A global compilation of coccolithophore calcification rates

The biological production of calcium carbonate (CaCO3), a process termed calcification, is a key term in the marine carbon cycle. A major planktonic group responsible for such pelagic CaCO3 production (CP) is the coccolithophores, single-celled haptophytes that inhabit the euphotic zone of the ocean. Satellite-based estimates of areal CP are limited to surface waters and open-ocean areas, with current algorithms utilising the unique optical properties of the cosmopolitan bloom-forming species Emiliania huxleyi, whereas little understanding of deep-water ecology, optical properties or environmental responses by species other than E. huxleyi is currently available to parameterise algorithms or models. To aid future areal estimations and validate future modelling efforts we have constructed a database of 2765 CP measurements, the majority of which were measured using 12 to 24 h incorporation of radioactive carbon (14C) into acid-labile inorganic carbon (CaCO3). We present data collated from over 30 studies covering the period from 1991 to 2015, sampling the Atlantic, Pacific, Indian, Arctic and Southern oceans. Globally, CP in surface waters ( < 20 m) ranged from 0.01 to 8398 µmol C m−3 d−1 (with a geometric mean of 16.1 µmol C m−3 d−1). An integral value for the upper euphotic zone (herein surface to the depth of 1 % surface irradiance) ranged from  < 0.1 to 6 mmol C m−2 d−1 (geometric mean 1.19 mmol C m−2 d−1). The full database is available for download from PANGAEA at https://doi.org/10.1594/PANGAEA.888182

Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019

Background: In an era of shifting global agendas and expanded emphasis on non-communicable diseases and injuries along with communicable diseases, sound evidence on trends by cause at the national level is essential. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) provides a systematic scientific assessment of published, publicly available, and contributed data on incidence, prevalence, and mortality for a mutually exclusive and collectively exhaustive list of diseases and injuries. Methods: GBD estimates incidence, prevalence, mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) due to 369 diseases and injuries, for two sexes, and for 204 countries and territories. Input data were extracted from censuses, household surveys, civil registration and vital statistics, disease registries, health service use, air pollution monitors, satellite imaging, disease notifications, and other sources. Cause-specific death rates and cause fractions were calculated using the Cause of Death Ensemble model and spatiotemporal Gaussian process regression. Cause-specific deaths were adjusted to match the total all-cause deaths calculated as part of the GBD population, fertility, and mortality estimates. Deaths were multiplied by standard life expectancy at each age to calculate YLLs. A Bayesian meta-regression modelling tool, DisMod-MR 2.1, was used to ensure consistency between incidence, prevalence, remission, excess mortality, and cause-specific mortality for most causes. Prevalence estimates were multiplied by disability weights for mutually exclusive sequelae of diseases and injuries to calculate YLDs. We considered results in the context of the Socio-demographic Index (SDI), a composite indicator of income per capita, years of schooling, and fertility rate in females younger than 25 years. Uncertainty intervals (UIs) were generated for every metric using the 25th and 975th ordered 1000 draw values of the posterior distribution. Findings: Global health has steadily improved over the past 30 years as measured by age-standardised DALY rates. After taking into account population growth and ageing, the absolute number of DALYs has remained stable. Since 2010, the pace of decline in global age-standardised DALY rates has accelerated in age groups younger than 50 years compared with the 1990–2010 time period, with the greatest annualised rate of decline occurring in the 0–9-year age group. Six infectious diseases were among the top ten causes of DALYs in children younger than 10 years in 2019: lower respiratory infections (ranked second), diarrhoeal diseases (third), malaria (fifth), meningitis (sixth), whooping cough (ninth), and sexually transmitted infections (which, in this age group, is fully accounted for by congenital syphilis; ranked tenth). In adolescents aged 10–24 years, three injury causes were among the top causes of DALYs: road injuries (ranked first), self-harm (third), and interpersonal violence (fifth). Five of the causes that were in the top ten for ages 10–24 years were also in the top ten in the 25–49-year age group: road injuries (ranked first), HIV/AIDS (second), low back pain (fourth), headache disorders (fifth), and depressive disorders (sixth). In 2019, ischaemic heart disease and stroke were the top-ranked causes of DALYs in both the 50–74-year and 75-years-and-older age groups. Since 1990, there has been a marked shift towards a greater proportion of burden due to YLDs from non-communicable diseases and injuries. In 2019, there were 11 countries where non-communicable disease and injury YLDs constituted more than half of all disease burden. Decreases in age-standardised DALY rates have accelerated over the past decade in countries at the lower end of the SDI range, while improvements have started to stagnate or even reverse in countries with higher SDI. Interpretation: As disability becomes an increasingly large component of disease burden and a larger component of health expenditure, greater research and developm nt investment is needed to identify new, more effective intervention strategies. With a rapidly ageing global population, the demands on health services to deal with disabling outcomes, which increase with age, will require policy makers to anticipate these changes. The mix of universal and more geographically specific influences on health reinforces the need for regular reporting on population health in detail and by underlying cause to help decision makers to identify success stories of disease control to emulate, as well as opportunities to improve. Funding: Bill & Melinda Gates Foundation. © 2020 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 licens

