Seeking Optimum System Settings for Physical Activity Recognition on Smartwatches

Physical activity recognition (PAR) using wearable devices can provide valued information regarding an individual's degree of functional ability and lifestyle. In this regards, smartphone-based physical activity recognition is a well-studied area. Research on smartwatch-based PAR, on the other hand, is still in its infancy. Through a large-scale exploratory study, this work aims to investigate the smartwatch-based PAR domain. A detailed analysis of various feature banks and classification methods are carried out to find the optimum system settings for the best performance of any smartwatch-based PAR system for both personal and impersonal models. To further validate our hypothesis for both personal (The classifier is built using the data only from one specific user) and impersonal (The classifier is built using the data from every user except the one under study) models, we tested single subject validation process for smartwatch-based activity recognition.Comment: 15 pages, 2 figures, Accepted in CVC'1

Modifying dental composites to formulate novel methacrylate-based bone cements with improved polymerisation kinetics, and mechanical properties

Objectives: The aim was to develop bone composites with similar working times, faster polymerisation and higher final conversion in comparison to Cortoss™. Additionally, low shrinkage/heat generation and improved short and longer-term mechanical properties are desirable. Methods: Four urethane dimethacrylate based composites were prepared using tri-ethylene-glycol dimethacrylate (TEGDMA) or polypropylene dimethacrylate (PPGDMA) diluent and 0 or 20 wt% fibres in the glass filler particles. FTIR was used to determine reaction kinetics, final degrees of conversions, and polymerisation shrinkage/heat generation at 37 °C. Biaxial flexural strength, Young's modulus and compressive strength were evaluated after 1 or 30 days in water. Results: Experimental materials all had similar inhibition times to Cortoss™ (140 s) but subsequent maximum polymerisation rate was more than doubled. Average experimental composite final conversion (76%) was higher than that of Cortoss™ (58%) but with less heat generation and shrinkage. Replacement of TEGDMA by PPGDMA gave higher polymerisation rates and conversions while reducing shrinkage. Early and aged flexural strengths of Cortoss™ were 93 and 45 MPa respectively. Corresponding compressive strengths were 164 and 99 MPa. Early and lagged experimental composite flexural strengths were 164–186 and 240–274 MPa whilst compressive strengths were 240–274 MPa and 226–261 MPa. Young's modulus for Cortoss™ was 3.3 and 2.2 GPa at 1 day and 1 month. Experimental material values were 3.4–4.8 and 3.0–4.1 GPa, respectively. PPGDMA and fibres marginally reduced strength but caused greater reduction in modulus. Fibres also made the composites quasi-ductile instead of brittle. Significance: The improved setting and higher strengths of the experimental materials compared to Cortoss™, could reduce monomer leakage from the injection site and material fracture, respectively. Lowering modulus may reduce stress shielding whilst quasi-ductile properties may improve fracture tolerance. The modified dental composites could therefore be a promising approach for future bone cements

A molecular phylogeny of selected species of genus Prunus L. (Rosaceae) from Pakistan using the internal transcribed spacer (ITS) spacer DNA

Prunus is found in all four provinces of Pakistan, that is, Punjab, NWFP, Sindh and Baluchistan including Azad Kashmir region. Studies on the family Rosaceae is scanty in the Flora of Pakistan and there is a lot of taxonomic work yet to be done, for the proper classification and placement of different genera under different sub-families. In the present study, the genus Prunus was studied in detail to find out the phylogenetic relationship among the 23 species of Prunus, selected from different regions of Pakistan and GenBank using maximum parsimony analysis of sequence polymorphism in nuclear ITS-9 and ITS-6 spacer DNA. The results for the internal transcribed spacer (ITS)- 9 and ITS- 6 primers confirm the work done by early phylogenetists with additions of new species from Pakistan including Prunus bokhariensis, Prunus dulcis (Mill.) D.A. Webb. (Syn. Prunus amygdalus) and Prunus cornuta (Wall. ex. Royle) Steudel. These are indigenous to Pakistan. In the ITS strict consensus results for example, the clade consisting of Laurocerasus, Padus and Cerasus subgenera are sister to the rest of the clades in the phylogenetic tree.Key words: Phylogeny, Prunus, Pakistan, molecular phylogeny, nuclear primers

