In September, 2015, the UN General Assembly established the Sustainable Development Goals (SDGs). The SDGs specify 17 universal goals, 169 targets, and 230 indicators leading up to 2030. We provide an analysis of 33 health-related SDG indicators based on the Global Burden of Diseases, Injuries, and Risk Factors Study 2015 (GBD 2015).
We applied statistical methods to systematically compiled data to estimate the performance of 33 health-related SDG indicators for 188 countries from 1990 to 2015. We rescaled each indicator on a scale from 0 (worst observed value between 1990 and 2015) to 100 (best observed). Indices representing all 33 health-related SDG indicators (health-related SDG index), health-related SDG indicators included in the Millennium Development Goals (MDG index), and health-related indicators not included in the MDGs (non-MDG index) were computed as the geometric mean of the rescaled indicators by SDG target. We used spline regressions to examine the relations between the Socio-demographic Index (SDI, a summary measure based on average income per person, educational attainment, and total fertility rate) and each of the health-related SDG indicators and indices.
In 2015, the median health-related SDG index was 59∙3 (95% uncertainty interval 56∙8–61∙8) and varied widely by country, ranging from 85∙5 (84∙2–86∙5) in Iceland to 20∙4 (15∙4–24∙9) in Central African Republic. SDI was a good predictor of the health-related SDG index (r²=0∙88) and the MDG index (r²=0∙92), whereas the non-MDG index had a weaker relation with SDI (r²=0∙79). Between 2000 and 2015, the health-related SDG index improved by a median of 7∙9 (IQR 5∙0–10∙4), and gains on the MDG index (a median change of 10∙0 [6∙7–13∙1]) exceeded that of the non-MDG index (a median change of 5∙5 [2∙1–8∙9]). Since 2000, pronounced progress occurred for indicators such as met need with modern contraception, under-5 mortality, and neonatal mortality, as well as the indicator for universal health coverage tracer interventions. Moderate improvements were found for indicators such as HIV and tuberculosis incidence, minimal changes for hepatitis B incidence took place, and childhood overweight considerably worsened.Interpretation
GBD provides an independent, comparable avenue for monitoring progress towards the health-related SDGs. Our analysis not only highlights the importance of income, education, and fertility as drivers of health improvement but also emphasises that investments in these areas alone will not be sufficient.
Although considerable progress on the health-related MDG indicators has been made, these gains will need to be sustained and, in many cases, accelerated to achieve the ambitious SDG targets. The minimal improvement in or worsening of health-related indicators beyond the MDGs highlight the need for additional resources to effectively address the expanded scope of the health-related SDGs
Quantitatively characterizing the intracellular carbon flux distribution provides useful information for both fundamental and applied investigations into the cellular metabolism at the system level, such as the roles of different metabolic pathways and individual reactions, metabolic state characterization, metabolic differences between the strains, and clues regarding strategies for producer-strain improvement. A variety of methods have been developed to characterize the metabolic state of the cell by determining its intracellular flux distribution, and together, they are called metabolic flux analysis (MFA) or fluxomics. These methods, in addition to other X-omics technologies (i.e., genomics, transcriptomics, proteomics, and metabolomics) constitute a recent arsenal of the system biology estimation approaches. One of the most well-developed approaches for intracellular carbon flux estimation in vivo in (quasi) steady-state conditions is 13C-MFA, which uses substrates that are labeled with a heavy carbon (13C). Applying 13C-MFA requires the coordination of experts in biochemistry, applied mathematics and nuclear magnetic resonance (NMR) or mass spectrometry. Therefore, the authors have prepared a three-part review highlighting the different but equally important aspects of 13C-MFA. In the first part, which is presented below, the focus is on the basic principles of 13C-MFA, such as stoichiometric model development, labeling experiments and experimental data extraction. The principles of the labeling experiments modeling and quantitative carbon flux estimation and statistics are discussed in the second part. The final part reviews recent achievements in fundamental and applied investigations of bacterial metabolism achieved using 13C-MFA.
