Advances in Patient Classification for Traditional Chinese Medicine: A Machine Learning Perspective

As a complementary and alternative medicine in medical field, traditional Chinese medicine (TCM) has drawn great attention in the domestic field and overseas. In practice, TCM provides a quite distinct methodology to patient diagnosis and treatment compared to western medicine (WM). Syndrome (ZHENG or pattern) is differentiated by a set of symptoms and signs examined from an individual by four main diagnostic methods: inspection, auscultation and olfaction, interrogation, and palpation which reflects the pathological and physiological changes of disease occurrence and development. Patient classification is to divide patients into several classes based on different criteria. In this paper, from the machine learning perspective, a survey on patient classification issue will be summarized on three major aspects of TCM: sign classification, syndrome differentiation, and disease classification. With the consideration of different diagnostic data analyzed by different computational methods, we present the overview for four subfields of TCM diagnosis, respectively. For each subfield, we design a rectangular reference list with applications in the horizontal direction and machine learning algorithms in the longitudinal direction. According to the current development of objective TCM diagnosis for patient classification, a discussion of the research issues around machine learning techniques with applications to TCM diagnosis is given to facilitate the further research for TCM patient classification

Graph Theoretic and Pearson Correlation-Based Discovery of Network Biomarkers for Cancer

Two graph theoretic concepts—clique and bipartite graphs—are explored to identify the network biomarkers for cancer at the gene network level. The rationale is that a group of genes work together by forming a cluster or a clique-like structures to initiate a cancer. After initiation, the disease signal goes to the next group of genes related to the second stage of a cancer, which can be represented as a bipartite graph. In other words, bipartite graphs represent the cross-talk among the genes between two disease stages. To prove this hypothesis, gene expression values for three cancers— breast invasive carcinoma (BRCA), colorectal adenocarcinoma (COAD) and glioblastoma multiforme (GBM)—are used for analysis. First, a co-expression gene network is generated with highly correlated gene pairs with a Pearson correlation coefficient ≥ 0.9. Second, clique structures of all sizes are isolated from the co-expression network. Then combining these cliques, three different biomarker modules are developed—maximal clique-like modules, 2-clique-1-bipartite modules, and 3-clique-2-bipartite modules. The list of biomarker genes discovered from these network modules are validated as the essential genes for causing a cancer in terms of network properties and survival analysis. This list of biomarker genes will help biologists to design wet lab experiments for further elucidating the complex mechanism of cancer

A Comprehensive Review of Techniques for Processing and Analyzing Fetal Heart Rate Signals

The availability of standardized guidelines regarding the use of electronic fetal monitoring (EFM) in clinical practice has not effectively helped to solve the main drawbacks of fetal heart rate (FHR) surveillance methodology, which still presents inter- and intra-observer variability as well as uncertainty in the classification of unreassuring or risky FHR recordings. Given the clinical relevance of the interpretation of FHR traces as well as the role of FHR as a marker of fetal wellbeing autonomous nervous system development, many different approaches for computerized processing and analysis of FHR patterns have been proposed in the literature. The objective of this review is to describe the techniques, methodologies, and algorithms proposed in this field so far, reporting their main achievements and discussing the value they brought to the scientific and clinical community. The review explores the following two main approaches to the processing and analysis of FHR signals: traditional (or linear) methodologies, namely, time and frequency domain analysis, and less conventional (or nonlinear) techniques. In this scenario, the emerging role and the opportunities offered by Artificial Intelligence tools, representing the future direction of EFM, are also discussed with a specific focus on the use of Artificial Neural Networks, whose application to the analysis of accelerations in FHR signals is also examined in a case study conducted by the authors

Human activity recognition for pervasive interaction

PhD ThesisThis thesis addresses the challenge of computing food preparation context in the kitchen. The automatic recognition of fine-grained human activities and food ingredients is realized through pervasive sensing which we achieve by instrumenting kitchen objects such as knives, spoons, and chopping boards with sensors. Context recognition in the kitchen lies at the heart of a broad range of real-world applications. In particular, activity and food ingredient recognition in the kitchen is an essential component for situated services such as automatic prompting services for cognitively impaired kitchen users and digital situated support for healthier eating interventions. Previous works, however, have addressed the activity recognition problem by exploring high-level-human activities using wearable sensing (i.e. worn sensors on human body) or using technologies that raise privacy concerns (i.e. computer vision). Although such approaches have yielded significant results for a number of activity recognition problems, they are not applicable to our domain of investigation, for which we argue that the technology itself must be genuinely “invisible”, thereby allowing users to perform their activities in a completely natural manner. In this thesis we describe the development of pervasive sensing technologies and algorithms for finegrained human activity and food ingredient recognition in the kitchen. After reviewing previous work on food and activity recognition we present three systems that constitute increasingly sophisticated approaches to the challenge of kitchen context recognition. Two of these systems, Slice&Dice and Classbased Threshold Dynamic Time Warping (CBT-DTW), recognize fine-grained food preparation activities. Slice&Dice is a proof-of-concept application, whereas CBT-DTW is a real-time application that also addresses the problem of recognising unknown activities. The final system, KitchenSense is a real-time context recognition framework that deals with the recognition of a more complex set of activities, and includes the recognition of food ingredients and events in the kitchen. For each system, we describe the prototyping of pervasive sensing technologies, algorithms, as well as real-world experiments and empirical evaluations that validate the proposed solutions.Vietnamese government’s 322 project, executed by the Vietnamese Ministry of Education and Training