De novo structural modeling and computational sequence analysis of a bacteriocin protein isolated from Rhizobium leguminosarum bv. viciae strain LC-31

Bacteriocins produced by different groups of bacteria are ribosomally synthesized peptides or proteins with antimicrobial and specific antagonistic bacterial interaction activity. Rhizobium leguminosarum is a Gram-negative soil bacterium which plays an important role in nitrogen fixation in leguminose plants. Bacteriocins produced by different strains of R. leguminosarum are known to impart antagonistic effects on other closely related strains. Recently, a bacteriocin gene was isolated from R. leguminosarum bv. viceae strain LC-31. Our study was aimed towards computational proteomic analysis and 3D structural modeling of this novel bacteriocin protein encoded by the earlier aforementioned gene. Different bioinformatics tools and machine learning techniques were used for protein structural classification. De novo protein modeling was performed by using I-TASSER server. The final model obtained was accessed by PROCHECK and DFIRE2, which confirmed that the final model is reliable. Until complete biochemical and structural data of bacteriocin protein produced by R. leguminosarum bv. viceae strain LC-31 are determined by experimental means, this model can serve as a valuable reference for characterizing this multifunctional protein.Key words: Bacteriocin, rhizobium, protein modelling, nodulation, symbiosis, nitrogen fixation

Human Activity Recognition Using Deep Models and Its Analysis from Domain Adaptation Perspective

© 2019, Springer Nature Switzerland AG. Human activity recognition (HAR) is a broad area of research which solves the problem of determining a user’s activity from a set of observations recorded on video or low-level sensors (accelerometer, gyroscope, etc.) HAR has important applications in medical care and entertainment. In this paper, we address sensor-based HAR, because it could be deployed on a smartphone and eliminates the need to use additional equipment. Using machine learning methods for HAR is common. However, such, methods are vulnerable to changes in the domain of training and test data. More specifically, a model trained on data collected by one user loses accuracy when utilised by another user, because of the domain gap (differences in devices and movement pattern results in differences in sensors’ readings.) Despite significant results achieved in HAR, it is not well-investigated from domain adaptation (DA) perspective. In this paper, we implement a CNN-LSTM based architecture along with several classical machine learning methods for HAR and conduct a series of cross-domain tests. The result of this work is a collection of statistics on the performance of our model under DA task. We believe that our findings will serve as a foundation for future research in solving DA problem for HAR

Pattern of benzodiazepine use in psychiatric outpatients in Pakistan: a cross-sectional survey.

Background: Benzodiazepines (BDZ) are the largest-selling drug group in the world. The potential of dependence with BDZ has been known for almost three decades now. In countries like Pakistan where laws against unlicensed sale of BDZ are not implemented vigorously the risk of misuse of and dependence on these drugs is even higher. Previous studies have shown that BDZ prevalence among Patients/visitors to general outPatient clinics in Pakistan may be as high as 30%. However, no research has been carried out on the prevalence of BDZ use in psychiatric Patients in Pakistan. Methods: We carried out a cross-sectional survey over 3 months in psychiatry outPatient clinics of two tertiary care hospitals in Karachi and Lahore. Besides basic socio-demographic data the participants were asked if they were taking a BDZ at present and if yes, the frequency, route and dosage of the drug, who had initiated the drug and why it had been prescribed. We used chi-square test and t-test to find out which socio-demographic or clinical factors were associated with an increased risk of BDZ use. We used Logistic Regression to find out which variable(s) best predicted the increased likelihood of BDZ use. Results: Out of a total of 419 participants 187 (45%) of the participants had been currently using at least one BDZ. Seventy-three percent of the users had been using the drug for 4 weeks or longer and 87% were taking it every day. In 90% of cases the BDZ had been initiated by a doctor, who was a psychiatrist in 70% of the cases. Female gender, increasing age, living in Lahore, and having seen a psychiatrist before, were associated with an increased likelihood of using BDZ. Conclusion: The study shows how high BDZ use is in psychiatric outPatients in Pakistan. Most of the users were taking it for a duration and with a frequency which puts them at risk of becoming dependent on BDZ. In most of the cases it had been initiated by a doctor. Both Patients and doctors need to be made aware of the risk of dependence associated with the use of BDZ

