Eating disorders (ED) have a multifactorial aetiology in which genetics play an important role. Several studies have found an association between the Val66Met (G196A) polymorphism of the Brain-Derived Neurotrophic Factor (BDNF) and Eating disorders.The aim of this study was to determine the association of the Val66Met (G196A) polymorphism of the BDNF gene and its effect on eating disorders (ED), energy intake and BMI in schoolchildren.Two-year cohort study (preadolescence to adolescence). From an initial sample of 1336 Caucasian children (mean age = 11.37 years), a group at risk of ED (n = 141) and a control group (n = 117) were selected using the Children's Eating Attitudes Test. Two years later, they were re-classified into an at-risk group (n = 41) and a control group (n = 159) using the Eating Attitudes Test. The diagnosis of the individuals at risk of ED was confirmed by means of the Diagnostic Interview for Children and Adolescents. BMI, energy intake and the Val66Met (G196A) polymorphism of the BDNF gene were analysed in the at-risk and control groups.The frequency of genotypes of the Val66Met (G196A) polymorphism of the BDNF gene is 28.6% (95% CI: 22.4-34.9) in the heterozygous form  and 5% (95% CI: 2.4-9) in the homozygous form (Met/Met). We detected no association between Val66Met genotypes and the severity of ED. Over time, the carriers of the Met66 allele with a persistent risk of ED significantly restricted energy intake .We have not found an association between Val66Met (G196A) polymorphism of the BDNF and ED in schoolchildren from general population. The relationship found between this polymorphism and energy intake restriction in adolescents with a persistent risk of ED should be replicated in a larger sample. Eating disorders (ED) have a multifactorial aetiology and negatively affect both the psychosocial and developmental levels. A high proportion of adolescents--between 20% and 25%--have inappropriate attitudes towards weight control -3. The cMany studies have investigated the role of genetics in ED. Brain-derived neurotrophic factor (BDNF) is a protein belonging to the neurotrophin family that is involved in the proliferation, differentiation and survival of neurons in the nervous system . In the Restricting anorexia nervosa (AN) and minimum body mass index (BMI) has been associated with the aforementioned Val66Met (G196A) polymorphism of the BDNF gene, and it has been suggested that the A allele (Met) may affect susceptibility to ED ,15. In aA recent meta-analysis study  suggesteBDNF is also known to play a key role in eating behaviour and weight regulation . Both BDAttempts have been made to establish an association between polymorphism and BMI in the general adult population of both sexes, but the results are contradictory. Compared with the Val/Met and Val/Val genotypes, the association between the Met/Met genotype and low BMI is much greater . NeverthIn light of the lack of research on the frequency of BDNF gene alteration in the adolescent population, the contradictory results reported to date with regard to the association between BDNF gene alteration and ED, and the main consequences of this type of alteration on energy intake and BMI, we decided to undertake a longitudinal study to clarify these aspects. The objectives of our study were: a) to determine the relation between the frequency of the Val66Met (G196A) polymorphism of the BDNF gene and the severity of risk of ED, b) to establish the association between genetic alteration and risk of ED, and c) to study the effects of this genetic alteration on BMI and energy intake according to the evolution of the risk of ED.Two-year cohort study.We studied 258 Spanish schoolchildren of Caucasian origin of both sexes aged 10-13  taken from an initial sample of 1,336 schoolchildren (649 boys and 687 girls). The participants were taken from 17 randomly selected primary schools so that the group was representative of the social and cultural backgrounds of various places (cities and suburbs) within the province of Tarragona (Spain). This sample was studied in two phases. In the first phase of the study, an ED screening test  was administered to the entire sample in order to select at-risk schoolchildren. A total of 141 were included in the at-risk group and 117 were included in the control group; the ages of the children ranged from 11 to 13 . In the preadolescent stage of the second phase, controls were randomly selected from those whose score on the ChEAT was at or below the cut-off and whose characteristics of age, gender and type of school were similar. Two years later, in the adolescent stage, the individuals