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In clinical settings, significant resources are spent on data collection and monitoring patients' health parameters to improve decision-making and provide better care. With increased digitization, the healthcare sector is shifting towards implementing digital technologies for data management and in administration. New technologies offer better treatment opportunities and streamline clinical workflow, but the complexity can cause ineffectiveness, frustration, and errors. To address this, we believe digital solutions alone are not sufficient. Therefore, we take a human-centred design approach for AI development, and apply systems engineering methods to identify system leverage points. We demonstrate how automation enables monitoring clinical parameters, using existing non-intrusive sensor technology, resulting in more resources toward patient care. Furthermore, we provide a framework on digitization of clinical data for integration with data management.
Background:
Childhood and adolescence are critical stages of life for mental health and well-being. Schools are a key setting for mental health promotion and illness prevention. One in five children and adolescents have a mental disorder, about half of mental disorders beginning before the age of 14. Beneficial and explainable artificial intelligence can replace current paper- based and online approaches to school mental health surveys. This can enhance data acquisition, interoperability, data driven analysis, trust and compliance. This paper presents a model for using chatbots for non-obtrusive data collection and supervised machine learning models for data analysis; and discusses ethical considerations pertaining to the use of these models.
Methods:
For data acquisition, the proposed model uses chatbots which interact with students. The conversation log acts as the source of raw data for the machine learning. Pre-processing of the data is automated by filtering for keywords and phrases.
Existing survey results, obtained through current paper-based data collection methods, are evaluated by domain experts (health professionals). These can be used to create a test dataset to validate the machine learning models. Supervised learning
can then be deployed to classify specific behaviour and mental health patterns.
Results:
We present a model that can be used to improve upon current paper-based data collection and manual data analysis methods. An open-source GitHub repository contains necessary tools and components of this model. Privacy is respected through
rigorous observance of confidentiality and data protection requirements. Critical reflection on these ethics and law aspects is included in the project.
Conclusions:
This model strengthens mental health surveillance in schools. The same tools and components could be applied to other public health data. Future extensions of this model could also incorporate unsupervised learning to find clusters and patterns
of unknown effects.
Type 2 diabetes (T2D) is a complex metabolic disease regulated by an interaction of genetic predisposition and environmental factors. To understand the genetic contribution in the development of diabetes, mice varying in their disease susceptibility were crossed with the obese and diabetes-prone New Zealand obese (NZO) mouse. Subsequent whole-genome sequence scans revealed one major quantitative trait loci (QTL),Nidd/DBAon chromosome 4, linked to elevated blood glucose and reduced plasma insulin and low levels of pancreatic insulin. Phenotypical characterization of congenic mice carrying 13.6 Mbp of the critical fragment of DBA mice displayed severe hyperglycemia and impaired glucose clearance at week 10, decreased glucose response in week 13, and loss of beta-cells and pancreatic insulin in week 16. To identify the responsible gene variant(s), further congenic mice were generated and phenotyped, which resulted in a fragment of 3.3 Mbp that was sufficient to induce hyperglycemia. By combining transcriptome analysis and haplotype mapping, the number of putative responsible variant(s) was narrowed from initial 284 to 18 genes, including gene models and non-coding RNAs. Consideration of haplotype blocks reduced the number of candidate genes to four (Kti12,Osbpl9,Ttc39a, andCalr4) as potential T2D candidates as they display a differential expression in pancreatic islets and/or sequence variation. In conclusion, the integration of comparative analysis of multiple inbred populations such as haplotype mapping, transcriptomics, and sequence data substantially improved the mapping resolution of the diabetes QTLNidd/DBA. Future studies are necessary to understand the exact role of the different candidates in beta-cell function and their contribution in maintaining glycemic control.
