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Most existing TWAS tools require individual-level eQTL reference data and thus are not applicable to summary-level reference eQTL datasets. The development of TWAS methods that can harness summary-level reference data is valuable to enable TWAS in broader settings and enhance power due to increased reference sample size. Thus, we develop a TWAS framework called OTTERS (Omnibus Transcriptome Test using Expression Reference Summary data) that adapts multiple polygenic risk score (PRS) methods to estimate eQTL weights from summary-level eQTL reference data and conducts an omnibus TWAS. We show that OTTERS is a practical and powerful TWAS tool by both simulations and application studies. Godkänd;2023;Nivå 0;2023-04-06 (hanlid);Funder: for more funders see the article https://doi.org/10.1038/s41467-023-36862-w

Plant productivity varies due to environmental heterogeneity, and theory suggests that plant diversity can reduce this variation. While there is strong evidence of diversity effects on temporal variability of productivity, whether this mechanism extends to variability across space remains elusive. Here we determine the relationship between plant diversity and spatial variability of productivity in 83 grasslands, and quantify the effect of experimentally increased spatial heterogeneity in environmental conditions on this relationship. We found that communities with higher plant species richness (alpha and gamma diversity) have lower spatial variability of productivity as reduced abundance of some species can be compensated for by increased abundance of other species. In contrast, high species dissimilarity among local communities (beta diversity) is positively associated with spatial variability of productivity, suggesting that changes in species composition can scale up to affect productivity. Experimentally increased spatial environmental heterogeneity weakens the effect of plant alpha and gamma diversity, and reveals that beta diversity can simultaneously decrease and increase spatial variability of productivity. Our findings unveil the generality of the diversity-stability theory across space, and suggest that reduced local diversity and biotic homogenization can affect the spatial reliability of key ecosystem functions.

Availability of light and CO2, substrates of microalgae photosynthesis, is frequently far from optimal. Microalgae activate photoprotection under strong light, to prevent oxidative damage, and the CO2 Concentrating Mechanism (CCM) under low CO2, to raise intracellular CO2 levels. The two processes are interconnected; yet, the underlying transcriptional regulators remain largely unknown. Employing a large transcriptomic data compendium of Chlamydomonas reinhardtii’s responses to different light and carbon supply, we reconstruct a consensus genome-scale gene regulatory network from complementary inference approaches and use it to elucidate transcriptional regulators of photoprotection. We show that the CCM regulator LCR1 also controls photoprotection, and that QER7, a Squamosa Binding Protein, suppresses photoprotection- and CCM-gene expression under the control of the blue light photoreceptor Phototropin. By demonstrating the existence of regulatory hubs that channel light- and CO2-mediated signals into a common response, our study provides an accessible resource to dissect gene expression regulation in this microalga.

Fast, efficient information transfer is essential for the brain to ensure survival. As recently shown in functional magnetic resonance imaging with high spatial resolution, turbulence appears to offer a fundamental way to facilitate energy and information transfer across spatiotemporal scales in brain dynamics. However, given that this imaging modality is comparably slow and not directly linked with neuronal activity, here we investigated the existence of turbulence in fast whole-brain neural dynamics measured with magnetoencephalography (MEG). The coarse spatial observations in MEG necessitated that we created and validated a empirical measure of turbulence. We found that the measure of edge-centric metastability perfectly detected turbulence in a ring of non-local coupled oscillators where the ground-truth was analytically known, even at a coarse spatial scale of observations. This allowed us to use this measure in the spatially coarse, empirical large-scale MEG data from 89 human participants. We demonstrated turbulence in fast neuronal dynamics and used this to quantify information transfer in the brain. The results demonstrate that the necessary efficiency of brain function is dependent on an underlying turbulent regime. G.D. is supported by the Human Brain Project Specific Grant Agreement 3 Grant agreement no. 945539 and by the Spanish Research Project AWAKENING: using whole-brain models perturbational approaches for predicting external stimulation to force transitions between different brain states, ref. PID2019-105772GB-I00/AEI/10.13039/501100011033, financed by the Spanish Ministry of Science, Innovation and Universities (MCIU), State Research Agency (AEI). Y.S.P is supported by European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant 896354. M.L.K. is supported by the Centre for Eudaimonia and Human Flourishing (funded by the Pettit and Carlsberg Foundations) and Center for Music in the Brain (funded by the Danish National Research Foundation, DNRF117).

The wiring of the brain connects micro-architecturally diverse neuronal populations. The conventional graph model encodes macroscale brain connectivity as a network of nodes and edges, but abstracts away the rich biological detail of each regional node. Regions are different in terms of their microscale attributes, many of which are readily available through modern technological advances and data-sharing initiatives. How is macroscale connectivity related to nodal attributes? Here we investigate the systematic arrangement of white-matter connectivity with respect to multiple biological annotations. Namely, we formally study assortative mixing in annotated connectomes by quantifying the tendency for regions to be connected with each other based on the similarity of their micro-architectural attributes. We perform all experiments using four cortico-cortical connectome datasets from three different species (human, macaque and mouse), and consider a range of molecular, cellular and laminar annotations, including gene expression, neurotransmitter receptors, neuron density, laminar thickness and intracortical myelin. Importantly, we disentangle the relationship between neural wiring, regional heterogeneity and spatial embedding using spatial autocorrelation-preserving null models. We show that mixing between micro-architecturally diverse neuronal populations is supported by long-distance connections. Using meta-analytic decoding, we find that the arrangement of connectivity patterns with respect to biological annotations shape patterns of regional functional specialization. Specifically, regions that connect to biologically similar regions are associated with executive function; conversely, regions that connect with biologically dissimilar regions are associated with memory function. By bridging scales of cortical organization, from microscale attributes to macroscale connectivity, this work lays the foundation for next-generation annotated connectomics.

13 p.-6 fig., The yeast Ty1 retrotransposon integrates upstream of genes transcribed by RNA polymerase III (Pol III). Specificity of integration is mediated by an interaction between the Ty1 integrase (IN1) and Pol III, currently uncharacterized at the atomic level. We report cryo-EM structures of Pol III in complex with IN1, revealing a 16-residue segment at the IN1 C-terminus that contacts Pol III subunits AC40 and AC19, an interaction that we validate by in vivo mutational analysis. Binding to IN1 associates with allosteric changes in Pol III that may affect its transcriptional activity. The C-terminal domain of subunit C11, involved in RNA cleavage, inserts into the Pol III funnel pore, providing evidence for a two-metal mechanism during RNA cleavage. Additionally, ordering next to C11 of an N-terminal portion from subunit C53 may explain the connection between these subunits during termination and reinitiation. Deletion of the C53 N-terminal region leads to reduced chromatin association of Pol III and IN1, and a major fall in Ty1 integration events. Our data support a model in which IN1 binding induces a Pol III configuration that may favor its retention on chromatin, thereby improving the likelihood of Ty1 integration., This work was supported by the Agence Nationale de la Recherche (ANR-17-CE11-0025 to P.L., J.A., J.R. and C.F.-T.), the Spanish Ministry of Science/Agencia Estatal de Investigación (BFU2017-87397-P, RED2018-102467-T and PID2020-116722GB-I00 to C.F.-T.; PRE2018-087012 to A.P.-P.), intramural funding from CSIC (2020AEP152 and PIE-202120E047-Conexiones-Life to C. F.-T.), Fondation pour la Recherche Médicale (FRM-EQU202203014635 to P.L.; FRM-SPF20170938755 to A.A.-L.; FRM-ECO202206015504 to B.C.), and intramural funding from CNRS, Université Paris Cité and INSERM (to P.L.).

