Miracle-dz/UniFAR-FTUs huggingface dataset:
Utility of Non-invasive Cardiac Imaging Assessment in Coronavirus Disease 2019

Coronavirus disease 2019 (COVID-19) was initially regarded as a disease of the lungs, which manifests as an acute respiratory illness and pneumonia, although more recently cardiac complications have been well-characterised. Serological cardiac biomarkers have been used to define acute myocardial injury, with significant elevation of high-sensitivity cardiac troponin (hs-cTn) associated with poor prognosis. Accordingly, 20–25% patients with acute myocardial injury (as defined by an elevated hs-cTn greater than the 99th percentile) have clinical signs of heart failure and increased mortality. An important outstanding clinical question is how best to determine the extent and nature of cardiac involvement in COVID-19. Non-invasive cardiac imaging has a well-established role in assessing cardiac structure and function in a wide range of cardiac diseases. It offers the potential to differentiate between direct and indirect COVID-19 effects upon the heart, providing incremental diagnostic and prognostic utility beyond the information yielded by elevated cardiac biomarkers in isolation. This review will focus on the non-invasive imaging assessment of cardiac involvement in COVID-19.
Sweat the Fall Stuff: Physical Activity Moderates the Association of White Matter Hyperintensities With Falls Risk in Older Adults

Background: Falls in older adults are a major public health problem. White matter hyperintensities (WMHs) are highly prevalent in older adults and are a risk factor for falls. In the absence of a cure for WMHs, identifying potential strategies to counteract the risk of WMHs on falls are of great importance. Physical activity (PA) is a promising countermeasure to reduce both WMHs and falls risk. However, no study has yet investigated whether PA attenuates the association of WMHs with falls risk. We hypothesized that PA moderates the association between WMHs and falls risk. Methods: Seventy-six community-dwelling older adults aged 70–80 years old were included in this cross-sectional study. We indexed PA using the Physical Activity Score for the Elderly (PASE) Questionnaire. Falls risk was assessed using the Physiological Profile Assessment (PPA), and WMH volume (mm3) was determined by an experienced radiologist on T2-weighted and PD-weighted MRI scans. We first examined the independent associations of WMH volume and PASE score with PPA. Subsequently, we examined whether PASE moderated the relationship between WMH volume and PPA. We plotted simple slopes to interpret the interaction effects. Age, sex, and Montreal Cognitive Assessment (MoCA) score were included as covariates in all models. Results: Participants had a mean age of 74 years (SD = 3 years) and 54 (74%) were female. Forty-nine participants (66%) had a Fazekas score of 1, 19 (26%) had a score of 2, and 6 (8%) a score of 3. Both PASE (β = −0.26 ± 0.11; p = 0.022) and WMH volume (β = 0.23 ± 0.11; p = 0.043) were each independently associated with PPA score. The interaction model indicated that PASE score moderated the association between WMH volume and PPA (β = −0.27 ± 0.12; p = 0.030), whereby higher PASE score attenuated the association between WMHs and falls risk. Conclusion: PA is an important moderator of falls risk. Importantly, older adults with WMH can reduce their risk of falls by increasing their PA.
Unravelling the Spatial and Temporal Plasticity of Eelgrass Meadows

The phenotypic plasticity of seagrasses enables them to adapt to changes in environmental conditions and withstand or recover from disturbance. This plasticity was demonstrated in the large variation recorded throughout a suite of bioindicators measured within Zostera marina meadows around Wales and SW England, United Kingdom. Short-term spatial data were analysed alongside long-term monitoring data to determine which bioindicators best described the status of eelgrass meadows subjected to a range of environmental and anthropogenic drivers. Shoot density, leaf length, leaf nutrients (C:N ratio, %N, %P) including stable isotope of δ13C and δ15N provided insight into the longer-term status of the meadows studied and a good indication of the causes of long-term decline. Meadows ranged from those in the Isles of Scilly with little evidence of impact to those in Littlewick in Milford Haven, Wales that showed the highest levels of impacts of all sites. Bioindicators at Littlewick showed clear warning signs of nutrient loading reflected in the long-term decline in shoot density, and prevalence of wasting disease. This study highlights the need for continuous consistent monitoring and the benefits of using extra tools in the form of shoot nutrient analysis to determine causes of decline.
Polygenic Scores and Parental Predictors: An Adult Height Study Based on the United Kingdom Biobank and the Framingham Heart Study

