MOTIVATION: Recent advances in long-read sequencing technologies have significantly facilitated the production of high-quality genome assembly. The telomere-to-telomere (T2T) gapless assembly has become the new golden standard of genome assembly efforts. Several recent efforts have claimed to produce T2T-level reference genomes. However, a universal standard is still missing to qualify a genome assembly to be at T2T standard. Traditional genome assembly assessment metrics (N50 and its derivatives) have no capacity in differentiating between nearly T2T assembly and the truly T2T assembly in continuity either globally or locally. Additionally, these metrics are independent of raw reads, making them inflated easily by artificial operations. Therefore, a gaplessness evaluation tool at single-nucleotide resolution to reflect true completeness is urgently needed in the era of complete genomes. RESULTS: Here, we present a tool called Genome Continuity Inspector (GCI), designed to assess genome assembly continuity at single-base resolution, and evaluate how close an assembly is to the T2T level. GCI utilizes multiple aligners to map long reads from various sequencing platforms back to the assembly. By incorporating curated mapping coverage of high-confidence read alignments, GCI identifies potential assembly issues. Meanwhile, it provides GCI scores that quantify overall assembly continuity on the whole genome or chromosome scales. AVAILABILITY AND IMPLEMENTATION: The open-source GCI code is freely available on Github (https://github.com/yeeus/GCI) under the MIT license.

Speech emotion recognition (SER) based on multi-view learning has made some progress on speaker-independent scenarios. How-ever, the existing SER methods always rely on excessive feature views and ignore the importance of heterogeneous feature learning. In this paper, we propose a novel multi-level attention method to effectively learn the heterogeneous information from the hand-crafted feature (MFCC) and the feature (W2V2) extracted from the pre-trained model. Specifically, we first design an Attention based Multi-scale Low-level Feature (A-MLF) extractor to extract scale-specific emotion-related regions from MFCC. Then, the Multi-Unit Attention (MUA) module is used to simultaneously learn discriminative features in three different dimensions. Finally, a two-stage feature fusion strategy is used for joint representation space learning. We demonstrate our method on two speaker-independent validation strategies and interpret the SOTA performance by visualizing the feature distribution.

Multi-view Speech Emotion Recognition (SER) based on the pre-trained model has achieved success in speaker-independent scenarios. However, the existing SER methods rely on excessive feature views and have complicated feature fusion strategies. In this paper, we propose a novel method to learn effective emotion-related information from two feature views. First, we present a Discriminative Channel Weighting (DCW) module to weight the channel dimension of the features produced by a set of multi-scale convolution layers. This module allows for discriminative weighting of complex channel dimensions. Second, a concise Two-stream Pooling Attention (TsPA) strategy is proposed to generate two groups of fusion features based on different channel-level embeddings with different emphasis. Finally, the SER task is completed by three consecutive fully connected layers. The effectiveness of the proposed method has been demonstrated on two speaker-independent validation strategies, outperforming other state-of-the-art approaches.

Aiming to improve the performance of human speech emotion recognition (SER), the existing work has made great progress based on the popular mel-scale frequency cepstral coefficient (MFCC). However, the existing work rarely pays attention to the low-level emotion related features in MFCC, such as the underlying interactive relations. In this letter, we propose a novel multi-level attention network (MLAnet), which contains a multi-scale low-level feature (MLF) extractor and a multi-unit attention (MUA) module. Within the MLF extractor, we minimize the task-irrelevant information which harms the performance of SER by applying the attention mechanism. Since the features extracted by the MLF extractor contain rich domain-specific emotion information, we further present a MUA module to simultaneously weight the features in terms of time, frequency and channel dimensions. In this way, the discriminative emotion features in different dimensions can be extracted by corresponding weighting blocks. Experimental results on two benchmark datasets demonstrate that the proposed method outperforms other state-of-the-art approaches.

BACKGROUND: Data from genome-wide association studies (GWASs) have been used to estimate the heritability of human complex traits in recent years. Existing methods are based on the linear mixed model, with the assumption that the genetic effects are random variables, which is opposite to the fixed effect assumption embedded in the framework of quantitative genetics theory. Moreover, heritability estimators provided by existing methods may have large standard errors, which calls for the development of reliable and accurate methods to estimate heritability. RESULTS: In this paper, we first investigate the influences of the fixed and random effect assumption on heritability estimation, and prove that these two assumptions are equivalent under mild conditions in the theoretical aspect. Second, we propose a two-stage strategy by first performing sparse regularization via cross-validated elastic net, and then applying variance estimation methods to construct reliable heritability estimations. Results on both simulated data and real data show that our strategy achieves a considerable reduction in the standard error while reserving the accuracy. CONCLUSIONS: The proposed strategy allows for a reliable and accurate heritability estimation using GWAS data. It shows the promising future that reliable estimations can still be obtained with even a relatively restricted sample size, and should be especially useful for large-scale heritability analyses in the genomics era.