Lassa fever hits West African countries annually in the absence of licensed vaccine to limit the burden of this viral hemorrhagic fever. We previously developed MeV-NP, a single-shot vaccine protecting cynomolgus monkeys against divergent strains one month or more than a year before Lassa virus infection. Given the limited dissemination area during outbreaks and the risk of nosocomial transmission, a vaccine inducing rapid protection could be useful to protect exposed people during outbreaks in the absence of preventive vaccination. Here, we test whether the time to protection can be reduced after immunization by challenging measles virus pre-immune male cynomolgus monkeys sixteen or eight days after a single shot of MeV-NP. None of the immunized monkeys develop disease and they rapidly control viral replication. Animals immunized eight days before the challenge are the best controllers, producing a strong CD8 T-cell response against the viral glycoprotein. A group of animals was also vaccinated one hour after the challenge, but was not protected and succumbed to the disease as the control animals. This study demonstrates that MeV-NP can induce a rapid protective immune response against Lassa fever in the presence of MeV pre-existing immunity but can likely not be used as therapeutic vaccine.

Giant viruses (GVs) are a hotspot of unresolved controversies since their discovery, including the definition of “Virus” and their origin. While increasing knowledge of genome diversity has accumulated, GV functional genomics was largely neglected. Here, we describe an experimental framework to genetically modify nuclear GVs and their host Acanthamoeba castellanii using CRISPR/Cas9, shedding light on the evolution from small icosahedral viruses to amphora-shaped GVs. Ablation of the icosahedral major capsid protein in the phylogenetically-related mollivirus highlights a transition in virion shape and size. We additionally demonstrate the existence of a reduced core essential genome in pandoravirus, reminiscent of their proposed smaller ancestors. This proposed genetic expansion led to increased genome robustness, indicating selective pressures for adaptation to uncertain environments. Overall, we introduce new tools for manipulation of the unexplored genome of nuclear GVs and provide experimental evidence suggesting that viral gigantism has aroused as an emerging trait.

The cell nucleus is a primary target for intracellular bacterial pathogens to counteract immune responses and hijack host signalling pathways to cause disease. Here we identify two Brucella abortus effectors, NyxA and NyxB, that interfere with host protease SENP3, and this facilitates intracellular replication of the pathogen. The translocated Nyx effectors directly interact with SENP3 via a defined acidic patch (identified from the crystal structure of NyxB), preventing nucleolar localisation of SENP3 at late stages of infection. By sequestering SENP3, the effectors promote cytoplasmic accumulation of nucleolar AAA-ATPase NVL and ribosomal protein L5 (RPL5) in effector-enriched structures in the vicinity of replicating bacteria. The shuttling of ribosomal biogenesis-associated nucleolar proteins is inhibited by SENP3 and requires the autophagy-initiation protein Beclin1 and the SUMO-E3 ligase PIAS3. Our results highlight a nucleomodulatory function of two Brucella effectors and reveal that SENP3 is a crucial regulator of the subcellular localisation of nucleolar proteins during Brucella infection, promoting intracellular replication of the pathogen.

Funding Information: This study was partly supported by the Centre National d’Études Spatiale (CNES) through the project “Hydrodynamics of wet foams". A.S. acknowledges Véronique Trappe and Eric Dufresne for interesting discussions. Publisher Copyright: © 2023, The Author(s). Foams are unstable jammed materials. They evolve over timescales comparable to their “time of use", which makes the study of their destabilisation mechanisms crucial for applications. In practice, many foams are made from viscoelastic fluids, which are observed to prolong their lifetimes. Despite their importance, we lack understanding of the coarsening mechanism in such systems. We probe the effect of continuous phase viscoelasticity on foam coarsening with foamed emulsions. We show that bubble size evolution is strongly slowed down and foam structure hugely impacted. The main mechanisms responsible are the absence of continuous phase redistribution and a non-trivial link between foam structure and mechanical properties. These combine to give spatially heterogeneous coarsening. Beyond their importance in the design of foamy materials, the results give a macroscopic vision of phase separation in a viscoelastic medium.

Antigen binding by B cell receptors (BCR) on cognate B cells elicits a response that eventually leads to production of antibodies. However, it is unclear what the distribution of BCRs is on the naïve B cell and how antigen binding triggers the first step in BCR signaling. Using DNA-PAINT super-resolution microscopy, we find that most BCRs are present as monomers, dimers, or loosely associated clusters on resting B cells, with a nearest-neighbor inter-Fab distance of 20–30 nm. We leverage a Holliday junction nanoscaffold to engineer monodisperse model antigens with precision-controlled affinity and valency, and find that the antigen exerts agonistic effects on the BCR as a function of increasing affinity and avidity. Monovalent macromolecular antigens can activate the BCR at high concentrations, whereas micromolecular antigens cannot, demonstrating that antigen binding does not directly drive activation. Based on this, we propose a BCR activation model determined by the antigen footprint.

New satellite remote sensing and machine learning techniques offer untapped possibilities to monitor global biodiversity with unprecedented speed and precision. These efficiencies promise to reveal novel ecological insights at spatial scales which are germane to the management of populations and entire ecosystems. Here, we present a robust transferable deep learning pipeline to automatically locate and count large herds of migratory ungulates (wildebeest and zebra) in the Serengeti-Mara ecosystem using fine-resolution (38-50 cm) satellite imagery. The results achieve accurate detection of nearly 500,000 individuals across thousands of square kilometers and multiple habitat types, with an overall F1-score of 84.75% (Precision: 87.85%, Recall: 81.86%). This research demonstrates the capability of satellite remote sensing and machine learning techniques to automatically and accurately count very large populations of terrestrial mammals across a highly heterogeneous landscape. We also discuss the potential for satellite-derived species detections to advance basic understanding of animal behavior and ecology.

Mutations in glucocerebrosidase cause the lysosomal storage disorder Gaucher’s disease and are the most common risk factor for Parkinson’s disease. Therapies to restore the enzyme’s function in the brain hold great promise for treating the neurological implications. Thus, we developed blood-brain barrier penetrant therapeutic molecules by fusing transferrin receptor-binding moieties to β-glucocerebrosidase (referred to as GCase-BS). We demonstrate that these fusion proteins show significantly increased uptake and lysosomal efficiency compared to the enzyme alone. In a cellular disease model, GCase-BS rapidly rescues the lysosomal proteome and lipid accumulations beyond known substrates. In a mouse disease model, intravenous injection of GCase-BS leads to a sustained reduction of glucosylsphingosine and can lower neurofilament-light chain plasma levels. Collectively, these findings demonstrate the potential of GCase-BS for treating GBA1-associated lysosomal dysfunction, provide insight into candidate biomarkers, and may ultimately open a promising treatment paradigm for lysosomal storage diseases extending beyond the central nervous system.