At present, 13C-MFA is a primary method for quantitatively characterizing intracellular carbon fluxes in cells in vivo under steady-state conditions. The method has been successfully used to investigate both the fundamental characteristics of prokaryotic and eukaryotic cell metabolism and to improve producer strains for more than twenty years. This publication is the last in a set of reviews that describe various aspects of the method. Here, the authors highlight recent achievements that involved using 13C-MFA to elucidate bacterial metabolism. Analyses of well-characterized bacterial model strains revealed that central metabolism robustness is provided by a set of alternative metabolic pathways; these analyses also helped develop a better understanding of the physiological significance of these pathways and identified previously unknown functions of well-studied metabolic pathways. Several examples of 13C-MFA-based fundamental investigations of poorly characterized bacteria are also analyzed. In applied investigations, flux analysis of strains that produce amino acids, vitamins and antibiotics indicated targets for modifications, suggested unconventional metabolic engineering approaches, and, most importantly, confirmed their utility. In the last section of this article, 13C-MFA prospects, including the monitoring of the dynamics of metabolic flux distribution during culture growth, are discussed.
The paper presents analysis of a new kind of educational courses based on multidisciplinary approach. The course synthesizes the methodologies and advances of regional studies and regional geography, cultural and cross-cultural studies and communication, oriental studies, civilization studies, second language acquisition and second language teaching. The course is a part of a wider language program elaborated and implemented at NRU HSE (Saint Petersburg) and bases essentially on the inclusive strategies of Arab countries study, primarily language learning techniques (Arabic). It requires preliminary commandment of elementary course of Arabic. This study aims to analyze a year’s experience of constructing the Arab countries studies course and its teaching process, and to evaluate the merits and demerits of its aspects, taking into account the peculiarities of the academic activity, language skills, basic dictionary, and comparative analysis of several similar courses.
Surveys of environmental microbial communities using metagenomic approach produce vast volumes of multidimensional data regarding the phylogenetic and functional composition of the microbiota. Faced with such complex data, a metagenomic researcher needs to select the means for data analysis properly. Data visualization became an indispensable part of the exploratory data analysis and serves a key to the discoveries. While the molecular-genetic analysis of even a single bacterium presents multiple layers of data to be properly displayed and perceived, the studies of microbiota are significantly more challenging. Here we present a review of the state-of-art methods for the visualization of metagenomic data in a multi-level manner: from the methods applicable to an in-depth analysis of a single metagenome to the techniques appropriate for large-scale studies containing hundreds of environmental samples.
Climate, fire, and human activities strongly affected the development of vegetation communities during the Holocene, yet the relative importance of these individual factors remains unclear in many areas. This paper presents new multi-proxy records of environmental change for the Meshchera Lowlands (the central part of the East European Plain) during the Holocene. Changes in regional vegetation during the Mid- and Late Holocene were influenced by climate, fire regime and human impact, as indicated by pollen, plant macrofossil, charcoal and testate amoebae analysis from several peat cores, along with reconstruction of tree cover from pollen assemblages. Since 8500 cal yr BP, the vegetation history represented a series of consecutive phases of birch, birch-pine and pine-broadleaf forests, with introduction of spruce after 2500 cal yr BP. Maximal abundance of broadleaf tree species was detected from 4700 to 2000 cal yr BP. Vegetation dynamics were strongly influenced by human activity since 1400 cal yr BP. High fire frequency was recorded for the periods 8500–4500 cal yr BP and 3500–2000 cal yr BP, when the fire return interval varied from 40 to 80 years. Since 2000 cal yr BP, the fire return period exceeded 500 years suggesting a significant decline in fire frequency during the last two millennia.
The article assesses the dynamics of migration effectiveness by Russian regions over a long time period. Russian and foreign studies have found that people with migration experience change their place of residence more easily compared with those who have never moved. Migrants are divided into two main groups, namely, newcomers and long-time residents who have lived in a migration destination for a long time, and a transitional group from newcomers to long-time residents. Moscow, St. Petersburg, and their oblasts are subjects where migrants adapt the best. For a long time, in most Far Eastern and Siberian subjects (except for the Khanty-Mansi Autonomous Okrug and Yamalo-Nenets Autonomous Okrug), the large number of migrants who departed a region were compensated by large number of arriving migrants. The collapse of the Soviet Union and subsequent socioeconomic crisis have shown that population outflow occurs primarily in regions with the highest share of new settlers. Attempts to force the development of areas with harsh natural conditions and low adaptation by the population led to a massive return migration. Ensuring the adaptation of new settlers and their transition to long-time residents, rather than a high number of arrivals, is important for regional migration policy. Adaptation largely depends on the level of socioeconomic development of regions and particular localities.
