GA-UNet: A Lightweight Ghost and Attention U-Net for Medical Image Segmentation.

U-Net has demonstrated strong performance in the field of medical image segmentation and has been adapted into various variants to cater to a wide range of applications. However, these variants primarily focus on enhancing the model's feature extraction capabilities, often resulting in increased parameters and floating point operations (Flops). In this paper, we propose GA-UNet (Ghost and Attention U-Net), a lightweight U-Net for medical image segmentation. GA-UNet consists mainly of lightweight GhostV2 bottlenecks that reduce redundant information and Convolutional Block Attention Modules that capture key features. We evaluate our model on four datasets, including CVC-ClinicDB, 2018 Data Science Bowl, ISIC-2018, and BraTS 2018 low-grade gliomas (LGG). Experimental results show that GA-UNet outperforms other state-of-the-art (SOTA) models, achieving an F1-score of 0.934 and a mean Intersection over Union (mIoU) of 0.882 on CVC-ClinicDB, an F1-score of 0.922 and a mIoU of 0.860 on the 2018 Data Science Bowl, an F1-score of 0.896 and a mIoU of 0.825 on ISIC-2018, and an F1-score of 0.896 and a mIoU of 0.853 on BraTS 2018 LGG. Additionally, GA-UNet has fewer parameters (2.18M) and lower Flops (4.45G) than other SOTA models, which further demonstrates the superiority of our model.

A novel fusion framework of deep bottleneck residual convolutional neural network for breast cancer classification from mammogram images.

With over 2.1 million new cases of breast cancer diagnosed annually, the incidence and mortality rate of this disease pose severe global health issues for women. Identifying the disease's influence is the only practical way to lessen it immediately. Numerous research works have developed automated methods using different medical imaging to identify BC. Still, the precision of each strategy differs based on the available resources, the issue's nature, and the dataset being used. We proposed a novel deep bottleneck convolutional neural network with a quantum optimization algorithm for breast cancer classification and diagnosis from mammogram images. Two novel deep architectures named three-residual blocks bottleneck and four-residual blocks bottle have been proposed with parallel and single paths. Bayesian Optimization (BO) has been employed to initialize hyperparameter values and train the architectures on the selected dataset. Deep features are extracted from the global average pool layer of both models. After that, a kernel-based canonical correlation analysis and entropy technique is proposed for the extracted deep features fusion. The fused feature set is further refined using an optimization technique named quantum generalized normal distribution optimization. The selected features are finally classified using several neural network classifiers, such as bi-layered and wide-neural networks. The experimental process was conducted on a publicly available mammogram imaging dataset named INbreast, and a maximum accuracy of 96.5% was obtained. Moreover, for the proposed method, the sensitivity rate is 96.45, the precision rate is 96.5, the F1 score value is 96.64, the MCC value is 92.97%, and the Kappa value is 92.97%, respectively. The proposed architectures are further utilized for the diagnosis process of infected regions. In addition, a detailed comparison has been conducted with a few recent techniques showing the proposed framework's higher accuracy and precision rate.

Overexpression of BAPT and DBTNBT genes in Taxus baccata in vitro cultures to enhance the biotechnological production of paclitaxel.

Paclitaxel is one of the most effective anticancer drugs ever developed. Although the most sustainable approach to its production is provided by plant cell cultures, the yield is limited by bottleneck enzymes in the taxane biosynthetic pathway: baccatin-aminophenylpropanoyl-13-O-transferase (BAPT) and 3'-N-debenzoyltaxol N-benzoyltransferase (DBTNBT). With the aim of enhancing paclitaxel production by overcoming this bottleneck, we obtained distinct lines of Taxus baccata in vitro roots, each independently overexpressing either of the two flux-limiting genes, BAPT or DBTNBT, through a Rhizobium rhizogenes A4-mediated transformation. Due to the slow growth rate of the transgenic Taxus roots, they were dedifferentiated to obtain callus lines and establish cell suspensions. The transgenic cells were cultured in a two-stage system and stimulated for taxane production by a dual elicitation treatment with 1 µm coronatine plus 50 mm of randomly methylated-ß-cyclodextrins. A high overexpression of BAPT (59.72-fold higher at 48 h) and DBTNBT (61.93-fold higher at 72 h) genes was observed in the transgenic cell cultures, as well as an improved taxane production. Compared to the wild type line (71.01 mg/L), the DBTNBT line produced more than four times higher amounts of paclitaxel (310 mg/L), while the content of this taxane was almost doubled in the BAPT line (135 mg/L). A transcriptional profiling of taxane biosynthetic genes revealed that GGPPS, TXS and DBAT genes were the most reactive to DBTNBT overexpression and the dual elicitation, their expression increasing gradually and constantly. The same genes exhibited a pattern of isolated peaks of expression in the elicited BAPT-overexpressing line.

