SURFACE URBAN HEAT ISLAND IN MOSCOW DURING THE COVID-19 PANDEMIC LOCKDOWN IN 2020

The influence of the COronaVIrus Disease 2019 (COVID-19) pandemic lockdown (the period of strict quarantine measures) in the spring of 2020 on the ‘Surface Urban Heat Island' (SUHI) geographical phenomenon in Moscow has been studied. For this purpose, we used the measurements of the surface temperature TS made by Moderate Resolution Imaging Spectroradiometer (MODIS) radiometer installed on Terra and Aqua satellites. As a result, TS during the 2020 lockdown, both in the city and surrounding rural zone, was found lower than at the same calendar time in the previous 20 years due to the relatively cold spring. The SUHI intensity as the difference between TS inside Moscow and the surrounding rural zone around it during the lockdown was also lower than usual (on average in the previous 20 years), but this decrease is relatively small and nonsignificant. The Normalized Difference Vegetation Index (NDVI) in Moscow and Moscow region during the lockdown was close to its usual values, but the leaf area index (LAI) was significantly lower than its average values in the previous 20 years. Thus, the weakening of the SUHI during the lockdown in 2020 was caused mostly by lower heat loss due to transpiration in the rural zone. This was associated with the slowdown in vegetation development as a result of the cold spring. Besides, an additional possible reason was the reduction of human activity due to the collapse of many anthropogenic heat sources in the city. According to long-term MODIS data, the SUHI intensity in Moscow and the surface temperature in Moscow region, as well as the NDVI and LAI values, do not demonstrate statistically significant long-term trends in the spring season over the past 21 years, despite climate changes. In spring, during faster snow melting in cities, when it still persists in the rural zone, the SUHI intensity can be record high (up to 8 ºC). © 2022, Russian Geographical Society. All rights reserved.

Long-acting Injectable Antipsychotics during the COVID-19 pandemic in schizophrenia: An observational study in a real-world clinical setting.

The COVID-19 pandemic is having an important impact on the practice of mental health services and on schizophrenia patients, and heterogeneous and conflicting findings are being reported on the reduction of long-acting injectable (LAI) antipsychotics use. Aims of the study were to assess the total number of patients treated with LAI, the start of novel LAI and the discontinuation of LAI treatments, analyzing register data of the first year of the pandemic, 2020, compared to a pre-pandemic reference year, 2019. Data from two outpatient centers were retrieved, for a total of 236 participants in 2020: no significant differences were observed comparing 2020 and 2019 when considering the total number of patients on LAI treatment (p = 0.890) and the number of dropouts (p = 0.262); however, a significant reduction in the start of LAI was observed (p = 0.022). In 2020, second generation LAI were more prescribed than first generation LAI (p = 0.040) while no difference was observed in 2019 (p = 0.191). These findings attest the efficacy of measures adopted in mental health services to face the consequences of COVID-19 and shed further light on the impact of the pandemic on the clinical practice of mental health services and on the continuity of care of people with schizophrenia.

Long-acting paliperidone in Ekbom's syndrome in Lewy body dementia: A case report.

Introduction: Ekbom Syndrome (ES) is characterised by fixed, delusional beliefs that one's body is infested by parasites or other vermin in absence of supporting clinical evidence. Antipsychotic (AP) treatment, including long-acting injectable (LAI) AP in subjects with poor compliance, is prescribed to manage behavioural and psychotic symptomatology. Objectives: We describe a 70-year-old woman who was hospitalised after experiencing new-onset delusions of infestation with visual and tactile hallucinations that led to bizarre behaviours and progressive social withdrawal. Methods: She was diagnosed with ES and was initially treated with risperidone 3 mg; however, due to poor compliance and a lack of insight, she was switched to LAI palmitate paliperidone (LAI-PP). She was followed up for 8 months, administering Positive and Negative Syndrome Scale, Montreal Cognitive Assessment, Global Assessment of Functioning, Brief Psychiatric Rating Scale, neurocognitive assessment, and neuroimaging. Results: After a progressive cognitive deterioration, she was diagnosed with an ES secondary to Lewy body dementia (DLB). Conclusion: The LAI-PP treatment determined a complete clinical remission of psychotic symptoms despite the emergence of an iatrogenic akinetic-rigid syndrome. The delay of confirmatory neurological diagnosis, the associated risky behaviours of the patient, and poor treatment adherence led clinicians to prescribe LAI-PP following a good clinical response to oral paliperidone. However, in the case of a suspected DLB diagnosis, the prescription of an LAI-PP as a first-line strategy should be carefully evaluated.

