Combination Of Descriptors Research Articles

Evaluation of QSAR Equations for Virtual Screening.

Quantitative Structure Activity Relationship (QSAR) models can inform on the correlation between activities and structure-based molecular descriptors. This information is important for the understanding of the factors that govern molecular properties and for designing new compounds with favorable properties. Due to the large number of calculate-able descriptors and consequently, the much larger number of descriptors combinations, the derivation of QSAR models could be treated as an optimization problem. For continuous responses, metrics which are typically being optimized in this process are related to model performances on the training set, for example, and . Similar metrics, calculated on an external set of data (e.g., ), are used to evaluate the performances of the final models. A common theme of these metrics is that they are context -” ignorant”. In this work we propose that QSAR models should be evaluated based on their intended usage. More specifically, we argue that QSAR models developed for Virtual Screening (VS) should be derived and evaluated using a virtual screening-aware metric, e.g., an enrichment-based metric. To demonstrate this point, we have developed 21 Multiple Linear Regression (MLR) models for seven targets (three models per target), evaluated them first on validation sets and subsequently tested their performances on two additional test sets constructed to mimic small-scale virtual screening campaigns. As expected, we found no correlation between model performances evaluated by “classical” metrics, e.g., and and the number of active compounds picked by the models from within a pool of random compounds. In particular, in some cases models with favorable and/or values were unable to pick a single active compound from within the pool whereas in other cases, models with poor and/or values performed well in the context of virtual screening. We also found no significant correlation between the number of active compounds correctly identified by the models in the training, validation and test sets. Next, we have developed a new algorithm for the derivation of MLR models by optimizing an enrichment-based metric and tested its performances on the same datasets. We found that the best models derived in this manner showed, in most cases, much more consistent results across the training, validation and test sets and outperformed the corresponding MLR models in most virtual screening tests. Finally, we demonstrated that when tested as binary classifiers, models derived for the same targets by the new algorithm outperformed Random Forest (RF) and Support Vector Machine (SVM)-based models across training/validation/test sets, in most cases. We attribute the better performances of the Enrichment Optimizer Algorithm (EOA) models in VS to better handling of inactive random compounds. Optimizing an enrichment-based metric is therefore a promising strategy for the derivation of QSAR models for classification and virtual screening.

Percepção de usuárias no climatério sobre as práticas integrativas

O objetivo do presente estudo foi identificar em bases cientifica a percepção de mulheres sobre o uso de práticas integrativas como estratégias de alivio das alterações do climatério. Trata-se de uma revisão integrativa de literatura do tipo descritivo, com abordagem qualitativa, os dados coletados através da estratégia PICo e a combinação dos descritores em ciências da saúde (DESCS) A análise das publicações/artigos foi feita através de leituras minuciosas e de forma crítica dos estudos na integra. Foram revisados 12 artigos publicados entre os anos de 2014 e 2020 e que avaliavam a percepção das mulheres a respeito de práticas integrativas. Ao analisar as publicações e artigos encontrados, foram agrupados em Da análise temática emergiram dois temas: Categoria 1. O (des)conhecimento das mulheres em relação ao climatério e o uso de práticas integrativas como forma de tratamento; Categoria 2. A falta de preparo dos enfermeiros e a ausência de estratégias do serviço básico de saúde para lidar com mulheres climatéricas. Evidenciou-se que diversos fatores dificultam a introdução e utilização de terapias complementares como forma de tratamento aos sintomas do climatério, apresentando que se necessita de ações públicas que visem proporcionar informações para as usuárias e um melhor preparo do sistema de saúde.

A Sequence-segment Neighbor Encoding Schema for Protein Hotspot Residue Prediction

Background: Hotspots are those residues that contribute major free energy of binding in protein-protein interactions. Protein functions are frequently dependent on hotspot residues. At present, hotspot residues are always identified by Alanine scanning mutagenesis technology, which is costly, time-consuming and laborious. Objective: Therefore, more accurate and efficient methods have to be developed to identify protein hotspot residues. Methods: This paper proposed a novel encoding schema of sequence-segment neighbors and constructed a random forest-based model to identify hotspots in protein interaction interfaces. Firstly, 10 amino acid physicochemical properties, 16 features related to the PI and DI, and 25 features related to ASA were extracted. Different from the previous residue encoding schemas, such as auto correlation descriptor or triplet combination information, this paper employed the influence of amino acids neighbors to hotspot residues and amino acids with a certain distance in sequence to the hotspot. Results: Moreover, the proposed model was compared with other hotspot prediction methods, including APIS, Robetta, FOLDEF, KFC, MINERVA models, etc. Conclusion: The experimental results showed that the proposed model can improve the prediction ability of protein hotspot residues on the same test set.

