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Dopamine decline is suggested to underlie aging-related cognitive decline, but longitudinal examinations of this link are currently missing. We analyzed 5-year longitudinal data for a sample of healthy, older adults (baseline: n = 181, age: 64–68 years; 5-year follow-up: n = 129) who underwent positron emission tomography with 11C-raclopride to assess dopamine D2-like receptor (DRD2) availability, magnetic resonance imaging to evaluate structural brain measures, and cognitive tests. Health, lifestyle, and genetic data were also collected. A data-driven approach (k-means cluster analysis) identified groups that differed maximally in DRD2 decline rates in age-sensitive brain regions. One group (n = 47) had DRD2 decline exclusively in the caudate and no cognitive decline. A second group (n = 72) had more wide-ranged DRD2 decline in putamen and nucleus accumbens and also in extrastriatal regions. The latter group showed significant 5-year working memory decline that correlated with putamen DRD2 decline, along with higher dementia and cardiovascular risk and a faster biological pace of aging. Taken together, for individuals with more extensive DRD2 decline, dopamine decline is associated with memory decline in aging.

Aims/hypothesis: People with type 2 diabetes are heterogeneous in their disease trajectory, with some progressing more quickly to insulin initiation than others. Although classical biomarkers such as age, HbA1c and diabetes duration are associated with glycaemic progression, it is unclear how well such variables predict insulin initiation or requirement and whether newly identified markers have added predictive value. Methods: In two prospective cohort studies as part of IMI-RHAPSODY, we investigated whether clinical variables and three types of molecular markers (metabolites, lipids, proteins) can predict time to insulin requirement using different machine learning approaches (lasso, ridge, GRridge, random forest). Clinical variables included age, sex, HbA1c, HDL-cholesterol and C-peptide. Models were run with unpenalised clinical variables (i.e. always included in the model without weights) or penalised clinical variables, or without clinical variables. Model development was performed in one cohort and the model was applied in a second cohort. Model performance was evaluated using Harrel’s C statistic. Results: Of the 585 individuals from the Hoorn Diabetes Care System (DCS) cohort, 69 required insulin during follow-up (1.0–11.4 years); of the 571 individuals in the Genetics of Diabetes Audit and Research in Tayside Scotland (GoDARTS) cohort, 175 required insulin during follow-up (0.3–11.8 years). Overall, the clinical variables and proteins were selected in the different models most often, followed by the metabolites. The most frequently selected clinical variables were HbA1c (18 of the 36 models, 50%), age (15 models, 41.2%) and C-peptide (15 models, 41.2%). Base models (age, sex, BMI, HbA1c) including only clinical variables performed moderately in both the DCS discovery cohort (C statistic 0.71 [95% CI 0.64, 0.79]) and the GoDARTS replication cohort (C 0.71 [95% CI 0.69, 0.75]). A more extensive model including HD, Pattern Recognition and Bioinformatics

Obesity is considered by many as a lifestyle choice rather than a chronic progressive disease. The Innovative Medicines Initiative (IMI) SOPHIA (Stratification of Obesity Phenotypes to Optimize Future Obesity Therapy) project is part of a momentum shift aiming to provide better tools for the stratification of people with obesity according to disease risk and treatment response. One of the challenges to achieving these goals is that many clinical cohorts are siloed, limiting the potential of combined data for biomarker discovery. In SOPHIA, we have addressed this challenge by setting up a federated database building on open-source DataSHIELD technology. The database currently federates 16 cohorts that are accessible via a central gateway. The database is multi-modal, including research studies, clinical trials, and routine health data, and is accessed using the R statistical programming environment where statistical and machine learning analyses can be performed at a distance without any disclosure of patient-level data. We demonstrate the use of the database by providing a proof-of-concept analysis, performing a federated linear model of BMI and systolic blood pressure, pooling all data from 16 studies virtually without any analyst seeing individual patient-level data. This analysis provided similar point estimates compared to a meta-analysis of the 16 individual studies. Our approach provides a benchmark for reproducible, safe federated analyses across multiple study types provided by multiple stakeholders.

Dopamine decline is suggested to underlie aging-related cognitive decline, but longitudinal examinations of this link are currently missing. We analyzed 5-year longitudinal data for a sample of healthy, older adults (baseline: n = 181, age: 64–68 years; 5-year follow-up: n = 129) who underwent positron emission tomography with 11C-raclopride to assess dopamine D2-like receptor (DRD2) availability, magnetic resonance imaging to evaluate structural brain measures, and cognitive tests. Health, lifestyle, and genetic data were also collected. A data-driven approach (k-means cluster analysis) identified groups that differed maximally in DRD2 decline rates in age-sensitive brain regions. One group (n = 47) had DRD2 decline exclusively in the caudate and no cognitive decline. A second group (n = 72) had more wide-ranged DRD2 decline in putamen and nucleus accumbens and also in extrastriatal regions. The latter group showed significant 5-year working memory decline that correlated with putamen DRD2 decline, along with higher dementia and cardiovascular risk and a faster biological pace of aging. Taken together, for individuals with more extensive DRD2 decline, dopamine decline is associated with memory decline in aging.

In the classical twin design, researchers compare trait resemblance in cohorts of identical and non-identical twins to understand how genetic and environmental factors correlate with resemblance in behaviour and other phenotypes. The twin design is also a valuable tool for studying causality, intergenerational transmission, and gene–environment correlation and interaction. Here we review recent developments in twin studies, recent results from twin studies of new phenotypes and recent insights into twinning. We ask whether the results of existing twin studies are representative of the general population and of global diversity, and we conclude that stronger efforts to increase representativeness are needed. We provide an updated overview of twin concordance and discordance for major diseases and mental disorders, which conveys a crucial message: genetic influences are not as deterministic as many believe. This has important implications for public understanding of genetic risk prediction tools, as the accuracy of genetic predictions can never exceed identical twin concordance rates.

We identify biomarkers for disease progression in three type 2 diabetes cohorts encompassing 2,973 individuals across three molecular classes, metabolites, lipids and proteins. Homocitrulline, isoleucine and 2-aminoadipic acid, eight triacylglycerol species, and lowered sphingomyelin 42:2;2 levels are predictive of faster progression towards insulin requirement. Of ~1,300 proteins examined in two cohorts, levels of GDF15/MIC-1, IL-18Ra, CRELD1, NogoR, FAS, and ENPP7 are associated with faster progression, whilst SMAC/DIABLO, SPOCK1 and HEMK2 predict lower progression rates. In an external replication, proteins and lipids are associated with diabetes incidence and prevalence. NogoR/RTN4R injection improved glucose tolerance in high fat-fed male mice but impaired it in male db/db mice. High NogoR levels led to islet cell apoptosis, and IL-18R antagonised inflammatory IL-18 signalling towards nuclear factor kappa-B in vitro. This comprehensive, multi-disciplinary approach thus identifies biomarkers with potential prognostic utility, provides evidence for possible disease mechanisms, and identifies potential therapeutic avenues to slow diabetes progression.

