Social media mental health analysis framework through applied computational approaches

Studies have shown that mental illness burdens not only public health and productivity but also established market economies throughout the world. However, mental disorders are difficult to diagnose and monitor through traditional methods, which heavily rely on interviews, questionnaires and surveys, resulting in high under-diagnosis and under-treatment rates. The increasing use of online social media, such as Facebook and Twitter, is now a common part of people’s everyday life. The continuous and real-time user-generated content often reflects feelings, opinions, social status and behaviours of individuals, creating an unprecedented wealth of person-specific information. With advances in data science, social media has already been increasingly employed in population health monitoring and more recently mental health applications to understand mental disorders as well as to develop online screening and intervention tools. However, existing research efforts are still in their infancy, primarily aimed at highlighting the potential of employing social media in mental health research. The majority of work is developed on ad hoc datasets and lacks a systematic research pipeline. [Continues.]</div

Linker Histone H1 Regulates Specific Gene Expression but Not Global Transcription In Vivo

AbstractIn a linker histone H1 knockout strain (ΔH1) of Tetrahymena thermophila, the number of mature RNAs produced by genes transcribed by pol I and pol III and of most genes transcribed by pol II remains unchanged. However, H1 is required for the normal basal repression of a gene (ngoA) in growing cells but is not required for its activated expression in starved cells. Surprisingly, H1 is required for the activated expression of another gene (CyP) in starved cells but not for its repression in growing cells. Thus, H1 does not have a major effect on global transcription but can act as either a positive or negative gene-specific regulator of transcription in vivo

Conducting Polymer Aerogels

Conducting polymers are an important class of organic materials with electric conductivity and have experienced a rapid development. Meanwhile, as a novel class of porous nanomaterials, aerogels attract people’s great interest for their ultra-low densities, large specific areas, rich open pores, etc. Thus, conducting polymer aerogels, combining the unique merits of aerogels with physicochemical properties relevant to conducting polymers, become a newly developed area. In this chapter, we give a brief introduction describing (1) synthesis strategies of conducting polymer (PEDOT, PPy, and PANi) aerogels through rational design for oxidant, cross-linker, soft template, sol-gel process, drying process; (2) advantages of these aerogels in physical and chemical performance, compared with the counterparts in bulk or membrane; and (3) their applications in energy storage, adsorption to metal-ions/dye-molecules, stress sensing, Joule heating. The chapter ends with a reflection on limitations of already proposed materials and a prospection of how conducting polymer aerogels developing in the future. As such, this chapter can act as a roadmap to guide researchers toward how conducting polymer aerogels produced and how these materials can be utilized, while also highlighting the current advancements in the field

Graphene Aerogel-Directed Fabrication of Phase Change Composites

Although phase change materials have been extensively used for thermal energy storage, various shortcomings such as low thermal conductivity, leakage during work, and shortage of multiple driving ways greatly hinder their practical applications. Among the new materials that can overcome these problems, graphene aerogel has attracted special interest owing to its 3D conductive network and extraordinary capillary force. In this chapter, we review recent progress of graphene-aerogel-based phase change composites (PCCs) and provide a brief introduction on the following topics: 1) why graphene aerogels can be used for PCCs, 2) the sol-gel transition synthesis of graphene aerogels, 3) the fabrication of graphene-aerogel-based PCCs, and 4) their applications in thermal energy storage, electric-thermal conversion and storage, solar-thermal conversion and storage, and thermal buffer. Finally, we also discuss the limitation and future development of these graphene-based materials

Distributed Logistic Regression for Massive Data with Rare Events

Large-scale rare events data are commonly encountered in practice. To tackle the massive rare events data, we propose a novel distributed estimation method for logistic regression in a distributed system. For a distributed framework, we face the following two challenges. The first challenge is how to distribute the data. In this regard, two different distribution strategies (i.e., the RANDOM strategy and the COPY strategy) are investigated. The second challenge is how to select an appropriate type of objective function so that the best asymptotic efficiency can be achieved. Then, the under-sampled (US) and inverse probability weighted (IPW) types of objective functions are considered. Our results suggest that the COPY strategy together with the IPW objective function is the best solution for distributed logistic regression with rare events. The finite sample performance of the distributed methods is demonstrated by simulation studies and a real-world Sweden Traffic Sign dataset

Development of Statistical Models for Functional Near-infrared Spectroscopy Data Analysis Incorporating Anatomical and Probe Registration Prior Information

Functional near-infrared spectroscopy (fNIRS) is a non-invasive technology that uses low-levels of non-ionizing light in the range of 650 -- 900 nm (red and near-infrared) to record changes in the optical absorption and scattering of tissue. In particular, oxy-hemoglobin (HbO) and deoxy-hemoglobin (HbR) have characteristic absorption spectra at these wavelengths, which are used to discriminate blood flow and oxygen metabolism changes. As compared with functional magnetic resonance imaging (fMRI), fNIRS is less costly, more portable, and allows for a wider range of experimental scenarios because it neither requires a dedicated scanner nor needs the subject to lay supine. Current challenges in fNIRS data analysis include: (i) a small change in brain anatomy or optical probe positioning can create huge differences in fNIRS measurements even though the underlying brain activity remains the same due to the existence of ``blind-spots"; (ii) fNIRS image reconstruction is a high-dimensional, under-determined, and ill-posed problem, in which there are thousands of parameters to estimate while only tens of measurements available and existing methods notably overestimate the false positive rate; (iii) brain anatomical information has rarely been used in current fNIRS data analyses. This dissertation proposes two new methods aiming to improve fNIRS data analysis and overcome these challenges -- one of which is a channel-space method based on anatomically defined region-of-interest (ROI) and the other one is an image reconstruction method incorporating anatomical and physiological prior information. The two methods are developed using advanced statistical models including a combination of regularization models and Bayesian hierarchical modeling. The performance of the two methods is validated via numerical simulations and evaluated using receiver operating characteristics (ROC)-based tools. The statistical comparisons with conventional methods suggest significant improvements