Concurrent Patterns Research Articles

Sexual minority disparities in opioid and benzodiazepine misuse among adults with opioid use disorder.

Sexual minority individuals demonstrate disparate rates of substance use. Research suggests that bisexual women are vulnerable to substance use disorders when compared to other sexual minority groups. This study explored differences in prevalence of past-year alcohol use disorder (AUD) with and without concurrent past-year opioid and/or benzodiazepine misuse. The present study utilized responses from the National Survey on Drug Use and Health (NSDUH) public dataset between the years 2015-2019 (N = 16,002) to examine the association between sexual orientation and concurrent misuse of opioids and/or benzodiazepines among individuals with past-year AUD, stratified by sex. Bisexual females demonstrated higher rates of concurrent opioid and benzodiazepine use compared to all other groups. Althoughthere was no association between sexual orientation and concurrent substance use patterns among males, female respondents with past-year AUD endorsing past-year misuse of opioids and benzodiazepines, both alone and in combination, were more likely to be bisexual compared to heterosexual. Lesbians were less likely to endorse concurrent misuse of opioids and benzodiazepines compared to bisexual females. In a national sample, bisexual females demonstrated higher odds of risky concurrent substance use patterns. Identifying sexual minority individuals who exhibit elevated risk of co-occurring alcohol, opioid, and/or benzodiazepine misuse is an important step to targeted prevention efforts and allocation of resources to combat rising overdose deaths. For the first time, this study explored risky concurrent alcohol, opioid, and benzodiazepine misuse patterns among individuals of different sexual orientations.

Onset and temporal sequencing patterns of comorbidity between lifetime major depression, panic disorder and fibromyalgia.

Fibromyalgia (FM) is a syndrome of unknown aetiology characterised by chronic widespread musculoskeletal pain and associated with high rates of psychiatric comorbidities, mainly mood and anxiety disorders.This study aims to determine the age at onset (AAO) and temporal sequencing patterns of FM and its frequent and distinguishable psychiatric comorbidities, the major depressive episode/s (MDE), and panic disorder (PD). Diagnosis of MDE and PD were assigned using the Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders fourth edition (DSM-IV). The AAO of FM, MDE, and PD was defined using the event history calendar. All patients completed a sociodemographic data form, self-report questionnaires measuring FM-related symptoms and function, and the Childhood Trauma Questionnaire-28 (CTQ-28). 98 (83%) of the 118 recruited patients with FM had at least one psychiatric comorbidity. Two main temporal patterns were identified among the 83 patients (70.3 %) who could reliably report the age at onset of FM and psychiatric comorbidities. In the concurrent comorbidity pattern (CCP), MDE and/or PD co-occurred with the onset of FM. In the sequential pattern (SP), the patients first developed PD, then MDE, and finally FM. FM patients with SP are overweight and younger than those with a CCP (FM concurrent with MDE and PD) and reported more childhood adversities, mainly sexual abuse. AAO of psychiatric comorbidities significantly differed between the two patterns. The presence of different temporal comorbidity patterns may suggest prevention/early treatment interventions, especially in patients with childhood adversities and early-onset PD.

Chemical Tracking of Temperature by Concurrent Periodic Precipitation Pattern Formation in Polyacrylamide Gels.

In nature, nonequilibrium systems reflect environmental changes, and these changes are often “recorded” in their solid body as they develop. Periodic precipitation patterns, aka Liesegang patterns (LPs), are visual sums of complex events in nonequilibrium reaction–diffusion processes. Here we aim to achieve an artificial system that “records” the temperature changes in the environment with the concurrent LP formation. We first illustrate the differences in 1-D LPs developing at different temperatures in terms of band spacings, which can demonstrate the time, ramp steepness, and extent of a temperature change. These results are discussed and augmented by a mathematical model. Using scanning electron microscopy, we show that the average size of the CuCrO4 precipitate also reflects the temperature changes. Finally, we show that these changes can also be “recorded” in the 2-D and 3-D LPs, which can have applications in long-term temperature tracking and complex soft material design.