Molecular Docking and QSAR Studies of Coumarin Derivatives as NMT Inhibitors: Simple Structural Features as Potential Modulators of Antifungal Activity

Background:Treatments of fungal diseases, including Candidiasis, remain not up to scratch in spite of the mounting catalog of synthetic antifungal agents. These have served as the impetus for investigating new antifungal agents based on natural products. Consequently, genetic algorithm-multiple linear regression (GA-MLR) based QSAR (Quantitative Structure-Activity Relationship) studies of coumarin analogues along with molecular docking were carried out.Methods:Coumarin analogues with their MIC values were used to generate the training and test sets of compounds for QSAR models development; the analogues were also docked into the binding pocket of NMT (MyristoylCoA: protein N-myristoyltransferase).Results and Discussion:The statistical parameters for internal and external validation of QSAR analysis (R2= 0.830, Q2= 0.758, R2Pred= 0.610 and R2m overall= 0.683 ), Y Randomization, Ridge trace, VIF, tolerance and model criteria of Golbraikh and Tropsha data illustrate the robustness of the best proposed QSAR model. Most of the analogues bind to the electrostatic, hydrophobic clamp and display hydrogen bonding with amino acid residues of NMT. Interestingly, the most active coumarin analogue (MolDock score of -189.257) was docked deeply within the binding pocket of NMT, thereby displaying hydrogen bonding with Tyr107, Leu451, Leu450, Gln226, Cys393 and Leu394 amino acid residues.Conclusion:The combinations of descriptors from various descriptor subsets in QSAR analysis have highlighted the role of atomic properties such as polarizability and atomic van der Waals volume to explain the inhibitory activity. The models and related information may pave the way for important insight into the designing of putative NMT inhibitors for Candida albicans.

Risk prediction model using eye movements during simulated driving with logistic regressions and neural networks

BackgroundMany studies have found that eye movement behavior provides a real-time index of mental activity. Risk management architectures embedded in autonomous vehicles fail to include human cognitive aspects. We set out to evaluate whether eye movements during a risk driving detection task are able to predict risk situations. MethodsThirty-two normally sighted subjects (15 female) saw 20 clips of recorded driving scenes while their gaze was tracked. They reported when they considered the car should brake, anticipating any hazard. We applied both a mixed-effect logistic regression model and feedforward neural networks between hazard reports and eye movement descriptors. ResultsAll subjects reported at least one major collision hazard in each video (average 3.5 reports).We found that hazard situations were predicted by larger saccades, more and longer fixations, fewer blinks, and a smaller gaze dispersion in both horizontal and vertical dimensions.Performance between models incorporating a different combination of descriptors was compared running a test equality of receiver operating characteristic areas. Feedforward neural networks outperformed logistic regressions in accuracies. The model including saccadic magnitude, fixation duration, dispersion in ×, and pupil returned the highest ROC area (0.73). ConclusionWe evaluated each eye movement descriptor successfully and created separate models that predicted hazard events with an average efficacy of 70% using both logistic regressions and feedforward neural networks.The use of driving simulators and hazard detection videos can be considered a reliable methodology to study risk prediction.

Structure–Property Correlation for Calculating the Critical Pressures of Liquid–Vapor Phase Transitions from the Topological Characteristics of Alkene Molecules

A multidimensional QSPR model is proposed for calculating the critical pressure of normal and substituted alkenes from combinations of the topological descriptors of molecular graphs, i.e., the Wiener and Randic indices and function of the eigenvalues of the topological matrix, and indices that consider the differences between cis- and trans-isomers. It is shown that the QSPR model adequately describes the critical pressure of olefins for the liquid–vapor phase transition. It is concluded that the obtained results can be recommended for determining the critical properties of known and novel alkenes. They can be used when making scientific and engineering calculations in petroleum chemistry and the technology of supercritical fluids.

Predictive Modeling of Angiotensin I-Converting Enzyme Inhibitory Peptides Using Various Machine Learning Approaches.

