Progeria: Translational insights from cell biology

Cell biologists love to think outside the box, pursuing many surprising twists and unexpected turns in their quest to unravel the mysteries of how cells work. But can cell biologists think outside the bench? We are certain that they can, and clearly some already do. To encourage more cell biologists to venture into the realm of translational research on a regular basis, we would like to share a handful of the many lessons that we have learned in our effort to develop experimental treatments for Hutchinson-Gilford progeria syndrome (HGPS), an endeavor that many view as a “poster child” for how basic cell biology can be translated to the clinic

A genome-wide linkage and association study using COGA data

BACKGROUND: Genome-wide association will soon be available to use as an adjunct to traditional linkage analysis. We studied alcoholism in 119 families collected by the Collaborative Study on the Genetics of Alcoholism and made available in Genetic Analysis Workshop 14, using genome-wide linkage and association analyses. METHODS: Genome-wide linkage analysis was first performed using microsatellite markers and a region with the strongest linkage evidence was further analyzed using single-nucleotide polymorphisms (SNPs). Family based genome-wide association test was also conducted using the SNPs. RESULTS: Nonparametric linkage analysis revealed weak linkage evidence on chromosome 7, and association analysis identified SNP tsc0515272 on chromosome 3 as significantly associated with alcoholism. CONCLUSION: Linkage analysis may require large sample sizes and high quality genotyping and marker maps to adequately improve power, while association analysis could hold more promise in efforts to identify variants responsible for complex traits

hMAF, a Small Human Transcription Factor That Heterodimerizes Specifically with Nrf1 and Nrf2

A 1.6-kilobase pair full-length cDNA encoding a transcription factor homologous to the Maf family of proteins was isolated by screening a K562 cDNA library with the NFE2 tandem repeat probe derived from the globin locus control region. The protein, which was designated hMAF, contains a basic DNA binding domain and an extended leucine zipper but lacks any recognizable activation domain. Expressed in vitro, the hMAF protein is able to homodimerize in solution and band-shift the NFE2 tandem repeat probe. In addition to homodimers, hMAF can also form high affinity heterodimers with two members of the NFE2/CNC-bZip family (Nrf1 and Nrf2) but not with a third family member, p45-NFE2. Although hMAF/hMAF homodimers and hMAF/Nrf1 and hMAF/Nrf2 heterodimers bind to the same NFE2 site, they exert functionally opposite effects on the activity of a linked gamma-globin gene. In fact, whereas all hMAF/CNC-bZip heterodimers stimulate the activity of a gamma-promoter reporter construct in K562 cells, the association into homodimers that is induced by overexpressing hMAF inhibits the activity of the same construct. Thus variations in the expression of hMAF may account for the modulation in the activity of the genes that bear NFE2 recognition sites

Fine Particulate Matter Constituents and Cardiopulmonary Mortality in a Heavily Polluted Chinese City

Background: Although ambient fine particulate matter (PM2.5; particulate matter ≤ 2.5 µm in aerodynamic diameter) has been linked to adverse human health effects, the chemical constituents that cause harm are unknown. To our knowledge, the health effects of PM2.5 constituents have not been reported for a developing country

Expertise Style Transfer: A New Task Towards Better Communication between Experts and Laymen

The curse of knowledge can impede communication between experts and laymen. We propose a new task of expertise style transfer and contribute a manually annotated dataset with the goal of alleviating such cognitive biases. Solving this task not only simplifies the professional language, but also improves the accuracy and expertise level of laymen descriptions using simple words. This is a challenging task, unaddressed in previous work, as it requires the models to have expert intelligence in order to modify text with a deep understanding of domain knowledge and structures. We establish the benchmark performance of five state-of-the-art models for style transfer and text simplification. The results demonstrate a significant gap between machine and human performance. We also discuss the challenges of automatic evaluation, to provide insights into future research directions. The dataset is publicly available at https://srhthu.github.io/expertise-style-transfer.Comment: 11 pages, 6 figures; To appear in ACL 202

From Cellular Characteristics to Disease Diagnosis: Uncovering Phenotypes with Supercells