We identify biomarkers for disease progression in three type 2 diabetes cohorts encompassing 2,973 individuals across three molecular classes, metabolites, lipids and proteins. Homocitrulline, isoleucine and 2-aminoadipic acid, eight triacylglycerol species, and lowered sphingomyelin 42:2;2 levels are predictive of faster progression towards insulin requirement. Of ~1,300 proteins examined in two cohorts, levels of GDF15/MIC-1, IL-18Ra, CRELD1, NogoR, FAS, and ENPP7 are associated with faster progression, whilst SMAC/DIABLO, SPOCK1 and HEMK2 predict lower progression rates. In an external replication, proteins and lipids are associated with diabetes incidence and prevalence. NogoR/RTN4R injection improved glucose tolerance in high fat-fed male mice but impaired it in male db/db mice. High NogoR levels led to islet cell apoptosis, and IL-18R antagonised inflammatory IL-18 signalling towards nuclear factor kappa-B in vitro. This comprehensive, multi-disciplinary approach thus identifies biomarkers with potential prognostic utility, provides evidence for possible disease mechanisms, and identifies potential therapeutic avenues to slow diabetes progression.

Aims/hypothesis: Five clusters based on clinical characteristics have been suggested as diabetes subtypes: one autoimmune and four subtypes of type 2 diabetes. In the current study we replicate and cross-validate these type 2 diabetes clusters in three large cohorts using variables readily measured in the clinic. Methods: In three independent cohorts, in total 15,940 individuals were clustered based on age, BMI, HbA1c, random or fasting C-peptide, and HDL-cholesterol. Clusters were cross-validated against the original clusters based on HOMA measures. In addition, between cohorts, clusters were cross-validated by re-assigning people based on each cohort’s cluster centres. Finally, we compared the time to insulin requirement for each cluster. Results: Five distinct type 2 diabetes clusters were identified and mapped back to the original four All New Diabetics in Scania (ANDIS) clusters. Using C-peptide and HDL-cholesterol instead of HOMA2-B and HOMA2-IR, three of the clusters mapped with high sensitivity (80.6–90.7%) to the previously identified severe insulin-deficient diabetes (SIDD), severe insulin-resistant diabetes (SIRD) and mild obesity-related diabetes (MOD) clusters. The previously described ANDIS mild age-related diabetes (MARD) cluster could be mapped to the two milder groups in our study: one characterised by high HDL-cholesterol (mild diabetes with high HDL-cholesterol [MDH] cluster), and the other not having any extreme characteristic (mild diabetes [MD]). When these two milder groups were combined, they mapped well to the previously labelled MARD cluster (sensitivity 79.1%). In the cross-validation between cohorts, particularly the SIDD and MDH clusters cross-validated well, with sensitivities ranging from 73.3% to 97.1%. SIRD and MD showed a lower sensitivity, ranging from 36.1% to 92.3%, where individuals shifted from SIRD to MD and vice versa. People belonging to the SIDD cluster showed the fastest progression towards insulin requiremen

Aims/hypothesis: Five clusters based on clinical characteristics have been suggested as diabetes subtypes: one autoimmune and four subtypes of type 2 diabetes. In the current study we replicate and cross-validate these type 2 diabetes clusters in three large cohorts using variables readily measured in the clinic. Methods: In three independent cohorts, in total 15,940 individuals were clustered based on age, BMI, HbA1c, random or fasting C-peptide, and HDL-cholesterol. Clusters were cross-validated against the original clusters based on HOMA measures. In addition, between cohorts, clusters were cross-validated by re-assigning people based on each cohort’s cluster centres. Finally, we compared the time to insulin requirement for each cluster. Results: Five distinct type 2 diabetes clusters were identified and mapped back to the original four All New Diabetics in Scania (ANDIS) clusters. Using C-peptide and HDL-cholesterol instead of HOMA2-B and HOMA2-IR, three of the clusters mapped with high sensitivity (80.6–90.7%) to the previously identified severe insulin-deficient diabetes (SIDD), severe insulin-resistant diabetes (SIRD) and mild obesity-related diabetes (MOD) clusters. The previously described ANDIS mild age-related diabetes (MARD) cluster could be mapped to the two milder groups in our study: one characterised by high HDL-cholesterol (mild diabetes with high HDL-cholesterol [MDH] cluster), and the other not having any extreme characteristic (mild diabetes [MD]). When these two milder groups were combined, they mapped well to the previously labelled MARD cluster (sensitivity 79.1%). In the cross-validation between cohorts, particularly the SIDD and MDH clusters cross-validated well, with sensitivities ranging from 73.3% to 97.1%. SIRD and MD showed a lower sensitivity, ranging from 36.1% to 92.3%, where individuals shifted from SIRD to MD and vice versa. People belonging to the SIDD cluster showed the fastest progression towards insulin requiremen

Type 2 diabetes is a multifactorial disease with multiple underlying aetiologies. To address this heterogeneity, investigators of a previous study clustered people with diabetes according to five diabetes subtypes. The aim of the current study is to investigate the etiology of these clusters by comparing their molecular signatures. In three independent cohorts, in total 15,940 individuals were clustered based on five clinical characteristics. In a subset, genetic (N = 12,828), metabolomic (N = 2,945), lipidomic (N = 2,593), and proteomic (N = 1,170) data were obtained in plasma. For each data type, each cluster was compared with the other four clusters as the reference. The insulin-resistant cluster showed the most distinct molecular signature, with higher branched-chain amino acid, diacylglycerol, and triacylglycerol levels and aberrant protein levels in plasma were enriched for proteins in the intracellular PI3K/Akt pathway. The obese cluster showed higher levels of cytokines. The mild diabetes cluster with high HDL showed the most beneficial molecular profile with effects opposite of those seen in the insulin-resistant cluster. This study shows that clustering people with type 2 diabetes can identify underlying molecular mechanisms related to pancreatic islets, liver, and adipose tissue metabolism. This provides novel biological insights into the diverse aetiological processes that would not be evident when type 2 diabetes is viewed as a homogeneous disease.

