Lewis Acid-Catalyzed Tandem Reaction Strategy for the Synthesis of Dihydrophenalene-Fused Lactones.

A Lewis acid-catalyzed tandem reaction strategy for the construction of a dihydrophenalene-lactone tetracyclic skeleton has been disclosed. Starting with 2-naphthol-tethered ketones and active methylene esters, the tandem reaction catalyzed by Sc(OTf)3 proceeded well to afford an array of dihydrophenalene-fused lactones with moderate to high efficiency and diastereoselectivity. Moreover, the synthetic utility of this protocol was demonstrated by easy gram-scale preparation and diverse product transformations.

A blockchain-enabled sharing platform for personal health records.

Background: Longitudinal personal health record (PHR) provides a foundation for managing patients' health care, but we do not have such a system in the U.S. except for the patients in the Department of Veterans Affairs. Such a gap exists mainly in the rest of the U.S. by the fact that patients' electronic health records are scattered across multiple health care facilities and often not shared due to privacy, security, and business interests concerns from both patients and health care organizations. In addition, patients have ethical concerns related to consent. To patients, data security, privacy, and consent are based on trustfulness, rather than patients' engagement in ensuring only authorized people can view their PHRs with patient-managed granularity. Resolving these challenges is an important step in making longitudinal PHR useful for patient care. Objective: This research aims to design and implement a blockchain-enabled sharing platform prototype for PHR with desired patient-controlled data security, privacy, and consent granularity. Methods: Built upon our prior work of a blockchain-enabled access control (BAC) model, we design a blockchain-enabled sharing platform for PHR with patient-controlled security, privacy, and consent granularity. We further implement the construct by building a prototypical platform among a patient and two typical health care organizations. Health organizations that hold the patient's electronic health records can join the platform with trust based on the validation from the patient. The mutual trust can be established through a rigorous validation process by both the patient and the built-in Hyperledger Fabric blockchain consensus mechanism. Results: We proposed a system trusted by patients and health care providers and constructed a Web-based PHR sharing platform with patient-controlled security, privacy, and consent granularity. We analyzed the system scalability in three aspects and showed millisecond range of performance when simultaneously changing access permissions on hundreds of PHRs. Consent, security and privacy of the model are ensured by the merits of the BAC model. We discovered the current blockchain model limits the system scalability due to using a non-graphical database. A new graphical database is suggested for future improvements. Conclusions: In this research, we report a solution to electronically sharing and managing patients' electronic health records originating from multiple organizations, focusing on privacy, security, and granularity control of consent in the U.S. Specifically, the system protects data security and privacy, and provides auditability, scalability, distributedness, patient consent autonomy, and zero-trust capabilities. The prototypical instantiation of the designed model suggested the feasibility of combining emerging blockchain technology with next generation access control model to tackle a longstanding longitudinal PHR problem.

Gene selection for multiclass prediction by weighted Fisher criterion.

Gene expression profiling has been widely used to study molecular signatures of many diseases and to develop molecular diagnostics for disease prediction. Gene selection, as an important step for improved diagnostics, screens tens of thousands of genes and identifies a small subset that discriminates between disease types. A two-step gene selection method is proposed to identify informative gene subsets for accurate classification of multiclass phenotypes. In the first step, individually discriminatory genes (IDGs) are identified by using one-dimensional weighted Fisher criterion (wFC). In the second step, jointly discriminatory genes (JDGs) are selected by sequential search methods, based on their joint class separability measured by multidimensional weighted Fisher criterion (wFC). The performance of the selected gene subsets for multiclass prediction is evaluated by artificial neural networks (ANNs) and/or support vector machines (SVMs). By applying the proposed IDG/JDG approach to two microarray studies, that is, small round blue cell tumors (SRBCTs) and muscular dystrophies (MDs), we successfully identified a much smaller yet efficient set of JDGs for diagnosing SRBCTs and MDs with high prediction accuracies (96.9% for SRBCTs and 92.3% for MDs, resp.). These experimental results demonstrated that the two-step gene selection method is able to identify a subset of highly discriminative genes for improved multiclass prediction.

Nuclear envelope dystrophies show a transcriptional fingerprint suggesting disruption of Rb-MyoD pathways in muscle regeneration.

Mutations of lamin A/C (LMNA) cause a wide range of human disorders, including progeria, lipodystrophy, neuropathies and autosomal dominant Emery-Dreifuss muscular dystrophy (EDMD). EDMD is also caused by X-linked recessive loss-of-function mutations of emerin, another component of the inner nuclear lamina that directly interacts with LMNA. One model for disease pathogenesis of LMNA and emerin mutations is cell-specific perturbations of the mRNA transcriptome in terminally differentiated cells. To test this model, we studied 125 human muscle biopsies from 13 diagnostic groups (125 U133A, 125 U133B microarrays), including EDMD patients with LMNA and emerin mutations. A Visual and Statistical Data Analyzer (VISDA) algorithm was used to statistically model cluster hierarchy, resulting in a tree of phenotypic classifications. Validations of the diagnostic tree included permutations of U133A and U133B arrays, and use of two probe set algorithms (MAS5.0 and MBEI). This showed that the two nuclear envelope defects (EDMD LMNA, EDMD emerin) were highly related disorders and were also related to fascioscapulohumeral muscular dystrophy (FSHD). FSHD has recently been hypothesized to involve abnormal interactions of chromatin with the nuclear envelope. To identify disease-specific transcripts for EDMD, we applied a leave-one-out (LOO) cross-validation approach using LMNA patient muscle as a test data set, with reverse transcription-polymerase chain reaction (RT-PCR) validations in both LMNA and emerin patient muscle. A high proportion of top-ranked and validated transcripts were components of the same transcriptional regulatory pathway involving Rb1 and MyoD during muscle regeneration (CRI-1, CREBBP, Nap1L1, ECREBBP/p300), where each was specifically upregulated in EDMD. Using a muscle regeneration time series (27 time points) we develop a transcriptional model for downstream consequences of LMNA and emerin mutations. We propose that key interactions between the nuclear envelope and Rb and MyoD fail in EDMD at the point of myoblast exit from the cell cycle, leading to poorly coordinated phosphorylation and acetylation steps. Our data is consistent with mutations of nuclear lamina components leading to destabilization of the transcriptome in differentiated cells.

Optimized multilayer perceptrons for molecular classification and diagnosis using genomic data.

MOTIVATION: Multilayer perceptrons (MLP) represent one of the widely used and effective machine learning methods currently applied to diagnostic classification based on high-dimensional genomic data. Since the dimensionalities of the existing genomic data often exceed the available sample sizes by orders of magnitude, the MLP performance may degrade owing to the curse of dimensionality and over-fitting, and may not provide acceptable prediction accuracy. RESULTS: Based on Fisher linear discriminant analysis, we designed and implemented an MLP optimization scheme for a two-layer MLP that effectively optimizes the initialization of MLP parameters and MLP architecture. The optimized MLP consistently demonstrated its ability in easing the curse of dimensionality in large microarray datasets. In comparison with a conventional MLP using random initialization, we obtained significant improvements in major performance measures including Bayes classification accuracy, convergence properties and area under the receiver operating characteristic curve (A(z)). SUPPLEMENTARY INFORMATION: The Supplementary information is available on http://www.cbil.ece.vt.edu/publications.htm