Instability Metrics Research Articles

Mood instability metrics to stratify individuals and measure outcomes in bipolar disorder.

Clinical care for bipolar disorder (BD) has a narrow focus on prevention and remission of episodes with pre/post treatment reductions in symptom severity as the 'gold standard' for outcomes in clinical trials and measurement-based care strategies. The study aim was to provide a innovative method for measuring outcomes in BD that has clinical utility and can stratify individuals with BD based on mood instability. Participants were 603 with a BD (n=385), other or non-affective disorder (n=71), or no psychiatric history (n=147) enrolled in an longitudinal cohort for at least 10 years that collects patient reported outcomes measures (PROMs) assessing depression, (hypo)mania, anxiety, and functioning every two months. Mood instability was calculated as the intraindividual standard deviation (s.d.) of PROMs over one-year rolling windows and stratified into low, moderate, and high thresholds, respectively. Individuals with BD had significantly higher one-year rolling SDs for depression, (hypo)mania, and anxiety compared to psychiatric comparisons (small - moderate effects) and healthy controls (large effects). A significantly greater proportion of scores for those with BD fell into the moderate (depression: 50.6%; anxiety: 36.5%; (hypo)mania: 52.1%) and high thresholds (depression: 9.4%; anxiety: 6·1%; (hypo)mania: 10·1%) compared to psychiatric comparisons (moderate: 32.3 - 42·9%; high: 2.6% - 6·6%) and healthy controls (moderate: 11.5% - 31.7%; high: 0.4% - 5.8%). Being in the high or moderate threshold predicted worse mental health functioning (small to large effects). Mood instability, as measured in commonly used PROMs, characterized the course of illness over time, correlated with functional outcomes, and significantly differentiated those with BD from healthy controls and psychiatric comparisons. Results suggest a paradigm shift in monitoring outcomes in BD, by measuring intraindividual SDs as a primary outcome index.

Can instability variations warn developers when open-source projects boost?

Although architecture instability has been studied and measured using a variety of metrics, a deeper analysis of which project parts are less stable and how such instability varies over time is still needed. While having more information on architecture instability is, in general, useful for any software development project, it is especially important in Open Source Software (OSS) projects where the supervision of the development process is more difficult to achieve. In particular, we are interested when OSS projects grow from a small controlled environment (i.e., the cathedral phase) to a community-driven project (i.e., the bazaar phase). In such a transition, the project often explodes in terms of software size and number of contributing developers. Hence, the complexity of the newly added features, and the frequency of the commits and files modified may cause significant variations of the instability of the structure of the classes and packages. Consequently, in this article we analyze the instability in OSS projects, especially during that sensitive phase where they become community-driven. Our results show that instability metrics can be easily obtained in such type of transitions. We also observed from our case studies that instability metrics can help finding out the balance between adding new functionality and performing refactoring. As a conclusions we state that instability metrics offer relevant information in the transition phase from the cathedral to the bazaar.

CGAS-STING pathway expression correlates with genomic instability and immune cell infiltration in breast cancer

Genomic instability, as caused by oncogene-induced replication stress, can lead to the activation of inflammatory signaling, involving the cGAS-STING and JAK-STAT pathways. Inflammatory signaling has been associated with pro-tumorigenic features, but also with favorable response to treatment, including to immune checkpoint inhibition. In this study, we aim to explore relations between inflammatory signaling, markers of replication stress, and immune cell infiltration in breast cancer. Expression levels of cGAS-STING signaling components (STING, phospho-TBK1, and phospho-STAT1), replication stress markers (γH2AX and pRPA), replication stress-related proto-oncogenes (Cyclin E1 and c-Myc) and immune cell markers (CD20, CD4, and CD57) are determined immunohistochemically on primary breast cancer samples (n = 380). RNA-sequencing data from TCGA (n = 1082) and METABRIC (n = 1904) are used to calculate cGAS-STING scores. pTBK1, pSTAT1 expression and cGAS-STING pathway scores are all increased in triple-negative breast cancers compared to other subtypes. Expression of γH2AX, pRPA, Cyclin E1, c-Myc, and immune cell infiltration positively correlate with p-STAT1 expression (P < 0.001). Additionally, we observe significant positive associations between expression of pTBK1 and γH2AX, pRPA, c-Myc, and number of CD4+ cells and CD20+ cells. Also, cGAS-STING scores are correlated with genomic instability metrics, such as homologous recombination deficiency (P < 0.001) and tumor mutational burden (P < 0.01). Moreover, data from the I-SPY2 clinical trial (n = 71) confirms that higher cGAS-STING scores are observed in breast cancer patients who responded to immunotherapy combined with chemotherapy. In conclusion, the cGAS-STING pathway is highly expressed in TNBCs and is correlated with genomic instability and immune cell infiltration.

