High-throughput Tool Research Articles

Rapid and specific differentiation of Salmonella enterica serotypes typhi and Paratyphi by multicolor melting curve analysis

Rapid and accurate identification of Salmonella enterica serotypes Typhi and Paratyphi (A, B and C), the causal agents of enteric fever, is critical for timely treatment, case management and evaluation of health policies in low and middle-income countries where the disease still remains a serious public health problem. The present study describes the development of a multiplex assay (EFMAtyping) for simultaneous identification of pathogens causing typhoid and paratyphoid fever in a single reaction by the MeltArray approach, which could be finished within 2.5 h. Seven specific genes were chosen for differentiation of typhoidal and nontyphoidal Salmonella. All gene targets were able to be detected by the EFMAtyping assay, with expected Tm values and without cross-reactivity to other relevant Salmonella serovars. The limit of detection (LOD) for all gene targets was 50 copies per reaction. The LOD reached 102–103 CFU/ml for each pathogen in simulated clinical samples. The largest standard deviation value for mean Tm was below 0.5 °C. This newly developed EFMAtyping assay was further evaluated by testing 551 clinical Salmonella isolates, corroborated in parallel by the traditional Salmonella identification workflow, and serotype prediction was enabled by whole-genome sequencing. Compared to the traditional method, our results exhibited 100% of specificity and greater than 96% of sensitivity with a kappa correlation ranging from 0.96 to 1.00. Thus, the EFMAtyping assay provides a rapid, high throughput, and promising tool for public health laboratories to monitor typhoid and paratyphoid fever.

Predicting tissue distribution and tumor delivery of nanoparticles in mice using machine learning models

Nanoparticles (NPs) can be designed for targeted delivery in cancer nanomedicine, but the challenge is a low delivery efficiency (DE) to the tumor site. Understanding the impact of NPs' physicochemical properties on target tissue distribution and tumor DE can help improve the design of nanomedicines. Multiple machine learning and artificial intelligence models, including linear regression, support vector machine, random forest, gradient boosting, and deep neural networks (DNN), were trained and validated to predict tissue distribution and tumor delivery based on NPs' physicochemical properties and tumor therapeutic strategies with the dataset from Nano-Tumor Database. Compared to other machine learning models, the DNN model had superior predictions of DE to tumors and major tissues. The determination coefficients (R2) for the test datasets were 0.41, 0.42, 0.45, 0.79, 0.87, and 0.83 for DE in tumor, heart, liver, spleen, lung, and kidney, respectively. All the R2 and root mean squared error (RMSE) results of the test datasets were similar to the 5-fold cross validation results. Feature importance analysis showed that the core material of NPs played an important role in output predictions among all physicochemical properties. Furthermore, multiple NP formulations with greater DE to the tumor were determined by the DNN model. To facilitate model applications, the final model was converted to a web dashboard. This model could serve as a high-throughput pre-screening tool to support the design of new and efficient nanomedicines with greater tumor DE and serve as an alternative tool to reduce, refine, and partially replace animal experimentation in cancer nanomedicine research.

Saturation transposon mutagenesis enables genome-wide identification of genes required for growth and fluconazole resistance in the human fungal pathogen Cryptococcus neoformans.

Fungi can cause devastating invasive infections, typically in immunocompromised patients. Treatment is complicated both by the evolutionary similarity between humans and fungi and by the frequent emergence of drug resistance. Studies in fungal pathogens have long been slowed by a lack of high-throughput tools and community resources that are common in model organisms. Here we demonstrate a high-throughput transposon mutagenesis and sequencing (TN-seq) system in Cryptococcus neoformans that enables genome-wide determination of gene essentiality. We employed a random forest machine learning approach to classify the Cryptococcus neoformans genome as essential or nonessential, predicting 1,465 essential genes, including 302 that lack human orthologs. These genes are ideal targets for new antifungal drug development. TN-seq also enables genome-wide measurement of the fitness contribution of genes to phenotypes of interest. As proof of principle, we demonstrate the genome-wide contribution of genes to growth in fluconazole, a clinically used antifungal. We show a novel role for the well-studied RIM101 pathway in fluconazole susceptibility. We also show that 5' insertions of transposons can drive sensitization of essential genes, enabling screenlike assays of both essential and nonessential components of the genome. Using this approach, we demonstrate a role for mitochondrial function in fluconazole sensitivity, such that tuning down many essential mitochondrial genes via 5' insertions can drive resistance to fluconazole. Our assay system will be valuable in future studies of C. neoformans, particularly in examining the consequences of genotypic diversity.

