MindShift: Leveraging Large Language Models for Mental-States-Based Problematic Smartphone Use Intervention

Problematic smartphone use negatively affects physical and mental health. Despite the wide range of prior research, existing persuasive techniques are not flexible enough to provide dynamic persuasion content based on users' physical contexts and mental states. We first conduct a Wizard-of-Oz study (N=12) and an interview study (N=10) to summarize the mental states behind problematic smartphone use: boredom, stress, and inertia. This informs our design of four persuasion strategies: understanding, comforting, evoking, and scaffolding habits. We leverage large language models (LLMs) to enable the automatic and dynamic generation of effective persuasion content. We develop MindShift, a novel LLM-powered problematic smartphone use intervention technique. MindShift takes users' in-the-moment physical contexts, mental states, app usage behaviors, users' goals & habits as input, and generates high-quality and flexible persuasive content with appropriate persuasion strategies. We conduct a 5-week field experiment (N=25) to compare MindShift with baseline techniques. The results show that MindShift significantly improves intervention acceptance rates by 17.8-22.5% and reduces smartphone use frequency by 12.1-14.4%. Moreover, users have a significant drop in smartphone addiction scale scores and a rise in self-efficacy. Our study sheds light on the potential of leveraging LLMs for context-aware persuasion in other behavior change domains

Attenuation of antigen-induced airway hyperresponsiveness and inflammation in CXCR3 knockout mice

<p>Abstract</p> <p>Background</p> <p>CD8+ T cells participate in airway hyperresponsiveness (AHR) and allergic pulmonary inflammation that are characteristics of asthma. CXCL10 by binding to CXCR3 expressed preferentially on activated CD8+ T cells, attracts T cells homing to the lung. We studied the contribution and limitation of CXCR3 to AHR and airway inflammation induced by ovalbumin (OVA) using CXCR3 knockout (KO) mice.</p> <p>Methods</p> <p>Mice were sensitized and challenged with OVA. Lung histopathological changes, AHR, cellular composition and levels of inflammatory mediators in bronchoalveolar lavage (BAL) fluid, and lungs at mRNA and protein levels, were compared between CXCR3 KO mice and wild type (WT) mice.</p> <p>Results</p> <p>Compared with the WT controls, CXCR3 KO mice showed less OVA-induced infiltration of inflammatory cells around airways and vessels, and less mucus production. CXCR3 KO mice failed to develop significant AHR. They also demonstrated significantly fewer CD8+ T and CD4+ T cells in BAL fluid, lower levels of TNFα and IL-4 in lung tissue measured by real-time RT-PCR and in BAL fluid by ELISA, with significant elevation of IFNγ mRNA and protein expression levels.</p> <p>Conclusions</p> <p>We conclude that CXCR3 is crucial for AHR and airway inflammation by promoting recruitment of more CD8+ T cells, as well as CD4+ T cells, and initiating release of proinflammatory mediators following OVA sensitization and challenge. CXCR3 may represent a novel therapeutic target for asthma.</p

Case report: Clinical complete response in advanced ALK-positive lung squamous cell carcinoma: a case study of successful anti-PD-1 immunotherapy post ALK-TKIs failure