Is Fatty Liver Associated with Increased Mortality and Morbidity in Coronavirus Disease 2019 (COVID-19) Pneumonia?

Background: Fatty liver has been shown to be associated with severe COVID-19 disease without any impact on mortality. This is based on heterogenous criteria for defining both fatty liver as well as the severity parameters. This study aimed to study the impact of fatty liver on the mortality and severity of disease in patients with COVID-19 pneumonia. Methods: In a case control study design, patients with COVID-19 pneumonia (COVID-19 computed tomography severity index [CTSI] on high-resolution computed tomography chest of ≥1) with fatty liver (defined as liver to spleen attenuation index ≤5 on noncontrast computed tomography cuts of upper abdomen) were compared with those without fatty liver. The primary outcome measure was in-hospital mortality, and the secondary outcome measures were CTSI score, need for intensive care unit (ICU) care, need for ventilatory support, duration of ICU stay, and duration of hospital stay. Results: Of 446 patients with COVID-19 pneumonia, 289 (64.7%)admitted to Max Hospital, Saket, India, between January 1, 2021, and October 30, 2021, had fatty liver. Fifty-nine of 446 patients died during the index admission. In-hospital mortality was not different between patients with fatty liver (38 [13.24%]) or without fatty liver (21 [13.81%]). COVID-19 CTSI score was found to be significantly higher among patients who had fatty liver (13.40 [5.16] vs 11.81 [5.50]; P = 0.003). There was no difference in the requirement of ICU (94 [32%] vs 62 [39.49%]; P = 0.752), requirement of ventilatory support (27 [9.34%] vs 14 [8.91%]; P = 0.385), duration of ICU stay (8.29 [6.87] vs 7.07 [5.71] days; P = 0.208), and duration of hospital stay (10.10 [7.14] vs 10.69 [8.13] days; P = 0.430) between the groups with fatty liver or no fatty liver. Similarly, no difference was found in primary or secondary outcomes measure between the group with severe fatty liver vs mild/moderate or no fatty liver. High total leucocyte count and Fibrosis-4 (FIB-4) index were independently associated with mortality. Conclusions: Fatty liver may not be associated with increased mortality or clinical morbidity in patients who have COVID-19 pneumonia.

Remote Prediction of Oilseed Rape Yield via Gaofen-1 Images and a Crop Model

The fast and accurate prediction of crop yield at the regional scale is of great significance to food policies or trade. In this study, a new model is developed to predict the yield of oilseed rape from high-resolution remote sensing images. In order to derive this model, the ground experiment and remote sensing data analysis are carried out successively. In the ground experiment, the leaf area index (LAI) of four growing stages are measured, and a regression model is established to predict yield from ground LAI. In the remote sensing analysis, a new model is built to predict ground LAI from Gaofen-1 images where the simple ratio vegetation index at the bolting stage and the VARIgreen vegetation index at the flowering stage are used. The WOFOSTWOrld FOod STudy (WOFOST) crop model is used to generate time-series ground LAI from discontinuous ground LAI, which is calibrated coarsely with the MODerate resolution imaging spectroradiometer LAI product and finely with the ground-measured data. By combining the two conclusive formulas, an estimation model is built from Gaofen-1 images to the yield of oilseed rape. The effectiveness of the proposed model is verified in Wuxue City, Hubei Province from 2014 to 2019, with the pyramid bottleneck residual network to extract oilseed rape planting areas, the proposed model to estimate yields, and the China statistical yearbooks for comparison. The validation shows that the prediction error of the proposed algorithm is less than 5.5%, which highlights the feasibility of our method for accurate prediction of the oilseed rape yield in a large area. [ FROM AUTHOR] Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Leaf Area Index Estimation of Pergola-Trained Vineyards in Arid Regions Based on UAV RGB and Multispectral Data Using Machine Learning Methods

