Signatures of Adaptation and Purifying Selection in Highland Populations of Dasiphora fruticosa.

High mountains harbor a considerable proportion of biodiversity, but we know little about how diverse plants adapt to the harsh environment. Here we finished a high-quality genome assembly for Dasiphora fruticosa, an ecologically important plant distributed in the Qinghai-Tibetan Plateau and lowland of the Northern Hemisphere, and resequenced 592 natural individuals to address how this horticulture plant adapts to highland. Demographic analysis revealed D. fruticosa underwent a bottleneck after Naynayxungla Glaciation. Selective sweep analysis of two pairs of lowland and highland populations identified 63 shared genes related to cell wall organization or biogenesis, cellular component organization, and dwarfism, suggesting parallel adaptation to highland habitats. Most importantly, we found that stronger purging of estimated genetic load due to inbreeding in highland populations apparently contributed to their adaptation to the highest mountain. Our results revealed how plants could tolerate the extreme plateau, which could provide potential insights for species conservation and crop breeding.

WASPSYN: A Challenge for Domain Adaptive Synapse Detection in Microwasp Brain Connectomes.

The size of image volumes in connectomics studies now reaches terabyte and often petabyte scales with a great diversity of appearance due to different sample preparation procedures. However, manual annotation of neuronal structures (e.g., synapses) in these huge image volumes is time-consuming, leading to limited labeled training data often smaller than 0.001% of the large-scale image volumes in application. Methods that can utilize in-domain labeled data and generalize to out-of-domain unlabeled data are in urgent need. Although many domain adaptation approaches are proposed to address such issues in the natural image domain, few of them have been evaluated on connectomics data due to a lack of domain adaptation benchmarks. Therefore, to enable developments of domain adaptive synapse detection methods for large-scale connectomics applications, we annotated 14 image volumes from a biologically diverse set of Megaphragma viggianii brain regions originating from three different whole-brain datasets and organized the WASPSYN challenge at ISBI 2023. The annotations include coordinates of pre-synapses and post-synapses in the 3D space, together with their one-to-many connectivity information. This paper describes the dataset, the tasks, the proposed baseline, the evaluation method, and the results of the challenge. Limitations of the challenge and the impact on neuroscience research are also discussed. The challenge is and will continue to be available at https://codalab.lisn.upsaclay.fr/competitions/9169. Successful algorithms that emerge from our challenge may potentially revolutionize real-world connectomics research and further the cause that aims to unravel the complexity of brain structure and function.

Forces driving transposable element load variation during Arabidopsis range expansion.

Genetic load refers to the accumulated and potentially life-threatening deleterious mutations in populations. Understanding the mechanisms underlying genetic load variation of transposable element (TE) insertion, a major large-effect mutation, during range expansion is an intriguing question in biology. Here, we used 1,115 global natural accessions of Arabidopsis (Arabidopsis thaliana) to study the driving forces of TE load variation during its range expansion. TE load increased with range expansion, especially in the recently established Yangtze River basin population. Effective population size, which explains 62.0% of the variance in TE load, high transposition rate, and selective sweeps contributed to TE accumulation in the expanded populations. We genetically mapped and identified multiple candidate causal genes and TEs, and revealed the genetic architecture of TE load variation. Overall, this study reveals the variation in TE genetic load during Arabidopsis expansion and highlights the causes of TE load variation from the perspectives of both population genetics and quantitative genetics.

Molecular mechanisms of adaptive evolution in wild animals and plants.

Wild animals and plants have developed a variety of adaptive traits driven by adaptive evolution, an important strategy for species survival and persistence. Uncovering the molecular mechanisms of adaptive evolution is the key to understanding species diversification, phenotypic convergence, and inter-species interaction. As the genome sequences of more and more non-model organisms are becoming available, the focus of studies on molecular mechanisms of adaptive evolution has shifted from the candidate gene method to genetic mapping based on genome-wide scanning. In this study, we reviewed the latest research advances in wild animals and plants, focusing on adaptive traits, convergent evolution, and coevolution. Firstly, we focused on the adaptive evolution of morphological, behavioral, and physiological traits. Secondly, we reviewed the phenotypic convergences of life history traits and responding to environmental pressures, and the underlying molecular convergence mechanisms. Thirdly, we summarized the advances of coevolution, including the four main types: mutualism, parasitism, predation and competition. Overall, these latest advances greatly increase our understanding of the underlying molecular mechanisms for diverse adaptive traits and species interaction, demonstrating that the development of evolutionary biology has been greatly accelerated by multi-omics technologies. Finally, we highlighted the emerging trends and future prospects around the above three aspects of adaptive evolution.

