Sugar shock: Probing Streptococcus pyogenes metabolism through bioluminescence imaging

Streptococcus pyogenes (S. pyogenes) can thrive in its host during an infection, and, as a result, it must be able to respond to external stimuli and available carbon sources. The pre-clinical use of engineered pathogens capable of constitutive light production may provide real-time information on microbial-specific metabolic processes. Here we mapped the central metabolism of a luxABCDE-modified S. pyogenes Xen20 (Strep. Xen20) to its de novo synthesis of luciferase substrates as assessed by the rate of light production in response to different environmental triggers. Previous characterization predicted that the lux operon was under the myo-inositol iolE promotor. Here we show that supplementation with myo-inositol generated increased Xen20 luminescence. Surprisingly, when supplemented with infection-relevant carbon sources, such as glucose or glycine, light production was diminished. This was presumably due to the scavenging of pyruvate by L-lactate dehydrogenase (LDH). Inhibition of LDH by its inhibitor, oxamate, partially restored luminescent signal in the presence of glucose, presumably by allowing the resulting pyruvate to proceed to acetyl-coenzyme A (CoA). This phenomenon appeared specific to the lactic acid bacterial metabolism as glucose or glycine did not reduce signal in an analogous luxABCDE-modified Gram-positive pathogen, Staph. Xen29. The Strep. Xen20 cells produced light in a concentration-dependent manner, inversely related to the amount of glucose present. Taken together, our measures of microbial response could provide new information regarding the responsiveness of S. pyogenes metabolism to acute changes in its local environments and cellular health.

Identifying the impact of the covalent-bonded carbon matrix to FeN4 sites for acidic oxygen reduction

The atomic configurations of FeNx moieties are the key to affect the activity of oxygen rection reaction (ORR). However, the traditional synthesis relying on high-temperature pyrolysis towards combining sources of Fe, N, and C often results in the plurality of local environments for the FeNx sites. Unveiling the effect of carbon matrix adjacent to FeNx sites towards ORR activity is important but still is a great challenge due to inevitable connection of diverse N as well as random defects. Here, we report a proof-of-concept study on the evaluation of covalent-bonded carbon environment connected to FeN4 sites on their catalytic activity via pyrolysis-free approach. Basing on the closed π conjugated phthalocyanine-based intrinsic covalent organic polymers (COPs) with well-designed structures, we directly synthesized a series of atomically dispersed Fe-N-C catalysts with various pure carbon environments connected to the same FeN4 sites. Experiments combined with density functional theory demonstrates that the catalytic activities of these COPs materials appear a volcano plot with the increasement of delocalized π electrons in their carbon matrix. The delocalized π electrons changed anti-bonding d-state energy level of the single FeN4 moieties, hence tailored the adsorption between active centers and oxygen intermediates and altered the rate-determining step.

MIDI-AM Model to Identify a Methodology for the Creation of Innovative Educational Digital Games

Educational game applications' production considers technical, pedagogical, and aesthetic resources guided by the type of device used, interfaces, and themes. In addition, it considers users' tastes and preferences, trends in society, and the environment. However, no evidence in the recent literature allows developers to identify entire patterns or structures in serious games production. This chapter analyzes university experiences and research related to the design, development, and use of ludic games application for mobile devices' MIDI-AM model series games as tested in local environments. These games are aimed at children in their first years of primary school education. The MIDI-AM model is proposed as a methodology that considers a technical and aesthetic platform to guide serious game applications' production and monitoring. These are suggested as supporting tools for the traditional teaching-learning process.

Multiscale selection in spatially structured populations

The spatial structure of natural populations is key to many of their evolutionary processes. Formal theories analysing the interplay between natural selection and spatial structure have mostly focused on populations divided into distinct, non-overlapping groups. Most populations, however, are not structured in this way, but rather (self-)organise into dynamic patterns unfolding at various spatial scales. Here, we present a mathematical framework that quantifies how patterns and processes at different spatial scales contribute to natural selection in such populations. To that end, we define the Local Selection Differential (LSD): a measure of the selection acting on a trait within a given local environment. Based on the LSD, natural selection in a population can be decomposed into two parts: the contribution of local selection, acting within local environments, and the contribution of interlocal selection, acting among them. Varying the size of the local environments subsequently allows one to measure the contribution of each length scale. To illustrate the use of this new multiscale selection framework, we apply it to two simulation models of the evolution of traits known to be affected by spatial population structure: altruism and pathogen transmissibility. In both models, the spatial decomposition of selection reveals that local and interlocal selection can have opposite signs, thus providing a mathematically rigorous underpinning to intuitive explanations of how processes at different spatial scales may compete. It furthermore identifies which length scales - and hence which patterns - are relevant for natural selection. The multiscale selection framework can thus be used to address complex questions on evolution in spatially structured populations.

