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ABSTRACT: One suggested approach to improve the reproductive performance of dairy herds is through the targeted management of subgroups of biologically similar animals, such as those with similar probabilities of becoming pregnant, termed pregnancy risk. We aimed to use readily available farm data to develop predictive models of pregnancy risk in dairy cows. Data from a convenience sample of 108 dairy herds in the UK were collated, and each herd was randomly allocated, at a ratio of 80:20, to either training or testing datasets. Following data cleaning, there were a total of 78 herds in the training dataset and 20 herds in the testing dataset. Data were further split by parity into nulliparous, primiparous, and multiparous subsets. An XGBoost model was trained to predict the insemination outcome in each parity subset, with predictors from farm records of breeding, calving, and milk recording. Training data comprised 74,511 inseminations in 45,909 nulliparous animals, 86,420 inseminations in 39,439 primiparous animals, and 158,294 inseminations in 32,520 multiparous animals. The final models were evaluated by predicting with the testing data, comprising 31,740 inseminations in 19,647 nulliparous animals, 38,588 inseminations in 16,215 primiparous animals, and 65,049 inseminations in 12,439 multiparous animals. Model discrimination was assessed by calculating the area under receiver operating characteristic curves (AUC); model calibration was assessed by plotting calibration curves and compared across test herds by calculating the expected calibration error (ECE) in each test herd. The models were unable to discriminate between insemination outcomes with high accuracy, with an AUC of 0.63, 0.59, and 0.62 in the nulliparous, primiparous, and multiparous subsets, respectively. The models were generally well calibrated, meaning the model-predicted pregnancy risks were similar to the observed pregnancy risks. The mean (SD) ECE in the test herds was 0.038 (0.023), 0.028 (0.012), and 0.020 (0.008) in the nulliparous, primiparous, and multiparous subsets respectively. The predictive models reported here could theoretically be used to identify subgroups of animals with similar pregnancy risk to facilitate targeted reproductive management; or provide information about cows' relative pregnancy risk compared with the herd average, which may support on-farm decision making. Further research is needed to evaluate the generalizability of these predictive models and understand the source of variation in ECE between herds; however, this study demonstrates that it is possible to accurately predict pregnancy risk in dairy cows using readily available farm data.

ABSTRACT: Currently, the dairy industry is facing many challenges that could affect its sustainability, including climate change and public perception of the industry. As a result, interest is increasing in the concept of identifying resilient animals, those with a long productive lifespan, as well as good reproductive performance and milk yield. There is much evidence that events in utero, that is, the developmental origins of health and disease hypothesis, alter the life-course health of offspring and we hypothesized that these could alter resilience in calves, where resilience is identified using lifetime data. The aim of this study was to quantify lifetime resilience scores (LRS) using an existing scoring system, based on longevity with secondary corrections for age at first calving and calving interval, and to quantify the effects of in utero events on the LRS using 2 datasets. The first was a large dataset of cattle on 83 farms in Great Britain born from 2006 to 2015 and the second was a smaller, more granular dataset of cattle born between 2003 and 2015 in the Langhill research herd at Scotland's Rural College. Events during dam's pregnancy included health events (lameness, mastitis, use of an antibiotic or anti-inflammatory medication), the effect of heat stress as measured by temperature-humidity index, and perturbations in milk yield and quality (somatic cell count, percentage fat, percentage protein and fat:protein ratio). Daughters born to dams that experienced higher temperature-humidity indexes while they were in utero during the first and third trimesters of pregnancy had lower LRS. Daughter LRS were also lower where milk yields or median fat percentages in the first trimester were low, and when milk yields were high in the third trimester. Dam LRS was positively associated with LRS of their offspring; however, as parity of the dam increased, LRS of their calves decreased. Similarly, in the Langhill herd, dams of a higher parity produced calves with lower LRS. Additionally, dams that recorded a high maximum locomotion score in the third trimester of pregnancy were negatively associated with lower calf LRS in the Langhill herd. Our results suggest that events that occur during pregnancy have lifelong consequences for the calf's lifetime performance. However, experience of higher temperature-humidity indexes, higher dam LRS, and mothers in higher parities explained a relatively small proportion of variation in offspring LRS, which suggests that other factors play a substantial role in determining calf LRS. Although “big data” can contain a considerable amount of noise, similar findings between the 2 datasets indicate it is likely these findings are real.

Abstract Cells within the tumour microenvironment (TME) can impact tumour development and influence treatment response. Computational approaches have been developed to deconvolve the TME from bulk RNA-seq. Using scRNA-seq profiling from breast tumours we simulate thousands of bulk mixtures, representing tumour purities and cell lineages, to compare the performance of nine TME deconvolution methods (BayesPrism, Scaden, CIBERSORTx, MuSiC, DWLS, hspe, CPM, Bisque, and EPIC). Some methods are more robust in deconvolving mixtures with high tumour purity levels. Most methods tend to mis-predict normal epithelial for cancer epithelial as tumour purity increases, a finding that is validated in two independent datasets. The breast cancer molecular subtype influences this mis-prediction. BayesPrism and DWLS have the lowest combined numbers of false positives and false negatives, and have the best performance when deconvolving granular immune lineages. Our findings highlight the need for more single-cell characterisation of rarer cell types, and suggest that tumour cell compositions should be considered when deconvolving the TME.