Deep learning-assisted diagnosis of parotid gland tumors by using contrast-enhanced CT imaging.

OBJECTIVES: Imaging interpretation of the benignancy or malignancy of parotid gland tumors (PGTs) is a critical consideration prior to surgery in view of therapeutic and prognostic values of such discrimination. This study investigates the application of a deep learning-based method for preoperative stratification of PGTs. MATERIALS AND METHODS: Using the 3D DenseNet-121 architecture and a dataset consisting of 117 volumetric arterial-phase contrast-enhanced CT scans, we developed a binary classifier for PGT distinction and tested it. We compared the discriminative performance of the model on the test set to that of 12 junior and 12 senior head and neck clinicians. Besides, potential clinical utility of the model was evaluated by measuring changes in unassisted and model-assisted performance of junior clinicians. RESULTS: The model finally reached the sensitivity, specificity, PPV, NPV, F1-score of 0.955 (95% CI 0.751-0.998), 0.667 (95% CI 0.241-0.940), 0.913 (95% CI 0.705-0.985), 0.800 (95% CI 0.299-0.989) and 0.933, respectively, comparable to that of practicing clinicians. Furthermore, there were statistically significant increases in junior clinicians' specificity, PPV, NPV and F1-score in differentiating benign from malignant PGTs when unassisted and model-assisted performance of junior clinicians were compared. CONCLUSION: Our results provide evidence that deep learning-based method may offer assistance for PGT's binary distinction.

Arabidopsis EXECUTER1 interacts with WRKY transcription factors to mediate plastid-to-nucleus singlet oxygen signaling.

Chloroplasts produce singlet oxygen (1O2), which causes changes in nuclear gene expression through plastid-to-nucleus retrograde signaling to increase plant fitness. However, the identity of this 1O2-triggered pathway remains unclear. Here, we identify mutations in GENOMES UNCOUPLED4 (GUN4) and GUN5 as suppressors of phytochrome-interacting factor1 (pif1) pif3 in regulating the photo-oxidative response in Arabidopsis thaliana. GUN4 and GUN5 specifically interact with EXECUTER1 (EX1) and EX2 in plastids, and this interaction is alleviated by treatment with Rose Bengal (RB) or white light. Impaired expression of GUN4, GUN5, EX1, or EX2 leads to insensitivity to excess light and overexpression of EX1 triggers photo-oxidative responses. Strikingly, upon light irradiation or RB treatment, EX1 transiently accumulates in the nucleus and the nuclear fraction of EX1 shows a similar molecular weight as the plastid-located protein. Point mutagenesis analysis indicated that nuclear localization of EX1 is required for its function. EX1 acts as a transcriptional co-activator and interacts with the transcription factors WRKY18 and WRKY40 to promote the expression of 1O2-responsive genes. This study suggests that EX1 may act in plastid-to-nucleus signaling and establishes a 1O2-triggered retrograde signaling pathway that allows plants adapt to changing light environments during chloroplast development.

Polishing Performance and Removal Mechanism of Core-Shell Structured Diamond/SiO2 Abrasives on Sapphire Wafer.

Corrosive and toxic solutions are normally employed to polish sapphire wafers, which easily cause environmental pollution. Applying green polishing techniques to obtain an ultrasmooth sapphire surface that is scratch-free and has low damage at high polishing efficiency is a great challenge. In this paper, novel diamond/SiO2 composite abrasives were successfully synthesized by a simplified sol-gel strategy. The prepared composite abrasives were used in the semi-fixed polishing technology of sapphire wafers, where the polishing slurry contains only deionized water and no other chemicals during the whole polishing process, effectively avoiding environmental pollution. The experimental results showed that diamond/SiO2 composite abrasives exhibited excellent polishing performance, along with a 27.2% decrease in surface roughness, and the material removal rate was increased by more than 8.8% compared with pure diamond. Furthermore, through characterizations of polished sapphire surfaces and wear debris, the chemical action mechanism of composite abrasives was investigated, which confirmed the solid-state reaction between the SiO2 shell and the sapphire surface. Finally, applying the elastic-plastic contact model revealed that the reduction of indentation depth and the synergistic effect of chemical corrosion and mechanical removal are the keys to improving polishing performance.

