Online Gaming Network Communication Dynamics, Depressive Symptoms, and Social Support: A Longitudinal Network Analysis

Video games are part of everyday life for many Americans despite concerns for social isolation and depressive symptoms. Preliminary studies show gamers may compensate for lack of in-real-life (IRL) support with online connections. This longitudinal social network study investigated the social structure of an online gaming site and how social support, sense of community, and depressive symptoms relate to communication. Members (N = 40) of an online gaming site reported online and IRL support, sense of community, depressive symptoms, and usernames of other members whom they spoke to about important life matters. IRL and online social support, sense of community, and depressive symptoms significantly influenced changes in online gaming network structure over time. These results are timely given social isolation and mental health impacts related to the COVID-19 pandemic. Exploring how to healthfully build online connections through gaming may be an avenue for greater social support when IRL social support is lacking. © 2023 North Central Sociological Association.

Informative Initialization and Kernel Selection Improves t-SNE for Biological Sequences

The t-distributed stochastic neighbor embedding (t-SNE) is a method for interpreting high dimensional (HD) data by mapping each point to a low dimensional (LD) space (usually two-dimensional). It seeks to retain the structure of the data. An important component of the t-SNE algorithm is the initialization procedure, which begins with the random initialization of an LD vector. Points in this initial vector are then updated to minimize the loss function (the KL divergence) iteratively using gradient descent. This leads comparable points to attract one another while pushing dissimilar points apart. We believe that, by default, these algorithms should employ some form of informative initialization. Another essential component of the t-SNE is using a kernel matrix, a similarity matrix comprising the pairwise distances among the sequences. For t-SNE-based visualization, the Gaussian kernel is employed by default in the literature. However, we show that kernel selection can also play a crucial role in the performance of t-SNE.In this work, we assess the performance of t-SNE with various alternative initialization methods and kernels, using four different sets, out of which three are biological sequences (nucleotide, protein, etc.) datasets obtained from various sources, such as the well-known GISAID database for sequences of the SARS-CoV-2 virus. We perform subjective and objective assessments of these alternatives. We use the resulting t-SNE plots and k-ary neighborhood agreement (k-ANA) to evaluate and compare the proposed methods with the baselines. We show that by using different techniques, such as informed initialization and kernel matrix selection, that t-SNE performs significantly better. Moreover, we show that t-SNE also takes fewer iterations to converge faster with more intelligent initialization. © 2022 IEEE.

Clustering SARS-CoV-2 Variants from Raw High-Throughput Sequencing Reads Data

The massive amount of genomic data appearing over the past two years for SARS-CoV-2 has challenged traditional methods for studying the dynamics of the COVID-19 pandemic. As a result, new methods, such as the Pangolin tool, have appeared which can scale to the millions of samples of SARS-CoV-2 currently available. Such a tool is tailored to take assembled, aligned and curated full-length sequences, such as those provided by GISAID, as input. As high-throughput sequencing technologies continue to advance, such assembly, alignment and curation may become a bottleneck, creating a need for methods which can process raw sequencing reads directly. In this paper, we propose several alignment-free embedding approaches, which can generate a fixed-length feature vector representation directly from the raw sequencing reads, without the need for assembly. Moreover, because such an embedding is a numerical representation, it can be passed to already highly optimized clustering methods such as k-means. We show that the clusterings we obtain with the proposed embeddings are more suited to this setting than the Pangolin tool, based on several internal clustering evaluation metrics. Moreover, we show that a disproportionate number of positions in the spike region of the SARS-CoV-2 genome are informing such clusterings (in terms of information gain), which is consistent with current biological knowledge of SARS-CoV-2. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Spike2Signal: Classifying Coronavirus Spike Sequences with Deep Learning