Stratospheric Injection of Brominated Very Short‐Lived Substances: Aircraft Observations in the Western Pacific and Representation in Global Models

We quantify the stratospheric injection of brominated very short‐lived substances (VSLS) based on aircraft observations acquired in winter 2014 above the Tropical Western Pacific during the CONvective TRansport of Active Species in the Tropics (CONTRAST) and the Airborne Tropical TRopopause EXperiment (ATTREX) campaigns. The overall contribution of VSLS to stratospheric bromine was determined to be 5.0 ± 2.1 ppt, in agreement with the 5 ± 3 ppt estimate provided in the 2014 World Meteorological Organization (WMO) Ozone Assessment report (WMO 2014), but with lower uncertainty. Measurements of organic bromine compounds, including VSLS, were analyzed using CFC‐11 as a reference stratospheric tracer. From this analysis, 2.9 ± 0.6 ppt of bromine enters the stratosphere via organic source gas injection of VSLS. This value is two times the mean bromine content of VSLS measured at the tropical tropopause, for regions outside of the Tropical Western Pacific, summarized in WMO 2014. A photochemical box model, constrained to CONTRAST observations, was used to estimate inorganic bromine from measurements of BrO collected by two instruments. The analysis indicates that 2.1 ± 2.1 ppt of bromine enters the stratosphere via inorganic product gas injection. We also examine the representation of brominated VSLS within 14 global models that participated in the Chemistry‐Climate Model Initiative. The representation of stratospheric bromine in these models generally lies within the range of our empirical estimate. Models that include explicit representations of VSLS compare better with bromine observations in the lower stratosphere than models that utilize longer‐lived chemicals as a surrogate for VSLS

Using bioinformatics techniques for gene identification in drug discovery and development

As more and more evidence has become available, the link between gene and emergent disease has been made including cancer, heart disease and parkinsonism. Analyzing the diseases and designing drugs with respect to the gene and protein level obviously help to find the underlying causes of the diseases, and to improve their rate of cure. The development of modern molecular biology, biochemistry, data collection and analysis techniques provides the scientists with a large amount of gene data. To draw a link between genes and their relation to disease outcomes and drug discovery is a big challenge: How to analyze large datasets and extract useful knowledge? Combining bioinformatics with drug discovery is a promising method to tackle this issue. Most techniques of bioinformatics are used in the first two phases of drug discovery to extract interesting information and find important genes and/or proteins for speeding the process of drug discovery, enhancing the accuracy of analysis and reducing the cost. Gene identification is a very fundamental and important technique among them. In this paper, we have reviewed gene identification algorithms and discussed their usage, relationships and challenges in drug discovery and development.<br /

Bioinformatics

Bioinformatics can be broadly described as the application of information technology to the field of molecular biology. It aims to solve practical problems arising from the management and analysis of biological data, such as natural products with pharmacological or biological activities. In pharmacology, natural products have provided the inspiration for most of the active ingredients in medicines and have been the most productive source of leads for new drugs. For example, around 80% of medicinal products up to 1996 were either directly derived from naturally occurring compounds or were inspired by a natural product, and more recent analyses confirm the continuing importance of natural products for drug discovery. In an extensive review of new drugs introduced between 1981 and 2002, 28% of the 868 new chemical entities were natural products or derived from natural products, with another 24% created around a pharmacophore from a natural product.3 In addition to launched products, at least 70 natural productrelated compounds were in clinical trials in 20044 and exploration of the bioactivity of natural products continues to provide novel chemical scaffolds for further drug inventions. New approaches to source novel compounds from untapped areas of biodiversity coupled with the technical advances in analytical techniques (such as microcoil NMR and linked LC–MS–NMR) have removed many of the difficulties when using natural products in screening campaigns. As the 'chemical space' occupied by natural products is both more varied and more drug-like than that of combinatorial chemical collections, synthetic and biosynthetic methods are being developed to produce screening libraries of natural product-like compounds. A renaissance of drug discovery inspired by natural products can be predicted