Reduced genetic diversity and the success of the invasive peacock bass (Cichliformes: Cichlidae)

Abstract Several species of Cichla successfully colonized lakes and reservoirs of Brazil, since the 1960s, causing serious damage to local wildlife. In this study, 135 peacock bass were collected in a reservoir complex in order to identify if they represented a single dominant species or multiple ones, as several Cichla species have been reported in the basin. Specimens were identified by color pattern, morphometric and meristic data, and using mitochondrial markers COI, 16S rDNA and Control Region (CR). Overlapping morphological data and similar coloration patterns prevented their identification using the taxonomic keys to species identification available in the literature. However, Bayesian and maximum likelihood from sequencing data demonstrated the occurrence of a single species, Cichla kelberi. A single haplotype was observed for the 16S and CR, while three were detected for COI, with a dominant haplotype present in 98.5% of the samples. The extreme low diversity of the transplanted C. kelberi evidenced a limited number of founding maternal lineages. The success of this colonization seems to rely mainly on abiotic factors, such as increased water transparency of lentic environments that favor visual predators that along with the absence of predators, have made C. kelberi a successful invader of these reservoirs.

Resumo Muitas espécies de Cichla colonizaram com sucesso lagos e reservatórios do Brasil desde os anos 1960, causando graves prejuízos à vida selvagem nesses locais. Neste estudo, 135 tucunarés foram coletados em um complexo de reservatórios a fim de identificar se representavam uma espécie dominante ou múltiplas espécies, uma vez que diversas espécies de Cichla foram registradas na bacia. Os espécimes foram identificados com base na coloração, dados morfométricos e merísticos, e por marcadores mitocondriais COI, 16S rDNA e Região Controle (RC). A sobreposição dos dados morfométricos e o padrão similar de coloração impediram a identificação utilizando as chaves de identificação disponíveis na literatura. Entretanto, as análises bayesiana e de máxima verossimilhança de dados moleculares demonstraram a ocorrência de uma única espécie, Cichla kelberi. Um único haplótipo foi observado para o 16S e RC, enquanto três foram detectados para o COI, com um haplótipo dominante presente em 98,5% das amostras. A baixa diversidade nos exemplares introduzidos de C. kelberi evidenciou um número limitado de linhagens maternas fundadoras. O sucesso da invasão parece depender de fatores abióticos, como a maior transparência da água de ambientes lênticos que favorece predadores visuais que, atrelado à ausência de predadores, fez do C. kelberi um invasor bem-sucedido nesses reservatórios.

Reduced genetic diversity and the success of the invasive peacock bass (Cichliformes: Cichlidae) / Diversidade genética reduzida e o sucesso da espécie invasora tucunaré (Cichliformes: Cichlidae)

Abstract Several species of Cichla successfully colonized lakes and reservoirs of Brazil, since the 1960's, causing serious damage to local wildlife. In this study, 135 peacock bass were collected in a reservoir complex in order to identify if they represented a single dominant species or multiple ones, as several Cichla species have been reported in the basin. Specimens were identified by color pattern, morphometric and meristic data, and using mitochondrial markers COI, 16S rDNA and Control Region (CR). Overlapping morphological data and similar coloration patterns prevented their identification using the taxonomic keys to species identification available in the literature. However, Bayesian and maximum likelihood from sequencing data demonstrated the occurrence of a single species, Cichla kelberi. A single haplotype was observed for the 16S and CR, while three were detected for COI, with a dominant haplotype present in 98.5% of the samples. The extreme low diversity of the transplanted C. kelberi evidenced a limited number of founding maternal lineages. The success of this colonization seems to rely mainly on abiotic factors, such as increased water transparency of lentic environments that favor visual predators that along with the absence of predators, have made C. kelberi a successful invader of these reservoirs.

