Automatic depression scale prediction using facial expression dynamics and regression

Depression is a state of low mood and aversion to activity that can affect a person's thoughts, behaviour, feelings and sense of well-being. In such a low mood, both the facial expression and voice appear different from the ones in normal states. In this paper, an automatic system is proposed to predict the scales of Beck Depression Inventory from naturalistic facial expression of the patients with depression. Firstly, features are extracted from corresponding video and audio signals to represent characteristics of facial and vocal expression under depression. Secondly, dynamic features generation method is proposed in the extracted video feature space based on the idea of Motion History Histogram (MHH) for 2-D video motion extraction. Thirdly, Partial Least Squares (PLS) and Linear regression are applied to learn the relationship between the dynamic features and depression scales using training data, and then to predict the depression scale for unseen ones. Finally, decision level fusion was done for combining predictions from both video and audio modalities. The proposed approach is evaluated on the AVEC2014 dataset and the experimental results demonstrate its effectiveness.The work by Asim Jan was supported by School of Engineering & Design/Thomas Gerald Gray PGR Scholarship. The work by Hongying Meng and Saeed Turabzadeh was partially funded by the award of the Brunel Research Initiative and Enterprise Fund (BRIEF). The work by Yona Falinie Binti Abd Gaus was supported by Majlis Amanah Rakyat (MARA) Scholarship

Deep learning based facial expression recognition and its applications

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonFacial expression recognition (FER) is a research area that consists of classifying the human emotions through the expressions on their face. It can be used in applications such as biometric security, intelligent human-computer interaction, robotics, and clinical medicine for autism, depression, pain and mental health problems. This dissertation investigates the advanced technologies for facial expression analysis and develops the artificial intelligent systems for practical applications. The first part of this work applies geometric and texture domain feature extractors along with various machine learning techniques to improve FER. Advanced 2D and 3D facial processing techniques such as Edge Oriented Histograms (EOH) and Facial Mesh Distances (FMD) are then fused together using a framework designed to investigate their individual and combined domain performances. Following these tests, the face is then broken down into facial parts using advanced facial alignment and localising techniques. Deep learning in the form of Convolutional Neural Networks (CNNs) is also explored also FER. A novel approach is used for the deep network architecture design, to learn the facial parts jointly, showing an improvement over using the whole face. Joint Bayesian is also adapted in the form of metric learning, to work with deep feature representations of the facial parts. This provides a further improvement over using the deep network alone. Dynamic emotion content is explored as a solution to provide richer information than still images. The motion occurring across the content is initially captured using the Motion History Histogram descriptor (MHH) and is critically evaluated. Based on this observation, several improvements are proposed through extensions such as Average Spatial Pooling Multi-scale Motion History Histogram (ASMMHH). This extension adds two modifications, first is to view the content in different spatial dimensions through spatial pooling; influenced by the structure of CNNs. The other modification is to capture motion at different speeds. Combined, they have provided better performance over MHH, and other popular techniques like Local Binary Patterns – Three Orthogonal Planes (LBP-TOP). Finally, the dynamic emotion content is observed in the feature space, with sequences of images represented as sequences of extracted features. A novel technique called Facial Dynamic History Histogram (FDHH) is developed to capture patterns of variations within the sequence of features; an approach not seen before. FDHH is applied in an end to end framework for applications in Depression analysis and evaluating the induced emotions through a large set of video clips from various movies. With the combination of deep learning techniques and FDHH, state-of-the-art results are achieved for Depression analysis

CRISPR-Cas system:A new paradigm for bacterial stress response through genome rearrangement

Bacteria can receive genetic material from other bacteria or invading bacteriophages primarily through horizontal gene transfer. These genetic exchanges can result in genome rearrangement and the acquisition of novel traits that assist cells with stresses and adverse environmental conditions. Bacteria have a relatively small genome with >90% of sequences consisting of protein coding genes, stable RNA biomolecules, and gene regulatory sequences. The remaining genome fraction is primarily large repeat elements, such as retrotransposons, interspersed repeat elements, insertion sequences, and the more recently discovered clustered regularly interspaced short palindromic repeats (CRISPRs), with CRISPR-associated gene sequences (cas) that code for various Cas proteins. The CRISPR genetic locus is a series of direct repeats that are interspersed by unique spacer sequences. These unique spacer sequences represent signatures of bacteriophage genomes as the "working memory" for a bacterium to identify and destroy an invading phage genome that has previously infected the host. The protective function of the CRISPR-Cas systems are found in ∼40% of sequenced bacterial genomes, and it is often defined as bacterial acquired immunity. This chapter will elaborate the origin, structure, and function of CRISPR-Cas genetic systems acquired by bacteria, and their role in adaptive fitness while being subjected to environmental stress conditions