Type 2 diabetes is a multifactorial disease with multiple underlying aetiologies. To address this heterogeneity, investigators of a previous study clustered people with diabetes according to five diabetes subtypes. The aim of the current study is to investigate the etiology of these clusters by comparing their molecular signatures. In three independent cohorts, in total 15,940 individuals were clustered based on five clinical characteristics. In a subset, genetic (N = 12,828), metabolomic (N = 2,945), lipidomic (N = 2,593), and proteomic (N = 1,170) data were obtained in plasma. For each data type, each cluster was compared with the other four clusters as the reference. The insulin-resistant cluster showed the most distinct molecular signature, with higher branched-chain amino acid, diacylglycerol, and triacylglycerol levels and aberrant protein levels in plasma were enriched for proteins in the intracellular PI3K/Akt pathway. The obese cluster showed higher levels of cytokines. The mild diabetes cluster with high HDL showed the most beneficial molecular profile with effects opposite of those seen in the insulin-resistant cluster. This study shows that clustering people with type 2 diabetes can identify underlying molecular mechanisms related to pancreatic islets, liver, and adipose tissue metabolism. This provides novel biological insights into the diverse aetiological processes that would not be evident when type 2 diabetes is viewed as a homogeneous disease.

Modern commercial antivirus systems increasingly rely on machine learning to keep up with the rampant inflation of new malware. However, it is well-known that machine learning models are vulnerable to adversarial examples (AEs). Previous works have shown that ML malware classifiers are fragile to the white-box adversarial attacks. However, ML models used in commercial antivirus products are usually not available to attackers and only return hard classification labels. Therefore, it is more practical to evaluate the robustness of ML models and real-world AVs in a pure black-box manner. We propose a black-box Reinforcement Learning (RL) based framework to generate AEs for PE malware classifiers and AV engines. It regards the adversarial attack problem as a multi-armed bandit problem, which finds an optimal balance between exploiting the successful patterns and exploring more varieties. Compared to other frameworks, our improvements lie in three points. 1) Limiting the exploration space by modeling the generation process as a stateless process to avoid combination explosions. 2) Due to the critical role of payload in AE generation, we design to reuse the successful payload in modeling. 3) Minimizing the changes on AE samples to correctly assign the rewards in RL learning. It also helps identify the root cause of evasions. As a result, our framework has much higher black-box evasion rates than other off-the-shelf frameworks. Results show it has over 74\%--97\% evasion rate for two state-of-the-art ML detectors and over 32\%--48\% evasion rate for commercial AVs in a pure black-box setting. We also demonstrate that the transferability of adversarial attacks among ML-based classifiers is higher than the attack transferability between purely ML-based and commercial AVs.

Modern commercial antivirus systems increasingly rely on machine learning to keep up with the rampant inflation of new malware. However, it is well-known that machine learning models are vulnerable to adversarial examples (AEs). Previous works have shown that ML malware classifiers are fragile to the white-box adversarial attacks. However, ML models used in commercial antivirus products are usually not available to attackers and only return hard classification labels. Therefore, it is more practical to evaluate the robustness of ML models and real-world AVs in a pure black-box manner. We propose a black-box Reinforcement Learning (RL) based framework to generate AEs for PE malware classifiers and AV engines. It regards the adversarial attack problem as a multi-armed bandit problem, which finds an optimal balance between exploiting the successful patterns and exploring more varieties. Compared to other frameworks, our improvements lie in three points. 1) Limiting the exploration space by modeling the generation process as a stateless process to avoid combination explosions. 2) Due to the critical role of payload in AE generation, we design to reuse the successful payload in modeling. 3) Minimizing the changes on AE samples to correctly assign the rewards in RL learning. It also helps identify the root cause of evasions. As a result, our framework has much higher black-box evasion rates than other off-the-shelf frameworks. Results show it has over 74\%--97\% evasion rate for two state-of-the-art ML detectors and over 32\%--48\% evasion rate for commercial AVs in a pure black-box setting. We also demonstrate that the transferability of adversarial attacks among ML-based classifiers is higher than the attack transferability between purely ML-based and commercial AVs.

MotivationThe potentially low precision associated with the geographic origin of sampled sequences represents an important limitation for spatially explicit (i.e. continuous) phylogeographic inference of fast-evolving pathogens such as RNA viruses. A substantial proportion of publicly available sequences is geo-referenced at broad spatial scale such as the administrative unit of origin, rather than more precise locations (e.g. geographic coordinates). Most frequently, such sequences are either discarded prior to continuous phylogeographic inference or arbitrarily assigned to the geographic coordinates of the centroid of their administrative area of origin for lack of a better alternative.ResultsWe here implement and describe a new approach that allows to incorporate heterogeneous prior sampling probabilities over a geographic area. External data, such as outbreak locations, are used to specify these prior sampling probabilities over a collection of sub-polygons. We apply this new method to the analysis of highly pathogenic avian influenza H5N1 clade data in the Mekong region. Our method allows to properly include, in continuous phylogeographic analyses, H5N1 sequences that are only associated with large administrative areas of origin and assign them with more accurate locations. Finally, we use continuous phylogeographic reconstructions to analyse the dispersal dynamics of different H5N1 clades and investigate the impact of environmental factors on lineage dispersal velocities.Availability and implementationOur new method allowing heterogeneous sampling priors for continuous phylogeographic inference is implemented in the open-source multi-platform software package BEAST 1.10.Supplementary informationSupplementary data are available at Bioinformatics online.

Modern commercial antivirus systems increasingly rely on machine learning to keep up with the rampant inflation of new malware. However, it is well-known that machine learning models are vulnerable to adversarial examples (AEs). Previous works have shown that ML malware classifiers are fragile to the white-box adversarial attacks. However, ML models used in commercial antivirus products are usually not available to attackers and only return hard classification labels. Therefore, it is more practical to evaluate the robustness of ML models and real-world AVs in a pure black-box manner. We propose a black-box Reinforcement Learning (RL) based framework to generate AEs for PE malware classifiers and AV engines. It regards the adversarial attack problem as a multi-armed bandit problem, which finds an optimal balance between exploiting the successful patterns and exploring more varieties. Compared to other frameworks, our improvements lie in three points. 1) Limiting the exploration space by modeling the generation process as a stateless process to avoid combination explosions. 2) Due to the critical role of payload in AE generation, we design to reuse the successful payload in modeling. 3) Minimizing the changes on AE samples to correctly assign the rewards in RL learning. It also helps identify the root cause of evasions. As a result, our framework has much higher black-box evasion rates than other off-the-shelf frameworks. Results show it has over 74\%--97\% evasion rate for two state-of-the-art ML detectors and over 32\%--48\% evasion rate for commercial AVs in a pure black-box setting. We also demonstrate that the transferability of adversarial attacks among ML-based classifiers is higher than the attack transferability between purely ML-based and commercial AVs.