Hydrometric networks play a vital role in providing information for decision-making in water resource management. They should be set up optimally to provide as much information as possible that is as accurate as possible and, at the same time, be cost-effective. Although the design of hydrometric networks is a well-identified problem in hydrometeorology and has received considerable attention, there is still scope for further advancement. In this study, we use complex network analysis, defined as a collection of nodes interconnected by links, to propose a new measure that identifies critical nodes of station networks. The approach can support the design and redesign of hydrometric station networks. The science of complex networks is a relatively young field and has gained significant momentum over the last few years in different areas such as brain networks, social networks, technological networks, or climate networks. The identification of influential nodes in complex networks is an important field of research. We propose a new node-ranking measure – the weighted degree–betweenness (WDB) measure – to evaluate the importance of nodes in a network. It is compared to previously proposed measures used on synthetic sample networks and then applied to a real-world rain gauge network comprising 1229 stations across Germany to demonstrate its applicability. The proposed measure is evaluated using the decline rate of the network efficiency and the kriging error. The results suggest that WDB effectively quantifies the importance of rain gauges, although the benefits of the method need to be investigated in more detail.
Hydrometric networks play a vital role in providing information for decision-making in water resource management. They should be set up optimally to provide as much information as possible that is as accurate as possible and, at the same time, be cost-effective. Although the design of hydrometric networks is a well-identified problem in hydrometeorology and has received considerable attention, there is still scope for further advancement. In this study, we use complex network analysis, defined as a collection of nodes interconnected by links, to propose a new measure that identifies critical nodes of station networks. The approach can support the design and redesign of hydrometric station networks. The science of complex networks is a relatively young field and has gained significant momentum over the last few years in different areas such as brain networks, social networks, technological networks, or climate networks. The identification of influential nodes in complex networks is an important field of research. We propose a new node-ranking measure – the weighted degree–betweenness (WDB) measure – to evaluate the importance of nodes in a network. It is compared to previously proposed measures used on synthetic sample networks and then applied to a real-world rain gauge network comprising 1229 stations across Germany to demonstrate its applicability. The proposed measure is evaluated using the decline rate of the network efficiency and the kriging error. The results suggest that WDB effectively quantifies the importance of rain gauges, although the benefits of the method need to be investigated in more detail.
This opinion article describes recent approaches to use the "biorefinery" concept to lower the carbon footprint of typical mass polymers, by replacing parts of the fossil monomers with similar or even the same monomer made from regrowing dendritic biomass. Herein, the new and green catalytic synthetic routes are for lactic acid (LA), isosorbide (IS), 2,5-furandicarboxylic acid (FDCA), and p-xylene (pXL). Furthermore, the synthesis of two unconventional lignocellulosic biomass derivable monomers, i.e., alpha-methylene-gamma-valerolactone (MeGVL) and levoglucosenol (LG), are presented. All those have the potential to enter in a cost-effective way, also the mass market and thereby recover lost areas for polymer materials. The differences of catalytic unit operations of the biorefinery are also discussed and the challenges that must be addressed along the synthesis path of each monomers.
This opinion article describes recent approaches to use the "biorefinery" concept to lower the carbon footprint of typical mass polymers, by replacing parts of the fossil monomers with similar or even the same monomer made from regrowing dendritic biomass. Herein, the new and green catalytic synthetic routes are for lactic acid (LA), isosorbide (IS), 2,5-furandicarboxylic acid (FDCA), and p-xylene (pXL). Furthermore, the synthesis of two unconventional lignocellulosic biomass derivable monomers, i.e., alpha-methylene-gamma-valerolactone (MeGVL) and levoglucosenol (LG), are presented. All those have the potential to enter in a cost-effective way, also the mass market and thereby recover lost areas for polymer materials. The differences of catalytic unit operations of the biorefinery are also discussed and the challenges that must be addressed along the synthesis path of each monomers.