High-Throughput Absorbance-Activated Droplet Sorting for Engineering Aldehyde Dehydrogenases.

Recent decades have seen a dramatic increase in the commercial use of biocatalysts, transitioning from energy-intensive traditional chemistries to more sustainable methods. Current enzyme engineering techniques, such as directed evolution, require the generation and testing of large mutant libraries to identify optimized variants. Unfortunately, conventional screening methods are unable to screen such large libraries in a robust and timely manner. Droplet-based microfluidic systems have emerged as a powerful high-throughput tool for library screening at kilohertz rates. Unfortunately, almost all reported systems are based on fluorescence detection, restricting their use to a limited number of enzyme types that naturally convert fluorogenic substrates or require the use of surrogate substrates. To expand the range of enzymes amenable to evolution using droplet-based microfluidic systems, we present an absorbance-activated droplet sorter that allows of droplet sorting at kilohertz rates without the need for optical monitoring of the microfluidic system. To demonstrate the utility of the sorter, we rapidly screen a 105-member aldehyde dehydrogenase library towards D-glyceraldehyde using a NADH mediated coupled assay that generates WST-1 formazan as the colorimetric product. We successfully identify a variant with a 51% improvement in catalytic efficiency and a significant increase in overall activity across a broad substrate spectrum.

Development and validation of a 40 K liquid SNP array for the mud crab (Scylla paramamosain)

Genomic selection (GS) and genome-wide association studies (GWAS) have been widely implemented to accelerate the genetic improvement of important economic traits. Developing an efficient and reliable high-throughput genotyping tool is the premise and basis of implementing GS and GWAS on the mud crab (Scylla paramamosain). In this study, leveraging the whole-genome resequencing data of 146 mud crabs from the southeast coast of China, a total of 16,714,943 high-quality SNPs were discovered. Based on polymorphic information content and evenly distribution on 49 chromosomes, a total of 40,352 SNPs was selected to develop a 40 K liquid SNP array (named “Xiexin No. 1”) using the genotyping by target sequencing (GBTS) technology. The genotyping performance test showed that this SNP array had a high site polymorphism (over 92.70%) and individual calling rate (97.02%). Furthermore, the mean of SNP concordance, genotype concordance, non-reference sensitivity, and non-reference discrepancy between “Xiexin No. 1” SNP array and resequencing were identified to be 0.986, 0.954, 0.942, and 0.097, respectively. Using this SNP array data, the genomic heritabilities were evaluated as 0.887 ± 0.065, 0.643 ± 0.108, 0.599 ± 0.111, and 0.624 ± 0.111 for body weight (BW), carapace length (CL), carapace length (CW), and body height (BH) in a cultured population (233 crabs). Furthermore, five significant SNPs associated with growth traits were identified by GWAS. After annotation, one candidate gene (XP_027221997.1) associated with BW and one candidate gene (XP_027210446.1) associated with CW and BH were identified. Most importantly, the prediction accuracy of GBLUP for BW, BH, CL, and CW were 0.535 ± 0.064, 0.544 ± 0.101, 0.574 ± 0.087, and 0.585 ± 0.083, with the unbiasedness values of 0.988 ± 0.22, 1.015 ± 0.274, 1.011 ± 0.196 and 1.037 ± 0.235. In conclusion, the “Xiexin No. 1” SNP array provides an efficient and reliable platform for high-quality genotyping, which would greatly promote the genetic studies and thus accelerate the genetic improvement of mud crabs.

Enhancing efficiency in detection of COVID-19 through AI-driven colorimetric isothermal detection with multiplex primers

COVID-19 has afflicted millions of lives worldwide. Although there are many rapid methods to detect it based on colorimetric loop-mediated isothermal amplification, there remains room for improvement. This study aims to 1) integrate multiple primers into a singleplex assay to enhance the diagnostic sensitivity, and 2) utilize a high-throughput smartphone-operatable AI-driven color reading tool to enable a rapid result analysis. This setup can improve the sensitivity by 10–100 times and can analyze approximately 6700 samples per minute. The assay is simpler than RT-qPCR, with a turnaround time of less than 75 min. It can detect various types of SARS-CoV-2 by targeting 3 genes, increasing the likelihood that it will remain effective even if the virus undergoes mutations in any single target gene. In summary, it affords potential for adaptation to detection of new/re-emerging diseases with the visual readout for maximum assay simplicity and AI-operated mode for large-scale testing.