Control Chart Concurrent Pattern Classification Using Multi-Label Convolutional Neural Networks

The detection and identification of non-random patterns is an important task in statistical process control (SPC). When a non-random pattern appears on a control chart, it means that there are assignable causes which will gradually deteriorate the process quality. In addition to the study of a single pattern, many researchers have also studied concurrent non-random patterns. Although concurrent patterns have multiple characteristics from different basic patterns, most studies have treated them as a special pattern and used the multi-class classifier to perform the classification work. This study proposed a new method that uses a multi-label convolutional neural network to construct a classifier for concurrent patterns of a control chart. This study used data from previous studies to evaluate the effectiveness of the proposed method with appropriate multi-label classification metrics. The results of the study show that the recognition performance of multi-label convolutional neural network is better than traditional machine learning algorithms. This study also used real-world data to demonstrate the applicability of the proposed method to online monitoring. This study aids in the further realization of smart SPC.

Image Co-Saliency Detection and Instance Co-Segmentation Using Attention Graph Clustering Based Graph Convolutional Network

Co-Saliency Detection (CSD) is to explore the concurrent patterns and salient objects from a group of relevant images, while Instance Co-Segmentation (ICS) aims to identify and segment out all of these co-salient instances, generating corresponding mask for each instance. To simultaneously tackle these two tasks, we present a novel adaptive graph convolutional network with attention graph clustering (GCAGC) for CSD and ICS, termed as GCAGC-CSD and GCAGC-ICS, respectively. The GCAGC-CSD contains three key model designs: first, we develop a graph convolutional network architecture to extract multi-scale representations to characterize the intra- and inter-image consistency. Second, we propose an attention graph clustering algorithm to distinguish the salient foreground objects from common areas in an unsupervised manner. Third, we present a unified framework with encoder-decoder structure to jointly train and optimize the graph convolutional network, attention graph cluster, and CSD decoder in an end-to-end fashion. Afterwards, we design a salient instance segmentation network for GCAGC-ICS, and combine the outputs of GCAGC-CSD and the instance segmentation branch to obtain instance-aware co-segmentation masks. The proposed GCAGC-CSD and GCAGC-ICS are extensively evaluated on four CSD benchmark datasets (iCoseg, Cosal2015, COCO-SEG and CoSOD3k) and five ICS benchmark datasets (CoSOD3k, COCO-NONVOC, COCO-VOC, VOC12 and SOC), and achieve superior performance over state-of-the-arts on both tasks.

Photoinduced Collective Motion of Oil Droplets and Concurrent Pattern Formation in Surfactant Solution

Photoinduced Collective Motion of Oil Droplets and Concurrent Pattern Formation in Surfactant Solution

Trends in Exclusive, Dual and Polytobacco Use among U.S. Adults, 2014-2019: Results from Two Nationally Representative Surveys.

Although increases in the variety of tobacco products available to consumers have led to investigations of dual/polytobacco use patterns, few studies have documented trends in these patterns over time. We used data from the 2014/2015 and 2018/2019 Tobacco Use Supplement to the Current Population Survey (TUS-CPS) and the 2015–2019 National Health Interview Survey (NHIS) to estimate trends in the following use patterns: exclusive use of cigarettes, electronic nicotine delivery systems (ENDS), other combustibles (cigars/cigarillos/little filtered cigars and traditional pipes/hookah), and smokeless tobacco (four categories); dual use (two product groups) of each product group with cigarettes (three categories); polyuse with cigarettes (all four product groups; one category); and dual/polyuse without cigarettes (one category). We estimated trends in product use patterns overall and by age, sex, and race/ethnicity using two-sample tests for differences in linear proportions. From 2014/2015 to 2018/2019, exclusive ENDS use increased, whereas cigarettes and ENDS dual use decreased. Furthermore, polyuse with cigarettes decreased, whereas dual/polyuse without cigarettes increased, with trends varying by age, sex, and race/ethnicity. Our findings suggest that patterns of dual/polyuse with and without cigarettes have changed in recent years, indicating the need for further surveillance of concurrent tobacco product use patterns.

The Cannabis-Dependent Relationship Between Methadone Treatment Dose and Illicit Opioid Use in a Community-Based Cohort of People Who Use Drugs.

