Match experiences affect interest: Impacts of matchmaking and performance on churn in a competitive game.

This study assessed how matchmaking and match results affect player churn in a multiplayer competitive game. In competitive games, matchmaking is crucial in gathering players with similar skills and creating balanced player-versus-player matches. Players are highly motivated when they win matches, whereas losing matches is demotivating, leading to churn. We performed a two-way fixed effects estimation using our panel data to analyze the relationship between players' churn and match experience. The panel data retrieved 42 days of server-side in-game logs, comprising approximately six million matches played by more than 262k players in the casual commercial game "Everybody's Marble." The experimental results indicate that churn is positively influenced by being matched with stronger opponents. Interestingly, being matched with weaker opponents decreases the possibility of churn more than fair matches (being matched with equally skilled opponents). Furthermore, large differences in opponents' skill levels positively influence churn, while more frequent and consecutive wins negatively influence it. The results also reveal that consecutive losses can affect churn differently, depending on the players' level. This study provides theoretical and practical implications for researchers who want to understand the factors that affect user churn and game developers who want to maximize user retention rates in commercial games.

Identity-Based Matchmaking Encryption with Equality Test.

The identity-based encryption with equality test (IBEET) has become a hot research topic in cloud computing as it provides an equality test for ciphertexts generated under different identities while preserving the confidentiality. Subsequently, for the sake of the confidentiality and authenticity of the data, the identity-based signcryption with equality test (IBSC-ET) has been put forward. Nevertheless, the existing schemes do not consider the anonymity of the sender and the receiver, which leads to the potential leakage of sensitive personal information. How to ensure confidentiality, authenticity, and anonymity in the IBEET setting remains a significant challenge. In this paper, we put forward the concept of the identity-based matchmaking encryption with equality test (IBME-ET) to address this issue. We formalized the system model, the definition, and the security models of the IBME-ET and, then, put forward a concrete scheme. Furthermore, our scheme was confirmed to be secure and practical by proving its security and evaluating its performance.

Collaborative Platforms and Matchmaking Algorithms for Research and Education, Establishment, and Optimization of Consortia.

Matchmaking has a great position in the rational allocation of resources in several fields, ranging from market operation to people's daily lives. Matchmakers have evolved through artificial intelligence technologies and are being introduced in numerous aspects of industry, research, and academia in solving decision issues, research innovation design, and building robust and efficient networks. The goal of this report is to describe the collaborative platforms and matchmaking algorithms for research and education, as well as the establishment and optimization of consortia.

bioTCIs: Middle-to-Macro Biomolecular Targeted Covalent Inhibitors Possessing Both Semi-Permanent Drug Action and Stringent Target Specificity as Potential Antibody Replacements.

Monoclonal antibody therapies targeting immuno-modulatory targets such as checkpoint proteins, chemokines, and cytokines have made significant impact in several areas, including cancer, inflammatory disease, and infection. However, antibodies are complex biologics with well-known limitations, including high cost for development and production, immunogenicity, a limited shelf-life because of aggregation, denaturation, and fragmentation of the large protein. Drug modalities such as peptides and nucleic acid aptamers showing high-affinity and highly selective interaction with the target protein have been proposed alternatives to therapeutic antibodies. The fundamental limitation of short in vivo half-life has prevented the wide acceptance of these alternatives. Covalent drugs, also known as targeted covalent inhibitors (TCIs), form permanent bonds to target proteins and, in theory, eternally exert the drug action, circumventing the pharmacokinetic limitation of other antibody alternatives. The TCI drug platform, too, has been slow in gaining acceptance because of its potential prolonged side-effect from off-target covalent binding. To avoid the potential risks of irreversible adverse drug effects from off-target conjugation, the TCI modality is broadening from the conventional small molecules to larger biomolecules possessing desirable properties (e.g., hydrolysis resistance, drug-action reversal, unique pharmacokinetics, stringent target specificity, and inhibition of protein-protein interactions). Here, we review the historical development of the TCI made of bio-oligomers/polymers (i.e., peptide-, protein-, or nucleic-acid-type) obtained by rational design and combinatorial screening. The structural optimization of the reactive warheads and incorporation into the targeted biomolecules enabling a highly selective covalent interaction between the TCI and the target protein is discussed. Through this review, we hope to highlight the middle to macro-molecular TCI platform as a realistic replacement for the antibody.