Fish species authentication in commercial fish products using mass spectrometry and spectral library matching approach

Seafood fraud has become a global issue, threatening food security and safety. Adulteration, substitution, dilution, and incorrect labeling of seafood products are fraudulent practices that violate consumer safety. In this context, developing sensitive, robust, and high-throughput molecular tools for food and feed authentication is becoming crucial for regulatory purposes. Analytical approaches such as proteomics mass spectrometry have shown promise in detecting incorrectly labeled products. For the application of these tools, genome information is crucial, but currently, for many marine species of commercial importance, such information is unavailable. However, when combining proteomic analysis with spectral library matching, commercially important fish species were successfully identified, differentiated, and quantified in pure muscle samples and mixtures, even when genome information was scarce. This study further tested the previously developed spectral library matching approach to differentiate between 29 fish species from the North Sea and examined samples including individual fish, laboratory-prepared mixtures and commercial products. For authenticating libraries generated from 29 fish species, fresh muscle samples from the fish samples were matched against the reference spectral libraries. Species of the fresh fish samples were correctly authenticated using the spectral library approach. The same result was obtained when evaluating the laboratory-prepared mixtures. Furthermore, processed commercial products containing mixtures of two or three fish species were matched against these reference spectral libraries to test the accuracy and robustness of this method for authentication of fish species. The results indicated that the method is suitable for the authentication of fish species from highly processed samples such as fish cakes and burgers. The study shows that current and future challenges in food and feed authentication can efficiently be tackled by reference spectral libraries method when prospecting new resources in the Arctic.

High-risk ARGs (HRA) Chip: A high-throughput qPCR-based array for assessment of high-risk ARGs from the environment

The global surge in antibiotic resistance genes (ARGs) presents a serious public health challenge. While methods like metagenomic analysis and qPCR arrays have been instrumental in investigating ARG distributions and dynamics, the vast diversity of ARGs often complicates effective monitoring and risk assessment. Here, we developed a High-Risk ARGs (HRA) chip based on high-capacity quantitative PCR array targeting previously identified high-risk ARGs. These ARGs are known to be prevalent in human-related environments, exhibit gene mobility, and are present in ESKAPE pathogens. The HRA chip include 101 primer sets and the 16S rRNA gene as a reference. These primer sets consist of 34 obtained from previous studies, and 67 newly designed primer sets which were validated in silico and experimentally. Absolute quantification of targeted ARGs is accomplished by generating standard curves for all ARGs with serially ten-fold diluted mixed plasmids containing targeted ARG sequences. The amplification efficiencies of all ARGs exceed 99% via plasmid template dilution tests, suggesting high reliability in quantification. The performance of HRA chip is further evaluated by practical applications in environmental samples from wastewater treatment plants (WWTPs) and soils with various land use types and fertilization regimes. The results indicate the dynamics of high-risk ARGs during wastewater treatment process, and reveal the profiles of soil high-risk ARGs which is distinct from those derived via metagenomic approaches. These findings highlight the potentials of HRA Chip in the evaluation of anthropogenic impacts on the environmental resistome with a more focused spectrum of high-risk ARGs. Overall, the HRA Chip emerges as a powerful and efficient high-throughput tool for rapid detection and quantification of high-risk ARGs, facilitating comprehensive profiling of high-risk resistomes in environmental samples which is essential for human health risk assessment of ARGs.

Artificial intelligence system for outcome evaluations of human in vitro fertilization-derived embryos.

