Dynamic physical activity recommendation on personalised mobile health information service: A deep reinforcement learning approach

Mobile health (mHealth) information service makes healthcare management easier for users, who want to increase physical activity and improve health. However, the differences in activity preference among the individual, adherence problems, and uncertainty of future health outcomes may reduce the effect of the mHealth information service. The current health service system usually provides recommendations based on fixed exercise plans that do not satisfy the user specific needs. This paper seeks an efficient way to make physical activity recommendation decisions on physical activity promotion in personalised mHealth information service by establishing data-driven model. In this study, we propose a real-time interaction model to select the optimal exercise plan for the individual considering the time-varying characteristics in maximising the long-term health utility of the user. We construct a framework for mHealth information service system comprising a personalised AI module, which is based on the scientific knowledge about physical activity to evaluate the individual exercise performance, which may increase the awareness of the mHealth artificial intelligence system. The proposed deep reinforcement learning (DRL) methodology combining two classes of approaches to improve the learning capability for the mHealth information service system. A deep learning method is introduced to construct the hybrid neural network combing long-short term memory (LSTM) network and deep neural network (DNN) techniques to infer the individual exercise behavior from the time series data. A reinforcement learning method is applied based on the asynchronous advantage actor-critic algorithm to find the optimal policy through exploration and exploitation

Beyond Smoothing: Unsupervised Graph Representation Learning with Edge Heterophily Discriminating

Unsupervised graph representation learning (UGRL) has drawn increasing research attention and achieved promising results in several graph analytic tasks. Relying on the homophily assumption, existing UGRL methods tend to smooth the learned node representations along all edges, ignoring the existence of heterophilic edges that connect nodes with distinct attributes. As a result, current methods are hard to generalize to heterophilic graphs where dissimilar nodes are widely connected, and also vulnerable to adversarial attacks. To address this issue, we propose a novel unsupervised Graph Representation learning method with Edge hEterophily discriminaTing (GREET) which learns representations by discriminating and leveraging homophilic edges and heterophilic edges. To distinguish two types of edges, we build an edge discriminator that infers edge homophily/heterophily from feature and structure information. We train the edge discriminator in an unsupervised way through minimizing the crafted pivot-anchored ranking loss, with randomly sampled node pairs acting as pivots. Node representations are learned through contrasting the dual-channel encodings obtained from the discriminated homophilic and heterophilic edges. With an effective interplaying scheme, edge discriminating and representation learning can mutually boost each other during the training phase. We conducted extensive experiments on 14 benchmark datasets and multiple learning scenarios to demonstrate the superiority of GREET.Comment: 14 pages, 7 tables, 6 figures, accepted by AAAI 202

Finding the Missing-half: Graph Complementary Learning for Homophily-prone and Heterophily-prone Graphs

Real-world graphs generally have only one kind of tendency in their connections. These connections are either homophily-prone or heterophily-prone. While graphs with homophily-prone edges tend to connect nodes with the same class (i.e., intra-class nodes), heterophily-prone edges tend to build relationships between nodes with different classes (i.e., inter-class nodes). Existing GNNs only take the original graph during training. The problem with this approach is that it forgets to take into consideration the ``missing-half" structural information, that is, heterophily-prone topology for homophily-prone graphs and homophily-prone topology for heterophily-prone graphs. In our paper, we introduce Graph cOmplementAry Learning, namely GOAL, which consists of two components: graph complementation and complemented graph convolution. The first component finds the missing-half structural information for a given graph to complement it. The complemented graph has two sets of graphs including both homophily- and heterophily-prone topology. In the latter component, to handle complemented graphs, we design a new graph convolution from the perspective of optimisation. The experiment results show that GOAL consistently outperforms all baselines in eight real-world datasets.Comment: Accepted by ICML 202

Disruption of Murine mp29/Syf2/Ntc31 Gene Results in Embryonic Lethality with Aberrant Checkpoint Response

Human p29 is a putative component of spliceosomes, but its role in pre-mRNA is elusive. By siRNA knockdown and stable overexpression, we demonstrated that human p29 is involved in DNA damage response and Fanconi anemia pathway in cultured cells. In this study, we generated p29 knockout mice (mp29GT/GT) using the mp29 gene trap embryonic stem cells to study the role of mp29 in DNA damage response in vivo. Interruption of mp29 at both alleles resulted in embryonic lethality. Embryonic abnormality occurred as early as E6.5 in mp29GT/GT mice accompanied with decreased mRNA levels of α-tubulin and Chk1. The reduction of α-tubulin and Chk1 mRNAs is likely due to an impaired post-transcriptional event. An aberrant G2/M checkpoint was found in mp29 gene trap embryos when exposed to aphidicolin and UV light. This embryonic lethality was rescued by crossing with mp29 transgenic mice. Additionally, the knockdown of zfp29 in zebrafish resulted in embryonic death at 72 hours of development postfertilization (hpf). A lower level of acetylated α-tubulin was also observed in zfp29 morphants. Together, these results illustrate an indispensable role of mp29 in DNA checkpoint response during embryonic development

Molecular and cellular mechanisms underlying the evolution of form and function in the amniote jaw.