Human height is a polygenic trait, influenced by a large number of genomic loci. In the pre-genomic era, height prediction was based largely on parental height. More recent predictions of human height have made great strides by integrating genotypic data from large biobanks with improved statistical techniques. Nevertheless, recent studies have not leveraged parental height, an added feature that we hypothesized would offer complementary predictive value. In this study, we assessed the predictive power of polygenic risk scores (PRS) combined with the traditional parental height predictors. Our study analyzed genotypic data and parental height from 1,071 trios from the United Kingdom Biobank and 444 trios from the Framingham Heart Study. We explored a series of statistical models to fully evaluate the performance of several PRS constructed together with parental information and proposed a model we call PRS++ that includes gender, parental height, and PRSs of parents and proband. Our estimate of height with an R2 of ∼0.82 is, to our knowledge, the most accurate estimate yet achieved for predicting human adult height. Without parental information, the R2 from the best PRS-driven model is ∼0.73. In summary, using adult height prediction as an example, we demonstrated that traditional predictors still play important roles and merit integration into the current trends of intensive PRS approaches.
Changes in Reproductive Traits in Physalis philadelphica; An Unexpected Shift Toward Self-Incompatibility in a Domesticated Annual Fruit Crop

Domestication is an evolutionary process with an impact on plant reproduction. Many domesticated plants are self-compatible (i.e., they lack mechanisms to reject their own pollen), but few domesticated plants are fully or partially self-incompatible. We used the husk tomato, Physalis philadelphica, as a study model to investigate changes in the reproductive strategy of an annual partially self-incompatible plant during the process of domestication. Wild and cultivated populations of this species coexist in close proximity. These different populations present a high level of morphological and genetic variation associated with different degrees of domestication. We hypothesized that artificial selection favors self-compatibility in cultivated plants through changes in their reproductive strategy and some reproductive parameters associated with domestication. To test this hypothesis, we characterized the floral morphology and some reproductive parameters of weedy plants (wild plants), landraces (semi-domesticated plant), and commercial plants (domesticated plants). We conducted an artificial crossing experiment, germinated the seeds, and recorded seedling growth. Commercial plants had the largest flowers and the highest number of ovules. Yet, they did not differ in other reproductive parameters (e.g., herkogamy, size of pollen grains, stigmatic area, and pollen:ovule ratio) from landraces and weedy plants. Physalis philadelphica produced fruits by autonomous autogamy in the artificial crossing experiment. These fruits were the smallest and lightest fruits at all degrees of domestication; however, fruit set of autonomous autogamy was higher in weedy plants. In addition, fruit production was higher when weedy plants donated pollen to commercial plants. Although seeds produced by autonomous autogamy of weedy plants had a low germination percentage, their cotyledons and the embryonic foliage leaves appeared earlier than in landraces and commercial plants. In conclusion, the domestication syndrome in this plant was manifested as increments in flower size and ovule production. Contrary to expectations, there was higher fruit production by autonomous autogamy in weedy plants than in cultivated plants. It seems that artificial selection in P. philadelphica favors self-incompatibility in cultivated plants. Nonetheless, spontaneous self-pollination seems to be advantageous in weedy populations because they produced viable seeds from which cotyledons and the embryonic foliage leaves emerged earlier than in cultivated plants.
Cancer and Relationship Dissolution: Perspective of Partners of Cancer Patients