In vitro fertilization (IVF) has emerged as a transformative solution for infertility. However, achieving favorable live-birth outcomes remains challenging. Current clinical IVF practices in IVF involve the collection of heterogeneous embryo data through diverse methods, including static images and temporal videos. However, traditional embryo selection methods, primarily reliant on visual inspection of morphology, exhibit variability and are contingent on the experience of practitioners. Therefore, an automated system that can evaluate heterogeneous embryo data to predict the final outcomes of live births is highly desirable. We employed artificial intelligence (AI) for embryo morphological grading, blastocyst embryo selection, aneuploidy prediction, and final live-birth outcome prediction. We developed and validated the AI models using multitask learning for embryo morphological assessment, including pronucleus type on day 1 and the number of blastomeres, asymmetry, and fragmentation of blastomeres on day 3, using 19,201 embryo photographs from 8271 patients. A neural network was trained on embryo and clinical metadata to identify good-quality embryos for implantation on days or day 5, and predict live-birth outcomes. Additionally, a 3D convolutional neural network was trained on 418 time-lapse videos of preimplantation genetic testing (PGT)-based ploidy outcomes for aneuploidy prediction and consequent live-birth outcomes. These two approaches enabled us to automatically assess the implantation potential. By combining embryo and maternal metrics in an ensemble AI model, we evaluated live-birth outcomes in a prospective cohort that achieved higher accuracy than experienced embryologists (46.1% vs. 30.7% on day 3, 55.0% vs. 40.7% on day 5). Our results demonstrate the potential for AI-based selection of embryos based on characteristics beyond the observational abilities of human clinicians (area under the curve: 0.769, 95% confidence interval: 0.709-0.820). These findings could potentially provide a noninvasive, high-throughput, and low-cost screening tool to facilitate embryo selection and achieve better outcomes. Our study underscores the AI model's ability to provide interpretable evidence for clinicians in assisted reproduction, highlighting its potential as a noninvasive, efficient, and cost-effective tool for improved embryo selection and enhanced IVF outcomes. The convergence of cutting-edge technology and reproductive medicine has opened new avenues for addressing infertility challenges and optimizing IVF success rates.

A Benchmark Test of High-Throughput Atomistic Modeling for Octa-Acid Host–Guest Complexes

Years of massive applications of high-throughput atomistic modeling tools such as molecular docking and end-point free energy calculations in the drug industry and academic exploration have made them indispensable parts of hierarchical screening. While the similarities between host–guest and protein–ligand complexes lead to the direct extension of techniques for protein–ligand screening to host–guest systems, the practical performance of these hit identification tools remains unclear in host-–-guest binding. Recent reports on specific host–guest complexes suggest that the experience on the accuracy ladder accumulated from protein–ligand cases could be invalid in host–guest complexes, which makes it an urgent need to perform a systematic benchmark to secure solid numerical supports and guidance of practical setups. Concerning molecular docking, there still lacks a comprehensive benchmark considering popular docking programs. As for end-point reranking, quantitative and rigorous free energy estimation via end-point formulism requires establishing statistically meaningful measurements of uncertainties due to finite sampling, which is neglected or underestimated by a significant portion in almost all main-stream applications. Further, a face-to-face comparison between different screening tools is required for the design of a hierarchical workflow. To fill the above-mentioned critical gaps, in this work, using a dataset containing tens of host–guest complexes involving basket-like macromolecular hosts from the octa acid family, we extensively benchmark seven academic docking protocols and perform post-docking end-point rescoring with twenty protocols. The resulting comprehensive benchmark provides conclusive pictures of the practical value of docking and end-point screening in OA host–guest binding.

Potential of Thermal and RGB Imaging Combined with Artificial Neural Networks for Assessing Salt Tolerance of Wheat Genotypes Grown in Real-Field Conditions

Developing new bread wheat varieties that can be successfully grown in saline conditions has become a pressing task for plant breeders. High-throughput phenotyping tools are crucial for this task. Proximal remote sensing is gaining popularity in breeding programs as a quick, cost-effective, and non-invasive tool to assess canopy structure and physiological traits in large genetic pools. Limited research has been conducted on the effectiveness of combining RGB and thermal imaging to assess the salt tolerance of different wheat genotypes. This study aimed to evaluate the effectiveness of combining several indices derived from thermal infrared and RGB images with artificial neural networks (ANNs) for assessing relative water content (RWC), chlorophyll a (Chla), chlorophyll b (Chlb), total chlorophyll (Chlt), and plant dry weight (PDW) of 18 recombinant inbred lines (RILs) and their 3 parents irrigated with saline water (150 mM NaCl). The results showed significant differences in various traits and indices among the tested genotypes. The normalized relative canopy temperature (NRCT) index exhibited strong correlations with RWC, Chla, Chlb, Chlt, and PDW, with R2 values ranging from 0.50 to 0.73, 0.53 to 0.76, 0.68 to 0.84, 0.68 to 0.84, and 0.52 to 0.76, respectively. Additionally, there was a strong relationship between several RGB indices and measured traits, with the highest R2 values reaching up to 0.70. The visible atmospherically resistant index (VARI), a popular index derived from RGB imaging, showed significant correlations with NRCT, RWC, Chla, Chlb, Chlt, and PDW, with R2 values ranging from 0.49 to 0.62 across two seasons. The different ANNs models demonstrated high predictive accuracy for NRCT and other measured traits, with R2 values ranging from 0.62 to 0.90 in the training dataset and from 0.46 to 0.68 in the cross-validation dataset. Thus, our study shows that integrating high-throughput digital image tools with ANN models can efficiently and non-invasively assess the salt tolerance of a large number of wheat genotypes in breeding programs.