Resumo Muitas espécies de Cichla colonizaram com sucesso lagos e reservatórios do Brasil desde os anos 1960, causando graves prejuízos à vida selvagem nesses locais. Neste estudo, 135 tucunarés foram coletados em um complexo de reservatórios a fim de identificar se representavam uma espécie dominante ou múltiplas espécies, uma vez que diversas espécies de Cichla foram registradas na bacia. Os espécimes foram identificados com base na coloração, dados morfométricos e merísticos, e por marcadores mitocondriais COI, 16S rDNA e Região Controle (RC). A sobreposição dos dados morfométricos e o padrão similar de coloração impediram a identificação utilizando as chaves de identificação disponíveis na literatura. Entretanto, as análises bayesiana e de máxima verossimilhança de dados moleculares demonstraram a ocorrência de uma única espécie, Cichla kelberi. Um único haplótipo foi observado para o 16S e RC, enquanto três foram detectados para o COI, com um haplótipo dominante presente em 98,5% das amostras. A baixa diversidade nos exemplares introduzidos de C. kelberi evidenciou um número limitado de linhagens maternas fundadoras. O sucesso da invasão parece depender de fatores abióticos, como a maior transparência da água de ambientes lênticos que favorece predadores visuais que, atrelado à ausência de predadores, fez do C. kelberi um invasor bem-sucedido nesses reservatórios.

MV-CVIB: a microbiome-based multi-view convolutional variational information bottleneck for predicting metastatic colorectal cancer.

Introduction: Imbalances in gut microbes have been implied in many human diseases, including colorectal cancer (CRC), inflammatory bowel disease, type 2 diabetes, obesity, autism, and Alzheimer's disease. Compared with other human diseases, CRC is a gastrointestinal malignancy with high mortality and a high probability of metastasis. However, current studies mainly focus on the prediction of colorectal cancer while neglecting the more serious malignancy of metastatic colorectal cancer (mCRC). In addition, high dimensionality and small samples lead to the complexity of gut microbial data, which increases the difficulty of traditional machine learning models. Methods: To address these challenges, we collected and processed 16S rRNA data and calculated abundance data from patients with non-metastatic colorectal cancer (non-mCRC) and mCRC. Different from the traditional health-disease classification strategy, we adopted a novel disease-disease classification strategy and proposed a microbiome-based multi-view convolutional variational information bottleneck (MV-CVIB). Results: The experimental results show that MV-CVIB can effectively predict mCRC. This model can achieve AUC values above 0.9 compared to other state-of-the-art models. Not only that, MV-CVIB also achieved satisfactory predictive performance on multiple published CRC gut microbiome datasets. Discussion: Finally, multiple gut microbiota analyses were used to elucidate communities and differences between mCRC and non-mCRC, and the metastatic properties of CRC were assessed by patient age and microbiota expression.

RGSB-UNet: Hybrid Deep Learning Framework for Tumour Segmentation in Digital Pathology Images.

