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Sweet sorghum (Sorghum bicolor (L.) moench), a crop that is grown by subsistence farmers in Zimbabwe was used to extract silica gel in order to assess its possible use as a raw material for the production of silica-based products. The gel was prepared from sodium silicate extracted from sweet sorghum bagasse ash by sodium hydroxide leaching. Results show that maximum yield can be obtained at pH 5 and with 3 M sodium concentration. The silica gel prepared at optimum pH 5 had a bulk density of 0.5626 g/cm3 and anestimated porosity of 71.87%. Silica gel aged over 10 h had improved moisture adsorption properties. X-ray fluorescence (XRF) determinations show that the silica content in the ash is 40.1%. Characterization of sweet sorghum ash and silica gels produced at pH 5, 7 and 8.5 by Fourier Transform Infrared spectroscopy gave absorption bands similar to those reported by other researchers.Transmission electron micrographs show that silica prepared under optimum conditions is amorphous and consisted of irregular particles. Sweet sorghum proved to be a potential low cost raw material for the production of silica gel.
We present our approach to extend a Virtual Reality software framework towards the use for Augmented Reality applications. Although VR and AR applications have very similar requirements in terms of abstract components (like 6DOF input, stereoscopic output, simulation engines), the requirements in terms of hardware and software vary considerably. In this article we would like to share the experience gained from adapting our VR software framework for AR applications. We will address design issues for this task. The result is a VR/AR basic software that allows us to implement interactive applications without fixing their type (VR or AR) beforehand. Switching from VR to AR is a matter of changing the configuration file of the application. We also give an example of the use of the extended framework: Augmenting the magnetic field of bar magnets in physics classes. We describe the setup of the system and the real-time calculation of the magnetic field, using a GPU.
Background: Falls are common in older adults and can result in serious injuries. Due to demographic changes, falls and related healthcare costs are likely to increase over the next years. Participation and motivation of older adults in fall prevention measures remain a challenge. The iStoppFalls project developed an information and communication technology (ICT)-based system for older adults to use at home in order to reduce common fall risk factors such as impaired balance and muscle weakness. The system aims at increasing older adults’ motivation to participate in ICT-based fall prevention measures. This article reports on usability, user-experience and user-acceptance aspects affecting the use of the iStoppFalls system by older adults.
Methods: In the course of a 16-week international multicenter study, 153 community-dwelling older adults aged 65+ participated in the iStoppFalls randomized controlled trial, of which half used the system in their home to exercise and assess their risk of falling. During the study, 60 participants completed questionnaires regarding the usability, user experience and user acceptance of the iStoppFalls system. Usability was measured with the System Usability Scale (SUS). For user experience the Physical Activity Enjoyment Scale (PACES) was applied. User acceptance was assessed with the Dynamic Acceptance Model for the Re-evaluation of Technologies (DART). To collect more detailed data on usability, user experience and user acceptance, additional qualitative interviews and observations were conducted with participants.
Results: Participants evaluated the usability of the system with an overall score of 62 (Standard Deviation, SD 15.58) out of 100, which suggests good usability. Most users enjoyed the iStoppFalls games and assessments, as shown by the overall PACES score of 31 (SD 8.03). With a score of 0.87 (SD 0.26), user acceptance results showed that participants accepted the iStoppFalls system for use in their own home. Interview data suggested that certain factors such as motivation, complexity or graphical design were different for gender and age.
Conclusions: The results suggest that the iStoppFalls system has good usability, user experience and user acceptance. It will be important to take these along with factors such as motivation, gender and age into consideration when designing and further developing ICT-based fall prevention systems.
Although work events can be regarded as pivotal elements of organizational life, only a few studies have examined how positive and negative events relate to and combine to affect work engagement over time. Theory suggests that, to better understand how current events affect work engagement (WE), we have to account for recent events that have preceded these current events. We present competing theoretical views on how recent and current work events may affect employees (e.g., getting used to a high frequency of negative events or becoming more sensitive to negative events). Although the occurrence of events implies discrete changes in the experience of work, prior research has not considered whether work events actually accumulate to sustained mid-term changes in WE. To address these gaps in the literature, we conducted a week-level longitudinal study across a period of 15 consecutive weeks among 135 employees, which yielded 849 weekly observations. While positive events were associated with higher levels of WE within the same week, negative events were not. Our results support neither satiation nor sensitization processes. However, a high frequency of negative events in the preceding week amplified the beneficial effects of positive events on WE in the current week. Growth curve analyses show that the benefits of positive events accumulate to sustain high levels of WE. WE dissipates in the absence of a continuous experience of positive events. Our study adds a temporal component by highlighting that positive events affect work engagement, particularly in light of recent negative events. Our study informs research that has taken a feature-oriented perspective on the dynamic interplay of job demands and resources.