Food-derived angiotensin I-converting enzyme (ACE) inhibitory peptides could potentially be used as safe supportive therapeutic products for high blood pressure. Theoretical approaches are promising methods with the advantage through exploring the relationships between peptide structures and their bioactivities. In this study, peptides with ACE inhibitory activity were collected and curated. Quantitative structure-activity relationship (QSAR) models were developed by using the combination of various machine learning approaches and chemical descriptors. The resultant models have revealed several structure features accounting for the ACE inhibitions. 14 new dipeptides predicted to lower blood pressure by inhibiting ACE were selected. Molecular docking indicated that these dipeptides formed hydrogen bonds with ACE. Five of these dipeptides were synthesized for experimental testing. The QSAR models developed were proofed to design and propose novel ACE inhibitory peptides. Machine learning algorithms and properly selected chemical descriptors can be promising modeling approaches for rational design of natural functional food components.

Systemic Evaluation of the Effects of Regional Self-Supply Targets on the German Electricity System Using Consistent Scenarios and System Optimization

This paper analyses the effects of regional renewable electricity self-sufficiency targets on the power system in Germany. For this purpose, an interdisciplinary approach from social sciences and energy system modelling was chosen, which allows considering qualitative factors such as public acceptance or political stability. Following the concept of context scenarios, consistent raw scenarios are generated by a cross-impact balance analysis (CIB), and the scenarios are quantified by the unit commitment and expansion cost minimisation model ENTIGRIS considering power plants, storages, and the electricity grid. This approach enables an understanding of the system framework conditions and their relationships and allows the combination of qualitative and quantitative scenario descriptors. The most important factors for setting regional self-sufficiency targets were identified through interviews. The main system effects identified are: The regional distribution of generation capacities is strongly influenced by a more demand-oriented installation of generation capacities. This leads to less grid reinforcement, but higher rates of curtailment. In all scenarios, higher utilization of the PV roof potential instead of ground mounted could be observed. The total system costs are increasing only slightly with regional self-supply targets. In general, it was found that the influence of regional self-sufficiency targets is less pronounced in scenarios that already achieve high national RES shares than in scenarios that achieve lower shares, since technology, storage and grid expansion measures are necessary anyway to achieve high RES shares. Overall, the effects here are rather small and the regional objective is not associated with major disadvantages for the system. In a future characterised by stagnation, the system can benefit from regional targeting, as higher renewable shares and lower costs can result. The main conclusion therefore is that regional target setting seem to be beneficial for the overall power system, in terms of system cost, national RE share, acceptance and CO2-emissions.

Human motion recognition based on limit learning machine

With the development of technology, human motion capture data have been widely used in the fields of human–computer interaction, interactive entertainment, education, and medical treatment. As a problem in the field of computer vision, human motion recognition has become a key technology in somatosensory games, security protection, and multimedia information retrieval. Therefore, it is important to improve the recognition rate of human motion. Based on the above background, the purpose of this article is human motion recognition based on extreme learning machine. Based on the existing action feature descriptors, this article makes improvements to features and classifiers and performs experiments on the Microsoft model specific register (MSR)-Action3D data set and the Bonn University high density metal (HDM05) motion capture data set. Based on displacement covariance descriptor and direction histogram descriptor, this article described both combine to produce a new combination; the description can statically reflect the joint position relevant information and at the same time, the change information dynamically reflects the joint position, uses the extreme learning machine for classification, and gets better recognition result. The experimental results show that the combined descriptor and extreme learning machine recognition rate on these two data sets is significantly improved by about 3% compared with the existing methods.

Conformational analysis and QSAR modeling of 14-membered macrolide analogues against mycobacterium tuberculosis

Electronic structures, effect of the substitution and structure physical-chemistry properties relationship for macrolide derivatives, have been studied by PM3 and ab initio methods. In the present work, the calculated values, namely net charges, bond lengths, MESP, dipole moments, electron-affinities, heats of formation, then, we treated the structural, physical and chemical relationships for a series of macrolide derivatives with inhibition activity against Mycobacterium tuberculosis. QSAR studies were done for these macrolide derivatives using a combination of various physicochemical descriptors. A multiple linear regression procedure was used to design the relationships between molecular descriptor and the activity of macrolide derivatives. Results validate the derived QSAR model.

First report on chemometric modeling of hydrolysis half-lives of organic chemicals.

