Deep network fault diagnosis for imbalanced small-sized samples via a coupled adversarial autoencoder based on the Bayesian method.

Deep network fault diagnosis methods heavily rely on abundant labeled data for effective model training. However, small-sized samples and imbalanced samples often lead to insufficient features, resulting in accuracy degradation and even instability in the diagnosis model. To address this challenge, this paper introduces a coupled adversarial autoencoder (CoAAE) based on the Bayesian method. This model aims to solve the issue of insufficient samples by generating fake samples and integrating them with the original ones. Within the CoAAE framework, the probability density distribution of the original data is captured using an encoder and fake samples are generated by random sampling from this distribution and decoding them. This process is the adversarial interaction between the encoder and a classifier to obtain the prior distribution of the encoder's parameters. The encoder's parameters are updated through the decoder's reconstruction process, leading to the posterior distribution. Concurrently, the decoder is trained to enhance its ability to reconstruct samples accurately. To address the imbalance in the original samples, a parallel coupled network is employed. This network shares the weights of the extraction layer in the encoder, enabling it to learn the joint distribution between fault-related and normal samples. To evaluate the effectiveness of the proposed data augmentation method, experiments were conducted on a bearing database from Case Western Reserve University using ResNet18 as the deep learning diagnosis model representative. The results demonstrate that CoAAE can effectively augment imbalanced datasets and outperform other advanced methods.

Meta-analysis of the global distribution of clinically relevant CYP2C8 alleles and their inferred functional consequences.

BACKGROUND: CYP2C8 is responsible for the metabolism of 5% of clinically prescribed drugs, including antimalarials, anti-cancer and anti-inflammatory drugs. Genetic variability is an important factor that influences CYP2C8 activity and modulates the pharmacokinetics, efficacy and safety of its substrates. RESULTS: We profiled the genetic landscape of CYP2C8 variability using data from 96 original studies and data repositories that included a total of 33,185 unrelated participants across 44 countries and 43 ethnic groups. The reduced function allele CYP2C8*2 was most common in West and Central Africa with frequencies of 16-36.9%, whereas it was rare in Europe and Asia (< 2%). In contrast, CYP2C8*3 and CYP2C8*4 were common throughout Europe and the Americas (6.9-19.8% for *3 and 2.3-7.5% for *4), but rare in African and East Asian populations. Importantly, we observe pronounced differences (> 2.3-fold) between neighboring countries and even between geographically overlapping populations. Overall, we found that 20-60% of individuals in Africa and Europe carry at least one CYP2C8 allele associated with reduced metabolism and increased adverse event risk of the anti-malarial amodiaquine. Furthermore, up to 60% of individuals of West African ancestry harbored variants that reduced the clearance of pioglitazone, repaglinide, paclitaxel and ibuprofen. In contrast, reduced function alleles are only found in < 2% of East Asian and 8.3-12.8% of South and West Asian individuals. CONCLUSIONS: Combined, the presented analyses mapped the genetic and inferred functional variability of CYP2C8 with high ethnogeographic resolution. These results can serve as a valuable resource for CYP2C8 allele frequencies and distribution estimates of CYP2C8 phenotypes that could help identify populations at risk upon treatment with CYP2C8 substrates. The high variability between ethnic groups incentivizes high-resolution pharmacogenetic profiling to guide precision medicine and maximize its socioeconomic benefits, particularly for understudied populations with distinct genetic profiles.

An adaptive fully convolutional network for bearing fault diagnosis under noisy environments.

