Universidad de Valladolid

Living species are continuously subjected to all extrinsic forms of reactive oxidants and others that are produced endogenously. There is extensive literature on the generation and effects of reactive oxygen species (ROS) in biological processes, both in terms of alteration and their role in cellular signaling and regulatory pathways. Cells produce ROS as a controlled physiological process, but increasing ROS becomes pathological and leads to oxidative stress and disease. The induction of oxidative stress is an imbalance between the production of radical species and the antioxidant defense systems, which can cause damage to cellular biomolecules, including lipids, proteins and DNA. Cellular and biochemical experiments have been complemented in various ways to explain the biological chemistry of ROS oxidants. However, it is often unclear how this translates into chemical reactions involving redox changes. This review addresses this question and includes a robust mechanistic explanation of the chemical reactions of ROS and oxidative stress.

Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives.

A brain-computer interface (BCI) is a hardware and software communications system that permits cerebral activity alone to control computers or external devices. The immediate goal of BCI research is to provide communications capabilities to severely disabled people who are totally paralyzed or 'locked in' by neurological neuromuscular disorders, such as amyotrophic lateral sclerosis, brain stem stroke, or spinal cord injury. Here, we review the state-of-the-art of BCIs, looking at the different steps that form a standard BCI: signal acquisition, preprocessing or signal enhancement, feature extraction, classification and the control interface. We discuss their advantages, drawbacks, and latest advances, and we survey the numerous technologies reported in the scientific literature to design each step of a BCI. First, the review examines the neuroimaging modalities used in the signal acquisition step, each of which monitors a different functional brain activity such as electrical, magnetic or metabolic activity. Second, the review discusses different electrophysiological control signals that determine user intentions, which can be detected in brain activity. Third, the review includes some techniques used in the signal enhancement step to deal with the artifacts in the control signals and improve the performance. Fourth, the review studies some mathematic algorithms used in the feature extraction and classification steps which translate the information in the control signals into commands that operate a computer or other device. Finally, the review provides an overview of various BCI applications that control a range of devices.

We present a Raman scattering study of wurtzite $\mathrm{ZnO}$ over a temperature range from 80 to $750\phantom{\rule{0.3em}{0ex}}\mathrm{K}$. Second-order Raman features are interpreted in the light of recent ab initio phonon density of states calculations. The temperature dependence of the Raman intensities allows the assignment of difference modes to be made unambiguously. Some weak, sharp Raman peaks are detected whose temperature dependence suggests they may be due to impurity modes. High-resolution spectra of the ${E}_{2}^{\mathrm{high}}$, ${A}_{1}(\mathrm{LO})$, and ${E}_{1}(\mathrm{LO})$ modes were recorded, and an analysis of the anharmonicity and lifetimes of these phonons is carried out. The ${E}_{2}^{\mathrm{high}}$ mode displays a visibly asymmetric line shape. This can be attributed to anharmonic interaction with transverse and longitudinal acoustic phonon combinations in the vicinity of the $K$ point, where the two-phonon density of states displays a sharp edge around the ${E}_{2}^{\mathrm{high}}$ frequency. The temperature dependence of the linewidth and frequency of the ${E}_{2}^{\mathrm{high}}$ mode is well described by a perturbation-theory renormalization of the harmonic ${E}_{2}^{\mathrm{high}}$ frequency resulting from the interaction with the acoustic two-phonon density of states. In contrast, the ${A}_{1}(\mathrm{LO})$ and ${E}_{1}(\mathrm{LO})$ frequencies lie in a region of nearly flat two-phonon density of states, and they exhibit a nearly symmetric Lorentzian line shape with a temperature dependence that is well accounted for by a dominating asymmetric decay channel.

Can carbon nanotubes be efficient structural reinforcements for high-strength polymer composites? Here it is shown that, during loading, individual single-walled nanotubes pull out of their bundles (see Figure), making load transfer difficult. It is thus concluded that the effectiveness of reinforcement is determined by the stability and collective behavior of the bundles rather than the strength of individual nanotube components.