44.1: Invited Paper: The Effect Investigation and Objective Quantification of Dynamic Spatial Distortions on VIMS

The mismatches among 3D content acquisition, display, and perception configurations can result in geometric distortion, which is a potential factor affecting the symptom of VIMS. 3D videos simulating two mismatch types and the reference matched situation were customized for the perception experiment. The subjective SSQ, five‐scale VIMS level rating and objective postural instability are adopted as the evaluation metrics. The results show that the factor matched type represented spatial distortion has a significant effect on the subjective VIMS level and objective postural instability metrics (p&lt;0.05). The VIMS level and postural instability increments of the match type are significantly lower than that of mismatch types. Besides, indexes based on Elastic Potential Energy are proposed for the objective quantification of dynamic spatial distortions.

Abstract P4-09-08: AI-based quantitation of cancer cell and fibroblast nuclear morphology reflects transcriptomic heterogeneity and predicts survival in breast cancer

Abstract Background: Morphological features of cancer cell nuclei are routinely used to assess disease severity and prognosis, and cancer nuclear morphology has been linked to genomic alterations. Quantitative analyses of the nuclear features of cancer cells and other tumor-resident cell types, such as cancer-associated fibroblasts (CAFs), may reveal novel biomarkers for prognosis and treatment response. Here, we applied a pan-cancer nucleus detection and segmentation algorithm and a cell classification model to hematoxylin and eosin (H&amp;E)-stained whole slide images (WSIs) of breast cancer specimens, enabling the measurement of morphological features of nuclei of multiple cell types within a tumor. Methods: Convolutional Neural Network models for 1) nucleus detection and segmentation and 2) cell classification were deployed on H&amp;E-stained WSIs from The Cancer Genome Atlas (TCGA) breast cancer dataset (primary surgical resections; N=890). Separate models were trained to segment regions of stromal subtypes, such as inflamed and fibroblastic stroma. Nuclear features (area, axis length, eccentricity, color, and texture) were computed and aggregated across each slide to summarize slide-level nuclear morphology for each cell type. Next-generation sequencing-based metrics of genomic instability (N=774) and gene expression (N=868) were acquired and paired with TCGA WSIs. Gene set enrichment analysis was performed using the Molecular Signatures Database. Spearman correlation compared nuclear features to genomic instability metrics. Linear regression was used to assess the relationship between nuclear features and bulk gene expression. Multivariable Cox regression with age and ordinal tumor stage as covariates was used to find association between overall survival (OS) and nuclear features. All reported results were significant (p&amp;lt; 0.05) when adjusted for false discovery rate via the Benjamini-Hochberg procedure. Results: Variation in cancer cell nuclear area, a quantitative metric related to pathologist-assessed nuclear pleomorphism, was calculated by the standard deviation of the nuclear area of cancer cells across a WSI. This feature was associated with genomic instability, as measured by aneuploidy score (r=0.448) and homologous recombination deficiency score (r=0.382), and reduced OS. In contrast, the variability in fibroblast and lymphocyte nuclear areas did not correlate with either metric of genomic instability (all r&amp;lt; 0.1, p&amp;gt;0.05). Furthermore, an association between variation in cancer cell nuclear area with the expression of cell cycle and proliferation pathway genes was observed, suggesting that increased nuclear size heterogeneity may indicate a more aggressive cancer phenotype. Features quantifying CAF nuclear morphology were also assessed, revealing that CAF nucleus shape (larger minor axis length) was associated with lower OS, as well as the expression of gene sets involved in extracellular matrix remodeling and degradation. Conclusions: The nuclear morphologies of breast cancer cells and CAFs reflect underlying genomic and transcriptomic properties of the tumor and correlates with patient outcome. The application of digital pathology analysis of breast cancer histopathology slides enables the integrative study of genomics, transcriptomics, tumor morphology, and overall survival to support research into disease biology research and biomarker discovery. Citation Format: John Abel, Christian Kirkup, Filip Kos, Ylaine Gerardin, Sandhya Srinivasan, Jacqueline Brosnan-Cashman, Ken Leidal, Sanjana Vasudevan, Deepta Rajan, Suyog Jain, Aaditya Prakash, Harshith Padigela, Jake Conway, Neel Patel, Benjamin Trotter, Limin Yu, Amaro Taylor-Weiner, Emma L. Krause, Matthew Bronnimann, Laura Chambre, Ben Glass, Chintan Parmar, Stephanie Hennek, Archit Khosla, Murray Resnick, Andrew H. Beck, Michael Montalto, Fedaa Najdawi, Michael G. Drage, Ilan Wapinski. AI-based quantitation of cancer cell and fibroblast nuclear morphology reflects transcriptomic heterogeneity and predicts survival in breast cancer [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P4-09-08.

