The Impact of Heavy Load Carrying on Musculoskeletal Pain and Disability Among Women in Shinyanga Region, Tanzania.

BackgroundHeavy load carrying has been associated with musculoskeletal discomfort (MSD) and disability. However, there is a lack of research investigating this association in resource-constrained settings where heavy load carrying by women is common.ObjectivesWe assessed the impact of heavy load carrying on musculoskeletal pain and disability among women in Shinyanga Region, Tanzania, in an exploratory cross-sectional study.MethodsEligible participants were a convenience sample of women, at least 18 years of age, who passed a study recruitment site carrying a load. We collected information on load-carrying practices, including frequency and time spent carrying water, wood, agricultural products, coal, sand, or rocks, and measured the weight of the load carried at the time. Outcomes included self-reported MSDs, defined as experiencing pain lasting >3 days in the neck, head, back, knees, feet and/or ankles within the last 1 year, and related disability. Using multivariable logistic regression we assessed for associations between load carrying exposures and MSDs and disability.FindingsResults showed a high prevalence of MSDs across the body regions assessed and evidence to suggest a relationship of back pain and related disability with several measures of load-carrying, including duration, frequency, and weight. Multivariable analyses revealed associations of increased load carrying exposures with low back pain (LBP) and related disability, including statistically significant increases in odds of LBP with increasing weight, total duration of load carrying/week and cumulative loads/week.ConclusionsFindings indicate a substantial burden of MSDs and disability in this population of women who carry heavy loads daily. The extent of discomfort and disability increased with increasing exposure to various load-carrying measures, especially for LBP. Larger epidemiologic studies that definitively assess relationships of load carrying with MSDs and disability are warranted

Sex differences in functional connectivity between resting state brain networks in autism spectrum disorder

Functional brain connectivity (FBC) has previously been examined in autism spectrum disorder (ASD) between-resting-state networks (RSNs) using a highly sensitive and reproducible hypothesis-free approach. However, results have been inconsistent and sex differences have only recently been taken into consideration using this approach. We estimated main effects of diagnosis and sex and a diagnosis by sex interaction on between-RSNs FBC in 83 ASD (40 females/43 males) and 85 typically developing controls (TC; 43 females/42 males). We found increased connectivity between the default mode (DM) and (a) the executive control networks in ASD (vs. TC); (b) the cerebellum networks in males (vs. females); and (c) female-specific altered connectivity involving visual, language and basal ganglia (BG) networks in ASD—in suggestive compatibility with ASD cognitive and neuroscientific theories.info:eu-repo/semantics/publishedVersio

Satellite detection of volcanic ash from Eyjafjallajökull and the threat to aviation

Earth orbiting satellites provide an excellent means for monitoring and measuring emissions from volcanic eruptions. The recent eruption of Eyjafjallajökull in Iceland on 14 April, 2010 and the subsequent movement of the ash clouds were tracked using a variety of satellite instruments as they moved over Europe. Data from the rapid sampling (every 15 minutes) SEVIRI on Meteosat Second Generation were especially useful during this event as the thermal channels between 10–12 micron could be used to detect the ash signal and perform quantitative ash retrievals of mass loadings, optical depths and effective particle size. Higher-spatial resolution ( 1 km2) information from the MODIS sensors on NASA’s Terra and Aqua platforms were also analysed to determine ash microphysics and also to provide ash cloud top height. High-spectral resolution data from the IASI and AIRS sensors showed that initially quantities of ice, potentially with ash cores, were present, and that multi-species retrievals could be performed by exploiting the spectral content of the data. Vertically resolved ash layers were detected using the Caliop lidar on board the Calipso platform. Ash was clearly detected over Europe using the infra-red sensors with mass loadings typically in the range 0.1–5 gm-2, which for layers of 500–1000 m thickness, suggests ash concentrations in the range 0.1–10 mg m-3, and therefore represent a potential hazard to aviation.Little SO2 was detected at the start of the eruption, although both OMI and AIRS detected upper-level SO2 on 15 April. By late April and early May, 0.1–0.3 Tg (SO2) could be detected using these sensors. The wealth of satellite data available, some in near real-time, and the ability of infrared and ultra-violet sensors to detect volcanic ash and SO2 are emphasised in this presentation. The ash/aviation problem can be addressed using remote sensing measurements, validated with ground-based and air-borne, and combined with dispersion modelling. The volcanic ash threat to aviation can be ameliorated by utilising these space-based resources