The leaf area index (LAI), a valuable variable for assessing vine vigor, reflects nutrient concentrations in vineyards and assists in precise management, including fertilization, improving yield, quality, and vineyard uniformity. Although some vegetation indices (VIs) have been successfully used to assess LAI variations, they are unsuitable for vineyards of different types and structures. By calibrating the light extinction coefficient of a digital photography algorithm for proximal LAI measurements, this study aimed to develop VI-LAI models for pergola-trained vineyards based on high-resolution RGB and multispectral images captured by an unmanned aerial vehicle (UAV). The models were developed by comparing five machine learning (ML) methods, and a robust ensemble model was proposed using the five models as base learners. The results showed that the ensemble model outperformed the base models. The highest R2 and lowest RMSE values that were obtained using the best combination of VIs with multispectral data were 0.899 and 0.434, respectively;those obtained using the RGB data were 0.825 and 0.547, respectively. By improving the results by feature selection, ML methods performed better with multispectral data than with RGB images, and better with higher spatial resolution data than with lower resolution data. LAI variations can be monitored efficiently and accurately for large areas of pergola-trained vineyards using this framework.

Greenspace exposure and COVID-19 mortality in the United States: January-July 2020.

BACKGROUND: Mortality from the novel coronavirus disease-2019 (COVID-19) continues to rise across the United States. Evidence is emerging that environmental factors may contribute to susceptibility to disease and mortality. Greenspace exposure promotes enhanced immunity and may protect against risk of mortality among those with COVID-19. OBJECTIVES: Our objective was to determine if high county level greenspace exposure is associated with reduced risk of COVID-19 mortality. METHODS: Greenspace exposure was characterized in 3049 counties across the conterminous United States using Leaf Area Index (LAI) deciles that were derived from satellite imagery via Moderate Resolution Imaging Spectroradiometer from 2011 to 2015. COVID-19 mortality data were obtained from the Center for Systems Science and Engineering at Johns Hopkins University. We used a generalized linear mixed model to evaluate the association between county level LAI and COVID-19 mortality rate in analyses adjusted for 2015-2019 county level average total county population, older population, race, overcrowding in home, Medicaid, education, and physical inactivity. RESULTS: A dose-response association was found between greenness and reduced risk of COVID-19 mortality. COVID-19 mortality was negatively associated with LAI deciles 8 [MRR = 0.82 (95% CI: 0.72, 0.93)], 9 [MRR = 0.78 (95% CI: 0.68, 0.89)], and 10 [MRR = 0.59 (95% CI: 0.50, 0.69)]. Aside from LAI decile 5, no associations were found between the remaining LAI deciles and COVID-19 mortality. Increasing prevalence of counties with older age residents, low education attainment, Native Americans, Black Americans, and housing overcrowding were significantly associated with increased risk of COVID-19 mortality, whereas Medicaid prevalence was associated with a reduced risk. DISCUSSION: Counties with a higher amount of greenspace may be at a reduced risk of experiencing mortality due to COVID-19.

Regional technology gap and innovation efficiency trap in Chinese pharmaceutical manufacturing industry

Objective There is a huge technology gap between regions in Chinese pharmaceutical manufacturing industry, which is the reality that must be faced. However, most of the available researches on innovation efficiency are based on the logic of a given technology level, ignoring the regional technological gap. This paper will stand from the perspective of technology gap and re-examine the innovation efficiency of pharmaceutical manufacturing industry in different regions of China and its impact on regional industrial competitiveness. Methods We use the DEA-BCC input-oriented model to measure innovation efficiency of 28 provinces from the data of China's pharmaceutical manufacturing industry. The threshold model is constructed, with technology level as the threshold variable, innovation efficiency as the main explanatory variable, and industrial competitiveness as the dependent variable. In the threshold model, 28 regions are divided into three technical groups, and further, the impact of innovation efficiency on industrial competitiveness in different groups is analyzed and compared. Results According to the empirical research results, an U-shaped efficiency trap has been found in Chinese pharmaceutical manufacturing industry, and the areas with medium technical level are at the bottom of the trap. The improvement of innovation efficiency does not necessarily promote the improvement of regional industrial competitiveness. Only in high-level and low-level technology groups, innovation efficiency has effectively promoted the improvement of industrial competitiveness. In addition, the intensity of R&D investment has a similar impact on industrial competitiveness. Conclusions The findings suggest that, regions in the efficiency trap should strive to seek opportunities for industrial transformation and focus on the industrial transformation of new technology, new industry and new opportunities, instead of blindly pursuing R&D investment intensity and superstitious innovation efficiency. So as to free up innovation resources for high-quality technological innovation in other regions. In addition, the Chinese government should make use of its public hospital system to normalize and expand the centralized drug procurement and eliminate the low-quality innovation.