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes the COVID-19 disease in humans, which has reached the scale of a global pandemic. Changes in the composition of the genome of the virus, in the form of mutations, can alter its ability to infect host cells. These modified forms of the virus are known as variants. The spike region of the SARS-CoV-2 genome has a crown-like structure - where 'coronavirus' gets its name. In SARS-CoV-2, it has been noted that mutations happen disproportionately many in the spike region, making this region important for distinguishing different variants. Since amino acids (of the spike protein sequence) are not in a numerical form, they are of no direct use to machine learning algorithms. Thus we use various embedding techniques to make such spike sequence data amenable to machine learning approaches. However, there is ongoing research to find better solutions to study these variants using classification. This paper presents a transformation for spike sequences, called Spike2Signal, to allow the classification of different variants of SARS-CoV-2 using deep learning algorithms. Spike2Signal converts spike sequences into a signal-like representation to allow the classification by state-of-the-art time-series classifiers. Further, we transform this Spike2Signal representation into an image (Spike2Image) to allow the usage of state-of-the-art image classifiers and compare these results with those obtained purely with Spike2Signal. In a wider comparison with existing feature engineering-based methods, we show that the Spike2Signal representation allows to outperform all baselines in predictive power. © 2022 IEEE.

Patient and Provider Perspectives on Telehealth Clinic Visits Precipitated by the Covid-19 Viral Pandemic

Purpose: The transplant community had to adjust to a new way of practicing medicine during the COVID-19 viral pandemic. Our transplant center quickly adapted to virtual clinic visits to maintain the safety of our immunosuppressed patients. The purpose of this study was to examine how patients and providers regarded this new method of delivering healthcare. Method(s): Patients with a telehealth transplant clinic visit between March 2020 and April 2021 were recruited to participate in a telephone survey, which consisted of 19 statements rating their experience on a 5-point Likert Scale. Two additional questions allowed participants to offer suggestions for improvement. Demographic information was also collected. Multi-specialty healthcare providers and support staff in the transplant division who had conducted telehealth visits were contacted via email to participate in an electronic survey, consisting of 25 statements that asked providers to assess their telehealth experience on a 5-point Likert Scale. They were also able to provide additional comments regarding their experience. Result(s): Results are summarized in Table 1. The majority of patients and providers had an electronic device that allowed access to video telehealth visits and felt that the telehealth platform was easy to navigate. Statistical significance was found between not pursuing higher education and not having a video visit-capable device (p=0.035). Retired or disabled patients were more likely to find that setting up an account and navigating the video platform was difficult (p=0.022). Both patients and providers agreed that when they experienced connectivity issues with the video platform, it was easy to convert to a telephone call. Both groups reported having ample time during their telehealth visit and felt that all questions and concerns were adequately addressed. More patients than providers preferred the telehealth visit to an in-person clinic visit, as they mentioned the convenience of the telehealth visit when a lengthy commute was involved. Conclusion(s): The viral pandemic required both patients and providers to adapt to telehealth visits. Social determinants of health should be taken into consideration to provide sufficient care, as certain populations may require extra assistance to utilize this platform effectively. Overall, most providers and patients agreed that adequate care can be provided via telehealth, supporting the continued use of this platform in the future.