Background:
Access to electricity is one of the enabling factors for healthcare service provision. From the sustainable development perspective, an essential requirement for improving health and caring for our environment is to assure that health facilities have sufficient and reliable access to the supply of clean and sustainable energy. The objective of this work is to investigate the users’ perceptions of electricity needs and electricity sources and the way those influence different attributes and their relevance for the diffusion of renewable electricity systems in healthcare facilities.
Methods:
To identify preferences and choices, Stated Choice modelling was applied as the use of solar PV systems in health facilities is not widespread in Ghana. This method allows to present the respondents with hypothetical options, which have attributes close to the real world. Four attributes were considered, namely electricity system configuration, initial investment cost, monthly costs, and improvements to the reliability of the electricity supply.
Results:
The largest share of the 200 health facilities interviewed reported services provision as outpatient treatment, provision of maternity services and family planning, which are relatively low electricity-intensive services. However, there was a general perception that increased reliability on the electricity supply can improve the health service provision and operation of the facilities. Moreover, despite that preferences towards the solar systems, the initial investment costs of the solar systems is still perceived as preventing the adoption of this technology
Conclusion:
From this study we can conclude that health facilities in Ghana rely greatly on the national supply which has issues with reliability, compromising the delivery of healthcare services. However, the adoption of alternative electricity technologies based on renewable sources is not likely to occur at the facility level without the engagement of other actors that can help bridging the barriers for adoption, as initial investment costs.
Applied privacy research has so far focused mainly on consumer relations in private life. Privacy in the context of employment relationships is less well studied, although it is subject to the same legal privacy framework in Europe. The European General Data Protection Regulation (GDPR) has strengthened employees’ right to privacy by obliging that employers provide transparency and intervention mechanisms. For such mechanisms to be effective, employees must have a sound understanding of their functions and value. We explored possible boundaries by conducting a semistructured interview study with 27 office workers in Germany and elicited mental models of the right to informational self-determination, which is the European proxy for the right to privacy. We provide insights into (1) perceptions of different categories of data, (2) familiarity with the legal framework regarding expectations for privacy controls, and (3) awareness of data processing, data flow, safeguards, and threat models. We found that legal terms often used in privacy policies used to describe categories of data are misleading. We further identified three groups of mental models that differ in their privacy control requirements and willingness to accept restrictions on their privacy rights. We also found ignorance about actual data flow, processing, and safeguard implementation. Participants’ mindsets were shaped by their faith in organizational and technical measures to protect privacy. Employers and developers may benefit from our contributions by understanding the types of privacy controls desired by office workers and the challenges to be considered when conceptualizing and designing usable privacy protections in the workplace.
Background: Bloodstream infections (BSIs) remain a significant cause of mortality worldwide. Causative pathogens are routinely identified and susceptibility tested but only very rarely investigated for their resistance genes, virulence factors, and clonality. Our aim was to gain insight into the clonality patterns of different species causing BSI and the clinical relevance of distinct virulence genes.
Methods: For this study, we whole-genome-sequenced over 400 randomly selected important pathogens isolated from blood cultures in our diagnostic department between 2016 and 2021. Genomic data on virulence factors, resistance genes, and clonality were cross-linked with in-vitro data and demographic and clinical information.
Results: The investigation yielded extensive and informative data on the distribution of genes implicated in BSI as well as on the clonality of isolates across various species.
Conclusion: Associations between survival outcomes and the presence of specific genes must be interpreted with caution, and conducting replication studies with larger sample sizes for each species appears mandatory. Likewise, a deeper knowledge of virulence and host factors will aid in the interpretation of results and might lead to more targeted therapeutic and preventive measures. Monitoring transmission dynamics more efficiently holds promise to serve as a valuable tool in preventing in particular BSI caused by nosocomial pathogens.