Satellite detection of volcanic ash from Eyjafjallajökull and the threat to aviation

Earth orbiting satellites provide an excellent means for monitoring and measuring emissions from volcanic eruptions. The recent eruption of Eyjafjallajökull in Iceland on 14 April, 2010 and the subsequent movement of the ash clouds were tracked using a variety of satellite instruments as they moved over Europe. Data from the rapid sampling (every 15 minutes) SEVIRI on Meteosat Second Generation were especially useful during this event as the thermal channels between 10–12 micron could be used to detect the ash signal and perform quantitative ash retrievals of mass loadings, optical depths and effective particle size. Higher-spatial resolution ( 1 km2) information from the MODIS sensors on NASA’s Terra and Aqua platforms were also analysed to determine ash microphysics and also to provide ash cloud top height. High-spectral resolution data from the IASI and AIRS sensors showed that initially quantities of ice, potentially with ash cores, were present, and that multi-species retrievals could be performed by exploiting the spectral content of the data. Vertically resolved ash layers were detected using the Caliop lidar on board the Calipso platform. Ash was clearly detected over Europe using the infra-red sensors with mass loadings typically in the range 0.1–5 gm-2, which for layers of 500–1000 m thickness, suggests ash concentrations in the range 0.1–10 mg m-3, and therefore represent a potential hazard to aviation.Little SO2 was detected at the start of the eruption, although both OMI and AIRS detected upper-level SO2 on 15 April. By late April and early May, 0.1–0.3 Tg (SO2) could be detected using these sensors. The wealth of satellite data available, some in near real-time, and the ability of infrared and ultra-violet sensors to detect volcanic ash and SO2 are emphasised in this presentation. The ash/aviation problem can be addressed using remote sensing measurements, validated with ground-based and air-borne, and combined with dispersion modelling. The volcanic ash threat to aviation can be ameliorated by utilising these space-based resources

SO2 AND ASH VOLCANIC PLUME RETRIEVALS FROM THE 24 NOVEMBER 2006 Mt. ETNA ERUPTION USING MSG-SEVIRI DATA: SO2 VALIDATION AND ASH CORRECTION PROCEDURE

Estimation of the daily trend of sulfur dioxide and ash from the thermal infrared measurements of the Spin Enhanced Visible and Infrared Imager (SEVIRI), on board the Meteosat Second Generation (MSG) geosynchronous satellite, has been carried out. The SO2 retrieval is validated vicariously by using satellite sensors and with ground measurements. The 24 November 2006 tropospheric eruption of Etna volcano is used as a test case. MSG-SEVIRI is an optical imaging radiometer characterized by 12 spectral channels, a high temporal resolution (one image every 15 minutes), and a 10 km2 footprint. The instrument’s spectral range includes the 7.3 and 8.7 mm bands (channels 6 and 7) used for SO2 retrieval and the 10.8 and 12.0 mm (channels 9 and 10) split window bands used for ash detection and retrievals. The SO2 columnar abundance and ash are retrieved simultaneously by means of a Look-Up Table least squares fit procedure for SO2 and using a Brightness Temperature Difference algorithm for ash. The SO2 retrievals obtained using different satellite sensors such as AIRS and MODIS have been carried out and compared with SEVIRI estimations. The results were validated using the permanent mini-DOAS ground system network (FLAME) installed and operated by INGV on Mt. Etna. Results show that the simultaneous presence of SO2 and ash in a volcanic plume yields a significant error in the SO2 columnar abundance retrieval in multispectral Thermal Infrared (TIR) data. The ash plume particles with high effective radius (from 1 to 10 mm) reduce the top of atmosphere radiance in the entire TIR spectral range, including the channels used for the SO2 retrieval. The net effect is a significant SO2 overestimation. To take this effect into account a novel ash correction procedure is presented and applied to the retrieval

