Conventional laboratory housing increases morbidity and mortality in research rodents: results of a meta-analysis

Background Over 120 million mice and rats are used annually in research, conventionally housed in shoebox-sized cages that restrict natural behaviours (e.g. nesting and burrowing). This can reduce physical fitness, impair thermoregulation and reduce welfare (e.g. inducing abnormal stereotypic behaviours). In humans, chronic stress has biological costs, increasing disease risks and potentially shortening life. Using a pre-registered protocol (https://atrium.lib.uoguelph.ca/xmlui/handle/10214/17955), this meta-analysis therefore tested the hypothesis that, compared to rodents in ‘enriched’ housing that better meets their needs, conventional housing increases stress-related morbidity and all-cause mortality. Results Comprehensive searches (via Ovid, CABI, Web of Science, Proquest and SCOPUS on May 24 2020) yielded 10,094 publications. Screening for inclusion criteria (published in English, using mice or rats and providing ‘enrichments’ in long-term housing) yielded 214 studies (within 165 articles, using 6495 animals: 59.1% mice; 68.2% male; 31.8% isolation-housed), and data on all-cause mortality plus five experimentally induced stress-sensitive diseases: anxiety, cancer, cardiovascular disease, depression and stroke. The Systematic Review Center for Laboratory animal Experimentation (SYRCLE) tool assessed individual studies’ risks of bias. Random-effects meta-analyses supported the hypothesis: conventional housing significantly exacerbated disease severity with medium to large effect sizes: cancer (SMD = 0.71, 95% CI = 0.54–0.88); cardiovascular disease (SMD = 0.72, 95% CI = 0.35–1.09); stroke (SMD = 0.87, 95% CI = 0.59–1.15); signs of anxiety (SMD = 0.91, 95% CI = 0.56–1.25); signs of depression (SMD = 1.24, 95% CI = 0.98–1.49). It also increased mortality rates (hazard ratio = 1.48, 95% CI = 1.25–1.74; relative median survival = 0.91, 95% CI = 0.89–0.94). Meta-regressions indicated that such housing effects were ubiquitous across species and sexes, but could not identify the most impactful improvements to conventional housing. Data variability (assessed via coefficient of variation) was also not increased by ‘enriched’ housing. Conclusions Conventional housing appears sufficiently distressing to compromise rodent health, raising ethical concerns. Results also add to previous work to show that research rodents are typically CRAMPED (cold, rotund, abnormal, male-biased, poorly surviving, enclosed and distressed), raising questions about the validity and generalisability of the data they generate. This research was funded by NSERC, Canada.

A Power Demand Analytical Model of Self-Propelled Vessels

The article herein presents a closed-form mathematical equation by which it is possible to estimate the propulsion power demand of ships as a function of the propeller parameters and total Resistance. The validation of the derived model is conducted by use of the Series 60 Model data and of the Korea Research Institute of Ships and Ocean Engineering (KRISO) Very Large Crude-oil Carrier 2 (KVLCC2) data. In all the cases tested, the derived model explained more than 99.9% of the data variability. Furthermore, the paper describes a practical method for quantifying changes in hull and propeller performance and provides an application example.

The effect of COVID-19 on the Egyptian exchange using principal component analysis

PurposeSince the beginning of 2020, economies faced many changes as a result of coronavirus disease 2019 (COVID-19) pandemic. The effect of COVID-19 on the Egyptian Exchange (EGX) is investigated in this research.Design/methodology/approachTo explore the impact of COVID-19, three periods were considered: (1) 17 months before the spread of COVID-19 and the start of the lockdown, (2) 17 months after the spread of COVID-19 and the during the lockdown and (3) 34 months comprehending the whole period (before and during COVID-19). Due to the large number of variables that could be considered, dimensionality reduction method, such as the principal component analysis (PCA) is followed. This method helps in determining the most individual stocks contributing to the main EGX index (EGX 30). The PCA, also, addresses the multicollinearity between the variables under investigation. Additionally, a principal component regression (PCR) model is developed to predict the future behavior of the EGX 30.FindingsThe results demonstrate that the first three principal components (PCs) could be considered to explain 89%, 85%, and 88% of data variability at (1) before COVID-19, (2) during COVID-19 and (3) the whole period, respectively. Furthermore, sectors of food and beverage, basic resources and real estate have not been affected by the COVID-19. The resulted Principal Component Regression (PCR) model performs very well. This could be concluded by comparing the observed values of EGX 30 with the predicted ones (R-squared estimated as 0.99).Originality/valueTo the best of our knowledge, no research has been conducted to investigate the effect of the COVID-19 on the EGX following an unsupervised machine learning method.

Organisation and Maintenance of Library Authority Files in Russia and Abroad

The article provides review of foreign and domestic publications on the problems of creation, development and use of authority files (AF) of names of persons, names of organizations, geographical names and other objects both at the international, national and regional levels. The paper presents analysis of the foreign experience of AF maintenance. The authors note that, due to the availability of universal collections and qualified specialists, AF formation abroad is usually carried out by national libraries. A substantive analysis of foreign publications has shown that national AFs (NAF) are characterized by data variability and diversity of approaches. The authors studied the experience of successful combination of NAF created according to different methods within the framework of the international corporate project — Virtual International Authority File (VIAF). The article notes that most of the Russian libraries do not use AF, since AF, created in republican and regional scientific libraries, as a rule, are not publicly available. At the same time, creation by a separate library of its own AF leads to high labour and material costs, and the formation of a large number of AF leads to the variability of the AFs created for the same objects. The authors conclude that for efficient use of AFs within the country, it is necessary to apply unified methods and rules for creation of authority records. Another way out is the application of the Semantic Web technology, which allows linking AFs created according to different methods. It is necessary to make maximum use of existing dictionaries or create dictionaries based on the World Wide Web Consortium (W3C), Resource Description Framework (RDF), RDF Schema (RDFS) and Web Ontology Language (OWL) standards.