Manipulating local environment of atomically dispersed Fe electrocatalyst for high energy and long cycle Li-S pouch cell

High-energy lithium-sulfur (Li-S) pouch cell are limited by the insufficient capacities and stabilities of their cathodes under practical electrolyte/sulfur (E/S), electrolyte/capacity (E/C), and negative/positive (N/P) ratios. Herein, we fabricated an advanced cathode comprising highly active Fe single atom catalysts (SACs) and porous carbon to attain 361.8 W h kg−1 Li-S pouch cells satisfying low E/S (2.0), E/C (1.9), and N/P (2.3) ratios with high sulfur loadings (8.4 mg cm−2). We designed high-activity Fe SACs by manipulating their local environments using electron exchangeable binding (EEB) sites. Introducing EEB sites composed of two different types of S species, namely, thiophene-like-S and oxidized-S, adjacent to Fe SACs, promoted the kinetics of the Li2S redox reaction by providing additional binding sites and modulating the Fe d-orbital levels via electron exchange with Fe. This desirable cathode electrocatalysis is maximized by the intimate contact of Fe SACs with the S species, confined together into porous carbon.

The Case of the Kumbhalgarh Wildlife Sanctuary and Camel Pastoralism in Rajasthan (India)

The Indian forest management system introduced during colonial times has led to the progressive loss of the grazing rights of the country’s pastoralists, culminating in the abolishment of grazing fees and replacement with grazing fines in 2004. This scenario has had a negative knock-on effect on the conservation of many of the livestock breeds that pastoralists have developed in adaptation to local environments and that are the basis of the country’s food security. This paper illustrates the dilemma with the example of the Kumbhalgarh Wildlife Sanctuary (KWS) in Rajasthan that represents the traditional monsoon grazing area for local camel, sheep and goat pastoralists. Raika herders have engaged in a long-standing but losing legal battle with the state for their continued seasonal access to this area. This situation contributes to the rapid decline of the camel which is an iconic part of Rajasthan’s desert identity, a major attraction for tourists and was declared state animal in 2014. The aims of the forest department to conserve wild animals and those of pastoralists and camel conservationists could easily be integrated into a more equitable governance system as is endorsed by Aichi Target 11 of the CBD Strategic Plan 2011–2020. However, deeply engrained concepts about nature being separate from (agri-)culture, as well as unequal power structures, stand in the way.

Inuvialuit Knowledge of Pacific Salmon Range Expansion in the Western Canadian Arctic

Rapid climate change is altering Arctic ecosystems and significantly affecting the livelihoods and cultural traditions of Arctic Indigenous peoples. In the Inuvialuit Settlement Region (ISR), an increase in the harvest of Pacific salmon indicates largescale changes influencing Inuvialuit fisheries. In this project we recorded and synthesized Inuvialuit knowledge of Pacific salmon. We conducted 54 interviews with Inuvialuit fishers about the history of Pacific salmon harvest, how it has changed in recent decades, and concurrent changes to local environments and fish species. Our interviews show that historic, incidental salmon harvest in the ISR ranged from infrequent to common among western communities, but was rare or unprecedented among eastern communities. Participants in all six communities reported a recent increase in salmon harvest and attributed this shift to regional environmental change. Fishers were concerned that salmon would negatively affect their cultural traditions and preferred fish species. Given uncertainty about the effects of salmon on local fisheries, research on salmon diets in the Arctic, their subsidies to Arctic freshwater systems, and the likelihood of their establishment is vital.

