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1.
Transl Psychiatry ; 11(1): 377, 2021 07 06.
Artigo em Inglês | MEDLINE | ID: mdl-34230451

RESUMO

Human endogenous retroviruses (HERVs) are remnants of infections that took place several million years ago and represent around 8% of the human genome. Despite evidence implicating increased expression of HERV type W envelope (HERV-W ENV) in schizophrenia and bipolar disorder, it remains unknown whether such expression is associated with distinct clinical or biological characteristics and symptoms. Accordingly, we performed unsupervised two-step clustering of a multivariate data set that included HERV-W ENV protein antigenemia, serum cytokine levels, childhood trauma scores, and clinical data of cohorts of patients with schizophrenia (n = 29), bipolar disorder (n = 43) and healthy controls (n = 32). We found that subsets of patients with schizophrenia (~41%) and bipolar disorder (~28%) show positive antigenemia for HERV-W ENV protein, whereas the large majority (96%) of controls was found to be negative for ENV protein. Unsupervised cluster analysis identified the presence of two main clusters of patients, which were best predicted by the presence or absence of HERV-W ENV protein. HERV-W expression was associated with increased serum levels of inflammatory cytokines and higher childhood maltreatment scores. Furthermore, patients with schizophrenia who were positive for HERV-W ENV protein showed more manic symptoms and higher daily chlorpromazine (CPZ) equivalents, whereas HERV-W ENV positive patients with bipolar disorder were found to have an earlier disease onset than those who were negative for HERV-W ENV protein. Taken together, our study suggest that HERV-W ENV protein antigenemia and cytokines can be used to stratify patients with major mood and psychotic disorders into subgroups with differing inflammatory and clinical profiles.


Assuntos
Transtorno Bipolar , Retrovirus Endógenos , Esquizofrenia , Análise por Conglomerados , Produtos do Gene env/genética , Humanos , Esquizofrenia/genética
2.
PLoS Comput Biol ; 17(7): e1009120, 2021 07.
Artigo em Inglês | MEDLINE | ID: mdl-34237051

RESUMO

Widespread school closures occurred during the COVID-19 pandemic. Because closures are costly and damaging, many jurisdictions have since reopened schools with control measures in place. Early evidence indicated that schools were low risk and children were unlikely to be very infectious, but it is becoming clear that children and youth can acquire and transmit COVID-19 in school settings and that transmission clusters and outbreaks can be large. We describe the contrasting literature on school transmission, and argue that the apparent discrepancy can be reconciled by heterogeneity, or "overdispersion" in transmission, with many exposures yielding little to no risk of onward transmission, but some unfortunate exposures causing sizeable onward transmission. In addition, respiratory viral loads are as high in children and youth as in adults, pre- and asymptomatic transmission occur, and the possibility of aerosol transmission has been established. We use a stochastic individual-based model to find the implications of these combined observations for cluster sizes and control measures. We consider both individual and environment/activity contributions to the transmission rate, as both are known to contribute to variability in transmission. We find that even small heterogeneities in these contributions result in highly variable transmission cluster sizes in the classroom setting, with clusters ranging from 1 to 20 individuals in a class of 25. None of the mitigation protocols we modeled, initiated by a positive test in a symptomatic individual, are able to prevent large transmission clusters unless the transmission rate is low (in which case large clusters do not occur in any case). Among the measures we modeled, only rapid universal monitoring (for example by regular, onsite, pooled testing) accomplished this prevention. We suggest approaches and the rationale for mitigating these larger clusters, even if they are expected to be rare.


Assuntos
COVID-19/prevenção & controle , COVID-19/transmissão , Instituições Acadêmicas , Adolescente , COVID-19/epidemiologia , COVID-19/virologia , Criança , Análise por Conglomerados , Surtos de Doenças , Humanos , Máscaras , Pandemias , Distanciamento Físico , SARS-CoV-2/isolamento & purificação
3.
Environ Monit Assess ; 193(8): 494, 2021 Jul 19.
Artigo em Inglês | MEDLINE | ID: mdl-34279739

RESUMO

The monitoring and assessment of a river system is a complex process and not restricted to urban areas only. The discharge of wastewater drains in the river increases the river system complexity further. The abstraction of freshwater at regular intervals and the discharge of the wastewater from various sources cause significant spatial and temporal variation in water quality. The multivariate statistical analysis is performed to identify water quality parameters' variability on the 5-year dataset from four monitoring sites. Hierarchical agglomerative cluster analysis (HACA) and principal component analysis (PCA) are applied to characterize the water quality parameters and identify the significant pollution sources. The clusters are formed considering the similarities between parameters, and eigenvalues are determined from the covariance of parameters. The box plots are designed to identify the spatial and temporal variations. The highest variability of the first principal component is 60.78% of the total variance at the second sampling location, the ITO bridge. The significant varifactors obtained from the PCA indicate the parameters responsible for the maximum variation in water quality. The study reveals the importance of multivariate statistical techniques in identifying a pattern of variability of parameters and developing management strategies to improve river water quality by identifying dominant parameters causing the maximum degradation in water quality.