Earlier research primarily attributed the effects of mesenchymal stem cell (MSC) therapies to their capacity for local engrafting and differentiating into multiple tissue types. However, recent studies have revealed that implanted cells do not survive for long, and that the benefits of MSC therapy could be due to the vast array of bioactive factors they produce, which play an important role in the regulation of key biologic processes. Secretome derivatives, such as conditioned media or exosomes, may present considerable advantages over cells for manufacturing, storage, handling, product shelf life and their potential as a ready-to-go biologic product. Nevertheless, regulatory requirements for manufacturing and quality control will be necessary to establish the safety and efficacy profile of these products. Among MSCs, human uterine cervical stem cells (hUCESCs) may be a good candidate for obtaining secretome-derived products. hUCESCs are obtained by Pap cervical smear, which is a less invasive and painful method than those used for obtaining other MSCs (for example, from bone marrow or adipose tissue). Moreover, due to easy isolation and a high proliferative rate, it is possible to obtain large amounts of hUCESCs or secretome-derived products for research and clinical use.

We present a comparative study of the energetic, structural, and elastic properties of carbon and composite single-wall nanotubes, including BN, ${\mathrm{BC}}_{3}$, and ${\mathrm{BC}}_{2}\mathrm{N}$ nanotubes, using a nonorthogonal tight-binding formalism. Our calculations predict that carbon nanotubes have a higher Young modulus than any of the studied composite nanotubes, and of the same order as that found for defect-free graphene sheets. We obtain good agreement with the available experimental results.

Abstract The genetic make-up of an individual contributes to the susceptibility and response to viral infection. Although environmental, clinical and social factors have a role in the chance of exposure to SARS-CoV-2 and the severity of COVID-19 1,2 , host genetics may also be important. Identifying host-specific genetic factors may reveal biological mechanisms of therapeutic relevance and clarify causal relationships of modifiable environmental risk factors for SARS-CoV-2 infection and outcomes. We formed a global network of researchers to investigate the role of human genetics in SARS-CoV-2 infection and COVID-19 severity. Here we describe the results of three genome-wide association meta-analyses that consist of up to 49,562 patients with COVID-19 from 46 studies across 19 countries. We report 13 genome-wide significant loci that are associated with SARS-CoV-2 infection or severe manifestations of COVID-19. Several of these loci correspond to previously documented associations to lung or autoimmune and inflammatory diseases 3–7 . They also represent potentially actionable mechanisms in response to infection. Mendelian randomization analyses support a causal role for smoking and body-mass index for severe COVID-19 although not for type II diabetes. The identification of novel host genetic factors associated with COVID-19 was made possible by the community of human genetics researchers coming together to prioritize the sharing of data, results, resources and analytical frameworks. This working model of international collaboration underscores what is possible for future genetic discoveries in emerging pandemics, or indeed for any complex human disease.

Non-destructive techniques are used widely in the metal industry in order to control the quality of materials. Eddy current testing is one of the most extensively used non-destructive techniques for inspecting electrically conductive materials at very high speeds that does not require any contact between the test piece and the sensor. This paper includes an overview of the fundamentals and main variables of eddy current testing. It also describes the state-of-the-art sensors and modern techniques such as multi-frequency and pulsed systems. Recent advances in complex models towards solving crack-sensor interaction, developments in instrumentation due to advances in electronic devices, and the evolution of data processing suggest that eddy current testing systems will be increasingly used in the future.

A study based on ab initio calculations is presented on the structural, elastic, and vibrational properties of single-wall carbon nanotubes with different radii and chiralities. These properties are obtained using an implementation of pseudopotential-density-functional theory, which allows calculations on systems with a large number of atoms per cell. Different quantities are monitored versus tube radius. The validity of expectations based on graphite is explored down to small radii, where some deviations appear related to the curvature-induced rehybridization of the carbon orbitals. Young moduli are found to be very similar to graphite and do not exhibit a systematic variation with either the radius or the chirality. The Poisson ratio also retains graphitic values except for a possible slight reduction for small radii. It shows, however, chirality dependence. The behavior of characteristic phonon branches as the breathing mode, twistons, and high-frequency optic modes, is also studied, the latter displaying a small chirality dependence at the top of the band. The results are compared with the predictions of the simple zone-folding approximation. Except for the known deficiencies of the zone-folding procedure in the low-frequency vibrational regions, it offers quite accurate results, even for relatively small radii.

Eighteen years ago in Angewandte Chemie John K. Stille reviewed a novel methodology, which eventually became known by his name, for the coupling of organostannanes with organic electrophiles. Since then that seed has blossomed into a multifaceted methodology full of hidden possibilities to explore, discover, and enjoy. Very recent modifications are making synthetic wishes come true that were only dreamed of a few years ago. Moreover, as important advances are being made in the understanding of the mechanistic details of the process, it is becoming increasingly possible to apply this essential reaction and its new variants in a less empirical way. The purpose of this Review is to give a critical account of this progress.