Scaling-up and proteomic analysis reveals photosynthetic and metabolic insights toward prolonged H2 photoproduction in Chlamydomonas hpm91 mutant lacking proton gradient regulation 5 (PGR5).

Clean and sustainable H2 production is crucial to a carbon-neutral world. H2 generation by Chlamydomonas reinhardtii is an attractive approach for solar-H2 from H2O. However, it is currently not large-scalable because of lacking desirable strains with both optimal H2 productivity and sufficient knowledge of underlying molecular mechanism. We hereby carried out extensive and in-depth investigations of H2 photoproduction of hpm91 mutant lacking PGR5 (Proton Gradient Regulation 5) toward its up-scaling and fundamental mechanism issues. We show that hpm91 is at least 100-fold scalable (up to 10 L) with continuous H2 collection of 7287 ml H2/10L-HPBR in averagely 26 days under sulfur deprivation. Also, we show that hpm91 is robust and active during sustained H2 photoproduction, most likely due to decreased intracellular ROS relative to wild type. Moreover, we obtained quantitative proteomic profiles of wild type and hpm91 at four representing time points of H2 evolution, leading to 2229 and 1350 differentially expressed proteins, respectively. Compared to wild type, major proteome alterations of hpm91 include not only core subunits of photosystems and those related to anti-oxidative responses but also essential proteins in photosynthetic antenna, C/N metabolic balance, and sulfur assimilation toward both cysteine biosynthesis and sulfation of metabolites during sulfur-deprived H2 production. These results reveal not only new insights of cellular and molecular basis of enhanced H2 production in hpm91 but also provide additional candidate gene targets and modules for further genetic modifications and/or in artificial photosynthesis mimics toward basic and applied research aiming at advancing solar-H2 technology.

Local Intensity Order Transformation for Robust Curvilinear Object Segmentation.

Segmentation of curvilinear structures is important in many applications, such as retinal blood vessel segmentation for early detection of vessel diseases and pavement crack segmentation for road condition evaluation and maintenance. Currently, deep learning-based methods have achieved impressive performance on these tasks. Yet, most of them mainly focus on finding powerful deep architectures but ignore capturing the inherent curvilinear structure feature (e.g., the curvilinear structure is darker than the context) for a more robust representation. In consequence, the performance usually drops a lot on cross-datasets, which poses great challenges in practice. In this paper, we aim to improve the generalizability by introducing a novel local intensity order transformation (LIOT). Specifically, we transfer a gray-scale image into a contrast-invariant four-channel image based on the intensity order between each pixel and its nearby pixels along with the four (horizontal and vertical) directions. This results in a representation that preserves the inherent characteristic of the curvilinear structure while being robust to contrast changes. Cross-dataset evaluation on three retinal blood vessel segmentation datasets demonstrates that LIOT improves the generalizability of some state-of-the-art methods. Additionally, the cross-dataset evaluation between retinal blood vessel segmentation and pavement crack segmentation shows that LIOT is able to preserve the inherent characteristic of curvilinear structure with large appearance gaps. An implementation of the proposed method is available at https://github.com/TY-Shi/LIOT.

Cell Localization and Counting Using Direction Field Map.

Automatic cell counting in pathology images is challenging due to blurred boundaries, low-contrast, and overlapping between cells. In this paper, we train a convolutional neural network (CNN) to predict a two-dimensional direction field map and then use it to localize cell individuals for counting. Specifically, we define a direction field on each pixel in the cell regions (obtained by dilating the original annotation in terms of cell centers) as a two-dimensional unit vector pointing from the pixel to its corresponding cell center. Direction field for adjacent pixels in different cells have opposite directions departing from each other, while those in the same cell region have directions pointing to the same center. Such unique property is used to partition overlapped cells for localization and counting. To deal with those blurred boundaries or low contrast cells, we set the direction field of the background pixels to be zeros in the ground-truth generation. Thus, adjacent pixels belonging to cells and background will have an obvious difference in the predicted direction field. To further deal with cells of varying density and overlapping issues, we adopt geometry adaptive (varying) radius for cells of different densities in the generation of ground-truth direction field map, which guides the CNN model to separate cells of different densities and overlapping cells. Extensive experimental results on three widely used datasets (i.e., VGG Cell, CRCHistoPhenotype2016, and MBM datasets) demonstrate the effectiveness of the proposed approach.

