Water quality assessment and mapping using inverse distance weighted interpolation: a case of River Kaduna, Nigeria

Several researchers have studied the water quality of the upper and lower stretches of River Kaduna with little on the middle stretch of the river. Besides, no work has ever been done on mapping the water quality of the said river. Hence, the middle stretc h of River Kaduna was monitored for 12 months starting from June, 2016 to May, 2017, covering both rainy and dry seasons in 15 sampling locations to generate data for water quality mapping. However, eight water quality parameters were analyzed namely; temp erature, turbidity, pH, dissolved oxygen, biochemical oxygen demand (BOD), chemical oxygen demand (COD), total nitrogen and total phosphorus using standard methods. Rainy season results were separated from dry season results and were used in mapping the wa ter quality of the river for both seasons using ArcGIS 10.5. It was concluded that the water temperature of the river was within the permissible limit set by U.S. EPA during both seasons while the other water quality parameters apart from turbidity and pH deteriorated more during dry season. In addition, COD and total phosphorus were found to be the only parameters that never met the requirement set by U.S. EPA throughout the sampling period irrespective of the sampling location and season. This is because the least measured concentrations of COD and total phosphorus were 35.34 mg/l and 0.109 mg/l, respectively. It was recommended that farming activities at the river banks should be banned as the fertilizers used by farmers easily drain into the river and in crease the phosphorus and COD concentrations. Key words: River, Kaduna, Interpolation, GIS Map, Water Quality

Achievement goals, self-handicapping, and performance: A 2 × 2 achievement goal perspective

Elliot and colleagues (2006) examined the effects of experimentally induced achievement goals, proposed by the trichotomous model, on self-handicapping and performance in physical education. Our study replicated and extended the work of Elliot et al. by experimentally promoting all four goals proposed by the 262 model (Elliot & McGregor, 2001), measuring the participants’ own situational achievement goals, using a relatively novel task, and testing the participants in a group setting. We used a randomized experimental design with four conditions that aimed to induce one of the four goals advanced by the 262 model. The participants (n¼138) were undergraduates who engaged in a dart-throwing task. The results pertaining to self-handicapping partly replicated Elliot and colleagues’ findings by showing that experimentally promoted performance-avoidance goals resulted in less practice. In contrast, the promotion of mastery-avoidance goals did not result in less practice compared with either of the approach goals. Dart-throwing performance did not differ among the four goal conditions. Personal achievement goals did not moderate the effects of experimentally induced goals on selfhandicapping and performance. The extent to which mastery-avoidance goals are maladaptive is discussed, as well as the interplay between personal and experimentally induced goals

Reading, Trauma and Literary Caregiving 1914-1918: Helen Mary Gaskell and the War Library

This article is about the relationship between reading, trauma and responsive literary caregiving in Britain during the First World War. Its analysis of two little-known documents describing the history of the War Library, begun by Helen Mary Gaskell in 1914, exposes a gap in the scholarship of war-time reading; generates a new narrative of "how," "when," and "why" books went to war; and foregrounds gender in its analysis of the historiography. The Library of Congress's T. W. Koch discovered Gaskell's ground-breaking work in 1917 and reported its successes to the American Library Association. The British Times also covered Gaskell's library, yet researchers working on reading during the war have routinely neglected her distinct model and method, skewing the research base on war-time reading and its association with trauma and caregiving. In the article's second half, a literary case study of a popular war novel demonstrates the extent of the "bitter cry for books." The success of Gaskell's intervention is examined alongside H. G. Wells's representation of textual healing. Reading is shown to offer sick, traumatized and recovering combatants emotional and psychological caregiving in ways that she could not always have predicted and that are not visible in the literary/historical record

Selected reactive oxygen species and antioxidant enzymes in common bean after Pseudomonas syringae pv. phaseolicola and Botrytis cinerea infection

Phaseolus vulgaris cv. Korona plants were inoculated with the bacteria Pseudomonas syringae pv. phaseolicola (Psp), necrotrophic fungus Botrytis cinerea (Bc) or with both pathogens sequentially. The aim of the experiment was to determine how plants cope with multiple infection with pathogens having different attack strategy. Possible suppression of the non-specific infection with the necrotrophic fungus Bc by earlier Psp inoculation was examined. Concentration of reactive oxygen species (ROS), such as superoxide anion (O2 -) and H2O2 and activities of antioxidant enzymes such as superoxide dismutase (SOD), catalase (CAT) and peroxidase (POD) were determined 6, 12, 24 and 48 h after inoculation. The measurements were done for ROS cytosolic fraction and enzymatic cytosolic or apoplastic fraction. Infection with Psp caused significant increase in ROS levels since the beginning of experiment. Activity of the apoplastic enzymes also increased remarkably at the beginning of experiment in contrast to the cytosolic ones. Cytosolic SOD and guaiacol peroxidase (GPOD) activities achieved the maximum values 48 h after treatment. Additional forms of the examined enzymes after specific Psp infection were identified; however, they were not present after single Bc inoculation. Subsequent Bc infection resulted only in changes of H2O2 and SOD that occurred to be especially important during plant–pathogen interaction. Cultivar Korona of common bean is considered to be resistant to Psp and mobilises its system upon infection with these bacteria. We put forward a hypothesis that the extent of defence reaction was so great that subsequent infection did not trigger significant additional response