Intelligent diagnostic algorithms based on convolutional neural networks (CNNs) have shown great potential in diagnosing various conditions. However, accurately and robustly diagnosing faults in noisy situations remains challenging. This study presents an adaptive fully convolutional network (AFCN) for identifying bearing defects in noisy environments. First, we use a novel large kernel convolution method for high-frequency noise reduction and wide-area temporal feature extraction. By utilizing a sequence of stacked residual adaptive convolution blocks, the AFCN achieves a selective emphasis on significant features and adaptive adjustment of feature weights at various convolution scales. The experimental results have shown that the AFCN achieves a diagnostic accuracy of over 90% for the faults in the CWRU dataset under the -8 dB noise and over 77% for the PU dataset in the case of -6 dB noise. The comparison results with five advanced baseline models have demonstrated the superiority of the AFCN in feature extraction, noise immunity, and robustness to the noise environment. The AFCN provides a better adaption to noise interference than conventional CNNs and other advanced adaptive networks.

Population-specific variations in KCNH2 predispose patients to delayed ventricular repolarization upon dihydroartemisinin-piperaquine therapy.

Dihydroartemisinin-piperaquine is efficacious for the treatment of uncomplicated malaria and its use is increasing globally. Despite the positive results in fighting malaria, inhibition of the Kv11.1 channel (hERG; encoded by the KCNH2 gene) by piperaquine has raised concerns about cardiac safety. Whether genetic factors could modulate the risk of piperaquine-mediated QT prolongations remained unclear. Here, we first profiled the genetic landscape of KCNH2 variability using data from 141,614 individuals. Overall, we found 1,007 exonic variants distributed over the entire gene body, 555 of which were missense. By optimizing the gene-specific parametrization of 16 partly orthogonal computational algorithms, we developed a KCNH2-specific ensemble classifier that identified a total of 116 putatively deleterious missense variations. To evaluate the clinical relevance of KCNH2 variability, we then sequenced 293 Malian patients with uncomplicated malaria and identified 13 variations within the voltage sensing and pore domains of Kv11.1 that directly interact with channel blockers. Cross-referencing of genetic and electrocardiographic data before and after piperaquine exposure revealed that carriers of two common variants, rs1805121 and rs41314375, experienced significantly higher QT prolongations (ΔQTc of 41.8 ms and 61 ms, respectively, vs 14.4 ms in controls) with more than 50% of carriers having increases in QTc >30 ms. Furthermore, we identified three carriers of rare population-specific variations who experienced clinically relevant delayed ventricular repolarization. Combined, our results map population-scale genetic variability of KCNH2 and identify genetic biomarkers for piperaquine-induced QT prolongation that could help to flag at-risk patients and optimize efficacy and adherence to antimalarial therapy.

Inactivation of Malic Enzyme 1 in Endothelial Cells Alleviates Pulmonary Hypertension.

BACKGROUND: Pulmonary hypertension (PH) is a progressive cardiopulmonary disease with a high mortality rate. Although growing evidence has revealed the importance of dysregulated energetic metabolism in the pathogenesis of PH, the underlying cellular and molecular mechanisms are not fully understood. In this study, we focused on ME1 (malic enzyme 1), a key enzyme linking glycolysis to the tricarboxylic acid cycle. We aimed to determine the role and mechanistic action of ME1 in PH. METHODS: Global and endothelial-specific ME1 knockout mice were used to investigate the role of ME1 in hypoxia- and SU5416/hypoxia (SuHx)-induced PH. Small hairpin RNA and ME1 enzymatic inhibitor (ME1*) were used to study the mechanism of ME1 in pulmonary artery endothelial cells. Downstream key metabolic pathways and mediators of ME1 were identified by metabolomics analysis in vivo and ME1-mediated energetic alterations were examined by Seahorse metabolic analysis in vitro. The pharmacological effect of ME1* on PH treatment was evaluated in PH animal models induced by SuHx. RESULTS: We found that ME1 protein level and enzymatic activity were highly elevated in lung tissues of patients and mice with PH, primarily in vascular endothelial cells. Global knockout of ME1 protected mice from developing hypoxia- or SuHx-induced PH. Endothelial-specific ME1 deletion similarly attenuated pulmonary vascular remodeling and PH development in mice, suggesting a critical role of endothelial ME1 in PH. Mechanistic studies revealed that ME1 inhibition promoted downstream adenosine production and activated A2AR-mediated adenosine signaling, which leads to an increase in nitric oxide generation and a decrease in proinflammatory molecule expression in endothelial cells. ME1 inhibition activated adenosine production in an ATP-dependent manner through regulating malate-aspartate NADH (nicotinamide adenine dinucleotide plus hydrogen) shuttle and thereby balancing oxidative phosphorylation and glycolysis. Pharmacological inactivation of ME1 attenuated the progression of PH in both preventive and therapeutic settings by promoting adenosine production in vivo. CONCLUSIONS: Our findings indicate that ME1 upregulation in endothelial cells plays a causative role in PH development by negatively regulating adenosine production and subsequently dysregulating endothelial functions. Our findings also suggest that ME1 may represent as a novel pharmacological target for upregulating protective adenosine signaling in PH therapy.

