Metrics for measuring distances in configuration spaces

In order to characterize molecular structures we introduce configurational fingerprint vectors which are counterparts of quantities used experimentally to identify structures. The Euclidean distance between the configurational fingerprint vectors satisfies the properties of a metric and can therefore safely be used to measure dissimilarities between configurations in the high dimensional configuration space. We show that these metrics correlate well with the RMSD between two configurations if this RMSD is obtained from a global minimization over all translations, rotations and permutations of atomic indices. We introduce a Monte Carlo approach to obtain this global minimum of the RMSD between configurations

A fingerprint based metric for measuring similarities of crystalline structures

Measuring similarities/dissimilarities between atomic structures is important for the exploration of potential energy landscapes. However, the cell vectors together with the coordinates of the atoms, which are generally used to describe periodic systems, are quantities not suitable as fingerprints to distinguish structures. Based on a characterization of the local environment of all atoms in a cell we introduce crystal fingerprints that can be calculated easily and allow to define configurational distances between crystalline structures that satisfy the mathematical properties of a metric. This distance between two configurations is a measure of their similarity/dissimilarity and it allows in particular to distinguish structures. The new method is an useful tool within various energy landscape exploration schemes, such as minima hopping, random search, swarm intelligence algorithms and high-throughput screenings

Biology of Parapodia sinaica (Lep.: Gelechiidae) in Qom province

Parapodia sinaica Frau. is among one of the important pests of tamarisk (Tamarix spp.) in Qom province. During 2000-2003, the biology of this pest was studied by periodic samplings of its different life stages every 15 days using light traps in natural tamarisk fields (Masileh region - Qom salt lake) and rearing some life stages in transparent dishes containing a tamarisk stem in laboratory conditions (50 ± 5% RH, 25 ± 1ºC and a photoperiod of 16L: 8D h.). P. sinaica overwinters as 4th or 5th larval instars in the galls. In early March, larvae change into pupa and the pupal period lasts 52 ± 5 and 44 ± 3 days in field and laboratory conditions, respectively. Flight peak of the adult moth took place in early May. The moth being fed on water and sugar survived 14 ± 3 days and preoviposition period lasted 2.3 ± 0.49 days. Average egg number deposited by a female in natural conditions was 32 ± 7. Oviposition period lasted 7.2 ± 0.83 days and incubation period of eggs was 29 ± 3 and 25 ± 2 days in the field and laboratory conditions, respectively. This insect has five larval instars and the mean developmental time of the first to the fifth larval instars were 63 ± 5, 46 ±.34, 44 ± 3.8, 50 ± 5.3 and 97 ± 6.2 days, respectively. The pest has one generation in a year. Larvae never feed on the wood but they feed on bark and cause die back of the branches in the end of the next year. Activity of this pest was also observed in Isfahan, Khuzestan, Qom and Yazd provinces

Engineering of 2D nanomaterials to trap and kill SARS-CoV-2 : a new insight from multi-microsecond atomistic simulations

In late 2019, coronavirus disease 2019 (COVID-19) was caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Spike protein is one of the surface proteins of SARS-CoV-2 that is essential for its infectious function. Therefore, it received lots of attention for the preparation of antiviral drugs, vaccines, and diagnostic tools. In the current study, we use computational methods of chemistry and biology to study the interaction between spike protein and its receptor in the body, angiotensin-I-converting enzyme-2 (ACE2). Additionally, the possible interaction of two-dimensional (2D) nanomaterials, including graphene, bismuthene, phosphorene, p-doped graphene, and functionalized p-doped graphene, with spike protein is investigated. The functionalized p-doped graphene nanomaterials were found to interfere with spike protein better than the other tested nanomaterials. In addition, the interaction of the proposed nanomaterials with the main protease (M-pro) of SARS-CoV-2 was studied. Functionalized p-doped graphene nanomaterials showed more capacity to prevent the activity of M-pro. These 2D nanomaterials efficiently reduce the transmissibility and infectivity of SARS-CoV-2 by both the deformation of the spike protein and inhibiting the M-pro. The results suggest the potential use of 2D nanomaterials in a variety of prophylactic approaches, such as masks or surface coatings, and would deserve further studies in the coming years.Peer reviewe

Corrigendum to "Repeated stimulation of the dorsolateral-prefrontal cortex improves executive dysfunctions and craving in drug addiction: A randomized, double-blind, parallel-group study" [Brain Stimul 13 (3) (2020) 582-593].

