Infectious disease dynamics can be inferred from pathogen genomic data using phylodynamic methods, but the applicability of many such approaches to large data sets is constrained by computational cost. Recent deep-learning approaches to phylodynamics have improved scalability, yet challenges remain when genetic divergence is limited during fast spreading outbreaks. To address this, we use pathogen-specific models to show that deep-learning models trained on outbreak-like phylogenies can accurately estimate the reproductive number (R) when both the birth-death model and the expected phylogenetic resolution are matched to the target pathogen, highlighting the importance of realistic training conditions. Focusing on three major respiratory pathogens of public health importance (SARS-CoV-2, seasonal human influenza virus, and respiratory syncytial virus (RSV)), we introduce PhyloRt, a scalable framework for estimating the time-varying reproductive number (Rt) from large outbreak phylogenies. PhyloRt decomposes large trees into overlapping subtrees and applies a hierarchical deep-learning-based inference strategy to classify subtrees as exhibiting constant or time-varying reproduction numbers, enabling identifiable and computationally efficient estimation of Rt as a piecewise-constant trajectory through time. Applications to SARS-CoV-2 and influenza outbreaks show that PhyloRt recovers transmission dynamics consistent with estimates derived from mathematical epidemiological and Bayesian phylodynamic analyses. Our work enables scalable and rapid estimation of time-varying transmission dynamics from very large-scale outbreak genomic data sets, supporting real-time genomic epidemiology of emerging pathogens. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This work was supported by the Marie Sklodowska-Curie Actions (Project No. 101203810) and the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. HKU PDFS2425-7S01) (R.X.). ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced are available online at https://github.com/xieruopeng/PhyloRt

Phylodynamics bridges the gap between epidemiology and pathogen genetic data by estimating epidemiological parameters from time-scaled pathogen phylogenies. Multi-type birth-death (MTBD) models are phylodynamic analogies of compartmental models in classical epidemiology. They serve to infer the average number of secondary infections R and the infection duration d. Moreover, more complex MTBD models add extra parameters, such as the average length of the incubation period or the proportion of superspreaders in the infected population. However, these additional parameters come at an important computational cost: Apart from the simplest, BD, model, MTBD models do not have a closed-form solution and require numerical methods for their likelihood computation. This leads to increased computational times and potential numerical errors. Therefore, the BD model remains the favorite researchers' choice for real dataset analyses, and is often applied even in cases where more complex epidemiological aspects are present. We investigated, using simulations, how model misspecification influences inference of R and d in the phylodynamic framework. We showed that the use of models not accounting for various epidemiological aspects leads to bias. In particular the simplest, BD, estimator tends to underestimate R in the presence of super-spreading or incubation, which might be dangerous from the public health prospective. However, deep-learning-based estimators for complex models, which account for multiple epidemiological factors, perform well both on the data where those factors are present and where they are absent. This advocates for the use of complex epidemiologically realistic estimators, whose design has recently become possible thanks to deep learning. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This work is supported in part by funds from the Marie Sklodowska- Curie Actions (Project No. 101203810, R.X.). ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All code and data produced are available online at https://github.com/modpath/bdeissct

Estimating how fast infections spread or how long they last is essential to control outbreaks. Phylodynamics methods enable the inference of key epidemiological parameters from viral genomic data but remain limited in terms of biological realism and speed because they need to derive and compute likelihoods. We address this issue using simulation-based inference and introduce a deep learning-based framework directly trained on alignments of viral genetic sequences. Our neural posterior estimation matches the accuracy of leading Bayesian likelihood-based methods while running a thousand times faster and avoiding a phylogeny reconstruction step. This performance can be harnessed to analyze large datasets and opens new perspectives to tackle biologically realistic models in terms of pathogen life histories or genomic evolution. ### Competing Interest Statement The authors have declared no competing interest. Agence Nationale de la Recherche, https://ror.org/00rbzpz17, ANR-23-CE45-0027 DIM One HEALTH PhD fellowship

