Could climate change benefit invasive snakes? Modelling the potential distribution of the California Kingsnake in the Canary Islands.

The interaction between climate change and biological invasions is a global conservation challenge with major consequences for invasive species management. However, our understanding of this interaction has substantial knowledge gaps; this is particularly relevant for invasive snakes on islands because they can be a serious threat to island ecosystems. Here we evaluated the potential influence of climate change on the distribution of invasive snakes on islands, using the invasion of the California kingsnake (Lampropeltis californiae) in Gran Canaria. We analysed the potential distribution of L. californiae under current and future climatic conditions in the Canary Islands, with the underlying hypothesis that the archipelago might be suitable for the species under these climate scenarios. Our results indicate that the Canary Islands are currently highly suitable for the invasive snake, with increased suitability under the climate change scenarios tested here. This study supports the idea that invasive reptiles represent a substantial threat to near-tropical regions, and builds on previous studies suggesting that the menace of invasive reptiles may persist or even be exacerbated by climate change. We suggest future research should continue to fill the knowledge gap regarding invasive reptiles, in particular snakes, to clarify their potential future impacts on global biodiversity.

A new acroeimerian (Apicomplexa: Eimeriidae) in spotted house gecko, Gekko monarchus (Schlegel) (Sauria: Gekkonidae) from Peninsular Malaysia.

A new species of Acroeimeria Paperna & Landsberg, 1989 is described from the spotted house gecko, Gekko monarchus (Schlegel) from Peninsular Malaysia. Oöcysts of Acroeimeria grismeri n. sp. are spheroidal to subspheroidal with a smooth bi-layered wall, measure on average 18.4 × 17.3 µm, and have a length/width (L/W) ratio of 1.1; a micropyle and an oöcyst residuum are absent but variable polar granule(s) are present, commonly in Brownian movement. Sporocysts are ellipsoidal and measure on average 8.6 × 6.7 µm, L/W 1.3; Stieda, sub-Stieda and para-Stieda bodies are absent. The sporocyst residuum is composed of numerous spheroidal granules in the center of the sporocyst. This is the initial species of coccidian reported from G. monarchus and one of the few reported from any reptile from Peninsular Malaysia.

Testicular Core Extraction: Important Technique for Determining Sperm Retrieval Method in Non-obstructive Azoospermia.

OBJECTIVE: To assess the value of preliminary testicular core extraction (TCE) as a tool for determining whether to pursue clinic testicular extraction (cTESE) versus microscopic testicular sperm extraction (mTESE) to minimize both financial burden and procedural complexity. METHODS: All men with non-obstructive azoospermia (NOA) from 2018 to 2022 who underwent clinic TCE were analyzed. Patients who were found to have sperm on initial TCE were recommended cTESE. Patients who did not have sperm on initial TCE were recommended for mTESE. Sensitivity, specificity, positive predictive value, and negative predictive value of TCE were calculated. Cost reductions were determined based on this institution's fees. A nonparametric Wilcoxon test was performed to determine statistical significance between the results of the TCE sperm present and sperm absent groups. RESULTS: Of the 82 NOA patients undergoing TCE, 51 (62.2%) core biopsies were positive for sperm and 31 (37.8%) were negative for sperm. The SRR for 35 men who then underwent cTESE following sperm seen on TCE was 97.1%. The SRR for 8 men who underwent mTESE after no sperm was found on TCE was 75%. The positive predictive value of TCE for successful TESE result is 94.4% for men with NOA. Treatment success rate of TCE and cTESE was 79.1% with a cost reduction of 59.4%. CONCLUSION: TCE is a prognostic tool to guide decision making between cTESE and mTESE and maximize sperm retrieval rate while mitigating financial burden and operative complexity. TCE is important to identify ideal candidates for both procedures to maximize efficacy and safety amongst men with NOA.

Investigation of machine learning algorithms for taxonomic classification of marine metagenomes.

Microbial communities play key roles in ocean ecosystems through regulation of biogeochemical processes such as carbon and nutrient cycling, food web dynamics, and gut microbiomes of invertebrates, fish, reptiles, and mammals. Assessments of marine microbial diversity are therefore critical to understanding spatiotemporal variations in microbial community structure and function in ocean ecosystems. With recent advances in DNA shotgun sequencing for metagenome samples and computational analysis, it is now possible to access the taxonomic and genomic content of ocean microbial communities to study their structural patterns, diversity, and functional potential. However, existing taxonomic classification tools depend upon manually curated phylogenetic trees, which can create inaccuracies in metagenomes from less well-characterized communities, such as from ocean water. Herein, we explore the utility of deep learning tools-DeepMicrobes and a novel Residual Network architecture-that leverage natural language processing and convolutional neural network architectures to map input sequence data (k-mers) to output labels (taxonomic groups) without reliance on a curated taxonomic tree. We trained both models using metagenomic reads simulated from marine microbial genomes in the MarRef database. The performance of both models (accuracy, precision, and percent microbe predicted) was compared with the standard taxonomic classification tool Kraken2 using 10 complex metagenomic data sets simulated from MarRef. Our results demonstrate that time, compute power, and microbial genomic diversity still pose challenges for machine learning (ML). Moreover, our results suggest that high genome coverage and rectification of class imbalance are prerequisites for a well-trained model, and therefore should be a major consideration in future ML work. IMPORTANCE Taxonomic profiling of microbial communities is essential to model microbial interactions and inform habitat conservation. This work develops approaches in constructing training/testing data sets from publicly available marine metagenomes and evaluates the performance of machine learning (ML) approaches in read-based taxonomic classification of marine metagenomes. Predictions from two models are used to test accuracy in metagenomic classification and to guide improvements in ML approaches. Our study provides insights on the methods, results, and challenges of deep learning on marine microbial metagenomic data sets. Future machine learning approaches can be improved by rectifying genome coverage and class imbalance in the training data sets, developing alternative models, and increasing the accessibility of computational resources for model training and refinement.

Unusual presentation of Lynch Syndrome.

Lynch Syndrome/HNPCC is a syndrome of cancer predisposition linked to inherited mutations of genes participating in post-replicative DNA mismatch repair (MMR). The spectrum of cancer associated with Lynch Syndrome includes tumours of the colorectum, endometrium, ovary, upper gastrointestinal tract and the urothelium although other cancers are rarely described. We describe a family of Lynch Syndrome with an hMLH1 mutation, that harbours an unusual tumour spectrum and its diagnostic and management challenges.