Purpose Cancer can be a burden on the relationship and even lead to relationship dissolution. Previous studies about the impact of cancer on close relationships almost exclusively involve cancer patients. So far, little is known about the views of spouses. Therefore, this study focuses on partners or ex-partners of cancer patients. Methods In this cross-sectional study, N = 265 partners or ex-partners of cancer patients are examined regarding a possible separation, the reasons for separation and the influence of the cancer on the relationship. In addition, predictors of separation and the positive or negative perception of the impact of cancer on the relationship were investigated. Results The separation rate (23.4%) was marginally lower than in the general population in Germany (35.79%). The most frequent reason for separation was the death of the cancer patient (59.6%), followed by relationship problems (26.9%), and the cancer disease itself (9.6%). Among those who were separated, 57.4% reported that cancer contributed to the separation. On average, the influence of cancer on relationship dissolution is indicated with 82.9%. Also, for those who stayed together, 83.7% reported an impact of the cancer on the relationship, of which 55.9% reported a negative impact. Logistic regressions indicated that higher levels of depression were associated with greater odds of a more negative perception of the influence of cancer on the relationship, whereas a more satisfied relationship tended to be associated with a more positive perception. Those who had no psychological treatment in the past, lower anxiety levels and lower relationship satisfaction had an increased risk of separation. Overall, relationship satisfaction was significantly lower than in the general population in Germany. Conclusion In particular, psychological factors such as depression and anxiety as well as relationship satisfaction appear to be factors influencing separation and the perception of the influence of cancer on the relationship as positive or negative. Therefore, it seems to be reasonable to consider these aspects in the psychosocial support and also to include the partners in order to achieve a stable and satisfied relationship which has a positive effect on health and psychological well-being.
MCTS Pruning in Turn-Based Strategy Games

Large action spaces is one of the most problematic aspects of turn-based strategy games for all types of AI methods. Some of the state-of-the-art algorithms such as Online Evolutionary Planning and Evolutionary Monte Carlo Tree Search (MCTS) have tried to deal with this problem, but they required a ﬁxed number of actions in each turn. In general strategy games, this assumption can’t be held, as the number of actions that can be executed in a turn is ﬂexible and will vary during the game. This paper studies pruning techniques and the insertion of domain knowledge to deal with high branching factors in a new turn-based strategy game: Tribes. The experiments show that, with the help of these techniques, MCTS can increase its performance and outperform the rule-based agents and Rolling Horizon Evolutionary Algorithms. Moreover, some insights into the tree shape and the behaviour of MCTS with and without pruning techniques are provided.
Warm Rocks for Cold Lizards: Generating Meaningful Quests in Caves of Qud (short paper)

Dynamic quest generation is a topic of interest in the game AI community, with examples appearing in AAA games (Lenhardt 2012), indie games (Coxon 2016), and academia (Sullivan et al. 2012). In July 2018, we released an update to our simulative science fantasy roguelike Caves of Qud (Freehold Games 2015) that added its own take on dynamically generated villages and quests. These systems were built on top of a framework we previously developed for generating historical biographies (Grinblat and Bucklew 2017). Like that framework, our approach to dynamic quest generation—novel in the space as far as we know—was shaped primarily by two design forces at work in the project’s development. The first is an architectural constraint; to support its large-scale game worlds, Caves of Qud uses a two-phase approach to world creation (Bucklew and Grinblat 2019) that delays the full fabrication of game “zones” until the player enters them. The quest generation system was necessarily designed to fit this two-tiered architecture. The second is an aesthetic choice of procedural content design; we tend to be nonprescriptive in deciding which of our content modules can be procedurally combined, preferring instead to “let our generators run wild,” as we say, but ensure that they smooth the rough edges off their output. For our storyful generators, this means eschewing causal logic in favor of randomness that’s parameterized by the richly narrative world the generators are embedded in. The resulting outputs are ripe for apophenic readings. As mentioned, the quest generation work is split between the two phases of world creation, world-gen and zone-gen, plus some pre-authoring of templates. World-gen happens at the start of each game, where abstract representations of the game zones, their contents, and their relationships are generated. Zone-gen happens when a player enters a new zone and those abstractions are reified and fabricated as game objects. With this architectural context in mind, the components of the quest generation system are as follows: 1. A catalog of pre-authored quest templates (e.g. “DiscoverAnImportantLocation”, “FindAnImportantItem”)
SMES: Adapting Dexterity-based Games for Deliberative Play