TELEHEALTH USE IN RURAL SASKATCHEWAN AND INFLAMMATORY BOWEL DISEASE OUTCOMES

Background: Appropriate management of inflammatory bowel disease (IBD) often requires multiple specialist appointments per year. Living in rural locations may pose a barrier to regular specialist care. Saskatchewan (SK) has a large rural population. Prior to COVID-19, telehealth (TH) in SK was not routinely used for either patient assessment or follow up. Furthermore, TH was exclusively between hospitals and specific TH sites without direct contact using patient's personal phones. Aims: The objective of this study was to assess the differences in demographics, disease characteristics, outcomes, and health care utilization between patients from rural SK with IBD who used TH and those who did not. Methods: A retrospective chart review was completed on all rural patients (postal code S0∗) with IBD in SK who were followed at the Multidisciplinary IBD Clinic in Saskatoon between January 2018 and February 2020. Patients were classified as using TH if they had ever used it. Information on demographics, disease characteristics, and access to IBD-related health care in the year prior to their last IBD clinic visit or endoscopy was collected. Data was not collected for clinic visits after March 1, 2020 as all outpatient care became remote secondary to the COVID-19 pandemic. Mean, standard deviations, median and interquartile ranges (IQR) were reported. Mann-Witney U and Chi-Square tests were used to determine differences between the groups. Results: In total, 288 rural SK IBD patients were included, 30 (10.4%) used TH and 258 (89.6%) did not. Patient demographics were not significantly different between the two groups;although, there was a statistically significant difference in the proportion of ulcerative colitis patients (17% TH vs. 38% non-TH, p=0.02). The percentage of patients with clinical remission was 87% for TH patients and 74% for non-TH patients (p=0.13). There were no significant differences in health care utilization patterns and biochemical markers of disease, including c-reactive protein (CRP) and fecal calprotectin (FCP) (p>0.05). Conclusions: Prior to the pandemic, a small percentage of patients with IBD in rural SK ever used TH. A small proportion of UC patients used TH. No significant differences in disease characteristics, outcomes, or health care utilization were identified. Further study is warranted to identify barriers to use of this technology to tailor care to this patient group and improve access to care, especially now as the COVID-19 pandemic has drastically changed the use of virtual care.

Interdisciplinary Integration: Linking Differentiated P(arts) to Create Accessible Dance Experiences

This article introduces Performing Ourselves, an interdisciplinary community dance program that utilizes principles from dance education and dance/movement therapy to serve beginning dancers in grades PK-8 in schools and community centers. After defining the integrative elements of the curriculum, the article outlines how Performing Ourselves used the shift to virtual programming during the COVID-19 pandemic as a means of creating accessible dance opportunities. In addition to the exploration of how one arts organization approached this challenging time, the article offers ideas and further questions for integrative interdisciplinarity in the arts. © 2022 National Dance Education Organization.

Clustering Based Identification of SARS-CoV-2 Subtypes

With the availability of more than half a million SARS-CoV-2 sequences and counting, many approaches have recently appeared which aim to leverage this information towards understanding the genomic diversity and dynamics of this virus. Early approaches involved building transmission networks or phylogenetic trees, the latter for which scalability becomes more of an issue with each day, due to its high computational complexity. In this work, we propose an alternative approach based on clustering sequences to identify novel subtypes of SARS-CoV-2 using methods designed for haplotyping intra-host viral populations. We assess this approach using cluster entropy, a notion which very naturally captures the underlying process of viral mutation-the first time entropy was used in this context. Using our approach, we were able to identify the well-known B.1.1.7 subtype from the sequences of the EMBL-EBI (UK) database, and also show that the associated cluster is consistent with a measure of fitness. This demonstrates that our approach as a viable and scalable alternative to unveiling trends in the spread of SARS-CoV-2.

Spike2Vec: An Efficient and Scalable Embedding Approach for COVID-19 Spike Sequences

With the rapid global spread of COVID-19, more and more data related to this virus is becoming available, including genomic sequence data. The total number of genomic sequences that are publicly available on platforms such as GISAID is currently several million, and is increasing with every day. The availability of such Big Data creates a new opportunity for researchers to study this virus in detail. This is particularly important with all of the dynamics of the COVID-19 variants which emerge and circulate. This rich data source will give us insights on the best ways to perform genomic surveillance for this and future pandemic threats, with the ultimate goal of mitigating or eliminating such threats. Analyzing and processing the several million genomic sequences is a challenging task. Although traditional methods for sequence classification are proven to be effective, they are not designed to deal with these specific types of genomic sequences. Moreover, most of the existing methods also face the issue of scalability. Previous studies which were tailored to coronavirus genomic data proposed to use spike sequences (corresponding to a subsequence of the genome), rather than using the complete genomic sequence, to perform different machine learning (ML) tasks such as classification and clustering. However, those methods suffer from scalability issues. In this paper, we propose an approach called Spike2Vec, an efficient and scalable feature vector representation for each spike sequence that can be used for downstream ML tasks. Through experiments, we show that Spike2Vec is not only scalable on several million spike sequences, but also outperforms the baseline models in terms of prediction accuracy, F1 score, etc. Since this type of study on such huge numbers of spike sequences has not been done before (to the best of our knowledge), we believe that it will open new doors for researchers to use this data and perform different tasks to unfold new information that was not available before. We also use information gain (IG) to compute the importance of each amino acid in the spike sequence. The amino acids with higher IG values tend to be the same as many reported by the USA based Centers for Disease Control and Prevention (CDC) for different variants.

