Deep learning analysis of mobile physiological, environmental and location sensor data for emotion detection

The detection and monitoring of emotions are important in various applications, e.g. to enable naturalistic and personalised human-robot interaction. Emotion detection often require modelling of various data inputs from multiple modalities, including physiological signals (e.g.EEG and GSR), environmental data (e.g. audio and weather), videos (e.g. for capturing facial expressions and gestures) and more recently motion and location data. Many traditional machine learning algorithms have been utilised to capture the diversity of multimodal data at the sensors and features levels for human emotion classification. While the feature engineering processes often embedded in these algorithms are beneficial for emotion modelling, they inherit some critical limitations which may hinder the development of reliable and accurate models. In this work, we adopt a deep learning approach for emotion classification through an iterative process by adding and removing large number of sensor signals from different modalities. Our dataset was collected in a real-world study from smart-phones and wearable devices. It merges local interaction of three sensor modalities: on-body, environmental and location into global model that represents signal dynamics along with the temporal relationships of each modality. Our approach employs a series of learning algorithms including a hybrid approach using Convolutional Neural Network and Long Short-term Memory Recurrent Neural Network (CNN-LSTM) on the raw sensor data, eliminating the needs for manual feature extraction and engineering. The results show that the adoption of deep-learning approaches is effective in human emotion classification when large number of sensors input is utilised (average accuracy 95% and F-Measure=%95) and the hybrid models outperform traditional fully connected deep neural network (average accuracy 73% and F-Measure=73%). Furthermore, the hybrid models outperform previously developed Ensemble algorithms that utilise feature engineering to train the model average accuracy 83% and F-Measure=82%

Two-dimensional Hamiltonian systems

This survey article contains various aspects of the direct and inverse spectral problem for twodimensional Hamiltonian systems, that is, two dimensional canonical systems of homogeneous differential equations of the form Jy'(x) = -zH(x)y(x); x ∈ [0;L); 0 < L ≤ ∞; z ∈ C; with a real non-negative definite matrix function H ≥ 0 and a signature matrix J, and with a standard boundary condition of the form y1(0+) = 0. Additionally it is assumed that Weyl's limit point case prevails at L. In this case the spectrum of the canonical system is determined by its Titchmarsh-Weyl coefficient Q which is a Nevanlinna function, that is, a function which maps the upper complex half-plane analytically into itself. In this article an outline of the Titchmarsh-Weyl theory for Hamiltonian systems is given and the solution of the direct spectral problem is shown. Moreover, Hamiltonian systems comprehend the class of differential equations of vibrating strings with a non-homogenous mass-distribution function as considered by M.G. Krein. The inverse spectral problem for two{dimensional Hamiltonian systems was solved by L. de Branges by use of his theory of Hilbert spaces of entire functions, showing that each Nevanlinna function is the Titchmarsh-Weyl coefficient of a uniquely determined normed Hamiltonian. More detailed results of this connection for e.g. systems with a semibounded or discrete or finite spectrum are presented, and also some results concerning spectral perturbation, which allow an explicit solution of the inverse spectral problem in many cases

FOXA1 and adaptive response determinants to HER2 targeted therapy in TBCRC 036

Inhibition of the HER2/ERBB2 receptor is a keystone to treating HER2-positive malignancies, particularly breast cancer, but a significant fraction of HER2-positive (HER2+) breast cancers recur or fail to respond. Anti-HER2 monoclonal antibodies, like trastuzumab or pertuzumab, and ATP active site inhibitors like lapatinib, commonly lack durability because of adaptive changes in the tumor leading to resistance. HER2+ cell line responses to inhibition with lapatinib were analyzed by RNAseq and ChIPseq to characterize transcriptional and epigenetic changes. Motif analysis of lapatinib-responsive genomic regions implicated the pioneer transcription factor FOXA1 as a mediator of adaptive responses. Lapatinib in combination with FOXA1 depletion led to dysregulation of enhancers, impaired adaptive upregulation of HER3, and decreased proliferation. HER2-directed therapy using clinically relevant drugs (trastuzumab with or without lapatinib or pertuzumab) in a 7-day clinical trial designed to examine early pharmacodynamic response to antibody-based anti-HER2 therapy showed reduced FOXA1 expression was coincident with decreased HER2 and HER3 levels, decreased proliferation gene signatures, and increased immune gene signatures. This highlights the importance of the immune response to anti-HER2 antibodies and suggests that inhibiting FOXA1-mediated adaptive responses in combination with HER2 targeting is a potential therapeutic strategy