The recent discovery of superconductivity in paramagnetic UTe2 turns spotlight on a serious candidate for spin-triplet state. To draw a complete picture of the superconducting state in UTe2 precise knowledge of the electronic properties of the 5 f states of Uranium is missing. We report on x-ray absorption and magnetic circular dichroism experiments performed at the U M4,5 edges at 2.7 K. At ambient pressure the 5 f electron count is found to be in-between 2.6 and 2.8. Partial delocalization of the 5 f electrons is further confirmed by the reduced value of the U orbital to spin magnetic moment ratio. The 5 f count is reduced by as large as 7 percent at the transition to a magnetically ordered state at Pc ≈ 1.5 GPa. At pressures above 4 GPa, the 5 f count increases back towards U3+ in the tetragonal phase. The observed “valence instabilities” and their interplay with magnetism seem to be important ingredients to understand the electronic structure in UTe2 in different phases.

Women of reproductive age are a group of particular concern with regards to vaccine uptake, related to their unique considerations of menstruation, fertility, and pregnancy. To obtain vaccine uptake data specific to this group, we obtained vaccine surveillance data from the Office for National Statistics, linked with COVID-19 vaccination status from the National Immunisation Management Service, England, from 8 Dec 2020 to 15 Feb 2021; data from 13,128,525 such women at population-level, were clustered by age (18–29, 30–39, and 40–49 years), self-defined ethnicity (19 UK government categories), and index of multiple deprivation (IMD, geographically-defined IMD quintiles). Here we show that among women of reproductive age, older age, White ethnicity and being in the least-deprived index of multiple deprivation are each independently associated with higher vaccine uptake, for first and second doses; however, ethnicity exerts the strongest influence (and IMD the weakest). These findings should inform future vaccination public messaging and policy.

Photosynthetic algae have evolved mechanisms to cope with suboptimal light and CO2 conditions. When light energy exceeds CO2 fixation capacity, Chlamydomonas reinhardtii activates photoprotection, mediated by LHCSR1/3 and PSBS, and the CO2 Concentrating Mechanism (CCM). How light and CO2 signals converge to regulate these processes remains unclear. Here, we show that excess light activates photoprotection- and CCM-related genes by altering intracellular CO2 concentrations and that depletion of CO2 drives these responses, even in total darkness. High CO2 levels, derived from respiration or impaired photosynthetic fixation, repress LHCSR3/CCM genes while stabilizing the LHCSR1 protein. Finally, we show that the CCM regulator CIA5 also regulates photoprotection, controlling LHCSR3 and PSBS transcript accumulation while inhibiting LHCSR1 protein accumulation. This work has allowed us to dissect the effect of CO2 and light on CCM and photoprotection, demonstrating that light often indirectly affects these processes by impacting intracellular CO2 levels.

Resumen (Objetivo) Se estudió la variación de las concentraciones del sulfuro de hidrógeno (H2S) y del amoniaco (NH3) en aire, en un sistema de tratamiento de aguas residuales en Costa Rica, para conocer el posible alcance de los olores producidos, con mediciones en el lugar y un modelo de dispersión de sustancias en el aire. (Metodología) Fueron realizados 13 muestreos en periodos de 12 horas y 24 horas, entre octubre del 2016 y febrero del 2017; con las mediciones de campo, se alimentó un modelo de dispersión. Se midieron los parámetros meteorológicos: dirección y velocidad de viento, radiación global, temperatura, porcentaje de humedad relativa, precipitación y presión atmosférica. Mediante el uso de los programas Aermed View y AERMOD, se demostró la dispersión de los gases alrededor de los puntos de emisión seleccionados en la planta de tratamiento. Al Aermet View se le incorporaron los parámetros de rugosidad superficial, la proporción de Bowen y el albedo para una zona rural. Los datos recolectados se incluyeron en AERMOD. (Resultados) La concentración de los gases emitidos por la planta se encuentra por debajo de los valores de percepción de estas sustancias, 0,70 µg/m3 (< 0,50 ppb) para el H2S y 26,6 µg/m3 (< 35,5 ppb) para el NH3. Se determinó que el canal de vertido es la fuente principal de emisión. Los gases generados se dispersaron hacia las comunidades vecinas de La Carpio, María Auxiliadora y Rincón Grande, en dirección noroeste; Carvajal Castro, Rossiter Carballo, Residencial Real Santamaría y Lagunilla, en dirección noreste, y la Comisión Nacional de Emergencia, en dirección sur. Los valores de error se encontraron en un ámbito entre el 5 % y el 48 % para el H2S y entre el 8 % y el 75 % para el NH3. El índice de concordancia (IC) mostró una similitud entre los valores predichos y los valores observados, tanto para el H2S como para el NH3. (Conclusiones) Las emisiones gaseosas de H2S y NH3 provenientes del sistema de tratamiento de aguas residuales no representan un riesgo para la salud ni el medio ambiente, en las poblaciones cercanas. Abstract (Objective) The variation of hydrogen sulfide (H2S) and ammonia (NH3) concentrations in air was studied in a wastewater treatment system in Costa Rica to obtain information about the dispersion of odors produced using on-site measurements and an air dispersion model. (Methodology) Thirteen samples were taken in periods of 12 hours and 24 hours between October 2016 and February 2017, and the measurements taken in the field were entered into a dispersion model. Meteorological parameters including direction and wind speed, global radiation, temperature, percentage of relative humidity, precipitation, and atmospheric pressure were recorded. Dispersion of gases around the selected emission points in the treatment plant was determined using the Aermed View and AERMOD programs. Data on roughness parameters, Bowen ratio and albedo for a rural area were incorporated into the Aermet View and the data collected was incorporated into the AERMOD model. (Results) The concentrations of gases emitted by the plant were below the perception values of these substances, 0.70 µg/m3 (< 0.50 ppb) for H2S and 26.6 µg/m3 (< 35.5 ppb) for NH3. The discharge channel is the main source of emissions; the gases emitted were dispersed to the neighboring communities of La Carpio, María Auxiliadora and Rincón Grande to the Northwest; Carvajal Castro, Rossiter Carballo, Residencial Real Santamaría and Lagunilla to the Northeast, and the National Emergency Commission to the South. Error values ranged between 5% and 48% for H2S and between 8% and 75% for NH3. The concordance index (CI) showed agreement between the predicted and observed values for both H2S and NH3. (Conclusions) H2S and NH3 emissions from the wastewater treatment system do not represent a risk to health or the environment for nearby populations. Resumo (Objetivo) A variação nas concentrações de sulfeto de hidrogênio (H2S) e amoníaco (NH3) no ar, em um sistema de tratamento de águas residuais na Costa Rica, para conhecer a possível extensão dos odores produzidos, com medições no local e um modelo de dispersão de substâncias no ar. (Metodologia). Foram realizadas treze amostragens em períodos de 12 horas e 24 horas, entre outubro de 2016 e fevereiro de 2017; com as medidas de campo, um modelo de dispersão foi alimentado. Os parâmetros meteorológicos foram medidos: direção e velocidade do vento, radiação global, temperatura, porcentagem de umidade relativa, precipitação e pressão atmosférica. Usando o Aermed View e o software AERMOD, foi demonstrada a dispersão de gases em torno dos pontos de emissão selecionados na estação de tratamento. Os parâmetros de rugosidade superficial, razão de Bowen e albedo para uma área rural foram incorporados na Aermet View. Os dados coletados foram incluídos no AERMOD. (Resultados) A concentração dos gases emitidos pela planta está abaixo dos valores de percepção para estas substâncias, 0,70 µg/m3 (< 0,50 ppb) para H2S e 26,6 µg/m3 (< 35,5 ppb) para NH3. O canal de deposição de descarte foi identificado como a principal fonte de emissões. Os gases gerados foram dispersos para as comunidades vizinhas de La Carpio, María Auxiliadora e Rincón Grande, na direção noroeste; Carvajal Castro, Rossiter Carballo, Residencial Real Santamaría e Lagunilla, na direção nordeste, e a Comissão Nacional de Emergência, na direção sul. Foram encontrados valores de erro entre 5% e 48% para o H2S e entre 8% e 75% para o NH3. O índice de concordância (CI) mostrou uma semelhança entre os valores previstos e observados tanto para o H2S quanto para o NH3. (Conclusões) As emissões gasosas de H2S y NH3 do sistema de tratamento de águas residuais não representam um risco sanitário e ambiental para as populações próximas.