This concept article discusses the possibilities for identifying sedimentary-origin meteorites. The paper concerns (i) the macroscopic candidate for sedimentary meteorite in the epicenter of the 1908 Tunguska catastrophe; (ii) potential parent bodies for sedimentary meteorites; (iii) isotopic heterogeneity of unmixed silicate reservoirs on Mars; (iv) possible terrestrial loss or contamination in the noble gas signatures in new type meteorites that spent time in extreme weather conditions; (v) cosmogenic isotopes and shielding; and (vi) pseudo meteorites. We conclude that the list of candidate parent bodies for sedimentary meteorites includes, but is not limited by the Earth, Mars, Enceladus, Ganymede, Europa, and hypothetical planets that could exist between orbits of Mars and Jupiter in the past. A parent body for extraterrestrial sedimentary rocks on the Earth should be identified based on the entire body of evidence which is not limited solely by tests of oxygen and noble gas isotopes whose signatures may undergo terrestrial contamination and may exhibit significant heterogeneity within the parent bodies. Observed fall of cosmic body, evidence of hypervelocity impact complying with the criteria of impact structures, and the presence of fusion crust on the fragments should be considered as priority signs of meteoritic origin., Comment: 20 pages, 3 figures, 75 references. arXiv admin note: text overlap with arXiv:1605.01892

This concept article discusses the possibilities for identifying sedimentary-origin meteorites. The paper concerns (i) the macroscopic candidate for sedimentary meteorite in the epicenter of the 1908 Tunguska catastrophe; (ii) potential parent bodies for sedimentary meteorites; (iii) isotopic heterogeneity of unmixed silicate reservoirs on Mars; (iv) possible terrestrial loss or contamination in the noble gas signatures in new type meteorites that spent time in extreme weather conditions; (v) cosmogenic isotopes and shielding; and (vi) pseudo meteorites. We conclude that the list of candidate parent bodies for sedimentary meteorites includes, but is not limited by the Earth, Mars, Enceladus, Ganymede, Europa, and hypothetical planets that could exist between orbits of Mars and Jupiter in the past. A parent body for extraterrestrial sedimentary rocks on the Earth should be identified based on the entire body of evidence which is not limited solely by tests of oxygen and noble gas isotopes whose signatures may undergo terrestrial contamination and may exhibit significant heterogeneity within the parent bodies. Observed fall of cosmic body, evidence of hypervelocity impact complying with the criteria of impact structures, and the presence of fusion crust on the fragments should be considered as priority signs of meteoritic origin., Comment: 20 pages, 3 figures, 75 references. arXiv admin note: text overlap with arXiv:1605.01892

ExPASy (http://www.expasy.org) has worldwide reputation as one of the main bioinformatics resources for proteomics. It has now evolved, becoming an extensible and integrative portal accessing many scientific resources, databases and software tools in different areas of life sciences. Scientists can henceforth access seamlessly a wide range of resources in many different domains, such as proteomics, genomics, phylogeny/evolution, systems biology, population genetics, transcriptomics, etc. The individual resources (databases, web-based and downloadable software tools) are hosted in a ‘decentralized' way by different groups of the SIB Swiss Institute of Bioinformatics and partner institutions. Specifically, a single web portal provides a common entry point to a wide range of resources developed and operated by different SIB groups and external institutions. The portal features a search function across ‘selected' resources. Additionally, the availability and usage of resources are monitored. The portal is aimed for both expert users and people who are not familiar with a specific domain in life sciences. The new web interface provides, in particular, visual guidance for newcomers to ExPASy

The article provides an open discussion and a critical feedback to the comments of Haack et al. (2015) and emphasizes a significance of the first macroscopic evidence for a candidate meteorite of a new type: planetary-origin meteorite composed of silica-rich sedimentary rock. Discussion concerns the arguments for (i) candidate parental bodies including the Earth, Mars and icy moons of Saturn and Jupiter; (ii) PGE anomaly versus glassy silicate microspherules and quartz grains anomaly in the area of the 1908 Tunguska catastrophe; (iii) isotopic heterogeneity of unmixed silicate reservoirs on Mars; (iv) possible terrestrial loss or contamination in the noble gas signatures in meteorites that spent time in the extreme weather conditions; (v) cosmogenic isotopes and shielding; and (vi) pseudo meteorites. We conclude that the list of candidate parental bodies for hypothetical sedimentary-origin meteorites includes, but is not limited by the Earth, Mars, Enceladus, Ganymede, and Europa. A parental body should be identified based on the entire body of evidence which is not limited solely by tests of oxygen and noble gas isotopes whose signatures may undergo terrestrial contamination and may exhibit significant heterogeneity within the parental bodies., Comment: 11 pages, 49 references

The article provides an open discussion and a critical feedback to the comments of Haack et al. (2015) and emphasizes a significance of the first macroscopic evidence for a candidate meteorite of a new type: planetary-origin meteorite composed of silica-rich sedimentary rock. Discussion concerns the arguments for (i) candidate parental bodies including the Earth, Mars and icy moons of Saturn and Jupiter; (ii) PGE anomaly versus glassy silicate microspherules and quartz grains anomaly in the area of the 1908 Tunguska catastrophe; (iii) isotopic heterogeneity of unmixed silicate reservoirs on Mars; (iv) possible terrestrial loss or contamination in the noble gas signatures in meteorites that spent time in the extreme weather conditions; (v) cosmogenic isotopes and shielding; and (vi) pseudo meteorites. We conclude that the list of candidate parental bodies for hypothetical sedimentary-origin meteorites includes, but is not limited by the Earth, Mars, Enceladus, Ganymede, and Europa. A parental body should be identified based on the entire body of evidence which is not limited solely by tests of oxygen and noble gas isotopes whose signatures may undergo terrestrial contamination and may exhibit significant heterogeneity within the parental bodies., Comment: 11 pages, 49 references

The agricultural sector is the backbone of economic activities to the farmers in Tanzania. Tanzania produces cotton as a commercial crop that is also produced by more than 80 countries in the world particularly those located in the tropics and temperate climate regions. In Tanzania, cotton is mostly grown in the western cotton growing area (WCGA) and eastern cotton growing area (ECGA). The serious problem facing farmers for cotton production is the continuous increasing costs of inputs in Tanzania. The aim of this paper was to build a mathematical model that estimates input demand in respect to the costs for cotton production. The primary data from 2003 to 2014 were collected from cotton farmers at Bariadi in Shinyanga. Moreover, secondary data were collected from the Tanzania Cotton Board, Ukiliguru Agriculture Training Institute, and Shinyanga Regional Commissioners. A mathematical model was estimated after taking out the reasonable statistical tests. The fixed effect and random effect were compared in the Hausman’s specification test. The coefficients (elasticities) in respect to the inputs and other elasticities were estimated by applying Ordinary Least Squares techniques facilitated by STATA 11 and EXCEL.