Understanding the dynamics of HIV epidemics is important to control them effectively. Classical methods that mainly rely on occurrence data are limited by the fact that an unknown part of the epidemic eludes sampling. Since the early 2000s, phylodynamic methods have enabled the estimation of key epidemiological parameters from virus genetic sequence data. These methods have the advantage of being less sensitive to partial sampling and to provide insights about epidemic history that even predates the first samples. In this study, we analysed 2,205 HIV sequences from the French ANRS PRIMO C06 cohort. We identified and were able to reconstruct the temporal dynamics of two large clades that represent the HIV-1 epidemics in the country. Using Bayesian phylodynamic inference models, we found that the first clade, from subtype B, originated in the end of 1970s, grew rapidly during the 80s before decreasing from 2000 to 2015 and stagnating since then. The second clade, from circulating recombinant form CRF02_AG, emerged and spread in the 80s, grew again in the early 2000s, before declining slightly. We also estimated key epidemiological parameters associated with each clade. Finally, using numerical simulations, we investigated prospective scenarios and assessed the possibility to meet the 2030 UNAIDS targets. This is one of the rare studies to analyse the HIV epidemic in France using molecular epidemiology methods. It highlights the value of routine HIV sequence data for studying past epidemic trends or designing public health policies. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement LC is supported by a PhD fellowship 13643 from Sidaction (https://www.sidaction.org/). PP is supported by a PhD fellowship 20275 from Sidaction (https://www.sidaction.org/). VG is supported by a PhD fellowship by the Agence Nationale de la Recherche (https://anr.fr/) for the DEELOGENY project (ANR-23-CE45-0027). This work was also partly supported by the by the Agence Nationale de la Recherche (https://anr.fr/) for the DEELOGENY project (ANR-23-CE45-0027). ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The data from the ANRS PRIMO Cohort used in my study has been anonymized. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present study are available upon reasonable request to the authors

Phylodynamics bridges the gap between classical epidemiology and pathogen genome sequence data by estimating epidemiological parameters from time-scaled pathogen phylogenetic trees. The models used in phylodynamics typically assume that the sampling procedure is independent between infected individuals. However, this assumption does not hold for many epidemics, in particular for such sexually transmitted infections as HIV-1, for which contact tracing schemes are included in health policies of many countries. We extended phylodynamic multi-type birth-death (MTBD) models with contact tracing (CT), and developed a simulator to generate trees under MTBD and MTBD-CT models. We proposed a non-parametric test for detecting contact tracing in pathogen phylogenetic trees. Its application to simulated data showed that it is both highly specific and sensitive. For the simplest representative of the MTBD-CT family, the BD-CT(1) model, where only the last contact can be notified, we solved the differential equations and proposed a closed form solution for the likelihood function. We implemented a maximum-likelihood program, which estimates the BD-CT(1) model parameters and their confidence intervals from phylogenetic trees. It performed accurate parameter inference on BD and BD-CT(1) simulated data, and detected contact tracing in HIV-1 B epidemics in Zurich and the UK. Importantly, we showed that not accounting for contact tracing when it is present, leads to bias in parameter estimation with the BD model (overestimation of the becoming-non-infectious rate). This bias is also present, but greatly reduced, when the BD-CT(1) model is used on data where multiple contacts can be notified. Our CT test, MTBD-CT tree simulator and BD-CT(1) parameter estimator are freely available at GitHub (evolbioinfo/treesimulator and evolbioinfo/bdct).

Paratyphoid B fever (PTB) is caused by an invasive lineage (phylogroup 1, PG1) of Salmonella enterica serotype Paratyphi B (SPB). However, little was known about the global population structure, geographic distribution, and evolution of this pathogen. Here, we report a whole-genome analysis of 568 historical and contemporary SPB PG1 isolates, obtained globally, between 1898 and 2021. We show that this pathogen existed in the 13th century, subsequently diversifying into 11 lineages and 38 genotypes with strong phylogeographic patterns. Following its discovery in 1896, it circulated across Europe until the 1970s, after which it was mostly reimported into Europe from South America, the Middle East, South Asia, and North Africa. Antimicrobial resistance recently emerged in various genotypes of SPB PG1, mostly through mutations of the quinolone-resistance-determining regions of gyrA and gyrB. This study provides an unprecedented insight into SPB PG1 and essential genomic tools for identifying and tracking this pathogen, thereby facilitating the global genomic surveillance of PTB.

