CD44 aptamer mediated cargo delivery to lysosomes of retinal pigment epithelial cells to prevent age-related macular degeneration

Age related macular degeneration (AMD) is a progressive, neurodegenerative disorder that leads to the severe loss of central vision in elderlies. The health of retinal pigment epithelial (RPE) cells is critical for the onset of AMD. Chronic oxidative stress along with loss of lysosomal activity is a major cause for RPE cell death during AMD. Hence, development of a molecule for targeted lysosomal delivery of therapeutic protein/drugs in RPE cells is important to prevent RPE cell death during AMD. Using human RPE cell line (ARPE-19 cells) as a study model, we confirmed that hydrogen peroxide (H2O2) induced oxidative stress results in CD44 cell surface receptor overexpression in RPE cells; hence, an important target for specific delivery to RPE cells during oxidative stress. We also demonstrate that the known nucleic acid CD44 aptamer - conjugated with a fluorescent probe (FITC) - is delivered into the lysosomes of CD44 expressing ARPE-19 cells. Hence, as a proof of concept, we demonstrate that CD44 aptamer may be used for lysosomal delivery of cargo to RPE cells under oxidative stress, similar to AMD condition. Since oxidative stress may induce wet and dry AMD, both, along with proliferative vitreoretinopathy, CD44 aptamer may be applicable as a carrier for targeted lysosomal delivery of therapeutic cargoes in ocular diseases showing oxidative stress in RPE cells. © 2019Peer reviewe

MintHint: Automated Synthesis of Repair Hints

Being able to automatically repair programs is an extremely challenging task. In this paper, we present MintHint, a novel technique for program repair that is a departure from most of today's approaches. Instead of trying to fully automate program repair, which is often an unachievable goal, MintHint performs statistical correlation analysis to identify expressions that are likely to occur in the repaired code and generates, using pattern-matching based synthesis, repair hints from these expressions. Intuitively, these hints suggest how to rectify a faulty statement and help developers find a complete, actual repair. MintHint can address a variety of common faults, including incorrect, spurious, and missing expressions. We present a user study that shows that developers' productivity can improve manyfold with the use of repair hints generated by MintHint -- compared to having only traditional fault localization information. We also apply MintHint to several faults of a widely used Unix utility program to further assess the effectiveness of the approach. Our results show that MintHint performs well even in situations where (1) the repair space searched does not contain the exact repair, and (2) the operational specification obtained from the test cases for repair is incomplete or even imprecise

Deep optical survey of the stellar content of Sh2-311 region

The stellar content in and around Sh2-311 region have been studied using the deep optical observations as well as near-infrared (NIR) data from 2MASS. The region contains three clusters, viz. NGC 2467, Haffner 18 and Haffner 19. We have made an attempt to distinguish the stellar content of these individual regions as well as to re-determine their fundamental parameters such as distance, reddening, age, onto the basis of a new and more extended optical and infrared photometric data set. NGC 2467 and Haffner 19 are found to be located in the Perseus arm at the distances of 5.0 $\pm$ 0.4 kpc and 5.7 $\pm$ 0.4 kpc, respectively, whereas Haffner 18 is located at the distance of 11.2 $\pm$ 1.0 kpc. The clusters NGC 2467 and Haffner 19 might have formed from the same molecular cloud, whereas the cluster Haffner 18 is located in the outer galactic arm, i.e. the Norma-Cygnus arm. We identify 8 class II young stellar objects (YSOs) using the NIR $(J - H)/(H - K)$ two colour diagram. We have estimated the age and mass of the YSOs identified in the present work and those by Snider et al. (2009) using the $V/(V - I)$ colour-magnitude diagram. The estimated ages and mass range of the majority of the YSOs are $\lesssim$1 Myr and $\sim$0.4 - 3.5 \msun, respectively, indicating that these sources could be T-Tauri stars or their siblings. Spatial distribution of the YSOs shows that some of the YSOs are distributed around the H II region Sh2-311, suggesting a triggered star formation at its periphery.Comment: 19 pages, 13 figures, 9 table; Accepted for publication in New Astronom

Stoichiometries and Affinities of Interacting Proteins from Concentration Series of Solution Scattering Data: Decomposition by Least Squares and Quadratic Optimization