Background: Methadone maintenance treatment (MMT) is an effective treatment for opioid use disorder. However, subtherapeutic dosing may lead to continued opioid use by failing to suppress opioid withdrawal and craving. Preclinical and pilot experimental research suggests that cannabinoids may reduce opioid withdrawal and craving. We sought to test whether the association between low methadone dose and illicit opioid use differs according to concurrent cannabis use patterns. Methods: Data for this study were derived from two community-recruited cohorts of people (≥18 years old) who use illicit drugs in Vancouver, Canada. We used generalized estimating equations to estimate the adjusted association between lower daily MMT dose (<90 mg/day) and daily illicit opioid use, testing for interaction between dose and daily cannabis use. Results: Between December 2005 and December 2018, 1389 participants reported MMT enrolment and were included in the study. We observed a significant interaction (p<0.01) between daily cannabis and lower MMT dose on concurrent daily illicit opioid use: lower MMT doses increased the odds of daily illicit opioid use by 86% (adjusted odds ratio [AOR]=1.86, 95% confidence interval [CI]=1.61-2.16) during periods of no or low-frequency cannabis use and by 30% during periods of daily cannabis use (AOR=1.30, 95% CI=1.01-1.67). Discussion: This study provides preliminary observational evidence that cannabis may mitigate some of the negative effects of subtherapeutic MMT dosing, guiding future clinical investigations into the safety and efficacy of cannabis and cannabinoids as adjunct treatment for MMT.

Evolution of song and color in island birds

Island birds are often less colorful and sing different songs than their mainland counterparts. Given that trade-offs can occur in the expression of costly sexually selected traits, it is not clear whether island species with less colorful plumage than their mainland counterparts have compensated with increased complexity of song structure. To examine potential trade-offs in sexually selected traits in island birds, we examined concurrent patterns of coloration and song structure in island and mainland (sub)species across 3 widespread and speciose songbird families: Fringillidae, Meliphagidae, and Monarchidae. Phylogenetically controlled analyses revealed a shift away from white plumage and toward melanin-based plumage in island (sub)species but only in the Meliphagidae. Contrary to our predictions, we found no evidence that song structure in island birds differed from their mainland counterparts. Although a number of inverse correlations were identified between song structure and plumage coloration when all birds were considered, this pattern became weaker when only island populations were examined. Instead, we found island size affected song structure leading to a lower number of syllables and lower minimum frequency on smaller islands. Birds on smaller islands also had reduced carotenoid plumage but this pattern was only significant in the Fringillidae. These results indicate that island environments can influence the evolution of both song and color in some taxa, but that selection is tempered by island size, rather than through correlated evolution across signaling modalities.

MRNA coexpression patterns of Wnt pathway components and their clinicopathological associations in breast and colorectal cancer

Aberrant Wnt signaling is implicated in carcinogenesis triggering efforts for the development of new therapeutic agents, many of which have entered clinical trials. We extend our previous analysis of WNT3, FZD7, LEF1 expression levels in breast and colorectal cancer including WNT2, FZD4 and β-catenin expression, in an effort to delineate their relative expression levels along with concurrent expression patterns and possible prognostic value. We analyzed 82 breast and 102 colorectal carcinomas for relative mRNA expression levels of the investigated genes by RT-PCR relative quantification with the ΔΔCt method. Statistical analysis was performed in order to determine associations of relative mRNA expression and linear correlations. β-catenin expression was determined by immunochemistry. Regarding breast carcinomas, decreased relative mRNA expression levels of WNT2, FZD4 were found frequently and WNT2 expression was correlated with ER/ PR status (p = 0.045/p = 0.028), whereas β-catenin with grade (p = 0.026). In colorectal carcinomas, increased relative mRNA expression levels of WNT2 and FZD4 were found in 59% and 32% of cases respectively, whereas β-catenin showed decreased mRNA expression levels in 57% of cases and a correlation with pN-category (p = 0.037). Linear correlations were observed between WNT2/FZD4 (R=0.542, p < 0.001), WNT2/β-catenin (R=0.254, p = 0.010), FZD4/β-catenin (R=0.406, p < 0.001) expression and a correlation between mRNA expression and membranous/cytoplasmic β-catenin emerged (p = 0.039/0.046). Our results suggest a possible clinical significance for Wnt pathway gene expression levels in both tumour types. The concurrent expression of the investigated genes as well as the different expression profiles, underlines the complexity of this pathway and the necessity of patient selection in order to maximize the efficacy of drugs targeting Wnt pathway.