Bridging clinical care and research in Ontario, Canada: Maximizing diagnoses from reanalysis of clinical exome sequencing data.

We examined the utility of clinical and research processes in the reanalysis of publicly-funded clinical exome sequencing data in Ontario, Canada. In partnership with eight sites, we recruited 287 families with suspected rare genetic diseases tested between 2014 and 2020. Data from seven laboratories was reanalyzed with the referring clinicians. Reanalysis of clinically relevant genes identified diagnoses in 4% (13/287); four were missed by clinical testing. Translational research methods, including analysis of novel candidate genes, identified candidates in 21% (61/287). Of these, 24 families have additional evidence through data sharing to support likely diagnoses (8% of cohort). This study indicates few diagnoses are missed by clinical laboratories, the incremental gain from reanalysis of clinically-relevant genes is modest, and the highest yield comes from validation of novel disease-gene associations. Future implementation of translational research methods, including continued reporting of compelling genes of uncertain significance by clinical laboratories, should be considered to maximize diagnoses.

The implementation of an enhanced clinical model to improve the diagnostic yield of exome sequencing for patients with a rare genetic disease: A Canadian experience.

The introduction of clinical exome sequencing (ES) has provided a unique opportunity to decrease the diagnostic odyssey for patients living with a rare genetic disease (RGD). ES has been shown to provide a diagnosis in 29%-57% of patients with a suspected RGD, with as many as 70% remaining undiagnosed. There is a need to advance the clinical model of care by more formally integrating approaches that were previously considered research into an enhanced diagnostic workflow. We developed an Exome Clinic, which set out to evaluate a workflow for improving the diagnostic yield of ES for patients with an undiagnosed RGD. Here, we report the outcomes of 47 families who underwent clinical ES in the first year of the clinic. The diagnostic yield from clinical ES was 40% (19/47). Families who remained undiagnosed after ES had the opportunity for follow-up studies that included phenotyping and candidate variant segregation in relatives, genomic matchmaking, and ES reanalysis. This enhanced diagnostic workflow increased the diagnostic yield to 55% (26/47), predominantly through the resolution of variants and genes of uncertain significance. We advocate that this approach be integrated into mainstream clinical practice and highlight the importance of a coordinated translational approach for patients with RGD.

The factors that affect members' use of a beauty industry matchmaking platform: Validation of the COM-B extended model.

The global impact of COVID-19 has seriously affected health and livelihood in every country or region, especially in terms of physical consumption behaviors. Hairdressing is an essential physical consumption behavior. To prevent infection, the consumption model for using the beauty industry matchmaking platform (BIMP) has been used during the pandemic. This study investigates the changes in the behavior of media app users in the beauty industry in the post-epidemic era of COVID-19. The COM-B model is the basis for a research framework to study the factors that affect changes in behavior in the areas of Capability, Motivation, and Opportunity of the theoretical framework. A new dimension of fashion sense has expanded the application and validation of the COM-B model to determine the causal relationship between the ability to pursue beauty, motivation, fashion sense, and opportunities by using the platform and the dimension of user behavior. The study finds that fashion sense in the BIMP has a positive and significant impact on beauty care ability, self-motivation to pursue beauty and future cooperation opportunities. The ability, motivation and opportunity to act are all positively significant, which is in agreement with the theoretical framework of the COM-B model. There is no mediating effect for motivation between fashion sense and behavior. The results of this study show that increasing the sense of fashion for members using the BIMP will increases active behavior for members using the platform. This study also proposes practical suggestions for the operation of the BIMP based on the results.

A match made in heaven - "Indian matchmaking" in contemporary times.