The agricultural sector is the backbone of economic activities to the farmers in Tanzania. Tanzania produces cotton as a commercial crop that is also produced by more than 80 countries in the world particularly those located in the tropics and temperate climate regions. In Tanzania, cotton is mostly grown in the western cotton growing area (WCGA) and eastern cotton growing area (ECGA). The serious problem facing farmers for cotton production is the continuous increasing costs of inputs in Tanzania. The aim of this paper was to build a mathematical model that estimates input demand in respect to the costs for cotton production. The primary data from 2003 to 2014 were collected from cotton farmers at Bariadi in Shinyanga. Moreover, secondary data were collected from the Tanzania Cotton Board, Ukiliguru Agriculture Training Institute, and Shinyanga Regional Commissioners. A mathematical model was estimated after taking out the reasonable statistical tests. The fixed effect and random effect were compared in the Hausman’s specification test. The coefficients (elasticities) in respect to the inputs and other elasticities were estimated by applying Ordinary Least Squares techniques facilitated by STATA 11 and EXCEL.

The agricultural sector is the backbone of economic activities to the farmers in Tanzania. Tanzania produces cotton as a commercial crop that is also produced by more than 80 countries in the world particularly those located in the tropics and temperate climate regions. In Tanzania, cotton is mostly grown in the western cotton growing area (WCGA) and eastern cotton growing area (ECGA). The serious problem facing farmers for cotton production is the continuous increasing costs of inputs in Tanzania. The aim of this paper was to build a mathematical model that estimates input demand in respect to the costs for cotton production. The primary data from 2003 to 2014 were collected from cotton farmers at Bariadi in Shinyanga. Moreover, secondary data were collected from the Tanzania Cotton Board, Ukiliguru Agriculture Training Institute, and Shinyanga Regional Commissioners. A mathematical model was estimated after taking out the reasonable statistical tests. The fixed effect and random effect were compared in the Hausman’s specification test. The coefficients (elasticities) in respect to the inputs and other elasticities were estimated by applying Ordinary Least Squares techniques facilitated by STATA 11 and EXCEL.

The agricultural sector is the backbone of economic activities to the farmers in Tanzania. Tanzania produces cotton as a commercial crop that is also produced by more than 80 countries in the world particularly those located in the tropics and temperate climate regions. In Tanzania, cotton is mostly grown in the western cotton growing area (WCGA) and eastern cotton growing area (ECGA). The serious problem facing farmers for cotton production is the continuous increasing costs of inputs in Tanzania. The aim of this paper was to build a mathematical model that estimates input demand in respect to the costs for cotton production. The primary data from 2003 to 2014 were collected from cotton farmers at Bariadi in Shinyanga. Moreover, secondary data were collected from the Tanzania Cotton Board, Ukiliguru Agriculture Training Institute, and Shinyanga Regional Commissioners. A mathematical model was estimated after taking out the reasonable statistical tests. The fixed effect and random effect were compared in the Hausman’s specification test. The coefficients (elasticities) in respect to the inputs and other elasticities were estimated by applying Ordinary Least Squares techniques facilitated by STATA 11 and EXCEL.

The agricultural sector is the backbone of economic activities to the farmers in Tanzania. Tanzania produces cotton as a commercial crop that is also produced by more than 80 countries in the world particularly those located in the tropics and temperate climate regions. In Tanzania, cotton is mostly grown in the western cotton growing area (WCGA) and eastern cotton growing area (ECGA). The serious problem facing farmers for cotton production is the continuous increasing costs of inputs in Tanzania. The aim of this paper was to build a mathematical model that estimates input demand in respect to the costs for cotton production. The primary data from 2003 to 2014 were collected from cotton farmers at Bariadi in Shinyanga. Moreover, secondary data were collected from the Tanzania Cotton Board, Ukiliguru Agriculture Training Institute, and Shinyanga Regional Commissioners. A mathematical model was estimated after taking out the reasonable statistical tests. The fixed effect and random effect were compared in the Hausman’s specification test. The coefficients (elasticities) in respect to the inputs and other elasticities were estimated by applying Ordinary Least Squares techniques facilitated by STATA 11 and EXCEL.

The aim of the study was to discover remnants of the 1908 Tunguska meteorite. Main objective of the field studies was identification of exotic rocks, furrows, and penetration funnels reported by the first eyewitnesses, residents of the area with severe forest destruction. Main methods included decoding of aerial survey photographs, systematic survey of the epicenter area of the Tunguska explosion, exploratory excavations of the objects of interest, reconstruction studies of exotic boulder by using its splinters, mineralogical and spectral analysis of specimens, experimental attempt of plasma-induced reproduction of the fusion crust on specimen. The authors present results on discovery of penetration funnel-like structures; exotic boulder (known as John's Stone)) with its shear-fractured splinters and fresh furrow in the permafrost; several splinters with glassy coatings; evidence of high-speed John's Stone deceleration in the permafrost; and clear consistency in geometry of spacial arrangements of all splinters, furrows, cleaved pebbles according to data of reconstruction studies. John's Stone is composed of highly silicified gravelite sandstone (98.5% SiO2) with grain size of 0.5 to 1.5 cm. Outer surface of several splinters showed continuous glassy coating similar to shiny fusion crust reminiscent of freshly applied enamel. Plasma-induced heating of John's Stone specimen led to its explosive disintegration; residue presented with whitish semi-transparent pumice-like grains and irregularly shaped fused particles. Overall, our data suggest that John's Stone is Tunguska meteorite candidate. Recent discoveries of sedimentary rocks, lithified gravelite sandstone, clay, and quartz on Mars provide rationale for search and identification of silica-rich Martian meteorites of sedimentary origin., Comment: Number of Pages: 19 Number of References: 27 Number of Tables: 1 Number of Figures: 5