Resumen (Objetivo) En este trabajo se identifica y caracteriza el conocimiento especializado de tres profesores del área de matemáticas al analizar una secuencia de actividades, cuyo propósito es la enseñanza de la suma de fracciones empleando las Regletas de Cuisenaire. (Metodología) Se optó por un estudio cualitativo bajo un paradigma interpretativo, la intervención con los docentes se realizó durante cinco sesiones. Como instrumentos para la recolección de información se utilizaron entrevistas semiestructuradas, una secuencia de actividades y las Regletas de Cuisenaire en presentación digital. Para realizar la caracterización de los conocimientos se hizo uso del modelo del conocimiento especializado del profesor de matemáticas desarrollado por Carrillo y colaboradores. Tal modelo permite identificar el conocimiento que moviliza el docente de matemáticas en el campo de la enseñanza, así mismo permite organizarlo, clasificarlo y caracterizarlo. (Resultados) Desde los resultados de la investigación se obtuvo un mayor predominio en algunos de los subdominios que conforman el modelo, en particular, se encontró mayor influencia en el conocimiento de los temas y el conocimiento de las características del aprendizaje de las matemáticas. (Conclusiones) Finalmente, las reflexiones obtenidas en el estudio muestran la importancia de identificar los conocimientos que movilizan los docentes al momento de pensarse un proceso de instrucción, pues estos podrían ayudar a otros profesores a mejorar o reflexionar sobre sus prácticas de enseñanza y aprendizaje de las matemáticas, en este caso particular, el estudio de las fracciones. Abstract (Objective) This paper is intended to identify and characterize the specialized knowledge of three mathematics teachers through the analysis of a sequence of activities intended to teach the sum of fractions using Cuisenaire Rulers. (Methodology) A qualitative study using an interpretative paradigm was designed, and an intervention with the teachers was carried out during five sessions. Semi-structured interviews, a sequence of activities and Cuisenaire Rulers presented digitally were used as instruments for information collection. A model of specialized knowledge of the mathematics teacher developed by Carrillo and collaborators was used to characterize knowledge. This model makes it possible to identify the knowledge used by teachers in the field of mathematics, and to organize, classify and characterize it. (Results) The results of the investigation show a predominance of some of the subdomains that make up the model; in particular, a greater influence of knowledge of the topics and characteristics of learning mathematics. (Conclusions) Insights obtained during the investigation show the importance of identifying the knowledge that teachers make use of when thinking about an educational process, since this knowledge could help other teachers to improve or reflect on their teaching and learning practices in mathematics - in this particular case, in the study of fractions. Resumo (Objetivo) Neste artigo é identificado e caracterizado o conhecimento especializado de três professores de matemática por meio da análise de uma sequência de atividades, cujo objetivo é ensinar a soma de frações usando a Escala Cuisenaire. (Metodologia) Optou-se por um estudo qualitativo sob um paradigma interpretativo; a intervenção com os professores foi realizada durante cinco sessões. Como instrumentos de coleta de informações foram utilizadas entrevistas semiestruturadas, uma sequência de atividades e a Escala Cuisenaire em apresentação digital. Para caracterizar o conhecimento, foi utilizado o modelo de conhecimento especializado do professor de matemática desenvolvido por Carrillo e colaboradores. Tal modelo permite identificar o conhecimento que o professor de matemática mobiliza no campo da educação, assim como permite organizá-lo, classificá-lo e caracterizá-lo. (Resultados) Dos resultados da pesquisa, obteve-se uma maior predominância em alguns dos subdomínios que compõem o modelo, em particular, uma maior influência foi encontrada no conhecimento dos tópicos e no conhecimento das características da aprendizagem da matemática. (Conclusões) Finalmente, as reflexões obtidas no estudo mostram a importância de identificar os conhecimentos que os professores mobilizam ao pensar em um processo instrucional, pois podem ajudar outros professores a melhorar ou refletir sobre suas práticas de ensino e aprendizagem da matemática, neste caso particular, o estudo das frações.

We give a comprehensive review of recent developments on using the pure spinor formalism to compute massless superstring scattering amplitudes at tree level. The main results of the pure spinor computations are placed into the context of related topics including the color-kinematics duality in field theory and the mathematical structure of $\alpha'$-corrections., Comment: 196 pp. Invited review for Physics Reports. We welcome the readers' help in spotting typos or technical mistakes. Every correction that is firstly brought to our attention will be rewarded with 20 Euro Cent per numbered equation, to be paid in cash during the next in-person encounter with one of the authors. v2: version accepted for publication in Physics Reports