Assuntos
Rios , Poluentes Químicos da Água , Análise por Conglomerados , Monitoramento Ambiental , Índia , Análise de Componente Principal , Poluentes Químicos da Água/análise , Qualidade da Água
4.
Sensors (Basel) ; 21(13)2021 Jun 25.
Artigo em Inglês | MEDLINE | ID: mdl-34202090

RESUMO

Wi-Fi-based indoor positioning systems have a simple layout and a low cost, and they have gradually become popular in both academia and industry. However, due to the poor stability of Wi-Fi signals, it is difficult to accurately decide the position based on a received signal strength indicator (RSSI) by using a traditional dataset and a deep learning classifier. To overcome this difficulty, we present a clustering-based noise elimination scheme (CNES) for RSSI-based datasets. The scheme facilitates the region-based clustering of RSSIs through density-based spatial clustering of applications with noise. In this scheme, the RSSI-based dataset is preprocessed and noise samples are removed by CNES. This experiment was carried out in a dynamic environment, and we evaluated the lab simulation results of CNES using deep learning classifiers. The results showed that applying CNES to the test database to eliminate noise will increase the success probability of fingerprint location. The lab simulation results show that after using CNES, the average positioning accuracy of margin-zero (zero-meter error), margin-one (two-meter error), and margin-two (four-meter error) in the database increased by 17.78%, 7.24%, and 4.75%, respectively. We evaluated the simulation results with a real time testing experiment, where the result showed that CNES improved the average positioning accuracy to 22.43%, 9.15%, and 5.21% for margin-zero, margin-one, and margin-two error, respectively.


Assuntos
Aprendizado Profundo , Tecnologia sem Fio , Algoritmos , Análise por Conglomerados
5.
Sensors (Basel) ; 21(13)2021 Jun 26.
Artigo em Inglês | MEDLINE | ID: mdl-34206718

RESUMO

Heat loss quantification (HLQ) is an essential step in improving a building's thermal performance and optimizing its energy usage. While this problem is well-studied in the literature, most of the existing studies are either qualitative or minimally driven quantitative studies that rely on localized building envelope points and are, thus, not suitable for automated solutions in energy audit applications. This research work is an attempt to fill this gap of knowledge by utilizing intensive thermal data (on the order of 100,000 plus images) and constitutes a relatively new area of analysis in energy audit applications. Specifically, we demonstrate a novel process using deep-learning methods to segment more than 100,000 thermal images collected from an unmanned aerial system (UAS). To quantify the heat loss for a building envelope, multiple stages of computations need to be performed: object detection (using Mask-RCNN/Faster R-CNN), estimating the surface temperature (using two clustering methods), and finally calculating the overall heat transfer coefficient (e.g., the U-value). The proposed model was applied to eleven academic campuses across the state of North Dakota. The preliminary findings indicate that Mask R-CNN outperformed other instance segmentation models with an mIOU of 73% for facades, 55% for windows, 67% for roofs, 24% for doors, and 11% for HVACs. Two clustering methods, namely K-means and threshold-based clustering (TBC), were deployed to estimate surface temperatures with TBC providing consistent estimates across all times of the day over K-means. Our analysis demonstrated that thermal efficiency not only depended on the accurate acquisition of thermal images but also relied on other factors, such as the building geometry and seasonal weather parameters, such as the outside/inside building temperatures, wind, time of day, and indoor heating/cooling conditions. Finally, the resultant U-values of various building envelopes were compared with recommendations from the American Society of Heating, Refrigerating, and Air-conditioning Engineers (ASHRAE) building standards.