How are functionally similar code clones syntactically different? An empirical study and a benchmark

Background. Today, redundancy in source code, so-called ‘‘clones’’ caused by copy&paste can be found reliably using clone detection tools. Redundancy can arise also independently, however, not caused by copy&paste. At present, it is not clear how only functionally similar clones (FSC) differ from clones created by copy&paste. Our aim is to understand and categorise the syntactical differences in FSCs that distinguish them from copy&paste clones in a way that helps clone detection research. Methods. We conducted an experiment using known functionally similar programs in Java and C from coding contests. We analysed syntactic similarity with traditional detection tools and explored whether concolic clone detection can go beyond syntax. We ran all tools on 2,800 programs and manually categorised the differences in a random sample of 70 program pairs. Results. We found no FSCs where complete files were syntactically similar. We could detect a syntactic similarity in a part of the files in <16% of the program pairs. Concolic detection found 1 of the FSCs. The differences between program pairs were in the categories algorithm, data structure, OO design, I/O and libraries. We selected 58 pairs for an openly accessible benchmark representing these categories. Discussion. The majority of differences between functionally similar clones are beyond the capabilities of current clone detection approaches. Yet, our benchmark can help to drive further clone detection research

Enhanced effects of dietary tannic acid with chlorantraniliprole on life table parameters and nutritional physiology of Spodoptera exigua (Hübner)

The beet armyworm, Spodoptera exigua (Hübner) (Lepidoptera: Noctuidae) is a highly polyphagous pest which causes considerable economic losses to cotton and many vegetable crops. Tannins are among the most important secondary metabolites in cotton plants. We show that tannic acid enhances the toxic effect of chlorantraniliprole on S. exigua when presented in combination. Bioassays using third-instar S. exigua larvae on an artificial diet showed that consumption of tannic acid with chlorantraniliprole at the concentration of (2 mg/g and LC50 0.018 mg/L) had higher toxicity when compared to either chlorantraniliprole or tannic acid alone (LC50 0.027 mg/L). The diet containing tannic acid with chlorantraniliprole significantly prolonged larval and pupal developmental time and extended mean generation time and total preoviposition period compared to either chemical alone. Moreover, fecundity, survival rate, reproductive value, intrinsic rate of increase, finite rate of increase and net reproduction rate declined significantly when exposed to the combined treatment. No difference was observed between tannic acid and the control. Meanwhile, tannic acid with chlorantraniliprole had markedly antifeedant effects; causing significant decline in the relative growth rate (RGR), the relative consumption rate (RCR), the efficiency of conversion of ingested food (ECI), the efficiency of conversion of digested food and an increase in the approximate digestibility (AD) compared to either chemical alone. Tannic acid with chlorantraniliprole also decreased the insect’s carbohydrate, lipid and protein contents significantly. The results showed that the interaction between tannic acid and chlorantraniliprole on the growth inhibition of larvae was additive and tannic acid increased the toxicity of chlorantraniliprole to insects. The results of this study provide information useful in integrated pest management programs for S. exigua and show that tannic acid combined with chlorantraniliprole may be a route to reducing the use of synthetic pesticides

All Sugars Ain’t Sweet: Selection of Particular Mono-, Di- and Trisaccharides by Western Carpenter Ants and European Fire Ants

Ants select sustained carbohydrate resources, such as aphid honeydew, based on many factors including sugar type, volume and concentration. We tested the hypotheses (H1– H3) that western carpenter ants, Camponotus modoc, seek honeydew excretions from Cinara splendens aphids based solely on the presence of sugar constituents (H1), prefer sugar solutions containing aphid-specific sugars (H2) and preferentially seek sugar solutions with higher sugar content (H3). We further tested the hypothesis (H4) that workers of both Ca. modoc and European fire ants, Myrmica rubra, selectively consume particular mono-, di- and trisaccharides. In choice bioassays with entire ant colonies, sugar constituents in honeydew (but not aphid-specific sugar) as well as sugar concentration affected foraging decisions by Ca. modoc. Both Ca. modoc and M. rubra foragers preferred fructose to other monosaccharides (xylose, glucose) and sucrose to other disaccharides (maltose, melibiose, trehalose). Conversely, when offered a choice between the aphid-specific trisaccharides raffinose and melezitose, Ca. modoc and M. rubra favoured raffinose and melezitose, respectively. Testing the favourite mono-, di- and trisaccharide head-to-head, both ant species favoured sucrose. While both sugar type and sugar concentration are the ultimate cause for consumption by foraging ants, strong recruitment of nest-mates to superior sources is probably the major proximate cause