COVID and the club: conversations with Boys & Girls Club leaders on providing services during the COVID-19 pandemic

Purpose Boys & Girls Clubs of America (BGCs) provide numerous avenues for youth to connect, be physically active and have healthy meals/snacks. These services are often provided to low-income families at reduced cost to bridge the gap in after school and summer childcare. However, many of these clubs were forced to dramatically change their services during the COVID-19 pandemic. This study aims to examine how 13 BGCs in Texas, USA, experienced COVID-19 and persevered to provide services. Design/methodology/approach Interviews were conducted with 16 BGC leaders from 13 different BGCs. Open-ended questions were used to elicit leaders' experiences with the pandemic, services their clubs were able to offer, barriers overcome and supports crucial to their ability to serve their communities. Thematic analysis was used to generate findings from these interviews. Findings BGC services changed significantly during the pandemic. Normal activities were no longer possible;however, leaders (alongside their communities) continually provided services for their families. Further, leaders reiterated the power of the community coming together in support of their families. Social implications While BGC leaders had to adapt services, they found ways to reach families and serve their community. These adaptations can have dramatic impacts on the social and physical well-being of children in their communities. Learning from this adversity can improve services as clubs start to build back. Originality/value This study provides vital context to the changing care and setting children were exposed to during the pandemic response. Additionally, these results provide understanding of the adaptations that took place in these services.

Effective and scalable clustering of SARS-CoV-2 sequences

SARS-CoV-2, like any other virus, continues to mutate as it spreads, according to an evolutionary process. Unlike any other virus, the number of currently available sequences of SARS-CoV-2 in public databases such as GISAID is already several million. This amount of data has the potential to uncover the evolutionary dynamics of a virus like never before. However, a million is already several orders of magnitude beyond what can be processed by the traditional methods designed to reconstruct a virus's evolutionary history, such as those that build a phylogenetic tree. Hence, new and scalable methods will need to be devised in order to make use of the ever increasing number of viral sequences being collected. Since identifying variants is an important part of understanding the evolution of a virus, in this paper, we propose an approach based on clustering sequences to identify the current major SARS-CoV-2 variants. Using a k-mer based feature vector generation and efficient feature selection methods, our approach is effective in identifying variants, as well as being efficient and scalable to millions of sequences. Such a clustering method allows us to show the relative proportion of each variant over time, giving the rate of spread of each variant in different locations - something which is important for vaccine development and distribution. We also compute the importance of each amino acid position of the spike protein in identifying a given variant in terms of information gain. Positions of high variant-specific importance tend to agree with those reported by the USA's Centers for Disease Control and Prevention (CDC), further demonstrating our approach. © 2021 ACM.