The Indian subcontinent is ubiquitous with some social factors such as caste, gender (discrimination), poverty. One particular factor that has taken up the imaginations of the Netflix-watching audience of late is the practice of arranged marriages. A series called Indian Matchmaking catapulted the notion of arranged marriages into the drawing rooms of both people who are highly aware of the notion (probably having been through it themselves), as well as people who have a very vague idea about it. Nevertheless, it has become a highly talked about television show across the Anglophone world. A little before its release, another English-language reality show, What the Love! with Karan Johar was released by Netflix. This explores the world of romantic connections with a few chosen people from India. While placing itself on the opposite side of the spectrum when compared to Indian Matchmaking, in many ways, it lends itself to similar tropes, albeit under a progressive garb. This paper delves into the portrayal of people from India or of Indian origin in the reality shows Indian Matchmaking and What the Love! with Karan Johar. I examine the two shows through the lens of postfeminism and how, while raising several social issues that plague Indian society, both citizens and the diaspora, they inadvertently propagate a certain self-policing and conservatism that people, particularly women, are expected to adhere to.

Seven years since the launch of the Matchmaker Exchange: The evolution of genomic matchmaking.

The Matchmaker Exchange (MME) was launched in 2015 to provide a robust mechanism to discover novel disease-gene relationships. It operates as a federated network connecting databases holding relevant data using a common application programming interface, where two or more users are looking for a match for the same gene (two-sided matchmaking). Seven years from its launch, it is clear that the MME is making outstanding contributions to understanding the morbid anatomy of the genome. The number of unique genes present across the MME has steadily increased over time; there are currently >13,520 unique genes (~68% of all protein-coding genes) connected across the MME's eight genomic matchmaking nodes, GeneMatcher, DECIPHER, PhenomeCentral, MyGene2, seqr, Initiative on Rare and Undiagnosed Disease, PatientMatcher, and the RD-Connect Genome-Phenome Analysis Platform. The collective data set accessible across the MME currently includes more than 120,000 cases from over 12,000 contributors in 98 countries. The discovery of potential new disease-gene relationships is happening daily and international collaborative teams are moving these advances forward to publication, now numbering well over 500. Expansion of data sharing into routine clinical practice by clinicians, genetic counselors, and clinical laboratories has ensured access to discovery for even more individuals with undiagnosed rare genetic diseases. Tens of thousands of patients and their family members have been directly or indirectly impacted by the discoveries facilitated by two-sided genomic matchmaking. MME supports further connections to the literature (PubCaseFinder) and to human and model organism resources (Monarch Initiative) and scientists (ModelMatcher). Efforts are now underway to explore additional approaches to matchmaking at the gene or variant level where there is only one querier (one-sided matchmaking). Genomic matchmaking has proven its utility over the past 7 years and will continue to facilitate discoveries in the years to come.

ModelMatcher: A scientist-centric online platform to facilitate collaborations between stakeholders of rare and undiagnosed disease research.

Next-generation sequencing is a prevalent diagnostic tool for undiagnosed diseases and has played a significant role in rare disease gene discovery. Although this technology resolves some cases, others are given a list of possibly damaging genetic variants necessitating functional studies. Productive collaborations between scientists, clinicians, and patients (affected individuals) can help resolve such medical mysteries and provide insights into in vivo function of human genes. Furthermore, facilitating interactions between scientists and research funders, including nonprofit organizations or commercial entities, can dramatically reduce the time to translate discoveries from bench to bedside. Several systems designed to connect clinicians and researchers with a shared gene of interest have been successful. However, these platforms exclude some stakeholders based on their role or geography. Here we describe ModelMatcher, a global online matchmaking tool designed to facilitate cross-disciplinary collaborations, especially between scientists and other stakeholders of rare and undiagnosed disease research. ModelMatcher is integrated into the Rare Diseases Models and Mechanisms Network and Matchmaker Exchange, allowing users to identify potential collaborators in other registries. This living database decreases the time from when a scientist or clinician is making discoveries regarding their genes of interest, to when they identify collaborators and sponsors to facilitate translational and therapeutic research.

PhenomeCentral: 7 years of rare disease matchmaking.