In this paper we describe the Sharp Multi-input Editing Software (SMES), a prototype system which allows players to convert dexterity challenges to planning challenges in existing games. SMES accomplishes this by recording the player input, displaying it in a visual form, allowing the player to edit the recorded playtrace, and replay the edited sequence back again. Our system combines ideas from tool-assisted speedruns (TAS) with interaction design patterns from Ca-sual Creators and Accessible Player Experiences. Because our implementation is based on intercepting input events at the operating system level, no engineering effort is needed to apply the tool to new games. The result is a new mode of play for existing games that further allows players to realize new behaviors in the game. SMES could support making games more inclusive by letting players adapt the challenge type they face. Furthermore we claim that AI systems that operate over playtraces to assist the player is a rich avenue to explore.
Transfer Learning Between RTS Combat Scenarios Using Component-Action Deep Reinforcement Learning

Real-time Strategy (RTS) games provide a challenging environment for AI research, due to their large state and action spaces, hidden information, and real-time gameplay. Star-Craft II has become a new test-bed for deep reinforcement learning systems using the StarCraft II Learning Environment (SC2LE). Recently the full game of StarCraft II has been approached with a complex multi-agent reinforcement learning (RL) system, however this is currently only possible with extremely large ﬁnancial investments out of the reach of most researchers. In this paper we show progress on using variations of easier to use RL techniques, modiﬁed to accommodate actions with multiple components used in the SC2LE. Our experiments show that we can eﬀectively transfer trained policies between RTS combat scenarios of varying complexity. First, we train combat policies on varying numbers of Star-Craft II units, and then carry out those policies on larger scale battles, maintaining similar win rates. Second, we demonstrate the ability to train combat policies on one StarCraft II unit type (Terran Marine) and then apply those policies to another unit type (Protoss Stalker) with similar success.
Arti�cial Intelligence Is a Promising Prospect for the Detection of Prostate Cancer Extracapsular Extension With Mp-mri: A Two-center Comparative Study

Purpose: A balance between preserving urinary continence and achievement of negative margins is of clinical relevance while implementary di�culty. Preoperatively accurate detection of prostate cancer (PCa) extracapsular extension (ECE) is thus crucial for determining appropriate treatment options. We aimed to develop and clinically validate an arti�cial intelligence (AI)-assisted tool for the detection of ECE in patients with PCa using multiparametric MRI. Methods: 849 patients with localized PCa underwent multiparametric MRI before radical prostatectomy were retrospectively included from two medical centers. The AI tool was built on a ResNeXt network embedded with a spatial attention map of experts’ prior knowledges (PAGNet) from 596 training data sets. The tool was validated in 150 internal and 103 external data sets, respectively; and its clinical applicability was compared with expert-based interpretation and AI-expert interaction. Results: An index PAGNet model using a single-slice image yielded the highest areas under the receiver operating characteristic curve (AUC) of 0.857 (95% con�dence interval [CI], 0.827-0.884), 0.807 (95% CI, 0.735-0.867) and 0.728 (95% CI, 0.631-0.811) in the training, internal test and external test cohorts, compared to the conventional ResNeXt networks. For experts, the inter-reader agreement was observed in only 437/849 (51.5%) patients with a Kappa value 0.343. And the performance of two experts (AUC, 0.632 to 0.741 vs 0.715 to 0.857) was lower (paired comparison, all p values < 0.05) than that of AI assessment. When expert’ interpretations were adjusted by the AI assessments, the performance of both two experts was improved. Conclusion: Our AI tool, showing improved accuracy, offers a promising alternative to human experts for imaging staging of PCa ECE using multiparametric MRI.
MISQ Archivist Cross-Lingual Cybersecurity Analytics in the International Dark Web with Adversarial Deep Representation Learning