(Objective) The current study explored the correlation and contribution of critical thinking and metacognitive skills toward female and male students' retention in senior high school. (Methodology) This study used a descriptive correlational design, where critical thinking and metacognitive skills served as the predictor, during retention as the criterion. A total of 230 students participated in this study. The participants comprised 112 (48.70%) male students and 118 (51.30%) female students. An essay test and a scoring rubric were used to collect data on participants' critical thinking and metacognitive skills. The research data were analyzed using a multiple regression analysis at a 5% significance level, followed by ANOVA to examine the contribution of critical thinking and metacognitive skills toward male and female students' retention. (Results) The statistical analysis revealed that the simultaneous contribution of critical thinking and metacognitive skills toward female students' retention as higher han that toward male students' retention. The effective contribution values of critical thinking and metacognitive skills toward male students' retention were 2.44% and 10.06%, respectively. Meanwhile, critical thinking and metacognitive skills contributed 7.89% and 12.81% toward female students' retention. (Conclusions) There was a simultaneous correlation between critical thinking, metacognitive skills, and retention of male and female high school students. The effective contribution of critical thinking and metacognitive skills toward female students' retention was more significant than that toward male students' retention. The findings of this study suggest that high school teachers need to consider gender equality when implementing learning strategies to improve critical thinking and metacognitive skills. Resumen (Objetivo) Este estudio tiene como objetivo revelar la relación múltiple entre las habilidades de pensamiento crítico y las habilidades metacognitivas sobre la retención en función del género de estudiantes de secundaria. (Metodología) Este estudio utiliza un diseño descriptivo correlacional. En el contexto de esta investigación, las habilidades de pensamiento crítico y las habilidades metacognitivas son predictores, mientras que la retención es un criterio. Los sujetos participantes en este estudio fueron 230 estudiantes, de los cuales 112 estudiantes varones (48.70 %) y 118 estudiantes mujeres (51.30 %). El instrumento de investigación utilizado para medir las habilidades de pensamiento crítico, las habilidades metacognitivas y la retención de estudiantes es una prueba de ensayo. Los datos obtenidos se analizaron mediante análisis de regresión múltiple con un nivel de significancia del 5 %. (Resultado) Los resultados mostraron que el valor de la contribución simultánea de las habilidades de pensamiento crítico y las habilidades metacognitivas a la retención de las estudiantes fue mayor que el de los estudiantes varones. Las habilidades de pensamiento crítico y las habilidades metacognitivas tienen una contribución de 2.44 % y 10.06 %, respectivamente, a la retención de estudiantes varones. Las habilidades de pensamiento crítico y las habilidades metacognitivas contribuyen en un 7.89 % y un 12.81 % a la retención de las estudiantes, respectivamente. (Conclusión) Existe una relación simultánea entre las habilidades de pensamiento crítico y las habilidades metacognitivas en la retención de alumnos y alumnas. La contribución efectiva de las habilidades de pensamiento crítico y las habilidades metacognitivas de las alumnas es mayor que la de los alumnos varones. Se sugiere que el personal docente debe prestar atención a la igualdad de género al potenciar las habilidades de pensamiento crítico y las habilidades metacognitivas mediante la aplicación de estrategias de aprendizaje adecuadas. Resumo (Objetivo) Este estudo visa revelar a relação múltipla entre as habilidades de pensamento crítico e as habilidades metacognitivas de retenção em função do sexo dos alunos do ensino médio. (Metodologia) Este estudo utiliza um desenho descritivo correlacional. No contexto desta pesquisa, as habilidades de pensamento crítico e as habilidades metacognitivas são preditoras, enquanto a retenção é um critério. Os participantes deste estudo foram 230 estudantes, dos quais 112 estudantes eram homens (48,70%) e 118 mulheres (51,30%). O instrumento de pesquisa utilizado para medir as habilidades de pensamento crítico, metacognitivas e de retenção dos estudantes é um teste de ensaio. Os dados obtidos foram analisados utilizando a análise de regressão múltipla com um nível de significância de 5%. (Resultado) Os resultados mostraram que o valor da contribuição simultânea das habilidades de pensamento crítico e metacognitivas para a retenção de estudantes do sexo feminino era maior do que o dos estudantes do sexo masculino. As habilidades de pensamento crítico e as metacognitivas têm uma contribuição de 2,44% e 10,06%, respectivamente, para a retenção de estudantes do sexo masculino.As habilidades de pensamento crítico e as metacognitivas contribuem com 7,89% e 12,81% para a retenção dos estudantes, respectivamente. (Conclusão) Existe uma relação simultânea entre as habilidades de pensamento crítico e as habilidades metacognitivas na retenção de estudantes do sexo masculino e feminino. A contribuição efetiva das habilidades de pensamento crítico e metacognitivas das estudantes do sexo feminino é maior do que a dos estudantes do sexo masculino. Sugere-se que os professores devem prestar atenção à igualdade de gênero, melhorando as habilidades de pensamento crítico e metacognitivas através da aplicação de estratégias de aprendizagem apropriadas.

(Background) Discovery learning is a model that guides students to actively learn in finding concepts or knowledge through an inquiry process based on the data or information obtained from experiments or observations. (Objective) The present study examined the implementation of a modification of discovery learning using mind mapping in promoting students' mathematical curiosity and creativity. (Method) A Classroom Action Research (CAR) design was employed in this study. The participants were 250 students in Middle Indonesian who registered in the academic year of 2020/2021. (Results) The descriptive analysis showed that the students achieved an average score of 44.04 with a standard deviation of 18.716 in preCAR, 52.48 with a standard deviation of 22.978 after cycle I, and 76.72 with a standard deviation of 17.097 after cycle II. Based on the students' mathematical creative thinking scores, 2 (8%) students could perform creative thinking in pre-CAR, 6 (24%) students after cycle I, and 22 (88%) students after cycle II. These figures indicated that the students classically achieved the ability to think creatively in mathematics after cycle II. (Conclusion) It was concluded that the implementation of modified discovery learning and mind mapping could promote students' mathematical creative thinking ability. The interview results also suggest that the learning model could increase mathematical curiosity of both the low and high achievers. Resumen (Antecedentes) El razonamiento ha sido ampliamente estudiado por muchas personas expertas. Sin embargo, la investigación sobre el razonamiento de estudiantes en la resolución de problemas trigonométricos, en particular los relacionados con las habilidades de pensamiento lógico, sigue siendo muy necesaria. (Objetivo) Esta investigación es un estudio cualitativo que tuvo como objetivo explorar el razonamiento de estudiantes en la resolución de problemas trigonométricos en términos de su habilidad lógica. (Método) Los sujetos participantes del estudio fueron tres estudiantes con diferentes habilidades de pensamiento lógico (baja, alta y media). El instrumento en este estudio fue el propio investigador como instrumento principal apoyado en tareas de resolución de problemas matemáticos y pautas de entrevista válidas y confiables. La recolección de datos se llevó a cabo mediante entrevistas basadas en tareas y pensamiento en voz alta. (Resultados) En cuanto a los resultados obtenidos: (1) Lo que tienen en común participantes con habilidad de pensamiento lógico alta (ST) y media (SS) en cuanto a su razonamiento es que solían comenzar con el razonamiento inductivo y luego razonaban deductivamente, y (2) la diferencia en su razonamiento es que el estudiante con baja habilidad de pensamiento lógico (SR) no mostró ningún proceso de razonamiento en absoluto. (Conclusión) Para resumir, el razonamiento de estudiantes podría usarse como referencia en el desarrollo de modelos de aprendizaje matemático para mejorar sus habilidades de razonamiento. Resumo (Contexto) O raciocínio tem sido extensivamente estudado por muitos especialistas. No entanto, a pesquisa sobre o raciocínio do aluno na resolução de problemas trigonométricos, particularmente aqueles relacionados às habilidades de pensamento lógico, ainda é muito necessária. (Objetivo) Esta pesquisa é um estudo qualitativo que teve como objetivo explorar o raciocínio de alunos na resolução de problemas trigonométricos em termos de sua habilidade lógica. (Método) Os participantes do estudo foram três alunos com diferentes habilidades de raciocínio lógico (baixa, alta e média). O instrumento neste estudo foi o próprio pesquisador como o principal instrumento apoiado por tarefas de resolução de problemas matemáticos e modelos de entrevista válidos e confiáveis. A coleta de dados foi realizada por meio de entrevistas baseadas em tarefas e reflexões em voz alta. (Resultados) Em relação aos resultados obtidos: (1) O que os participantes com capacidade de raciocínio lógico alta (ST) e média (SS) têm em comum em termos de raciocínio é que costumavam começar com raciocínio indutivo e depois raciocinar dedutivamente, e (2) a diferença em seu raciocínio é que o aluno com baixa habilidade de raciocínio lógico (RL) não apresentou nenhum processo de raciocínio. (Conclusão) Resumindo, o raciocínio dos alunos pode ser usado como referência no desenvolvimento de modelos de aprendizagem matemática para melhorar as habilidades de raciocínio dos alunos.