The amniote jaw complex is a remarkable amalgamation of derivatives from distinct embryonic cell lineages. During development, the cells in these lineages experience concerted movements, migrations, and signaling interactions that take them from their initial origins to their final destinations and imbue their derivatives with aspects of form including their axial orientation, anatomical identity, size, and shape. Perturbations along the way can produce defects and disease, but also generate the variation necessary for jaw evolution and adaptation. We focus on molecular and cellular mechanisms that regulate form in the amniote jaw complex, and that enable structural and functional integration. Special emphasis is placed on the role of cranial neural crest mesenchyme (NCM) during the species-specific patterning of bone, cartilage, tendon, muscle, and other jaw tissues. We also address the effects of biomechanical forces during jaw development and discuss ways in which certain molecular and cellular responses add adaptive and evolutionary plasticity to jaw morphology. Overall, we highlight how variation in molecular and cellular programs can promote the phenomenal diversity and functional morphology achieved during amniote jaw evolution or lead to the range of jaw defects and disease that affect the human condition

Increased Expression of PITX2 Transcription Factor Contributes to Ovarian Cancer Progression

BACKGROUND: Paired-like homeodomain 2 (PITX2) is a bicoid homeodomain transcription factor which plays an essential role in maintaining embryonic left-right asymmetry during vertebrate embryogenesis. However, emerging evidence suggests that the aberrant upregulation of PITX2 may be associated with tumor progression, yet the functional role that PITX2 plays in tumorigenesis remains unknown. PRINCIPAL FINDINGS: Using real-time quantitative RT-PCR (Q-PCR), Western blot and immunohistochemical (IHC) analyses, we demonstrated that PITX2 was frequently overexpressed in ovarian cancer samples and cell lines. Clinicopathological correlation showed that the upregulated PITX2 was significantly associated with high-grade (P = 0.023) and clear cell subtype (P = 0.011) using Q-PCR and high-grade (P<0.001) ovarian cancer by IHC analysis. Functionally, enforced expression of PITX2 could promote ovarian cancer cell proliferation, anchorage-independent growth ability, migration/invasion and tumor growth in xenograft model mice. Moreover, enforced expression of PITX2 elevated the cell cycle regulatory proteins such as Cyclin-D1 and C-myc. Conversely, RNAi mediated knockdown of PITX2 in PITX2-high expressing ovarian cancer cells had the opposite effect. CONCLUSION: Our findings suggest that the increased expression PITX2 is involved in ovarian cancer progression through promoting cell growth and cell migration/invasion. Thus, targeting PITX2 may serve as a potential therapeutic modality in the management of high-grade ovarian tumor.published_or_final_versio

Conservation of the role of INNER NO OUTER in development of unitegmic ovules of the Solanaceae despite a divergence in protein function

The P-SlINO::SlINO-GFP transgene continues to be expressed after fertilization during the onset of fruit development. A-C: Ovules from P-SlINO::SlINO-GFP plants. D, E: Ovules from control plants. Images A (confocal) and B (DIC overlaid with GFP channel) show expression in the outer cell layer in an ovule post-anthesis. C-E are images of the surface cells of the integument of ovules taken from 3–4 mm fruits. C and D are images taken on an epifluorescence microscope (Axioplan) using a Chroma GFP filter set 41017 (Chroma, Bellows Falls, VT). E is a dark-field image of the same ovule in D. These images show expression is present in developing fruit. Scale bar in B represents 20 μm, scale bar in E represents 20 μm in C-E. (TIF 4435 kb

Antibacterial resistance and their genetic location in MRSA isolated in Kuwait hospitals, 1994-2004

BACKGROUND: Methicillin-resistant Staphylococcus aureus (MRSA) continues to be a major cause of serious infections in hospitals and in the community worldwide. In this study, MRSA isolated from patients in Kuwait hospitals were analyzed for resistance trends and the genetic location of their resistance determinants. METHODS: Between April 1994 and December 2004, 5644 MRSA isolates obtained from different clinical samples were studied for resistance to antibacterial agents according to guidelines from the National Committee for Clinical Laboratory Standards and the British Society for Antimicrobial Chemotherapy. The genetic location of their resistance determinants was determined by curing and transfer experiments. RESULTS: They were resistant to aminoglycosides, erythromycin, tetracycline, trimethoprim, fusidic acid, ciprofloxacin, chloramphenicol, rifampicin, mupirocin, cadmium acetate, mercuric chloride, propamidine isethionate and ethidium bromide but susceptible to vancomycin, teicoplanin and linezolid. The proportion of the isolates resistant to erythromycin, ciprofloxacin and fusidic acid increased during the study period. In contrast, the proportion of isolates resistant to gentamicin, tetracycline, chloramphenicol and trimethoprim declined. High-level mupirocin resistance increased rapidly from 1996 to 1999 and then declined. They contained plasmids of 1.9, 2.8, 3.0, 4.4, 27 and 38 kilobases. Genetic studies revealed that they carried plasmid-borne resistance to high-level mupirocin resistance (38 kb), chloramphenicol (2.8 – 4.4 kb), erythromycin (2.8–3.0 kb) and cadmium acetate, mercuric chloride, propamidine isethionate and ethidium bromide (27 kb) and chromosomal location for methicillin, the aminoglycosides, tetracycline, fusidic acid, ciprofloxacin and trimethoprim resistance. Thus, the 27 kb plasmids had resistance phenotypes similar to plasmids reported in MRSA isolates in South East Asia. CONCLUSION: The prevalence of resistance to erythromycin, ciprofloxacin, high-level mupirocin and fusidic acid increased whereas the proportion of isolates resistant to gentamicin, tetracycline, chloramphenicol and trimethoprim declined during the study period. They contained 27-kb plasmids encoding resistance to cadmium acetate, mercuric chloride, propamidine isethionate and ethidium bromide similar to plasmids isolated in MRSA from South East Asia. Molecular typing of these isolates will clarify their relationship to MRSA from South East Asia