Assuntos
Ar Condicionado , Ambiente Construído , Análise por Conglomerados , Calefação , North Dakota
6.
Sensors (Basel) ; 21(13)2021 Jun 30.
Artigo em Inglês | MEDLINE | ID: mdl-34209456

RESUMO

Monitoring of an underwater environment and communication is essential for many applications, such as sea habitat monitoring, offshore investigation and mineral exploration, but due to underwater current, low bandwidth, high water pressure, propagation delay and error probability, underwater communication is challenging. In this paper, we proposed a sensor node clustering technique for UWSNs named as adaptive node clustering technique (ANC-UWSNs). It uses a dragonfly optimization (DFO) algorithm for selecting ideal measure of clusters needed for routing. The DFO algorithm is inspired by the swarming behavior of dragons. The proposed methodology correlates with other algorithms, for example the ant colony optimizer (ACO), comprehensive learning particle swarm optimizer (CLPSO), gray wolf optimizer (GWO) and moth flame optimizer (MFO). Grid size, transmission range and nodes density are used in a performance matrix, which varies during simulation. Results show that DFO outperform the other algorithms. It produces a higher optimized number of clusters as compared to other algorithms and hence optimizes overall routing and increases the life span of a network.


Assuntos
Algoritmos , Tecnologia sem Fio , Análise por Conglomerados , Simulação por Computador , Sistemas Computacionais
7.
BMC Health Serv Res ; 21(1): 669, 2021 Jul 08.
Artigo em Inglês | MEDLINE | ID: mdl-34238287

RESUMO

BACKGROUND: The aim of this study was to determine how clusters or subgroups of insulin-treated people with diabetes, based upon healthcare resource utilization, select social demographic and clinical characteristics, and diabetes management parameters, are related to health outcomes including acute care visits and hospital admissions. METHODS: This was a non-experimental, retrospective cluster analysis. We utilized Aetna administrative claims data to identify insulin-using people with diabetes with service dates from 01 January 2015 to 30 June 2018. The study included adults over the age of 18 years who had a diagnosis of type 1 (T1DM) or type 2 diabetes mellitus (T2DM) on insulin therapy and had Aetna medical and pharmacy coverage for at least 18 months (6 months prior and 12 months after their index date, defined as either their first insulin prescription fill date or their earliest date allowing for 6 months' prior coverage). We used K-means clustering methods to identify relevant subgroups of people with diabetes based on 13 primary outcome variables. RESULTS: A total of 100,650 insulin-using people with diabetes were identified in the Aetna administrative claims database and met study criteria, including 11,826 (11.7%) with T1DM and 88,824 (88.3%) with T2DM. Of these 79,053 (78.5%) people were existing insulin users. Seven distinct clusters were identified with different characteristics and potential risks of diabetes complications. Overall, clusters were significantly associated with differences in healthcare utilization (emergency room visits, inpatient admissions, and total inpatient days) after multivariable adjustment. CONCLUSIONS: This analysis of healthcare claims data using clustering methodologies identified meaningful subgroups of patients with diabetes using insulin. The subgroups differed in comorbidity burden, healthcare utilization, and demographic factors which could be used to identify higher risk patients and/or guide the management and treatment of diabetes.


Assuntos
Diabetes Mellitus Tipo 2 , Insulina , Adulto , Análise por Conglomerados , Demografia , Diabetes Mellitus Tipo 2/tratamento farmacológico , Diabetes Mellitus Tipo 2/epidemiologia , Custos de Cuidados de Saúde , Humanos , Insulina/uso terapêutico , Pessoa de Meia-Idade , Estudos Retrospectivos
8.
BMC Plant Biol ; 21(1): 313, 2021 Jul 02.
Artigo em Inglês | MEDLINE | ID: mdl-34215178

RESUMO

BACKGROUND: Harnessing heterosis is one of the major approaches to increase rice yield and has made a great contribution to food security. The identification and selection of outstanding parental genotypes especially among male sterile lines is a key step for exploiting heterosis. Two-line hybrid system is based on the discovery and application of photoperiod- and thermo-sensitive genic sensitive male sterile (PTGMS) materials. The development of wide-range of male sterile lines from a common gene pool leads to a narrower genetic diversity, which is vulnerable to biotic and abiotic stress. Hence, it is valuable to ascertain the genetic background of PTGMS lines and to understand their relationships in order to select and design a future breeding strategy. RESULTS: A collection of 118 male sterile rice lines and 13 conventional breeding lines from the major rice growing regions of China was evaluated and screened against the photosensitive (pms3) and temperature sensitive male sterility (tms5) genes. The total gene pool was divided into four major populations as P1 possessing the pms3, P2 possessing tms5, P3 possessing both pms3 and tms5 genes, and P4 containing conventional breeding lines without any male sterility allele. The high genetic purity was revealed by homozygous alleles in all populations. The population admixture, principle components and the phylogenetic analysis revealed the close relations of P2 and P3 with P4. The population differentiation analysis showed that P1 has the highest differentiation coefficient. The lines from P1 were observed as the ancestors of other three populations in a phylogenetic tree, while the lines in P2 and P3 showed a close genetic relation with conventional lines. A core collection of top 10% lines with maximum within and among populations genetic diversity was constructed for future research and breeding efforts. CONCLUSION: The low genetic diversity and close genetic relationship among PTGMS lines in P2, P3 and P4 populations suggest a selection sweep and they might result from a backcrossing with common ancestors including the pure lines of P1. The core collection from PTGMS panel updated with new diverse germplasm will serve best for further two-line hybrid breeding.