Evaluating approximate flavor instability metrics in neutron star mergers

Neutrinos can rapidly change flavor in the inner dense regions of core-collapse supernovae and neutron star mergers due to the neutrino fast flavor instability. If the amount of flavor transformation is significant, the FFI could significantly affect how supernovae explode and how supernovae and mergers enrich the universe with heavy elements. Since many state of the art supernova and merger simulations rely on neutrino transport algorithms based on angular moments of the radiation field, there is incomplete information with which to determine if the distributions are unstable to the FFI. In this work we test the performance of several proposed moment-based instability tests in the literature. We perform time-independent general relativistic neutrino transport on a snapshot of a 3D neutron star merger simulation to generate reasonable neutrino distributions and check where each of these criteria correctly predict instability. In addition, we offer a new ``maximum entropy'' instability test that is somewhat more complex, but offers more detailed (though still approximate) estimates of ELN crossing width and depth. We find that this maximum entropy test and the resonant trajectory test are particularly accurate at predicting instability in this snapshot, though all tests predict instability where significant flavor transformation is most likely.

Where Are We on the Animal Welfare Map? Using GIS to Assess Stakeholder Diversity and Inclusion.

Inequities exist in all facets of society, and animal welfare organizations (AWOs) and their communities are no exception. These organizations interface with multiple stakeholder groups. An active analysis of stakeholder groups to identify under-served areas and communities has not been performed. Using stakeholder data from Toronto Humane Society (THS) from 2015–2019, this study performed a retrospective spatial analysis to identify well served and under-served geographic areas for adopters, surrenders, public veterinary service (PVS) clients, volunteers and foster parents, using Hot Spot analysis. Correlation analysis was performed to determine whether the spatial distribution of the groups correlated with the four socioeconomic metrics of the 2016 Ontario Marginalization Index (residential instability, material deprivation, dependency, and ethnic concentration), and a metric representing the distribution of Indigenous residents. For each stakeholder group, there were well served areas, typically in central Toronto where THS is located, and under-served areas, typically in the north-west and north-east corners of Toronto and in the surrounding cities of the Greater Toronto Area. The area served by THS PVS extended further north than the other hot spot areas. The number of adopters increased as the residential instability metric increased, whereas the number of adopters decreased as the ethnic concentration metric increased. The rate of surrenders increased as the Indigenous metric increased. Public Veterinary Service clients increased as the residential instability, material deprivation, and Indigenous metrics increased. One of the primary limitations of this study was the confounding factor of distance from THS. Individuals living further from THS are less likely to utilize its services, particularly if there is another accessible AWO nearby, and therefore may appear to reflect an under-served population that may not truly be under-served. A regional approach would help to overcome this limitation. The results provide useful insights into stakeholder engagement and provide a foundation for analysis of more targeted areas, as well as for strategies to reach under-served demographics. Similar analyses by other AWOs would be helpful to address inequities in a larger geographic area. Animal welfare organizations can improve program effectiveness by adding data analytics skills to the more traditional skills associated with this sector.