With all learning institutions pre-maturely closed on 20 March 2020 and all citizens advised to self-isolate in a bid to control the spread of COVID-19, it was hypothesized that COVID-19 would negatively impact on the performance of students in the 2020 Grade 12 national examinations vis-à-vis mathematics, science and design and technology subjects. An observed steady increase in the number of COVID-19 confirmed cases and the low levels of technology use in secondary schools in Zambia due to limited technology resources signifies a very difficult period in a young country which has just rolled out a nation-wide implementation of STEM education, This study collected data from three teachers at a public secondary school in Chipata District of Eastern Province in the Republic of Zambia. The Head of Department for Mathematics, the Head of Natural Sciences Department and one science teacher were interviewed. Semi-structured interviews via mobile phone were used to collect views of what these specialists thought would be the COVID-19 effects on the general performance of students in their subject areas. Results of this study revealed that there is likely to be a drop in the pass percentage of secondary school students in this year’s national examinations if the COVID-19 epidemic is not contained in the shortest possible time considering that the school academic calendar was abruptly disturbed by the early untimely closure of all schools in the country.

The current need for large multimodal databases to evaluate automatic biometric recognition systems has motivated the development of the MCYT bimodal database. The main purpose has been to consider a large scale population, with statistical significance, in a real multimodal procedure, and including several sources of variability that can be found in real environments. The acquisition process, contents and availability of the single-session baseline corpus are fully described. Some experiments showing consistency of data through the different acquisition sites and assessing data quality are also presented.

A network traffic classifier (NTC) is an important part of current network monitoring systems, being its task to infer the network service that is currently used by a communication flow (e.g., HTTP and SIP). The detection is based on a number of features associated with the communication flow, for example, source and destination ports and bytes transmitted per packet. NTC is important, because much information about a current network flow can be learned and anticipated just by knowing its network service (required latency, traffic volume, and possible duration). This is of particular interest for the management and monitoring of Internet of Things (IoT) networks, where NTC will help to segregate traffic and behavior of heterogeneous devices and services. In this paper, we present a new technique for NTC based on a combination of deep learning models that can be used for IoT traffic. We show that a recurrent neural network (RNN) combined with a convolutional neural network (CNN) provides best detection results. The natural domain for a CNN, which is image processing, has been extended to NTC in an easy and natural way. We show that the proposed method provides better detection results than alternative algorithms without requiring any feature engineering, which is usual when applying other models. A complete study is presented on several architectures that integrate a CNN and an RNN, including the impact of the features chosen and the length of the network flows used for training.

In recent years, honeybees (Apis mellifera) have been strangely disappearing from their hives, and strong colonies have suddenly become weak and died. The precise aetiology underlying the disappearance of the bees remains a mystery. However, during the same period, Nosema ceranae, a microsporidium of the Asian bee Apis cerana, seems to have colonized A. mellifera, and it's now frequently detected all over the world in both healthy and weak honeybee colonies. For first time, we show that natural N. ceranae infection can cause the sudden collapse of bee colonies, establishing a direct correlation between N. ceranae infection and the death of honeybee colonies under field conditions. Signs of colony weakness were not evident until the queen could no longer replace the loss of the infected bees. The long asymptomatic incubation period can explain the absence of evident symptoms prior to colony collapse. Furthermore, our results demonstrate that healthy colonies near to an infected one can also become infected, and that N. ceranae infection can be controlled with a specific antibiotic, fumagillin. Moreover, the administration of 120 mg of fumagillin has proven to eliminate the infection, but it cannot avoid reinfection after 6 months. We provide Koch's postulates between N. ceranae infection and a syndrome with a long incubation period involving continuous death of adult bees, non-stop brood rearing by the bees and colony loss in winter or early spring despite the presence of sufficient remaining pollen and honey.

In recent years, the debate about the efficiency of corporate governance mechanisms has focused on the activity of the corporate boards of directors. This paper analyses the effect of the size of the board, its composition and internal functioning on firm value in a sample of 450 non-financial companies from ten countries in Western Europe and North America. The econometric method combines uniequational regression analysis with simultaneous equations in order to control for the possibility of board size and composition endogeneity. The results show a negative relationship between firm value and the size of the board of directors. This relation holds when we control for alternative definitions of firm size and for board composition, the board's internal functioning, country effect and industry effect. We find no significant relationship between the composition of the board and the value of the firm. These results are consistent with previous relevant papers and show that companies with oversized boards of directors have poorer performance both in countries where internal mechanisms of governance dominate and in countries where external mechanisms are predominant.