in the sample from the second phase, now aged 13-15  were assessed once again. Of these individuals, a total of 113 at-risk individuals and 87 controls were identified. These subjects were given the Eating Attitudes Test (EAT), and 41 were found to be at risk and 159 were selected as controls. Figure The reasons for non-participation were lack of parental consent, absence from school or refusal by the potential subject. The percentages of participation were 87.8% in the screening phase and 88.97% in the preadolescent stage. Of these, 77.52% were monitored in the adolescent stage.Before starting the study, we obtained permission from the Catalan Government and from the schools' headmasters. Informed consent was also obtained from the children's parents in both phases of the study. The study was approved by the Ethics Committee for Clinical Research at the Sant Joan University Hospital in Reus (Spain).In the first stage of the study, the preadolescent participants in the initial sample took the adapted ChEAT  in the cTwo years later, we phoned the families that had participated in the preadolescent stage to inform them that the adolescent stage of the study was to be held. Individuals who agreed to participate again were assessed using the EAT  and wereWe classified the participants into four groups defined by the evolution of the risk of ED according to the results of the ChEAT and the EAT: A) no risk in either stage (No→No); B) risk in the first stage but not in the second stage (Yes→No); C) no risk in the first stage but risk in the second stage (No→Yes); and D) risk in both the first and second stages (Yes→Yes). These groups were checked using the DICA-C and the DICA-A in order to eliminate false controls, which accounted for 3.4% in the preadolescent stage and 13.2% in the adolescent stage. We classified participants into four groups in order to study the evolution of ED according to the DICA-C (preadolescent stage) and the DICA-A (adolescent stage). We considered individuals with subclinical syndromes, EDNOS and full syndromes as part of the ED diagnostic group .We used DSM-IV criteria to define the cases of EDNOS and subclinical syndromes on the basis of information collected with the DICA-C and the DICA-A. We defined participants with subclinical anorexia as those who fulfil the psychopathological criteria of ED based on fear of gaining weight (B criteria for AN) or self-esteem that is completely based on body weight and shape (C criteria for AN), but who do not satisfy clinical criteria such as amenorrhea (D criteria for AN) or weight loss (A criteria for AN). For subclinical bulimia, we did not consider the frequency of binge eating (C criteria for BN) or the loss of control over eating during the episode (A-2 criteria for BN).This 26-item scale is designed to assess maladaptive or problematic eating attitudes and behaviours among children. In this study, we used a 20-item experimental adapted Spanish version (reliability α = 0.73) to detect possible risk of ED and used a cut-off score of 17 .This 40-item self-report has six possible answers and is designed to assess ED behaviours. It has good psychometric characteristics of reliability and validity. In this study, we administered the Spanish validated version of the EAT  . We usedThe Spanish computerised adaptations  of the DFood consumption was assessed quantitatively over the course of three non-consecutive days, one of which was not a weekday. Because adolescents are more aware than preadolescents of the food they have consumed and of family consumption habits, we used a different method for assessing food consumption that was better adapted to their characteristics and gave better results. In the preadolescent stage, we used a food record , whereasThe parents or guardians of preadolescents recorded the quantity of food and drink consumed by their children. They expressed these quantities using simple measurements and gave information about how the food was prepared, what ingredients were used and at what time their children ate. The data were checked in a meeting with the parents and subsequently with the child. Particular care was taken to determine food intake outside of the home. This method was carried out by trained nutritionists.This method involved interviewing the adolescents. An extensive collection of photographs with a variety of portion sizes was used to provide a better assessment of the food intake. The interviews were carried out by trained nutritionists.In both phases, energy intake was calculated using the French REGAL food composition table , and the2). Height was measured using an ordinary