WARP: a WIMP double phase Argon detector

The WARP programme for dark matter search with a double phase argon detector is presented. In such a detector both excitation and ionization produced by an impinging particle are evaluated by the contemporary measurement of primary scintillation and secondary (proportional) light signal, this latter being produced by extracting and accelerating ionization electrons in the gas phase. The proposed technique, verified on a 2.3 liters prototype, could be used to efficiently discriminate nuclear recoils, induced by WIMP's interactions, and measure their energy spectrum. An overview of the 2.3 liters results and of the proposed 100 liters detector is shown.Comment: Proceeding for IDM200

Volcanic cloud detection using Sentinel-3 satellite data by means of neural networks: the Raikoke 2019 eruption test case

The accurate automatic volcanic cloud detection by means of satellite data is a challenging task and of great concern for both scientific community and stakeholder due to the well-known issues generated by a strong eruption event in relation to aviation safety and health impact. In this context, machine learning techniques applied to recent spaceborne sensors acquired data have shown promising results in the last years. This work focuses on the application of a neural network based model to Sentinel-3 SLSTR (Sea and Land Surface Temperature Radiometer) daytime products in order to detect volcanic ash plumes generated by the 2019 Raikoke eruption. The classification of the clouds and of the other surfaces composing the scene is also carried out. The neural network has been trained with MODIS (MODerate resolution Imaging Spectroradiometer) daytime imagery collected during the 2010 Eyjafjallaj&ouml;kull eruption. The similar acquisition channels of SLSTR and MODIS sensors and the events comparable latitudes foster the robustness of the approach, which allows overcoming the lack in SLSTR products collected in previous mid-high latitude eruptions. The results show that the neural network model is able to detect volcanic ash with good accuracy if compared with RGB visual inspection and BTD (Brightness Temperature Difference) procedure. Moreover, the comparison between the ash cloud obtained by neural network and a plume mask manually generated for the specific SLSTR considered images, shows significant agreement. Thus, the proposed approach allows an automatic image classification during eruption events, which it is also considerably faster than time-consuming manually algorithms (e.g. find the best BTD product-specific threshold). Furthermore, the whole image classification indicates an overall reliability of the algorithm, in particular for meteo-clouds recognition and discrimination from volcanic clouds. Finally, the results show that the NN developed for the SLSTR nadir view is able to properly classify also the SLSTR oblique view images.</p

Natural Astaxanthin Is a Green Antioxidant Able to Counteract Lipid Peroxidation and Ferroptotic Cell Death

Astaxanthin is a red orange xanthophyll carotenoid produced mainly by microalgae but which can also be chemically synthesized. As demonstrated by several studies, this lipophilic molecule is endowed with potent antioxidant properties and is able to modulate biological functions. Unlike synthetic astaxanthin, natural astaxanthin (NAst) is considered safe for human nutrition, and its production is considered eco-friendly. The antioxidant activity of astaxanthin depends on its bioavailability, which, in turn, is related to its hydrophobicity. In this study, we analyzed the water-solubility of NAst and assessed its protective effect against oxidative stress by means of different approaches using a neuroblastoma cell model. Moreover, due to its highly lipophilic nature, astaxanthin is particularly protective against lipid peroxidation; therefore, the role of NAst in counteracting ferroptosis was investigated. This recently discovered process of programmed cell death is indeed characterized by iron-dependent lipid peroxidation and seems to be linked to the onset and development of oxidative-stress-related diseases. The promising results of this study, together with the “green sources” from which astaxanthin could derive, suggest a potential role for NAst in the prevention and co-treatment of chronic degenerative diseases by means of a sustainable approach