Social anxiety disorder in DSM-5

With the publication of DSM-5, the diagnostic criteria for social anxiety disorder (SAD, also known as social phobia) have undergone several changes, which have important conceptual and clinical implications. In this paper, we first provide a brief history of the diagnosis. We then review a number of these changes, including (1) the primary name of the disorder, (2) the increased emphasis on fear of negative evaluation, (3) the importance of sociocultural context in determining whether an anxious response to a social situation is out of proportion to the actual threat, (4) the diagnosis of SAD in the context of a medical condition, and (5) the way in which we think about variations in the presentation of SAD (the specifier issue). We then consider the clinical implications of changes in DSM-5 related to these issues

Population study of causes, treatment, and outcome of infertility

Specialist infertility practice was studied in a group of 708 couples within a population of residents of a single health district in England. They represented an annual incidence of 1.2 couples for every 1000 of the population. At least one in six couples needed specialist help at some time in their lives because of an average of infertility of 21/2 years, 71% of whom were trying for their first baby. Those attending gynaecology clinics made up 10% of new and 22% of all attendances. Failure of ovulation (amenorrhoea or oligomenorrhoea) occurred in 21% of cases and was successfully treated (two year conception rates of 96% and 78%). Tubal damage (14%) had a poor outlook (19%) despite surgery. Endometriosis accounted for infertility in 6%, although seldom because of tubal damage, cervical mucus defects or dysfunction in 3%, and coital failure in up to 6%. Sperm defects or dysfunction were the commonest defined cause of infertility (24%) and led to a poor chance of pregnancy (0-27%) without donor insemination. Obstructive azoospermia or primary spermatogenic failure was uncommon (2%) and hormonal causes of male infertility rare. Infertility was unexplained in 28% and the chance of pregnancy (overall 72%) was mainly determined by duration of infertility. In vitro fertilisation could benefit 80% of cases of tubal damage and 25% of unexplained infertility--that is, 18% of all cases, representing up to 216 new cases each year per million of the total population

Population study of causes, treatment, and outcome of infertility

Specialist infertility practice was studied in a group of 708 couples within a population of residents of a single health district in England. They represented an annual incidence of 1.2 couples for every 1000 of the population. At least one in six couples needed specialist help at some time in their lives because of an average of infertility of 21/2 years, 71% of whom were trying for their first baby. Those attending gynaecology clinics made up 10% of new and 22% of all attendances. Failure of ovulation (amenorrhoea or oligomenorrhoea) occurred in 21% of cases and was successfully treated (two year conception rates of 96% and 78%). Tubal damage (14%) had a poor outlook (19%) despite surgery. Endometriosis accounted for infertility in 6%, although seldom because of tubal damage, cervical mucus defects or dysfunction in 3%, and coital failure in up to 6%. Sperm defects or dysfunction were the commonest defined cause of infertility (24%) and led to a poor chance of pregnancy (0-27%) without donor insemination. Obstructive azoospermia or primary spermatogenic failure was uncommon (2%) and hormonal causes of male infertility rare. Infertility was unexplained in 28% and the chance of pregnancy (overall 72%) was mainly determined by duration of infertility. In vitro fertilisation could benefit 80% of cases of tubal damage and 25% of unexplained infertility--that is, 18% of all cases, representing up to 216 new cases each year per million of the total population