Hydrolysis is one of the most important processes of transformation of organic chemicals in water. The rates of reactions, final chemical entities of these processes, and half-lives of organic chemicals are of considerable interest to environmental chemists as well as authorities involved in the controlling the processing and disposal of such organic chemicals. In this study, we have proposed QSPR models for the prediction of hydrolysis half-life of organic chemicals as a function of different pH and temperature conditions using only two-dimensional molecular descriptors with definite physicochemical significance. For each model, suitable subsets of variables were elected using a genetic algorithm method; next, the elected subsets of variables were subjected to the best subset selection with a key objective to determine the best combination of descriptors for model generation. Finally, QSPR models were constructed using the best combination of variables employing the partial least squares (PLS) regression technique. Next, every final model was subjected for strict validation employing the internationally accepted internal and external validation parameters. The proposed models could be applicable for data gap filling to determine hydrolysis half-lives of organic chemicals at different environmental conditions. Generally, presence of aliphatic ether and ether functional groups, high percentage of oxygen content in the molecule and presence of O-Si pairs of atoms at topological distance one, results in a shorter hydrolysis half-life of organic chemicals. On the other hand, higher unsaturation content and high percentage of nitrogen content in molecules lead to higher hydrolysis half-life. It is also found that branched and compact molecules will have a lower half-life while straight chain analogues will have a higher half-life. To the best of our knowledge, the presented models are the first reported QSPR models for hydrolysis half-lives of organic chemicals at different pH values.

Predicting Chemical-Induced Liver Toxicity Using High-Content Imaging Phenotypes and Chemical Descriptors: A Random Forest Approach.

Hepatotoxicity is a major reason for the withdrawal or discontinuation of drugs from clinical trials. Thus, better tools are needed to filter potential hepatotoxic drugs early in drug discovery. Our study demonstrates utilization of HCI phenotypes, chemical descriptors, and both combined (hybrid) descriptors to construct random forest classifiers (RFCs) for the prediction of hepatotoxicity. HCI data published by Broad Institute provided HCI phenotypes for about 30 000 samples in multiple replicates. Phenotypes belonging to 346 chemicals, which were tested in up to eight replicates, were chosen as a basis for our analysis. We then constructed individual RFC models for HCI phenotypes, chemical descriptors, and hybrid (chemical and HCI) descriptors. The model that was constructed using selective hybrid descriptors showed high predictive performance with 5-fold cross validation (CV) balanced accuracy (BA) at 0.71, whereas within the given applicability domain (AD), independent test set and external test set prediction BAs were equal to 0.61 and 0.60, respectively. The model constructed using chemical descriptors showed a similar predictive performance with a 5-fold CV BA equal to 0.66, a test set prediction BA within the AD equal to 0.56, and an external test set prediction BA within the AD equal to 0.50. In conclusion, the hybrid and chemical descriptor-based models presented here should be considered as a new tool for filtering hepatotoxic molecules during compound prioritization in drug discovery.

Máscaras de proteção e os reveses da busca por itens de proteção individual em tempos de COVID-19 - uma revisão integrativa

Introdução: A COVID-19 – doença viral que se tornou emergência em saúde pública – possui como agente etiológico o SARS-CoV-2 e é transmitida por gotículas ou aerossóis advindos de indivíduos infectados. Em virtude da alta taxa de contaminação pelo vírus e das limitações quanto ao tratamento da doença, instituições públicas e privadas passaram a recomendar o uso de máscaras como uma forma de proteção respiratória em massa. Objetivo: delinear um panorama mais amplo sobre aspectos relativos à produção, acesso, uso e manuseio das máscaras caseiras e especificar o tempo, a forma de uso e as recomendações de cada tipo de máscara profissional. Metodologia: Para tanto, foi feita uma revisão integrativa da literatura nas bases de dados PubMed, LILACS, Web of Science, Scopus, Science Direct, The BMJ e LEXML, utilizando várias combinações dos descritores principais “masks”, “personal protective equipment” e “virus diseases”. Resultados: Entre os principais achados destaca-se a maior eficácia de proteção dos respiradores N95 e FFP2, em detrimento das máscaras cirúrgicas. Ademais, evidências mostram métodos de desinfecção eficientes para os respiradores, como o uso de peróxido de hidrogênio e radiação UV, e a alternativa do uso de máscaras artesanais, de baixo custo e fácil fabricação, para a população em geral, frente a escassez de equipamentos de proteção individual. Conclusão: Em meio a uma pandemia com agente etiológico viral, é imprescindível o estudo de dispositivos de proteção respiratória, diferenciando-os de acordo com suas particularidades, com o objetivo de se conhecer o mais indicado para proteção individual e coletiva.