The perspective of cAMP/cGMP signaling and cyclic nucleotide phosphodiesterases in aortic aneurysm and dissection.

Aortic aneurysm (AA) and dissection (AD) are aortic diseases caused primarily by medial layer degeneration and perivascular inflammation. They are lethal when the rupture happens. Vascular smooth muscle cells (SMCs) play critical roles in the pathogenesis of medial degeneration, characterized by SMC loss and elastin fiber degradation. Many molecular pathways, including cyclic nucleotide signaling, have been reported in regulating vascular SMC functions, matrix remodeling, and vascular structure integrity. Intracellular cyclic adenosine monophosphate (cAMP) and cyclic guanosine monophosphate (cGMP) are second messengers that mediate intracellular signaling transduction through activating effectors, such as protein kinase A (PKA) and PKG, respectively. cAMP and cGMP are synthesized by adenylyl cyclase (AC) and guanylyl cyclase (GC), respectively, and degraded by cyclic nucleotide phosphodiesterases (PDEs). In this review, we will discuss the roles and mechanisms of cAMP/cGMP signaling and PDEs in AA/AD formation and progression and the potential of PDE inhibitors in AA/AD, whether they are beneficial or detrimental. We also performed database analysis and summarized the results showing PDEs with significant expression changes under AA/AD, which should provide rationales for future research on PDEs in AA/AD.

Next-generation sequencing in pharmacogenomics - fit for clinical decision support?

INTRODUCTION: The technological advances of sequencing methods during the past 20 years have fuelled the generation of large amounts of sequencing data that comprise common variations, as well as millions of rare and personal variants that would not be identified by conventional genotyping. While comprehensive sequencing is technically feasible, its clinical utility for guiding personalized treatment decisions remains controversial. AREAS COVERED: We discuss the opportunities and challenges of comprehensive sequencing compared to targeted genotyping for pharmacogenomic applications. Current pharmacogenomic sequencing panels are heterogeneous and clinical actionability of the included genes is not a major focus. We provide a current overview and critical discussion of how current studies utilize sequencing data either retrospectively from biobanks, databases or repurposed diagnostic sequencing, or prospectively using pharmacogenomic sequencing. EXPERT OPINION: While sequencing-based pharmacogenomics has provided important insights into genetic variations underlying the safety and efficacy of a multitude pharmacological treatments, important hurdles for the clinical implementation of pharmacogenomic sequencing remain. We identify gaps in the interpretation of pharmacogenetic variants, technical challenges pertaining to complex loci and variant phasing, as well as unclear cost-effectiveness and incomplete reimbursement. It is critical to address these challenges in order to realize the promising prospects of pharmacogenomic sequencing.

Pharmacogenomics Beyond Single Common Genetic Variants: The Way Forward.