Tre-O2-03 - Mogamulizumab-associated rash frequently mimics cutaneous T-cell lymphoma recurrence but heralds a positive response to therapy: an updated single-center case series

Mogamulizumab is a humanized antibody that targets CCR4, a transmembrane chemokine receptor predominantly expressed on effector T helper type 2 and malignant cutaneous T-cell lymphoma (CTCL) cells. This biologic was FDA approved for relapsed/refractory mycosis fungoides (MF)/Sézary syndrome (SS) in 2018. MARs are the most common adverse events reported; however, the clinicopathologic nature of these eruptions remains unclear. To inform future management and recognition of MAR, we report an update on our retrospective case series of MF/SS patients treated with mogamulizumab since its approval as standard of care. Among 24 CTCL (16 SS, 8 MF) patients, 17 developed MAR (68%). MAR development was associated with complete (11/17) or partial (4/17) responses, for an overall response rate (ORR) of 88% to mogamulizumab among patients with MAR. A lower ORR of 28% (2/7) was observed in patients that did not develop MAR. Median MAR time to onset (TTO) was 2.1 mo. (0.7–9.0). Median time to resolution (TTR) was 6.9 mo. (3.5–9.2) in 8/17 patients with MAR resolution. Five distinct but frequently concurrent clinical patterns were identified: lichenoid papules and plaques (9/17), MF-like lesions (9/17), photodistribution (7/17), psoriasiform plaques (7/17), and SS-like erythroderma (3/17). Histopathologic patterns included spongiotic (n=21), lichenoid (n=14), psoriasiform (n=10), and interface (n=8) features across 27 skin biopsies. Eosinophils (n=15), epithelioid granulomas (n=4), and bizarre bi- and multinucleated cells (n=6) that may represent highly activated macrophages were also noted. Although mild, features associated with CTCL were noted in all suspected MAR tissue samples [epidermotropism (n=10), exocytosis (n=12), and lymphoid atypia (n=16)]. When compared to baseline studies, flow cytometry analysis for circulating Sézary cells and TCR rearrangement studies in skin and blood were most often negative during suspected MAR despite the presence of CTCL-associated histological features, emphasizing the importance of ancillary studies in distinguishing MAR from CTCL. In conclusion, MAR may be characterized by distinct clinical, histopathologic, and molecular patterns. Notably, MAR can clinically mimic CTCL lesions and may occur more often in patients with clinical responses, highlighting the need for clinicians to rule out progressive disease vs. drug eruption in treating a condition that historically suffers from poor outcomes and treatment response.

Evolution of size and composition of fine particulate matter in the Delhi megacity during later winter

This study presents the first multi-site evolution analysis of fine Organic Aerosols (OA) and inorganics in the Delhi megacity (National Capital Region (NCR)) using high-resolution-particle time of flight (HR-PToF) size distribution obtained from Aerodyne HR-ToF-AMS. It provides a comprehensive view of the atmospheric dynamic processes responsible for the aerosol size and composition transformation. The measurements were performed at two different sampling sites (1) Indian Institute of Technology, Delhi (IITD), and (2) Manav Rachna International University, Faridabad (MRIU) during a late winter period (February to March 2018). Among organics, primary OA (POA) showed particle growth, whereas it was absent in secondary OA proxies. IITD was observed to have a distinct growth pattern as compared to MRIU because of high vehicular density and heterogeneity, and the presence of growth-promoting factors such as acidity and RH conditions. IITD aerosols were observed to be more acidic (ANR∼ 0.75) compared to MRIU (ANR∼1) and showed a distinct diurnal bin-wise increase during the photochemically active period (PAP). Such high acidic conditions are responsible for promoting acid-catalyzed SOA productions over the broader size bins, especially during the PAPs. Primary OA (POA) such as HOA and BBOA proxies show a diurnal growth from ∼440 to 970 nm and from ∼370 to 550 nm, at IITD and from ∼270 to 400 nm and ∼430–470 nm, at MRIU respectively. Further, OA families (amines and hydrocarbons), and inorganics such as chloride and nitrate also showed distinct concurrent diurnal growth patterns at IITD (from an MMDs of ∼380–520 nm to 650–960 nm). We have also observed a positive correlation between OA and inorganics growth and the mass fraction (MF = SOA/OA) of SOA at both sites of NCR. For 0.1 (MF) increase in SOA, the sizes of HOA, chloride, primary CH family and amines (CHN family), and secondary amines (CHON family) are increased by ∼93, 80, 64, 66, and 47 nm at IITD and ∼14, 35, 8, 11, and 16 nm at MRIU respectively. The concurrent growth of these species with the increase in the SOA concentration indicates the existence of a similar size evolution mechanism at both NCR sites, further promoting the formation of internally mixed aerosols during some phase of their evolution. This suggests that condensation is significantly governing the growth mechanism in NCR. Our study indicates the inclusion of such dynamic growth patterns arising from local prevailing conditions into the models for a better understanding of atmospheric aerosol evolution and estimation of fine particulate matter budget.

