Two phase feature-ranking for new soil dataset for Coxiella burnetii persistence and classification using machine learning models

Global and regional seroprevalence of coxiellosis in small ruminants: A systematic review and meta-analysis

BACKGROUND

Coxiellosis is a neglected zoonosis for occupationally exposed people in many parts of the world. Sheep and goats are two important small ruminants that act as reservoirs for human contamination; however, there is a lack of comprehensive data on the epidemiological aspects of coxiellosis in sheep and goats at regional and global levels. The aim of this study was to systematically review the available articles on seroprevalence of coxiellosis in sheep and goats and estimate the overall seroprevalence in different regions.

METHODS

A systematic search strategy was performed in five electronic repositories for articles published until December 2021. Relevant data were extracted from the selected articles based on the inclusion criteria. A random effect meta-analysis model was used to analyse the data. Results are presented as the prevalence of seropositivity as a percentage and 95% confidence intervals.

RESULTS

The global pooled seroprevalence of coxiellosis in sheep was 17.38% (95% confidence interval [CI]: 15.59%-19.17%). Overall, the regional level pooled prevalence estimates in sheep ranged from 15.04% (95% CI: 7.68%-22.40%) to 19.14% (95% CI: 15.51%-22.77%), depending on region. The global pooled seroprevalence of coxiellosis in goats was 22.60% (95% CI: 19.54%-25.66%). Overall, the regional level pooled prevalence estimates in goats ranged from 6.33% (95% CI: 2.96%-9.71%) to 55.13% (95% CI: 49.61%-60.65%), depending on the region. The prevalence estimates also varied significantly in both sheep and goats depending on age, sex, and rearing systems of the animals (p < 0.001).

CONCLUSION

Seroprevalence of coxiellosis in both sheep and goats is considerable. Routine monitoring of the sheep and goat populations is needed to prevent spillover infection in other livestock and humans.

Interpretable CRISPR/Cas9 off-target activities with mismatches and indels prediction using BERT

Off-target effects of CRISPR/Cas9 can lead to suboptimal genome editing outcomes. Numerous deep learning-based approaches have achieved excellent performance for off-target prediction; however, few can predict the off-target activities with both mismatches and indels between single guide RNA (sgRNA) and target DNA sequence pair. In addition, data imbalance is a common pitfall for off-target prediction. Moreover, due to the complexity of genomic contexts, generating an interpretable model also remains challenged. To address these issues, firstly we developed a BERT-based model called CRISPR-BERT for enhancing the prediction of off-target activities with both mismatches and indels. Secondly, we proposed an adaptive batch-wise class balancing strategy to combat the noise exists in imbalanced off-target data. Finally, we applied a visualization approach for investigating the generalizable nucleotide position-dependent patterns of sgRNA-DNA pair for off-target activity. In our comprehensive comparison to existing methods on five mismatches-only datasets and two mismatches-and-indels datasets, CRISPR-BERT achieved the best performance in terms of AUROC and PRAUC. Besides, the visualization analysis demonstrated how implicit knowledge learned by CRISPR-BERT facilitates off-target prediction, which shows potential in model interpretability. Collectively, CRISPR-BERT provides an accurate and interpretable framework for off-target prediction, further contributes to sgRNA optimization in practical use for improved target specificity in CRISPR/Cas9 genome editing. The source code is available at https://github.com/BrokenStringx/CRISPR-BERT.

Advances in machine learning-based bacteria analysis for forensic identification: identity, ethnicity, and site of occurrence

When faced with an unidentified body, identifying the victim can be challenging, particularly if physical characteristics are obscured or masked. In recent years, microbiological analysis in forensic science has emerged as a cutting-edge technology. It not only exhibits individual specificity, distinguishing different human biotraces from various sites of occurrence (e.g., gastrointestinal, oral, skin, respiratory, and genitourinary tracts), each hosting distinct bacterial species, but also offers insights into the accident's location and the surrounding environment. The integration of machine learning with microbiomics provides a substantial improvement in classifying bacterial species compares to traditional sequencing techniques. This review discusses the use of machine learning algorithms such as RF, SVM, ANN, DNN, regression, and BN for the detection and identification of various bacteria, including Bacillus anthracis, Acetobacter aceti, Staphylococcus aureus, and Streptococcus, among others. Deep leaning techniques, such as Convolutional Neural Networks (CNN) models and derivatives, are also employed to predict the victim's age, gender, lifestyle, and racial characteristics. It is anticipated that big data analytics and artificial intelligence will play a pivotal role in advancing forensic microbiology in the future.

Soil microbiome influences human health in the context of climate change

Soil microbiomes continue to evolve and shape the human microbiota according to external anthropogenic and climate change effects. Ancient microbes are being exposed as a result of glacier melting, soil erosion and poor agricultural practices. Soil microbes subtly regulate greenhouse gas emissions and undergo profound alterations due to poor soil maintenance. This review highlights how the soil microbiome influences human digestion processes, mineral and vitamin production, mental health and mood stimulation. Although much about microbial functions remains unknown, increasing evidence suggests that beneficial soil microbes are vital for enhancing human tolerance to diseases and pathogens. Further research is essential to delineate the specific role of the soil microbiome in promoting human health, especially in light of the increasing anthropogenic pressures and changing climatic conditions.

Deep ensemble approach for pathogen classification in large-scale images using patch-based training and hyper-parameter optimization

Pathogenic bacteria present a major threat to human health, causing various infections and illnesses, and in some cases, even death. The accurate identification of these bacteria is crucial, but it can be challenging due to the similarities between different species and genera. This is where automated classification using convolutional neural network (CNN) models can help, as it can provide more accurate, authentic, and standardized results.In this study, we aimed to create a larger and balanced dataset by image patching and applied different variations of CNN models, including training from scratch, fine-tuning, and weight adjustment, and data augmentation through random rotation, reflection, and translation. The results showed that the best results were achieved through augmentation and fine-tuning of deep models. We also modified existing architectures, such as InceptionV3 and MobileNetV2, to better capture complex features. The robustness of the proposed ensemble model was evaluated using two data splits (7:2:1 and 6:2:2) to see how performance changed as the training data was increased from 10 to 20%. In both cases, the model exhibited exceptional performance. For the 7:2:1 split, the model achieved an accuracy of 99.91%, F-Score of 98.95%, precision of 98.98%, recall of 98.96%, and MCC of 98.92%. For the 6:2:2 split, the model yielded an accuracy of 99.94%, F-Score of 99.28%, precision of 99.31%, recall of 98.96%, and MCC of 99.26%. This demonstrates that automatic classification using the ensemble model can be a valuable tool for diagnostic staff and microbiologists in accurately identifying pathogenic bacteria, which in turn can help control epidemics and minimize their social and economic impact.