Deep learning has emerged as a powerful tool for phylodynamic analysis, addressing common computational limitations affecting existing methods. However, notable disparities exist between simulated phylogenetic trees used for training existing deep learning models and those derived from real-world sequence data, necessitating a thorough examination of their practicality. We conducted a comprehensive evaluation of model performance by assessing an existing deep learning inference tool for phylodynamics, PhyloDeep, against realistic phylogenetic trees characterized from SARS-CoV-2. Our study reveals the poor predictive accuracy of PhyloDeep models trained on simulated trees when applied to realistic data. Conversely, models trained on realistic trees demonstrate improved predictions, despite not being infallible, especially in scenarios where superspreading dynamics are challenging to capture accurately. Consequently, we find markedly improved performance through the integration of minimal contact tracing data. Applying this approach to a sample of SARS-CoV-2 sequences partially matched to contact tracing from Hong Kong yields informative estimates of SARS-CoV-2 superspreading potential beyond the scope of contact tracing data alone. Our findings demonstrate the potential for enhancing deep learning phylodynamic models processing low resolution trees through complementary data integration, ultimately increasing the precision of epidemiological predictions crucial for public health decision making and outbreak control. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement National Institutes of Health contract number 75N93021C00016 (VD) Research Grants Council of the Hong Kong SAR, China (Project No. [T11-705/21-N]) (VD) The Collaborative Research Scheme (Project No. C7123-20G) of the Research Grants Council of the Hong Kong Special Administrative Region, China (BC, DA) Health and Medical Research Fund Seed Grant Scheme (Project No. 22211192) of the Hong Kong SAR (DA) HKU-Pasteur Research Pole Fellowship 2023 (S-AC23005-01) (RX) PaRis AI Research InstitutE (PRAIRIE; ANR-19-P3IA-0001) (OG) ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present work are contained in the manuscript

Cells are unceasingly confronted by oxidative stresses that oxidize proteins on their cysteines. The thioredoxin (Trx) system, which is a ubiquitous system for thiol and protein repair, is composed of a thioredoxin (TrxA) and a thioredoxin reductase (TrxB). TrxAs reduce disulfide bonds of oxidized proteins and are then usually recycled by a single pleiotropic NAD(P)H-dependent TrxB (NTR). In this work, we first analyzed the composition of Trx systems across Bacteria. Most bacteria have only one NTR, but organisms in some Phyla have several TrxBs. In Firmicutes, multiple TrxBs are observed only in Clostridia, with another peculiarity being the existence of ferredoxin-dependent TrxBs. We used Clostridioides difficile, a pathogenic sporulating anaerobic Firmicutes, as a model to investigate the biological relevance of TrxB multiplicity. Three TrxAs and three TrxBs are present in the 630Δerm strain. We showed that two systems are involved in the response to infection-related stresses, allowing the survival of vegetative cells exposed to oxygen, inflammation-related molecules and bile salts. A fourth TrxB copy present in some strains also contributes to the stress-response arsenal. One of the conserved stress-response Trx system was found to be present both in vegetative cells and in the spores and is under a dual transcriptional control by vegetative cell and sporulation sigma factors. This Trx system contributes to spore survival to hypochlorite and ensure proper germination in the presence of oxygen. Finally, we found that the third Trx system contributes to sporulation through the recycling of the glycine-reductase, a Stickland pathway enzyme that allows the consumption of glycine and contributes to sporulation. Altogether, we showed that Trx systems are produced under the control of various regulatory signals and respond to different regulatory networks. The multiplicity of Trx systems and the diversity of TrxBs most likely meet specific needs of Clostridia in adaptation to strong stress exposure, sporulation and Stickland pathways.

Several coronaviruses infect humans, with three, including the SARS-CoV2, causing diseases. While coronaviruses are especially prone to induce pandemics, we know little about their evolutionary history, host-to-host transmissions, and biogeography, which impedes the prediction of future transmission scenarios. One of the difficulties lies in dating the origination of the family, a particularly challenging task for RNA viruses in general. Previous cophylogenetic tests of virus-host associations, including in the Coronaviridae family, have suggested a virus-host codiversification history stretching many millions of years. Here, we establish a framework for robustly testing scenarios of ancient origination and codiversification versus recent origination and diversification by host switches. Applied to coronaviruses and their mammalian hosts, our results support a scenario of recent origination of coronaviruses in bats and diversification by host switches, with preferential host switches within mammalian orders. Hotspots of coronavirus diversity, concentrated in East Asia and Europe, are consistent with this scenario of relatively recent origination and localized host switches. Spillovers from bats to other species are rare, but have the highest probability to be towards humans than to any other mammal species, implicating humans as the evolutionary intermediate host. The high host-switching rates within orders, as well as between humans, domesticated mammals, and non-flying wild mammals, indicates the potential for rapid additional spreading of coronaviruses across the world. Our results suggest that the evolutionary history of extant mammalian coronaviruses is recent, and that cases of long-term virus–host codiversification have been largely over-estimated.