Enhancement of student performance prediction using modified K-nearest neighbor

The traditional K-nearest neighbor (KNN) algorithm uses an exhaustive search for a complete training set to predict a single test sample. This procedure can slow down the system to consume more time for huge datasets. The selection of classes for a new sample depends on a simple majority voting system that does not reflect the various significance of different samples (i.e. ignoring the similarities among samples). It also leads to a misclassification problem due to the occurrence of a double majority class. In reference to the above-mentioned issues, this work adopts a combination of moment descriptor and KNN to optimize the sample selection. This is done based on the fact that classifying the training samples before the searching actually takes place can speed up and improve the predictive performance of the nearest neighbor. The proposed method can be called as fast KNN (FKNN). The experimental results show that the proposed FKNN method decreases original KNN consuming time within a range of (75.4%) to (90.25%), and improve the classification accuracy percentage in the range from (20%) to (36.3%) utilizing three types of student datasets to predict whether the student can pass or fail the exam automatically.

Clinical Research Informatics.

Summary Objectives : To summarize key contributions to current research in the field of Clinical Research Informatics (CRI) and to select best papers published in 2019. Method : A bibliographic search using a combination of MeSH descriptors and free-text terms on CRI was performed using PubMed, followed by a double-blind review in order to select a list of candidate best papers to be then peer-reviewed by external reviewers. After peer-review ranking, a consensus meeting between the two section editors and the editorial team was organized to finally conclude on the selected three best papers. Results : Among the 517 papers, published in 2019, returned by the search, that were in the scope of the various areas of CRI, the full review process selected three best papers. The first best paper describes the use of a homomorphic encryption technique to enable federated analysis of real-world data while complying more easily with data protection requirements. The authors of the second best paper demonstrate the evidence value of federated data networks reporting a large real world data study related to the first line treatment for hypertension. The third best paper reports the migration of the US Food and Drug Administration (FDA) adverse event reporting system database to the OMOP common data model. This work opens the combined analysis of both spontaneous reporting system and electronic health record (EHR) data for pharmacovigilance. Conclusions : The most significant research efforts in the CRI field are currently focusing on real world evidence generation and especially the reuse of EHR data. With the progress achieved this year in the areas of phenotyping, data integration, semantic interoperability, and data quality assessment, real world data is becoming more accessible and reusable. High quality data sets are key assets not only for large scale observational studies or for changing the way clinical trials are conducted but also for developing or evaluating artificial intelligence algorithms guiding clinical decision for more personalized care. And lastly, security and confidentiality, ethical and regulatory issues, and more generally speaking data governance are still active research areas this year.

MAIN COMPLICATIONS IN INTESTINAL OSTOMIES: AN INTEGRATIVE REVIEW

Abstract: The word stoma means the opening of a surgical mouth, when there is a need to divert the normal transit of food or elimination temporarily or permanently. The intestinal stoma has several reasons that lead to the need for its manufacture, among them: rectal colon cancer, inflammatory bowel diseases, abdominal injuries due to trauma and perforations, among others. The individual with an ostomy is subject to immediate or long-term postoperative complications, even if the stoma is temporary, affecting the person on several aspects. The objective of this study was to identify the main complications of intestinal ostomies and their impact on the lives of these individuals. To this end, an integrative literature review was carried out with a qualitative approach, carried out by consulting the Scielo and Lilacs databases. The following descriptors (DeCS) were used: “Colostomy”, “Postoperative complications” and “Surgical stomas”. These were combined using the Boolean operator and. The criteria adopted for inclusion of articles were: articles published from 2013, in the languages: Portuguese, English and Spanish, available and free. The following were excluded: Monographs, theses, reviews and duplicate articles. A total of 2566 articles were found, using the combination of descriptors, after applying the inclusion and exclusion criteria 64 articles remained, these were subjected to a previous analysis of their summary, leaving 10 articles to compose the present study. There was a predominance of complications related to surgery, (50%) followed by complications related to peri-stoma skin (30%) and complications related to the lack of professional guidance in the preoperative period (20%). It is concluded, therefore, that the incidence of complications in intestinal ostomies not directly related to the surgical act, evidences the individual's adaptation process regarding the care with the stoma and peri-stoma skin, having as aggravating the lack of monitoring of trained professionals during the preoperatively and continuously in the following phases.Keywords: Colostomy. Postoperative complications. Surgical stomas.

