number of dependents, rabbit breeding income, and the level of experience in breeding rabbits

Highly heterogeneous probiotic Lactobacillus species in healthy iranians with low functional activities

Background Lactic acid bacteria (LAB) have been considered as potentially probiotic organisms due to their potential human health properties. This study aimed to evaluate both in vitro and in vivo, the potential probiotic properties of Lactobacillus species isolated from fecal samples of healthy humans in Iran. Methods and Results A total of 470 LAB were initially isolated from 53 healthy individual and characterized to species level. Of these, 88 (86) were Lactobacillus species. Biochemical and genetic fingerprinting with Phene-Plate system (PhP-LB) and RAPD-PCR showed that the isolates were highly diverse consisted of 67(76.1) and 75 (85.2) single types (STs) and a diversity indices of 0.994 and 0.997, respectively. These strains were tested for production of adhesion to Caco-2 cells, antibacterial activity, production of B12, anti-proliferative effect and interleukin-8 induction on gut epithelial cell lines and antibiotic resistance against 9 commonly used antibiotics. Strains showing the characteristics consistent with probiotic strains, were further tested for their anti-inflammatory effect in mouse colitis model. Only one L. brevis; one L. rhamnosus and two L. plantarum were shown to have significant probiotic properties. These strains showed shortening the length of colon compared to dextran sulfate sodium and disease activity index (DAI) was also significantly reduced in mouse. Conclusion Low number of LAB with potential probiotic activity as well as high diversity of lactobacilli species was evident in Iranian population. It also suggest that specific strains of L. plantarum, L. brevis and L. rhamnosus with anti-inflammatory effect in mouse model of colitis could be used as a potential probiotic candidate in inflammatory bowel disease to decrease the disease activity index. © 2015 Rohani et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

The Composition of Species and Structure of Seagrass Fish Community in Tanjung Tiram – Inner Ambon Bay

The study was conducted in March - May 2011 in the coastal waters of Tanjung Tiram – inner Ambon bay. The aims of the study were to determine the composition of species and structure of fish communities in seagrass beds ecosystems. Fish were collected every spring and neap tide for three month periode with a swept area method using beach seine. Fishes were collected as many as 6444 individuals representing 68 species from 29 families. Siganus canaliculatus was contributed up to 62.91% of the total individual fish found.The fish community structure was varied between spring and neap tide. Index of dominance was in low category, diversity in medium, and evenness in unstable conditions. Moreover, the results indicated that seagrass ecosystems in Tanjung Tiram (TAD) have an important role as spawning, nursery ground, and feeding ground. Therefore, management and conservation efforts are urgently needed to maintain the ecological role of seagrass ecosystems for the sustainability of the fish resources

THE WORK TIME SPENT BY FEMALE RABBIT BREEDERS AND ITS’ INFLUENTIAL FACTORS

The aim of this research was to find out the work time spent by female rabbit breeders and factors influencing the work time spent to raise rabbits in Soppeng, South Sulawesi. The research used survey method with female rabbit breeders as analysis unit. Population of 356 female rabbit breeders in Soppeng was used and a sample of 78 female rabbit breeders were selected by region. The result of the research showed that average work time of female rabbit breeders was 3.044 hours per day, which was spent cleaning cages and equipment, preparing feedstuffs and feeding on rabbits, assisting bunnies breastfeed to their mother and health care. The F-test results reveal that the most significantly influential factors affecting female rabbit breeders work time was household income, number of dependents, rabbit breeding income, and the level of experience in breeding rabbits

Leak Detection Modeling and Simulation for Oil Pipeline with Artificial Intelligence Method

Leak detection is always interesting research topic, where leak location and leak rate are two pipeline leaking parameters that should be determined accurately to overcome pipe leaking problems. In this research those two parameters are investigated by developing transmission pipeline model and the leak detection model which is developed using Artificial Neural Network. The mathematical approach needs actual leak data to train the leak detection model, however such data could not be obtained from oil fields. Therefore, for training purposes hypothetical data are developed using the transmission pipeline model, by applying various physical configuration of pipeline and applying oil properties correlations to estimate the value of oil density and viscosity. The various leak locations and leak rates are also represented in this model. The prediction of those two leak parameters will be completed until the total error is less than certain value of tolerance, or until iterations level is reached. To recognize the pattern, forward procedure is conducted. The application of this approach produces conclusion that for certain pipeline network configuration, the higher number of iterations will produce accurate result. The number of iterations depend on the leakage rate, the smaller leakage rate, the higher number of iterations are required. The accuracy of this approach is clearly determined by the quality of training data. Therefore, in the preparation of training data the results of pressure drop calculations should be validated by the real measurement of pressure drop along the pipeline. For the accuracy purposes, there are possibility to change the pressure drop and fluid properties correlations, to get the better results. The results of this research are expected to give real contribution for giving an early detection of oil-spill in oil fields

Delusions in frontotemporal lobar degeneration

We assessed the significance and nature of delusions in frontotemporal lobar degeneration (FTLD), an important cause of young-onset dementia with prominent neuropsychiatric features that remain incompletely characterised. The case notes of all patients meeting diagnostic criteria for FTLD attending a tertiary level cognitive disorders clinic over a three year period were retrospectively reviewed and eight patients with a history of delusions were identified. All patients underwent detailed clinical and neuropsychological evaluation and brain MRI. The diagnosis was confirmed pathologically in two cases. The estimated prevalence of delusions was 14 %. Delusions were an early, prominent and persistent feature. They were phenomenologically diverse; however paranoid and somatic delusions were prominent. Behavioural variant FTLD was the most frequently associated clinical subtype and cerebral atrophy was bilateral or predominantly right-sided in most cases. We conclude that delusions may be a clinical issue in FTLD, and this should be explored further in future work

Classifying the unknown: discovering novel gravitational-wave detector glitches using similarity learning

The observation of gravitational waves from compact binary coalescences by LIGO and Virgo has begun a new era in astronomy. A critical challenge in making detections is determining whether loud transient features in the data are caused by gravitational waves or by instrumental or environmental sources. The citizen-science project \emph{Gravity Spy} has been demonstrated as an efficient infrastructure for classifying known types of noise transients (glitches) through a combination of data analysis performed by both citizen volunteers and machine learning. We present the next iteration of this project, using similarity indices to empower citizen scientists to create large data sets of unknown transients, which can then be used to facilitate supervised machine-learning characterization. This new evolution aims to alleviate a persistent challenge that plagues both citizen-science and instrumental detector work: the ability to build large samples of relatively rare events. Using two families of transient noise that appeared unexpectedly during LIGO's second observing run (O2), we demonstrate the impact that the similarity indices could have had on finding these new glitch types in the Gravity Spy program