Based on the data on addresses of real estate buyers, we assess the investment activity of residents of Russian regions and cities in the primary housing market of the Moscow capital region (MCR) compared to the activity of their labor migrations to the MCR. The objects of our analysis are 149 Russian cities and 80 remaining parts of regions. This enabled us to analyze the specifics of migration and investment behavior for the first time, taking into account differentiation between cities and rural areas, between size classes of cities, and between individual large cities. This enabled us to fill in the gap in assessing the mobility of inputs, i.e., capital and labor. A sharp contrast between settlements of different sizes was revealed in the nature of their interaction with the MCR agglomeration. The intensity of labor migration to the Moscow agglomeration is decreasing rapidly and monotonically with increasing settlement size. The activity of nonresident homebuyers, depending on the population of the city of their residence, varies nonmonotonically, reaching its highest level for cities with populations of 250000–500000 people for Moscow’s housing market and 100000–500000 people in Moscow oblast. Small towns and rural areas (except for the Khanty–Mansi and Yamalo–Nenets autonomous okrugs) are a source of labor for the Moscow agglomeration and show low investment activity in the capital’s housing market. Million-plus cities provide a negligible inflow of labor migrants and are characterized by moderate activity in the MCR housing market, close to the national average. Compared to the premium housing and labor market of the City of Moscow, investment and migration flows to Moscow oblast are shifted to smaller settlements and lower-income regions. The attraction of Moscow oblast rapidly decreases with distance, extending to first- and second-order neighbors, while Moscow’s influence is nationwide.
On the basis of data for the 1989–2002 and 2003–2010, the migration of young people at the level of cities and areas of 19 Russian regions is analyzed. Migration is estimated by the “age-group shift” for the corresponding periods between censuses which provides more accurate estimates in comparison with the data of current statistics. Migration of young people has an expressed centripetal nature everywhere; their migration rate from the province is higher the farther one goes from regional centers. All regional capitals attracted young people in the period under review which has a positive effect on the age structure of their population, and only large cities could retain young people among their population. Migration of young people from the periphery is sustainable; it depends on the common migration attractiveness of regions and reaches the greatest extent in the East and in the depressed areas of the Center. In small and medium-sized cities on the periphery of regions, the outflow of young people almost always reaches the same intensity as in the countryside.
An explicit description of a finite minimal basis of generators is given for the algebra of symmetries of a generic quantum three-frequency resonance oscillator.
Navigated transcranial magnetic stimulation (nTMS) can be applied to locate and outline cortical motor representations. This may be important, e.g., when planning neurosurgery or focused nTMS therapy, or when assessing plastic changes during neurorehabilitation. Conventionally, a cortical location is considered to belong to the motor cortex if the maximum electric field (E-field) targeted there evokes a motor-evoked potential in a muscle. However, the cortex is affected by a broad E-field distribution, which tends to broaden estimates of representation areas by stimulating also the neighboring areas in addition to the maximum E-field location. Our aim was to improve the estimation of nTMS-based motor maps by taking into account the E-field distribution of the stimulation pulse. The effect of the E-field distribution was considered by calculating the minimum-norm estimate (MNE) of the motor representation area. We tested the method on simulated data and then applied it to recordings from six healthy volunteers and one stroke patient. We compared the motor representation areas obtained with the MNE method and a previously introduced interpolation method. The MNE hotspots and centers of gravity were close to those obtained with the interpolation method. The areas of the maps, however, depend on the thresholds used for outlining the areas. The MNE method may improve the definition of cortical motor areas, but its accuracy should be validated by comparing the results with maps obtained with direct cortical stimulation of the cortex where the E-field distribution can be better focused.
This paper addresses the important problem of efficiently mining numerical data with formal concept analysis (FCA). Classically, the only way to apply FCA is to binarize the data, thanks to a so-called scaling procedure. This may either involve loss of information, or produce large and dense binary data known as hard to process. In the context of gene expression data analysis, we propose and compare two FCA-based methods for mining numerical data and we show that they are equivalent. The first one relies on a particular scaling, encoding all possible intervals of attribute values, and uses standard FCA techniques. The second one relies on pattern structures without a priori transformation, and is shown to be more computationally efficient and to provide more readable results. Experiments with real-world gene expression data are discussed and give a practical basis for the comparison and evaluation of the methods.