Wild grass-derived alleles represent a genetic architecture for the resilience of modern common wheat to stresses.

This review explores the integration of wild grass-derived alleles into modern bread wheat breeding to tackle the challenges of climate change and increasing food demand. With a focus on synthetic hexaploid wheat, this review highlights the potential of genetic variability in wheat wild relatives, particularly Aegilops tauschii, for improving resilience to multifactorial stresses like drought, heat, and salinity. The evolutionary journey of wheat (Triticum spp.) from diploid to hexaploid species is examined, revealing significant genetic contributions from wild grasses. We also emphasize the importance of understanding incomplete lineage sorting in the genomic evolution of wheat. Grasping this information is crucial as it can guide breeders in selecting the appropriate alleles from the gene pool of wild relatives to incorporate into modern wheat varieties. This approach improves the precision of phylogenetic relationships and increases the overall effectiveness of breeding strategies. This review also addresses the challenges in utilizing the wheat wild genetic resources, such as the linkage drag and cross-compatibility issues. Finally, we culminate the review with future perspectives, advocating for a combined approach of high-throughput phenotyping tools and advanced genomic techniques to comprehensively understand the genetic and regulatory architectures of wheat under stress conditions, paving the way for more precise and efficient breeding strategies.

Specific quantification of inducible HIV-1 reservoir by RT-LAMP

BackgroundStrategies toward HIV-1 cure aim to clear, inactivate, reduce, or immunologically control the virus from a pool of latently infected cells such that combination antiretroviral therapy (cART) can be safely interrupted. In order to assess the impact of any putative curative interventions on the size and inducibility of the latent HIV-1 reservoir, robust and scalable assays are needed to precisely quantify the frequency of infected cells containing inducible HIV-1.MethodsWe developed Specific Quantification of Inducible HIV−1 by RT-LAMP (SQuHIVLa), leveraging the high sensitivity and specificity of RT-LAMP, performed in a single reaction, to detect and quantify cells expressing tat/rev HIV-1 multiply spliced RNA (msRNA) upon activation. The LAMP primer/probe used in SQuHIVLa was designed to exclusively detect HIV-1 tat/rev msRNA and adapted for different HIV-1 subtypes.ResultsUsing SQuHIVLa, we successfully quantify the inducible viral reservoir in CD4+ T cells from people living with HIV-1 subtypes B and C on cART. The assay demonstrates high sensitivity, specificity, and reproducibility.ConclusionsSQuHIVLa offers a high throughput, scalable, and specific HIV-1 reservoir quantification tool that is amenable to resource-limited settings. This assay poses remarkable potential in facilitating the evaluation of potential interventional strategies toward achieving HIV-1 cure.

Slipping through the cracks: Challenges and prospects for investigating fungal plant disease complexes

Plant pathogens frequently do not operate alone when causing diseases. Since the infection process is affected by the interaction between members of the parasite community, this question asks about the efficiency of disease control strategies which are usually tailored to manage only one microbial pathogen at a time. Fungi and oomycetes are among plant disease's most prevalent and damaging causal agents. With the increasing ability to distinguish individual species, thanks to new high-throughput molecular tools, numerous diseases once attributed to a single species are now recognized as being caused by complexes of fungal phytopathogens. How should we approach the study of these complexes? What tools and methodologies are needed to characterize them and decipher their functional interactions? How can we understand and master the drivers of these coinfections and their dynamics under field conditions? Here, we review the current literature on fungal disease complexes to define their commonalities and address some of the current challenges regarding the identification of preferential associations among fungal species indicative of a disease complex and its community dynamics and regulation. This review highlights that fungal species complexes are highly dynamic at geographic and temporal scales and that human actions contributed to the dissemination of new members of species complexes worldwide and to disequilibrium within species complexes, often resulting in more damaging diseases. This review also points to the generally insufficient (or lacking) knowledge of the diversity, dynamics, and functioning of fungal disease complexes, and the risks linked with inappropriate management strategies focusing on only one dominant member of the complex.