International dark web platforms operating within multiple geopolitical regions and languages host a myriad of hacker assets such as malware, hacking tools, hacking tutorials, and malicious source code. Cybersecurity analytics organizations employ machine learning models trained on human-labeled data to automatically detect these assets and bolster their situational awareness. However, the lack of human-labeled training data is prohibitive when analyzing foreign-language dark web content. In this research note, we adopt the computational design science paradigm to develop a novel IT artifact for cross-lingual hacker asset detection (CLHAD). CLHAD automatically leverages the knowledge learned from English content to detect hacker assets in non-English dark web platforms. CLHAD encompasses a novel adversarial deep representation learning (ADREL) method, which generates multilingual text representations using generative adversarial networks (GANs). Drawing upon the state of the art in cross-lingual knowledge transfer, ADREL is a novel approach to automatically extract transferable text representations and facilitate the analysis of multilingual content. We evaluate CLHAD on Russian, French, and Italian dark web platforms and demonstrate its practical utility in hacker asset profiling, and conduct a proof-of-concept case study. Our analysis suggests that cybersecurity managers may benefit more from focusing on Russian to identify sophisticated hacking assets. In contrast, financial hacker assets are scattered among several dominant dark web languages. Managerial insights for security managers are discussed at operational and strategic levels.
Integrated modelling and pilot plant assessments

Sharifzadeh, Mahdi; Shah, Nilay 3 a Centre for Process Systems Engineering (CPSE), Department of Chemical Engineering, Imperial College. London SW7 2AZ 4 5 AbstractNatural gas accounts for 21.3% of the total primary energy supply. In particular, with the shale gas 6 boom, its application has experienced an exponential growth rate. Nevertheless, natural gas has the highest 7 hydrogen to carbon ratio among all hydrocarbons and the dilute (with respect to CO2) exhausts of its 8 combustion pose considerable thermodynamic challenges for carbon capture. Solvent-based carbon capture 9 can potentially mitigate the carbon emissions. Nonetheless, minimizing the penalties associated with the costs 10 of carbon capture requires detailed understanding of the underlying physical and chemical phenomena. The 11 present research exploits a rigorous methodology based on rate-based distributed modelling and statistical 12 associating fluid theory (SAFT). The important characteristics of this modelling approach include abstract 13 formulation in conjunction with high predictability. The present research aims at establishing the performance 14 of a new solvent (an amine-promoted buffer salt, APBS) in comparison to the baseline solvent, i.e., 15 monoethanolamine (MEA). The features of interest include: (i) developing a high fidelity mathematical model 16 of the carbon capture process, (ii) validating the model with pilot plant data, (iii) quantification of the energetic 17 and technical performances of the solvents using key process indicators (KPIs), and (iv) employing optimization 18 programming for further solvent performance improvement. 19 20
Internet of Things Based Smart Society

Internet of Things (IoT) is a fledgling and popular technology, which has given the provision to people for doing daily work like, at just a click or on a command. The Internet of Things has helped develop many amazing Things like, smart street where streetlight gets automatically switched on or off with the help of sensor data. Well sensor is a crucial factor in the field of Internet of Things applications. The great developers and people from the industry are working continuously to develop a system which will help sectors like healthcare, agricultural, industrial, construction, etc. A lot of researchers do research on the Internet of Things as a part of interest and as a result they end up developing amazing and unique systems. This paper produces a summary of the Internet of Things system named as “Smart Society” which consists of 5 unique modules which are Smart Street Lighting, Disaster Management System, House Safety, Smart Gardening and Healthy Environment. In the further part of the paper, the components and the software systems are discussed which have been utilized to develop this model, and at last, the challenges and future scope of “Smart Society System”.