Renad Alyautdin,1 Igor Khalin,2 Mohd Ismail Nafeeza,1 Muhammad Huzaimi Haron,1 Dmitry Kuznetsov31Faculty of Medicine, Universiti Teknologi MARA (UiTM), Sungai Buloh, Selangor, Malaysia; 2Faculty of Medicine and Defence Health, National Defence University of Malaysia (NDUM), Kuala Lumpur, Malaysia; 3Department of Medicinal Nanobiotechnologies, N. I. Pirogoff Russian State Medical University, Moscow, RussiaAbstract: The protective properties of the blood–brain barrier (BBB) are conferred by the intricate architecture of its endothelium coupled with multiple specific transport systems expressed on the surface of endothelial cells (ECs) in the brain's vasculature. When the stringent control of the BBB is disrupted, such as following EC damage, substances that are safe for peripheral tissues but toxic to neurons have easier access to the central nervous system (CNS). As a consequence, CNS disorders, including degenerative diseases, can occur independently of an individual's age. Although the BBB is crucial in regulating the biochemical environment that is essential for maintaining neuronal integrity, it limits drug delivery to the CNS. This makes it difficult to deliver beneficial drugs across the BBB while preventing the passage of potential neurotoxins. Available options include transport of drugs across the ECs through traversing occludins and claudins in the tight junctions or by attaching drugs to one of the existing transport systems. Either way, access must specifically allow only the passage of a particular drug. In general, the BBB allows small molecules to enter the CNS; however, most drugs with the potential to treat neurological disorders other than infections have large structures. Several mechanisms, such as modifications of the built-in pumping-out system of drugs and utilization of nanocarriers and liposomes, are among the drug-delivery systems that have been tested; however, each has its limitations and constraints. This review

Renad Alyautdin,1 Igor Khalin,2 Mohd Ismail Nafeeza,1 Muhammad Huzaimi Haron,1 Dmitry Kuznetsov31Faculty of Medicine, Universiti Teknologi MARA (UiTM), Sungai Buloh, Selangor, Malaysia; 2Faculty of Medicine and Defence Health, National Defence University of Malaysia (NDUM), Kuala Lumpur, Malaysia; 3Department of Medicinal Nanobiotechnologies, N. I. Pirogoff Russian State Medical University, Moscow, RussiaAbstract: The protective properties of the blood–brain barrier (BBB) are conferred by the intricate architecture of its endothelium coupled with multiple specific transport systems expressed on the surface of endothelial cells (ECs) in the brain's vasculature. When the stringent control of the BBB is disrupted, such as following EC damage, substances that are safe for peripheral tissues but toxic to neurons have easier access to the central nervous system (CNS). As a consequence, CNS disorders, including degenerative diseases, can occur independently of an individual's age. Although the BBB is crucial in regulating the biochemical environment that is essential for maintaining neuronal integrity, it limits drug delivery to the CNS. This makes it difficult to deliver beneficial drugs across the BBB while preventing the passage of potential neurotoxins. Available options include transport of drugs across the ECs through traversing occludins and claudins in the tight junctions or by attaching drugs to one of the existing transport systems. Either way, access must specifically allow only the passage of a particular drug. In general, the BBB allows small molecules to enter the CNS; however, most drugs with the potential to treat neurological disorders other than infections have large structures. Several mechanisms, such as modifications of the built-in pumping-out system of drugs and utilization of nanocarriers and liposomes, are among the drug-delivery systems that have been tested; however, each has its limitations and constraints. This review

The aim of the study was to discover remnants of the 1908 Tunguska meteorite. Main objective of the field studies was identification of exotic rocks, furrows, and penetration funnels reported by the first eyewitnesses, residents of the area with severe forest destruction. Main methods included decoding of aerial survey photographs, systematic survey of the epicenter area of the Tunguska explosion, exploratory excavations of the objects of interest, reconstruction studies of exotic boulder by using its splinters, mineralogical and spectral analysis of specimens, experimental attempt of plasma-induced reproduction of the fusion crust on specimen. The authors present results on discovery of penetration funnel-like structures; exotic boulder (known as John's Stone)) with its shear-fractured splinters and fresh furrow in the permafrost; several splinters with glassy coatings; evidence of high-speed John's Stone deceleration in the permafrost; and clear consistency in geometry of spacial arrangements of all splinters, furrows, cleaved pebbles according to data of reconstruction studies. John's Stone is composed of highly silicified gravelite sandstone (98.5% SiO2) with grain size of 0.5 to 1.5 cm. Outer surface of several splinters showed continuous glassy coating similar to shiny fusion crust reminiscent of freshly applied enamel. Plasma-induced heating of John's Stone specimen led to its explosive disintegration; residue presented with whitish semi-transparent pumice-like grains and irregularly shaped fused particles. Overall, our data suggest that John's Stone is Tunguska meteorite candidate. Recent discoveries of sedimentary rocks, lithified gravelite sandstone, clay, and quartz on Mars provide rationale for search and identification of silica-rich Martian meteorites of sedimentary origin., Comment: Number of Pages: 19 Number of References: 27 Number of Tables: 1 Number of Figures: 5

Access to online repositories for genomic and associated "-omics" datasets is now an essential part of everyday research activity. It is important therefore that the Tuberculosis community is aware of the databases and tools available to them online, as well as for the database hosts to know what the needs of the research community are. One of the goals of the Tuberculosis Annotation Jamboree, held in Washington DC on March 7th-8th 2012, was therefore to provide an overview of the current status of three key Tuberculosis resources, TubercuList (tuberculist.epfl.ch), TB Database (www.tbdb.org), and Pathosystems Resource Integration Center (PATRIC, www.patricbrc.org). Here we summarize some key updates and upcoming features in TubercuList, and provide an overview of the PATRIC site and its online tools for pathogen RNA-Seq analysis. (C) 2012 Elsevier Ltd. All rights reserved.

Access to online repositories for genomic and associated "-omics" datasets is now an essential part of everyday research activity. It is important therefore that the Tuberculosis community is aware of the databases and tools available to them online, as well as for the database hosts to know what the needs of the research community are. One of the goals of the Tuberculosis Annotation Jamboree, held in Washington DC on March 7th-8th 2012, was therefore to provide an overview of the current status of three key Tuberculosis resources, TubercuList (tuberculist.epfl.ch), TB Database (www.tbdb.org), and Pathosystems Resource Integration Center (PATRIC, www.patricbrc.org). Here we summarize some key updates and upcoming features in TubercuList, and provide an overview of the PATRIC site and its online tools for pathogen RNA-Seq analysis. (C) 2012 Elsevier Ltd. All rights reserved.

