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Ophthalmology Ophthalmology

Important Tools for Use by Pediatric Endocrinologists in the Assessment of Short Stature.

In Journal of clinical research in pediatric endocrinology

Assessment and management of children with growth failure has improved greatly over recent years. However, there remains a strong potential for further improvements by use of novel digital techniques. A panel of experts discussed developments in digitalization of a number of important tools used by pediatric endocrinologists at the third 360° European Meeting on Growth and Endocrine Disorders, funded by Merck KGaA, Germany, and this review is based on those discussions. It was reported that electronic monitoring and new algorithms have been devised that are providing more sensitive referral for short stature, and computer programs have improved ways in which diagnoses are coded for use by various groups such as healthcare providers and government health systems. Innovative cranial imaging techniques have been devised that are considered safer than using gadolinium contrast agents and are also more sensitive and accurate. Deep-learning neural networks are changing the way that bone age and bone health are assessed, which are more objective than standard methodologies. Models for prediction of growth response to GH treatment are being improved by applying novel artificial intelligence methods that can identify non-linear and linear factors that relate to response, providing more accurate predictions. Determination and interpretation of IGF-I levels are becoming more standardized and consistent, for evaluation across different patient groups, and computer-learning models indicate that baseline IGF-I SDS is among the most important indicators of GH therapy response. While physicians involved in child growth and treatment of disorders resulting in growth failure need to be aware of and keep abreast of these latest developments, treatment decisions and management should continue to be based on clinical decisions. New digital technologies and advancements in the field should be aimed at making clinical decisions more efficient and consider patient-centered approaches. Keywords: Short stature, height monitoring, bone age, cranial imaging, growth hormone treatment, prediction models.

Labarta José I, Ranke Michael B, Maghnie Mohamad, Martin David, Guazzarotti Laura, Pfäffle Roland, Koledova Ekaterina, Wit Jan M

2020-Oct-02

General General

Evaluation of Country Dietary Habits Using Machine Learning Techniques in Relation to Deaths from COVID-19.

In Healthcare (Basel, Switzerland)

COVID-19 disease has affected almost every country in the world. The large number of infected people and the different mortality rates between countries has given rise to many hypotheses about the key points that make the virus so lethal in some places. In this study, the eating habits of 170 countries were evaluated in order to find correlations between these habits and mortality rates caused by COVID-19 using machine learning techniques that group the countries together according to the different distribution of fat, energy, and protein across 23 different types of food, as well as the amount ingested in kilograms. Results shown how obesity and the high consumption of fats appear in countries with the highest death rates, whereas countries with a lower rate have a higher level of cereal consumption accompanied by a lower total average intake of kilocalories.

García-Ordás María Teresa, Arias Natalia, Benavides Carmen, García-Olalla Oscar, Benítez-Andrades José Alberto

2020-Sep-29

COVID-19, K-Means, KCal, countries, deaths, fat, machine learning, protein

General General

A Novel Strategy for the Development of Vaccines for SARS-CoV-2 (COVID-19) and Other Viruses Using AI and Viral Shell Disorder.

In Journal of proteome research

A model that predicts levels of coronavirus (CoV) respiratory and fecal-oral transmission potentials based on the shell disorder has been built using neural network (artificial intelligence, AI) analysis of the percentage of disorder (PID) in the nucleocapsid, N, and membrane, M, proteins of the inner and outer viral shells, respectively. Using primarily the PID of N, SARS-CoV-2 is grouped as having intermediate levels of both respiratory and fecal-oral transmission potentials. Related studies, using similar methodologies, have found strong positive correlations between virulence and inner shell disorder among numerous viruses, including Nipah, Ebola, and Dengue viruses. There is some evidence that this is also true for SARS-CoV-2 and SARS-CoV, which have N PIDs of 48% and 50%, and case-fatality rates of 0.5-5% and 10.9%, respectively. The underlying relationship between virulence and respiratory potentials has to do with the viral loads of vital organs and body fluids, respectively. Viruses can spread by respiratory means only if the viral loads in saliva and mucus exceed certain minima. Similarly, a patient is likelier to die when the viral load overwhelms vital organs. Greater disorder in inner shell proteins has been known to play important roles in the rapid replication of viruses by enhancing the efficiency pertaining to protein-protein/DNA/RNA/lipid bindings. This paper suggests a novel strategy in attenuating viruses involving comparison of disorder patterns of inner shells (N) of related viruses to identify residues and regions that could be ideal for mutation. The M protein of SARS-CoV-2 has one of the lowest M PID values (6%) in its family, and therefore, this virus has one of the hardest outer shells, which makes it resistant to antimicrobial enzymes in body fluid. While this is likely responsible for its greater contagiousness, the risks of creating an attenuated virus with a more disordered M are discussed.

Goh Gerard Kian-Meng, Dunker A Keith, Foster James A, Uversky Vladimir N

2020-Oct-02

Nipah, antibody, attenuate, coronavirus, covid, disorder, ebola, function, shell, immune, intrinsic, matrix, nucleocapsid, nucleoprotein, protein, shell, structure, vaccine, viral, virulence

General General

New forest biomass carbon stock estimates in Northeast Asia based on multisource data.