Hybrid Systems to Boost EEG-Based Real-Time Action Decoding in Car Driving Scenarios

The complexity of concurrent cerebral processes underlying driving makes such human behavior one of the most studied real-world activities in neuroergonomics. Several attempts have been made to decode, both offline and online, cerebral activity during car driving with the ultimate goal to develop brain-based systems for assistive devices. Electroencephalography (EEG) is the cornerstone of these studies providing the highest temporal resolution to track those cerebral processes underlying overt behavior. Particularly when investigating real-world scenarios as driving, EEG is constrained by factors such as robustness, comfortability, and high data variability affecting the decoding performance. Hence, additional peripheral signals can be combined with EEG for increasing replicability and the overall performance of the brain-based action decoder. In this regard, hybrid systems have been proposed for the detection of braking and steering actions in driving scenarios to improve the predictive power of the single neurophysiological measurement. These recent results represent a proof of concept of the level of technological maturity. They may pave the way for increasing the predictive power of peripheral signals, such as electroculogram (EOG) and electromyography (EMG), collected in real-world scenarios when informed by EEG measurements, even if collected only offline in standard laboratory settings. The promising usability of such hybrid systems should be further investigated in other domains of neuroergonomics.

Advance of soy commodity in the southern Amazonia with deforestation via PRODES and ImazonGeo: a moratorium-based approach

The guidance on decision-making regarding deforestation in Amazonia has been efficient as a result of monitoring programs using remote sensing techniques. Thus, the objective of this study was to identify the expansion of soybean farming in disagreement with the Soy Moratorium (SoyM) in the Amazonia biome of Mato Grosso from 2008 to 2019. Deforestation data provided by two Amazonia monitoring programs were used: PRODES (Program for Calculating Deforestation in Amazonia) and ImazonGeo (Geoinformation Program on Amazonia). For the identification of soybean areas, the Perpendicular Crop Enhancement Index (PCEI) spectral model was calculated using a cloud platform. To verify areas (polygons) of largest converted forest-soybean occurrences, the Kernel Density (KD) estimator was applied. Mann–Kendall and Pettitt tests were used to identify trends over the time series. Our findings reveal that 1,387,288 ha were deforested from August 2008 to October 2019 according to PRODES data, of which 108,411 ha (7.81%) were converted into soybean. The ImazonGeo data showed 729,204 hectares deforested and 46,182 hectares (6.33%) converted into soybean areas. Based on the deforestation polygons of the two databases, the KD estimator indicated that the municipalities of Feliz Natal, Tabaporã, Nova Ubiratã, and União do Sul presented higher occurrences of soybean fields in disagreement with the SoyM. The results indicate that the PRODES system presents higher data variability and means statistically superior to ImazonGeo.

Precipitation extremes and anomalies of the Indian Sundarban 1984-2018

Gridded precipitation data products of 0.5º × 0.5º spatial resolution were analysed to understand the climatic variability in a spatial and temporal context. Data reliability of processed gridded data products were examined in the absence of gauge station data observations in the study area. However, the implementations of comparative analysis of the spatial and temporal data products in this study area are missing. The NASA Power Data (NPD) and Climate Research Unit (CRU TS 4.03) Data were scrutinized from 1984-2018. The data products were selected, compared, and interpreted grid wise. Annual and monsoonal precipitation pattern was also studied. Data variability has been analyzed using the Coefficient of Variation (CV), Anomaly, and Precipitation Concentration Index (PCI). The statistical analysis of R2, MAE, RMSE, MAPE and BIAS was performed to quantify the error and differences. Considering the independent grid point, the MAPE and BIAS indicate that only grid 4 performed better than the rest with 12.7% and 17%, respectively. The results regarding the data products illustrate significant differences both in averaged and grid wise context. The NPD shows an increasing trend, whereas CRU represents a decreasing trend from the year 1984-2018. Before the implementation of any processed secondary gridded data products in complex terrain, the critical evaluation and quantification of the magnitude of error is a prerequisite, like the Sundarbans, where the gauge stationed data is unavailable.

The Use of Multivariate Data Analysis (HCA and PCA) to Characterize Ashes from Biomass Combustion

The content of heavy metals Cd, Cr, Cu, Fe, Ni, Pb and Zn in ash samples from miscanthus, oak, pine, sunflower husk, wheat straw, and willow ashes burned at 500, 600, 700, 800, 900, and 1000 °C, respectively, was determined. The statistical analysis of the results was based on multivariate methods: hierarchical cluster analysis (HCA), and principal component analysis (PCA), which made it possible to classify the raw materials ashed at different temperatures into the most similar groups, and to study the structure of data variability. Using PCA, three principal components were extracted, which explain more than 88% of the variability of the studied elements. Therefore, it can be concluded that the application of multivariate statistical techniques to the analysis of the results of the study of heavy metal content allowed us to draw conclusions about the influence of biomass properties on its chemical characteristics during combustion.