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Abstract Molluscan mitochondrial genomes are unusual because they show wide variation in size, radical genome rearrangements and frequently show high variation (> 10%) within species. As progress in understanding this variation has been limited, we used whole genome sequencing of a six-generation matriline of the terrestrial snail Cepaea nemoralis, as well as whole genome sequences from wild-collected C. nemoralis, the sister species C. hortensis, and multiple other snail species to explore the origins of mitochondrial DNA (mtDNA) variation. The main finding is that a high rate of SNP heteroplasmy in somatic tissue was negatively correlated with mtDNA copy number in both Cepaea species. In individuals with under ten mtDNA copies per nuclear genome, more than 10% of all positions were heteroplasmic, with evidence for transmission of this heteroplasmy through the germline. Further analyses showed evidence for purifying selection acting on non-synonymous mutations, even at low frequency of the rare allele, especially in cytochrome oxidase subunit 1 and cytochrome b. The mtDNA of some individuals of Cepaea nemoralis contained a length heteroplasmy, including up to 12 direct repeat copies of tRNA-Val, with 24 copies in another snail, Candidula rugosiuscula, and repeats of tRNA-Thr in C. hortensis. These repeats likely arise due to error prone replication but are not correlated with mitochondrial copy number in C. nemoralis. Overall, the findings provide key insights into mechanisms of replication, mutation and evolution in molluscan mtDNA, and so will inform wider studies on the biology and evolution of mtDNA across animal phyla.

To transform Mexico's electric load infrastructure, accurate electric load forecasts are required that are crucial to efficiently allocate resources, maintain system stability, and manage energy. The purpose of this study is to use the Quantile Transformer Network (QTN) as a novel approach for a deep learning framework for load forecasting, emphasizing its potential and practical consequences in enhancing the accuracy of load forecasting in real-world energy systems. Moreover, it is shown that QTN efficiently captures complex patterns, temporal relationships and interconnections among factors that influence electric load. The dataset utilized consists of past records of energy consumption in the Baja California System in Mexico. It includes several factors such as electricity demand, marginal prices, temporal characteristics, temperature-related variables, seasonal patterns, and holidays. Additionally, QTN is combined with the Rainbow Technique (RT) to manage categorical variables, resulting in the creation of a unified feature called category. In this case, RT examines the connections between descriptive phrases that reflect distinct combinations of categorical factors depending on the load values. Moreover, this research shows that the common Quantile Regression (QR) outperforms QTN in capturing dependencies in sequential data. Finally, several recommendations for promoting Mexico's Electric Load Infrastructure are provided.

This research develops a new electric charge prediction method by using Convolutional Neural Networks with Quantile Regression (CNN-QR) combined with the Rainbow Technique for Categorical Features (RTCF) and using Deep Learning to create layers for the architecture of the neural network. This combination captures both local and global interdependencies within the load data. In particular, RTCF employs advanced natural language processing (NLP) techniques to convert several important categorical features into a single feature called “category,” which is tailored to the various attributes of the Baja California Sur system, in Mexico, taking into consideration climatic conditions, local circumstances and a significant increase in energy consumption. Furthermore, this research compares CNN-QR with classical quantile regression and shows that CNN-QR works better at capturing sophisticated load patterns and producing probabilistic estimates. The above methodology uses hourly data from January 2019 to October 2020. The results obtained provide valuable information for policy formulation in the energy sector, specifically in the areas of load forecasting and expansion of renewable energy and electricity consumption. Finally, it is worth mentioning that the utilization of CNN-QR with RTCF not only improves the accuracy of load forecasting, but also provides a strategic framework for energy management and resource planning in dynamic energy systems, which demonstrates its substantial importance for market participants and authorities, as well as regulators.

We present a genome assembly from an individual female Bombus hortorum (the garden bumblebee; Arthropoda; Insecta; Hymenoptera; Apidae). The genome sequence is 296 megabases in span. The majority of the assembly is scaffolded into 18 chromosomal pseudomolecules.

We present a genome assembly from an individual male Spilosoma lubricipeda (the white ermine; Arthropoda; Insecta; Lepidoptera; Erebidae). The genome sequence is 587 megabases in span. The majority of the assembly is scaffolded into 30 chromosomal pseudomolecules, with the Z sex chromosome assembled.

We present a genome assembly from an individual Lineus longissimus (the bootlace worm; Nemertea; Pilidiophora; Heteronemertea; Lineidae). The genome sequence is 391 megabases in span. The majority of the assembly is scaffolded into 19 chromosomal pseudomolecules.