tape measure, with the participants standing upright and wearing no shoes or headwear. We checked that all participants remained still, with their heels in line with the longitudinal axes of both feet. Weight was calculated using a digital scale (Tanita 305) equipped with an impedance analyzer. The participants were weighed in the morning, before their morning break at school. They had to wear as little clothing as possible and go barefoot in order to prevent interference with the bio impedance.The BMI was calculated by dividing the participants' weight in kilograms by their size in square metres  and stored in individual bags with a packet of desiccant.We collected samples of oral epithelium using sterilised swabs after the children had not eaten or drunk anything for at least 30 minutes. The samples were kept at room temperature on specimen collection cards . In samples in which little DNA was extracted or the DNA was weak, the entire genomic DNA was amplified using a GenomiPhi kit . The DNA was extracted and amplified at the BioBank of the Institute for Health Sciences Research (IRCIS) at Sant Joan University Hospital in Reus (Spain). In one case, the DNA extraction was not satisfactory.2O, 1× gold buffer , 1.5 mM MgCl2, 0.2 mM dNTPs, 0.5 μM BDNF-F (5'-AGGTGAGAAGAGTGATGACC-3'), 0.5 μM BDNF-R (5'-CTGGACGTGTACAAGTCTGC-3') and 0.5 U Taq DNA polymerase . The initial conditions of the PCR were 94°C for 5 min followed by 32 cycles of 30 s at 94°C, 30 s at 58°C and 30 s at 72°C, and finally 7 min at 74°C, until a 292 bp product was obtained. Digestion was then carried out using the NlaIII restriction enzyme. For an enzyme volume of 2 μl, it contained 1× NE buffer 4, 1 U NlaIII and H2O. Digestion was carried out at 37°C for 1 h 15 min. Digestion with NlaIII produced four fragments with 160, 59, 58 and 15 bp, respectively, in the presence of the Met66 allele, and five fragments with 83, 77, 59, 58 and 15 bp, respectively, in the presence of the Val66 allele. Finally, electrophoresis was applied to a polyacrylamide gel at 12% with a TBE buffer.The G196A variant of the BDNF gene was genotyped using the polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) technique. The SNP in the BDNF gene was categorised for each DNA sample using a PCR with a total volume of 10 μl, consisting of 20 ng of DNA, HThe genotyping was undertaken by the IRCIS UniLab at the Sant Joan University Hospital in Reus (Spain).The categorical variables have been presented as percentages and their corresponding confidence interval, and the quantitative variables (BMI and energy intake) as the median and the interquartile range because of its skewed distribution.The genotypes were stratified by the severity of ED risk. The genotype distribution at different severities of ED risk was tested for Hardy-Weinberg equilibrium by chi-square test or Fisher's exact test when necessary. Mann-Whitney U test, with Bonferroni correction to solve the multiple testing problems, were used to compare BMI and Energy intake in the different groups. Wilcoxon test were used to observe the relationship between genotype and repeated measures of BMI and energy intake in the same subjects. All data were analyzed using the SPSS package for Windows (version 17.0). The level of statistical significance was set at p < 0.05.χ2 = 2.96; d.f. = 4; p = 0.562) or in boys   polymorphism of the BDNF gene does not depend on the severity of ED risk in girls  or of ED diagnosis .Table χ2 = 0.06; d.f. = 2; p = 0.970) , the risk group   or the ED diagnosis group   in girls or boys, respectively. Nor was the genotype distribution significantly different in the groups classified according to evolution of risk and evolution of ED.The distribution of genotypes according to the severity of ED did not differ significantly from that expected from the Hardy-Weinberg equilibrium in the control group .In this study, we analysed a genetic component, the Val66Met (G196A) polymorphism of the BDNF gene, in a sample of schoolchildren susceptible to developing some kind of ED. The longitudinal design allowed us to observe the evolution of risk of ED and how it is associated with the genotypes of the polymorphism. The frequency of the polymorphism in our sample contributes to the intrapopulation differences in Val66Met in the Caucasian population. According to Shimizu et al., the distribution of genotypes varies according to population: participants of Asian origin carry a higher percentage of the Met66 allele than participants of Caucasian origin .Our results do not reveal any association between the Val66Met G196A) polymorphism of the BDNF gene and the different levels of