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Abstract Background and aims The gut microbiota is implicated in the pathogenesis of colorectal cancer (CRC). We aimed to map the CRC mucosal microbiota and metabolome and define the influence of the tumoral microbiota on oncological outcomes. Methods A multicentre, prospective observational study was conducted of CRC patients undergoing primary surgical resection in the UK (n = 74) and Czech Republic (n = 61). Analysis was performed using metataxonomics, ultra-performance liquid chromatography-mass spectrometry (UPLC-MS), targeted bacterial qPCR and tumour exome sequencing. Hierarchical clustering accounting for clinical and oncological covariates was performed to identify clusters of bacteria and metabolites linked to CRC. Cox proportional hazards regression was used to ascertain clusters associated with disease-free survival over median follow-up of 50 months. Results Thirteen mucosal microbiota clusters were identified, of which five were significantly different between tumour and paired normal mucosa. Cluster 7, containing the pathobionts Fusobacterium nucleatum and Granulicatella adiacens, was strongly associated with CRC (P FDR = 0.0002). Additionally, tumoral dominance of cluster 7 independently predicted favourable disease-free survival (adjusted p = 0.031). Cluster 1, containing Faecalibacterium prausnitzii and Ruminococcus gnavus, was negatively associated with cancer (P FDR = 0.0009), and abundance was independently predictive of worse disease-free survival (adjusted p = 0.0009). UPLC-MS analysis revealed two major metabolic (Met) clusters. Met 1, composed of medium chain (MCFA), long-chain (LCFA) and very long-chain (VLCFA) fatty acid species, ceramides and lysophospholipids, was negatively associated with CRC (P FDR = 2.61 × 10−11); Met 2, composed of phosphatidylcholine species, nucleosides and amino acids, was strongly associated with CRC (P FDR = 1.30 × 10−12), but metabolite clusters were not associated with disease-free survival (p = 0.358). An association was identified between Met 1 and DNA mismatch-repair deficiency (p = 0.005). FBXW7 mutations were only found in cancers predominant in microbiota cluster 7. Conclusions Networks of pathobionts in the tumour mucosal niche are associated with tumour mutation and metabolic subtypes and predict favourable outcome following CRC resection. Video Abstract

Several questions regarding the evolution of SARS-CoV-2 remain poorly elucidated. One of these questions is the possible evolutionary impact of SARS-CoV-2 after the infection in domestic animals. In this study, we aimed to evaluate the potential role of cats as generators of relevant SARS-CoV-2 lineages during the pandemic. A total of 105 full-length genome viral sequences obtained from naturally infected cats during the pandemic were evaluated by distinct evolutionary algorithms. Analyses were enhanced, including a set of highly related SARS-CoV-2 sequences recovered from human populations. Our results showed the apparent high susceptibility of cats to the infection SARS-CoV-2 compared with other animal species. Evolutionary analyses indicated that the phylogenomic characteristics displayed by cat populations were influenced by the dominance of specific SARS-CoV-2 genetic groups affecting human populations. However, disparate dN/dS rates at some genes between populations recovered from cats and humans suggested that infection in these two species may suggest a different evolutionary constraint for SARS-CoV-2. Interestingly, the branch selection analysis showed evidence of the potential role of natural selection in the emergence of five distinct cat lineages during the pandemic. Although these lineages were apparently irrelevant to public health during the pandemic, our results suggested that additional studies are needed to understand the role of other animal species in the evolution of SARS-CoV-2 during the pandemic.

Studying food recipes is indispensable to understand the science of cooking. An essential problem in food computing is the adaptation of recipes to user needs and preferences. The main difficulty when adapting recipes is in determining ingredients relations, which are compound and hard to interpret. Word embedding models can catch the semantics of food items in a recipe, helping to understand how ingredients are combined and substituted. In this work, we propose an unsupervised method for adapting ingredient recipes to user preferences. To learn food representations and relations, we create and apply a specific-domain word embedding model. In contrast to previous works, we not only use the list of ingredients to train the model but also the cooking instructions. We enrich the ingredient data by mapping them to a nutrition database to guide the adaptation and find ingredient substitutes. We performed three different kinds of recipe adaptation based on nutrition preferences, adapting to similar ingredients, and vegetarian and vegan diet restrictions. With a 95% of confidence, our method can obtain quality adapted recipes without a previous knowledge extraction on the recipe adaptation domain. Our results confirm the potential of using a specific-domain semantic model to tackle the recipe adaptation task.