In studying interacting proteins, complementary insights are provided by analyzing both the association model (the stoichiometry and affinity constants of the intermediate and final complexes) and the quaternary structure of the resulting complexes. Many current methods for analyzing protein interactions either give a binary answer to the question of association and no information about quaternary structure or at best provide only part of the complete picture. Presented here is a method to extract both types of information from X-ray or neutron scattering data for a series of equilibrium mixtures containing the initial components at different concentrations. The method determines the association pathway and constants, along with the scattering curves of the individual members of the mixture, so as to best explain the scattering data for the mixtures. The derived curves then enable reconstruction of the intermediate and final complexes. Using simulated solution scattering data for four hetero-oligomeric complexes with different structures, molecular weights and association models, it is demonstrated that this method accurately determines the simulated association model and scattering profiles for the initial components and complexes. Recognizing that experimental mixtures contain static contaminants and nonspecific complexes with the lowest affinities (inter-particle interference) as well as the desired specific complex(es), a new analytical method is also employed to extend this approach to evaluating the association models and scattering curves in the presence of static contaminants, testing both a nonparticipating monomer and a large homo-oligomeric aggregate. It is demonstrated that the method is robust to both random noise and systematic noise from such contaminants, and the treatment of nonspecific complexes is discussed. Finally, it is shown that this method is applicable over a large range of weak association constants typical of specific but transient protein-protein complexes

Scalable and Interpretable One-class SVMs with Deep Learning and Random Fourier features

One-class support vector machine (OC-SVM) for a long time has been one of the most effective anomaly detection methods and extensively adopted in both research as well as industrial applications. The biggest issue for OC-SVM is yet the capability to operate with large and high-dimensional datasets due to optimization complexity. Those problems might be mitigated via dimensionality reduction techniques such as manifold learning or autoencoder. However, previous work often treats representation learning and anomaly prediction separately. In this paper, we propose autoencoder based one-class support vector machine (AE-1SVM) that brings OC-SVM, with the aid of random Fourier features to approximate the radial basis kernel, into deep learning context by combining it with a representation learning architecture and jointly exploit stochastic gradient descent to obtain end-to-end training. Interestingly, this also opens up the possible use of gradient-based attribution methods to explain the decision making for anomaly detection, which has ever been challenging as a result of the implicit mappings between the input space and the kernel space. To the best of our knowledge, this is the first work to study the interpretability of deep learning in anomaly detection. We evaluate our method on a wide range of unsupervised anomaly detection tasks in which our end-to-end training architecture achieves a performance significantly better than the previous work using separate training.Comment: Accepted at European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD) 201

Online change detection for energy-efficient mobilec crowdsensing

Mobile crowdsensing is power hungry since it requires continuously and simultaneously sensing, processing and uploading fused data from various sensor types including motion sensors and environment sensors. Realizing that being able to pinpoint change points of contexts enables energy-efficient mobile crowdsensing, we modify histogram-based techniques to efficiently detect changes, which has less computational complexity and performs better than the conventional techniques. To evaluate our proposed technique, we conducted experiments on real audio databases comprising 200 sound tracks. We also compare our change detection with multivariate normal distribution and one-class support vector machine. The results show that our proposed technique is more practical for mobile crowdsensing. For example, we show that it is possible to save 80% resource compared to standard continuous sensing while remaining detection sensitivity above 95%. This work enables energy-efficient mobile crowdsensing applications by adapting to contexts

Prediction of bullying at work: A data-driven analysis of the Finnish public sector cohort study

AIM: To determine the extent to which change in (i.e., start and end of) workplace bullying can be predicted by employee responses to standard workplace surveys. METHODS: Responses to an 87-item survey from 48,537 Finnish public sector employees at T1 (2017–2018) and T2 (2019–2020) were analyzed with least-absolute-shrinkage-and-selection-operator (LASSO) regression. The predictors were modelled both at the individual- and the work unit level. Outcomes included both the start and the end of bullying. Predictive performance was evaluated with C-indices and density plots. RESULTS: The model with best predictive ability predicted the start of bullying with individual-level predictors, had a C-index of 0.68 and included 25 variables, of which 6 remained in a more parsimonious model: discrimination at work unit, unreasonably high workload, threat that some work tasks will be terminated, working in a work unit where everyone did not feel they are understood and accepted, having a supervisor who was not highly trusted, and a shorter time in current position. Other models performed even worse, either from the point of view of predictive performance, or practical useability. DISCUSSION: While many bivariate associations between socioeconomic characteristics, work characteristics, leadership, team climate, and job satisfaction were observed, reliable individualized detection of individuals at risk of becoming bullied at workplace was not successful. The predictive performance of the developed risk scores was suboptimal, and we do not recommend their use as an individual-level risk prediction tool. However, they might be useful tool to inform decision-making when planning the contents of interventions to prevent bullying at an organizational level