Understanding the Mechanism of Hard Metal (WC-Co) Toxicity: In vitro Studies and In vivo Exploration

Hard metals, such as tungsten carbide cobalt (WC-Co), are frequently used for a number of industrial applications such as surface coatings for heavy machinery and tools. In particular, WC-Co coatings are prevalent in mining and drilling applications where extensive, repetitive use of these tools causes wear over time. In enclosed environments, WC-Co wear particles become airborne and present an occupational inhalation hazard. It is known that inhalation of WC-Co dusts , composed of nano- and micro-sized WC-Co particles, contributes to the development of hard metal lung disease and increased risk for lung cancer; however, the relationship between acute WC-Co toxicity and disease progression remains poorly understood. To address this gap in knowledge, we systematically evaluated nano-WC-Co particle toxicity using a combination of in vitro and in vivo models. In Aim 1, we determined the toxicity of nano-WC-Co particles in BEAS-2B lung epithelial cells over concentrations ranging 0.1 to 1000 mug/mL and exposure periods from 0.5 to 48 hr. Our MTT-based cell viability assay indicated that nano-WC-Co exhibits greater toxicity than micro-WC-Co at concentrations ≥ 10 mug/mL. We also found that nano-WC-Co exposure induces oxidative stress at the highest particle concentration tested (1000 mug/mL) using a fluorescence-based (DCF/DHE) assay and that WC-Co particle exposure induced cellular apoptosis, marked by increased annexin-V staining in our flow cytometry apoptosis assay. The potential for nano-WC-Co particle internalization was also investigated using transmission electron microscopy (TEM) and confirmed that nano-WC-Co particles are capable of being internalized by BEAS-2B cells. In Aim 2, we determined the inflammatory response toward nano-WC-Co particles in a co-culture model of BEAS-2B cells and macrophages, to more closely represent the dynamic tissue environment of the lung. The results of our viability assay indicated that macrophages attenuated the toxicity of nano-WC-Co in the co-culture model compared to BEAS-2B alone, which indicated a protective effect of the macrophages. We found that nano-WC-Co exposure caused macrophage polarization toward the M1 pro-inflammatory phenotype and determined that nano-WC-Co exposure also stimulates the secretion of cytokines such as IL-12 and IL-1beta in macrophages, consistent with a pro-inflammatory response. In Aim 3, we investigated the potential systemic (extra-pulmonary) effects of nano-WC-Co exposure in an intra-tracheal instillation (IT) rat model and compared the outcomes with a known pulmonary irritant, cerium dioxide (CeO2). After 24 hr exposure, nano-WC-Co exposure did not induce pulmonary or systemic inflammation at a dose of 50, 250 or 500 mug compared to control or CeO2; this outcome highlights the need for future in vivo studies which examine the inflammatory effects of chronic or repeated nano-WC-Co exposure. Taken together, the results of our studies improve the current understanding of hard metal WC-Co toxicity and may point toward potential therapeutic or diagnostic strategies for the future

CERNAS: Current Evolution and Research Novelty in Agricultural Sustainability

Climate changes pose overwhelming impacts on primary production and, consequently, on agricultural and animal farming. Additionally, at present, agriculture still depends strongly on fossil fuels both for energy and production factors ,such as synthetized inorganic fertilizers and harmful chemicals such as pesticides. The need to feed the growing world population poses many challenges. The need to reduce environmental impacts to a minimum, maintain healthy ecosystems, and improve soil microbiota are central to ensuring a promising future for coming generations. Livestock production under cover crop systems helps to alleviate compaction so that oxygen and water can sufficiently flow in the soil, add organic matter, and help hold soil in place, reducing crusting and protecting against erosion. The use of organic plant production practices allied to the control of substances used in agriculture also decisively contributes to alleviating the pressure on ecosystems. Some of the goals of this new decade are to use enhanced sustainable production methodologies to improve the input/output ratios of primary production, reduce environmental impacts, and rely on new innovative technologies. This reprint addresses original studies and reviews focused on the current evolution and research novelty in agricultural sustainability. New developments are discussed on issues related to quality of soil, natural fertilizers, or the sustainable use of land and water. Also, crop protection techniques are pivotal for sustainable food production under the challenges of the Sustainable Development Goals of the United Nations, allied to innovative weed control methodologies as a way to reduce the utilization of pesticides. The role of precision and smart agriculture is becoming more pertinent as communication technologies improve at a rapid rate. Waste management, reuse of agro-industrial residues, extension of shelf life, and use of new technologies are ways to reduce food waste, all contributing to higher sustainability in food supply chains, leading to a more rational use of natural resources. The unquestionable role of bees as pollinators and contributors to biodiversity is adjacent to characterizing beekeeping activities, which in turn contributes, together with the valorization of endemic varieties of plant foods, to the development of local communities. Finally, the short circuits and local food markets have a decisive role in the preservation and enhancement of rural economies.info:eu-repo/semantics/publishedVersio