severity of risk of ED, the evolution of risk, or ED diagnosis. These results support those obtained for clinical populations of participants diagnosed with BN ,20 and A6A polymoAccording to various studies, the prevalence of heterozygote  in AN is about 35.8%, with values ranging from 24.4% to 45.9%, while the prevalence of homozygote (Met/Met) is 3.6% (2.3-4.9%) ,44. In BAs in other studies on ED ,19,20, wAlthough we observed no direct effect of the Val66Met polymorphism on those schoolchildren with some kind of ED, some previous studies ,50 have A high percentage of the general population studied was shown to have the Val66Met (G196A) polymorphism of the BDNF gene . However, no direct relationship was observed between the presence of the polymorphism and risk of ED. Nevertheless, in one small group (adolescents with a persistent risk of ED for at least two years), the presence of the polymorphism leads to a greater restriction of energy intake  than that found in the general population. No direct relationship was found between BMI and the polymorphism. Nevertheless, further studies with larger samples and long-term monitoring are required if the evolution of ED is to be better understood.The authors declare that they have no competing interests.The study's chief researchers VA and JC were responsible for identifying the research question, designing the study, obtaining ethics approval, acquiring funding, analysing the data, writing the manuscript and overseeing the study. MF contributed to developing the precise content of the study interventions and resources, recruited participants, implemented the study and wrote the paper. MF and NA reviewed the literature and analyzed the data. All authors were responsible for drafting the manuscript, and read and approved the final version.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2458/10/363/prepub


A mistake has been noted in our recently published (25 March 2009) article . This erAn overlapping of the lines has occurred in the fatty acid profile section of the Table, due to an unfortunate insertion of the 22:2 n-6, a fatty acid that has nothing to do there. This returns any impossible understanding, particularly of the DHA supply and so intake. Table


Alkaline phosphatase, a marker of differentiation in the human alveolar adenocarcinoma cell line A549, is inducible by conditioned medium from lung fibroblasts and by cytokines including oncostatin M and interleukin 6, but only in the presence of a glucocorticoid, dexamethasone. Dexamethasone was shown to induce incorporation of [3H]glucosamine into three fractions of medium and cell trypsinate from subconfluent A549 cells, eluting from DEAE ion-exchange chromatography. The first peak did not correspond to any of the unlabelled glycosaminoglycans and was not characterized further. Induction was seen in two other peaks, corresponding to hyaluronic acid and heparan sulphate. Of these, heparan sulphate, eluting as one well-defined peak (referred to as HS1) and another of lower activity and less well defined (HS2), was selected as the most likely to interact with growth factors and cytokines and was isolated from the eluate, concentrated and desalted, and used in alkaline phosphatase induction experiments in place of dexamethasone. HS1 isolated from the medium (HS1m) of subconfluent A549 cells was shown to replace dexamethasone in induction experiments with fibroblast-conditioned medium, oncostatin M and interleukin 6. HS1 from the cell trypsinate and HS2 from the medium and trypsinate were inactive. As the activity of HS1m could be abolished by heparinase and heparitinase but not by chondroitinase ABC, it was concluded that HS1m was a fraction of heparan sulphate involved in the regulation of paracrine growth factor activity in lung fibroblast-conditioned medium, and in the regulation of other growth factors with potential roles in the paracrine control of cell differentiation.


Untreated male and female BDX rats were observed over a period of 30 months for spontaneous tumours of a size suitable for transplantation. At the age of 13-30 months 60/97 animals developed tumours, 53 of which were considered as malignant, and 7 as benign tumours. The spectrum of malignant tumours included sarcomas of connective tissue and bone, skin carcinomas, tumours of the lung, the gastrointestinal tract, the genito-urinary tract, the mammary glands, the testis, the adrenal glands and also sarcomas of the neural system and malignancies of the lymphoreticular system. Out of 41 tumours implanted s.c., 34 of them could be passaged further. The primary latent period varied between 1 and 12 months.