Erratum: Author Correction: Advancing COVID-19 diagnosis with privacy-preserving collaboration in artificial intelligence.

[This corrects the article DOI: 10.1038/s42256-021-00421-z.].

Low Expression of Long Noncoding RNA SLC26A4 Antisense RNA 1 Is an Independent Prognostic Biomarker and Correlate of Immune Infiltrates in Breast Cancer.

BACKGROUND Aberrant expression of long noncoding RNA (lncRNA) SLC26A4 antisense RNA 1 (SLC26A4-AS1) plays an important role in some cancer types. However, the clinical significance of SLC26A4-AS1 in patients with breast cancer (BC) and the possible regulatory mechanisms of SLC26A4-AS1 are unclear. MATERIAL AND METHODS Statistical analysis was used to assess the correlation between SLC26A4-AS1 expression and patients' clinical characteristics. The Kaplan-Meier method and Cox regression analysis were used to assess the correlation between SLC26A4-AS1 expression and prognosis. Gene set enrichment analysis (GSEA) and immuno-infiltration analysis were used to investigate the possible regulatory mechanisms of SLC26A4-AS1. RESULTS Low SLC26A4-AS1 expression in BC was associated with age (P<0.001), estrogen-receptor status (P<0.001), PAM50 (P<0.001), and menopause status (P<0.001). Low SLC26A4-AS1 expression predicted a poorer overall survival (OS) (hazard ratio [HR]: 0.56; 95% confidence interval [CI]: 0.40-0.78; P=0.001) and disease-specific survival (DSS) (HR: 0.57; 95% CI: 0.37-0.88; P=0.011). Also, SLC26A4-AS1 expression (HR: 0.298; 95% CI: 0.154-0.579; P<0.001) was independently correlated with OS in patients with BC. SLC26A4-AS1 was related to CYP2E1 reactions, protein export, mitochondrial_ciii_assembly, formation of adenosine triphosphate by chemiosmotic coupling, budding and maturation of HIV virion, cristae formation, biocarta proteasome pathway, endosomal sorting complex required for transport, and histone modification. SLC26A4-AS1 expression was associated with some types of immune infiltrating cells. CONCLUSIONS SLC26A4-AS1 expression was significantly associated with poor survival and immune infiltration in patients with BC. It may be a promising prognostic biomarker for BC.

Advancing COVID-19 Diagnosis with Privacy-Preserving Collaboration in Artificial Intelligence.

Artificial intelligence (AI) provides a promising substitution for streamlining COVID-19 diagnoses. However, concerns surrounding security and trustworthiness impede the collection of large-scale representative medical data, posing a considerable challenge for training a well-generalised model in clinical practices. To address this, we launch the Unified CT-COVID AI Diagnostic Initiative (UCADI), where the AI model can be distributedly trained and independently executed at each host institution under a federated learning framework (FL) without data sharing. Here we show that our FL model outperformed all the local models by a large yield (test sensitivity /specificity in China: 0.973/0.951, in the UK: 0.730/0.942), achieving comparable performance with a panel of professional radiologists. We further evaluated the model on the hold-out (collected from another two hospitals leaving out the FL) and heterogeneous (acquired with contrast materials) data, provided visual explanations for decisions made by the model, and analysed the trade-offs between the model performance and the communication costs in the federated training process. Our study is based on 9,573 chest computed tomography scans (CTs) from 3,336 patients collected from 23 hospitals located in China and the UK. Collectively, our work advanced the prospects of utilising federated learning for privacy-preserving AI in digital health.

Identification of long noncoding natural antisense transcripts (lncNATs) correlated with drought stress response in wild rice (Oryza nivara).