Posttranslational modifications (PTMs) are an integral component to how cells respond to perturbation. While experimental advances have enabled improved PTM identification capabilities, the same throughput for characterizing how structural changes caused by PTMs equate to altered physiological function has not been maintained. In this Perspective, we cover the history of computational modeling and molecular dynamics simulations which have characterized the structural implications of PTMs. We distinguish results from different molecular dynamics studies based upon the timescales simulated and analysis approaches used for PTM characterization. Lastly, we offer insights into how opportunities for modern research efforts on in silico PTM characterization may proceed given current state-of-the-art computing capabilities and methodological advancements., Comment: 64 pages, 11 figures

Customizable gold nanoparticle platforms are motivating innovations in drug discovery with massive therapeutic potential due to their biocompatibility, stability, and imaging capabilities. Further development requires the understanding of how discrete differences in shape, charge, or surface chemistry affect the drug delivery process of the nanoparticle. The nanoparticle shape can have a significant impact on nanoparticle function as this can, for example, drastically change the surface area available for modifications, such as surface ligand density. In order to investigate the effects of nanoparticle shape on the structure of cell membranes, we directly probed nanoparticle–lipid interactions with an interface sensitive technique termed sum frequency generation (SFG) vibrational spectroscopy. Both gold nanostars and gold nanospheres with positively charged ligands were allowed to interact with a model cell membrane and changes in the membrane structure were directly observed by specific SFG vibrational modes related to molecular bonds within the lipids. The SFG results demonstrate that the +Au nanostars both penetrated and impacted the ordering of the lipids that made up the membrane, while very little structural changes to the model membrane were observed by SFG for the +Au nanospheres interacting with the model membrane. This suggests that the +Au nanostars, compared to the +Au nanospheres, are more disruptive to a cell membrane. Our findings indicate the importance of shape in nanomaterial design and provide strong evidence that shape does play a role in defining nanomaterial-biological interactions.

We introduce a perturbative approximation to the combined density functional theory and multireference configuration interaction (DFT/MRCI) approach. The method, termed DFT/MRCI(2), results from the application of quasi-degenerate perturbation theory (QDPT) and the Epstein–Nesbet partitioning to the DFT/MRCI Hamiltonian matrix. The application of QDPT obviates the need to diagonalize the large DFT/MRCI Hamiltonian; electronic energies are instead obtained as the eigenvalues of a small effective Hamiltonian, affording an orders of magnitude savings in the computational cost. Most importantly, the DFT/MRCI(2) approximation is found to be of excellent accuracy, furnishing excitation energies with a root mean squared deviation from the canonical DFT/MRCI values of less than 0.03 eV for an extensive test set of organic molecules.

Chirality is a fundamental feature in all domains of nature, ranging from particle physics over electromagnetism to chemistry and biology. Chiral objects lack a mirror plane and inversion symmetry and therefore cannot be spatially aligned with their mirrored counterpart, their enantiomer. Both natural molecules and artificial chiral nanostructures can be characterized by their light-matter interaction, which is reflected in circular dichroism (CD). Using DNA origami, we assemble model meta-molecules from multiple plasmonic nanoparticles, representing meta-atoms accurately positioned in space. This allows us to reconstruct piece by piece the impact of varying macromolecular geometries on their surrounding optical near fields. Next to the emergence of CD signatures in the instance that we architect a third dimension, we design and implement sign-flipping signals through addition or removal of single particles in the artificial molecules. Our data and theoretical modeling reveal the hitherto unrecognized phenomenon of chiral plasmonic-dielectric coupling, explaining the intricate electromagnetic interactions within hybrid DNA-based plasmonic nanostructures.

International audience; In this paper we develop a decoupled finite particle method (DFPM) to simulate non-isothermal free surface flow. This method can not only promote the accuracy of kernel approximation, but also insure the computational efficiency comparing with some improved particle methods. To model the non-isothermal viscous fluid flow, the particle discretization of heat conduction is derived explicitly. The Rusanov flux is introduced to alleviate the random oscillation of pressure. A corrected dynamic boundary method with a soft repulsive model and combined with particle shifting technology is developed further to model the complex wall-boundary problem. The comprehensive particle model is validated firstly through solving the non-isothermal Couette flow and the high-speed injection molding of a holey cavity, which is compared with other numerical results or experiments. Then, we extend the present method to solve the 2D/3D droplet impacting onto micro multi-structured surface for the capacity of present boundary treatment. The convergence, computational efficiency and operating conditions on the behavior of flow regime are analyzed. All numerical results show that the comprehensive numerical model has a considerable potential to simulate non-isothermal free surface flow.

Crystalline nanoporous molecular networks are assembled on the Ag(111) surface, where the pores confine electrons originating from the surface state of the metal. Depending on the pore sizes and their coupling, an antibonding level is shifted upwards by 0.1 to 0.3 eV as measured by scanning tunneling microscopy. On molecular sites, a down-shifted bonding state is observed, which is occupied under equilibrium conditions. Low-temperature force spectroscopy reveals energy dissipation peaks and jumps of frequency shifts at bias voltages, which are related to the confined states. The dissipation maps show delocalization on the supra-molecular assembly and a weak distance-dependence of the dissipation peaks. These observations indicate that two-dimensional arrays of coupled quantum dots are formed, which are quantitatively characterized by their quantum capacitances and resonant tunneling rates. Our work provides a method for studying the capacitive and dissipative response of quantum materials with nanomechanical oscillators., 24 pages, 4 figures

Spin-coated blend films with complex phase-separated morphology find broad applications while precise tailoring of the morphology is still challenging. In this study, graphene oxide (GO)-based Janus nanosheets were synthesized by interfacial polymerization in a GO nanosheet stabilized Pickering emulsion with polystyrene (PS) and poly(hydroxyethyl methacrylate) synchronously being grafted to the GO nanosheet from the oil and water sides. The Janus nanosheets make the morphology of spin-coated PS/poly(methyl methacrylate)(PMMA) blend films tunable over the full height of the film until the substrate as their preferential assembly at the PS/PMMA interface and attachment on the glass substrate drive the top PS phase to migrate towards the substrate and the bottom PMMA phase to dewet from the substrate towards the air. By varying blend composition and Janus nanosheet loading, morphologies are readily transformed from a PS network on top of PMMA to PS droplets in the PMMA matrix and from PS encapsulated by PMMA to PMMA cavities in the PS network, etc.. This enables generating thin films with various morphologies ranging from a flat surface to cavity-network structures, droplet-matrix structures or bi-continuous structures, etc. at will. Moreover, the morphologies trapped by jammed nanosheets at the interface are super stable against further evolution upon annealing.