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Studying molecular 2 species econimic shrimps (P. merguiensis, P. indicus) Northern coast of the Persian Gulf and Oman sea using microsatelitte markers to separating and identificating of their possible populations

This study focuses on molecular investigation of two commercial shrimp species of penaeus family namely as : P. merguiensis and P. indicus in order to find and introduce the genetic differentiations and also probable genotypes for monitoring and managing the genetic resources of populations in three major catch areas in the Persian Gulf and the Oman Sea. Only five out of the eight primers for P. merguiensis and four out of the eight primers for P. indicus produced good amplified PCR products with fixed annealing temperature. The rest of the primers were either not easily amplified or produced nonspecific bands. Seven and six alleles were found to be unique to each of the three populations in P.merguiensis and and two populations of P. indicus respectively. Occurrences of heterozygosity deficiency were found at most loci. These heterozygosity deficiencies in observed heterozygosity in compare to expected heterozygosity may be due to inbreeding, genetic drift and consequences of illegal overharvesting of P. merguiensis and P. indicus in the studied areas as well. Deviation from HWE in both studied species was significant in most microsatellite loci (P <0.001). We observed deviation from HWE in most loci with hetrozygosity deficits. The genetic variation results showed that the pairwise Fst values were significant between populations in both species. The assignment test for P. merguiensis revealed high gene flow between Hormoz and Jask and restricted genetic flow between Guatr and Hormoz populations .We observed high gene flow between Hormoz and Jask populations for P. indicus. It seems that the changes in immigration patterns of populations between Hormoz, Jask and Guatr areas in both species are depend on the influence of Persian Gulf currents or the life cycle of studied species. Alternatively, the presence of ecological barriers such as mangrove forests may result in restricted genetic flow between Guatr and both Hormoz and Jask populations

Global Distribution of Human Protoparvoviruses

Development of next-generation sequencing and metagenomics has revolutionized detection of novel viruses. Among these viruses are 3 human protoparvoviruses: bufavirus, tusavirus, and cutavirus. These viruses have been detected in feces of children with diarrhea. In addition, cutavirus has been detected in skin biopsy specimens of cutaneous T-cell lymphoma patients in France and in 1 melanoma patient in Denmark. We studied seroprevalences of IgG against bufavirus, tusavirus, and cutavirus in various populations (n = 840), and found a striking geographic difference in prevalence of bufavirus IgG. Although prevalence was low in adult populations in Finland (1.9%) and the United States (3.6%), bufavirus IgG was highly prevalent in populations in Iraq (84.8%), Iran (56.1%), and Kenya (72.3%). Conversely, cutavirus IgG showed evenly low prevalences (0%-5.6%) in all cohorts, and tusavirus IgG was not detected. These results provide new insights on the global distribution and endemic areas of protoparvoviruses.Peer reviewe

Predicting Breast Cancer Response to Neoadjuvant Chemotherapy Using Pretreatment Diffuse Optical Spectroscopic-Texture Analysis