Mobility scoring data can be used to estimate the prevalence, incidence, and duration of lameness in dairy herds. Mobility scoring is often performed infrequently with variable sensitivity, but how this impacts the estimation of lameness parameters is largely unknown. We developed a simulation model to investigate the impact of the frequency and accuracy of mobility scoring on the estimation of lameness parameters for different herd scenarios. Herds with a varying prevalence (10, 30, or 50%) and duration (distributed around median days 18, 36, 54, 72, or 108) of lameness were simulated at daily time steps for five years. The lameness parameters investigated were prevalence, duration, new case rate, time to first lameness, and probability of remaining sound in the first year. True parameters were calculated from daily data and compared to those calculated when replicating different frequencies (weekly, two-weekly, monthly, quarterly), sensitivities (60–100%), and specificities (95–100%) of mobility scoring. Our results showed that over-estimation of incidence and under-estimation of duration can occur when the sensitivity and specificity of mobility scoring are

Abstract Deep learning is a subdiscipline of artificial intelligence that uses a machine learning technique called artificial neural networks to extract patterns and make predictions from large data sets. The increasing adoption of deep learning across healthcare domains together with the availability of highly characterised cancer datasets has accelerated research into the utility of deep learning in the analysis of the complex biology of cancer. While early results are promising, this is a rapidly evolving field with new knowledge emerging in both cancer biology and deep learning. In this review, we provide an overview of emerging deep learning techniques and how they are being applied to oncology. We focus on the deep learning applications for omics data types, including genomic, methylation and transcriptomic data, as well as histopathology-based genomic inference, and provide perspectives on how the different data types can be integrated to develop decision support tools. We provide specific examples of how deep learning may be applied in cancer diagnosis, prognosis and treatment management. We also assess the current limitations and challenges for the application of deep learning in precision oncology, including the lack of phenotypically rich data and the need for more explainable deep learning models. Finally, we conclude with a discussion of how current obstacles can be overcome to enable future clinical utilisation of deep learning.

Lymphatic filariasis, commonly known as elephantiasis, is a painful and profoundly disfiguring disease. Wuchreria bancrofti (Wb) is responsible for >90% of infections and the remainder are caused by Brugia spp. Mosquitoes of the genera Culex (in urban and semi-urban areas), Anopheles (in rural areas of Africa and elsewhere), and Aedes (in Pacific islands) are the major vectors of W. bancrofti. A preventive chemotherapy called mass drug administration (MDA), including albendazole with ivermectin or diethylcarbamazine citrate (DEC) is used in endemic areas. Vector control strategies such as residual insecticide spraying and long-lasting insecticidal nets are supplemental to the core strategy of MDA to enhance elimination efforts. However, increasing insecticide resistance in mosquitoes and drug resistance in parasite limit the effectiveness of existing interventions, and new measures are needed for mosquito population control and disruption of mosquito-parasite interactions to reduce transmission. Mosquito insulin signaling regulates nutrient metabolism and has been implicated in reduced prevalence and intensity of malaria parasite, Plasmodium falciparum, infection in mosquitoes. Currently no data are available to assess how insulin signaling in mosquitoes affects the development of multi-cellular parasites, such as filarial nematodes. Here, we show that insulin receptor knockdown in blood fed C. quinquefasciatus, the major vector of Wb in India, completely blocks the development of filarial nematode parasite to the infective L3 stage, and results in decreased ecdysteroid production and trypsin activity leading to fewer mosquito eggs. These data indicate that a functional mosquito insulin receptor (IR) is necessary for filarial parasite development and mosquito reproduction. Therefore, insulin signaling may represent a new target for the development of vector control or parasite blocking strategies.

In terms of cost-efficiency, biocompatibility, environmental friendliness, and scalability, green nanoparticle (NP) synthesis is a novel field of nanotechnology that outperforms both physical and chemical approaches. Plants, bacteria, fungi, and algae have lately been used to produce metals and metal oxide nanoparticles as an alternate method. The development of alternative strategies to restrict the growth of hazardous bacteria, as well as the building of resistance by germs to various antibiotics, led to the introduction of nanoparticles as novel antimicrobial agents. Metal oxides have been found to form oxide monolayer structures for drug delivery when they react with a transporter's surface. Metal oxide nanoparticles have emerged as biomedical materials in recent years, with applications in immunotherapy, tissue treatment, diagnostics, regenerative medicine, wound healing, dentistry, and biosensing platforms. Biotoxicology and its antimicrobial, antifungal, and antiviral characteristics were hotly contested. Metal oxide nanoparticles have tremendous applicability and commercial value, as evidenced by important discoveries in the realm of nanobiomedicine in terms of locations and amounts. This paper describes the production of nanometal oxides from various green materials, as well as their applications.