International audience; Photopolymerization is an active research field and recently, a great deal of efforts has been devoted to replace the energy-consuming UV irradiation setups by the popular and easily available energy-saving light-emitting diodes (LEDs). With aim at going a step further, sunlight can be used as an inexpensive and unlimited light source to perform polymerization processes. In this case, no energy is required anymore, and the use of photopolymerization for various outdoor applications becomes possible. Over the years, and with aim developing reliable photoinitiating systems, various dyes have been developed, enabling in the case of sunny days to efficiently promote polymerization processes. In this review, an overview on the different photoinitiating systems developed for polymerization processes done under sunlight or even daylight is provided. Polymerization efficiency is often compared to that obtained with artificial light sources, enabling to determine the efficacy of the photoinitiating systems under sunlight.

Understanding the migration and transformation of nitrogen during high-alkali coal pyrolysis and the influence of sodium on the nitrogen-containing components of coal is of great significance for the clean and efficient utilization of coal. In this study, gas chromatography-mass spectrometry (GC-MS), X-ray photoelectron spectroscopy (XPS) and ultraviolet visible spectroscopy (UV-Vis) were used to analyze the conversion of tar-N, char-N and gas-N during coal pyrolysis and the effect of sodium on the nitrogen-containing pyrolysis products of coal. The experimental results were combined with density functional theory (DFT) calculations to investigate the effect of sodium on the internal conversion process of char-N. The results revealed that a large amount of Na (2.5 wt.%, 3.5 wt.%) can reduce the nitrogen content in coal pyrolysis char, promote the formation of amine and amide in tar-N and inhibit the release of gaseous nitrogen. A small amount of Na (0.5 wt.%, 1.5 wt.%) retains nitrogen in char, reducing the total amount of nitrogen-containing species in the gas phase and tar, which is an effective means to inhibit the transformation of nitrogen to volatile matter. In addition, a large amount of Na promotes the conversion of pyrrole nitrogen (N-5) and pyridine nitrogen (N-6) in char to heterocyclic N in tar. A small amount of sodium promotes the hydrogenation and ring opening of heterocyclic-N in tar to HCN. Na promotes the conversion of N-5 and N-6 in char. DFT calculations further confirmed that the addition of Na significantly reduced the energy barrier of the rate determining step by about 36.6 kJ/mol.

In this article, we looked at how to go about creating a CNC pen or drawing machine of your own. Inkscape, which translates graphics and text into g- code format, was utilized as the controller for this project, with the microcontroller serving as the interface between the computer and the language of the CNC machine. The g-code transmits a series of x, y, and z coordinates to the motors; the servo motor controls the pen's movement in response to the Z coordinates; stepper motor 1 controls the rail's horizontal motion; and stepper motor 2 controls the rail's vertical motion in response to the X coordinate. The laser machine employs industrial applications to expedite manufacturing and perform engraving and cutting, resulting in a superior and expertly finished output. The carbon laser beam emitted by the laser engraving machine may be used for engraving, cutting, and shaping a wide variety of materials and end products.

Among the most pressing issues in the field of illness diagnostics is identifying and diagnosing leukemia at its earliest stages, which requires accurate distinction of malignant leukocytes at a low cost. Leukemia is quite common, yet laboratory diagnostic centres often lack the necessary technology to diagnose the disease properly, and the available procedures take a long time. They are considering the efficacy of machine learning (ML) in illness diagnostics and that deep learning as a machine learning method is becoming critical. This study proposes a convolutional neural network (CNN) deep learning model for leukemia diagnosis utilizing the AML (acute myeloid leukemia) dataset. The classification using the proposed method achieved results that exceeded 98% accuracy, the sensitivity of 94.73% and specificity of 98.87%.

Smart building management systems today employ the architectural multi-level creation of a system of unified components. Such a market is dynamic and always expanding, and sometimes the developers of such components stop maintaining their products or even collapse and vanish. It's not always possible to use professional-level components from proven developers when a building automation system (BAS) project budget is limited. The construction and analysis of models that consider the dependability and cyber security aspects of maintenance as well as ways of validating the selection of components, strategies, and service parameters are both necessary steps in finding compromises between the BAS architecture's value and quality. Use of information technology to determine maintenance parameters based on a model and model development approach.

The Internet of Things (IoT) is a cutting-edge innovation that facilitates the cost-effective development of smart system architectures. Although current regulations necessitate installing an analog fire alarm system, such a system lacks the intelligence to instantly notify the appropriate parties in a timely fashion. In addition, since people are not always present, an analog fire alarm will not be able to prevent immediate danger or damage in the event of a fire. Therefore, the incident must be reported as soon as possible to the appropriate party in order to lessen the impact of a fire. In this study, we suggest a smart fire-alarm system made of a fire sensor and a sound sensor that can both detect fire and noise as well as the status of the analog fire alarm system to ascertain whether the analog fire alarm system is operational. We first tested our proposed smart fire alarm system to determine its effectiveness before putting it into use. From there, we ran experiments to determine how well it worked. The outcomes show that the system is trustworthy in a range of scenarios.

Alzheimer's disease (AD) is a neurodegenerative ailment that causes cognitive deterioration due to changes in brain structure. Individuals usually see diagnostic symptoms after irreversible brain damage has occurred. In order to slow the course of the illness and enhance the quality of life for AD patients, early diagnosis is crucial. Recent advances in machine learning and scanning have made the use of these methods to detect AD in its earliest stages possible. This article uses deep learning using CNN methods to extract picture characteristics from ADNI (Alzheimer's Disease Neuroimaging Initiative) datasets to improve Alzheimer's disease diagnosis techniques. This descriptor will be used in conjunction with the CNN to categorize the illness and add new characteristics that are more accurate, quicker, and stable than the current features. In this process, an Alzheimer's detection System will be implemented to mitigate the adverse effects of data imbalance on recognition performance, and an integrated multi-depth architectural technology will be introduced to boost recognition quality. Using the suggested model of the convolution neural network (CNN) technique, classification accuracy results were obtained above 97%.

One of the most common uses of computer vision, automatic number plate recognition (ANPR) is also a pretty well-explored subject with numerous effective solutions. Due to regional differences in license plate design, however, these solutions are often optimized for a specific setting. Number plate recognition algorithms are often dependent on these aspects, making a universal solution unlikely due to the fact that the image analysis methods used to develop these algorithms cannot guarantee a perfect success rate. In this research, we offer an algorithm tailor-made for use with brand-new license plates in Iraq. The method employs edge detection, Feature Detection, and mathematical morphology to find the plate; it was developed in C++ using the OpenCV library. When characters were found on the plate, they were entered into the Easy OCR engine for analysis.