In patients with advanced lung adenocarcinoma (LADC) harboring the echinoderm microtubule-associated protein-like 4 (EML4) -anaplastic lymphoma kinase (ALK) rearrangement, targeted therapy typically demonstrates superior efficacy as an initial treatment compared to chemotherapy. Following resistance to ALK-tyrosine kinase inhibitors (TKIs), regimens incorporating platinum-based dual agents or combined with bevacizumab often show effectiveness. However, therapeutic alternatives become constrained after resistance develops to both TKIs and platinum-based therapies. Given that the majority of ALK-positive non-small cell lung carcinomas (NSCLC) are LADC, the benefits of TKIs for patients with ALK-positive lung squamous cell carcinoma (LSCC) and the optimal treatment strategy for these patients remain a subject of debate. In this case study, we report on a patient with advanced LSCC, in whom the EML4-ALK rearrangement was identified via ARMS-PCR (Amplification Refractory Mutation System-Polymerase Chain Reaction). The patient underwent oral treatment with crizotinib and alectinib, showing effectiveness in both first-line and second-line ALK-TKI therapies, albeit with limited progression-free survival (PFS). Subsequent resistance to second-generation TKI was followed by the detection of tumors in the left neck region via computed tomography (CT). Biopsy pathology revealed non-squamous cell carcinoma, and subsequent treatment with platinum-based double-drug therapy proved ineffective. Further analysis through next-generation sequencing (NGS) indicated ALK negativity but a high expression of programmed death-ligand 1 (PD-L1). Immunotherapy was then initiated, resulting in a PFS of over 29 months and clinical complete remission (cCR). This case underscores the potential benefit of ALK-TKIs in patients with ALK-positive LSCC. Resistance to second-generation TKIs may lead to ALK negativity and histological transformation, highlighting the necessity of repeated biopsies post-TKI resistance for informed treatment decision-making. As of November 2023, imaging studies continue to indicate cCR in the patient, with a survival time exceeding 47 months

Comprehensive bioinformatics analysis and systems biology approaches to identify the interplay between COVID-19 and pericarditis

BackgroundIncreasing evidence indicating that coronavirus disease 2019 (COVID-19) increased the incidence and related risks of pericarditis and whether COVID-19 vaccine is related to pericarditis has triggered research and discussion. However, mechanisms behind the link between COVID-19 and pericarditis are still unknown. The objective of this study was to further elucidate the molecular mechanisms of COVID-19 with pericarditis at the gene level using bioinformatics analysis.MethodsGenes associated with COVID-19 and pericarditis were collected from databases using limited screening criteria and intersected to identify the common genes of COVID-19 and pericarditis. Subsequently, gene ontology, pathway enrichment, protein–protein interaction, and immune infiltration analyses were conducted. Finally, TF–gene, gene–miRNA, gene–disease, protein–chemical, and protein–drug interaction networks were constructed based on hub gene identification.ResultsA total of 313 common genes were selected, and enrichment analyses were performed to determine their biological functions and signaling pathways. Eight hub genes (IL-1β, CD8A, IL-10, CD4, IL-6, TLR4, CCL2, and PTPRC) were identified using the protein–protein interaction network, and immune infiltration analysis was then carried out to examine the functional relationship between the eight hub genes and immune cells as well as changes in immune cells in disease. Transcription factors, miRNAs, diseases, chemicals, and drugs with high correlation with hub genes were predicted using bioinformatics analysis.ConclusionsThis study revealed a common gene interaction network between COVID-19 and pericarditis. The screened functional pathways, hub genes, potential compounds, and drugs provided new insights for further research on COVID-19 associated with pericarditis

Machine Learning for Drug-Target Interaction Prediction

Identifying drug-target interactions will greatly narrow down the scope of search of candidate medications, and thus can serve as the vital first step in drug discovery. Considering that in vitro experiments are extremely costly and time-consuming, high efficiency computational prediction methods could serve as promising strategies for drug-target interaction (DTI) prediction. In this review, our goal is to focus on machine learning approaches and provide a comprehensive overview. First, we summarize a brief list of databases frequently used in drug discovery. Next, we adopt a hierarchical classification scheme and introduce several representative methods of each category, especially the recent state-of-the-art methods. In addition, we compare the advantages and limitations of methods in each category. Lastly, we discuss the remaining challenges and future outlook of machine learning in DTI prediction. This article may provide a reference and tutorial insights on machine learning-based DTI prediction for future researchers

A Meta-Analysis on Loyalty Program Membership Effects –The Influence from Firm, Industrial, and National Levels