In Global change biology

Forests play an important role in both regional and global C cycles. However, the spatial patterns of biomass C density and underlying factors in Northeast Asia remain unclear. Here, we characterized spatial patterns and important drivers of biomass C density for Northeast Asia, based on multisource data from in-situ forest inventories, as well as remote sensing, bioclimatic, topographic, and human footprint data. We derived, for the first time, high-resolution (1 km × 1 km) maps of the current and future forest biomass C density for this region. Based on these maps, we estimated that current biomass C stock in northeastern China, the Democratic People's Republic of Korea, and Republic of Korea to be 2.53, 0.40, and 0.35 Pg C, respectively. Biomass C stock in Northeast Asia has increased by 20-46% over the past 20 years, of which 40-76% was contributed by planted forests. We estimated the biomass C stock in 2080 to be 6.13 and 6.50 Pg C under RCP4.5 and RCP8.5 scenarios, respectively, which exceeded the present region-wide C stock value by 2.85-3.22 Pg C, and were 8-14% higher than the baseline C stock value (5.70 Pg C). The spatial patterns of biomass C densities were found to vary greatly across the Northeast Asia, and largely decided by mean diameter at breast height, dominant height, elevation, and human footprint. Our results suggest that reforestation and forest conservation in Northeast Asia have effectively expanded the size of the carbon sink in the region, and sustainable forest management practices such as precision forestry and close forest monitoring for fire and insect outbreaks would be important to maintain and improve this critical carbon sink for Northeast Asia.

Luo Weixue, Kim Hyun Seok, Zhao Xiuhai, Ryu Daun, Jung Ilbin, Cho Hyunkook, Harris Nancy, Ghosh Sayon, Zhang Chunyu, Liang Jingjing

2020-Oct-02

Carbon stock; Carbon density, Climate change, Korean Peninsula, Machine learning, Spatial variations, human influences, northeastern China

General General

A Novel Strategy for the Development of Vaccines for SARS-CoV-2 (COVID-19) and Other Viruses Using AI and Viral Shell Disorder.

In Journal of proteome research

A model that predicts levels of coronavirus (CoV) respiratory and fecal-oral transmission potentials based on the shell disorder has been built using neural network (artificial intelligence, AI) analysis of the percentage of disorder (PID) in the nucleocapsid, N, and membrane, M, proteins of the inner and outer viral shells, respectively. Using primarily the PID of N, SARS-CoV-2 is grouped as having intermediate levels of both respiratory and fecal-oral transmission potentials. Related studies, using similar methodologies, have found strong positive correlations between virulence and inner shell disorder among numerous viruses, including Nipah, Ebola, and Dengue viruses. There is some evidence that this is also true for SARS-CoV-2 and SARS-CoV, which have N PIDs of 48% and 50%, and case-fatality rates of 0.5-5% and 10.9%, respectively. The underlying relationship between virulence and respiratory potentials has to do with the viral loads of vital organs and body fluids, respectively. Viruses can spread by respiratory means only if the viral loads in saliva and mucus exceed certain minima. Similarly, a patient is likelier to die when the viral load overwhelms vital organs. Greater disorder in inner shell proteins has been known to play important roles in the rapid replication of viruses by enhancing the efficiency pertaining to protein-protein/DNA/RNA/lipid bindings. This paper suggests a novel strategy in attenuating viruses involving comparison of disorder patterns of inner shells (N) of related viruses to identify residues and regions that could be ideal for mutation. The M protein of SARS-CoV-2 has one of the lowest M PID values (6%) in its family, and therefore, this virus has one of the hardest outer shells, which makes it resistant to antimicrobial enzymes in body fluid. While this is likely responsible for its greater contagiousness, the risks of creating an attenuated virus with a more disordered M are discussed.

Goh Gerard Kian-Meng, Dunker A Keith, Foster James A, Uversky Vladimir N

2020-Oct-02

Nipah, antibody, attenuate, coronavirus, covid, disorder, ebola, function, shell, immune, intrinsic, matrix, nucleocapsid, nucleoprotein, protein, shell, structure, vaccine, viral, virulence

General General

Application of machine learning algorithms to identify cryptic reproductive habitats using diverse information sources.

In Oecologia

Information on ecological systems often comes from diverse sources with varied levels of complexity, bias, and uncertainty. Accordingly, analytical techniques continue to evolve that address these challenges to reveal the characteristics of ecological systems and inform conservation actions. We applied multiple statistical learning algorithms (i.e., machine learning) with a range of information sources including fish tracking data, environmental data, and visual surveys to identify potential spawning aggregation sites for a marine fish species, permit (Trachinotus falcatus), in the Florida Keys. Recognizing the potential complementarity and some level of uncertainty in each information source, we applied supervised (classic and conditional random forests; RF) and unsupervised (fuzzy k-means; FKM) algorithms. The two RF models had similar predictive performance, but generated different predictor variable importance structures and spawning site predictions. Unsupervised clustering using FKM identified unique site groupings that were similar to the likely spawning sites identified with RF. The conservation of aggregate spawning fish species depends heavily on the protection of key spawning sites; many of these potential sites were identified here for permit in the Florida Keys, which consisted of relatively deep-water natural and artificial reefs with high mean permit residency periods. The application of multiple machine learning algorithms enabled the integration of diverse information sources to develop models of an ecological system. Faced with increasingly complex and diverse data sources, ecologists, and conservation practitioners should find increasing value in machine learning algorithms, which we discuss here and provide resources to increase accessibility.

Brownscombe Jacob W, Griffin Lucas P, Morley Danielle, Acosta Alejandro, Hunt John, Lowerre-Barbieri Susan K, Adams Aaron J, Danylchuk Andy J, Cooke Steven J

2020-Oct-01

Conservation, Ecology, Machine learning, Marine biology, Spawning aggregations