Colorectal cancer (CRC) is a prevalent gastrointestinal tumour with high incidence and mortality rates. Early screening for CRC can improve cure rates and reduce mortality. Recently, deep convolution neural network (CNN)-based pathological image diagnosis has been intensively studied to meet the challenge of time-consuming and labour-intense manual analysis of high-resolution whole slide images (WSIs). Despite the achievements made, deep CNN-based methods still suffer from some limitations, and the fundamental problem is that they cannot capture global features. To address this issue, we propose a hybrid deep learning framework (RGSB-UNet) for automatic tumour segmentation in WSIs. The framework adopts a UNet architecture that consists of the newly-designed residual ghost block with switchable normalization (RGS) and the bottleneck transformer (BoT) for downsampling to extract refined features, and the transposed convolution and 1 × 1 convolution with ReLU for upsampling to restore the feature map resolution to that of the original image. The proposed framework combines the advantages of the spatial-local correlation of CNNs and the long-distance feature dependencies of BoT, ensuring its capacity of extracting more refined features and robustness to varying batch sizes. Additionally, we consider a class-wise dice loss (CDL) function to train the segmentation network. The proposed network achieves state-of-the-art segmentation performance under small batch sizes. Experimental results on DigestPath2019 and GlaS datasets demonstrate that our proposed model produces superior evaluation scores and state-of-the-art segmentation results.

LSD-YOLOv5: A Steel Strip Surface Defect Detection Algorithm Based on Lightweight Network and Enhanced Feature Fusion Mode.

In the field of metallurgy, the timely and accurate detection of surface defects on metallic materials is a crucial quality control task. However, current defect detection approaches face challenges with large model parameters and low detection rates. To address these issues, this paper proposes a lightweight recognition model for surface damage on steel strips, named LSD-YOLOv5. First, we design a shallow feature enhancement module to replace the first Conv structure in the backbone network. Second, the Coordinate Attention mechanism is introduced into the MobileNetV2 bottleneck structure to maintain the lightweight nature of the model. Then, we propose a smaller bidirectional feature pyramid network (BiFPN-S) and combine it with Concat operation for efficient bidirectional cross-scale connectivity and weighted feature fusion. Finally, the Soft-DIoU-NMS algorithm is employed to enhance the recognition efficiency in scenarios where targets overlap. Compared with the original YOLOv5s, the LSD-YOLOv5 model achieves a reduction of 61.5% in model parameters and a 28.7% improvement in detection speed, while improving recognition accuracy by 2.4%. This demonstrates that the model achieves an optimal balance between detection accuracy and speed, while maintaining a lightweight structure.

Integration of Radiomics and Tumor Biomarkers in Interpretable Machine Learning Models.

Despite the unprecedented performance of deep neural networks (DNNs) in computer vision, their clinical application in the diagnosis and prognosis of cancer using medical imaging has been limited. One of the critical challenges for integrating diagnostic DNNs into radiological and oncological applications is their lack of interpretability, preventing clinicians from understanding the model predictions. Therefore, we studied and propose the integration of expert-derived radiomics and DNN-predicted biomarkers in interpretable classifiers, which we refer to as ConRad, for computerized tomography (CT) scans of lung cancer. Importantly, the tumor biomarkers can be predicted from a concept bottleneck model (CBM) such that once trained, our ConRad models do not require labor-intensive and time-consuming biomarkers. In our evaluation and practical application, the only input to ConRad is a segmented CT scan. The proposed model was compared to convolutional neural networks (CNNs) which act as a black box classifier. We further investigated and evaluated all combinations of radiomics, predicted biomarkers and CNN features in five different classifiers. We found the ConRad models using nonlinear SVM and the logistic regression with the Lasso outperformed the others in five-fold cross-validation, with the interpretability of ConRad being its primary advantage. The Lasso is used for feature selection, which substantially reduces the number of nonzero weights while increasing the accuracy. Overall, the proposed ConRad model combines CBM-derived biomarkers and radiomics features in an interpretable ML model which demonstrates excellent performance for lung nodule malignancy classification.

Combinatorial Network of Transcriptional and miRNA Regulation in Colorectal Cancer.

