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Joins are essential and potentially expensive operations in database management systems. When data is associated with time periods, joins commonly include predicates that require pairs of argument tuples to overlap in order to qualify for the result. Our goal is to enable built-in systems support for such joins. In particular, we present an approach where overlap joins are formulated as unions of range joins, which are more general purpose joins compared to overlap joins, i.e., are useful in their own right, and are supported well by B+-trees. The approach is sufficiently flexible that it also supports joins with additional equality predicates, as well as open, closed, and half-open time periods over discrete and continuous domains, thus offering both generality and simplicity, which is important in a system setting. We provide both a stand-alone solution that performs on par with the state-of-the-art and a DBMS embedded solution that is able to exploit standard indexing and clearly outperforms existing DBMS solutions that depend on specialized indexing techniques. We offer both analytical and empirical evaluations of the proposals. The empirical study includes comparisons with pertinent existing proposals and offers detailed insight into the performance characteristics of the proposals.

Similarity search in metric spaces is used widely in areas such as multimedia retrieval, data mining, data integration, to name but a few. To accelerate metric similarity search, pivot-based indexing is often employed. Pivot-based indexing first computes the distances between data objects and pivots and then exploits filtering techniques that use the triangle inequality on pre-computed distances to prune search space during search. The performance of pivot-based indexing depends on the quality of the pivots used, and many algorithms have been proposed for selecting high-quality pivots. We present a comprehensive empirical study of pivot selection algorithms. Specifically, we classify all existing algorithms into three categories according to the types of distances they use for selecting pivots. We also propose a new pivot selection algorithm that exploits the power law probabilistic distribution. Next, we report on a comprehensive empirical study of the search performance enabled by different pivot selection approaches, using different datasets and indexes, thus contributing new insight into the strengths and weaknesses of existing selection techniques. Finally, we offer advice on how to select appropriate pivot selection algorithms for different settings.

Objectives The objective of this study was to evaluate a US hospital’s cost implications and outcomes of cangrelor use in percutaneous coronary intervention (PCI) patients with two or more angiographic high-risk features (HRFs), including avoidance of oral P2Y12 inhibitor pretreatment in patients requiring cardiac surgery. Intravenous cangrelor provides direct, immediate onset and rapid-offset P2Y12 inhibition, which may reduce the necessity for oral P2Y12 pretreatment. Methods A decision analytic model was developed, estimating the annual impact over 3 years of cangrelor availability. Ischemic and bleeding events (48 h) from randomized clinical trial data were extrapolated to 30 days. Event costs were from the CHAMPION PHOENIX Economics substudy. Rates of coronary artery disease (CAD) presentation, PCI, oral P2Y12 pretreatment, and inpatient hospitalization costs were from published literature and clinical experts. Scenario analyses evaluated the impact of cangrelor availability on potential reduced P2Y12 pretreatment rates by 50–100%. Drug costs were 2019 wholesale acquisition costs and, where necessary, all costs were adjusted to 2019 dollars. Results In a hospital treating 1000 CAD PCI inpatients annually, increasing cangrelor use from 11 to 32% resulted in a reduction in 48-h ischemic events/year by 5.7%, while bleeding events increased by 2.9%. Total costs of $1,135,472 declined 12.8%, with a 50% reduction in P2Y12 pretreatment or 30% with no pretreatment. Savings were driven by a decrease in ischemic events, decrease in glycoprotein IIb/IIIa inhibitor use, and less need for and shorter oral P2Y12 inhibitor washout period for surgery patients. Conclusion Use of cangrelor in patients with two or more angiographic HRFs may improve outcomes and lower hospital budgets, mainly from avoiding surgery delays necessitated by oral P2Y12 inhibitor pretreatment. Supplementary Information The online version contains supplementary material available at 10.1007/s40256-021-00491-9.

