Supplementary data are available at Bioinformatics online.

Background: Diagnosis of benign paroxysmal positional vertigo (BPPV) depends on the accurate interpretation of nystagmus induced by positional tests. However, difficulties in interpreting eye-movement often can arise in primary care practice or emergency room. We hypothesized that the use of machine learning would be helpful for the interpretation. Methods: From our clinical data warehouse, 91,778 nystagmus videos from 3467 patients with dizziness were obtained, in which the three-dimensional movement of nystagmus was annotated by four otologic experts. From each labeled video, 30 features changed into 255 grid images fed into the input layer of the neural network for the training dataset. For the model validation, video dataset of 3566 horizontal, 2068 vertical, and 720 torsional movements from 1005 patients with BPPV were collected. Results: The model had a sensitivity and specificity of 0.910 ± 0.036 and 0.919 ± 0.032 for horizontal nystagmus; of 0.879 ± 0.029 and 0.894 ± 0.025 for vertical nystagmus; and of 0.783 ± 0.040 and 0.799 ± 0.038 for torsional nystagmus, respectively. The affected canal was predicted with a sensitivity of 0.806 ± 0.010 and a specificity of 0.971 ± 0.003. Conclusions: As our deep-learning model had high sensitivity and specificity for the classification of nystagmus and localization of affected canal in patients with BPPV, it may have wide clinical applicability.

Summary Meiotic crossovers facilitate chromosome segregation and create new combinations of alleles in gametes. Crossover frequency varies along chromosomes and crossover interference limits the coincidence of closely spaced crossovers. Crossovers can be measured by observing the inheritance of linked transgenes expressing different colors of fluorescent protein in Arabidopsis pollen tetrads. Here we establish DeepTetrad, a deep learning‐based image recognition package for pollen tetrad analysis that enables high‐throughput measurements of crossover frequency and interference in individual plants. DeepTetrad will accelerate the genetic dissection of mechanisms that control meiotic recombination.

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Malformations of cortical development (MCD) are neurological conditions displaying focal disruption of cortical architecture and cellular organization arising during embryogenesis, largely from somatic mosaic mutations, and causing intractable epilepsy. Identifying the genetic causes of MCD has been a challenge, as mutations remain at low allelic fractions in brain tissue resected to treat condition-related epilepsy. Here, we report a genetic landscape from 283 brain resections, identifying 69 mutated genes through intensive profiling of somatic mutations, combining whole-exome and targeted-amplicon sequencing with functional validation including in utero electroporation of mice and single-nucleus RNA sequencing. Genotype-phenotype correlation analysis elucidated specific MCD gene sets associating distinct pathophysiological and clinical phenotypes. The unique single-cell level spatiotemporal expression patterns of mutated genes in control and patient brains implicate critical roles in excitatory neurogenic pools during brain development, and in promoting neuronal hyperexcitability after birth.

-In this paper, we propose an antenna module with high isolation between Tx and Rx in RFID reader module. The proposed module consists of a quadrafilar antenna and a directional coupler with a switchable dummy load to improve the directivity. The proposed module achieves the isolation higher than 20 dB between Tx and Rx. To show the validity of the proposed scheme, we have performed the measurement of tagging range and multi-tagging ability. The experiment results show that the detecting range and multi-tagging ability are enhanced by 81% and 200%, respectively.

15Meiotic crossovers facilitate chromosome segregation and create new combinations of alleles 16 in gametes. Crossover frequency varies along chromosomes and crossover interference limits 17 the coincidence of closely spaced crossovers. Crossovers can be measured by observing the 18 inheritance of linked transgenes expressing different colors of fluorescent protein in 19 Arabidopsis pollen tetrads. Here we establish DeepTetrad, a deep learning-based image 20 recognition package for pollen tetrad analysis that enables high-throughput measurements of 21 crossover frequency and interference in individual plants. DeepTetrad will accelerate genetic 22 dissection of mechanisms that control meiotic recombination. 23 24 Main 25 Meiosis consists of two consecutive nuclear divisions and produces four haploid gametes from 26 a single diploid cell in sexually reproducing eukaryotes 1 . In Arabidopsis male meiosis, ~200-27 DeepTetrad 2 250 meiotic DNA double-strand breaks (DSBs) are induced in the genome by a DNA 28 topoisomerase VI-like complex to initiate meiotic recombination 2-4 . Of these DSBs, only ~8-29 11 are repaired as crossovers (COs) using a homologous chromosome (homolog). Thus, male 30 meiosis in the Arabidopsis genome, which comprises five chromosomes, results in an average 31 of ~1.8 crossovers between homologs. This low number suggests the existence of 32 mechanisms that limit crossovers, a phenomenon that is observed in most eukaryotes 2 .33 Meiotic DSB and CO frequencies are controlled by genetic and epigenetic factors and are 34 non-randomly distributed along chromosomes, with higher levels around gene promoters and 35 terminators and lower levels across the centromeres 5-7 . 36 At least two pathways (Type I and Type II), contribute to CO formation 2,3 . The Type I pathway 37 leads to interfering COs that prevent the coincident occurrence of closely spaced CO on the 38 same pair of chromosomes 2,8,9 . In plants, interfering COs represent ~80-85% of total COs and 39 are dependent on the ZMM proteins (ZIP4, MSH4, MSH5, MER3, HEI10, SHOC1, PTD, MLH1, 40 MLH3). The remaining ~10-15% of COs are non-interfering and occur via the Type II 41 pathway 10 . Non-interfering COs are resolved by the MUS81 endonuclease and are restricted 42 by anti-recombination factors such as FANCM, RECQ4A, RECQ4B, and FIGL1 11-15 . 43 Disruption of anti-recombination factors can increase the number of Type II COs in plants, 44 which has the potential to create new combinations of desirable alleles that can improve crop 45 varieties 13,16 . Therefore, high-throughput detection and understanding of CO frequency and 46 interference have important implications for our understanding of the control of meiotic 47 QRT1 gene encoding a pectin methylestrase results in the four pollen products of male 51 meiosis remaining attached to one another, allowing classical tetrad analysis. Each FTL has 52 a transgenes that expresses eYFP (Y), dsRed (R) or eCFP (C) fluorescent proteins in mature 53 pollen using the post-meiotic LAT52 promoter. Gen...