Interindividual variability in genes encoding drug-metabolizing enzymes, transporters, receptors, and human leukocyte antigens has a major impact on a patient's response to drugs with regard to efficacy and safety. Enabled by both technological and conceptual advances, the field of pharmacogenomics is developing rapidly. Major progress in omics profiling methods has enabled novel genotypic and phenotypic characterization of patients and biobanks. These developments are paralleled by advances in machine learning, which have allowed us to parse the immense wealth of data and establish novel genetic markers and polygenic models for drug selection and dosing. Pharmacogenomics has recently become more widespread in clinical practice to personalize treatment and to develop new drugs tailored to specific patient populations. In this review, we provide an overview of the latest developments in the field and discuss the way forward, including how to address the missing heritability, develop novel polygenic models, and further improve the clinical implementation of pharmacogenomics.

Translating pharmacogenomic sequencing data into drug response predictions-How to interpret variants of unknown significance.

The rapid development of sequencing technologies during the past 20 years has provided a variety of methods and tools to interrogate human genomic variations at the population level. Pharmacogenes are well known to be highly polymorphic and a plethora of pharmacogenomic variants has been identified in population sequencing data. However, so far only a small number of these variants have been functionally characterized regarding their impact on drug efficacy and toxicity and the significance of the vast majority remains unknown. It is therefore of high importance to develop tools and frameworks to accurately infer the effects of pharmacogenomic variants and, eventually, aggregate the effect of individual variations into personalized drug response predictions. To address this challenge, we here first describe the technological advances, including sequencing methods and accompanying bioinformatic processing pipelines that have enabled reliable variant identification. Subsequently, we highlight advances in computational algorithms for pharmacogenomic variant interpretation and discuss the added value of emerging strategies, such as machine learning and the integrative use of omics techniques that have the potential to further contribute to the refinement of personalized pharmacological response predictions. Lastly, we provide an overview of experimental and clinical approaches to validate in silico predictions. We conclude that the iterative feedback between computational predictions and experimental validations is likely to rapidly improve the accuracy of pharmacogenomic prediction models, which might soon allow for an incorporation of the entire pharmacogenetic profile into personalized response predictions.

D-LMBmap: a fully automated deep-learning pipeline for whole-brain profiling of neural circuitry.

Recent proliferation and integration of tissue-clearing methods and light-sheet fluorescence microscopy has created new opportunities to achieve mesoscale three-dimensional whole-brain connectivity mapping with exceptionally high throughput. With the rapid generation of large, high-quality imaging datasets, downstream analysis is becoming the major technical bottleneck for mesoscale connectomics. Current computational solutions are labor intensive with limited applications because of the exhaustive manual annotation and heavily customized training. Meanwhile, whole-brain data analysis always requires combining multiple packages and secondary development by users. To address these challenges, we developed D-LMBmap, an end-to-end package providing an integrated workflow containing three modules based on deep-learning algorithms for whole-brain connectivity mapping: axon segmentation, brain region segmentation and whole-brain registration. D-LMBmap does not require manual annotation for axon segmentation and achieves quantitative analysis of whole-brain projectome in a single workflow with superior accuracy for multiple cell types in all of the modalities tested.

Structural variation of the coding and non-coding human pharmacogenome.

Genetic variants in drug targets and genes encoding factors involved in drug absorption, distribution, metabolism and excretion (ADME) can have pronounced impacts on drug pharmacokinetics, response, and toxicity. While the landscape of genetic variability at the level of single nucleotide variants (SNVs) has been extensively studied in these pharmacogenetic loci, their structural variation is only poorly understood. Thus, we systematically analyzed the genetic structural variability across 908 pharmacogenes (344 ADME genes and 564 drug targets) based on publicly available whole genome sequencing data from 10,847 unrelated individuals. Overall, we extracted 14,984 distinct structural variants (SVs) ranging in size from 50 bp to 106 Mb. Each individual harbored on average 10.3 and 1.5 SVs with putative functional effects that affected the coding regions of ADME genes and drug targets, respectively. In addition, by cross-referencing pharmacogenomic SVs with experimentally determined binding data of 224 transcription factors across 130 cell types, we identified 1276 non-coding SVs that overlapped with gene regulatory elements. Based on these data, we estimate that non-coding structural variants account for 22% of the genetically encoded pharmacogenomic variability. Combined, these analyses provide the first comprehensive map of structural variability across pharmacogenes, derive estimates for the functional impact of non-coding SVs and incentivize the incorporation of structural genomic data into personalized drug response predictions.