A major challenge in validating genetic causes for patients with rare diseases (RDs) is the difficulty in identifying other RD patients with overlapping phenotypes and variants in the same candidate gene. This process, known as matchmaking, requires robust data sharing solutions to be effective. In 2014 we launched PhenomeCentral, a RD data repository capable of collecting computer-readable genotypic and phenotypic data for the purposes of RD matchmaking. Over the past 7 years PhenomeCentral's features have been expanded and its data set has consistently grown. There are currently 1615 users registered on PhenomeCentral, which have contributed over 12,000 patient cases. Most of these cases contain detailed phenotypic terms, with a significant portion also providing genomic sequence data or other forms of clinical information. Matchmaking within PhenomeCentral, and with connections to other data repositories in the Matchmaker Exchange, have collectively resulted in over 60,000 matches, which have facilitated multiple gene discoveries. The collection of deep phenotypic and genotypic data has also positioned PhenomeCentral well to support next generation of matchmaking initiatives that utilize genome sequencing data, ensuring that PhenomeCentral will remain a useful tool in solving undiagnosed RD cases in the years to come.

PatientMatcher: A customizable Python-based open-source tool for matching undiagnosed rare disease patients via the Matchmaker Exchange network.

The amount of data available from genomic medicine has revolutionized the approach to identify the determinants underlying many rare diseases. The task of confirming a genotype-phenotype causality for a patient affected with a rare genetic disease is often challenging. In this context, the establishment of the Matchmaker Exchange (MME) network has assumed a pivotal role in bridging heterogeneous patient information stored on different medical and research servers. MME has made it possible to solve rare disease cases by "matching" the genotypic and phenotypic characteristics of a patient of interest with patient data available at other clinical facilities participating in the network. Here, we present PatientMatcher (https://github.com/Clinical-Genomics/patientMatcher), an open-source Python and MongoDB-based software solution developed by Clinical Genomics facility at the Science for Life Laboratory in Stockholm. PatientMatcher is designed as a standalone MME server, but can easily communicate via REST API with external applications managing genetic analyses and patient data. The MME node is being implemented in clinical routine in collaboration with the Genomic Medicine Center Karolinska at the Karolinska University Hospital. PatientMatcher is written to implement the MME API and provides several customizable settings, including a custom-fit similarity score algorithm and adjustable matching results notifications.

The RD-Connect Genome-Phenome Analysis Platform: Accelerating diagnosis, research, and gene discovery for rare diseases.

Rare disease patients are more likely to receive a rapid molecular diagnosis nowadays thanks to the wide adoption of next-generation sequencing. However, many cases remain undiagnosed even after exome or genome analysis, because the methods used missed the molecular cause in a known gene, or a novel causative gene could not be identified and/or confirmed. To address these challenges, the RD-Connect Genome-Phenome Analysis Platform (GPAP) facilitates the collation, discovery, sharing, and analysis of standardized genome-phenome data within a collaborative environment. Authorized clinicians and researchers submit pseudonymised phenotypic profiles encoded using the Human Phenotype Ontology, and raw genomic data which is processed through a standardized pipeline. After an optional embargo period, the data are shared with other platform users, with the objective that similar cases in the system and queries from peers may help diagnose the case. Additionally, the platform enables bidirectional discovery of similar cases in other databases from the Matchmaker Exchange network. To facilitate genome-phenome analysis and interpretation by clinical researchers, the RD-Connect GPAP provides a powerful user-friendly interface and leverages tens of information sources. As a result, the resource has already helped diagnose hundreds of rare disease patients and discover new disease causing genes.

Genomics4RD: An integrated platform to share Canadian deep-phenotype and multiomic data for international rare disease gene discovery.

Despite recent progress in the understanding of the genetic etiologies of rare diseases (RDs), a significant number remain intractable to diagnostic and discovery efforts. Broad data collection and sharing of information among RD researchers is therefore critical. In 2018, the Care4Rare Canada Consortium launched the project C4R-SOLVE, a subaim of which was to collect, harmonize, and share both retrospective and prospective Canadian clinical and multiomic data. Here, we introduce Genomics4RD, an integrated web-accessible platform to share Canadian phenotypic and multiomic data between researchers, both within Canada and internationally, for the purpose of discovering the mechanisms that cause RDs. Genomics4RD has been designed to standardize data collection and processing, and to help users systematically collect, prioritize, and visualize participant information. Data storage, authorization, and access procedures have been developed in collaboration with policy experts and stakeholders to ensure the trusted and secure access of data by external researchers. The breadth and standardization of data offered by Genomics4RD allows researchers to compare candidate disease genes and variants between participants (i.e., matchmaking) for discovery purposes, while facilitating the development of computational approaches for multiomic data analyses and enabling clinical translation efforts for new genetic technologies in the future.

