Mindfulness: an upward spiral process to combat depression Abstract

Many studies proved detrimental effects of depression at the workplace in terms of reducing employee performance, increased absenteeism and other psychological, physical and mental distress. Organizations are driving specific interventions and training services to reduce potential negative outcomes of depression (Holmes, 2016; Utley, 2018) and one such intervention that needs to be researched is mindfulness (Bear, 2003). The proposed model intends to examine the role of mindfulness in reducing depression. The model proved that change in mindfulness significantly brings change in positive reappraisal and depression. Further positive reappraisal partially mediates the relationship between mindfulness and depression and the change in rumination does not decrease depression. By implementing mindfulness programmes, managers could make a significant difference and help employees to fight depression. Mindfulness develops into a resource over a period of time and helps employees to be engaged in the work, increase their performance, job satisfaction, productivity, and develop overall wellbeing.Introduction: Many studies proved detrimental effects of depression at the workplace in terms of reducing employeeperformance, increased absenteeism and other psychological, physical and mental distress. Organizations are drivingspecific interventions and training services to reduce potential negative outcomes of depression and one such interventionthat needs to be researched is mindfulness. We postulate that a depressed individuals when engage in mindfulnesspractice, increases positivity and set an upward spiral processes that broaden the potentials that originate in mind andimproves coping potential through positive reappraisal. Material and methods: The participants (N = 155) enrolled in 8 week’s Mindfulness Based Symptom Management(MBSM) program were contacted to take part in the study, 105 participants volunteered to take part in the study. Thestudy used SPSS Amos to test the model. Results and conclusions: The results indicate that mindfulness change reduces depression and the relation betweenmindfulness and depression was mediated partially through positive reappraisal. The study also tests competing modelwith change in rumination, although mindfulness program helped participants in reducing rumination it did not mediatethe relationship

Explore the Node Representation Learning on Heterogeneous Information Networks

Node representation learning (NRL) has shown incredible success in recent years. It compresses the nodes as low-dimensional vectors, which can accurately represent the characteristics of the nodes. While many researchers have applied NRL to heterogeneous information networks (HIN), most of them only focus on the quality of the node embedding itself or some basic downstream tasks, such as node classification and link prediction. In this thesis, we study the following three problems to explore the power of graph representation learning on different heterogeneous information network mining tasks. Firstly, we investigate the problem of the meta-path prediction problem. Given an HIN H, a head node h, a meta-path P, and a tail node t, the meta-path prediction aims to predict whether h can be linked to t by an instance of P. Most existing solutions either require predefined meta-paths, which limits their scalability to schema-rich HINs and long meta-paths, or do not aim to predict the existence of an instance of P. To address these issues, we propose a novel prediction model, called ABLE, by exploiting the Attention mechanism and BiLSTM for Embedding. We conduct extensive experiments on four real datasets. The empirical results show that ABLE outperforms the state-of-the-art methods by up to 20\% and 22\% of improvement of AUC and AP scores, respectively. Secondly, we focus on the node importance value estimation problem. Node importance estimation is a fundamental task in graph data analysis. Extensive studies have focused on this task, and various downstream applications have benefited from it, such as recommendation, resource allocation optimization, and missing value completion. However, existing works either focus on the homogeneous network or only study importance-based ranking. We are the first to consider the node importance values as heterogeneous values in HINs. A typical HIN is built of several distinguished node types where each type has its own measure of importance value. This characteristic makes the above problem more challenging than computing the node importance in conventional homogeneous networks. In this thesis, we formally introduce the problem of node importance value estimation in HINs; that is, given the importance values of a subset of nodes in an HIN, we aim to estimate the importance values of the remaining nodes. To solve this problem, we propose an effective graph neural network (GNN) model, called HIN Importance Value Estimation Network (HIVEN). Extensive experiments on real-world HIN datasets demonstrate that HIVEN superiorly outperforms the baseline methods. Thirdly, we study the node importance estimation problem in dynamic HIN. The node importance in HIN is highly co-related to the HIN topology, while the node importance can also in turn influence the change of the HIN structure. All existing works assume that the HIN is static, and ignore their co-evolutionary natures. In addition, the historical node importance information is always available, which can further help to get accurate node importance estimation. Thus, we propose a novel temporal GNN model, CoGNN. We experimented with real-world dynamic HIN datasets and show that the proposed model outperforms the state of the arts

A Fog Computing Architecture for Disaster Response Networks

In the aftermath of a disaster, the impacted communication infrastructure is unable to provide first responders with a reliable medium of communication. Delay tolerant networks that leverage mobility in the area have been proposed as a scalable solution that can be deployed quickly. Such disaster response networks (DRNs) typically have limited capacity due to frequent disconnections in the network, and under-perform when saturated with data. On the other hand, there is a large amount of data being produced and consumed due to the recent popularity of smartphones and the cloud computing paradigm. Fog Computing brings the cloud computing paradigm to the complex environments that DRNs operate in. The proposed architecture addresses the key challenges of ensuring high situational awareness and energy efficiency when such DRNs are saturated with large amounts of data. Situational awareness is increased by providing data reliably, and at a high temporal and spatial resolution. A waypoint placement algorithm places hardware in the disaster struck area such that the aggregate good-put is maximized. The Raven routing framework allows for risk-averse data delivery by allowing the user to control the variance of the packet delivery delay. The Pareto frontier between performance and energy consumption is discovered, and the DRN is made to operate at these Pareto optimal points. The FuzLoc distributed protocol enables mobile self-localization in indoor environments. The architecture has been evaluated in realistic scenarios involving deployments of multiple vehicles and devices

