Machine-Learning Approach for Risk Estimation and Risk Prediction of the Effect of Climate on Bovine Respiratory Disease

Bovine respiratory disease (BRD) is a major cause of illness and death in cattle; however, its global extent and distribution remain unclear. As climate change continues to impact the environment, it is important to understand the environmental factors contributing to BRD’s emergence and re-emergence. In this study, we used machine-learning models and remotely sensed climate data at 2.5 min (21 km2) resolution environmental layers to estimate the risk of BRD and predict its potential future distribution. We analysed 13,431 BRD cases from 1727 cities worldwide between 2005 and 2021 using two machine-learning models, maximum entropy (MaxEnt) and Boosted Regression Trees (BRT), to predict the risk and geographical distribution of the risk of BRD globally with varying model parameters. Different re-sampling regimes were used to visualise and measure various sources of uncertainty and prediction performance. The best-fitting model was assessed based on the area under the receiver operator curve (AUC-ROC), positive predictive power and Cohen’s Kappa. We found that BRT had better predictive power compared with MaxEnt. Our findings showed that favourable habitats for BRD occurrence were associated with the mean annual temperature, precipitation of the coldest quarter, mean diurnal range and minimum temperature of the coldest month. Similarly, we showed that the risk of BRD is not limited to the currently known suitable regions of Europe and west and central Africa but extends to other areas, such as Russia, China and Australia. This study highlights the need for global surveillance and early detection systems to prevent the spread of disease across borders. The findings also underscore the importance of bio-security surveillance and livestock sector interventions, such as policy-making and farmer education, to address the impact of climate change on animal diseases and prevent emergencies and the spread of BRD to new areas

Multivariable regression analysis in Schistosoma mansoni-infected individuals in the Sudan reveals unique immunoepidemiological profiles in uninfected, egg+ and non-egg+ infected individuals

Background: In the Sudan, Schistosoma mansoni infections are a major cause of morbidity in schoolaged children and infection rates are associated with available clean water sources. During infection, immune responses pass through a Th1 followed by Th2 and Treg phases and patterns can relate to different stages of infection or immunity. Methodology: This retrospective study evaluated immunoepidemiological aspects in 234 individuals(range 4–85 years old) from Kassala and Khartoum states in 2011. Systemic immune profiles(cytokines and immunoglobulins) and epidemiological parameters were surveyed in n = 110 persons presenting patent S. mansoni infections (egg+), n = 63 individuals positive for S. mansoni via PCR in sera but egg negative (SmPCR+) and n = 61 people who were infection-free (Sm uninf). Immunoepidemiological findings were further investigated using two binary multivariable regression analysis. Principal Findings: Nearly all egg+ individuals had no access to latrines and over 90% obtained water via the canal stemming from the Atbara River. With regards to age, infection and an egg+ status was linked to young and adolescent groups. In terms of immunology, S. mansoni infection per se was strongly associated with increased SEA-specific IgG4 but not IgE levels. IL-6, IL-13 and IL-10 were significantly elevated in patently-infected individuals and positively correlated with egg load. In contrast, IL-2 and IL-1β were significantly lower in SmPCR+ individuals when compared to Sm uninf and egg+ groups which was further confirmed during multivariate regression analysis. Conclusions/Significance: Schistosomiasis remains an important public health problem in the Sudan with a high number of patent individuals. In addition, SmPCR diagnostics revealed another cohort of infected individuals with a unique immunological profile and provides an avenue for future studies on non-patent infection states. Future studies should investigate the downstream signalling pathways/mechanisms of IL-2 and IL-1β as potential diagnostic markers in order to distinguish patent from non-patent individuals

Nonlinear finite element analysis of axially crushed cotton fibre composite corrugated tubes

It is proven experimentally that introducing corrugation along a shell generator together with a proper advanced composite material will enhance the crashworthiness performance of energy device units. This is because corrugation along the shell generator will force the initial crushing to occur at a predetermined region along the tube generator. On the other hand, a proper composite material offers vast potential for optimally tailoring a design to meet crashworthiness performance requirements. In this paper, the energy absorption characteristics of cotton fibre/propylene corrugated tubes are numerically studied. Finite element simulation using ABAQUS/Explicit was carried out to examine the effects of parametric modifications on the tube’s energy absorption capability. Results showed that the tube’s energy absorption capability was affected significantly by varying the number of corrugation and aspect ratios. It is found that as the number of corrugations increases, the amount of absorbed energy significantly increases

Follicular helper T cell profiles predict response to costimulation blockade in type 1 diabetes

Follicular helper T (TFH) cells are implicated in type 1 diabetes (T1D), and their development has been linked to CD28 costimulation. We tested whether TFH cells were decreased by costimulation blockade using the CTLA-4–immunoglobulin (Ig) fusion protein (abatacept) in a mouse model of diabetes and in individuals with new-onset T1D. Unbiased bioinformatics analysis identified that inducible costimulatory molecule (ICOS)+ TFH cells and other ICOS+ populations, including peripheral helper T cells, were highly sensitive to costimulation blockade. We used pretreatment TFH profiles to derive a model that could predict clinical response to abatacept in individuals with T1D. Using two independent approaches, we demonstrated that higher frequencies of ICOS+ TFH cells at baseline were associated with a poor clinical response following abatacept administration. Therefore, TFH analysis may represent a new stratification tool, permitting the identification of individuals most likely to benefit from costimulation blockade

Follicular helper T cell profiles predict response to costimulation blockade in type 1 diabetes

Follicular helper T (TFH) cells are implicated in type 1 diabetes (T1D), and their development has been linked to CD28 costimulation. We tested whether TFH cells were decreased by costimulation blockade using the CTLA-4–immunoglobulin (Ig) fusion protein (abatacept) in a mouse model of diabetes and in individuals with new-onset T1D. Unbiased bioinformatics analysis identified that inducible costimulatory molecule (ICOS)+ TFH cells and other ICOS+ populations, including peripheral helper T cells, were highly sensitive to costimulation blockade. We used pretreatment TFH profiles to derive a model that could predict clinical response to abatacept in individuals with T1D. Using two independent approaches, we demonstrated that higher frequencies of ICOS+ TFH cells at baseline were associated with a poor clinical response following abatacept administration. Therefore, TFH analysis may represent a new stratification tool, permitting the identification of individuals most likely to benefit from costimulation blockade

Role of non-coding RNA networks in leukemia progression, metastasis and drug resistance.

Early-stage detection of leukemia is a critical determinant for successful treatment of the disease and can increase the survival rate of leukemia patients. The factors limiting the current screening approaches to leukemia include low sensitivity and specificity, high costs, and a low participation rate. An approach based on novel and innovative biomarkers with high accuracy from peripheral blood offers a comfortable and appealing alternative to patients, potentially leading to a higher participation rate.Recently, non-coding RNAs due to their involvement in vital oncogenic processes such as differentiation, proliferation, migration, angiogenesis and apoptosis have attracted much attention as potential diagnostic and prognostic biomarkers in leukemia. Emerging lines of evidence have shown that the mutational spectrum and dysregulated expression of non-coding RNA genes are closely associated with the development and progression of various cancers, including leukemia. In this review, we highlight the expression and functional roles of different types of non-coding RNAs in leukemia and discuss their potential clinical applications as diagnostic or prognostic biomarkers and therapeutic targets