Assuntos
Oryza/genética , Fotoperíodo , Infertilidade das Plantas/genética , Sementes/genética , Temperatura , Núcleo Celular/genética , Núcleo Celular/efeitos da radiação , Análise por Conglomerados , Ontologia Genética , Estudos de Associação Genética , Marcadores Genéticos , Luz , Nucleotídeos/genética , Oryza/efeitos da radiação , Filogenia , Infertilidade das Plantas/efeitos da radiação , Polimorfismo de Nucleotídeo Único/genética , Análise de Componente Principal , Reprodutibilidade dos Testes , Sementes/efeitos da radiação
9.
Anal Chim Acta ; 1174: 338716, 2021 Aug 22.
Artigo em Inglês | MEDLINE | ID: mdl-34247741

RESUMO

Kurtosis-based projection pursuit analysis (kPPA) has demonstrated the ability to visualize multivariate data in a way that complements other exploratory data analysis tools, such as principal components analysis (PCA). It is especially useful for partitioning binary data sets (2k classes) with a balanced design. Since kPPA is not a variance-based method, it can often provide unsupervised class separation where other methods fail. However, when multiple classifications are possible (e.g. by gender, age, disease state, etc.), the projection provided by kPPA (corresponding to the global minimum kurtosis) will not necessarily be the one of greatest interest to the researcher. Fortunately, the optimization algorithm for kPPA allows for interrogation of projections obtained from numerous local minima. This strategy provides the basis of a new method described here, referred to as combinatorial projection pursuit analysis (CombPPA) because it presents alternative combinations of class separation. The method is truly exploratory in that it allows the landscape of interesting projections to be more fully probed. The approach uses Procrustes rotation to map local minima among the kPPA solutions, whereupon the researcher can visualize different projections. To demonstrate the new method, the clustering of grape juice samples using visible spectroscopy is presented as a model problem. This problem is well-suited to this type of study because there are eight classes of samples symmetrically partitioned into two classes by type (organic/non-organic) or four classes by brand. Results presented show the different combinations of projections that can be obtained, including the desired partitions. In addition, this work describes new enhancements to the kPPA algorithm that improve the orthogonality of solutions obtained.


Assuntos
Algoritmos , Análise por Conglomerados , Análise de Componente Principal
10.
Molecules ; 26(12)2021 Jun 16.
Artigo em Inglês | MEDLINE | ID: mdl-34208619

RESUMO

Skin pigment disorders are common cosmetic and medical problems. Many known compounds inhibit the key melanin-producing enzyme, tyrosinase, but their use is limited due to side effects. Natural-derived peptides also display tyrosinase inhibition. Abalone is a good source of peptides, and the abalone proteins have been used widely in pharmaceutical and cosmetic products, but not for melanin inhibition. This study aimed to predict putative tyrosinase inhibitory peptides (TIPs) from abalone, Haliotis diversicolor, using k-nearest neighbor (kNN) and random forest (RF) algorithms. The kNN and RF predictors were trained and tested against 133 peptides with known anti-tyrosinase properties with 97% and 99% accuracy. The kNN predictor suggested 1075 putative TIPs and six TIPs from the RF predictor. Two helical peptides were predicted by both methods and showed possible interaction with the predicted structure of mushroom tyrosinase, similar to those of the known TIPs. These two peptides had arginine and aromatic amino acids, which were common to the known TIPs, suggesting non-competitive inhibition on the tyrosinase. Therefore, the first version of the TIP predictors could suggest a reasonable number of the TIP candidates for further experiments. More experimental data will be important for improving the performance of these predictors, and they can be extended to discover more TIPs from other organisms. The confirmation of TIPs in abalone will be a new commercial opportunity for abalone farmers and industry.