BACKGROUND: Wild rice, including Oryza nivara and Oryza rufipogon, which are considered as the ancestors of Asian cultivated rice (Oryza sativa), possess high genetic diversity and serve as a crucial resource for breeding novel cultivars of cultivated rice. Although rice domestication related traits, such as seed shattering and plant architecture, have been intensively studied at the phenotypic and genomic levels, further investigation is needed to understand the molecular basis of phenotypic differences between cultivated and wild rice. Drought stress is one of the most severe abiotic stresses affecting rice growth and production. Adaptation to drought stress involves a cascade of genes and regulatory factors that form complex networks. O. nivara inhabits swampy areas with a seasonally dry climate, which is an ideal material to discover drought tolerance alleles. Long noncoding natural antisense transcripts (lncNATs), a class of long noncoding RNAs (lncRNAs), regulate the corresponding sense transcripts and play an important role in plant growth and development. However, the contribution of lncNATs to drought stress response in wild rice remains largely unknown. RESULTS: Here, we conducted strand-specific RNA sequencing (ssRNA-seq) analysis of Nipponbare (O. sativa) and two O. nivara accessions (BJ89 and BJ278) to determine the role of lncNATs in drought stress response in wild rice. A total of 1246 lncRNAs were identified, including 1091 coding-noncoding NAT pairs, of which 50 were expressed only in Nipponbare, and 77 were expressed only in BJ89 and/or BJ278. Of the 1091 coding-noncoding NAT pairs, 240 were differentially expressed between control and drought stress conditions. Among these 240 NAT pairs, 12 were detected only in Nipponbare, and 187 were detected uniquely in O. nivara. Furthermore, 10 of the 240 coding-noncoding NAT pairs were correlated with genes enriched in stress responsive GO terms; among these, nine pairs were uniquely found in O. nivara, and one pair was shared between O. nivara and Nipponbare. CONCLUSION: We identified lncNATs associated with drought stress response in cultivated rice and O. nivara. These results will improve our understanding of the function of lncNATs in drought tolerance and accelerate rice breeding.

Automated brain MRI metrics in the EPIRMEX cohort of preterm newborns: Correlation with the neurodevelopmental outcome at 2 years.

PURPOSE: The purpose of this study was to identify in the EPIRMEX cohort the correlations between MRI brain metrics, including diffuse excessive high signal intensities (DEHSI) obtained with an automated quantitative method and neurodevelopmental outcomes at 2 years. MATERIALS AND METHODS: A total of 390 very preterm infants (gestational age at birth≤32 weeks) who underwent brain MRI at term equivalent age at 1.5T (n=338) or 3T (n=52) were prospectively included. Using a validated algorithm, automated metrics of the main brain surfaces (cortical and deep gray matter, white matter, cerebrospinal fluid) and DEHSI with three thresholds were obtained. Linear adjust regressions were performed to assess the correlation between brain metrics with the ages and stages questionnaire (ASQ) score at 2 years. RESULTS: Basal ganglia and thalami, cortex and white matter surfaces positively and significantly correlated with the global ASQ score. For all ASQ sub-domains, basal ganglia and thalami surfaces significantly correlated with the scores. DEHSI was present in 289 premature newborns (74%) without any correlation with the ASQ score. Metrics of DEHSI were greater at 3T than at 1.5T. CONCLUSION: Brain MRI metrics obtained in our multicentric cohort correlate with the neurodevelopmental outcome at 2 years of age. The quantitative detection of DEHSI is not predictive of adverse outcomes. Our automated algorithm might easily provide useful predictive information in daily practice.

Gliding Vertex on the Horizontal Bounding Box for Multi-Oriented Object Detection.

Object detection has recently experienced substantial progress. Yet, the widely adopted horizontal bounding box representation is not appropriate for ubiquitous oriented objects such as objects in aerial images and scene texts. In this paper, we propose a simple yet effective framework to detect multi-oriented objects. Instead of directly regressing the four vertices, we glide the vertex of the horizontal bounding box on each corresponding side to accurately describe a multi-oriented object. Specifically, We regress four length ratios characterizing the relative gliding offset on each corresponding side. This may facilitate the offset learning and avoid the confusion issue of sequential label points for oriented objects. To further remedy the confusion issue for nearly horizontal objects, we also introduce an obliquity factor based on area ratio between the object and its horizontal bounding box, guiding the selection of horizontal or oriented detection for each object. We add these five extra target variables to the regression head of faster R-CNN, which requires ignorable extra computation time. Extensive experimental results demonstrate that without bells and whistles, the proposed method achieves superior performances on multiple multi-oriented object detection benchmarks including object detection in aerial images, scene text detection, pedestrian detection in fisheye images.

Affinity Space Adaptation for Semantic Segmentation Across Domains.