The short-time dynamics of bacterial chromosomal loci is a mixture of subdiffusive and active motion, in the form of rapid relocations with near-ballistic dynamics. While previous work has shown that such rapid motions are ubiquitous, we still have little grasp on their physical nature, and no positive model is available that describes them. Here, we propose a minimal theoretical model for loci movements as a fractional Brownian motion subject to a constant but intermittent driving force, and compare simulations and analytical calculations to data from high-resolution dynamic tracking in E. coli. This analysis yields the characteristic time scales for intermittency. Finally, we discuss the possible shortcomings of this model, and show that an increase in the effective local noise felt by the chromosome associates to the active relocations.
The problem of functional localization in the brain is one of the most fundamental in neuroscience. For this problem two opposite ideologies: "modular" versus "holistic" nature of the brain also known as "localism" and "holism" have been discussed for a long time (Flourens 1825; Luria 1967). The debate in favor of one or another ideology still can be traced at all methodological levels - from a cell to a system. In this opinion paper we want to raise a question - what is nowadays meant by mapping of the brain? In addition we want to highlight the necessity of being aware of occasionally occurring discontinuity in the research at different methodological scales.
Although thalamic deep brain stimulation is an effective treatment for patients with essential tremor, little is known about its effect on cortical neural dynamics. Therefore, we investigated long-range temporal correlations and spectral power in electroencephalographic recordings of patients during OFF versus ON bilateral thalamic deep brain stimulation in comparison with healthy controls. Cortical dynamics were analyzed in the range of 6-30 Hz. We found the presence of long-range temporal correlations up to 20 s in patients and controls. Thalamic deep brain stimulation was associated with increased long-range temporal correlations in the high beta band (21-30 Hz) and decreased power in the low beta band (13-20 Hz) compared with OFF stimulation and healthy controls. Long-range temporal correlations in the 6-10 Hz range were increased with OFF stimulation compared with the controls. Importantly, deep brain stimulation-induced changes in long-range temporal correlations within 6-10 Hz and in the beta ranges (13-20, 21-30 Hz) were correlated with OFF-ON changes in the tremor severity and with the disease duration, respectively. The differential reactivity of long-range temporal correlations and spectral power to deep brain stimulation might suggest that both measures reflect distinct aspects of cortical dynamics and might represent biomarkers for stimulation-induced modulations of neural dynamics in electroencephalography. The fact that long-range temporal correlations, but not spectral power, were correlated with clinical information might suggest long-range temporal correlations as a potential marker for disease severity in essential tremor.
Previous studies demonstrated the presence of Monochromatic Ultra-Slow Oscillations (MUSO) in human EEG. In the present study we explored the biological origin of MUSO by simultaneous recordings of EEG, Near-Infrared Spectroscopy (NIRS), arterial blood pressure, respiration and Laser Doppler flowmetry. We used a head-up tilt test in order to check whether MUSO might relate to Mayer waves in arterial blood pressure, known to be enhanced by the tilting procedure. MUSO were detected in 8 out of 10 subjects during rest and showed a striking monochromatic spectrum (0.07–0.14 Hz). The spatial topography of MUSO was complex, showing multiple foci variable across subjects. While the head-up tilt test increased the relative power of Mayer waves, it had no effect on MUSO. On the other hand, the relative spectral power of 0.1 Hz oscillations in EEG, NIRS and blood pressure signals were positively correlated across subjects in the tilted condition. Eight subjects showed a coherence between MUSO and NIRS/arterial blood pressure. Moreover, MUSO at different electrode sites demonstrated coherence not reducible to volume conduction, thus indicating that MUSO are unlikely to be generated by one source. We related our experimental findings to known biological phenomena being generated at about 0.1 Hz, i.e.: arterial blood pressure, cerebral and skin vasomotion, respiration and neuronal activity. While no definite conclusion can yet be drawn as to an exact physiological mechanism of MUSO, we suggest that these oscillations might be of a rather extraneuronal origin reflecting cerebral vasomotion.