Assuntos
Gastrópodes/metabolismo , Monofenol Mono-Oxigenase/antagonistas & inibidores , Monofenol Mono-Oxigenase/metabolismo , Algoritmos , Animais , Análise por Conglomerados , Biologia Computacional/métodos , Gastrópodes/química , Aprendizado de Máquina , Monofenol Mono-Oxigenase/farmacologia , Peptídeos/farmacologia
11.
BMC Plant Biol ; 21(1): 327, 2021 Jul 07.
Artigo em Inglês | MEDLINE | ID: mdl-34233614

RESUMO

BACKGROUND: Grapevine cultivars of the Pinot family represent clonally propagated mutants with major phenotypic and physiological differences, such as different colour or shifted ripening time, as well as changes in important viticultural traits. Specifically, the cultivars 'Pinot Noir' (PN) and 'Pinot Noir Precoce' (PNP, early ripening) flower at the same time, but vary in the beginning of berry ripening (veraison) and, consequently, harvest time. In addition to genotype, seasonal climatic conditions (i.e. high temperatures) also affect ripening times. To reveal possible regulatory genes that affect the timing of veraison onset, we investigated differences in gene expression profiles between PN and PNP throughout berry development with a closely meshed time series and over two separate years. RESULTS: The difference in the duration of berry formation between PN and PNP was quantified to be approximately two weeks under the growth conditions applied, using plant material with a proven PN and PNP clonal relationship. Clusters of co-expressed genes and differentially expressed genes (DEGs) were detected which reflect the shift in the timing of veraison onset. Functional annotation of these DEGs fit to observed phenotypic and physiological changes during berry development. In total, we observed 3,342 DEGs in 2014 and 2,745 DEGs in 2017 between PN and PNP, with 1,923 DEGs across both years. Among these, 388 DEGs were identified as veraison-specific and 12 were considered as berry ripening time regulatory candidates. The expression profiles revealed two candidate genes for ripening time control which we designated VviRTIC1 and VviRTIC2 (VIT_210s0071g01145 and VIT_200s0366g00020, respectively). These genes likely contribute the phenotypic differences observed between PN and PNP. CONCLUSIONS: Many of the 1,923 DEGs show highly similar expression profiles in both cultivars if the patterns are aligned according to developmental stage. In our work, putative genes differentially expressed between PNP and PN which could control ripening time as well as veraison-specific genes were identified. We point out connections of these genes to molecular events during berry development and discuss potential candidate genes which may control ripening time. Two of these candidates were observed to be differentially expressed in the early berry development phase. Several down-regulated genes during berry ripening are annotated as auxin response factors / ARFs. Conceivably, general changes in auxin signaling may cause the earlier ripening phenotype of PNP.


Assuntos
Frutas/crescimento & desenvolvimento , Perfilação da Expressão Gênica , Regulação da Expressão Gênica no Desenvolvimento , Regulação da Expressão Gênica de Plantas , Genes de Plantas , Vitis/crescimento & desenvolvimento , Vitis/genética , Análise por Conglomerados , Flores/genética , Flores/fisiologia , Frutas/genética , Fenótipo , Análise de Componente Principal , Fatores de Tempo
12.
BMC Bioinformatics ; 22(1): 361, 2021 Jul 06.
Artigo em Inglês | MEDLINE | ID: mdl-34229612

RESUMO

BACKGROUND: Facing the diversity of omics data and the difficulty of selecting one result over all those produced by several methods, consensus strategies have the potential to reconcile multiple inputs and to produce robust results. RESULTS: Here, we introduce ClustOmics, a generic consensus clustering tool that we use in the context of cancer subtyping. ClustOmics relies on a non-relational graph database, which allows for the simultaneous integration of both multiple omics data and results from various clustering methods. This new tool conciliates input clusterings, regardless of their origin, their number, their size or their shape. ClustOmics implements an intuitive and flexible strategy, based upon the idea of evidence accumulation clustering. ClustOmics computes co-occurrences of pairs of samples in input clusters and uses this score as a similarity measure to reorganize data into consensus clusters. CONCLUSION: We applied ClustOmics to multi-omics disease subtyping on real TCGA cancer data from ten different cancer types. We showed that ClustOmics is robust to heterogeneous qualities of input partitions, smoothing and reconciling preliminary predictions into high-quality consensus clusters, both from a computational and a biological point of view. The comparison to a state-of-the-art consensus-based integration tool, COCA, further corroborated this statement. However, the main interest of ClustOmics is not to compete with other tools, but rather to make profit from their various predictions when no gold-standard metric is available to assess their significance. AVAILABILITY: The ClustOmics source code, released under MIT license, and the results obtained on TCGA cancer data are available on GitHub: https://github.com/galadrielbriere/ClustOmics .