Modeling spatially distributed snow instability at a regional scale using Alpine3D

AbstractAssessing the avalanche danger level requires snow stratigraphy and instability data. As such data are usually sparse, we investigated whether distributed snow cover modeling can be used to provide information on spatial instability patterns relevant for regional avalanche forecasting. Using Alpine3D, we performed spatially distributed simulations to evaluate snow instability for the winter season 2016–17 in the region of Davos, Switzerland. Meteorological data from automatic weather stations were interpolated to 100 m horizontal resolution and precipitation was scaled with snow depth measurements from airborne laser scanning. Modeled snow instability metrics assessed for two different weak layers suggested that the weak layer closer to the snow surface was more variable. Initially, it was less stable than the weak layer closer to the ground, yet it stabilized faster as the winter progressed. In spring, the simulated snowpack on south-facing slopes stabilized faster than on north-facing slopes, in line with the regional avalanche forecast. In the winter months January to March 2017, simulated instability metrics did not suggest that the snowpack on south-facing slopes was more stable, as reported in the regional avalanche forecast. Although a validation with field data is lacking, these model results still show the potential and challenges of distributed modeling for supporting operational avalanche forecasting.

Sensitivity of modeled snow stability data to meteorological input uncertainty

Abstract. To perform spatial snow cover simulations for numerical avalanche forecasting, interpolation and downscaling of meteorological data are required, which introduce uncertainties. The repercussions of these uncertainties on modeled snow stability remain mostly unknown. We therefore assessed the contribution of meteorological input uncertainty to modeled snow stability by performing a global sensitivity analysis. We used the numerical snow cover model SNOWPACK to simulate two snow instability metrics, i.e., the skier stability index and the critical crack length, for a field site equipped with an automatic weather station providing the necessary input for the model. Simulations were performed for a winter season, which was marked by a prolonged dry period at the beginning of the season. During this period, the snow surface layers transformed into layers of faceted and depth hoar crystals, which were subsequently buried by snow. The early-season snow surface was likely the weak layer of many avalanches later in the season. Three different scenarios were investigated to better assess the influence of meteorological forcing on snow stability during (a) the weak layer formation period, (b) the slab formation period, and (c) the weak layer and slab formation period. For each scenario, 14 000 simulations were performed, by introducing quasi-random uncertainties to the meteorological input. Uncertainty ranges for meteorological forcing covered typical differences observed within a distance of 2 km or an elevation change of 200 m. Results showed that a weak layer formed in 99.7 % of the simulations, indicating that the weak layer formation was very robust due to the prolonged dry period. For scenario a, modeled grain size of the weak layer was mainly sensitive to precipitation, while the shear strength of the weak layer was sensitive to most input variables, especially air temperature. Once the weak layer existed (case b), precipitation was the most prominent driver for snow stability. The sensitivity analysis highlighted that for all scenarios, the two stability metrics were mostly sensitive to precipitation. Precipitation determined the load of the slab, which in turn influenced weak layer properties. For cases b and c, the two stability metrics showed contradicting behaviors. With increasing precipitation, i.e., deep snowpacks, the skier stability index decreased (became less stable). In contrast, the critical crack length increased with increasing precipitation (became more stable). With regard to spatial simulations of snow stability, the high sensitivity to precipitation suggests that accurate precipitation patterns are necessary to obtain realistic snow stability patterns.

Detecting and Tracking Circulating Tumour DNA Copy Number Profiles during First Line Chemotherapy in Oesophagogastric Adenocarcinoma.

