Towards an AI-driven soft toy for automatically detecting and classifying infant-toy interactions using optical force sensors.

Introduction: It is crucial to identify neurodevelopmental disorders in infants early on for timely intervention to improve their long-term outcomes. Combining natural play with quantitative measurements of developmental milestones can be an effective way to swiftly and efficiently detect infants who are at risk of neurodevelopmental delays. Clinical studies have established differences in toy interaction behaviors between full-term infants and pre-term infants who are at risk for cerebral palsy and other developmental disorders. Methods: The proposed toy aims to improve the quantitative assessment of infant-toy interactions and fully automate the process of detecting those infants at risk of developing motor delays. This paper describes the design and development of a toy that uniquely utilizes a collection of soft lossy force sensors which are developed using optical fibers to gather play interaction data from infants laying supine in a gym. An example interaction database was created by having 15 adults complete a total of 2480 interactions with the toy consisting of 620 touches, 620 punches-"kick substitute," 620 weak grasps and 620 strong grasps. Results: The data is analyzed for patterns of interaction with the toy face using a machine learning model developed to classify the four interactions present in the database. Results indicate that the configuration of 6 soft force sensors on the face created unique activation patterns. Discussion: The machine learning algorithm was able to identify the distinct action types from the data, suggesting the potential usability of the toy. Next steps involve sensorizing the entire toy and testing with infants.

acCRISPR: an activity-correction method for improving the accuracy of CRISPR screens.

High throughput CRISPR screens are revolutionizing the way scientists unravel the genetic underpinnings of engineered and evolved phenotypes. One of the critical challenges in accurately assessing screening outcomes is accounting for the variability in sgRNA cutting efficiency. Poorly active guides targeting genes essential to screening conditions obscure the growth defects that are expected from disrupting them. Here, we develop acCRISPR, an end-to-end pipeline that identifies essential genes in pooled CRISPR screens using sgRNA read counts obtained from next-generation sequencing. acCRISPR uses experimentally determined cutting efficiencies for each guide in the library to provide an activity correction to the screening outcomes via calculation of an optimization metric, thus determining the fitness effect of disrupted genes. CRISPR-Cas9 and -Cas12a screens were carried out in the non-conventional oleaginous yeast Yarrowia lipolytica and acCRISPR was used to determine a high-confidence set of essential genes for growth under glucose, a common carbon source used for the industrial production of oleochemicals. acCRISPR was also used in screens quantifying relative cellular fitness under high salt conditions to identify genes that were related to salt tolerance. Collectively, this work presents an experimental-computational framework for CRISPR-based functional genomics studies that may be expanded to other non-conventional organisms of interest.

Population genomics-guided engineering of phenazine biosynthesis in Pseudomonas chlororaphis.

The emergence of next-generation sequencing (NGS) technologies has made it possible to not only sequence entire genomes, but also identify metabolic engineering targets across the pangenome of a microbial population. This study leverages NGS data as well as existing molecular biology and bioinformatics tools to identify and validate genomic signatures for improving phenazine biosynthesis in Pseudomonas chlororaphis. We sequenced a diverse collection of 34 Pseudomonas isolates using short- and long-read sequencing techniques and assembled whole genomes using the NGS reads. In addition, we assayed three industrially relevant phenotypes (phenazine production, biofilm formation, and growth temperature) for these isolates in two different media conditions. We then provided the whole genomes and phenazine production data to a unitig-based microbial genome-wide association study (mGWAS) tool to identify novel genomic signatures responsible for phenazine production in P. chlororaphis. Post-processing of the mGWAS analysis results yielded 330 significant hits influencing the biosynthesis of one or more phenazine compounds. Based on a quantitative metric (called the phenotype score), we elucidated the most influential hits for phenazine production and experimentally validated them in vivo in the most optimal phenazine producing strain. Two genes significantly increased phenazine-1-carboxamide (PCN) production: a histidine transporter (ProY_1), and a putative carboxypeptidase (PS__04251). A putative MarR-family transcriptional regulator decreased PCN titer when overexpressed in a high PCN producing isolate. Overall, this work seeks to demonstrate the utility of a population genomics approach as an effective strategy in enabling the identification of targets for metabolic engineering of bioproduction hosts.

Analyzing CRISPR screens in non-conventional microbes.