Colorectal cancer is one of the leading causes of cancer-associated mortality across the worldwide. One of the major challenges in colorectal cancer is the understanding of the regulatory mechanisms of biological molecules. In this study, we aimed to identify novel key molecules in colorectal cancer by using a computational systems biology approach. We constructed the colorectal protein-protein interaction network which followed hierarchical scale-free nature. We identified TP53, CTNBB1, AKT1, EGFR, HRAS, JUN, RHOA, and EGF as bottleneck-hubs. The HRAS showed the largest interacting strength with functional subnetworks, having strong correlation with protein phosphorylation, kinase activity, signal transduction, and apoptotic processes. Furthermore, we constructed the bottleneck-hubs' regulatory networks with their transcriptional (transcription factor) and post-transcriptional (miRNAs) regulators, which exhibited the important key regulators. We observed miR-429, miR-622, and miR-133b and transcription factors (EZH2, HDAC1, HDAC4, AR, NFKB1, and KLF4) regulates four bottleneck-hubs (TP53, JUN, AKT1 and EGFR) at the motif level. In future, biochemical investigation of the observed key regulators could provide further understanding about their role in the pathophysiology of colorectal cancer.

Temporal contexts for motion tracking in ultrasound sequences with information bottleneck.

BACKGROUND: Recently, deep convolutional neural networks (CNNs) have been widely adopted for ultrasound sequence tracking and shown to perform satisfactorily. However, existing trackers ignore the rich temporal contexts that exists between consecutive frames, making it difficult for these trackers to perceive information about the motion of the target. PURPOSE: In this paper, we propose a sophisticated method to fully utilize temporal contexts for ultrasound sequences tracking with information bottleneck. This method determines the temporal contexts between consecutive frames to perform both feature extraction and similarity graph refinement, and information bottleneck is integrated into the feature refinement process. METHODS: The proposed tracker combined three models. First, online temporal adaptive convolutional neural network (TAdaCNN) is proposed to focus on feature extraction and enhance spatial features using temporal information. Second, information bottleneck (IB) is incorporated to achieve more accurate target tracking by maximally limiting the amount of information in the network and discarding irrelevant information. Finally, we propose temporal adaptive transformer (TA-Trans) that efficiently encodes temporal knowledge by decoding it for similarity graph refinement. The tracker was trained on 2015 MICCAI Challenge on Liver Ultrasound Tracking (CLUST) dataset to evaluate the performance of the proposed method by calculating the tracking error (TE) between the predicted landmarks and the ground truth landmarks for each frame. The experimental results are compared with 13 state-of-the-art methods, and ablation studies are conducted. RESULTS: On CLUST 2015 dataset, our proposed model achieves a mean TE of 0.81 ± 0.74 mm and a maximum TE of 1.93 mm for 85 point-landmarks across 39 ultrasound sequences in the 2D sequences. Tracking speed ranged from 41 to 63 frames per second (fps). CONCLUSIONS: This study demonstrates a new integrated workflow for ultrasound sequences motion tracking. The results show that the model has excellent accuracy and robustness. Reliable and accurate motion estimation is provided for applications requiring real-time motion estimation in the context of ultrasound-guided radiation therapy.

Variational Information Bottleneck Regularized Deep Reinforcement Learning for Efficient Robotic Skill Adaptation.

Deep Reinforcement Learning (DRL) algorithms have been widely studied for sequential decision-making problems, and substantial progress has been achieved, especially in autonomous robotic skill learning. However, it is always difficult to deploy DRL methods in practical safety-critical robot systems, since the training and deployment environment gap always exists, and this issue would become increasingly crucial due to the ever-changing environment. Aiming at efficiently robotic skill transferring in a dynamic environment, we present a meta-reinforcement learning algorithm based on a variational information bottleneck. More specifically, during the meta-training stage, the variational information bottleneck first has been applied to infer the complete basic tasks for the whole task space, then the maximum entropy regularized reinforcement learning framework has been used to learn the basic skills consistent with that of basic tasks. Once the training stage is completed, all of the tasks in the task space can be obtained by a nonlinear combination of the basic tasks, thus, the according skills to accomplish the tasks can also be obtained by some way of a combination of the basic skills. Empirical results on several highly nonlinear, high-dimensional robotic locomotion tasks show that the proposed variational information bottleneck regularized deep reinforcement learning algorithm can improve sample efficiency by 200-5000 times on new tasks. Furthermore, the proposed algorithm achieves substantial asymptotic performance improvement. The results indicate that the proposed meta-reinforcement learning framework makes a significant step forward to deploy the DRL-based algorithm to practical robot systems.