Karyotypic analysis of Crucian carp, Carassius carassius (Linnaeus, 1758) from cold waters of Kashmir Himalayas

Carassius carassius (Linnaeus, 1758) is an exotic fish to Kashmir, locally known as “gang gad” and commonly called as “crucian carp”. It belongs to family Cyprinidae. The present study aimed to identify the chromosome number of the Carassius carassius and to optimize the colchicine concentrations (0.01%, 0.025%, and 0.05%) and hypotonic treatment timings (25, 35, and 45 minutes) for the chromosome preparation in Carassius carassius in order to obtain the highest number of clear and identifiable metaphasic chromosomal spreads. Data collected was analyzed and the means of each treatment was compared. The findings of the present study indicated that there was a significant influence of colchicine concentration, hypotonic timings as well as colchicine concentration× hypotonic timings (P<0.01) on the number of metaphase chromosome spreads. Furthermore a significant (P<0.01) strong positive correlation obtained between colchicine concentrations, hypotonic timings and the number of metaphase chromosome spreads. The findings of the present study recommends further research into chromosomal modification techniques such as fish polyploid production, gynogenesis, androgenesis, and inter or intra-species hybridization is needed to generate unique and good inbred lines in aquaculture

Loss of function mutations in HARS cause a spectrum of inherited peripheral neuropathies

Using linkage analysis and whole-exome sequencing, Safka Brozkova et al. reveal missense mutations in the histidyl-tRNA synthetase gene in 23 patients from four families with axonal and demyelinating neuropathies of varying severity. The mutations cause loss of function in yeast complementation assays and neurotoxicity in a C. elegans mode

A narrative review

Publisher Copyright: Copyright © 2022 Couto, Parreira, Power, Pinheiro, Madruga Dias, Novofastovski, Eshed, Sarzi-Puttini, Pappone, Atzeni, Verlaan, Kuperus, Bieber, Ambrosino, Kiefer, Khan, Mader, Baraliakos and Bruges-Armas.Diffuse Idiopathic Skeletal Hyperostosis (DISH) and Ossification of the Posterior Longitudinal Ligament (OPLL) are common disorders characterized by the ossification of spinal ligaments. The cause for this ossification is currently unknown but a genetic contribution has been hypothesized. Over the last decade, many studies on the genetics of ectopic calcification disorders have been performed, mainly on OPLL. Most of these studies were based on linkage analysis and case control association studies. Animal models have provided some clues but so far, the involvement of the identified genes has not been confirmed in human cases. In the last few years, many common variants in several genes have been associated with OPLL. However, these associations have not been at definitive levels of significance and evidence of functional significance is generally modest. The current evidence suggests a multifactorial aetiopathogenesis for DISH and OPLL with a subset of cases showing a stronger genetic component.publishersversionpublishe

Deep Learning for Medication Recommendation: A Systematic Survey

ABSTRACTMaking medication prescriptions in response to the patient's diagnosis is a challenging task. The number of pharmaceutical companies, their inventory of medicines, and the recommended dosage confront a doctor with the well-known problem of information and cognitive overload. To assist a medical practitioner in making informed decisions regarding a medical prescription to a patient, researchers have exploited electronic health records (EHRs) in automatically recommending medication. In recent years, medication recommendation using EHRs has been a salient research direction, which has attracted researchers to apply various deep learning (DL) models to the EHRs of patients in recommending prescriptions. Yet, in the absence of a holistic survey article, it needs a lot of effort and time to study these publications in order to understand the current state of research and identify the best-performing models along with the trends and challenges. To fill this research gap, this survey reports on state-of-the-art DL-based medication recommendation methods. It reviews the classification of DL-based medication recommendation (MR) models, compares their performance, and the unavoidable issues they face. It reports on the most common datasets and metrics used in evaluating MR models. The findings of this study have implications for researchers interested in MR models