A natural language processing model for supporting sustainable development goals: translating semantics, visualizing nexus, and connecting stakeholders.

Sharing successful practices with other stakeholders is important for achieving SDGs. In this study, with a deep-learning natural language processing model, bidirectional encoder representations from transformers (BERT), the authors aimed to build (1) a classifier that enables semantic mapping of practices and issues in the SDGs context, (2) a visualizing method of SDGs nexus based on co-occurrence of goals (3) a matchmaking process between local issues and initiatives that may embody solutions. A data frame was built using documents published by official organizations and multi-labels corresponding to SDGs. A pretrained Japanese BERT model was fine-tuned on a multi-label text classification task, while nested cross-validation was conducted to optimize the hyperparameters and estimate cross-validation accuracy. A system was then developed to visualize the co-occurrence of SDGs and to couple the stakeholders by evaluating embedded vectors of local challenges and solutions. The paper concludes with a discussion of four future perspectives to improve the natural language processing system. This intelligent information system is expected to help stakeholders take action to achieve the sustainable development goals. Supplementary Information: The online version contains supplementary material available at 10.1007/s11625-022-01093-3.

The Effect of Vitamin D on Coronary Atherosclerosis: A Propensity Score Matched Case-Control Coronary CTA Study.

BACKGROUND: Vitamin D supplementation may be associated with lower cardiovascular (CV) events, but the data are controversial. It remains speculative whether vitamin D supplementation has a direct effect on coronary atherosclerosis. We therefore set out to assess the influence of vitamin D supplementation on the coronary atherosclerosis profile quantified by coronary computed tomography angiography (CTA) in a retrospective case-control cohort study. METHODS: 176 patients (age: 62.4 ± 10.4) referred to coronary CTA for clinical indications were included. A total of 88 patients receiving vitamin D supplementation (mean duration 65.3 ± 81 months) were 1:1 propensity score matched with 88 controls for age, gender, smoking, arterial hypertension, positive family history, dyslipidemia, and diabetes. Coronary stenosis severity (CAD-RADSTM), mixed plaque burden (weighted for non-calcified), high-risk-plaque (HRP) features, and plaque density (HU) were quantified by CTA. Serum 25-hydroxyvitamin D (OH)-levels were measured in 138 patients and categorized into four groups (0: <20 ng/mL; 1: 20-40 ng/mL; 2: 40-60 ng/mL; and 3: >60 ng/mL) and compared with CTA. RESULTS: The prevalence of atherosclerosis by CTA was similar in both groups (75.6% versus 74.3%, p = 0.999), >50% coronary stenosis was slightly higher in controls (p = 0.046), but stenosis severity score (CAD-RADS) was not different (p = 0.106). Mixed plaque burden (weighted for non-calcified) was lower in patients receiving vitamin D supplementation (p = 0.002) and high-risk-plaque prevalence was markedly lower (3.8% versus 32%, p < 0.001). CT plaque density (HU) was higher (p < 0.001) in the vitamin D group. Patients with serum vitamin D (OH) levels >60 ng/mL had higher plaque density (p = 0.04), indicating more calcified and less vulnerable plaque. CONCLUSIONS: In this retrospective case-control cohort study, vitamin D supplementation was associated with less high-risk plaque, less non-calcified plaque burden, and a higher calcified plaque independent of CV risk factors.

Rare disease patient matchmaking: development and outcomes of an internet case-finding strategy in the Undiagnosed Diseases Network.