Pilot study of a randomised trial of a guided e-learning health promotion intervention for managers based on management standards for the improvement of employee well-being and reduction of sickness absence: the GEM (Guided E-learning for Managers) study

Background: Psychosocial work environments influence employee well-being. There is a need for an evaluation of organisational-level interventions to modify psychosocial working conditions and hence employee well-being. Objective: To test the acceptability of the trial and the intervention, the feasibility of recruitment and adherence to and likely effectiveness of the intervention within separate clusters of an organisation. Design: Mixed methods: pilot cluster randomised controlled trial and qualitative study (in-depth interviews, focus group and observation). Participants: Employees and managers of a NHS trust. Inclusion criteria were the availability of sickness absence data and work internet access. Employees on long-term sick leave and short-term contracts and those with a notified pregnancy were excluded. Intervention: E-learning program for managers based on management standards over 10 weeks, guided by a facilitator and accompanied by face-to-face meetings. Three clusters were randomly allocated to receive the guided e-learning intervention; a fourth cluster acted as a control. Main outcome measures: Recruitment and participation of employees and managers; acceptability of the intervention and trial; employee subjective well-being using the Warwick–Edinburgh Mental Wellbeing Scale (WEMWBS); and feasibility of collecting sickness absence data. Results: In total, 424 employees out of 649 approached were recruited and 41 managers out of 49 were recruited from the three intervention clusters. Of those consenting, 350 [83%, 95% confidence interval (CI) 79% to 86%] employees completed the baseline assessment and 291 (69%, 95% CI 64% to 73%) completed the follow-up questionnaires. Sickness absence data were available from human resources for 393 (93%, 95% CI 90% to 95%) consenting employees. In total, 21 managers adhered to the intervention, completing at least three of the six modules. WEMWBS scores fell slightly in all groups, from 50.4 to 49.0 in the control group and from 51.0 to 49.9 in the intervention group. The overall intervention effect was 0.5 (95% CI –3.2 to 4.2). The fall in WEMWBS score was significantly less among employees whose managers adhered to the intervention than among those employees whose managers did not (–0.7 vs. 1.6, with an adjusted difference of 1.6, 95% CI 0.1 to 3.2). The intervention and trial were acceptable to managers, although our study raises questions about the widely used concept of ‘acceptability’. Managers reported insufficient time to engage with the intervention and lack of senior management ‘buy-in’. It was thought that the intervention needed better integration into organisational processes and practice. Conclusions: The mixed-methods approach proved valuable in illuminating reasons for the trial findings, for unpacking processes of implementation and for understanding the influence of study context. We conclude from the results of our pilot study that further mixed-methods research evaluating the intervention and study design is needed. We found that it is feasible to carry out an economic evaluation of the intervention. We plan a further mixed-methods study to re-evaluate the intervention boosted with additional elements to encourage manager engagement and behaviour change in private and public sector organisations with greater organisational commitment

A study of the environments of large radio galaxies using SDSS

The distributions of galaxies in the environments of 16 large radio sources have been examined using the Sloan Digital Sky Survey. In the giant radio galaxy J1552+2005 (3C326) which has the highest arm-length ratio, the shorter arm is found to interact with a group of galaxies which forms part of a filamentary structure. Although most large sources occur in regions of low galaxy density, the shorter arm is brighter in most cases suggesting asymmetries in the intergalactic medium which may not be apparent in the distribution of galaxies. In two cases with strong and variable cores, J0313+4120 and J1147+3501, the large flux density asymmetries are possibly also caused by the effects of relativistic motion.Comment: Accepted for publication in MNRA