Phylodynamics bridges the gap between classical epidemiology and pathogen genome sequence data by estimating epidemiological parameters from time-scaled pathogen phylogenetic trees. The models used in phylodynamics typically assume that the sampling procedure is independent between infected individuals. However, this assumption does not hold for many epidemics, in particular for such sexually transmitted infections as HIV-1, for which partner notification schemes are included in health policies of many countries. We developed an extension of phylodynamic multi-type birth-death (MTBD) models with partner notification (PN), and a simulator to generate trees under MTBD and MTBD-PN models. We proposed a non-parametric test for detecting partner notification in pathogen phylogenetic trees. Its application to simulated data showed that it is both highly specific and sensitive. For the simplest representative of the MTBD-PN family, the BD-PN model, we solved the differential equations and proposed a closed form solution for the likelihood function. We implemented it in a program, which estimates the model parameters and their confidence intervals from phylogenetic trees. It performed accurate estimations on simulated data, and detected partner notification in HIV-1 B epidemics in Zurich and the UK. Importantly, we showed that not accounting for partner notification when it is present leads to bias in parameter estimation with the BD model, while BD-PN parameter estimator performs well both in presence and in absence of partner notification. Our PN test, MTBD-PN tree simulator and BD-PN parameter estimator are freely available at https://github.com/evolbioinfo/treesimulator and https://github.com/evolbioinfo/bdpn}{github.com/evolbioinfo/bdpn. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement O.G. was supported by the Paris Artificial Intelligence Research Institute (PRAIRIE, ANR-19-P3IA-0001). ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced are available online at https://github.com/evolbioinfo/bdpn

Development of stable Ni-based catalysts with high resistance to sintering and carbon deposition is a challenge in the catalytic ethanol dry reforming (EDR) process. An effective and practical strategy is to introduce a second metal to obtain Ni-based bimetallic catalysts. In this study, bimetallic Cu-Ni nanoparticles supported on Al-Zr-Ce (ACZ) complex oxides were successfully developed as a multifunctional catalyst for syngas production via EDR and were compared with monometallic Ni and Cu catalysts supported on ACZ oxides. The addition of a small amount of copper (1%) to the catalyst resulted in the formation of a Cu-Ni alloy with crystallite sizes ranging from 10 to 30 nm, exhibiting a high metal-support interaction and resistance to sintering. However, a high Cu content limited the activity of the catalysts due to side reactions of ethanol decomposition, which led to catalyst deactivation. The catalyst 1Cu-9Ni/50ACZ exhibited the highest H2 and CO yields (78% and 70%, respectively, at T = 750 degrees C) at H2/CO = 1.1. The addition of Cu enhanced the H2/CO ratio by shifting the water-gas shift (WGS) reaction pathway and increasing the reducibility and dispersibility of Ni, which is attributed to the formation of a Cu-Ni alloy. The Cu-Ni alloy is active in the WGS reaction and has a synergistic effect with Ni in dehydration and dehydrogenation of ethanol, which affects the product distribution. Furthermore, copper plays a role in the reduction of carbide forms of nickel, which are precursors of graphitized coke. The support composition was also found to have a significant effect on the activity and stability of the bimetallic catalysts. It was demonstrated that the Al/Zr ratio in the support enables tuning the crystallite size of the active phase, which affects the surface concentrations of nickel and copper and their ratio and determines the ratio of reactive oxygen species that contribute to the gasification of the formed coke. This work provides a strategy to design highly selective catalysts with functional metal sites for hydrogen or syngas production with a regulated H2/CO ratio.

Phylodynamics is central to understanding infectious disease dynamics through the integration of genomic and epidemiological data. Despite advancements, including the application of deep learning to overcome computational limitations, significant challenges persist due to data inadequacies and statistical unidentifiability of key parameters. These issues are particularly pronounced in poorly resolved phylogenies, commonly observed in outbreaks such as SARS-CoV-2. In this study, we conducted a thorough evaluation of PhyloDeep, a deep learning inference tool for phylodynamics, assessing its performance on poorly resolved phylogenies. Our findings reveal the limited predictive accuracy of PhyloDeep (and other state-of-the-art approaches) in these scenarios. However, models trained on poorly resolved, realistically simulated trees demonstrate improved predictive power, despite not being infallible, especially in scenarios with superspreading dynamics, whose parameters are challenging to capture accurately. Notably, we observe markedly improved performance through the integration of minimal contact tracing data, which refines poorly resolved trees. Applying this approach to a sample of SARS-CoV-2 sequences partially matched to contact tracing from Hong Kong yields informative estimates of superspreading potential, extending beyond the scope of contact tracing data alone. Our findings demonstrate the potential for enhancing phylodynamic analysis through complementary data integration, ultimately increasing the precision of epidemiological predictions crucial for public health decision-making and outbreak control.