Quantum Correlated Heat Engine In XY Chainwith Dzyaloshinskii-Moriya Interactions

In this paper, we consider a heat engines composed of two interactionalqubits with spin-orbit interaction (Dzyaloshinskii–Moriya (DM)) subjectto an external magnetic field, so that each qubit is considered as a cold orhot source. One intention of this work is to investigate the following question: is it possible the effects of DM lead to improve basic thermodynamicquantities in this heat engine are coupled to local environments that arenot necessarily at equilibrium? For this end, we investigate the effects ofthe temperature and the interaction rate of each qubit with its surrounding environment on quantum correlations such as quantum coherence andquantum discord and quantum entanglements, as well as the generatedwork. Finally we compare three quantum correlations (entanglement, discord, and coherence) with thermodynamic parameters and show that theoutput work is positive for what values of the magnetic field so that thiscycle can be considered as a thermal machine.

Enhanced Mineral Quantification and Uncertainty Analysis from Downhole Spectroscopy Logs Using Variational Autoencoders

This paper describes an innovative machine-learning application, based on variational autoencoder frameworks, to quantify the concentrations and associated uncertainties of common minerals in sedimentary formations using the measurement of atomic element concentrations from geochemical spectroscopy logs as inputs. The algorithm comprises an input(s), encoder, decoder, output(s), and a novel cost function to optimize the model coefficients during training. The input to the algorithm is a set of dry-weight concentrations of atomic elements with their associated uncertainty. The first output is a set of dry-weight fractions of 14 minerals, and the second output is a set of reconstructed dry-weight concentrations of the original elements. Both sets of outputs include estimates of uncertainty on their predictions. The encoder and decoder are multilayer feed-forward artificial neural networks (ANN), with their coefficients (weights) optimized during calibration (training). The cost function simultaneously minimizes error (accuracy metric) and variance (precision or robustness metric) on the mineral and reconstructed elemental outputs. Training of the weights is done using a set of several-thousand core samples with independent, high-fidelity elemental and mineral (quartz, potassium-feldspar, plagioclase-feldspar, illite, smectite, kaolinite, chlorite, mica, calcite, dolomite, ankerite, siderite, pyrite, and anhydrite) data. The algorithm provides notable advantages over existing methods to estimate formation lithology or mineralogy relying on simple linear, empirical, or nearest-neighbor functions. The ANN numerically capture the multidimensional and nonlinear geochemical relationship (mapping) between elements and minerals that is insufficiently described by prior methods. Training is iterative via backpropagation and samples from Gaussian distributions on each of the elemental inputs, rather than single values, for every sample at each iteration (epoch). These Gaussian distributions are chosen to specifically represent the unique statistical uncertainty of the dry-weight elements in the logging measurements. Sampling from Gaussian distributions during training reduces the potential for overfitting, provides robustness for log interpretations, and further enables a calibrated estimate of uncertainty on the mineral and reconstructed elemental outputs, all of which are lacking in prior methods. The framework of the algorithm is purposefully generalizable so that it can be adapted across geochemical spectroscopy tools. The algorithm reasonably approximates a “global-average” model that requires neither different calibrations nor expert parameterization or intervention for interpreting common oilfield sedimentary formations, although the framework is again purposefully generalizable so it can be optimized for local environments where desirable. The paper showcases a field application of the method for estimating mineral type and abundance in oilfield formations from wellbore-logging measurements.

Genome Assembly and Population Resequencing Reveal the Geographical Divergence of 'Shanmei'(Rubus corchorifolius)

Rubus corchorifolius (Shanmei or mountain berry, 2n =14) is widely distributed in China, and its fruit has high nutritional and medicinal values. Here, we report a high-quality chromosome-scale genome assembly of Shanmei, with a size of 215.69 Mb and encompassing 26696 genes. Genome comparisons among Rosaceae species show that Shanmei and Fupenzi(Rubus chingii Hu) are most closely related, and then is blackberry (Rubus occidentalis). Further resequencing of 101 samples of Shanmei collected from four regions in provinces of Yunnan, Hunan, Jiangxi and Sichuan in South China reveals that the Hunan population of Shanmei possesses the highest diversity and may represent the relatively more ancestral population. Moreover, the Yunnan population undergoes strong selection based on nucleotide diversity, linkage disequilibrium and the historical effective population size analyses. Furthermore, genes from candidate genomic regions that show strong divergence are significantly enriched in flavonoid biosynthesis and plant hormone signal transduction, indicating the genetic basis of adaptation of Shanmei to the local environments. The high-quality genome sequences and the variome dataset of Shanmei provide valuable resources for breeding applications and for elucidating the genome evolution and ecological adaptation of Rubus species.