COVID-19 publications: Database coverage, citations, readers, tweets, news, Facebook walls, Reddit posts

© 2020 The Authors. Published by MIT Press. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://doi.org/10.1162/qss_a_00066The COVID-19 pandemic requires a fast response from researchers to help address biological, medical and public health issues to minimize its impact. In this rapidly evolving context, scholars, professionals and the public may need to quickly identify important new studies. In response, this paper assesses the coverage of scholarly databases and impact indicators during 21 March to 18 April 2020. The rapidly increasing volume of research, is particularly accessible through Dimensions, and less through Scopus, the Web of Science, and PubMed. Google Scholar’s results included many false matches. A few COVID-19 papers from the 21,395 in Dimensions were already highly cited, with substantial news and social media attention. For this topic, in contrast to previous studies, there seems to be a high degree of convergence between articles shared in the social web and citation counts, at least in the short term. In particular, articles that are extensively tweeted on the day first indexed are likely to be highly read and relatively highly cited three weeks later. Researchers needing wide scope literature searches (rather than health focused PubMed or medRxiv searches) should start with Dimensions (or Google Scholar) and can use tweet and Mendeley reader counts as indicators of likely importance

Psychometric evaluation of the Basic Psychological Need Satisfaction and Frustration Scale (BPNSFS) in Italy

Two studies were conducted to adapt the Basic Psychological Need Satisfaction and Frustration Scale (BPNSFS) to the Italian context. Results suggest that the BPNSFS can be considered a promising instrument in the context of SDT-based research for investigating satisfaction and frustration of the three basic needs in Italy

BICEPP: an example-based statistical text mining method for predicting the binary characteristics of drugs

<p>Abstract</p> <p>Background</p> <p>The identification of drug characteristics is a clinically important task, but it requires much expert knowledge and consumes substantial resources. We have developed a statistical text-mining approach (BInary Characteristics Extractor and biomedical Properties Predictor: BICEPP) to help experts screen drugs that may have important clinical characteristics of interest.</p> <p>Results</p> <p>BICEPP first retrieves MEDLINE abstracts containing drug names, then selects tokens that best predict the list of drugs which represents the characteristic of interest. Machine learning is then used to classify drugs using a document frequency-based measure. Evaluation experiments were performed to validate BICEPP's performance on 484 characteristics of 857 drugs, identified from the Australian Medicines Handbook (AMH) and the PharmacoKinetic Interaction Screening (PKIS) database. Stratified cross-validations revealed that BICEPP was able to classify drugs into all 20 major therapeutic classes (100%) and 157 (of 197) minor drug classes (80%) with areas under the receiver operating characteristic curve (AUC) > 0.80. Similarly, AUC > 0.80 could be obtained in the classification of 173 (of 238) adverse events (73%), up to 12 (of 15) groups of clinically significant cytochrome P450 enzyme (CYP) inducers or inhibitors (80%), and up to 11 (of 14) groups of narrow therapeutic index drugs (79%). Interestingly, it was observed that the keywords used to describe a drug characteristic were not necessarily the most predictive ones for the classification task.</p> <p>Conclusions</p> <p>BICEPP has sufficient classification power to automatically distinguish a wide range of clinical properties of drugs. This may be used in pharmacovigilance applications to assist with rapid screening of large drug databases to identify important characteristics for further evaluation.</p

Scuba:Scalable kernel-based gene prioritization

Abstract Background The uncovering of genes linked to human diseases is a pressing challenge in molecular biology and precision medicine. This task is often hindered by the large number of candidate genes and by the heterogeneity of the available information. Computational methods for the prioritization of candidate genes can help to cope with these problems. In particular, kernel-based methods are a powerful resource for the integration of heterogeneous biological knowledge, however, their practical implementation is often precluded by their limited scalability. Results We propose Scuba, a scalable kernel-based method for gene prioritization. It implements a novel multiple kernel learning approach, based on a semi-supervised perspective and on the optimization of the margin distribution. Scuba is optimized to cope with strongly unbalanced settings where known disease genes are few and large scale predictions are required. Importantly, it is able to efficiently deal both with a large amount of candidate genes and with an arbitrary number of data sources. As a direct consequence of scalability, Scuba integrates also a new efficient strategy to select optimal kernel parameters for each data source. We performed cross-validation experiments and simulated a realistic usage setting, showing that Scuba outperforms a wide range of state-of-the-art methods. Conclusions Scuba achieves state-of-the-art performance and has enhanced scalability compared to existing kernel-based approaches for genomic data. This method can be useful to prioritize candidate genes, particularly when their number is large or when input data is highly heterogeneous. The code is freely available at https://github.com/gzampieri/Scuba