Loyalty programs are a common customer relationship management tool that has been adopted in various industries. Despite their prevalence, research on loyalty programs find inconsistent results on loyalty program effectiveness in terms of magnitude and direction. To clarify the effects of loyalty programs, the first aim of this thesis is to investigate whether loyalty program membership has an impact on a range of customer responses. A meta-analysis is used to solve this research question. In total, 432 effect sizes on the relationship between loyalty program membership and customer responses from 81 independent samples were collected. The average corrected sample size-weighted correlations show loyalty program membership generally has a positive yet small effect (r < .30) on 17 customer response outcomes. The results from the first meta-analysis also show substantial heterogeneity which is caused by between-study differences other than random sampling errors. Therefore, the next research question is to identify sources of heterogeneity in loyalty program effects, i.e. a moderator analysis on the underlying factors that influence the relationship between loyalty programs and customer response variables. Drawing on the existing research on loyalty programs, three levels of potential moderators were proposed. At the firm level, program structures and firm size were assessed. At the industrial level, the model incorporates product characteristics and market concentration. At the national level, Hofstede’s five cultural dimensions and national economic factors were tested. A number of variables of study characteristics were included to control for different study designs. To further explore loyalty program effects in complex situations, this study tests the interactions between national culture and product characteristics. This thesis provides an overview of the current research on loyalty programs by quantitatively integrate existing research results. It identifies the strength of loyalty program effects, which are generally weak. Therefore, managers should carefully evaluate the use of loyalty programs for their businesses, given the high initial investment of launching such a loyalty initiative. More importantly, this thesis assesses three levels of moderators that might influence the strength of the loyalty program effects within a single framework.Managers should take into account of these factors examined when evaluating and designing their loyalty program strategies to optimise the output of loyalty programs

Why CREM Should be Implemented by the Office-Based Companies in Shanghai, China?

The purpose of this paper is to investigate why CREM should be carried out within the office based companies in Shanghai. Besides, the current CREM practices and performances in the office based companies in Shanghai are examined as well. Design/methodology/approach - The CREM added value creating model for office based corporations in Shanghai is constructed based on literature review. Then, according to the model, the proposition as to why CREM should be carried out within the office based companies in Shanghai is developed. Later, two case studies, which are composed of online questionnaire, structured interviews, and secondary documentary review, are investigated to test the proposition. Findings - Although the result from the two-case study does not provide direct evidence to support the proposition that alternative added values listed in the model is the driving force of CREM implementation for Shanghai office users, CREM, or Corporate Office Estate Management, does help them to promote marketing and corporate branding, staff retention, as well as efficiency and cost control. Research limitations/implications – The research is based on two case studies, the logic of which is replication. Thus, it is not possible to draw any strong generalization. Future studies are needed to validate or contradict the findings in the research. Practical implications – The research process and result provides inspirations for the office occupiers in Shanghai, China, on how Corporate Real Estate Management (CREM) can contribute to the core business, and what specific added values CREM can create. Besides, the CREM added value creating model for office based corporations in Shanghai provides framework for CREM managers on how CREM can be strategically carried out. Originality/value – Since CREM is a brand new discipline in China, the research is the first one digging into Corporate Office Estate Management practices in Shanghai, with the incorporation of the CREM added value creating model. Keywords – CREM, office property, added value, Shanghai Paper type – Master degree thesi