Real time lane detection for autonomous vehicles

An increasing safety and reducing road accidents, thereby saving lives are one of great interest in the context of Advanced Driver Assistance Systems. Apparently, among the complex and challenging tasks of future road vehicles is road lane detection or road boundaries detection. It is based on lane detection (which includes the localization of the road, the determination of the relative position between vehicle and road, and the analysis of the vehicle’s heading direction). One of the principal approaches to detect road boundaries and lanes using vision system on the vehicle. However, lane detection is a difficult problem because of the varying road conditions that one can encounter while driving. In this paper, a vision-based lane detection approach capable of reaching real time operation with robustness to lighting change and shadows is presented. The system acquires the front view using a camera mounted on the vehicle then applying few processes in order to detect the lanes. Using a pair of hyperbolas which are fitting to the edges of the lane, those lanes are extracted using Hough transform. The proposed lane detection system can be applied on both painted and unpainted road as well as curved and straight road in different weather conditions. This approach was tested and the experimental results show that the proposed scheme was robust and fast enough for real time requirements. Eventually, a critical overview of the methods were discussed, their potential for future deployment were assis

Mapping the subcortical connectome using in vivo diffusion MRI: Feasibility and reliability

Tractography combined with regions of interest (ROIs) has been used to non-invasively study the structural connectivity of the cortex as well as to assess the reliability of these connections. However, the subcortical connectome (subcortex to subcortex) has not been comprehensively examined, in part due to the difficulty of performing tractography in this complex and compact region. In this study, we performed an in vivo investigation using tractography to assess the feasibility and reliability of mapping known connections between structures of the subcortex using the test-retest dataset from the Human Connectome Project (HCP). We further validated our observations using a separate unrelated subjects dataset from the HCP. Quantitative assessment was performed by computing tract densities and spatial overlap of identified connections between subcortical ROIs. Further, known connections between structures of the basal ganglia and thalamus were identified and visually inspected, comparing tractography reconstructed trajectories with descriptions from tract-tracing studies. Our observations demonstrate both the feasibility and reliability of using a data-driven tractography-based approach to map the subcortical connectome in vivo

Biopsy variability of lymphocytic infiltration in breast cancer subtypes and the ImmunoSkew score.

The number of tumour biopsies required for a good representation of tumours has been controversial. An important factor to consider is intra-tumour heterogeneity, which can vary among cancer types and subtypes. Immune cells in particular often display complex infiltrative patterns, however, there is a lack of quantitative understanding of the spatial heterogeneity of immune cells and how this fundamental biological nature of human tumours influences biopsy variability and treatment resistance. We systematically investigate biopsy variability for the lymphocytic infiltrate in 998 breast tumours using a novel virtual biopsy method. Across all breast cancers, we observe a nonlinear increase in concordance between the biopsy and whole-tumour score of lymphocytic infiltrate with increasing number of biopsies, yet little improvement is gained with more than four biopsies. Interestingly, biopsy variability of lymphocytic infiltrate differs considerably among breast cancer subtypes, with the human epidermal growth factor receptor 2-positive (HER2+) subtype having the highest variability. We subsequently identify a quantitative measure of spatial variability that predicts disease-specific survival in HER2+ subtype independent of standard clinical variables (node status, tumour size and grade). Our study demonstrates how systematic methods provide new insights that can influence future study design based on a quantitative knowledge of tumour heterogeneity

Multi-response optimization of face milling performance considering tool path strategies in machining of Al-2024

It is hypothesized that the orientation of tool maneuvering in the milling process defines the quality of machining. In that respect, here, the influence of different path strategies of the tool in face milling is investigated, and subsequently, the best strategy is identified following systematic optimization. The surface roughness, material removal rate and cutting time are considered as key responses, whereas the cutting speed, feed rate and depth of cut were considered as inputs (quantitative factors) beside the tool path strategy (qualitative factor) for the material Al 2024 with a torus end mill. The experimental plan, i.e., 27 runs were determined by using the Taguchi design approach. In addition, the analysis of variance is conducted to statistically identify the effects of parameters. The optimal values of process parameters have been evaluated based on Taguchi-grey relational analysis, and the reliability of this analysis has been verified with the confirmation test. It was found that the tool path strategy has a significant influence on the end outcomes of face milling. As such, the surface topography respective to different cutter path strategies and the optimal cutting strategy is discussed in detail