Assuntos
Algoritmos , Neoplasias , Análise por Conglomerados , Consenso , Humanos , Neoplasias/genética , Software
13.
Sensors (Basel) ; 21(13)2021 Jun 23.
Artigo em Inglês | MEDLINE | ID: mdl-34201656

RESUMO

Computer numerical control (CNC) is a machine used in the manufacturing industry to produce components quickly for the engineering field or the desired shape. In the milling process carried out by CNC machines, sometimes vibrations occur that cause unwanted cracks or damage, which if left unchecked, will cause more severe damage. For this reason, this study describes how to monitor and analyze the sound produced by CNC during the milling process. This study uses six sound sample videos from YouTube, and there are two modes: (1) the operating mode is three different shapes with XY, XZ, and XYZ axes, and the second (2) is based on material differences. Namely, wood, Styrofoam, and plastic. The sound generated from all samples of the CNC milling processes will be detected using a sound detection program that has been designed in the LabVIEW using a simple microphone. The resulting sound frequency will be analyzed using the fast Fourier transform (FFT) process in spectral measurements, which will produce the amplitude and frequency of the detected sound in real time in the form of a graph. All frequency results that have been obtained from the sound detection monitoring tool in the CNC milling machine will be imported into the K-means clustering algorithm where the different frequencies between the resonant frequency and noise will be classified. Based on the experiments conducted, the sound detection program can detect sounds with a significant level of sensitivity.


Assuntos
Algoritmos , Som , Análise por Conglomerados , Análise de Fourier
14.
Artigo em Inglês | MEDLINE | ID: mdl-34208307

RESUMO

(1) Background: Research on patterns of risky driving behaviors (RDBs) in adolescents is scarce. This study aims to identify distinctive patterns of RDBs and to explore their characteristics in a representative sample of adolescents. (2) Methods: this is a cross-sectional study of a representative sample of Tuscany Region students aged 14-19 years (n = 2162). The prevalence of 11 RDBs was assessed and a cluster analysis was conducted to identify patterns of RDBs. ANOVA, post hoc pairwise comparisons and multivariate logistic regression models were used to characterize cluster membership. (3) Results: four distinct clusters of drivers were identified based on patterns of RDBs; in particular, two clusters-the Reckless Drivers (11.2%) and the Careless Drivers (21.5%)-showed high-risk patterns of engagement in RDBs. These high-risk clusters exhibited the weakest social bonds, the highest psychological distress, the most frequent participation in health compromising and risky behaviors, and the highest risk of a road traffic accident. (4) Conclusion: findings suggest that it is possible to identify typical profiles of RDBs in adolescents and that risky driving profiles are positively interrelated with other risky behaviors. This clustering suggests the need to develop multicomponent prevention strategies rather than addressing specific RDBs in isolation.


Assuntos
Acidentes de Trânsito , Condução de Veículo , Adolescente , Análise por Conglomerados , Estudos Transversais , Humanos , Itália , Assunção de Riscos
15.
BMJ ; 374: n1585, 2021 07 13.
Artigo em Inglês | MEDLINE | ID: mdl-34257088