In this work one-dimensional (1D) triplex polymer of silver (I): mixture nucleosides of cytidine and 8-bromoguanosine was synthesised. The polymer showed high stability due to the presence Ag(I) ions in the structure of the polymer in addition to the stability that produces from the effect of Hoogsteen hydrogen bonding in the triplex CGC. Atomic Force Microscopy (AFM) and transmission electron microscopy (TEM) were used to investigate the morphology of the polymer. The AFM images revealed formation of nanofibres extending many microns in length with height in the range of 2–3 nm. Statistical analyses carried out to analyse the AFM images to determine the height of the loops that formed in the polymer. The data displayed that the height value was in the range between 10 nm to 15 nm. The data of TEM images were consistent with the data of AFM images by displaying a very long fibre. Gwyddion software program was used to investigate surface parameters (roughness and waviness), diameter (size distribution), and probability density of the fibre. The data showed that the diameter of the fibre was ∼0.4 nm. Based on the interactions of intermolecular hydrogen bonding, guanine and cytosine nucleobases can assemble to make long chains and forming networks, according to the known Watson-Crick pairing in both DNA and RNA Studying surface roughness properties of the compounds, e.g., surface of semiconductor materials, is very important to know the changing that occurs on in the surface during the chemical reaction by forming or removing a layer from the surface. The atoms in the surface possess high chemical reactivity than that in the bulk as these atoms have the ability to alter their electronic structure by reaction with different kinds of chemical environments, these properties have found applications in different industrial technologies 22.11H and 13C NMR spectra were performed on a Bruker Advance 300 spectrometer at 300 MHz, with DMSO-d6.All chemicals were purchased from Sigma Aldrich and were used as received without further purification. 2.22 and cleaning with 1:4 H2O2:H2SO4 (piranha solution) for 1 h, followed by washing the chips with deionised water and drying with nitrogen gas.P-silicon (100) wafers were used to achieve AFM measurements by cutting the wafers into 1 cm2.32 clean silicon chip, then the sample was dried by air prior to scan with NanoScope Analysis 1.5 software (Bruker).Sample for AFM measurements was prepared by drop-casting 2 μL of the sample onto 1 cm2.4TEM measurements were carried out by using Philips CM100 electron microscope at accelerating voltage 100 kV. 2 μL of the sample was dropped onto a carbon coated copper grid, the sample was left to dry by air overnight before imaging.2.5Gwyddion software program was used to analysis AFM images.2.68-Bromoguanosine was prepared according to the procedure of Srivastava 2.6.11H NMR  of guanosine spectrum in 1HNMR spectrum of 8-bromoguanosine in 2.72O) was added to cytidine solution , the mixture was shacked for several minutes then a solution of AgNO3  was added, then the mixture was shacked quickly and left in a dark place at room temperature. After 30 min colourless viscose solution was formed, the viscosity of the sample was increased with the time. After four days of the preparation, the sample be more viscous, but it was not stable to inversion test to indicate the stability, and this confirms that the sample was not gel. 1.5 μL of the sample was drop-casted on a silicon chip and left to dry by air prior to scan by AFM.The one-dimensional (1D) polymer of Ag(I):cytidine and 8-bromoguanosine was prepared with stoichiometry 1:1:1 of Ag(I):cytidine:8-bromoguanosine as followed: The solution of 8-bromoguanosine  ions with equimolar equivalents mixture nucleosides, cytidine and 8-bromoguanosine, in aqueous solution leads to form triplex pyrimidine motif (Y), CGCAg+ needs that the position of the third strand must be in the major groove of the double helix that is formed by Hoogsteen hydrogen bonding Building triplex block of CGCAg3.22. Many loops can be seen in image (a), +, can self-assemble to form nanofibres. Self-assembling of nucleosides was seen for guanosine and its derivatives. Different architectures structures such as cyclic The AFM technique was used to address the morphology of the polymer. Examination the dried polymer revealed formation of nanofibres extending many microns in length with a height in range of 2–3 nm, some few fibres revealed with height up to 4 nm. 3.32 by using Gwyddion software program, the data are presented in Surface texture analysis is very useful to understand the nature of the material, and it helps in the development of many material components. The surface sample parameters of waviness and roughness of the single polymer were carried out for the AFM image with scanning area 5 × 5 μmRMS is the root mean square average deviation of the roughness, it is known as Rq, also. L and Z (x) are the length of the profile and the function of height profile, respectively. The data demonstrated that the root mean square waviness (Wq) was 147.3 pm, while the waviness average (Wa) was 128.9 pm. The height of the waviness is normally higher than the average roughness height by three times 3.4Kurtosis parameter describes the height distribution of the surface 3.52) for the polymer of Ag(I): mixture nucleosides. The image was adjusted to greyscale for analysis, as shown in Gwyddion software program was used to obtain surface roughness parameters by analysis the tapping mode height AFM image, The height distribution of the AFM image (a) in 3.6+.TEM technique was used to investigate the morphology of the polymer. Carbon coated copper grid was used as a substrate to prepare the sample. 1.5 μL of the sample was drop-casted onto the substrate, left to dry by air prior to imaging. The inspection revealed the formation of a very long, entangled fibres, as shown in 4In summary, one dimensional triplex parallel pyrimidine polymer of Y motif based on self-assemble of Ag(I) with mixture complementary bases nucleosides (G & C) was prepared, to the best of our knowledge, this is the first report displays that complimentary nucleosides, cytidine & 8-bromoguanosine, are capable of self-assembling directly to produce nanostructure material with such length and height as shown by AFM measurements where the height of the polymer was in the range of 2–3 nm and the length was many microns. This feature makes this polymer analogous to the duplex DNA Lamia al-Mahamad: Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper.This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.The authors declare no conflict of interest.No additional information is available for this paper.


Google Glass is a head-mounted device designed in the shape of a pair of eyeglasses equipped with a 5.0-megapixel integrated camera and capable of taking pictures with simple voice commands.The objective of our study was to determine whether Google Glass is fit for veterinary forensic pathology purposes.A total of 44 forensic necropsies of 2 different species (22 dogs and 22 cats) were performed by 2 pathologists; each pathologist conducted 11 necropsies of each species and, for each photographic acquisition, the images were taken with a Google Glass device and a Nikon D3200 digital single-lens reflex (DSLR) camera. The pictures were collected, divided into 3 groups , and evaluated by 5 forensic pathologists using a 5-point score system. The parameters assessed were overall color settings, region of interest, sharpness, and brightness. To evaluate the difference in mean duration between necropsies conduced with Google Glass and DSLR camera and to assess the battery consumption of the devices, an additional number of 16 necropsies were performed by the 2 pathologists. In these cases, Google Glass was used for photographic reports in 8 cases (4 dogs and 4 cats) and a Nikon D3200 reflex camera in the other 8 cases. Statistical evaluations were performed to assess the differences in ratings between the quality of the images taken with both devices.P<.001). In particular, for the pictures of Groups A and B taken with Google Glass, the sum of frequency of ratings 5 (very good) and 4 (good) was between 50% and 77% for all 4 assessed parameters. The lowest ratings were observed for the pictures of Group C, with a sum of frequency of ratings 5 and 4 of 21.1% (342/1602) for region of interest, 26% (421/1602) for sharpness, 35.5% (575/1602) for overall color settings, and 61.4% (995/1602) for brightness. Furthermore, we found a significant reduction in the mean execution time for necropsy conduced with the Google Glass with respect to the reflex group (P<.001). However, Google Glass drained the battery very quickly.The images taken with Google Glass received significantly lower ratings than those acquired with reflex camera for all 4 assessed parameters (These findings suggest that Google Glass is usable in veterinary forensic pathology. In particular, the image quality of Groups A and B seemed adequate for forensic photographic documentation purposes, although the quality was lower than that with the reflex camera. However, in this step of development, the high frequency of poor ratings observed for the pictures of Group C suggest that the device is not suitable for taking pictures of small anatomical details or close-ups of the injuries. Google Glass is a device that was released for the first time as Google Glass explorer edition in 2013. It is a head-mounted device designed in the shape of a pair of eyeglasses with a 5.0-megapixel integrated camera; wireless connection; and the ability to take pictures, record a video, and call people with simple voice commands or manually by touching the frame. A small prism placed on the right side of the device allows a display of information to the user , clinicaOver the last years, forensic