Due to expected positive impacts on business, the application of artificial intelligence has been widely increased. The decision-making procedures of those models are often complex and not easily understandable to the company’s stakeholders, i.e. the people having to follow up on recommendations or try to understand automated decisions of a system. This opaqueness and black-box nature might hinder adoption, as users struggle to make sense and trust the predictions of AI models. Recent research on eXplainable Artificial Intelligence (XAI) focused mainly on explaining the models to AI experts with the purpose of debugging and improving the performance of the models. In this article, we explore how such systems could be made explainable to the stakeholders. For doing so, we propose a new convolutional neural network (CNN)-based explainable predictive model for product backorder prediction in inventory management. Backorders are orders that customers place for products that are currently not in stock. The company now takes the risk to produce or acquire the backordered products while in the meantime, customers can cancel their orders if that takes too long, leaving the company with unsold items in their inventory. Hence, for their strategic inventory management, companies need to make decisions based on assumptions. Our argument is that these tasks can be improved by offering explanations for AI recommendations. Hence, our research investigates how such explanations could be provided, employing Shapley additive explanations to explain the overall models’ priority in decision-making. Besides that, we introduce locally interpretable surrogate models that can explain any individual prediction of a model. The experimental results demonstrate effectiveness in predicting backorders in terms of standard evaluation metrics and outperform known related works with AUC 0.9489. Our approach demonstrates how current limitations of predictive technologies can be addressed in the business domain.
When the Artemis missions launch, NASA's Orion spacecraft (and crew as of the Artemis II mission) will be exposed to the deep space radiation environment beyond the protection of Earth's magnetosphere. Hence, it is essential to characterize the effects of space radiation, microgravity, and the combination thereof on cells and organisms, i.e., to quantify any correlations between the deep space radiation environment, genetic variation, and induced genetic changes in cells. To address this, the Artemis I mission will include the Peristaltic Laboratory for Automated Science with Multigenerations (PLASM) hardware containing the Deep Space Radiation Genomics (DSRG) experiment. The scientific aims of DSRG are (i) to identify the metabolic and genomic pathways in yeast affected by microgravity, space radiation, and their combination, and (ii) to differentiate between gravity and radiation exposure on single-gene deletion/overexpressing strains' ability to thrive in the spaceflight environment. Yeast is used as a model system because 70% of its essential genes have a human homolog, and over half of these homologs can functionally replace their human counterpart. As part of the experiment preparation towards spaceflight, an Experiment Verification Test (EVT) was performed at the Kennedy Space Center to verify that the experiment design, hardware, and approach to automated operations will enable achieving the scientific aims. For the EVT, fluidic systems were assembled, sterilized, loaded, and acceptance-tested, and subsequently integrated with the engineering parts to produce a flight-like PLASM unit. Each fluidic system consisted of (i) a Media Bag, (ii) four Culture Bags loaded with Saccharomyces cerevisiae (two with deletion series and the remaining two with overexpression series), and (iii) tubing and check valves. The EVT PLASM unit was put under a temperature profile replicating the anticipated different phases of flight, including handover to launch, spaceflight, and splashdown to handover back to the science team, for a 58-day period. At EVT completion, the rate of activation, cellular growth, RNA integrity, and sample contamination were interrogated. All of the experiment's success criteria were satisfied, encouraging our efforts to perform this investigation on Artemis I. This manuscript thus describes the process of spaceflight experiment design maturation with a focus on the EVT, its results, DSRG's preparation for its planned launch on Artemis I in 2022, and how the PLASM hardware can enable other scientific goals on future Artemis missions and/or the Lunar Orbital Platform – Gateway.
Exosomes derived from human autologous conditioned serum are nanocarriers for IL-6 and TNF-alfa
(2017)
Beta-ketothiolase deficiency, also known as mitochondrial acetoacetyl-CoA thiolase (T2) deficiency, is an autosomal recessive disease caused by mutations in the acetylCoA acetyltransferase 1 (ACAT1) gene. A German T2deficient patient that developed a severe ketoacidotic episode at the age of 11 months, was revealed to be a compound heterozygote of a previously reported null mutation, c.472A>G (p.N158D) and a novel mutation, c.949G>A (p.D317N), in ACAT1. The c.949G>A mutation was suspected to cause aberrant splicing as it is located within an exonic splicing enhancer sequence (c. 947CTGACGC) that is a potential binding site for serine/argininerich splicing factor 1. A mutation in this sequence, c.951C>T, results in exon 10 skipping. A minigene construct was synthesized that included exon 9truncated intron 9exon 10truncated intron 10exon 11, and the splicing of this minigene revealed that the c.949G>A mutant construct caused exon 10 skipping in a proportion of the transcripts. Furthermore, additional substitution of G for C at the first nucleotide of exon 10 (c.941G>C) abolished the effect of the c.949G>A mutation. Transient expression analysis of the c.949G>A mutant cDNA revealed no residual T2 activity in the mutated D317N enzyme. Therefore, c.949G>A (D317N) is a pathogenic missense mutation, and diminishes the effect of an exonic splicing enhancer and causes exon 10 skipping. The present study demonstrates that a missense mutation, or even a synonymous substitution, may disrupt enzyme function by interference with splicing.