Semantic segmentation with dense pixel-wise annotation has achieved excellent performance thanks to deep learning. However, the generalization of semantic segmentation in the wild remains challenging. In this paper, we address the problem of unsupervised domain adaptation (UDA) in semantic segmentation. Motivated by the fact that source and target domain have invariant semantic structures, we propose to exploit such invariance across domains by leveraging co-occurring patterns between pairwise pixels in the output of structured semantic segmentation. This is different from most existing approaches that attempt to adapt domains based on individual pixel-wise information in image, feature, or output level. Specifically, we perform domain adaptation on the affinity relationship between adjacent pixels termed affinity space of source and target domain. To this end, we develop two affinity space adaptation strategies: affinity space cleaning and adversarial affinity space alignment. Extensive experiments demonstrate that the proposed method achieves superior performance against some state-of-the-art methods on several challenging benchmarks for semantic segmentation across domains. The code is available at https://github.com/idealwei/ASANet.

Deep Learning for Detecting Cerebral Aneurysms with CT Angiography.

Background Cerebral aneurysm detection is a challenging task. Deep learning may become a supportive tool for more accurate interpretation. Purpose To develop a highly sensitive deep learning-based algorithm that assists in the detection of cerebral aneurysms on CT angiography images. Materials and Methods Head CT angiography images were retrospectively retrieved from two hospital databases acquired across four different scanners between January 2015 and June 2019. The data were divided into training and validation sets; 400 additional independent CT angiograms acquired between July and December 2019 were used for external validation. A deep learning-based algorithm was constructed and assessed. Both internal and external validation were performed. Jackknife alternative free-response receiver operating characteristic analysis was performed. Results A total of 1068 patients (mean age, 57 years ± 11 [standard deviation]; 660 women) were evaluated for a total of 1068 CT angiograms encompassing 1337 cerebral aneurysms. Of these, 534 CT angiograms (688 aneurysms) were assigned to the training set, and the remaining 534 CT angiograms (649 aneurysms) constituted the validation set. The sensitivity of the proposed algorithm for detecting cerebral aneurysms was 97.5% (633 of 649; 95% CI: 96.0, 98.6). Moreover, eight new aneurysms that had been overlooked in the initial reports were detected (1.2%, eight of 649). With the aid of the algorithm, the overall performance of radiologists in terms of area under the weighted alternative free-response receiver operating characteristic curve was higher by 0.01 (95% CI: 0.00, 0.03). Conclusion The proposed deep learning algorithm assisted radiologists in detecting cerebral aneurysms on CT angiography images, resulting in a higher detection rate. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Kallmes and Erickson in this issue.

Advancing COVID-19 diagnosis with privacy-preserving collaboration in artificial intelligence.

Artificial intelligence provides a promising solution for streamlining COVID-19 diagnoses; however, concerns surrounding security and trustworthiness impede the collection of large-scale representative medical data, posing a considerable challenge for training a well-generalized model in clinical practices. To address this, we launch the Unified CT-COVID AI Diagnostic Initiative (UCADI), where the artificial intelligence (AI) model can be distributedly trained and independently executed at each host institution under a federated learning framework without data sharing. Here we show that our federated learning framework model considerably outperformed all of the local models (with a test sensitivity/specificity of 0.973/0.951 in China and 0.730/0.942 in the United Kingdom), achieving comparable performance with a panel of professional radiologists. We further evaluated the model on the hold-out (collected from another two hospitals without the federated learning framework) and heterogeneous (acquired with contrast materials) data, provided visual explanations for decisions made by the model, and analysed the trade-offs between the model performance and the communication costs in the federated training process. Our study is based on 9,573 chest computed tomography scans from 3,336 patients collected from 23 hospitals located in China and the United Kingdom. Collectively, our work advanced the prospects of utilizing federated learning for privacy-preserving AI in digital health.

Less Is More, Natural Loss-of-Function Mutation Is a Strategy for Adaptation.

Gene gain and loss are crucial factors that shape the evolutionary success of diverse organisms. In the past two decades, more attention has been paid to the significance of gene gain through gene duplication or de novo genes. However, gene loss through natural loss-of-function (LoF) mutations, which is prevalent in the genomes of diverse organisms, has been largely ignored. With the development of sequencing techniques, many genomes have been sequenced across diverse species and can be used to study the evolutionary patterns of gene loss. In this review, we summarize recent advances in research on various aspects of LoF mutations, including their identification, evolutionary dynamics in natural populations, and functional effects. In particular, we discuss how LoF mutations can provide insights into the minimum gene set (or the essential gene set) of an organism. Furthermore, we emphasize their potential impact on adaptation. At the genome level, although most LoF mutations are neutral or deleterious, at least some of them are under positive selection and may contribute to biodiversity and adaptation. Overall, we highlight the importance of natural LoF mutations as a robust framework for understanding biological questions in general.