Asieh Hosseini1, Mohammad Sharifzadeh1, Seyed Mahdi Rezayat2, Gholamreza Hassanzadeh3, Shokoufeh Hassani1, Maryam Baeeri1, Vahid Shetab-Bushehri4, Dmitry A Kuznetsov5, Mohammad Abdollahi11Faculty of Pharmacy, and Pharmaceutical Sciences Research Center, Tehran University of Medical Sciences, Tehran, Iran; 2Faculty of Advanced Science and Technology in Medicine, Tehran University of Medical Sciences and Pharmaceutical Sciences Branch, Islamic Azad University, Tehran, Iran; 3Faculty of Medicine, Tehran University of Medical Sciences, Tehran, Iran; 4Faculty of Medicine, Iran University of Medical Sciences, Tehran, Iran; 5Department of Medicinal Nanobiotechnologies, N. I. Pirogoff Russian State Medical University, Moscow, Russian FederationAbstract: Diabetic neuropathy (DN) is a debilitating disorder occurring in most diabetic patients without a viable treatment yet. The present work examined the protective effect of 25Mg-PMC16 nanoparticle (porphyrin adducts of cyclohexil fullerene-C60) in a rat model of streptozotocin (STZ)-induced DN. 25Mg-PMC16 (0.5 lethal dose50 [LD50]) was administered intravenously in two consecutive days before intraperitoneal injection of STZ (45 mg/kg). 24Mg-PMC16 and MgCl2 were used as controls. Blood 2,3-diphosphoglycerate (2,3-DPG), oxidative stress biomarkers, adenosine triphosphate (ATP) level in dorsal root ganglion (DRG) neurons were determined as biomarkers of DN. Results indicated that 2,3-DPG and ATP decreased whereas oxidative stress increased by induction of DN which all were improved in 25Mg-PMC16-treated animals. No significant changes were observed by administration of 24Mg-PMC16 or MgCl2 in DN rats. It is concluded that in DN, oxidative stress initiates injuries to DRG neurons that finally results in death of neurons whereas administration of 25Mg-PMC16 by release of Mg and increasing ATP acts protectively.Keywords: diabetes, neuropathy, 25Mg-magnetic isotope effect, porphyrin-fullerene nanoparticles, oxidative stress, adenosin

Asieh Hosseini1, Mohammad Sharifzadeh1, Seyed Mahdi Rezayat2, Gholamreza Hassanzadeh3, Shokoufeh Hassani1, Maryam Baeeri1, Vahid Shetab-Bushehri4, Dmitry A Kuznetsov5, Mohammad Abdollahi11Faculty of Pharmacy, and Pharmaceutical Sciences Research Center, Tehran University of Medical Sciences, Tehran, Iran; 2Faculty of Advanced Science and Technology in Medicine, Tehran University of Medical Sciences and Pharmaceutical Sciences Branch, Islamic Azad University, Tehran, Iran; 3Faculty of Medicine, Tehran University of Medical Sciences, Tehran, Iran; 4Faculty of Medicine, Iran University of Medical Sciences, Tehran, Iran; 5Department of Medicinal Nanobiotechnologies, N. I. Pirogoff Russian State Medical University, Moscow, Russian FederationAbstract: Diabetic neuropathy (DN) is a debilitating disorder occurring in most diabetic patients without a viable treatment yet. The present work examined the protective effect of 25Mg-PMC16 nanoparticle (porphyrin adducts of cyclohexil fullerene-C60) in a rat model of streptozotocin (STZ)-induced DN. 25Mg-PMC16 (0.5 lethal dose50 [LD50]) was administered intravenously in two consecutive days before intraperitoneal injection of STZ (45 mg/kg). 24Mg-PMC16 and MgCl2 were used as controls. Blood 2,3-diphosphoglycerate (2,3-DPG), oxidative stress biomarkers, adenosine triphosphate (ATP) level in dorsal root ganglion (DRG) neurons were determined as biomarkers of DN. Results indicated that 2,3-DPG and ATP decreased whereas oxidative stress increased by induction of DN which all were improved in 25Mg-PMC16-treated animals. No significant changes were observed by administration of 24Mg-PMC16 or MgCl2 in DN rats. It is concluded that in DN, oxidative stress initiates injuries to DRG neurons that finally results in death of neurons whereas administration of 25Mg-PMC16 by release of Mg and increasing ATP acts protectively.Keywords: diabetes, neuropathy, 25Mg-magnetic isotope effect, porphyrin-fullerene nanoparticles, oxidative stress, adenosin

Accumulation and release of energy in the nucleoside triphosphate enzymatic synthesis and hydrolysis does not limited to a routine energy consuming nucleophilic mechanism. These processes require an overcoming the large energy barrier exceeding a total value of accumulated or released energy level by at least 3 – 4 times (~10 kcal/mol). This energy is supposed to be taken from the mechanical compression of the catalytic site and used to form P–O chemical bond by a direct nucleophilic addition of phosphate to nucleoside diphosphate (ADP as an example). A new, energetically “cheap”, ion-radical mechanism of the ATP biosynthesis has been proposed due to the observation of magnetic isotope and magnetic field effects on the ATP synthesis. This mechanism is about to generate a compression energy to “spend” on a partial dehydratation of magnesium ion inside the nucleotidyl transferase catalyric site (energy cost of this process is 3-5 kcal/mol, i.e. by 2-3 times less than a total accumulated or released energy). Dehydration of this ion is to increase its electron affinity and hence to stimulate an electron transfer from ADP3- to Mg2+. This reaction is a starting point of the ion-radical mechanism considering the molecular mechanics of enzymatic machines and its quantum chemistry background as well. To the contrast of a hardly controllable nucleophilic path, the ion-radical mechanism might be turned on/off by a targeted delivery of  paramagnetic magnesium ions, 25Mg2+, towards the phosphate transferring enzyme catalytic site. The magnesium isotope substitution is easily reachable by the endo-osmotic pressure techniques, which makes it attractive for further biotechnological and/or pharmacological application(s).

DNA Polymerase Beta (EC 2.7.7.7) is found to be operated by magnetic isotope effect (MIE) of Calcium once the Mg2+ ions replaced with the stable 43Ca2+ isotopes inside the enzyme catalytic sites. The isotope mentioned is the only paramagnetic species of the Calcium isotopic set with a 0.135 natural abundance value and the negative 7/2 nuclear spin providing a nuclear magnetic moment equal to 1.317 Bohr magnetons. As compared to the Mg/40Ca substitution, a 2.25-fold enzyme inhibition has been shown to provethe43Ca-MIE dependent mode of the catalysis turning down.An ion-radical mechanism based on the singlet – triplet conversion of the enzyme generated intermediates (ion-radical pairs) is found to be engaged once the paramagnetic metal isotope involved into the catalysis studied.The MIE promotes a primary reaction in DNA synthesis constituting in electron transfer between the ion – radical forming partners, [Ca(H2O)n2+] and [Ca2+(dNTP)]. Once the metal isotope substitution takes place inside just one of two DNA Polymerase Beta catalytic sites, a consequent43Ca – promoted inhibition leads to a residual synthesis of shorted DNA fragments that counts 25 – 35 nucleotides in length contrasting with the 180n – 210n DNA produced by either intact or40Ca – loaded polymerase. Being occurred simultaneously with a marked MIE – promoted enzyme inhibition, this fact itself makes possible to consider these short (“size-invalid”) DNA segments hardly efficient in the DNA base – excision repair. The latter is a survival factor in leukemic cells where the DNApolβ was found overexpressed. That supports a standpoint considering theDNApolβ a legitimate target for antitumor agents since its inhibition deprives the malignant cell from a DNA base – excision repair in neoplasma. A possible trend making role of these data in the current developments on a novel concept - establishing chemical background for cancer therapies is in a focus.

