Multifarious transparent glass nanocrystal composites

Glasses comprising well known ferroelectric crystalline phases have been a subject of curious investigation from the point of view of exploiting these composites for dielectric, pyroelectric, ferroelectric, electro and non-linear optical devices. Transparent glass-ceramics containing ferroelectric crystallites at nano scale have been of much interest owing to their promising physical properties. The advantages that are associated with glass-ceramics include very low levels of porosity and hence high break down voltages. It is of our interest to nanocrystallize Aurivillius family of ferroelectric oxides and tetragonal tungsten bronzes on borate and tellurite based glass matrices and demonstrate their promising optical and nonlinear optical properties. Apart from the above, the nanocrystallites of well known ferroelectric material LiNbO3 was grown in a reactive glass matrix. These nanocrystals of LiNbO3 exhibited intense second harmonic signals in transmission mode when exposed to IR light at 1064 nm. The most interesting result was the demonstration of optical diffraction of the second harmonic signals which was attributed to the presence of self- organized sub-micrometer sized LiNbO3 crystallites that were indeed inscribed by the IR laser light which was used to probe in the NLO property of these materials

The CRISPR/Cas9 System for Targeted Genome Engineering in Free-Living Fungi: Advances and Opportunities for Lichenized Fungi

Studies using whole genome sequencing, computational and gene expression, targeted genome engineering techniques for generating site-specific sequence alterations through non-homologous end joining (NHEJ) by genomic double-strand break (DSB) repair pathway with high precision, resulting in gene inactivation have elucidated the complexity of gene expression, and metabolic pathways in fungi. These tools and the data generated are crucial for precise generation of fungal products such as enzymes, secondary metabolites, antibiotics etc. Artificially engineered molecular scissors, zinc finger nucleases (ZFNs), Transcriptional activator-like effector nucleases (TALENs; that use protein motifs for DNA sequence recognition in the genome) and CRISPR associated protein 9 (Cas9;CRISPR/Cas9) system (RNA-DNA recognition) are being used in achieving targeted genome modifications for modifying traits in free-living fungal systems. Here, we discuss the recent research breakthroughs and developments which utilize CRISPR/Cas9 in the metabolic engineering of free-living fungi for the biosynthesis of secondary metabolites, enzyme production, antibiotics and to develop resistance against post-harvest browning of edible mushrooms and fungal pathogenesis. We also discuss the potential and advantages of using targeted genome engineering in lichenized fungal (mycobiont) cultures to enhance their growth and secondary metabolite production in vitro can be complemented by other molecular approaches

Genetic Basis of Growth Adaptation of Escherichia coli after Deletion of pgi, a Major Metabolic Gene

Bacterial survival requires adaptation to different environmental perturbations such as exposure to antibiotics, changes in temperature or oxygen levels, DNA damage, and alternative nutrient sources. During adaptation, bacteria often develop beneficial mutations that confer increased fitness in the new environment. Adaptation to the loss of a major non-essential gene product that cripples growth, however, has not been studied at the whole-genome level. We investigated the ability of Escherichia coli K-12 MG1655 to overcome the loss of phosphoglucose isomerase (pgi) by adaptively evolving ten replicates of E. coli lacking pgi for 50 days in glucose M9 minimal medium and by characterizing endpoint clones through whole-genome re-sequencing and phenotype profiling. We found that 1) the growth rates for all ten endpoint clones increased approximately 3-fold over the 50-day period; 2) two to five mutations arose during adaptation, most frequently in the NADH/NADPH transhydrogenases udhA and pntAB and in the stress-associated sigma factor rpoS; and 3) despite similar growth rates, at least three distinct endpoint phenotypes developed as defined by different rates of acetate and formate secretion. These results demonstrate that E. coli can adapt to the loss of a major metabolic gene product with only a handful of mutations and that adaptation can result in multiple, alternative phenotypes

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Spin injection and transport in organic semiconductors

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Materials Science and Engineering, 2011.Cataloged from PDF version of thesis. Vita.Includes bibliographical references (p. 141-155).The use of organic semiconductors to enable organic spintronic devices requires the understanding of transport and control of the spin state of the carriers. This thesis deals with the above issue, focusing on the interface between the organic semiconductor and the spin source. The morphology of the organic molecule at the interface is shown to play a dominant role in this study, influencing spin injection. The interface molecule morphology affects the form of interaction between the organic molecule and the ferromagnet. This interaction ranges from a weak Van der Waals' to a chemical interaction leading to charge transfer, hybridization and other interface chemistry. As a result, the spin-dependent density of states is modified, influencing the spin injection process. The first part of the thesis focuses on identifying the interface properties between the ferromagnet and the organic semiconductor, rubrene. This is performed in a vertical organic junction geometry using different interface characterization tools. The growth morphology of the rubrene molecule is shown to influence the electronic coupling between the molecule and the ferromagnet. This has a pronounced effect on the spin injection efficiency and the magnetoresistance signals in the devices. The complex nature of the top interface is dealt with in detail. These studies provide insights on the importance of tailoring the interface properties to control and realize optimum behavior. As an alternative to conventional ferromagnets, a spin-filter material, europium sulphide, is demonstrated as a spin-polarized source. Spin-filter materials have shown the possibility to inject spin-polarized electrons into the organic semiconductor at higher voltage bias. This knowledge encouraged to seek organic spin-filter materials. Understanding the importance of molecule morphology from the work on rubrene, the second part of the thesis involved the study on a new class of organic materials called the phenalenyl compounds. Study using the compound, Zinc methyl phenalenyl, was initiated that showed large magnetoresistance signals of 50% at 4.2 K to 20% at close to room temperature. The origin of this magnetoresistance is attributed to a new interface phenomena described by the spin-filtering effect. The interface molecule morphology is found to play a very important role at the interface inducing an antiferromagnetic state and dominating the device physics. This technique opens up a new approach to engineer the interface and realize new functional devices without worrying about the bulk disorder of the organic film. This thesis thus shows the feasibility of tuning the property of the interface using tailor-made molecules to realize new functional devices at room temperature that can lead to development of the field and technological applications.by Karthik V. Raman.Ph.D

Enabling the Use of Clinically Generated Datasets to Improve Diagnostic Methods in Multiparametric MRI of the Prostate

In this work, we aimed to develop methods and approaches to enable the use of unannotated or weakly annotated clinically generated datasets in clinical data science and deep learning, in the clinical context of prostate cancer and multiparametric MRI of the prostate. Specifically, we demonstrate: 1) The development of an optimized regional targeted biopsy strategy that could reduce the number of biopsies that need to retrieved in a targeted biopsy procedure, by creating a combined MRI, ultrasound, and histopathological evaluation dataset from the clinical record, 2) the creation of a state-of-the-art prostate organ segmentation model using unrefined clinically-generated annotations as well as an evaluation of the utility of those annotations to improve model training on small strongly annotated datasets, 3) the training of a high performance segmentation model on private data originating from three different healthcare institutions using the federated learning approach, without requiring any data to be transferred across institutional boundaries, and 4) the creation of patient-level predictive models for prostate cancer risk stratification from multiparametric MRI of the prostate, and an evaluation of the relative contribution of pretrained voxel-level feature extractors using unannotated, weakly annotated, and strongly annotated data with the finding that even an unannotated data-based pretrained model is effective. The contributions of this dissertation demonstrate the potential uses of unannotated and weakly annotated clinically generated data in clinical data science and machine learning model development for healthcare, and enable the development of clinical tools for the prostate cancer clinical workflow