Purpose: Diffuse optical spectroscopy (DOS) has been demonstrated capable of monitoring response to neoadjuvant chemotherapy (NAC) in locally advanced breast cancer (LABC) patients. In this study, we evaluate texture features of pre-treatment DOS functional maps for predicting LABC response to NAC. Methods: LABC patients (n = 37) underwent DOS-breast imaging before starting neoadjuvant chemotherapy. Breast-tissue parametric maps were constructed and texture analyses were performed based on grey level co-occurrence matrices (GLCM) for feature extraction. Ground-truth labels as responders (R) or non-responders (NR) were assigned to patients based on Miller-Payne pathological response criteria. The capability of DOS-textural features computed on volumetric tumour data before the start of treatment (i.e. “pre-treatment”) to predict patient responses to NAC was evaluated using a leave-one-out validation scheme at subject level. Data were analysed using a logistic regression, naïve Bayes, and k-nearest neighbour (k-NN) classifiers. Results: Data indicated that textural characteristics of pre-treatment DOS parametric maps can differentiate between treatment response outcomes. The HbO2-homogeneity resulted in the highest accuracy amongst univariate parameters in predicting response to chemotherapy: sensitivity (%Sn) and specificity (%Sp) were 86.5 and 89.0%, respectively and accuracy was 87.8%. The highest predictors using multivariate (binary) combination features were the Hb-Contrast + HbO2-Homogeneity which resulted in a %Sn/%Sp = 78.0/81.0% and an accuracy of 79.5%. Conclusions: This study demonstrated that pre-treatment tumour DOS-texture features can predict breast cancer response to NAC and potentially guide treatments

Cost-of-Illness Analysis of Type 2 Diabetes Mellitus in Iran

Diabetes is a worldwide high prevalence chronic progressive disease that poses a significant challenge to healthcare systems. The aim of this study is to provide a detailed economic burden of diagnosed type 2 diabetes mellitus (T2DM) and its complications in Iran in 2009 year.This is a prevalence-based cost-of-illness study focusing on quantifying direct health care costs by bottom-up approach. Data on inpatient hospital services, outpatient clinic visits, physician services, drugs, laboratory test, education and non-medical cost were collected from two national registries. The human capital approach was used to calculate indirect costs separately in male and female and also among different age groups.The total national cost of diagnosed T2DM in 2009 is estimated at 3.78 billion USA dollars (USD) including 2.04±0.28 billion direct (medical and non-medical) costs and indirect costs of 1.73 million. Average direct and indirect cost per capita was 842.6±102 and 864.8 USD respectively. Complications (48.9%) and drugs (23.8%) were main components of direct cost. The largest components of medical expenditures attributed to diabetes's complications are cardiovascular disease (42.3% of total Complications cost), nephropathy (23%) and ophthalmic complications (14%). Indirect costs include temporarily disability (335.7 million), permanent disability (452.4 million) and reduced productivity due to premature mortality (950.3 million).T2DM is a costly disease in the Iran healthcare system and consume more than 8.69% of total health expenditure. In addition to these quantified costs, T2DM imposes high intangible costs on society in terms of reduced quality of life. Identification of effective new strategies for the control of diabetes and its complications is a public health priority

Assessment of Digital Pathology Imaging Biomarkers Associated with Breast Cancer Histologic Grade

Background: Evaluating histologic grade for breast cancer diagnosis is standard and associated with prognostic outcomes. Current challenges include the time required for manual microscopic evaluation and interobserver variability. This study proposes a computer-aided diagnostic (CAD) pipeline for grading tumors using artificial intelligence. Methods: There were 138 patients included in this retrospective study. Breast core biopsy slides were prepared using standard laboratory techniques, digitized, and pre-processed for analysis. Deep convolutional neural networks (CNNs) were developed to identify the regions of interest containing malignant cells and to segment tumor nuclei. Imaging-based features associated with spatial parameters were extracted from the segmented regions of interest (ROIs). Clinical datasets and pathologic biomarkers (estrogen receptor, progesterone receptor, and human epidermal growth factor 2) were collected from all study subjects. Pathologic, clinical, and imaging-based features were input into machine learning (ML) models to classify histologic grade, and model performances were tested against ground-truth labels at the patient-level. Classification performances were evaluated using receiver-operating characteristic (ROC) analysis. Results: Multiparametric feature sets, containing both clinical and imaging-based features, demonstrated high classification performance. Using imaging-derived markers alone, the classification performance demonstrated an area under the curve (AUC) of 0.745, while modeling these features with other pathologic biomarkers yielded an AUC of 0.836. Conclusion: These results demonstrate an association between tumor nuclear spatial features and tumor grade. If further validated, these systems may be implemented into pathology CADs and can assist pathologists to expeditiously grade tumors at the time of diagnosis and to help guide clinical decisions