Objective The aim of the present study was to gain insight into the pathophysiology of diabetic polyneuropathy (DPN) and examine the diagnostic value of sensory and motor axonal excitability testing. Methods One hundred and eleven type 2 diabetics with and without DPN (disease duration: 6.36 ± 0.25 years) and 60 controls were included. All participants received a thorough clinical examination including Michigan Neuropathy Screening Instrument (MNSI) score, nerve conduction studies (NCS), and sensory and motor excitability tests. Patients were compared by the likelihood of neuropathy presence, ranging from no DPN (17), possible/probable DPN (46) to NCS-confirmed DPN (48). Results Motor excitability tests showed differences in rheobase and depolarizing threshold electrotonus measures between NCS-confirmed DPN group and controls but no changes in hyperpolarising threshold electrotonus or recovery cycle parameters. Sensory excitability showed even less changes despite pronounced sensory NCS abnormalities. There were only weak correlations between the above motor excitability parameters and clinical scores. Conclusions Changes in excitability in the examined patient group were subtle, perhaps because of the relatively short disease duration. Significance Less pronounced excitability changes than NCS suggest that axonal excitability testing is not of diagnostic value for early DPN and does not provide information on the mechanisms.

Many navigation applications take natural language speech as input, which avoids users typing in words and thus improves traffic safety. However, navigation applications often fail to understand a user's free-form description of a route. In addition, they only support input of a specific source or destination, which does not enable users to specify additional route requirements. We propose a SpeakNav framework that enables users to describe intended routes via speech and then recommends appropriate routes. Specifically, we propose a novel Route Template based Bidirectional Encoder Representation from Transformers (RT-BERT) model that supports the understanding of natural language route descriptions. The model enables extraction of information of intended POI keywords and related distances. Then we formalize a template-driven path query that uses the extracted information. To enable efficient query processing, we develop a hybrid label index for computing network distances between POIs, and we propose a branch-and-bound algorithm along with a pivot reverse B-tree (PB-tree) index. Experiments with real and synthetic data indicate that RT-BERT offers high accuracy and that the proposed algorithm is capable of outperforming baseline algorithms.

A new species of Macrosiphum Passerini is described based on all life cycle morphs: fundatrix, apterous and alate viviparae, oviparae, and males. Macrosiphum ginajo lives throughout the growing season on glandular–sticky species of Silene (Caryophyllaceae) in dry forest habitats of western North America. The new species is compared to other Macrosiphum known to feed on Silene. A very similar species, Macrosiphum aetheocornum, which feeds on native Geranium species in similar habitats, is also discussed including measurements, illustrations, and detailed comparison to the new species.

Associations between multifidus muscle morphology and degenerative pathologies have been implied in patients with non-specific low back pain, but it is unknown how these are influenced by pathology severity, number, or distribution. MRI measures of pure multifidus muscle cross-sectional area (CSA) were acquired from 522 patients presenting with low back and/or leg symptoms in an outpatient clinic. We explored cross-sectional associations between the presence, distribution, and/or severity of lumbar degenerative pathologies (individually and in aggregate) and muscle outcomes in multivariable analyses (beta coefficients [95% CI]). We identified associations between lower pure multifidus muscle CSA and disc degeneration (at two or more levels): − 4.51 [− 6.72; − 2.3], Modic 2 changes: − 4.06 [− 6.09; − 2.04], endplate defects: − 2.74 [− 4.58; − 0.91], facet arthrosis: − 4.02 [− 6.26; − 1.78], disc herniations: − 3.66 [− 5.8; − 1.52], and when > 5 pathologies were present: − 6.77 [− 9.76; − 3.77], with the last supporting a potential dose–response relationship between number of spinal pathologies and multifidus morphology. Our findings could hypothetically indicate that these spinal and muscle findings: (1) are part of the same degenerative process, (2) result from prior injury or other common antecedent events, or (3) have a directional relationship. Future longitudinal studies are needed to further examine the complex nature of these relationships.