Cheliped construction, in particular the teeth pattern on chelae fingers is considered as most important character suit (along with burrowing/swimming apparatus) for the diagnosis of Portunoidea. Heterochelic and heterodontic chelipeds with the molariform tooth in the larger chela and multi-lobed serial teeth are presumably ancestral and most common pattern for the group. New material (mostly species of Thalamitinae Paulson, 1875, Lupocyclus Adamd and White, 1848 and Portunus Weber, 1795 sensu lato) have been combined with the existing sequences from the GenBank to produce molecular phylogenetic reconstructions based on the histone H3 gene fragment and a multi-gene tree (for smaller set of species) based on partial sequences of H3, D1 region of 28S gene and mitochondrial COI gene. These reconstructions have not provided necessary support to the monophyly of Portunoidea sensu lato but indicated the presence of several monophyletic lineages, i.e. Portunidae sensu stricto, Polybiidae + Thiidae + Carcinidae + Pirimelidae, Benthochascon + Geryonidae (to lesser extent), and Ovalipes. Monophyly of the Portunidae sensu stricto is supported by both the H3 and multigene trees and morphological evidence. Swimming capacity probably evolves as a result of parallel evolution in at least three different lineages of portunoids. A new version of the family level classification of Portunoidea and a key to their families are provided with the following taxa: Geryonidae (Geryoninae + Benthochasconinaeˇ ́ 1991, Thiidae, Pirimelidae, Carcinidae McLeay, subfam. nov.), Ovalipidae fam. nov., Brusiniidae Stevˇci c,1838 (Carcininae + Portumninae Ortmann, 1893), Polybiidae Ortmann, 1893, and Portunidae Rafinesque, 1815 sensu stricto. The most radical change in the systematics of Portunidae sensu stricto is the final recognition of the polyphyly of Portunus sensu lato and the need for revalidization and re-diagnozing of several taxa that were synonymized by Stephenson and Campbell (1959) and Stephenson (1972) under Portunus. While some subfamilies of the Portunidae (Podophthalminae Dana, 1851, Thalamitinae, and Lupocyclinae Alcock, 1895) are well supported by molecular phylogenies and the presence of morphological synapomorphies, the others need re-assessment.
Are compound words represented as unitary lexical units, or as individual constituents that are processed combinatorially? We investigated the neuro-cognitive processing of compounds using EEG and a passive-listening oddball design in which lexical access and combinatorial processing elicit dissociating Mismatch Negativity (MMN) brain-response patterns. MMN amplitude varied with compound frequency and semantic transparency (the clarity of the relationship between compound and constituent meanings). Opaque compounds elicited an enhanced 'lexical' MMN, reflecting stronger lexical representations, to high- vs. low-frequency compounds. Transparent compounds showed no frequency effect, nor differed to pseudo-compounds, reflecting the combination of a reduced 'syntactic' MMN indexing combinatorial links, and an enhanced 'lexical' MMN for real-word compounds compared to pseudo-compounds. We argue that transparent compounds are processed combinatorially alongside parallel lexical access of the whole-form representation, but whole-form access is the dominant mechanism for opaque compounds, particularly those of high-frequency. Results support a flexible dual-route account of compound processing.
Spatial component analysis is often used to explore multidimensional time series data whose sources cannot be measured directly. Several methods may be used to decompose the data into a set of spatial components with temporal loadings. Component selection is of crucial importance, and should be supported by objective criteria. In some applications, the use of a well defined component selection criterion may provide for automation of the analysis. In this paper we describe a novel approach for ranking of spatial components calculated from the EEG or MEG data recorded within evoked response paradigm. Our method is called Mutual Information (MI) Spectrum and is based on gauging the amount of MI of spatial component temporal loadings with a synthetically created reference signal. We also describe the appropriate randomization based statistical assessment scheme that can be used for selection of components with statistically significant amount of MI. Using simulated data with realistic trial to trial variations and SNR corresponding to the real recordings we demonstrate the superior performance characteristics of the described MI based measure as compared to a more conventionally used power driven gauge. We also demonstrate the application of the MI Spectrum for the selection of task-related independent components from real MEG data. We show that the MI spectrum allows to identify task-related components reliably in a consistent fashion, yielding stable results even from a small number of trials. We conclude that the proposed method fits naturally the information driven nature of ICA and can be used for routine and automatic ranking of independent components calculated from the functional neuroimaging data collected within event-related paradigms.
Critical conditions for natural selection in multidimensional evolutionary spaces and general requirements following from these conditions and corresponding to the prebiotic evolutionary stage are discussed.