necropsies in veterinary medicine have rapidly increased due to the increasing demand for investigations of crimes against animals. For these reasons, the subfield of veterinary forensic pathology has emerged as a distinct discipline, essentially based on a transverse, multiorgan approach that includes necropsy, histological examination, immunohistochemistry, and collateral examinations such as laboratory analysis and diagnostic imaging to resolve obscure fatalities -17. The Photography is an important component of documentation in forensic pathology ,26,27. AA total of 44 necropsies of 2 different species  were performed by 2 pathologists (FP and GP) with training in forensic medicine. All necropsies were performed in the necropsy room of the Department of Veterinary Medicine and Animal Productions at the University of Naples Federico II, Naples, Italy, following a standard necropsy protocol summarized in Victim identification proceduresEvaluation of thanatological aspects and estimation of the time elapsed since deathExternal examination of the body Skinning with evaluation of subcutis and musclesOpening and evaluation of body cavities Extraction and general macroscopic evaluation of organsDissection of all organsSpecific evaluation of wounds or injuriesComplete photographic documentation of external appearance of the animals, body cavities, organs, and injuriesEach pathologist conducted 11 necropsies of both species. For each photographic acquisition, the images were taken using two different devices: Google Glass and a Nikon D3200 DSLR camera. During the external inspection of the body, in accordance with the guidelines for forensic veterinary autopsies, pathologists were asked to take pictures of the external appearance of the animals from many different angles. Furthermore, during the necropsies, pictures of organs before and after extraction and any other detail useful for documentation purposes was acquired. All images were acquired under standard lighting conditions and without using the internal flash of the camera. In addition, a standard background (blue table of 90 × 70 cm) was used to acquire pictures of organs and small anatomical details. Finally, during image acquisition, a photomacrographic scale  placed near the injuries was used to provide a geometrical reference in the forensic photographic documentation of the evidences.To evaluate the differences in time of necropsy between autopsies conduced with Google Glass and DSLR camera and the battery performance of the devices, an additional number of 16 necropsies of 2 different species  were performed by the 2 pathologists (FP and GP). In these cases, each pathologist conducted 4 necropsies of each species, with half of them conducted using Google Glass and another half using the Nikon D3200 DSLR camera. For each postmortem examination, we measured the time required to perform the necropsy using the stopwatch functionality available on a smartphone iPhone 6s Plus. To standardize the measurement, for all 16 forensic necropsies, the timer started at the first photographic acquisition and ended when the pathologist declared that he had acquired all pictures useful for the documentation purpose. Furthermore, each forensic examination began with devices (DSLR camera and Google Glass) charged to 100%, and at the end of each necropsy, the remaining battery power was noted.At the end of each necropsy performed with the Google Glass, pathologists were interviewed to acquire information about the user experience. The questions were designed to obtain information about the usability aspects, general experiences, and the main positive and negative features of the device.The device—a Google Glass explorer edition—available during our study, ran on Android 4.4.2. specifications of the available developer explorer unit that included Texas Instrument OMAP 4430 SoC 1.2 GHz Dual core (ARMv7) processor, 2 GB of RAM and 12 GB of usable storage space, a 640 × 360 display, 802.11b/g Wi-Fi, Bluetooth, and a 5-megapixel camera . It alsoFor image acquisition, we used the preinstalled camera app for two reasons. First, we did not know whether the use of an app other than the preinstalled one would increase the battery consumption or decrease the photographic quality. Second, the voice commands and gestures performed on running the preinstalled app were easy to perform, intuitive, and precise; thus, we were not inclined to use an accessory app. However, to properly use the Google Glass camera during the necropsies, both pathologists followed a training course that lasted approximately 15 minutes. At the end of it, the pathologists declared to be able to use the devise correctly. The voice commands used during our study were as follows: “show viewfinder,” to frame the anatomical reason of interest correctly, and “take a picture,” to acquire the images. All accepted images were stored in a folder on the device until the pictures were