The multifaceted nature of CRISPR screens has propelled advancements in the field of functional genomics. Pooled CRISPR screens involve creating programmed genetic perturbations across multiple genomic sites in a pool of host cells subjected to a challenge, empowering researchers to identify genetic causes of desirable phenotypes. These genome-wide screens have been widely used in mammalian cells to discover biological mechanisms of diseases and drive the development of targeted drugs and therapeutics. Their use in non-model organisms, especially in microbes to improve bioprocessing-relevant phenotypes, has been limited. Further compounding this issue is the lack of bioinformatic algorithms for analyzing microbial screening data with high accuracy. Here, we describe the general approach and underlying principles for conducting pooled CRISPR knockout screens in non-conventional yeasts and performing downstream analysis of the screening data, while also reviewing state-of-the-art algorithms for identification of CRISPR screening outcomes. Application of pooled CRISPR screens to non-model yeasts holds considerable potential to uncover novel metabolic engineering targets and improve industrial bioproduction. ONE-SENTENCE SUMMARY: This mini-review describes experimental and computational approaches for functional genomic screening using CRISPR technologies in non-conventional microbes.

Clinical Validation of Integrated Point-of-Care Devices for the Management of Non-Communicable Diseases.

Non-communicable diseases are the leading cause of death and disability across India, including in the poorest states. Effective disease management, particularly for cardiovascular diseases, requires the tracking of several biochemical and physiological parameters over an extended period of time. Currently, patients must go to diagnostic laboratories and doctors' clinics or invest in individual point-of-care devices for measuring the required parameters. The cost and inconvenience of current options lead to inconsistent monitoring, which contribute to suboptimal outcomes. Furthermore, managing multiple individual point-of-devices is challenging and helps track some parameters to the exclusion of others. To address these issues, HealthCubed, a primary care technology company, has designed integrated devices that measure blood glucose, hemoglobin, cholesterol, uric acid, blood pressure, capillary oxygen saturation and pulse rate. Here we report data from clinical studies undertaken in healthy subjects establishing the validity of an integrated device for monitoring multiple parameters.

A modular approach for the generation, storage, mixing, and detection of droplet libraries for high throughput screening.

The desire to make microfluidic technology more accessible to the biological research community has led to the notion of "modular microfluidics", where users can build a fluidic system using a toolkit of building blocks. This paper applies a modular approach for performing droplet-based screening, including the four integral steps of library generation, storage, mixing, and optical interrogation. Commercially available cross-junctions are used for drop generation, flexible capillary tubing for storage, and tee-junctions for serial mixing. Optical interrogation of the drops is achieved using fiber-optic detection modules which can be incorporated inline at one or more points in the system. Modularity enables the user to hand-assemble systems for functional assays or applications. Three examples are shown: (1) a "mix and read" assay commonly used in high throughput screening (HTS); (2) generation of chemically distinct droplets using microfractionation in droplets (microFD); and (3) in situ encapsulation and culture of eukaryotes. Using components with IDs ranging from 150 microm to 1.5 mm, this approach can accommodate drop assays with volumes ranging from 2 nL to 2 microL, and storage densities ranging from 300 to 3000 drops per metre tubing. Generation rates are up to 200 drops per second and merging rates are up to 10 drops per second. The impact of length scale, carrier fluid viscosity, and flow rates on system performance is considered theoretically and illustratively using 2D CFD simulations. Due to its flexibility, the widespread availability of components, and some favorable material properties compared to PDMS, this approach can be a useful part of a researcher's toolkit for prototyping droplet-based assays.

Microfluidic encapsulation of cells in alginate capsules for high throughput screening.

Microdroplet systems can drastically reduce costs and increase throughput in high throughput screening (HTS) assays. While droplets are well suited for biomolecular screening, cell-based screens are more problematic because eukaryotes typically require attachment to solid supports to maintain viability and function. This paper describes an economical, off-the-shelf microfluidic system which encapsulates eukaryotic cells in gelatinous alginate capsules for the purpose of HTS. The flow-through system consists of i) a cross junction, which forms monodisperse droplets of alginate and cell suspension in an immiscible carrier fluid, followed by ii) a T junction which delivers BaCl(2) to crosslink and solidify each droplet. With an appropriate carrier fluid, the system is self-synchronized and can produce cell-alginate-BaCl(2) capsules with virtually 100% reliability. Droplet volumes and frequency are determined by flow rates and the diameter of the cross junction. The present implementation, which utilizes 1.5 mm Teflon tubing and plastic junctions, can generate 0.4-1.4 microL droplets at frequencies >10 droplets/sec. Cell viability is >80% at 4 hours post-encapsulation. With low recurring cost (