A reusable, impurity-tolerant and noble metal-free catalyst for hydrocracking of waste polyolefins.

One-step conversion of low-purity polyolefins to value-added products without pretreatments represents a great opportunity for chemical recycling of waste plastics. However, additives, contaminants, and heteroatom-linking polymers tend to be incompatible with catalysts that break down polyolefins. Here, we disclose a reusable, noble metal-free and impurity-tolerant bifunctional catalyst, MoSx-Hbeta, for hydroconversion of polyolefins into branched liquid alkanes under mild conditions. The catalyst works for a wide scope of polyolefins, including different kinds of high-molecular weight polyolefins, polyolefins mixed with various heteroatom-linking polymers, contaminated polyolefins, and postconsumer polyolefins with/without cleaning under 250°C and 20 to 30 bar H2 in 6 to 12 hours. A 96% yield of small alkanes was successfully achieved even at a temperature as low as 180°C. These results demonstrate the great potentials of hydroconversion in practical use of waste plastics as a largely untapped carbon feedstock.

Toward Source-Free Cross Tissues Histopathological Cell Segmentation via Target-Specific Finetuning.

Recognition and quantitative analytics of histopathological cells are the golden standard for diagnosing multiple cancers. Despite recent advances in deep learning techniques that have been widely investigated for the automated segmentation of various types of histopathological cells, the heavy dependency on specific histopathological image types with sufficient supervised annotations, as well as the limited access to clinical data in hospitals, still pose significant challenges in the application of computer-aided diagnosis in pathology. In this paper, we focus on the model generalization of cell segmentation towards cross-tissue histopathological images. Remarkably, a novel target-specific finetuning-based self-supervised domain adaptation framework is proposed to transfer the cell segmentation model to unlabeled target datasets, without access to source datasets and annotations. When performed on the target unlabeled histopathological image set, the proposed method only needs to tune very few parameters of the pre-trained model in a self-supervised manner. Considering the morphological properties of pathological cells, we introduce two constraint terms at both local and global levels into this framework to access more reliable predictions. The proposed cross-domain framework is validated on three different types of histopathological tissues, showing promising performance in self-supervised cell segmentation. Additionally, the whole framework can be further applied to clinical tools in pathology without accessing the original training image data. The code and dataset are released at: https://github.com/NeuronXJTU/SFDA-CellSeg.

Global distribution of functionally important CYP2C9 alleles and their inferred metabolic consequences.

BACKGROUND: Genetic variability in the cytochrome P450 CYP2C9 constitutes an important predictor for efficacy and safety of various commonly prescribed drugs, including coumarin anticoagulants, phenytoin and multiple non-steroidal anti-inflammatory drugs (NSAIDs). A global map of CYP2C9 variability and its inferred functional consequences has been lacking. RESULTS: Frequencies of eight functionally relevant CYP2C9 alleles (*2, *3, *5, *6, *8, *11, *13 and *14) were analyzed. In total, 108 original articles were identified that included genotype data from a total of 81,662 unrelated individuals across 70 countries and 40 unique ethnic groups. The results revealed that CYP2C9*2 was most abundant in Europe and the Middle East, whereas CYP2C9*3 was the main reason for reduced CYP2C9 activity across South Asia. Our data show extensive variation within superpopulations with up to tenfold differences between geographically adjacent populations in Malaysia, Thailand and Vietnam. Translation of genetic CYP2C9 variability into functional consequences indicates that up to 40% of patients in Southern Europe and the Middle East might benefit from warfarin and phenytoin dose reductions, while 3% of patients in Southern Europe and Israel are recommended to reduce starting doses of NSAIDs. CONCLUSIONS: This study provides a comprehensive map of the genetic and functional variability of CYP2C9 with high ethnogeographic resolution. The presented data can serve as a useful resource for CYP2C9 allele and phenotype frequencies and might guide the optimization of genotyping strategies, particularly for indigenous and founder populations with distinct genetic profiles.