RESUMO

OBJECTIVE: To examine the effect of optimising drug treatment on drug related hospital admissions in older adults with multimorbidity and polypharmacy admitted to hospital. DESIGN: Cluster randomised controlled trial. SETTING: 110 clusters of inpatient wards within university based hospitals in four European countries (Switzerland, Netherlands, Belgium, and Republic of Ireland) defined by attending hospital doctors. PARTICIPANTS: 2008 older adults (≥70 years) with multimorbidity (≥3 chronic conditions) and polypharmacy (≥5 drugs used long term). INTERVENTION: Clinical staff clusters were randomised to usual care or a structured pharmacotherapy optimisation intervention performed at the individual level jointly by a doctor and a pharmacist, with the support of a clinical decision software system deploying the screening tool of older person's prescriptions and screening tool to alert to the right treatment (STOPP/START) criteria to identify potentially inappropriate prescribing. MAIN OUTCOME MEASURE: Primary outcome was first drug related hospital admission within 12 months. RESULTS: 2008 older adults (median nine drugs) were randomised and enrolled in 54 intervention clusters (963 participants) and 56 control clusters (1045 participants) receiving usual care. In the intervention arm, 86.1% of participants (n=789) had inappropriate prescribing, with a mean of 2.75 (SD 2.24) STOPP/START recommendations for each participant. 62.2% (n=491) had ≥1 recommendation successfully implemented at two months, predominantly discontinuation of potentially inappropriate drugs. In the intervention group, 211 participants (21.9%) experienced a first drug related hospital admission compared with 234 (22.4%) in the control group. In the intention-to-treat analysis censored for death as competing event (n=375, 18.7%), the hazard ratio for first drug related hospital admission was 0.95 (95% confidence interval 0.77 to 1.17). In the per protocol analysis, the hazard ratio for a drug related hospital admission was 0.91 (0.69 to 1.19). The hazard ratio for first fall was 0.96 (0.79 to 1.15; 237 v 263 first falls) and for death was 0.90 (0.71 to 1.13; 172 v 203 deaths). CONCLUSIONS: Inappropriate prescribing was common in older adults with multimorbidity and polypharmacy admitted to hospital and was reduced through an intervention to optimise pharmacotherapy, but without effect on drug related hospital admissions. Additional efforts are needed to identify pharmacotherapy optimisation interventions that reduce inappropriate prescribing and improve patient outcomes. TRIAL REGISTRATION: ClinicalTrials.gov NCT02986425.


Assuntos
Hospitalização/estatística & dados numéricos , Prescrição Inadequada/prevenção & controle , Multimorbidade , Polimedicação , Acidentes por Quedas/estatística & dados numéricos , Idoso , Idoso de 80 Anos ou mais , Análise por Conglomerados , Europa (Continente) , Humanos , Prescrição Inadequada/efeitos adversos
16.
Sensors (Basel) ; 21(13)2021 Jul 01.
Artigo em Inglês | MEDLINE | ID: mdl-34282759

RESUMO

This paper focuses on the binary classification of the emotion of fear, based on the physiological data and subjective responses stored in the DEAP dataset. We performed a mapping between the discrete and dimensional emotional information considering the participants' ratings and extracted a substantial set of 40 types of features from the physiological data, which represented the input to various machine learning algorithms-Decision Trees, k-Nearest Neighbors, Support Vector Machine and artificial networks-accompanied by dimensionality reduction, feature selection and the tuning of the most relevant hyperparameters, boosting classification accuracy. The methodology we approached included tackling different situations, such as resolving the problem of having an imbalanced dataset through data augmentation, reducing overfitting, computing various metrics in order to obtain the most reliable classification scores and applying the Local Interpretable Model-Agnostic Explanations method for interpretation and for explaining predictions in a human-understandable manner. The results show that fear can be predicted very well (accuracies ranging from 91.7% using Gradient Boosting Trees to 93.5% using dimensionality reduction and Support Vector Machine) by extracting the most relevant features from the physiological data and by searching for the best parameters which maximize the machine learning algorithms' classification scores.


Assuntos
Aprendizado de Máquina , Máquina de Vetores de Suporte , Algoritmos , Análise por Conglomerados , Medo , Humanos
17.
Sensors (Basel) ; 21(13)2021 Jul 01.
Artigo em Inglês | MEDLINE | ID: mdl-34282788

RESUMO

Gas-path anomalies account for more than 90% of all civil aero-engine anomalies. It is essential to develop accurate gas-path anomaly detection methods. Therefore, a weakly supervised gas-path anomaly detection method for civil aero-engines based on mapping relationship mining of gas-path parameters and improved density peak clustering is proposed. First, the encoder-decoder, composed of an attention mechanism and a long short-term memory neural network, is used to construct the mapping relationship mining model among gas-path parameters. The predicted values of gas-path parameters under the restriction of mapping relationships are obtained. The deviation degree from the original values to the predicted values is regarded as the feature. To force the extracted features to better reflect the anomalies and make full use of weakly supervised labels, a weakly supervised cross-entropy loss function under extreme class imbalance is deployed. This loss function can be combined with a simple classifier to significantly improve the feature extraction results, in which anomaly samples are more different from normal samples and do not reduce the mining precision. Finally, an anomaly detection method is deployed based on improved density peak clustering and a weakly supervised clustering parameter adjustment strategy. In the improved density peak clustering method, the local density is enhanced by K-nearest neighbors, and the clustering effect is improved by a new outlier threshold determination method and a new outlier treatment method. Through these settings, the accuracy of dividing outliers and clustering can be improved, and the influence of outliers on the clustering process reduced. By introducing weakly supervised label information and automatically iterating according to clustering and anomaly detection results to update the hyperparameter settings, a weakly supervised anomaly detection method without complex parameter adjustment processes can be implemented. The experimental results demonstrate the superiority of the proposed method.