Cell heterogeneity and the inherent complexity due to the interplay of multiple molecular processes within the cell pose difficult challenges for current single-cell biology. We introduce an approach that identifies a disease phenotype from multiparameter single-cell measurements, which is based on the concept of ‘‘supercell statistics’’, a single-cell-based averaging procedure followed by a machine learning classification scheme. We are able to assess the optimal tradeoff between the number of single cells averaged and the number of measurements needed to capture phenotypic differences between healthy and diseased patients, as well as between different diseases that are difficult to diagnose otherwise. We apply our approach to two kinds of single-cell datasets, addressing the diagnosis of a premature aging disorder using images of cell nuclei, as well as the phenotypes of two non-infectious uveitides (the ocular manifestations of Behc¸et’s disease and sarcoidosis) based on multicolor flow cytometry. In the former case, one nuclear shape measurement taken over a group of 30 cells is sufficient to classify samples as healthy or diseased, in agreement with usual laboratory practice. In the latter, our method is able to identify a minimal set of 5 markers that accurately predict Behc¸et’s disease and sarcoidosis. This is the first time that a quantitative phenotypic distinction between these two diseases has been achieved. To obtain this clear phenotypic signature, about one hundred CD8+ T cells need to be measured. Although the molecular markers identified have been reported to be important players in autoimmune disorders, this is the first report pointing out that CD8+ T cells can be used to distinguish two systemic inflammatory diseases. Beyond these specific cases, the approach proposed here is applicable to datasets generated by other kinds of state-of-the-art and forthcoming single-cell technologies, such as multidimensional mass cytometry, single-cell gene expression, and single-cell full genome sequencing techniques.Fil: Candia, Julian Marcelo. University of Maryland; Estados Unidos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Física de Líquidos y Sistemas Biológicos. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Física de Líquidos y Sistemas Biológicos; ArgentinaFil: Maunu, Ryan. University of Maryland; Estados UnidosFil: Driscoll, Meghan. University of Maryland; Estados UnidosFil: Biancotto, Angélique. National Institutes of Health; Estados UnidosFil: Dagur, Pradeep. National Institutes of Health; Estados UnidosFil: McCoy Jr., J Philip. National Institutes of Health; Estados UnidosFil: Nida Sen, H.. National Institutes of Health; Estados UnidosFil: Wei, Lai. National Institutes of Health; Estados UnidosFil: Maritan, Amos. Università di Padova; ItaliaFil: Cao, Kan. University of Maryland; Estados UnidosFil: Nussenblatt, Robert B. National Institutes of Health; Estados UnidosFil: Banavar, Jayanth R.. University of Maryland; Estados UnidosFil: Losert, Wolfgang. University of Maryland; Estados Unido

A simple and effective model for prediction of effective thermal conductivity of vacuum insulation panels

The excellent thermal insulation performance of vacuum insulation panels (VIPs) make them widely applied in energy conservation fields, especially in buildings engineering. This research work proposes a simple, yet extremely effective, alternative model for prediction of the effective thermal conductivity (ETC.) of VIPs. The ETC. of VIPs is function of the thermal conductivity of the core materials, the equivalent thermal conductivity of the rarefied gas embraced in the core and the equivalent thermal conductivity of radiation in the early studies. The micro structure of the porous core materials and vacuum degree are taken into consideration and the prediction numerical model for the ECT of VIPs is developed. The relationship of the vacuum degree versus the ETC. is theoretical analyzed. Three types VIPs are made from the polyurethane foam materials, superfine fibrous materials and nano-granular silica materials as the core materials. For each type, the vacuum degree and the thermal conductivity are collected, including the comparison between the testing results and the prediction model. The agreement between the model and the experimental results is fairly well when the air pressure is very low. The vacuum maintaining and service life of the VIPs are also discussed. The research work is meaningful for the enhancement of stability and the development of vacuum insulation panels

Wearable sensor-based human activity recognition using hybrid deep learning techniques

Human activity recognition (HAR) can be exploited to great benefits in many applications, including elder care, health care, rehabilitation, entertainment, and monitoring. Many existing techniques, such as deep learning, have been developed for specific activity recognition, but little for the recognition of the transitions between activities. This work proposes a deep learning based scheme that can recognize both specific activities and the transitions between two different activities of short duration and low frequency for health care applications. In this work, we first build a deep convolutional neural network (CNN) for extracting features from the data collected by sensors. Then, the long short-term memory (LTSM) network is used to capture long-term dependencies between two actions to further improve the HAR identification rate. By combing CNN and LSTM, a wearable sensor based model is proposed that can accurately recognize activities and their transitions. The experimental results show that the proposed approach can help improve the recognition rate up to 95.87% and the recognition rate for transitions higher than 80%, which are better than those of most existing similar models over the open HAPT dataset