Cross-Ancestry Genome-Wide Association Study Defines the Extended CYP2D6 Locus as the Principal Genetic Determinant of Endoxifen Plasma Concentrations.

The therapeutic efficacy of tamoxifen is predominantly mediated by its active metabolites 4-hydroxy-tamoxifen and endoxifen, whose formation is catalyzed by the polymorphic cytochrome P450 2D6 (CYP2D6). Yet, known CYP2D6 polymorphisms only partially determine metabolite concentrations in vivo. We performed the first cross-ancestry genome-wide association study with well-characterized patients of European, Middle-Eastern, and Asian descent (n = 497) to identify genetic factors impacting active and parent metabolite formation. Genome-wide significant variants were functionally evaluated in an independent liver cohort (n = 149) and in silico. Metabolite prediction models were validated in two independent European breast cancer cohorts (n = 287, n = 189). Within a single 1-megabase (Mb) region of chromosome 22q13 encompassing the CYP2D6 gene, 589 variants were significantly associated with tamoxifen metabolite concentrations, particularly endoxifen and metabolic ratio (MR) endoxifen/N-desmethyltamoxifen (minimal P = 5.4E-35 and 2.5E-65, respectively). Previously suggested other loci were not confirmed. Functional analyses revealed 66% of associated, mostly intergenic variants to be significantly correlated with hepatic CYP2D6 activity or expression (ρ = 0.35 to -0.52), and six hotspot regions in the extended 22q13 locus impacting gene regulatory function. Machine learning models based on hotspot variants (n = 12) plus CYP2D6 activity score (AS) increased the explained variability (~ 9%) compared with AS alone, explaining up to 49% (median R2 ) and 72% of the variability in endoxifen and MR endoxifen/N-desmethyltamoxifen, respectively. Our findings suggest that the extended CYP2D6 locus at 22q13 is the principal genetic determinant of endoxifen plasma concentration. Long-distance haplotypes connecting CYP2D6 with adjacent regulatory sites and nongenetic factors may account for the unexplained portion of variability.

Eosinophils protect against pulmonary hypertension through 14-HDHA and 17-HDHA.

BACKGROUND: Pulmonary hypertension (PH) is a life-threatening disease featuring pulmonary vessel remodelling and perivascular inflammation. The effect, if any, of eosinophils (EOS) on the development of PH remains unclear. METHODS: EOS infiltration and chemotaxis were investigated in peripheral blood and lung tissues from pulmonary arterial hypertension (PAH) patients without allergic history and from sugen/hypoxia-induced PH mice. The role of EOS deficiency in PH development was investigated using GATA1-deletion (ΔdblGATA) mice and anti-interleukin 5 antibody-treated mice and rats. Ultra-high-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) was conducted to identify the critical oxylipin molecule(s) produced by EOS. Culture supernatants and lysates of EOS were collected to explore the mechanisms in co-culture cell experiments. RESULTS: There was a lower percentage of EOS in peripheral blood but higher infiltration in lung tissues from PAH patients and PH mice. PAH/PH lungs showed increased EOS-related chemokine expression, mainly C-C motif chemokine ligand 11 derived from adventitial fibroblasts. EOS deficiency aggravated PH in rodents, accompanied by increased neutrophil and monocyte/macrophage infiltration. EOS highly expressed arachidonate 15-lipoxygenase (ALOX15). 14-hydroxy docosahexaenoic acid (14-HDHA) and 17-HDHA were critical downstream oxylipins produced by EOS, which showed anti-inflammatory effects on recruitment of neutrophils and monocytes/macrophages through N-formyl peptide receptor 2. They also repressed pulmonary artery smooth muscle cell (PASMC) proliferation by activating peroxisome proliferator-activated receptor γ and blunting Stat3 phosphorylation. CONCLUSIONS: In PH development without external stimuli, peripheral blood exhibits a low EOS level. EOS play a protective role by suppressing perivascular inflammation and maintaining PASMC homeostasis via 14/17-HDHA.