Quantification of histological information from excised human abdominal aortic aneurysm (AAA) specimens may provide essential information on the degree of infiltration of inflammatory cells in different regions of the AAA. Such information will support mechanistic insight in AAA pathology and can be linked to clinical measures for further development of AAA treatment regimens. We hypothesize that artificial intelligence can support high throughput analyses of histological sections of excised human AAA. We present an analysis framework based on supervised machine learning. We used TensorFlow and QuPath to determine the overall architecture of the AAA: thrombus, arterial wall, and adventitial loose connective tissue. Within the wall and adventitial zones, the content of collagen, elastin, and specific inflammatory cells was quantified. A deep neural network (DNN) was trained on manually annotated, Weigert stained, tissue sections (14 patients) and validated on images from two other patients. Finally, we applied the method on 95 new patient samples. The DNN was able to segment the sections according to the overall wall architecture with Jaccard coefficients after 65 epocs of 92% for the training and 88% for the validation data set, respectively. Precision and recall both reached 92%. The zone areas were highly variable between patients, as were the outputs on total cell count and elastin/collagen fiber content. The number of specific cells or stained area per zone was deterministically determined. However, combining the masks based on the Weigert stainings, with images of immunostained serial sections requires addition of landmark recognition to the analysis path. The combination of digital pathology, the DNN we developed, and landmark registration will provide a strong tool for future analyses of the histology of excised human AAA. In combination with biomechanical testing and microstructurally motivated mathematical models of AAA remodeling, the method has the potential to be a strong tool to provide mechanistic insight in the disease. In combination with each patients’ demographic and clinical profile, the method can be an interesting tool to in supportof a better treatment regime for the patients.

With the widespread diffusion of smartphones, Spatial Crowdsourcing (SC), which aims to assign spatial tasks to mobile workers, has drawn increasing attention in both academia and industry. One of the major issues is how to best assign tasks to workers. Given a worker and a task, the worker will choose to accept the task based on her affinity towards the task, and the worker can propagate the information of the task to attract more workers to perform it. These factors can be measured as worker-task influence. Since workers' affinities towards tasks are different and task issuers may ask workers who performed tasks to propagate the information of tasks to attract more workers to perform them, it is important to analyze worker-task influence when making assignments. We propose and solve a novel influence-aware task assignment problem in SC, where tasks are assigned to workers in a manner that achieves high worker-task influence. In particular, we aim to maximize the number of assigned tasks and worker-task influence. To solve the problem, we first determine workers' affinities towards tasks by identifying workers' historical task-performing patterns. Next, a Historical Acceptance approach is developed to measure workers' willingness of performing a task, i.e., the probability of workers visiting the location of the task when they are informed. Next, we propose a Random reverse reachable-based Propagation Optimization algorithm that exploits reverse reachable sets to calculate the probability of workers being informed about tasks in a social network. Based on worker-task influence derived from the above three factors, we propose three influence-aware task assignment algorithms that aim to maximize the number of assigned tasks and worker-task influence. Extensive experiments on two real-world datasets offer detailed insight into the effectiveness of our solutions.

In naturalistic auditory scenes, relevant information is rarely concentrated at a single location, but rather unpredictably scattered in- and out-field-of-view (in-/out-FOV). Although the parsing of a complex auditory scene is a fairly simple job for a healthy human auditory system, the uncertainty represents a major issue in the development of effective hearing aid (HA) processing strategies. Whereas traditional omnidirectional microphones (OM) amplify the complete auditory scene without enhancing signal-to-noise-ratio (SNR) between in- and out-FOV streams, directional microphones (DM) may greatly increase SNR at the cost of preventing HA users to perceive out-FOV information. The present study compares the conventional OM and DM HA settings to a split processing (SP) scheme differentiating between in- and out-FOV processing. We recorded electroencephalographic data of ten young, normal-hearing listeners who solved a cocktail-party-scenario-paradigm with continuous auditory streams and analyzed neural tracking of speech with a stimulus reconstruction (SR) approach. While results for all settings exhibited significantly higher SR accuracies for attended in-FOV than unattended out-FOV streams, there were distinct differences between settings. In-FOV SR performance was dominated by DM and SP and out-FOV SR accuracies were significantly higher for SP compared to OM and DM. Our results demonstrate the potential of a SP approach to combine the advantages of traditional OM and DM settings without introduction of significant compromises.