Aural attention training using computer software

Vecāki un skolotāji ikdienā saskaras ar nepietiekamām bērna koncentrēšanās spējām un uzmanības nenoturību, ko mūsdienās pedagogi uzskata par vienu no aktuālākajām problēmām skolās. Šī darba mērķis ir izstrādāt rīku-spēli, ko varētu izmantot uzmanības trenēšanai. Darbs izstrādāts sadarbībā ar Valdi Bernhofu, balstoties uz viņa topošā promocijas darba „Skaņaugstuma un ritma struktūrsistēma audiālās uzmanības treniņam” pētījuma praktisko daļu. Uzmanība ir katra kognitīva procesa priekšnoteikums un, kā rāda literatūras analīze, tieši skaņaugstumi un ritmi ir visbiežāk minētie parametri saistībā ar uzmanības spējām. Tāpēc Bernhofs pētījumā izstrādājis tieši skaņaugstumu un ritma struktūrsistēmu, kuras analīzei un realizācijai veltīts šis darbs. Darbs apraksta audiālās uzmanības trenēšanas rīka A.U.T. izstrādes ciklu no iepazīšanās ar doto sistēmu līdz tās izmantošanai uzmanības treniņam. Šis darbs ietver iepazīšanos ar Bernhofa struktūrsistēmas teorētisko pamatojumu, šīs sistēmas matemātisko un praktisko analīzi, pilota fāzes izstrādi un tās rezultātu, pielietojuma un tehnisko aspektu analīzi. Balstoties uz šo analīzi, ir veikta programmas gala versijas izstrāde un nobeigumā veikts kopējais programmas darbības pārskats, bet pētījuma gala rezultāti tiks publicēti Bernhofa disertācijā. Programma A.U.T. realizēta Javā, izmantojot modificētu datorpeli kā testu vadības rīku. Darba koncepcija, datorpgroammas izveides kritēriji un pilota fāzes rezultāti prezentēti un publicēti starptautiskajā konferencē „Cognitive and Behavioral Psychology (CBP) 2012”, gala versijas rezultāti aicināti publicēt „Global Science and Technology Forum: Journal of Law and Social Sciences”. Atslēgvārdi: audiālā uzmanība, mācību programma, MIDI, JavaAttention deficit disorder appears to be a common phenomenon among elementary school pupils and is recognized as one of the key problems in schools. This work aims to create a game/tool that would be used to train attention. This paper is written is collaboration with Valdis Bernhofs and addresses the practical part of his promotion paper "Pitch and Rhythm structural system for training aural attentiveness”. The hypothesis states that pitch and rhythm are the key music parameters to evoke a positive effect on aural attention system. Thus in his work Bernhofs develops a pitch and rhythm structural system for aural attentiveness training. This work covers the development cycle of aural attention training tool A.U.T. from introduction to the training system to the practical application of the developed learning tool. This paper includes brief summary of the theoretical background of Bernhofs’ paper, mathematical analysis of the system, pilot phase and both practical and technical analysis of its results, development of the final version and brief analysis of the final tests. The research is ongoing and the final results of the tests will be posted in Bernhofs’ dissertation. The program A.U.T. is developed in Java using modified mouse as a controller. The concept and results of the pilot phase were presented and published in the annual Cognitive and Behavioral Psychology (CBP) conference in Singapore, 2012. The results of the final paper have been asked to be published in “Global Science and Technology Forum: Journal of Law and Social Sciences”. Keywords: aural attentiveness, training program, Midi, Jav

The Use of an Improved LSSVM and Joint Normalization on Temperature Prediction of Gearbox Output Shaft in DFWT

In the working process of Double-Fed Wind Turbines (DFWT), it is very important to monitor and predict the temperature of the high-speed output shaft of the gearbox timely and effectively. Support vector machine has more advantages in the temperature prediction of wind turbines. Least squares support vector machine is suitable for online prediction due to reducing the computational complexity of support vector machine. In order to solve the sparsity of least squares support vector machine, an improved least squares support vector machine based on pruning algorithm is proposed in this paper to predict the temperature of the high-speed output shaft of gearbox using the practical data of Double-Fed Wind Turbines. At the same time, in order to improve the prediction accuracy and to solve the problem of few links between different feature parameters in common normalization method, the paper uses the method of joint normalization to preprocess the data. The principal component analysis is used to reduce the dimension of the data. Particle swarm optimization algorithm is used to optimize the parameters of the pruning least squares support vector machine. The proposed model that is established in this paper is a new model to forecast the temperature of the high-speed output shaft. The results show that its prediction accuracy is higher than that of other algorithms