DNA Polymerase Beta (EC 2.7.7.7) is found to be operated by magnetic isotope effect (MIE) of Calcium once the Mg2+ ions replaced with the stable 43Ca2+ isotopes inside the enzyme catalytic sites. The isotope mentioned is the only paramagnetic species of the Calcium isotopic set with a 0.135 natural abundance value and the negative 7/2 nuclear spin providing a nuclear magnetic moment equal to 1.317 Bohr magnetons. As compared to the Mg/40Ca substitution, a 2.25-fold enzyme inhibition has been shown to provethe43Ca-MIE dependent mode of the catalysis turning down.An ion-radical mechanism based on the singlet – triplet conversion of the enzyme generated intermediates (ion-radical pairs) is found to be engaged once the paramagnetic metal isotope involved into the catalysis studied.The MIE promotes a primary reaction in DNA synthesis constituting in electron transfer between the ion – radical forming partners, [Ca(H2O)n2+] and [Ca2+(dNTP)]. Once the metal isotope substitution takes place inside just one of two DNA Polymerase Beta catalytic sites, a consequent43Ca – promoted inhibition leads to a residual synthesis of shorted DNA fragments that counts 25 – 35 nucleotides in length contrasting with the 180n – 210n DNA produced by either intact or40Ca – loaded polymerase. Being occurred simultaneously with a marked MIE – promoted enzyme inhibition, this fact itself makes possible to consider these short (“size-invalid”) DNA segments hardly efficient in the DNA base – excision repair. The latter is a survival factor in leukemic cells where the DNApolβ was found overexpressed. That supports a standpoint considering theDNApolβ a legitimate target for antitumor agents since its inhibition deprives the malignant cell from a DNA base – excision repair in neoplasma. A possible trend making role of these data in the current developments on a novel concept - establishing chemical background for cancer therapies is in a focus.

Accumulation and release of energy in the nucleoside triphosphate enzymatic synthesis and hydrolysis does not limited to a routine energy consuming nucleophilic mechanism. These processes require an overcoming the large energy barrier exceeding a total value of accumulated or released energy level by at least 3 – 4 times (~10 kcal/mol). This energy is supposed to be taken from the mechanical compression of the catalytic site and used to form P–O chemical bond by a direct nucleophilic addition of phosphate to nucleoside diphosphate (ADP as an example). A new, energetically “cheap”, ion-radical mechanism of the ATP biosynthesis has been proposed due to the observation of magnetic isotope and magnetic field effects on the ATP synthesis. This mechanism is about to generate a compression energy to “spend” on a partial dehydratation of magnesium ion inside the nucleotidyl transferase catalyric site (energy cost of this process is 3-5 kcal/mol, i.e. by 2-3 times less than a total accumulated or released energy). Dehydration of this ion is to increase its electron affinity and hence to stimulate an electron transfer from ADP3- to Mg2+. This reaction is a starting point of the ion-radical mechanism considering the molecular mechanics of enzymatic machines and its quantum chemistry background as well. To the contrast of a hardly controllable nucleophilic path, the ion-radical mechanism might be turned on/off by a targeted delivery of  paramagnetic magnesium ions, 25Mg2+, towards the phosphate transferring enzyme catalytic site. The magnesium isotope substitution is easily reachable by the endo-osmotic pressure techniques, which makes it attractive for further biotechnological and/or pharmacological application(s).

ExPASy (http://www.expasy.org) has worldwide reputation as one of the main bioinformatics resources for proteomics. It has now evolved, becoming an extensible and integrative portal accessing many scientific resources, databases and software tools in different areas of life sciences. Scientists can henceforth access seamlessly a wide range of resources in many different domains, such as proteomics, genomics, phylogeny/evolution, systems biology, population genetics, transcriptomics, etc. The individual resources (databases, web-based and downloadable software tools) are hosted in a ‘decentralized' way by different groups of the SIB Swiss Institute of Bioinformatics and partner institutions. Specifically, a single web portal provides a common entry point to a wide range of resources developed and operated by different SIB groups and external institutions. The portal features a search function across ‘selected' resources. Additionally, the availability and usage of resources are monitored. The portal is aimed for both expert users and people who are not familiar with a specific domain in life sciences. The new web interface provides, in particular, visual guidance for newcomers to ExPASy

Motivation: Lipids are a large and diverse group of biological molecules with roles in membrane formation, energy storage and signaling. Cellular lipidomes may contain tens of thousands of structures, a staggering degree of complexity whose significance is not yet fully understood. High-throughput mass spectrometry-based platforms provide a means to study this complexity, but the interpretation of lipidomic data and its integration with prior knowledge of lipid biology suffers from a lack of appropriate tools to manage the data and extract knowledge from it. Results: To facilitate the description and exploration of lipidomic data and its integration with prior biological knowledge, we have developed a knowledge resource for lipids and their biology—SwissLipids. SwissLipids provides curated knowledge of lipid structures and metabolism which is used to generate an in silico library of feasible lipid structures. These are arranged in a hierarchical classification that links mass spectrometry analytical outputs to all possible lipid structures, metabolic reactions and enzymes. SwissLipids provides a reference namespace for lipidomic data publication, data exploration and hypothesis generation. The current version of SwissLipids includes over 244 000 known and theoretically possible lipid structures, over 800 proteins, and curated links to published knowledge from over 620 peer-reviewed publications. We are continually updating the SwissLipids hierarchy with new lipid categories and new expert curated knowledge. Availability: SwissLipids is freely available at http://www.swisslipids.org/. Contact: alan.bridge@isb-sib.ch Supplementary information: Supplementary data are available at Bioinformatics online

Presented paper describes the basic principles and features of the implementation of a robotic network of optical telescopes MASTER, designed to study the prompt (simultaneous with gamma radiation) optical emission of gamma-ray bursts and to perform the sky survey to detect unknown objects and transient phenomena. With joint efforts of Sternberg astronomical institute, High altitude astronomical station of the Pulkovo observatory, Ural state university, Irkutsk state university, Blagoveshchensk pedagogical university, the robotic telescopes MASTER II near Kislovodsk, Yekaterinburg, Irkutsk and Blagoveshchensk were installed and tested. The network spread over the longitudes is greater than 6 h. A further expansion of the network is considered. © 2011 Springer Science+Business Media B.V.