Information bottleneck-based interpretable multitask network for breast cancer classification and segmentation.

Breast cancer is one of the most common causes of death among women worldwide. Early signs of breast cancer can be an abnormality depicted on breast images (e.g., mammography or breast ultrasonography). However, reliable interpretation of breast images requires intensive labor and physicians with extensive experience. Deep learning is evolving breast imaging diagnosis by introducing a second opinion to physicians. However, most deep learning-based breast cancer analysis algorithms lack interpretability because of their black box nature, which means that domain experts cannot understand why the algorithms predict a label. In addition, most deep learning algorithms are formulated as a single-task-based model that ignores correlations between different tasks (e.g., tumor classification and segmentation). In this paper, we propose an interpretable multitask information bottleneck network (MIB-Net) to accomplish simultaneous breast tumor classification and segmentation. MIB-Net maximizes the mutual information between the latent representations and class labels while minimizing information shared by the latent representations and inputs. In contrast from existing models, our MIB-Net generates a contribution score map that offers an interpretable aid for physicians to understand the model's decision-making process. In addition, MIB-Net implements multitask learning and further proposes a dual prior knowledge guidance strategy to enhance deep task correlation. Our evaluations are carried out on three breast image datasets in different modalities. Our results show that the proposed framework is not only able to help physicians better understand the model's decisions but also improve breast tumor classification and segmentation accuracy over representative state-of-the-art models. Our code is available at https://github.com/jxw0810/MIB-Net.

Contextual Features and Information Bottleneck-Based Multi-Input Network for Breast Cancer Classification from Contrast-Enhanced Spectral Mammography.

In computer-aided diagnosis methods for breast cancer, deep learning has been shown to be an effective method to distinguish whether lesions are present in tissues. However, traditional methods only classify masses as benign or malignant, according to their presence or absence, without considering the contextual features between them and their adjacent tissues. Furthermore, for contrast-enhanced spectral mammography, the existing studies have only performed feature extraction on a single image per breast. In this paper, we propose a multi-input deep learning network for automatic breast cancer classification. Specifically, we simultaneously input four images of each breast with different feature information into the network. Then, we processed the feature maps in both horizontal and vertical directions, preserving the pixel-level contextual information within the neighborhood of the tumor during the pooling operation. Furthermore, we designed a novel loss function according to the information bottleneck theory to optimize our multi-input network and ensure that the common information in the multiple input images could be fully utilized. Our experiments on 488 images (256 benign and 232 malignant images) from 122 patients show that the method's accuracy, precision, sensitivity, specificity, and f1-score values are 0.8806, 0.8803, 0.8810, 0.8801, and 0.8806, respectively. The qualitative, quantitative, and ablation experiment results show that our method significantly improves the accuracy of breast cancer classification and reduces the false positive rate of diagnosis. It can reduce misdiagnosis rates and unnecessary biopsies, helping doctors determine accurate clinical diagnoses of breast cancer from multiple CESM images.

Genomic Assessment of Cancer Susceptibility in the Threatened Catalina Island Fox (Urocyon littoralis catalinae).

Small effective population sizes raise the probability of extinction by increasing the frequency of potentially deleterious alleles and reducing fitness. However, the extent to which cancers play a role in the fitness reduction of genetically depauperate wildlife populations is unknown. Santa Catalina island foxes (Urocyon littoralis catalinae) sampled in 2007-2008 have a high prevalence of ceruminous gland tumors, which was not detected in the population prior to a recent bottleneck caused by a canine distemper epidemic. The disease appears to be associated with inflammation from chronic ear mite (Otodectes) infections and secondary elevated levels of Staphyloccus pseudointermedius bacterial infections. However, no other environmental factors to date have been found to be associated with elevated cancer risk in this population. Here, we used whole genome sequencing of the case and control individuals from two islands to identify candidate loci associated with cancer based on genetic divergence, nucleotide diversity, allele frequency spectrum, and runs of homozygosity. We identified several candidate loci based on genomic signatures and putative gene functions, suggesting that cancer susceptibility in this population may be polygenic. Due to the efforts of a recovery program and weak fitness effects of late-onset disease, the population size has increased, which may allow selection to be more effective in removing these presumably slightly deleterious alleles. Long-term monitoring of the disease alleles, as well as overall genetic diversity, will provide crucial information for the long-term persistence of this threatened population.