Studies on the IC50 of Metabolically Stable 1-(3,3-diphenylpropyl)- piperidinyl Amides and Ureas as Human CCR5 Receptor Antagonists Based on QSAR

Background: In this study, modulators of human Chemotactic cytokine receptor 5 (CCR5) were described using a quantitative structure-activity relationship model (QSAR). This model was based on the molecule’s chemical structure. Methods:: All 56 compounds of CCR5 receptor antagonists were randomly separated into two sets, 43 were reserved for training and the other 13 for testing. In the course of this study, molecular models were drawn using ChemDraw software. By means of Hyperchem software as well as optimized with both AM1 (semi-empirical self-consistent-field molecular orbital) and MM+ (molecular mechanics plus force field), molecular models were described through numerous descriptors using CODESSA software. Results: Linear models were obtained by Heuristic Method (HM) software and nonlinear models were obtained using APS software with optimal descriptor combinations used to build linear QSAR models, involving a group of selected descriptors. As a result, values of the above two different sets were shown to result from 0.82 in testing and 0.86 in training in HM while 0.83 in testing and 0.88 in training in Gene Expression Programming (GEP). Conclusion: From this method, the activity of molecules could be predicted, and the molecular structure could be changed to alter its IC50, avoiding the testing of large numbers of compounds.

Treatment Adherence of Patients with Chronic Kidney Disease: an Integrative Literature Review

Chronic kidney disease is a serious health problem worldwide and the adherence to treatment is fraught with difficulty and needs to be understood. The objective of this study is to analyze the scientific production on the main factors that influence treatment adherence of patients with chronic kidney disease undergoing hemodialysis or peritoneal dialysis. An integrative literature review was performed, selecting articles published in Portuguese, English, and Spanish, between 2007 and 2017, from the Scielo, Regional Portal of the Virtual Health Library and PubMed databases. Through four combinations of descriptors and booleans, 1181 files were found. After applying the exclusion criteria, 20 articles remained. Most of the studies were in English and were empirical, cross-sectional, quantitative, and in the area of medical sciences. They indicate different modalities that determine adherence (diet therapy, pharmacology, and therapy) and various biosociodemographic factors (age, gender, education, cognition, knowledge about treatment, socioeconomic status, access to transport and treatment, pain, and faith) and psychosocial factors (quality of life, well-being, mental health, personality, and social and family support). It is concluded that the factors that strengthen, as well as impede, patients’ treatment adherence should be recognized to guide decision-making regarding the patient’s unique therapeutic planning.

ECG heartbeat classification by means of variable rational projection

In this paper we are concerned with ECG heartbeat classification. To this order we develop an adaptive transformation method. The transformation we use is a variable orthogonal projection that makes use of the large variability of rational functions. As a result the orthogonal system is adjusted to the signal. Consequently, not only the coefficients of the projection but also the parameters controlling the system carry significant information about the signal. An additional advantage of the adaptivity is that the dimension of the representation can be substantially reduced. We note that the key issue in the use of an adaptive transformation is the optimization according to the specific task, which is ECG heartbeat classification in our case. It is a non-linear problem and is discussed in our paper in detail. Then we construct a feature vector as a combination of dynamic and morphological descriptors. The morphological part is divided into patient-depending and individual heartbeat-depending features. In every step we provide reasoning that justifies our way to proceed. We used support vector machine algorithm for classifying the heartbeats into 5 and 16 classes. The comparison tests were performed on the MIT-BIH Arrhythmia Database. They proved that our method is better than the previous ones.

Image Retrieval Based on the Combination of Region and Orientation Correlation Descriptors

A large number of growing digital images require retrieval effectively, but the trade-off between accuracy and speed is a tricky problem. This paperwork proposes a lightweight and efficient image retrieval approach by combining region and orientation correlation descriptors (CROCD). The region color correlation pattern and orientation color correlation pattern are extracted by the region descriptor and the orientation descriptor, respectively. The feature vector of the image is extracted from the two correlation patterns. The proposed algorithm has the advantages of statistic and texture description methods, and it can represent the spatial correlation of color and texture. The feature vector has only 80 dimensions for full color images specifically. Therefore, it is very efficient in image retrieving. The proposed algorithm is extensively tested on three datasets in terms of precision and recall. The experimental results demonstrate that the proposed algorithm outperforms other state-of-the-art algorithms.