Exploring crop genomics: role of molecular markers in identifying genetic diversity and characterization

and facilitating germplasm characterization for crop improvement. Understanding and harnessing crop genetic diversity are paramount with the escalating demand for sustainable agricultural practices and the need to address global food security challenges. Molecular markers have emerged as powerful tools enabling precise identification, characterization, and utilization of genetic resources for crop enhancement. The paper provides an in-depth analysis of various molecular marker techniques, encompassing DNA-based markers such as single nucleotide polymorphisms (SNPs), simple sequence repeats (SSRs), and amplified fragment length polymorphisms (AFLPs), as well as RNA-based markers like expression sequence tags (ESTs) and microRNAs. By elucidating genetic relationships, population structure, and phylogenetic analysis, these markers facilitate the conservation and utilization of crop germplasm. Moreover, the review highlights recent advancements in high-throughput genotyping technologies and bioinformatics tools, which have revolutionized crop genomic research. These advancements enable comprehensive genome-wide analyses, accelerating breeding efforts for the development of improved crop varieties with enhanced traits such as yield, quality, and stress tolerance. Integration of molecular marker-assisted selection (MAS) into breeding programs further enhances the efficiency and precision of crop improvement strategies. Despite the significant contributions of molecular markers to crop genomics, challenges persist. The review addresses these challenges and discusses potential solutions, emphasizing the importance of collaborative efforts, data sharing, and interdisciplinary research endeavors. By overcoming these hurdles, the agricultural community can fully harness the potential of genomics for sustainable crop improvement, thus addressing global food security challenges in the face of changing environmental conditions and population growth. In conclusion, understanding the role of molecular markers in crop genomics is crucial for optimizing breeding strategies, conserving genetic resources, and developing resilient crop varieties to meet the demands of a growing population and ensure food security in a changing world.

Precise and High-Throughput Delivery of Micronutrients in Plants Enabled by Pollen-Inspired Spiny and Biodegradable Microcapsules.

Decarbonizing food production and mitigating agriculture's environmental impact require new technologies for precise delivery of fertilizers and pesticides to plants. The cuticle, a waxy barrier that protects the surface of leaves, causes 60%-90% runoff of fertilizers and pesticides, leading to the wastage of intensive resources, soil depletion, and water bodies pollution. Solutions to mitigate runoff include adding chemicals (e.g., surfactants) to decrease surface tension and enhance cuticles' permeability but have low efficacy. In this study, vapor-induced synergistic differentiation (VISDi) is used to nanomanufacture echinate pollen-like, high payload content (≈50 wt%) microcapsules decorated with robust spines that mechanically disrupt the cuticle and adhere to the leaf. VISDi induces a core-shell structure in the spines, enabling the release of agrochemicals from the microparticles' body into the leaf. As proof of concept, precise and highthroughput delivery of iron fertilizer in Fe-deficient spinach plants is demonstrated. Spray of spiny microparticles improves leaf adhesion by mechanical interlocking, reduces wash-off by an ≈12.5 fold, and enhances chlorophyll content by ≈7.3 times compared to the application of spherical counterparts. Together, these results show that spiny microparticles can mitigate agricultural runoff and provide a high-throughput tool for precise plant drug delivery.

Indirect evaluation of watermelon volatile profile: Detection of subtle changes with e-nose systems

The effectiveness of e-nose systems as high-throughput tools for volatile profiling in watermelon was investigated focusing on discerning subtle changes induced by the use of different rootstocks. Partial Least Square Discriminant Analysis (PLS-DA) models, both GC-MS and e-nose data, demonstrated moderate performance in classification due to nuanced differences among groups (the same F1 hybrid was used as scion). However, PLS-DA biplots revealed a clear correlation between GC-MS and e-nose data. This methodology enabled the e-nose system to identify the effects of specific root-scion combinations compared to non-grafted controls and detect combinations with more variable volatile profiles. Remarkably, the e-nose system identified samples with anomalous volatile profiles, mirroring the capabilities of GC-MS data. Additionally, PLS models were developed to provide reasonably accurate predictions of key compound contents like geranylacetone, (Z)-6-nonen-1-ol, or (Z)-6-nonenal, crucial for watermelon flavor and taste perception. Overall, this study highlights the potential of e-nose systems in discerning nuanced variations in watermelon volatile profiles affecting aroma. Incorporating volatile profile evaluation capabilities using such systems will significantly optimize quality control processes and plant breeding programs.