BACKGROUND: Although clinician, researcher, and patient resources for matchmaking exist, finding similar patients remains an obstacle for rare disease diagnosis. The goals of this study were to develop and test the effectiveness of an Internet case-finding strategy and identify factors associated with increased matching within a rare disease population. METHODS: Public web pages were created for consented participants. Matches made, time to each inquiry and match, and outcomes were recorded and analyzed using descriptive statistics. A Poisson regression model was run to identify characteristics associated with matches. RESULTS: 385 participants were referred to the project and 158 had pages posted. 579 inquiries were received; 89.0% were from the general public and 24.7% resulted in a match. 81.6% of pages received at least one inquiry and 15.0% had at least one patient match. Primary symptom category of neurology, diagnosis, gene page, and photo were associated with increased matches (p ≤ 0.05). CONCLUSIONS: This Internet case-finding strategy was of interest to patients, families, and clinicians, and similar patients were identified using this approach. Extending matchmaking efforts to the general public resulted in matches and suggests including this population in matchmaking activities can improve identification of similar patients.

ARC: An Open Web-Platform for Request/Supply Matching for a Prioritized and Controlled COVID-19 Response.

In 2020 the world was hit by the COVID-19 pandemic putting entire governments and civil societies in crisis mode. Around the globe unprecedented shortages of equipment and qualified personnel were reported in hospitals and diagnostic laboratories. When a crisis is global, supply chains are strained worldwide and external help may not be readily available. In Switzerland, as part of the efforts of the Swiss National COVID-19 Science Task Force, we developed a tailor-made web-based tool where needs and offers for critical laboratory equipment and expertise can be brought together, coordinated, prioritized, and validated. This Academic Resources for COVID-19 (ARC) Platform presents the specialized needs of diagnostic laboratories to academic research groups at universities, allowing the sourcing of said needs from unconventional supply channels, while keeping the entities tasked with coordination of the crisis response in control of each part of the process. An instance of the ARC Platform is operated in Switzerland (arc.epfl.ch) catering to the diagnostic efforts in Switzerland and sourcing from the Swiss academic sector. The underlying technology has been released as open source so that others can adopt the customizable web-platform for need/supply match-making in their own relief efforts, during the COVID-19 pandemic or any future disaster.

RNA matchmaking in chromatin regulation.

Beyond being the product of gene expression, RNA can also influence the regulation of chromatin. The majority of the human genome has the capacity to be transcribed and the majority of the non-protein-coding transcripts made by RNA Polymerase II are enriched in the nucleus. Many chromatin regulators can bind to these ncRNAs in the nucleus; in some cases, there are clear examples of direct RNA-mediated chromatin regulation mechanisms stemming from these interactions, while others have yet to be determined. Recent studies have highlighted examples of chromatin regulation via RNA matchmaking, a term we use broadly here to describe intermolecular base-pairing interactions between one RNA molecule and an RNA or DNA match. This review provides examples of RNA matchmaking that regulates chromatin processes and summarizes the technical approaches used to capture these events.

A Hybrid Semantic Knowledge Integration and Sharing Approach for Distributed Smart Environments.

Distributed systems provide smart functionality to everyday objects with the help of wireless sensors using the internet. Since the last decade, the industry is struggling to develop efficient and intelligent protocols to integrate a huge number of smart objects in distributed computing environments. However, the main challenge for smart and distributed system designers lies in the integration of a large number of heterogeneous components for faster, cheaper, and more efficient functionalities. To deal with this issue, practitioners are using edge computing along with server and desktop technology for the development of smart applications by using Service-Oriented Architecture (SOA) where every smart object offers its functionality as a service, enabling other objects to interact with them dynamically. In order to make such a system, researchers have considered context-awareness and Quality of Service (QoS) attributes of device services. However, context modeling is a complicated task since it could include everything around the applications. Moreover, it is also important to consider non-functional interactions that may have an impact on the behavior of the complete system. In this regard, various research dimensions are explored. However, rich context-aware modeling, QoS, user priorities, grouping, and value type direction along with uncertainty are not considered properly while modeling of incomplete or partial domain knowledge during ontology engineering, resulting in low accuracy of results. In this paper, we present a semantic and logic-based formal framework (hybrid) to find the best service among many candidate services by considering the limitations of existing frameworks. Experimental results of the proposed framework show the improvement of the discovered results.