Verbally reported long-term memory for past events typically improves with age. However, such findings are based exclusively on studies, where children are directly asked to recall. The present study showed that when 3- (n = 113, 59 girls) and 4-year-olds (n = 113, 62 girls), predominantly White, were brought back to a distinct lab-setting after either 1-, 4.5-, or 13-weeks, children—regardless of age and delay—spontaneously recalled the distinct event experienced at their first visit (all Cohen’s ds > 1.00). Meanwhile, the oldest children outperformed the youngest when being asked directly to retrieve the event (ηp2 > .088). These findings suggest that spontaneous retrieval facilitated by distinct environmental cues provides a short-cut to young children’s event memories. Verbally reported long-term memory for past events typically improves with age. However, such findings are based exclusively on studies, where children are directly asked to recall. The present study showed that when 3- (n = 113, 59 girls) and 4-year-olds (n = 113, 62 girls), predominantly White, were brought back to a distinct laboratory-setting after either 1-, 4.5-, or 13-weeks, children—regardless of age and delay—spontaneously recalled the distinct event experienced at their first visit (all Cohen's ds > 1.00). Meanwhile, the oldest children outperformed the youngest when being asked directly to retrieve the event (η2p>.088). These findings suggest that spontaneous retrieval facilitated by distinct environmental cues provides a short-cut to young children's event memories.

International audience; Neuropathic pain highly affects quality of life, well-being, and function. It has recently been shown based on cluster analysis studies that most patients with neuropathic pain may be categorized into 1 of 3 sensory phenotypes: sensory loss, mechanical hyperalgesia, and thermal hyperalgesia. If these phenotypes reflect underlying pathophysiological mechanisms, they may be more relevant for patient management than underlying neurological diagnosis or pain intensity. The aim of this study was thus to examine the impact of these sensory phenotypes on mental health, functionality, and quality of life. Data of 433 patients from the IMI/EuroPain network database were analyzed, and results of HADS-D/A, Pain Catastrophizing Scale, Euro Quality of Life 5D/-VAS, Brief Pain Inventory, and Graded Chronic Pain Scale between the sensory phenotypes were compared using multiple regression analysis. There was no difference in chronic pain grade, pain intensity, depression, or anxiety scores between phenotypes. Pain interference (Brief Pain Inventory) was higher (P = 0.002); self-reported health state lower (Euro Quality of Life 5D VAS, P = 0.02); and problems regarding mobility (P = 0.008), usual activities (P = 0.004), and self-care (P = 0.039) more prominent (EQ5-D) in the sensory loss compared with the thermal hyperalgesia phenotype. Patients with sensory loss also showed higher pain catastrophizing scores (P = 0.006 and 0.022, respectively) compared with the 2 other groups. Sensory phenotype is associated with the impact of neuropathic pain conditions on well-being, daily functionality, and quality of life but is less associated with pain intensity. These results suggest that the somatosensory phenotype should be considered for personalized pain management. Copyright

BackgroundPsilocybin is a psychedelic drug that has shown lasting positive effects on clinical symptoms and self-reported well-being following a single dose. There has been little research into the long-term effects of psilocybin on brain connectivity in humans.AimsEvaluate changes in resting-state functional connectivity (RSFC) at one-week and three-months after one psilocybin dose in 10 healthy psychedelic-naïve volunteers and explore associations between change in RSFC and related measures.MethodsParticipants received 0.2-0.3 mg/kg psilocybin in a controlled setting. Participants completed resting-state fMRI scans at baseline, one-week and three-months post-administration and [11C]Cimbi-36 PET scans at baseline and one-week. We examined changes in within-network, between-network and region-to-region RSFC. We explored associations between changes in RSFC and psilocybin-induced phenomenology as well as changes in psychological measures and neocortex serotonin 2A receptor binding.ResultsPsilocybin was well tolerated and produced positive changes in well-being. At one-week only, executive control network (ECN) RSFC was significantly decreased (Cohen’s d=-1.73, pFWE=0.010). We observed no other significant changes in RSFC at one-week or three-months, nor changes in region-to-region RSFC. Exploratory analyses indicated that decreased ECN RSFC at one-week predicted increased mindfulness at three-months (r =-0.65).ConclusionsThese findings in a small cohort indicate that psilocybin affects ECN function within the psychedelic “afterglow” period. Our findings implicate ECN modulation as mediating psilocybin-induced, long-lasting increases in mindfulness. Although our findings implicate a neural pathway mediating lasting psilocybin effects, it is notable that changes in neuroimaging measures at three-months, when personality changes are observed, remain to be identified.