Sourcing external digital solutions: Digital technologies' architecture and knowledge exchange

Sourcing of external digital solution has become an important strategy incumbent firms are using to accelerate their digital transformation. However, external knowledge and technology sourcing is a complex, multi-phase process that requires exchanging various kinds of knowledge across cognitive, technological and organizational boundaries. Using a longitudinal in-depth qualitative analysis of 15 early-stage pilot projects between startups offering digital solutions and electric utilities seeking to adopt them as empirical setting, we develop a framework of external digital technology sourcing. This framework explicates how and why knowledge exchange patterns differ given a derived set of digital technology integration processes - positioned along the digital technology stack. We show that service enablement at the data-application interface triggers a parallel knowledge iteration pattern, system harmonization at the data-network interface triggers a concurrent knowledge co-creation pattern and assuring hardware efficacy at the network-device interface triggers a sequential knowledge validation pattern. We discuss contributions to the literature on digital innovation and external knowledge sourcing.

On-line concurrent control chart pattern recognition using singular spectrum analysis and random forest

A scheme integrated with engineer process control (EPC) is a useful approach to identify the disturbances for statistical process control (SPC) charting of an auto-correlated process. Although the EPC is able to compensate for the underlying disturbance, the underlying disturbance is embedded in the control chart. Thus, this makes the control chart patterns (CCPs) difficult to be identified. As documented in the literature, much efforts have been put in the recognition of single basic patterns of unnatural variation. However, there could exist a combination of two unnatural patterns simultaneously in a real-world manufacturing process. Also for an automated real-time production line, data are collected automatically and monitored by a computer-based system. Thus, the early detection of abnormality is of high importance for the aforesaid problem. This study aims to establish an on-line detection system used for monitoring the mixture unnatural CCPs for a SPC-EPC process. This paper presents a hybrid approach based on singular spectrum analysis (SSA) and random forest (RF) to identify the concurrent CCPs in an on-line SPC-EPC process. A total of fifteen types of concurrent CCPs were utilized to validate the proposed method. The SSA method was also used to decompose the mixture patterns into single patterns. The RF was employed to identify the types of patterns to which it belongs. The results showed that the proposed method was able to handle most of the concurrent CCPs very successfully with an average accurate identification rate of 91.8%. Also, the proposed method was found to be more accurate and efficient than the use of the hybrid method of SSA and support vector machine (SVM). It is suggested that this proposed system could be possibly applied for monitoring an on-line process to a greater extent.

The assignment problem

In the allocation problem, asynchronous processors must partition a set of items so that each processor leaves knowing all items exclusively allocated to it. We introduce a new variant of the allocation problem called the assignment problem, in which processors might leave having only partial knowledge of their assigned items. The missing items in a processor's assignment must eventually be announced by other processors.While allocation has consensus number 2, we show that the assignment problem is solvable read-write wait-free when k processors compete for at least 2k−1 items. Moreover, we propose a long-lived read-write wait-free assignment algorithm which is fair, allocating no more than 2 items per processor, and in which a slow processor may delay the assignment of at most n items, where n is the number of processors.The assignment problem and its read-write solution may be of practical interest for implementing resource allocators and work queues, which are pervasive concurrent programming patterns, as well as stream-processing systems.