DNA somatic copy number aberrations (SCNAs) are key drivers in oesophagogastric adenocarcinoma (OGA). Whether minimally invasive SCNA analysis of circulating tumour (ct)DNA can predict treatment outcomes and reveal how SCNAs evolve during chemotherapy is unknown. We investigated this by low-coverage whole genome sequencing (lcWGS) of ctDNA from 30 patients with advanced OGA prior to first-line chemotherapy and on progression. SCNA profiles were detectable pretreatment in 23/30 (76.7%) patients. The presence of liver metastases, primary tumour in situ, or of oesophageal or junctional tumour location predicted for a high ctDNA fraction. A low ctDNA concentration associated with significantly longer overall survival. Neither chromosomal instability metrics nor ploidy correlated with chemotherapy outcome. Chromosome 2q and 8p gains before treatment were associated with chemotherapy responses. lcWGS identified all amplifications found by prior targeted tumour tissue sequencing in cases with detectable ctDNA as well as finding additional changes. SCNA profiles changed during chemotherapy, indicating that cancer cell populations evolved during treatment; however, no recurrent SCNA changes were acquired at progression. Tracking the evolution of OGA cancer cell populations in ctDNA is feasible during chemotherapy. The observation of genetic evolution warrants investigation in larger series and with higher resolution techniques to reveal potential genetic predictors of response and drivers of chemotherapy resistance. The presence of liver metastasis is a potential biomarker for the selection of patients with high ctDNA content for such studies.

A High‐Throughput Search for Functionally Stable Interfaces in Sulfide Solid‐State Lithium Ion Conductors

AbstractInterfacial reactions between ceramic‐sulfide solid‐electrolytes and common electrodes have remained a major impediment to the development of solid‐state lithium‐ion batteries. In practice, this means that ceramic‐sulfide batteries require a suitable coating material to isolate the electrolyte from the electrode materials. In this work, the interfacial stability of Li10SiP2S12 with over 67 000 materials is computationally evaluated. Over 2000 materials that are predicted to form stable interfaces in the cathode voltage range and over 1000 materials for the anode range are reported on and cataloged. LiCoO2 is chosen as an example cathode material to identify coating compounds that are stable with both Li10SiP2S12 and a common cathode. The correlation between elemental composition and multiple instability metrics (e.g., chemical/electrochemical) is analyzed, revealing key trends in, amongst others, the role of anion selection. A new binary‐search algorithm is introduced for evaluating the pseudo‐phase with improved speed and accuracy. Computational challenges posed by high‐throughput interfacial phase‐diagram calculations are highlighted as well as pragmatic computational methods to make such calculations routinely feasible. In addition to the over 3000 materials cataloged, representative materials from the anionic classes of oxides, fluorides, and sulfides are chosen to experimentally demonstrate chemical stability when in contact with Li10SiP2S12.

Does unstable mood increase risk of suicide? Theory, research and practice

Suicide represents a substantial problem, with significant societal and personal impact. The identification of factors influencing suicide risk is an important step in preventing self-harming behaviour. In this article the authors explore whether emotional instability increases risk of suicide, beyond that of mood intensity. This article provides a summary of existing theory and indirect evidence in support of an association between emotional instability and suicidality. A systematic literature search (Embase, Medline, PsychInfo) was carried out on literature conducted up to October, 2011. Meta-analysis was used to assess the strength of the proposed association. The systematic search identified 20 journal articles meeting the inclusion criteria, including retrospective questionnaire design studies and research conducted across several time-points. Meta-analysis revealed a moderate association, which remained statistically significant even when only including studies conducted over multiple time-points. This effect was attenuated, but remained significant, when controlling for study selection bias. Retrospective questionnaire studies failed to adequately control for mood level. Little is still currently understood about the types of emotional instability (e.g., dysoria, anxiety) most associated with suicidality. Future avenues of investigation include micro- to macro-longitudinal research and the differentiation of emotion subtypes and instability metrics. Momentary assessment techniques may help to detect subtle fluctuations in mood leading to more effective and immediate intervention. Psychosocial intervention strategies for treating unstable emotions are discussed.

Development of a mechanical ‘pivot shift' knee stability test

Anterior cruciate ligament (ACL) injuries are common in sports, with 135 000 ACL tears leading to over 95 000 annual reconstructions in the US. The manual ‘pivot shift' test is...