Assuntos
Redes Neurais de Computação , Análise por Conglomerados
18.
Sensors (Basel) ; 21(13)2021 Jul 05.
Artigo em Inglês | MEDLINE | ID: mdl-34283130

RESUMO

The classification and recognition of radar clutter is helpful to improve the efficiency of radar signal processing and target detection. In order to realize the effective classification of uniform circular array (UCA) radar clutter data, a classification method of ground clutter data based on the chaotic genetic algorithm is proposed. In this paper, the characteristics of UCA radar ground clutter data are studied, and then the statistical characteristic factors of correlation, non-stationery and range-Doppler maps are extracted, which can be used to classify ground clutter data. Based on the clustering analysis, results of characteristic factors of radar clutter data under different wave-controlled modes in multiple scenarios, we can see: in radar clutter clustering of different scenes, the chaotic genetic algorithm can save 34.61% of clustering time and improve the classification accuracy by 42.82% compared with the standard genetic algorithm. In radar clutter clustering of different wave-controlled modes, the timeliness and accuracy of the chaotic genetic algorithm are improved by 42.69% and 20.79%, respectively, compared to standard genetic algorithm clustering. The clustering experiment results show that the chaotic genetic algorithm can effectively classify UCA radar's ground clutter data.


Assuntos
Algoritmos , Radar , Análise por Conglomerados , Processamento de Sinais Assistido por Computador
19.
Nat Commun ; 12(1): 4073, 2021 07 01.
Artigo em Inglês | MEDLINE | ID: mdl-34210969

RESUMO

Cluster synchronization in networks of coupled oscillators is the subject of broad interest from the scientific community, with applications ranging from neural to social and animal networks and technological systems. Most of these networks are directed, with flows of information or energy that propagate unidirectionally from given nodes to other nodes. Nevertheless, most of the work on cluster synchronization has focused on undirected networks. Here we characterize cluster synchronization in general directed networks. Our first observation is that, in directed networks, a cluster A of nodes might be one-way dependent on another cluster B: in this case, A may remain synchronized provided that B is stable, but the opposite does not hold. The main contribution of this paper is a method to transform the cluster stability problem in an irreducible form. In this way, we decompose the original problem into subproblems of the lowest dimension, which allows us to immediately detect inter-dependencies among clusters. We apply our analysis to two examples of interest, a human network of violin players executing a musical piece for which directed interactions may be either activated or deactivated by the musicians, and a multilayer neural network with directed layer-to-layer connections.


Assuntos
Análise por Conglomerados , Redes Neurais de Computação , Animais , Humanos , Modelos Teóricos , Música , Dinâmica não Linear
20.
Bioinformatics ; 37(Suppl_1): i51-i58, 2021 07 12.
Artigo em Inglês | MEDLINE | ID: mdl-34252936

RESUMO

MOTIVATION: Single-cell RNA sequencing (scRNA-seq) technology has been widely applied to capture the heterogeneity of different cell types within complex tissues. An essential step in scRNA-seq data analysis is the annotation of cell types. Traditional cell-type annotation is mainly clustering the cells first, and then using the aggregated cluster-level expression profiles and the marker genes to label each cluster. Such methods are greatly dependent on the clustering results, which are insufficient for accurate annotation. RESULTS: In this article, we propose a semi-supervised learning method for cell-type annotation called CALLR. It combines unsupervised learning represented by the graph Laplacian matrix constructed from all the cells and supervised learning using sparse logistic regression. By alternately updating the cell clusters and annotation labels, high annotation accuracy can be achieved. The model is formulated as an optimization problem, and a computationally efficient algorithm is developed to solve it. Experiments on 10 real datasets show that CALLR outperforms the compared (semi-)supervised learning methods, and the popular clustering methods. AVAILABILITY AND IMPLEMENTATION: The implementation of CALLR is available at https://github.com/MathSZhang/CALLR. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.


Assuntos
RNA , Análise de Célula Única , Análise por Conglomerados , Perfilação da Expressão Gênica , Análise de Sequência de RNA
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