Crack cocaine use among aging Mexican American men with heroin use histories: motivations and polydrug use patterns

Background Knowledge about the motivations for crack cocaine use and the long-term effects among people who inject heroin is limited. This study aims to understand motivations associated with this co-use drug pattern among aging men entrenched in established intergenerational heroin subcultures. Methods: A sample of 60 men with diverse injecting heroin use histories was recruited from low-income Mexican American urban communities for semi-structured ethnographic interviews using respondent-driven sampling. Qualitative analysis was then employed to provide an in-depth understanding of patterns and motivations of polydrug use among this older population. Results: The men discussed motivations including tolerance, curiosity and experimentation, a paradox of affordability, alternatives to the health hassles and stigma, and the cross-fertilization of drug markets. Concurrent and switching use patterns were primarily reported among those seeking a new or different high, while replacement patterns were reported by men in methadone treatment or by those who had tired of injection. Discussion: Findings translate to the on-going opioid crisis and the emerging trend in polysubstance use involving crack cocaine, methamphetamine and other psychostimulants among people who use heroin. More research is needed to investigate the full range of social and health consequences associated with these co-use patterns. Targeted and tailored prevention and treatment services are essential to improve outcomes among aging Mexican American men who use heroin and other persons with similar polydrug use patterns.

Patterns of integrating palliative care into standard oncology in an early ESMO designated center: a 10-year experience

BackgroundIntegration of specialist palliative care (PC) into standard oncology care is recommended. This study investigated how integration at the Cantonal Hospital St. Gallen (KSSG) was manifested 10 years after initial accreditation as a European Society for Medical Oncology (ESMO) Designated Center (ESMO-DC) of Integrated Oncology and Palliative Care.MethodsA chart review covering the years 2006-2009 and 2016 was carried out in patients with an incurable malignancy receiving PC. Visual graphic analysis was utilized to identify patterns of integration of PC into oncology based on the number and nature of medical consultations recorded for both specialties. A follow-up cohort collected 10 years later was analyzed and changes in patterns of integrating specialist PC into oncology were compared.ResultsThree hundred and forty-five patients from 2006 to 2009 and 64 patients from 2016 were included into analyses. Four distinct patterns were identified using visual graphic analysis. The ‘specialist PC-led pattern’ (44.9%) and the ‘oncology-led pattern’ (20.3%) represent disciplines that took primary responsibility for managing patients, with occasional and limited involvement from other disciplines. Patients in the ‘concurrent integrated care pattern’ (18.3%) had medical consultations that frequently bounced between specialist PC and oncology. In the ‘segmented integrated care pattern’ (16.5%), patients had sequences of continuous consultations provided by one discipline before alternating to a stretch of consultations provided by the other specialty. In the 2016 follow-up, while the ‘oncology-led pattern’ occurred significantly less frequently relative to the ‘specialist PC-led pattern’ and the ‘segmented integrated care pattern’, the ‘concurrent integrated care pattern’ emerged more frequently when compared with the 2006-2009 follow-up.ConclusionThe ‘specialist PC-led pattern’ was the most prominent pattern in this data. The 2016 follow-up showed that a growing number of patients received a collaborative pattern of care, indicating that integration of specialist PC into standard oncology can manifest as either segmented or concurrent care pathways. Our data suggest a closer, more dynamic and flexible collaboration between oncology and specialist PC early in the disease course of patients with advanced cancer and concurrent with active treatment.

Computing infection distributions and longitudinal evolution patterns in lung CT images