Background. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causing coronavirus disease 2019 (COVID-19) is the most transmissible ß-coronavirus in history, affecting all population groups. Immunocompromised patients, particularly cancer patients, have been highlighted as a reservoir to promote accumulation of viral mutations throughout persistent infection. Case presentation. We aimed to describe the clinical course and SARS-CoV-2 mutation profile for 102 days in an immunocompromised patient with non-Hodgkin’s lymphoma and COVID-19. We used RT-qPCR to quantify SARS-CoV-2 viral load over time and whole-virus genome sequencing to identify viral lineage and mutation profile. The patient presented with a persistent infection through 102 days while being treated with cytotoxic chemotherapy for non-Hodgkin’s lymphoma and received targeted therapy for COVID-19 with remdesivir and hyperimmune plasma. All sequenced samples belonged to the BA.1.1 lineage. We detected nine amino acid substitutions in five viral genes (Nucleocapsid, ORF1a, ORF1b, ORF13a, and ORF9b), grouped in two clusters: the first cluster with amino acid substitutions only detected on days 39 and 87 of sample collection, and the second cluster with amino acid substitutions only detected on day 95 of sample collection. The Spike gene remained unchanged in all samples. Viral load was dynamic but consistent with the disease flares. Conclusions. This report shows that the multiple mutations that occur in an immunocompromised patient with persistent COVID-19 could provide information regarding viral evolution and emergence of new SARS-CoV-2 variants.

The Mexican lineage H7N3 highly pathogenic avian influenza virus (HPAIV) has persisted in Mexican poultry since its first isolation in 2012. To date, the detection of this virus has gradually expanded from the initial one state to 18 states in Mexico. Despite the HPAIV H7N3 outbreak occurring yearly, the transmission pathways have never been studied, disallowing the establishment of effective control measures. We used a phylogenetic approach to unravel the transmission pathways of 2022 H7N3 HPAIVs in the new outbreak areas in Northern Mexico. We present genetic data of H7N3 viruses produced from 18 poultry farms infected in the spring of 2022. Our results indicate that the virus responsible for the current outbreak in Northern Mexico evolved from the Mexican lineage H7N3 HPAIV discovered in 2012. In the current outbreak, we identified five clusters of infection with four noticeably different genetic backgrounds. It is a cluster IV-like virus that was transmitted into one northern state causing an outbreak, then spreading to another neighboring northern state, possibly via a human-mediated mechanical transmission mechanism. The long-distance transmission event highlights the necessity for the more rigorous enforcement of biosafety measures in outbreaks. Additionally, we examined the evolutionary processes shaping the viral genetic and antigenic diversities. It is imperative to enhance active surveillance to include birds, the environment, and humans to detect HPAI in domestic poultry at an earlier point and eliminate it.

Bovine viral diarrhea (BVD) is an infectious disease, globally-distributed, caused by bovine Pestiviruses, endemic of cattle and other ruminant populations. BVD leads to significant economic losses to the cattle industry due to the wide range of clinical manifestations, including respiratory and gastrointestinal diseases and reproductive disorders. Within the Pestivirus genus of the family Flaviviridae three viral species are associated with BVD; Pestivirus A (Bovine viral diarrhea virus 1, BVDV-1), Pestivirus B (Bovine viral diarrhea virus 2, BVDV-2), and Pestivirus H (HoBi-like pestivirus, atypical ruminant pestivirus). These species are subdivided into subgenotypes based on phylogenetic analysis. The extensive genetic diversity of BVDV has been reported for several countries, where the incidence and genetic variation are more developed in Europe than in the Americas. The first report of BVDV in Mexico was in 1975; this study revealed seropositivity of 75% in cows with a clinical history of infertility, abortions, and respiratory disease. Other studies have demonstrated the presence of antibodies against BVDV with a seroprevalence ranging from 7.4 to 100%. Recently, endemic BVDV strains affecting cattle populations started to be analyzed, providing evidence of the BVDV diversity in several states of the country, revealing that at least four subgenotypes (BVDV-1a, 1b, 1c, and 2a) are circulating in animal populations in Mexico. Little information regarding BVD epidemiological current status in Mexico is available. This review summarizes available information regarding the prevalence and genetic diversity viruses associated with BVD in cattle from Mexico.