A Fuzzy Logic-Based Approach for Node Localization in Mobile Sensor Networks

In most range-based localization methods, inferring distance from radio signal strength using mathematical modeling becomes increasingly unreliable and complicated in indoor and extreme environments, due to effects such as multipath propagation and signal interference. We propose FuzLoc, a range-based, anchor-based, fuzzy logic enabled system system for localization. Quantities like RSS and distance are transformed into linguistic variables such as Low, Medium, High etc. by binning. The location of the node is then solved for using a nonlinear system in the fuzzy domain itself, which outputs the location of the node as a pair of fuzzy numbers. An included destination prediction system activates when only one anchor is heard; it localizes the node to an area. It accomplishes this using the theoretical construct of virtual anchors, which are calculated when a single anchor is in the node’s vicinity. The fuzzy logic system is trained during deployment itself so that it learns to associate an RSS with a distance, and a set of distances to a probability vector. We implement the method in a simulator and compare it against other methods like MCL, Centroid and Amorphous. Extensive evaluation is done based on a variety of metrics like anchor density, node density etc

Clinical outcomes of ramucirumab plus docetaxel in the treatment of patients with non-small cell lung cancer after immunotherapy: a systematic literature review

IntroductionIn the REVEL trial, ramucirumab plus docetaxel demonstrated significant improvements in overall survival (OS), progression-free survival (PFS), and overall response rate (ORR) compared with placebo plus docetaxel for treatment of metastatic non-small cell lung cancer (NSCLC) that progressed during or after platinum-based chemotherapy. Since the approval of ramucirumab plus docetaxel, immune checkpoint inhibitors (ICIs), either as single agents or in combination with chemotherapy, have become the standard of care for first-line treatment of patients with advanced NSCLC. However, efficacy and safety data for ramucirumab plus docetaxel after prior ICI treatment from randomized controlled clinical studies are lacking.MethodsFollowing Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, a systematic literature review was performed. Electronic databases and select international oncology conference proceedings were searched. Studies published between 01 January 2014 and 01 July 2022, which evaluated 2 efficacy outcomes (and included at least 1 time-to-event endpoint) or safety outcomes of ramucirumab plus docetaxel in NSCLC that progressed after prior ICI treatment, were identified. Twelve studies were included in the analysis. Two treatment groups were selected: ramucirumab plus docetaxel after prior ICI ± chemotherapy (RAM + DTX ICI pre-treated) and ramucirumab plus docetaxel after prior chemotherapy only (RAM + DTX ICI naïve). OS, PFS, ORR, disease control rate (DCR), and safety data were extracted and descriptively summarized across both treatment groups.ResultsThe pooled weighted median PFS and median OS were 5.7 months (95% confidence interval [CI]: 3.9-6.8) and 11.2 months (95% CI: 7.5-17.5), respectively, in the RAM + DTX ICI pre-treated group and 3.8 months (95% CI: 2.3-4.1) and 13.5 months (95% CI: 8-24.0), respectively, in the RAM + DTX ICI naïve group. The ORR and DCR ranged from 20.9% to 60.0% and from 62.4% to 90.0%, respectively, in the RAM + DTX ICI pre-treated group and from 17.7% to 20.0% and from 57.1% to 75.0%, respectively, in the RAM + DTX ICI naïve group. The safety profile across studies was consistent between both treatment groups, and no new safety signals were reported.ConclusionsCumulatively, these results support the combination of ramucirumab plus docetaxel as an effective and safe subsequent therapy for the treatment of patients with metastatic NSCLC with disease progression irrespective of previous ICI treatment

Identification of Hyper-Methylated Tumor Suppressor Genes-Based Diagnostic Panel for Esophageal Squamous Cell Carcinoma (ESCC) in a Chinese Han Population

DNA methylation-based biomarkers were suggested to be promising for early cancer diagnosis. However, DNA methylation-based biomarkers for esophageal squamous cell carcinoma (ESCC), especially in Chinese Han populations have not been identified and evaluated quantitatively. Candidate tumor suppressor genes (N = 65) were selected through literature searching and four public high-throughput DNA methylation microarray datasets including 136 samples totally were collected for initial confirmation. Targeted bisulfite sequencing was applied in an independent cohort of 94 pairs of ESCC and normal tissues from a Chinese Han population for eventual validation. We applied nine different classification algorithms for the prediction to evaluate to the prediction performance. ADHFE1, EOMES, SALL1 and TFPI2 were identified and validated in the ESCC samples from a Chinese Han population. All four candidate regions were validated to be significantly hyper-methylated in ESCC samples through Wilcoxon rank-sum test (ADHFE1, P = 1.7 × 10-3; EOMES, P = 2.9 × 10-9; SALL1, P = 3.9 × 10-7; TFPI2, p = 3.4 × 10-6). Logistic regression based prediction model shown a moderately ESCC classification performance (Sensitivity = 66%, Specificity = 87%, AUC = 0.81). Moreover, advanced classification method had better performances (random forest and naive Bayes). Interestingly, the diagnostic performance could be improved in non-alcohol use subgroup (AUC = 0.84). In conclusion, our data demonstrate the methylation panel of ADHFE1, EOMES, SALL1 and TFPI2 could be an effective methylation-based diagnostic assay for ESCC