Ensemble Fuzzy Feature Selection Based on Relevancy, Redundancy, and Dependency Criteria

The main challenge of classification systems is the processing of undesirable data. Filter-based feature selection is an effective solution to improve the performance of classification systems by selecting the significant features and discarding the undesirable ones. The success of this solution depends on the extracted information from data characteristics. For this reason, many research theories have been introduced to extract different feature relations. Unfortunately, traditional feature selection methods estimate the feature significance based on either individually or dependency discriminative ability. This paper introduces a new ensemble feature selection, called fuzzy feature selection based on relevancy, redundancy, and dependency (FFS-RRD). The proposed method considers both individually and dependency discriminative ability to extract all possible feature relations. To evaluate the proposed method, experimental comparisons are conducted with eight state-of-the-art and conventional feature selection methods. Based on 13 benchmark datasets, the experimental results over four well-known classifiers show the outperformance of our proposed method in terms of classification performance and stability

Candidate working set strategy based SMO algorithm in support vector machine

Sequential minimal optimization (SMO) is quite an efficient algorithm for training the support vector machine. The most important step of this algorithm is the selection of the working set, which greatly affects the training speed. The feasible direction strategy for the working set selection can decrease the objective function, however, may augment to the total calculation for selecting the working set in each of the iteration. In this paper, a new candidate working set (CWS) Strategy is presented considering the cost on the working set selection and cache performance. This new strategy can select several greatest violating samples from Cache as the iterative working sets for the next several optimizing steps, which can improve the efficiency of the kernel cache usage and reduce the computational cost related to the working set selection. The results of the theory analysis and experiments demonstrate that the proposed method can reduce the training time, especially on the large-scale datasets.<br /

Comorbidities and predictors of health-related quality of life in Dravet syndrome: a ten-year prospective follow- up study

Objective: Dravet Syndrome (DS) is a severe developmental and epileptic encephalopathy, leading to reduced health related quality of life (HRQOL). Prospective outcome data on HRQOL are sparse and this study investigated long-term predictors of HRQOL in DS. Methods: 113 families of SCN1A-positive DS patients recruited as part of our 2010 study were contacted at 10-year follow-up, of which 68 (60%) responded. The mortality was 5.8%. Detailed clinical and demographic information was available for each patient. HRQOL was evaluated with two epilepsy-specific instruments, the Impact of Pediatric Epilepsy Scale (IPES) and the Epilepsy &amp; Learning Disabilities Quality of Life Questionnaire (ELDQOL); a generic HRQOL instrument, the Pediatric Quality of Life Inventory (PedsQL); and a behavioral screening tool, the Strength and Difficulties Questionnaire (SDQ). Results: Twenty-eight patients were aged 10-15 years (0-5 years at baseline) and 40 were aged ≥16 years (≥6 years at baseline). Patients 0-5 years old at baseline showed significant decline in mean scores on the PedsQL total score (p=0.004), physical score (p&lt;0.001), cognitive score (p=0.011), social score (p=0.003), and eating score (p=0.030) at follow-up. On multivariate regression, lower baseline and follow-up HRQOL for the whole cohort were associated with worse epilepsy severity and a high SDQ total score (R2=33% and 18% respectively). In the younger patient group, younger age at first seizure and increased severity of epilepsy were associated with a lower baseline HRQOL (R2=35%). In the older age group, worse epilepsy severity (F=6.40, p=0.016, R2=14%) and the use of sodium-channel blockers were independently associated with a lower HRQOL at 10-year follow-up (F=4.13, p=0.05, R2=8%). Significance: This 10-year prospective follow-up study highlights the significant HRQOL-associated cognitive, social and physical decline particularly affecting younger patients with DS. Sodium channel blocker use appears to negatively impact long-term HRQOL highlighting the importance of early diagnosis and disease specific management in DS