BackgroundSpatial and temporal lung infection distributions of coronavirus disease 2019 (COVID-19) and their changes could reveal important patterns to better understand the disease and its time course. This paper presents a pipeline to analyze statistically these patterns by automatically segmenting the infection regions and registering them onto a common template.MethodsA VB-Net is designed to automatically segment infection regions in CT images. After training and validating the model, we segmented all the CT images in the study. The segmentation results are then warped onto a pre-defined template CT image using deformable registration based on lung fields. Then, the spatial distributions of infection regions and those during the course of the disease are calculated at the voxel level. Visualization and quantitative comparison can be performed between different groups. We compared the distribution maps between COVID-19 and community acquired pneumonia (CAP), between severe and critical COVID-19, and across the time course of the disease.ResultsFor the performance of infection segmentation, comparing the segmentation results with manually annotated ground-truth, the average Dice is 91.6% ± 10.0%, which is close to the inter-rater difference between two radiologists (the Dice is 96.1% ± 3.5%). The distribution map of infection regions shows that high probability regions are in the peripheral subpleural (up to 35.1% in probability). COVID-19 GGO lesions are more widely spread than consolidations, and the latter are located more peripherally. Onset images of severe COVID-19 (inpatients) show similar lesion distributions but with smaller areas of significant difference in the right lower lobe compared to critical COVID-19 (intensive care unit patients). About the disease course, critical COVID-19 patients showed four subsequent patterns (progression, absorption, enlargement, and further absorption) in our collected dataset, with remarkable concurrent HU patterns for GGO and consolidations.ConclusionsBy segmenting the infection regions with a VB-Net and registering all the CT images and the segmentation results onto a template, spatial distribution patterns of infections can be computed automatically. The algorithm provides an effective tool to visualize and quantify the spatial patterns of lung infection diseases and their changes during the disease course. Our results demonstrate different patterns between COVID-19 and CAP, between severe and critical COVID-19, as well as four subsequent disease course patterns of the severe COVID-19 patients studied, with remarkable concurrent HU patterns for GGO and consolidations.

Evaluating Visual Cues Modulates Their Representation in Mouse Visual and Cingulate Cortex.

Choosing an action in response to visual cues relies on cognitive processes, such as perception, evaluation, and prediction, which can modulate visual representations even at early processing stages. In the mouse, it is challenging to isolate cognitive modulations of sensory signals because concurrent overt behavior patterns, such as locomotion, can also have brainwide influences. To address this challenge, we designed a task, in which head-fixed mice had to evaluate one of two visual cues. While their global shape signaled the opportunity to earn reward, the cues provided equivalent local stimulation to receptive fields of neurons in primary visual (V1) and anterior cingulate cortex (ACC). We found that mice evaluated these cues within few hundred milliseconds. During this period, ∼30% of V1 neurons became cue-selective, with preferences for either cue being balanced across the recorded population. This selectivity emerged in response to the behavioral demands because the same neurons could not discriminate the cues in sensory control measurements. In ACC, cue evaluation affected a similar fraction of neurons; emerging selectivity, however, was stronger than in V1, and preferences in the recorded population were biased toward the cue promising reward. Such a biased selectivity regime might allow the mouse to infer the promise of reward simply by the overall level of activity. Together, these experiments isolate the impact of task demands on neural responses in mouse cerebral cortex, and document distinct neural signatures of cue evaluation in V1 and ACC.SIGNIFICANCE STATEMENT Performing a cognitive task, such as evaluating visual cues, not only recruits frontal and parietal brain regions, but also modulates sensory processing stages. We trained mice to evaluate two visual cues, and show that, during this task, ∼30% of neurons recorded in V1 became selective for either cue, although they provided equivalent visual stimulation. We also show that, during cue evaluation, mice frequently move their eyes, even under head fixation, and that ignoring systematic differences in eye position can substantially obscure the modulations seen in V1 neurons. Finally, we document that modulations are stronger in ACC, and biased toward the reward-predicting cue, suggesting a transition in the neural representation of task-relevant information across processing stages in mouse cerebral cortex.

A Highly Configurable High-Level Synthesis Functional Pattern Library

FPGA has recently played an increasingly important role in heterogeneous computing, but Register Transfer Level design flows are not only inefficient in design, but also require designers to be familiar with the circuit architecture. High-level synthesis (HLS) allows developers to design FPGA circuits more efficiently with a more familiar programming language, a higher level of abstraction, and automatic adaptation of timing constraints. When using HLS tools, such as Xilinx Vivado HLS, specific design patterns and techniques are required in order to create high-performance circuits. Moreover, designing efficient concurrency and data flow structures requires a deep understanding of the hardware, imposing more learning costs on programmers. In this paper, we propose a set of functional patterns libraries based on the MapReduce model, implemented by C++ templates, which can quickly implement high-performance parallel pipelined computing models on FPGA with specified simple parameters. The usage of this pattern library allows flexible adaptation of parallel and flow structures in algorithms, which greatly improves the coding efficiency. The contributions of this paper are as follows. (1) Four standard functional operators suitable for hardware parallel computing are defined. (2) Functional concurrent programming patterns are described based on C++ templates and Xilinx HLS. (3) The efficiency of this programming paradigm is verified with two algorithms with different complexity.