Modern strategic management theories try to explain why firms differ, because new sources of competitive advantage are keenly sought in the dynamic and complex environment of global competition. Two areas in particular have attracted the attention of researchers: the role of dynamic capabilities, and the firm's abilities for knowledge management. In this paper, we argue that there is a link between these two concepts, which has not been fully articulated in the literature. The aim of the paper is therefore to ascertain the conceptual connection between them as a basis for future research. Our proposed framework acknowledges and critiques the distinct roots of each field, identifies boundaries, and proposes relationships between the constructs and firm performance.

The importance, extent, and mode of interspecific gene flow for the evolution of species has long been debated. Characterization of genomic differentiation in a classic example of hybridization between all-black carrion crows and gray-coated hooded crows identified genome-wide introgression extending far beyond the morphological hybrid zone. Gene expression divergence was concentrated in pigmentation genes expressed in gray versus black feather follicles. Only a small number of narrow genomic islands exhibited resistance to gene flow. One prominent genomic region (<2 megabases) harbored 81 of all 82 fixed differences (of 8.4 million single-nucleotide polymorphisms in total) linking genes involved in pigmentation and in visual perception-a genomic signal reflecting color-mediated prezygotic isolation. Thus, localized genomic selection can cause marked heterogeneity in introgression landscapes while maintaining phenotypic divergence.

Abstract Previous genome-wide association studies (GWASs) of stroke — the second leading cause of death worldwide — were conducted predominantly in populations of European ancestry 1,2 . Here, in cross-ancestry GWAS meta-analyses of 110,182 patients who have had a stroke (five ancestries, 33% non-European) and 1,503,898 control individuals, we identify association signals for stroke and its subtypes at 89 (61 new) independent loci: 60 in primary inverse-variance-weighted analyses and 29 in secondary meta-regression and multitrait analyses. On the basis of internal cross-ancestry validation and an independent follow-up in 89,084 additional cases of stroke (30% non-European) and 1,013,843 control individuals, 87% of the primary stroke risk loci and 60% of the secondary stroke risk loci were replicated ( P &lt; 0.05). Effect sizes were highly correlated across ancestries. Cross-ancestry fine-mapping, in silico mutagenesis analysis 3 , and transcriptome-wide and proteome-wide association analyses revealed putative causal genes (such as SH3PXD2A and FURIN ) and variants (such as at GRK5 and NOS3 ). Using a three-pronged approach 4 , we provide genetic evidence for putative drug effects, highlighting F11, KLKB1, PROC, GP1BA, LAMC2 and VCAM1 as possible targets, with drugs already under investigation for stroke for F11 and PROC. A polygenic score integrating cross-ancestry and ancestry-specific stroke GWASs with vascular-risk factor GWASs (integrative polygenic scores) strongly predicted ischaemic stroke in populations of European, East Asian and African ancestry 5 . Stroke genetic risk scores were predictive of ischaemic stroke independent of clinical risk factors in 52,600 clinical-trial participants with cardiometabolic disease. Our results provide insights to inform biology, reveal potential drug targets and derive genetic risk prediction tools across ancestries.

In this paper we address the problem of the numerical integration of the time-dependent Schrodinger equation i partial differential (t)phi=Hphi. In particular, we are concerned with the important case where H is the self-consistent Kohn-Sham Hamiltonian that stems from time-dependent functional theory. As the Kohn-Sham potential depends parametrically on the time-dependent density, H is in general time dependent, even in the absence of an external time-dependent field. The present analysis also holds for the description of the excited state dynamics of a many-electron system under the influence of arbitrary external time-dependent electromagnetic fields. Our discussion is separated in two parts: (i) First, we look at several algorithms to approximate exp(A), where A is a time-independent operator [e.g., A=-iDeltatH(tau) for some given time tau]. In particular, polynomial expansions, projection in Krylov subspaces, and split-operator methods are investigated. (ii) We then discuss different approximations for the time-evolution operator, such as the midpoint and implicit rules, and Magnus expansions. Split-operator techniques can also be modified to approximate the full time-dependent propagator. As the Hamiltonian is time dependent, problem (ii) is not equivalent to (i). All these techniques have been implemented and tested in our computer code OCTOPUS, but can be of general use in other frameworks and implementations.