Depending on the biochemical and biotechnical approach, the aim of this work was to understand the mechanism of protein-glucan interactions in regulation and control of starch degradation. Although starch degradation starts with the phosphorylation process, the mechanisms by which this process is controlling and adjusting starch degradation are not yet fully understood. Phosphorylation is a major process performed by the two dikinases enzymes α-glucan, water dikinase (GWD) and phosphoglucan water dikinase (PWD). GWD and PWD enzymes phosphorylate the starch granule surface; thereby stimulate starch degradation by hydrolytic enzymes. Despite these important roles for GWD and PWD, so far the biochemical processes by which these enzymes are able to regulate and adjust the rate of phosphate incorporation into starch during the degradation process haven‘t been understood. Recently, some proteins were found associated with the starch granule. Two of these proteins are named Early Starvation Protein 1 (ESV1) and its homologue Like-Early Starvation Protein 1 (LESV). It was supposed that both are involved in the control of starch degradation, but their function has not been clearly known until now. To understand how ESV1 and LESV-glucan interactions are regulated and affect the starch breakdown, it was analyzed the influence of ESV1 and LESV proteins on the phosphorylating enzyme GWD and PWD and hydrolysing enzymes ISA, BAM, and AMY. However, the analysis determined the location of LESV and ESV1 in the chloroplast stroma of Arabidopsis. Mass spectrometry data predicted ESV1and LESV proteins as a product of the At1g42430 and At3g55760 genes with a predicted mass of ~50 kDa and ~66 kDa, respectively. The ChloroP program predicted that ESV1 lacks the chloroplast transit peptide, but it predicted the first 56 amino acids N-terminal region as a chloroplast transit peptide for LESV. Usually, the transit peptide is processed during transport of the proteins into plastids. Given that this processing is critical, two forms of each ESV1 and LESV were generated and purified, a full-length form and a truncated form that lacks the transit peptide, namely, (ESV1and tESV1) and (LESV and tLESV), respectively. Both protein forms were included in the analysis assays, but only slight differences in glucan binding and protein action between ESV1 and tESV1 were observed, while no differences in the glucan binding and effect on the GWD and PWD action were observed between LESV and tLESV. The results revealed that the presence of the N-terminal is not massively altering the action of ESV1 or LESV. Therefore, it was only used the ESV1 and tLESV forms data to explain the function of both proteins.
However, the analysis of the results revealed that LESV and ESV1 proteins bind strongly at the starch granule surface. Furthermore, not all of both proteins were released after their incubation with starches after washing the granules with 2% [w/v] SDS indicates to their binding to the deeper layers of the granule surface. Supporting of this finding comes after the binding of both proteins to starches after removing the free glucans chains from the surface by the action of ISA and BAM. Although both proteins are capable of binding to the starch structure, only LESV showed binding to amylose, while in ESV1, binding was not observed. The alteration of glucan structures at the starch granule surface is essential for the incorporation of phosphate into starch granule while the phosphorylation of starch by GWD and PWD increased after removing the free glucan chains by ISA. Furthermore, PWD showed the possibility of starch phosphorylation without prephosphorylation by GWD.
Biochemical studies on protein-glucan interactions between LESV or ESV1 with different types of starch showed a potentially important mechanism of regulating and adjusting the phosphorylation process while the binding of LESV and ESV1 leads to altering the glucan structures of starches, hence, render the effect of the action of dikinases enzymes (GWD and PWD) more able to control the rate of starch degradation. Despite the presence of ESV1 which revealed an antagonistic effect on the PWD action as the PWD action was decreased without prephosphorylation by GWD and increased after prephosphorylation by GWD (Chapter 4), PWD showed a significant reduction in its action with or without prephosphorylation by GWD in the presence of ESV1 whether separately or together with LESV (Chapter 5). However, the presence of LESV and ESV1 together revealed the same effect compared to the effect of each one alone on the phosphorylation process, therefore it is difficult to distinguish the specific function between them. However, non-interactions were detected between LESV and ESV1 or between each of them with GWD and PWD or between GWD and PWD indicating the independent work for these proteins. It was also observed that the alteration of the starch structure by LESV and ESV1 plays a role in adjusting starch degradation rates not only by affecting the dikinases but also by affecting some of the hydrolysing enzymes since it was found that the presence of LESV and ESV1leads to the reduction of the action of BAM, but does not abolish it.