OBJECTIVE: The global rise in type 2 diabetes is associated with a concomitant increase in diabetic complications. Diabetic polyneuropathy is the most frequent type 2 diabetes complication and is associated with poor outcomes. The metabolic syndrome has emerged as a major risk factor for diabetic polyneuropathy; however, the metabolites associated with the metabolic syndrome that correlate with diabetic polyneuropathy are unknown.METHODS: We conducted a global metabolomics analysis on plasma samples from a subcohort of participants from the Danish arm of Anglo-Danish-Dutch study of Intensive Treatment of Diabetes in Primary Care (ADDITION-Denmark) with and without diabetic polyneuropathy versus lean control participants.RESULTS: Compared to lean controls, type 2 diabetes participants had significantly higher HbA1c (p = 0.0028), BMI (p = 0.0004), and waist circumference (p = 0.0001), but lower total cholesterol (p = 0.0001). Out of 991 total metabolites, we identified 15 plasma metabolites that differed in type 2 diabetes participants by diabetic polyneuropathy status, including metabolites belonging to energy, lipid, and xenobiotic pathways, among others. Additionally, these metabolites correlated with alterations in plasma lipid metabolites in type 2 diabetes participants based on neuropathy status. Further evaluating all plasma lipid metabolites identified a shift in abundance, chain length, and saturation of free fatty acids in type 2 diabetes participants. Importantly, the presence of diabetic polyneuropathy impacted the abundance of plasma complex lipids, including acylcarnitines and sphingolipids.INTERPRETATION: Our explorative study suggests that diabetic polyneuropathy in type 2 diabetes is associated with novel alterations in plasma metabolites related to lipid metabolism.

Evaluating associations between the five-factor personality domains and resting-state functional connectivity networks (e.g. default mode network, DMN) highlights distributed neurobiological systems linked to behaviorally relevant phenotypes. Establishing these associations can highlight a potential underlying role for these neural pathways in related clinical illness and treatment response. Here, we examined associations between within- and between-network resting-state functional connectivity with functional magnetic resonance imaging and the five-factor personality domains: Openness to experience (Openness), Extraversion, Neuroticism, Agreeableness and Conscientiousness. We included data from 470 resting-state scan sessions and personality assessments in 295 healthy participants. Within- and between-network functional connectivity from 32 a priori defined regions was computed across seven resting-state networks. The association between functional connectivity and personality traits was assessed using generalized least squares. Within-network DMN functional connectivity was significantly negatively associated with trait Openness (regression coefficient = −0.0010; [95% confidence interval] = [−0.0017, −0.0003]; PFWER = 0.033), seemingly driven by association with the Fantasy subfacet. Trait Extraversion was significantly negatively associated with functional connectivity between the visual and dorsal attention networks and positively associated with functional connectivity between the frontoparietal and language networks. Our findings provide evidence that resting-state DMN is associated with trait Openness and gives insight into personality neuroscience.

The article in this issue of JAACAP by the CAMELS (Child/Adolescent Anxiety Multimodal Extended Long-term Study) study team, Peris et al.1, offers 2 very important contributions. Conveniently, the first contribution is also the stated purpose of this report: to present findings detailing the extent to which children and adolescents who previously participated (averaging 6.5 years previously) in the landmark Child and Adolescent Multimodal Study of Anxiety (CAMS) multisite randomized controlled trial (RCT)2 did or did not receive follow-up services. This is not a good-news story. By and large, over the 6-year follow-up, only 35% of the previous RCT-treated children and youth received "consistent" continuing services, either through medication or cognitive-behavioral therapy (CBT), despite ongoing issues of anxiety and significant impairment.