Chronic rejection of solid organ allografts remains the major cause of transplant failure. Donor-derived tissue-resident lymphocytes are transferred to the recipient during transplantation, but their impact on alloimmunity is unknown. Using mouse cardiac transplant models, we show that graft-versus-host recognition by passenger donor CD4 T cells markedly augments recipient cellular and humoral alloimmunity, resulting in more severe allograft vasculopathy and early graft failure. This augmentation is enhanced when donors were pre-sensitized to the recipient, is dependent upon avoidance of host NK cell recognition, and is partly due to provision of cognate help for allo-specific B cells from donor CD4 T cells recognizing B cell MHC class II in a peptide-degenerate manner. Passenger donor lymphocytes may therefore influence recipient alloimmune responses and represent a therapeutic target in solid organ transplantation.

Summary: MHC alloantigen is recognized by two pathways: “directly,” intact on donor cells, or “indirectly,” as self-restricted allopeptide. The duration of each pathway, and its relative contribution to allograft vasculopathy, remain unclear. Using a murine model of chronic allograft rejection, we report that direct-pathway CD4 T cell alloresponses, as well as indirect-pathway responses against MHC class II alloantigen, are curtailed by rapid elimination of donor hematopoietic antigen-presenting cells. In contrast, persistent presentation of epitope resulted in continual division and less-profound contraction of the class I allopeptide-specific CD4 T cell population, with approximately 10,000-fold more cells persisting than following acute allograft rejection. This expanded population nevertheless displayed sub-optimal anamnestic responses and was unable to provide co-stimulation-independent help for generating alloantibody. Indirect-pathway CD4 T cell responses are heterogeneous. Appreciation that responses against particular alloantigens dominate at late time points will likely inform development of strategies aimed at improving transplant outcomes. : The T cell allorecognition pathways governing rejection of organ allografts are poorly understood. Using a model of chronic heart graft rejection, Ali et al. show that continual presentation of MHC class I allopeptide drives late division in the responding alloreactive CD4 T cell population, resulting in a markedly augmented maintenance phase.

Background. It is well recognized that there is significant variation between centers in access to kidney transplantation. In the absence of high-grade evidence, it is unclear whether variation is due to patient case mix, other center factors, or individual clinician decisions. This study sought consensus between UK clinicians on factors that should influence access to kidney transplantation. Methods. As part of the Access to Transplantation and Transplant Outcome Measures project, consultant nephrologists and transplant surgeons in 71 centers were invited to participate in a Delphi study involving 2 rounds. During rounds 1 and 2, participants rated their agreement to 29 statements covering 8 topics regarding kidney transplantation. A stakeholder meeting was used to discuss statements of interest after the 2 rounds. Results. In total, 122 nephrologists and 16 transplant surgeons from 45 units participated in rounds 1 and 2. After 2 rounds, 12 of 29 statements reached consensus. Fifty people participated in the stakeholder meeting. After the stakeholder meeting, a further 4 statements reached agreement. Of the 8 topics covered, consensus was reached in 6: use of a transplant protocol, patient age, body mass index, patient compliance with treatment, cardiac workup, and use of multidisciplinary meetings. Consensus was not reached on screening for malignancy and use of peripheral Doppler studies. Conclusions. The Delphi process identified factors upon which clinicians agreed and areas where consensus could not be achieved. The findings should inform national guidelines to support decision making in the absence of high quality evidence and to guide areas that warrant future research.

Ischemia–reperfusion (IR) injury to the kidney occurs in a range of clinically important scenarios including hypotension, sepsis and in surgical procedures such as cardiac bypass surgery and kidney transplantation, leading to acute kidney injury (AKI). Mitochondrial oxidative damage is a significant contributor to the early phases of IR injury and may initiate a damaging inflammatory response. Here we assessed whether the mitochondria targeted antioxidant MitoQ could decrease oxidative damage during IR injury and thereby protect kidney function. To do this we exposed kidneys in mice to in vivo ischemia by bilaterally occluding the renal vessels followed by reperfusion for up to 24 h. This caused renal dysfunction, measured by decreased creatinine clearance, and increased markers of oxidative damage. Administering MitoQ to the mice intravenously 15 min prior to ischemia protected the kidney from damage and dysfunction. These data indicate that mitochondrial oxidative damage contributes to kidney IR injury and that mitochondria targeted antioxidants such as MitoQ are potential therapies for renal dysfunction due to IR injury. Keywords: Kidney, Ischemia–reperfusion injury, Mitochondria, Oxidative damage, Mitochondria targeted antioxidants, MitoQ