Musculoskeletal modeling is a well-established method in spine biomechanics and generally employed for investigations concerning both the healthy and the pathological spine. It commonly involves inverse kinematics and optimization of muscle activity and provides detailed insight into joint loading. The aim of the present work was to develop and validate a procedure for the automatized generation of semi-subject-specific multi-rigid body models with an articulated lumbar spine. Individualization of the models was achieved with a novel approach incorporating information from annotated EOS images. The size and alignment of bony structures, as well as specific body weight distribution along the spine segments, were accurately reproduced in the 3D models. To ensure the pipeline’s robustness, models based on 145 EOS images of subjects with various weight distributions and spinopelvic parameters were generated. For validation, we performed kinematics-dependent and segment-dependent comparisons of the average joint loads obtained for our cohort with the outcome of various published in vivo and in situ studies. Overall, our results agreed well with literature data. The here described method is a promising tool for studying a variety of clinical questions, ranging from the evaluation of the effects of alignment variation on joint loading to the assessment of possible pathomechanisms involved in adjacent segment disease.

We investigated whether and how political misinformation is engineered using a dataset of four months worth of tweets related to the 2016 presidential election in the United States. The data contained tweets that achieved a significant level of exposure and was manually labelled into misinformation and regular information. We found that misinformation was produced by accounts that exhibit different characteristics and behaviour from regular accounts. Moreover, the content of misinformation is more novel, polarised and appears to change through coordination. Our findings suggest that engineering of political misinformation seems to exploit human traits such as reciprocity and confirmation bias. We argue that investigating how misinformation is created is essential to understand human biases, diffusion and ultimately better produce public policy.

Despite the increasing capabilities of information technologies for data acquisition and processing, building energy management systems still require manual configuration and supervision to achieve optimal performance. Model predictive control (MPC) aims to leverage equipment control-particularly heating, ventilation, and air conditioning (HVAC)-by using a model of the building to capture its dynamic characteristics and to predict its response to alternative control scenarios. Usually, MPC approaches are based on simplified linear models, which support faster computation but also present some limitations regarding interpretability, solution diversification, and longer-term optimization. In this paper, we propose a novel MPC algorithm that uses a full-complexity grey-box simulation model to optimize HVAC operation in non-residential buildings. Our system generates hundreds of candidate operation plans, typically for the next day, and evaluates them in terms of consumption and comfort by means of a parallel simulator configured according to the expected building conditions (weather and occupancy). The system has been implemented and tested in an office building in Helsinki, both in a simulated environment and in the real building, yielding energy savings around 35% during the intermediate winter season and 20% in the whole winter season with respect to the current operation of the heating equipment.

The analysis of the electronic structure of the isolated silver copper oxide Ag2Cu2O4 is presented along with its precursor Ag2Cu2O4 and similar binary oxides, Ag2O, AgO, CuO, and NaCuO2. The results for AgCu2O4 reveal an electronic distribution in which silver and copper share a delocalized valence scheme with both metals in formal oxidation states larger than the usual Ag(super I) and Cu(super II).

The encapsulation of magnetic clusters of the transition metal ions between diamagnetic fragments of metal oxides is a major characteristic of polyoxometalate complexes. The reaction of divalent metal ions with the trivalent tungstophospate ligands produces new types of compounds. These compounds are derived from the Keggin and Dawson-Wells polyoxoanions. The tetrametallic unit M4O16 is encapsulated by the individual polyoxometalate entities.

Crystals of a heteropolytungstate compound with the formula K5.5Na1.5(PW10Cu2 H2O 2O38).13H2O were subjected to X-ray diffraction, magneticsusceptibility and electronparamagnetic resonance spectroscopy examinations. Structural data identified the predominant alpha-Keggin coordination taken by the compound and the location of the copper ions. In addition, the exchange-coupled copper pairs, which were confirmed using magnetic measurements, formed two different exchange interactions and two anisotropies for the compounds.

The structure and redox properties of the reduced Keggin complex, PMo12O40(VO)2](super 5-), were assessed using the density functional calculations (DFT). The redox properties of the metal oxide complex clusters were investigated through the relative stabilities of the reduced species as a function of the number n of the metal electrons. The complex's six out of the eight d electrons were contained in the three symmetric molybdenum orbitals while the two d electrons were at the vanadium orbitals.

Discusses a study which examined the magnetic properties of the ordered bimetallic compound CoCo(NiEDTA)[sub2]4H[sub2]O. Structure of the bimetallic compound; Conditions that influence magnetic behavior; Computational equations for the transfer matrix of the bimetallic compound.