We present a genome assembly from an individual male Euproctis similis (the yellow-tail; Arthropoda; Insecta; Lepidoptera; Lymantriidae). The genome sequence is 508 megabases in span. The majority of the assembly is scaffolded into 22 chromosomal pseudomolecules, with the Z sex chromosome assembled.

Monitoring the spread of SARS-CoV-2 and reconstructing transmission chains has become a major public health focus for many governments around the world. The modest mutation rate and rapid transmission of SARS-CoV-2 prevents the reconstruction of transmission chains from consensus genome sequences, but within-host genetic diversity could theoretically help identify close contacts. Here we describe the patterns of within-host diversity in 1181 SARS-CoV-2 samples sequenced to high depth in duplicate. 95.1% of samples show within-host mutations at detectable allele frequencies. Analyses of the mutational spectra revealed strong strand asymmetries suggestive of damage or RNA editing of the plus strand, rather than replication errors, dominating the accumulation of mutations during the SARS-CoV-2 pandemic. Within- and between-host diversity show strong purifying selection, particularly against nonsense mutations. Recurrent within-host mutations, many of which coincide with known phylogenetic homoplasies, display a spectrum and patterns of purifying selection more suggestive of mutational hotspots than recombination or convergent evolution. While allele frequencies suggest that most samples result from infection by a single lineage, we identify multiple putative examples of co-infection. Integrating these results into an epidemiological inference framework, we find that while sharing of within-host variants between samples could help the reconstruction of transmission chains, mutational hotspots and rare cases of superinfection can confound these analyses.

We performed a systematic analysis of blood DNA methylation profiles from 4483 participants from seven independent cohorts identifying differentially methylated positions (DMPs) associated with psychosis, schizophrenia, and treatment-resistant schizophrenia. Psychosis cases were characterized by significant differences in measures of blood cell proportions and elevated smoking exposure derived from the DNA methylation data, with the largest differences seen in treatment-resistant schizophrenia patients. We implemented a stringent pipeline to meta-analyze epigenome-wide association study (EWAS) results across datasets, identifying 95 DMPs associated with psychosis and 1048 DMPs associated with schizophrenia, with evidence of colocalization to regions nominated by genetic association studies of disease. Many schizophrenia-associated DNA methylation differences were only present in patients with treatment-resistant schizophrenia, potentially reflecting exposure to the atypical antipsychotic clozapine. Our results highlight how DNA methylation data can be leveraged to identify physiological (e.g., differential cell counts) and environmental (e.g., smoking) factors associated with psychosis and molecular biomarkers of treatment-resistant schizophrenia.

In contrast to Eurasia and North America, powdery mildews (Ascomycota, Erysiphales) are understudied in Australia. There are over 900 species known globally, with fewer than currently 60 recorded from Australia. Some of the Australian records are doubtful as the identifications were presumptive, being based on host plant-pathogen lists from overseas. The goal of this study was to provide the first comprehensive catalog of all powdery mildew species present in Australia. The project resulted in (i) an up-to-date list of all the taxa that have been identified in Australia based on published DNA barcode sequences prior to this study; (ii) the precise identification of 117 specimens freshly collected from across the country; and (iii) the precise identification of 30 herbarium specimens collected between 1975 and 2013. This study confirmed 42 species representing 10 genera, including two genera and 13 species recorded for the first time in Australia. In Eurasia and North America, the number of powdery mildew species is much higher. Phylogenetic analyses of powdery mildews collected from Acalypha spp. resulted in the transfer of Erysiphe acalyphae to Salmonomyces, a resurrected genus. Salmonomyces acalyphae comb. nov. represents a newly discovered lineage of the Erysiphales. Another taxonomic change is the transfer of Oidium ixodiae to Golovinomyces. Powdery mildew infections have been confirmed on 13 native Australian plant species in the genera Acacia, Acalypha, Cephalotus, Convolvulus, Eucalyptus, Hardenbergia, Ixodia, Jagera, Senecio, and Trema. Most of the causal agents were polyphagous species that infect many other host plants both overseas and in Australia. All powdery mildews infecting native plants in Australia were phylogenetically closely related to species known overseas. The data indicate that Australia is a continent without native powdery mildews, and most, if not all, species have been introduced since the European colonization of the continent.

Reduced central corneal thickness (CCT) is observed in common eye diseases as well as in rare Mendelian disorders. Here, in a cross-ancestry GWAS, the authors identify 19 novel genetic loci associated with CCT, a subset of which is involved in rare corneal or connective tissue disorders.

Emmanuelle Souzeau, who contributed to analysis of data, was inadvertently omitted from the author list in the originally published version of this Article. This has now been corrected in both the PDF and HTML versions of the Article.