Using scalable computer vision to automate high-throughput semiconductor characterization

High-throughput materials synthesis methods, crucial for discovering novel functional materials, face a bottleneck in property characterization. These high-throughput synthesis tools produce 104 samples per hour using ink-based deposition while most characterization methods are either slow (conventional rates of 101 samples per hour) or rigid (e.g., designed for standard thin films), resulting in a bottleneck. To address this, we propose automated characterization (autocharacterization) tools that leverage adaptive computer vision for an 85x faster throughput compared to non-automated workflows. Our tools include a generalizable composition mapping tool and two scalable autocharacterization algorithms that: (1) autonomously compute the band gaps of 200 compositions in 6 minutes, and (2) autonomously compute the environmental stability of 200 compositions in 20 minutes, achieving 98.5% and 96.9% accuracy, respectively, when benchmarked against domain expert manual evaluation. These tools, demonstrated on the formamidinium (FA) and methylammonium (MA) mixed-cation perovskite system FA1−xMAxPbI3, 0 ≤ x ≤ 1, significantly accelerate the characterization process, synchronizing it closer to the rate of high-throughput synthesis.

Revealing biomolecular structure and motion with neural ab initio cryo-EM reconstruction.

Proteins and other biomolecules form dynamic macromolecular machines that are tightly orchestrated to move, bind, and perform chemistry. Cryo-electron microscopy (cryo-EM) can access the intrinsic heterogeneity of these complexes and is therefore a key tool for understanding mechanism and function. However, 3D reconstruction of the resulting imaging data presents a challenging computational problem, especially without any starting information, a setting termed ab initio reconstruction. Here, we introduce a method, DRGN-AI, for ab initio heterogeneous cryo-EM reconstruction. With a two-step hybrid approach combining search and gradient-based optimization, DRGN-AI can reconstruct dynamic protein complexes from scratch without input poses or initial models. Using DRGN-AI, we reconstruct the compositional and conformational variability contained in a variety of benchmark datasets, process an unfiltered dataset of the DSL1/SNARE complex fully ab initio, and reveal a new "supercomplex" state of the human erythrocyte ankyrin-1 complex. With this expressive and scalable model for structure determination, we hope to unlock the full potential of cryo-EM as a high-throughput tool for structural biology and discovery.

HPB-Chip: An accurate high-throughput qPCR-based tool for rapidly profiling waterborne human pathogenic bacteria in the environment

Waterborne pathogens are threatening public health globally, but profiling multiple human pathogenic bacteria (HPBs) in various polluted environments is still a challenge due to the absence of rapid, high-throughput and accurate quantification tools. This work developed a novel chip, termed the HPB-Chip, based on high-throughput quantitative polymerase chain reactions (HT-qPCR). The HPB-Chip with 33-nL reaction volume could simultaneously complete 10,752 amplification reactions, quantifying 27 HPBs in up to 192 samples with two technical replicates (including those for generating standard curves). Specific positive bands of target genes across different species and single peak melting curves demonstrated high specificity of the HPB-Chip. The mixed plasmid serial dilution test validated its high sensitivity with the limit of quantification (LoD) of averaged 82 copies per reaction for 25 target genes. PCR amplification efficiencies and R2 coefficients of standard curves of the HPB-Chip averaged 101 % and 0.996, respectively. Moreover, a strong positive correlation (Pearson’ r: 0.961–0.994, P < 0.001) of HPB concentrations (log10 copies/L) between HPB-Chip and conventional qPCR demonstrated high accuracy of the HPB-Chip. Subsequently, the HPB-Chip has been successfully applied to absolutely quantify 27 HPBs in municipal and hospital wastewater treatment plants (WWTPs) after PMA treatment. A total of 17 HPBs were detected in the 6 full-scale WWTPs, with an additional 19 in the hospital WWTP. Remarkably, Acinetobacter baumannii, Legionella pneumophila, and Arcobacter butzler were present in the final effluent of each municipal WWTP. Overall, the HPB-Chip is an efficient and accurate high-throughput quantification tool to comprehensively and rapidly quantify 27 HPBs in the environment.