The process development of the image processing can solve the problem of detection and recognition of the license plate by taking pictures of the cars and then recognizing them. Most traffic applications rely on automatic vehicle plate detection in parking lots, border control, speed control, etc. In this study, a smart visual system was presented to identify car plates in the College of Science for Girls - University of Baghdad parking lot. The work included distinguishing the car plate and identifying cars, whether they belonged to the college or not. This process was based on the Cascade Classifier method based on the Viola-Jones algorithm, and a database for all car plate features was stored in a file using the proposed method. The recognized car was compared with the characteristics of the database using Oriented FAST and Rotated BRIEF then features were extracted using Histograms of Oriented Gradients. The license plate is recognized when matching features are employed using the matching feature’s function. The results of congruence and discrimination were excellent and very highly efficient. The luminous intensity dependence is considered, as the work is based on the red band of the car's image.

In light of the fact that the global pandemic of Coronavirus Disease 2019 (COVID-19) is still having a significant impact on the health of people all over the world, there is a growing need for testing diagnosis and treatment that can be completed quickly. The primary imaging modalities used in the respiratory disease diagnostic process are the Chest X-ray (CXR) and the computed tomography scan. In this context, this paper aims to design a new Convolutional Neural Network (CNN) to diagnose COVID-19 in patients based on CXR images and determine whether they are COVID or healthy. We have tested the performance of our CNN on the COVID-19 Radiography Database with three classes (COVID, Pneumonia, and Normal). Also, we proposed a new enhancement technique to enhance the CXR image using the Laplacian kernel with Delta Function and Contrast-Limited Adaptive Histogram Equalization. The proposed CNN has been trained and tested on 15153 enhanced and original images, COVID (3616), Pneumonia (1345), and Normal (10192). Our enhancement technique increased the performance metrics scores of the proposed CNN. Hence, the proposed method obtained better results than the state-of-the-art methods in accuracy, sensitivity, precision, specificity, and F measure.

Post quantum is a general name to all the techniques which are safe against the quantum computer attack. The wireless network is one of the most important means of communication. Wireless network security is a top priority. Wireless networks use conventional cryptography, which has various flaws, whereas quantum cryptography claims to be completely secure. It wasn't long after quantum computers became operational that people began to think about new ways to secure electronic communications. After considering all of the weaknesses in conventional cryptosystems, individuals began to look for new ways to secure electronic communications. Traditional cryptography has many problems, but quantum cryptography addresses nearly all of them.

Chicken feathers are valuable solid waste that mainly contains insoluble proteins called keratin. Naturally, keratin is degraded by microbes that produce a specific protease called keratinase. Many reports have been addressed to study Bacillus species as keratin-degrading microbes. However, most reported works were using pathogenic soil Bacillus. Our work has been concentrated on keratinase produced by a soil bacterium, Bacillus sp. MD24 and we confirmed it as a non-pathogenic bacterium. This report describes the different profiles of keratinase fermentation between sub-merged fermentation (SmF) and solid-state fermentation (SSF) using chicken feathers as a sole carbon and nitrogen source. Keratinase production and weight loss were followed for 10 consecutive days, the keratinase molecular weight was investigated, the type and concentration of 18 amino acids were determined using LC-MS/MS after 10 days to seek its potential as the amino acid source, and protein by-product concentration was measured. The results showed that keratinase production as well as weight loss in SmF was better compared to SSF with different optimum production times. Surprisingly, L-arginine was a dominant amino acid in SmF hydrolysate which contained 42.06% of total amino acids and made Bacillus sp. MD24 is a potential organism for L-arginine production. As protein by-product concentration was relatively low in both conditions showing effective utilization of chicken feathers as matter and energy source, consequently, cell-free keratinase should be applied to degrade chicken feathers into valuable materials.

Melanoma, the deadliest form of skin cancer, is on the rise. The goal of this study is to present a deep learning system implementation for the detection of melanoma lesions on a server equipped with a graphics processing unit (GPU). When applied by a dermatologist, the recommended method might aid in the early detection of this kind of skin cancer. Evidence shows that deep learning may be used in a variety of settings to successfully extract patterns from data such as signals and images. This research presents a convolution neural network–based strategy for identifying early-stage melanoma skin cancer. Images are input into a deep learning model known as a convolutional neural network (CNN) that has already been pre-trained. The CNN classifier, which is trained with large amounts of data, can discriminate between malignant and nonmalignant melanoma. The method's success in the lab bodes well for its potential to aid dermatologists in the early detection of melanoma. However, the experimental results show that the proposed technique excels beyond the state-of-the-art methods in terms of diagnostic accuracy.

This paper implements a combined pseudo-random sequence generator based on neural networks and chaotic, random variable color images based on an algorithm for chaotic encryption, secure image storage, and transmission. The generator that controls the operation of the encryption algorithm: the arrangement of pixel positions, the use of random color images of the AES algorithm to achieve encryption, creation algorithms, and neural networks by dynamically updating control parameters and numbers to increase the generated chaotic sequence randomness by using simulation MATLAB for getting output. The chaotic function's iteration of the neural networks. The experimental results are presented in the form of a pixel correlation coefficient and security analysis to prove the security and effectiveness of the proposed chaos-based ANN encryption.

WDM (wavelength division multiplexing) is used in this project to simultaneously send data over several channels at high speed. Single mode fiber is favored over Multimode fiber for long-distance communication. As data rate and optical fiber length grow, the quality factor falls. Dispersion effects on an 8-channel dense WDM system at a high data rate will be examined using the Optisystem 10 simulator. A dispersion compensation fiber (DCF) and an Erbium doped fiber amplifier (EDFA) are used to compensate for the dispersion and attenuation in the transmitted optical signal, respectively. Bit error rate (BER) and eye diagram analyzers were used to evaluate the optical communication system's performance.

As the world continues to battle the devastating effects of the COVID-19 pandemic, it has become increasingly crucial to screen patients for contamination accurately and effectively. One of the primary screening methods is chest radiography, utilizing radiological imaging to detect the presence of the virus in the lungs. This study presents a cutting-edge solution to classify COVID-19 infections in chest X-ray images by utilizing the Gray-Level Co-occurrence Matrix (GLCM) and machine learning algorithms. The proposed method analyzes each X-ray image using the GLCM to extract 22 statistical texture features and then trains two machine learning classifiers - K-Nearest Neighbor and Support Vector Machine - on these features. The method was tested on the COVID-19 Radiography Database and was compared to a state-of-the-art method, delivering highly efficient results with impressive sensitivity, accuracy, precision, F1-score, specificity, and Matthew's correlation coefficient. The proposed approach offers a promising new way to classify COVID-19 infections in chest X-ray images and has the potential to play a crucial role in the ongoing fight against the pandemic.