Series of Ni/xAl(2)O(3)-(100-x)(0.88Zr + 0.12Ce)O-2 (denoted as Ni/xACZ) were prepared to elucidate the effect of Al/Zr ratio on Ni-support interaction and activity/stability in ethanol dry reforming (EDR). The change in the catalysts' physicochemical properties before and after EDR reaction was determined using various characterization techniques including X-ray diffraction, UV-Vis spectroscopy, transmission electron microscopy, H-2-temperature programmed reduction, CO2-temperature programmed desorption, ferromagnetic resonance, thermogravimetric analysis and Raman spectroscopy. Increasing the Al/Zr ratio (from 5 mol.% to 75 mol.% alumina content) resulted in Ni nanoparticles' size decreasing as well as the increasing metal-support interaction (MSI). The structural properties of the Al-rich (x = 50, 75 mol.%) oxide supports provided the highest activity and stability in EDR. In contrast, the Zr-rich samples (x = 5, 20 mol. %) showed lower EDR activity and stability due to the large nickel particle size and the deactivation of Ni-0 nanoparticles with graphitized carbon. The Ni/50ACZ catalyst achieved stable and balanced H-2 and CO yields (similar to 77% each, with a CO/H-2 ratio close to 1) and exceptional C2H5OH/CO2 conversions of 100/63% at 650 degrees C, owing to nanosized Ni-0, optimal MSI, and high thermal stability.

Evolutionary convergences are observed at all levels, from phenotype to DNA and protein sequences, and changes at these different levels tend to be correlated. Notably, convergent mutations can lead to convergent changes in phenotype, such as changes in metabolism, drug resistance, and other adaptations to changing environments. We propose a two-component approach to detect mutations subject to convergent evolution in protein alignments. The "Emergence" component selects mutations that emerge more often than expected, while the "Correlation" component selects mutations that correlate with the convergent phenotype under study. With regard to Emergence, a phylogeny deduced from the alignment is provided by the user and is used to simulate the evolution of each alignment position. These simulations allow us to estimate the expected number of mutations in a neutral model, which is compared to the observed number of mutations in the data studied. In Correlation, a comparative phylogenetic approach, is used to measure whether the presence of each of the observed mutations is correlated with the convergent phenotype. Each component can be used on its own, for example Emergence when no phenotype is available. Our method is implemented in a standalone workflow and a webserver, called ConDor. We evaluate the properties of ConDor using simulated data, and we apply it to three real datasets: sedge PEPC proteins, HIV reverse transcriptase, and fish rhodopsin. The results show that the two components of ConDor complement each other, with an overall accuracy that compares favorably to other available tools, especially on large datasets.

Bio-based acetaldehyde production by non-oxidative dehydrogenation of ethanol is a promising alternative for the fine chemistry industry, where acetaldehyde is an important part of the synthesis chain and co-production of hydrogen. In this paper, investigations on nickel containing alumina itterbia stabilized zirconia with microwave-assisted synthesis as catalysts for the ethanol dehydrogenation process to acetaldehyde are reported. Ni–xAl2O3–(100 − x)[Zr0,97Yb0,03]O2 (x = 35 and 65 mol

Based on our own and published results of the HKUST-1 (Cu3BTC2)-type organometallic polymers study by X-ray diffraction (cell parameters, intensity ratio of a series of indicator reflections) and X-ray absorption spectroscopy (Cu2+ ions local coordination environment), the phase compositions determined by the synthesis conditions were proposed. Different local environments of Cu(2+ )ions in Cu3BTC2 structures were established depending on the compositions of the precursors with copper ions and solvent and their ratio, solution for post-synthetic treatment of finished samples, temperature, and duration of the process. A relationship between the proposed compositions and the cell parameters, Cu-Cu interatomic distance, and sample morphology was found, which made it possible to explain the catalytic properties (conversion degree and selectivity) of Cu3BTC2 and to identify the most promising catalyst. The Cu(3)BTC(2 )antimicrobial properties against Escherichia coli, Pseudomonas aeruginosa, and Staphylococcus aureus and possible reasons for the maximum values of the growth inhibition zone were discussed. The concept of "topological structural type" was introduced, which combined phases of different compositions of the HKUST-1 type.