Adenovirus-mediated Overexpression of FcγRIIB Attenuates Pulmonary Inflammation and Fibrosis.

Progressive fibrosing interstitial lung diseases (PF-ILDs) result in high mortality and lack effective therapies. The pathogenesis of PF-ILDs involves macrophages driving inflammation and irreversible fibrosis. Fc-γ receptors (FcγRs) regulate macrophages and inflammation, but their roles in PF-ILDs remain unclear. We characterized the expression of FcγRs and found upregulated FcγRIIB in human and mouse lungs after exposure to silica. FcγRIIB deficiency aggravated lung dysfunction, inflammation, and fibrosis in silica-exposed mice. Using single-cell transcriptomics and in vitro experiments, FcγRIIB was found in alveolar macrophages, where it regulated the expression of fibrosis-related genes Spp1 and Ctss. In mice with macrophage-specific overexpression of FcγRIIB and in mice treated with adenovirus by intratracheal instillation to upregulate FcγRIIB, silica-induced functional and histological changes were ameliorated. Our data from three genetic models and a therapeutic model suggest that FcγRIIB plays a protective role that can be enhanced by adenoviral overexpression, representing a potential therapeutic strategy for PF-ILDs.

Challenges Related to the Use of Next-Generation Sequencing for the Optimization of Drug Therapy.

Over the last decade, next-generation sequencing (NGS) methods have become increasingly used in various areas of human genomics. In routine clinical care, their use is already implemented in oncology to profile the mutational landscape of a tumor, as well as in rare disease diagnostics. However, its utilization in pharmacogenomics is largely lacking behind. Recent population-scale genome data has revealed that human pharmacogenes carry a plethora of rare genetic variations that are not interrogated by conventional array-based profiling methods and it is estimated that these variants could explain around 30% of the genetically encoded functional pharmacogenetic variability.To interpret the impact of such variants on drug response a multitude of computational tools have been developed, but, while there have been major advancements, it remains to be shown whether their accuracy is sufficient to improve personalized pharmacogenetic recommendations in robust trials. In addition, conventional short-read sequencing methods face difficulties in the interrogation of complex pharmacogenes and high NGS test costs require stringent evaluations of cost-effectiveness to decide about reimbursement by national healthcare programs. Here, we illustrate current challenges and discuss future directions toward the clinical implementation of NGS to inform genotype-guided decision-making.

Scellseg: A style-aware deep learning tool for adaptive cell instance segmentation by contrastive fine-tuning.

Deep learning-based cell segmentation is increasingly utilized in cell biology due to the massive accumulation of large-scale datasets and excellent progress in model architecture and instance representation. However, the development of specialist algorithms has long been hampered by a paucity of annotated training data, whereas the performance of generalist algorithms is limited without experiment-specific calibration. Here, we present Scellseg, an adaptive pipeline that utilizes a style-aware pre-trained model coupled to a contrastive fine-tuning strategy that also learns from unlabeled data. Scellseg achieves state-of-the-art transferability in average precision and Aggregated Jaccard Index on disparate datasets containing microscopy images at three biological levels, from organelle, cell to organism. Interestingly, when fine-tuning Scellseg, we show that performance plateaued after approximately eight images, implying that a specialist model can be obtained with few manual efforts. For convenient dissemination, we develop a graphical user interface that allows biologists to easily specialize their self-adaptive segmentation model.