Population-genomic analyses reveal bottlenecks and asymmetric introgression from Persian into iron walnut during domestication.

BACKGROUND: Persian walnut, Juglans regia, occurs naturally from Greece to western China, while its closest relative, the iron walnut, Juglans sigillata, is endemic in southwest China; both species are cultivated for their nuts and wood. Here, we infer their demographic histories and the time and direction of possible hybridization and introgression between them. RESULTS: We use whole-genome resequencing data, different population-genetic approaches (PSMC and GONE), and isolation-with-migration models (IMa3) on individuals from Europe, Iran, Kazakhstan, Pakistan, and China. IMa3 analyses indicate that the two species diverged from each other by 0.85 million years ago, with unidirectional gene flow from eastern J. regia and its ancestor into J. sigillata, including the shell-thickness gene. Within J. regia, a western group, located from Europe to Iran, and an eastern group with individuals from northern China, experienced dramatically declining population sizes about 80 generations ago (roughly 2400 to 4000 years), followed by an expansion at about 40 generations, while J. sigillata had a constant population size from about 100 to 20 generations ago, followed by a rapid decline. CONCLUSIONS: Both J. regia and J. sigillata appear to have suffered sudden population declines during their domestication, suggesting that the bottleneck scenario of plant domestication may well apply in at least some perennial crop species. Introgression from introduced J. regia appears to have played a role in the domestication of J. sigillata.

Lynch syndrome testing of colorectal cancer patients in a high-income country with universal healthcare: a retrospective study of current practice and gaps in seven australian hospitals.

BACKGROUND: To inform effective genomic medicine strategies, it is important to examine current approaches and gaps in well-established applications. Lynch syndrome (LS) causes 3-5% of colorectal cancers (CRCs). While guidelines commonly recommend LS tumour testing of all CRC patients, implementation in health systems is known to be highly variable. To provide insights on the heterogeneity in practice and current bottlenecks in a high-income country with universal healthcare, we characterise the approaches and gaps in LS testing and referral in seven Australian hospitals across three states. METHODS: We obtained surgery, pathology, and genetics services data for 1,624 patients who underwent CRC resections from 01/01/2017 to 31/12/2018 in the included hospitals. RESULTS: Tumour testing approaches differed between hospitals, with 0-19% of patients missing mismatch repair deficiency test results (total 211/1,624 patients). Tumour tests to exclude somatic MLH1 loss were incomplete at five hospitals (42/187 patients). Of 74 patients with tumour tests completed appropriately and indicating high risk of LS, 36 (49%) were missing a record of referral to genetics services for diagnostic testing, with higher missingness for older patients (0% of patients aged ≤ 40 years, 76% of patients aged > 70 years). Of 38 patients with high-risk tumour test results and genetics services referral, diagnostic testing was carried out for 25 (89%) and identified a LS pathogenic/likely pathogenic variant for 11 patients (44% of 25; 0.7% of 1,624 patients). CONCLUSIONS: Given the LS testing and referral gaps, further work is needed to identify strategies for successful integration of LS testing into clinical care, and provide a model for hereditary cancers and broader genomic medicine. Standardised reporting may help clinicians interpret tumour test results and initiate further actions.

A Genetic Bottleneck of Mitochondrial DNA During Human Lymphocyte Development.