A deeper understanding of HIV-1 transmission and drug resistance mechanisms can lead to improvements in current treatment policies. However, the rates at which HIV-1 drug resistance mutations (DRMs) are acquired and which transmitted DRMs persist are multi-factorial and vary considerably between different mutations. We develop a method for the estimation of drug resistance acquisition and transmission patterns. The method uses maximum likelihood ancestral character reconstruction informed by treatment roll-out dates and allows for the analysis of very large datasets. We apply our method to transmission trees reconstructed on the data obtained from the UK HIV Drug Resistance Database to make predictions for known DRMs. Our results show important differences between DRMs, in particular between polymorphic and non-polymorphic DRMs and between the B and C subtypes. Our estimates of reversion times, based on a very large number of sequences, are compatible but more accurate than those already available in the literature, with narrower confidence intervals. We consistently find that large resistance clusters are associated with polymorphic DRMs and DRMs with long loss times, which require special surveillance. As in other high-income countries (e.g., Switzerland), the prevalence of sequences with DRMs is decreasing, but among these, the fraction of transmitted resistance is clearly increasing compared to the fraction of acquired resistance mutations. All this indicates that efforts to monitor these mutations and the emergence of resistance clusters in the population must be maintained in the long term.

Abstract Despite the rapid growth in viral genome sequencing, statistical methods face challenges in handling historical viral endemic diseases with large amounts of underutilized partial sequence data. We propose a phylogenetic pipeline that harnesses both full and partial viral genome sequences to investigate historical pathogen spread between countries. Its application to Rabies virus (RABV) yields precise dating and confident estimates of its geographic dispersal. By using full genomes and partial sequences, we reduce both geographic and genetic biases that often hinder studies that focus on specific genes. Our pipeline reveals an emergence of the present canine-mediated RABV between years 1301 and 1401 and reveals regional introductions over a 700-year period. This geographic reconstruction enables us to locate episodes of human-mediated introductions of RABV and examine the role that European colonization played in its spread. Our approach enables phylogeographic analysis of large and genetically diverse data sets for many viral pathogens.

Ni-based catalysts supported on xAl(2)O(3)-(100 - x)(Zr-Yb)O-2 mixed oxides xerogel matrix with various mole ratios (x = 35 and 65% alumina), prepared by a sol-gel method, were used as catalysts (Ni/xAZ) in the ethanol dry reforming (EDR) reaction in the temperature range 600-750 degrees C with different CO2/Ethanol feed molar ratios. The fresh and spent catalysts were characterized via X-ray diffraction (XRD), H-2-TPR, Brunauer-Emmett-Teller (BET), X-ray photoelectron spectroscopy (XPS), electron paramagnetic resonance (EPR), small angle X-ray scattering (SAXS), and Raman techniques. The results pointed to the key role of support composition (Zr:Al ratio) in the strength of metal-support interactions and natural carbon deactivation. Differences in the catalytic activity and stability of the samples are associated with different ratios of weakly and strongly interacting forms of nickel and their amounts depending on the Al/Zr ratio. The highest catalytic activity and stability of Ni/35AZ is due to the presence of ferromagnetic Ni-0 particles formed during the reduction of oxide forms of nickel with moderate metal-support interaction. The higher Zr content in support composition increases the Ni2+ reducibility and the Ni-0 formation due to its coordination in the form of the NiO with high metal-support interaction and defective spinel-type phase, improving resistance to graphite and carbonyl-type carbon formation under EDR reaction media. At the CO2/Ethanol = 1:1 reagent mixture ratio, Ni/35AZ-1:1 was more active at 600 degrees C (43% H-2 yield, 38% CO yield), the best H-2/CO ratio was achieved at 750 degrees C, and was at 1.2 regardless of the sample composition. Increasing the CO2 concentration at CO2/Ethanol = 1.4:1 increased the activity of the Ni/35AZ-1:1.4 sample (47% H-2 yield, 44% CO yield at 600 degrees C), and the best H-2/CO ratio was 1 at T = 750 degrees C.