A k-mer Based Approach for SARS-CoV-2 Variant Identification

With the rapid spread of the novel coronavirus (COVID-19) across the globe and its continuous mutation, it is of pivotal importance to design a system to identify different known (and unknown) variants of SARS-CoV-2. Identifying particular variants helps to understand and model their spread patterns, design effective mitigation strategies, and prevent future outbreaks. It also plays a crucial role in studying the efficacy of known vaccines against each variant, and modeling the likelihood of breakthrough infections. It is well known that the spike protein contains most of the information/variation pertaining to coronavirus variants. In this paper, we use spike sequences to classify different variants of the human SARS-CoV-2. We show that preserving order information of the amino acids helps the underlying classifiers to achieve better performance. We also show that we can train our model to outperform the baseline algorithms using only a small number of training samples (1 % of the data). Finally, we show the importance of the different amino acids which play a key role in identifying variants and how they coincide with those reported by the USA’s Centers for Disease Control and Prevention (CDC). © 2021, Springer Nature Switzerland AG.

Depressive symptoms, developing friendships, and social support through online gaming: a mixed-method analysis of online gaming network communication

Purpose Online gaming offers avenue to connect with others producing social capital especially for individuals lacking in-real-life (IRL) social support;however, there is concerns related to mental health and depressive symptoms (DS). Virtually mediated social connections are particularly important during times of social distancing. This paper aims to investigate discussant networks established through an online gaming site and their possible association with DS and social support. Design/methodology/approach Participants (n = 40) recruited from an online gaming site reported DS, online and IRL social support, and site members with whom they discussed important life matters. Participants also reported topics of conversation discussed and reason for communication. Quadratic assignment procedure multiple regression was used to determined significant associations between network structure, DS and social support. Findings DS were significantly associated with online (ss = 0.39) and IRL social support (ss = -0.44). Online social support was significantly associated with network structural factors. Topics reported by members most often were bridging capital topics while topics reported by members in most recent conversation were bonding capital topics. Members mentioned bonding social capital concepts as motivation for conversation. Social implications Building online relationships to provide bonding social capital could supply buffering effects for those feeling socially isolated during social distancing. Originality/value This paper is among the first to approach online gaming communication through social network analysis and qualitative analysis mixed method approach.

Ambulatory care pharmacy practice innovations and collaborative developments in response to COVID-19: A report from the ambulatory care practice and research network (PRN)

Service or Program: The Ambulatory Care PRN annually reports ambulatory care pharmacy practice innovations developed by PRN members. In 2020, the ACCP Clinical Pharmacy in Action series was reviewed to disseminate ambulatory clinical models developed to maintain continuity of care during the COVID-19 pandemic and their initial outcomes. Justification/Documentation: New approaches reported included non-clinic-based drive-through and telehealth services. Hospital- and private clinic-based pharmacist-led teams incorporating technicians and learners developed drive-through anticoagulation and COVID-19 testing and screening utilizing existing parking facilities and driveways. Increased pharmacist video and phone telehealth utilization for medication administration technique education and medication-related problem resolution were also reported. Collaborations were established with local health departments, clinics, shelters, and medical reserve corps units to enhance COVID-19 screening, personal protective equipment fitting, call center services, patient education and follow-up, and homeless assistance services. Patients were reported to express satisfaction and reduced anxiety due to improved convenience, safety, and expediency of drive-through testing, which led to reports of improved time to care, no-show rates, and workflows. Telehealth approaches were reported to help support full-time pharmacist practice hours, with telehealth services generating reimbursement potentially comparable to in-clinic encounters. Student pharmacists reported meaningful and fulfilling experiences. Adaptability: Prior to the development and deployment of an effective vaccine, COVID-19 is anticipated to have a sustained global impact on patient care access and clinical pharmacist practice and reimbursement. Approaches developed in response to the initial COVID-19 outbreak will likely serve as models for ambulatory pharmacy clinical services nationwide and adapted to a wide range of care needs and specialties. Significance: Patient-centric, non-clinic-based approaches developed during the COVID-19 pandemic potentially justify continuance of pharmacist telehealth reimbursement and out-of-clinic care services post-pandemic. These approaches may reduce patient barriers to care while expanding ambulatory care pharmacist reach. Expansion of collaboration with public health organizations may also support expanded pharmacist involvement in pandemic initiatives.