Mitochondria are essential organelles in eukaryotic cells that provide critical support for energetic and metabolic homeostasis. Although the elimination of pathogenic mitochondrial DNA (mtDNA) mutations in somatic cells has been observed, the mechanisms to maintain proper functions despite their mtDNA mutation load are poorly understood. In this study, we analyzed somatic mtDNA mutations in more than 30,000 single human peripheral and bone marrow mononuclear cells. We observed a significant overrepresentation of homoplasmic mtDNA mutations in B, T, and natural killer (NK) lymphocytes. Intriguingly, their overall mutational burden was lower than that in hematopoietic progenitors and myeloid cells. This characteristic mtDNA mutational landscape indicates a genetic bottleneck during lymphoid development, as confirmed with single-cell datasets from multiple platforms and individuals. We further demonstrated that mtDNA replication lags behind cell proliferation in both pro-B and pre-B progenitor cells, thus likely causing the genetic bottleneck by diluting mtDNA copies per cell. Through computational simulations and approximate Bayesian computation (ABC), we recapitulated this lymphocyte-specific mutational landscape and estimated the minimal mtDNA copies as <30 in T, B, and NK lineages. Our integrative analysis revealed a novel process of a lymphoid-specific mtDNA genetic bottleneck, thus illuminating a potential mechanism used by highly metabolically active immune cells to limit their mtDNA mutation load.

Protein network analysis to prioritize key genes in amyotrophic lateral sclerosis.

Amyotrophic Lateral Sclerosis (ALS) is a fatal disease, progressive nature characterizes by loss of both upper and lower motor neuron functions. One of the major challenge is to understand the mechanism of ALS multifactorial nature. We aimed to explore some key genes related to ALS through bioinformatics methods for its therapeutic intervention. Here, we applied a systems biology approach involving experimentally validated 148 ALS-associated proteins and construct ALS protein-protein interaction network (ALS-PPIN). The network was further statistically analysed and identified bottleneck-hubs. The network is also subjected to identify modules which could have similar functions. The interaction between the modules and bottleneck-hubs provides the functional regulatory role of the ALS mechanism. The ALS-PPIN demonstrated a hierarchical scale-free nature. We identified 17 bottleneck-hubs, in which CDC5L, SNW1, TP53, SOD1, and VCP were the high degree nodes (hubs) in ALS-PPIN. CDC5L was found to control highly cluster modules and play a vital role in the stability of the overall network followed by SNW1, TP53, SOD1, and VCP. HSPA5 and HSPA8 acting as a common connector for CDC5L and TP53 bottleneck-hubs. The functional and disease association analysis showed ALS has a strong correlation with mRNA processing, protein deubiquitination, and neoplasms, nervous system, immune system disease classes. In the future, biochemical investigation of the observed bottleneck-hubs and their interacting partners could provide a further understanding of their role in the pathophysiology of ALS.

Premetastatic shifts of endogenous and exogenous mutational processes support consolidative therapy in EGFR-driven lung adenocarcinoma.

The progression of cancer is an evolutionary process that is challenging to assess between sampling timepoints. However, investigation of cancer evolution over specific time periods is crucial to the elucidation of key events such as the acquisition of therapeutic resistance and subsequent fatal metastatic spread of therapy-resistant cell populations. Here we apply mutational signature analyses within clinically annotated cancer chronograms to detect and describe the shifting mutational processes caused by both endogenous (e.g. mutator gene mutation) and exogenous (e.g. mutagenic therapeutics) factors between tumor sampling timepoints. In one patient, we find that cisplatin therapy can introduce mutations that confer genetic resistance to subsequent targeted therapy with Erlotinib. In another patient, we trace detection of defective mismatch-repair associated mutational signature SBS3 to the emergence of known driver mutation CTNNB1 S37C. In both of these patients, metastatic lineages emerged from a single ancestral lineage that arose during therapy-a finding that argues for the consideration of local consolidative therapy over other therapeutic approaches in EGFR-positive non-small cell lung cancer. Broadly, these results demonstrate the utility of phylogenetic analysis that incorporates clinical time course and mutational signature deconvolution to inform therapeutic decision making and retrospective assessment of disease etiology.