transmitted via USB to an iOS-based laptop (MacBook Air 13").A total of 5 forensic pathologists  with a mean work experience of 19 (SD 7.41) years in the field of veterinary pathology, both diagnostic and teaching, were selected to evaluate the quality of the images taken with both devices. To avoid compromising the evaluation of the images by the memories of the necropsies, the pathologists selected for the evaluation of the images were different from those who physically performed them. Furthermore, before the beginning of the evaluation, the pictures were divided into 3 groups: Group A, pictures of the external appearance of the animals; Group B, pictures of the organs; and Group C, pictures of small anatomical details or close-ups of the injuries. Each group included images taken with both devices. However, the device used for the acquisition was known (DSLR camera or Glass) only to the coordinators of the study. All 5 pathologists separately evaluated each group. In addition, all pictures were presented on a same computer (MacBook Air 13") with fixed display settings and similar environmental lighting conditions to avoid differences of evaluation due to external variables. The pathologists gave their opinions individually about the quality of the images analyzing 4 parameters: (1) overall color setting; (2) sharpness; (3) region of interest; and (4) brightness. Each one of these 4 parameters was separately evaluated on a 5-point score system according to Albrecht et al  Table 1Table 1.t test was used to evaluate the difference in the mean duration between the necropsies conducted with Google Glass and the DSLR camera. The descriptive statistics for the ratings consisted of the tabulation of the frequency and percentages of scale items for each group for each item per device. To evaluate the differences between the ratings obtained for each group for both devices, we calculated an unpaired rank sum test  [Statistics were computed using SPSS Version 22.0 . Student’s pha=.05) . The sampha=.05) .During the 44 forensic autopsies, the pathologists took 985 pictures with Google Glass and 985 pictures with the D3200 DSLR camera (504 photos of dogs and 481 photos of cats with each device). Each picture was evaluated by 5 pathologists, resulting in 4925 single evaluations each for Google Glass and the DSLR camera. The ratings of the images taken with Google Glass during necropsies were significantly lower than those of the images acquired with the DSLR camera . In partThe lowest ratings were observed in the pictures of Group C taken with Google Glass, with a sum of frequency of ratings 5 and 4 of 21.1% (342/1602) for region of interest, 26% (421/1602) for sharpness, 35.5% (575/1602) for overall color settings, and 61.4% (995/1602) for brightness. In this group, the differences between devices were particularly noticeable for region of interest, overall color settings, and sharpness. With regard to region of interest, the sum of frequency of ratings 2 and 3 amounted to 4.6% (74/1602) for images acquired using the DSLR camera versus 78.9% (1278/1602) for those acquired using Google Glass. Similarly, for overall color settings, this sum was 4% (65/1602) for images acquired using the DSLR camera versus 64.5% (1045/1602) for those acquired using Google Glass. Finally, for sharpness, the sum was 8.6% (140/1602) for images taken with DSLR versus 74% (1199/1602) for those taken with Google Glass. Furthermore, with regard to the pictures taken with Google Glass, statistical differences were observed in the distribution of ratings between Group A and Group B and between Group C versus B and A for all 4 assessed parameters .P<.001). Furthermore, at the end of the necropsies conducted with Google Glass, the average percentage of battery power was 47% and 60% for the dogs and cats group, respectively (Of the 16 necropsies conducted with Google Glass (8 necropsies) and DSLR camera (8 necropsies), we observed a reduction in the time of necropsy with Google Glass compared with that with the DSLR camera group. The mean duration of a single postmortem examination in the DSLR camera group was 126.38 (SD 3.46) minutes for the dogs group and 68.90 (SD 2.30) minutes for the cats group, whereas for the Google Glass, the mean duration was 111.11 (SD 3.29) minutes for dogs group and 55.5 (SD 2.06) for cats group. The differences were significant , region of interest , sharpness , and brightness .The interrater reliability was high. The ICC for the ratings obtained based on the forensic necropsy pictures indicated a good positive relationship for overall color settings (0.815, 95% CI 0.771-0.853; Potentially disruptive technologies such as Google Glass create excitement for the possible applications that they can have in health care; however, the use of new tools should be thoroughly evaluated and validated before applying them in the medical or biomedical fields. Google Glass has been tested in many medical fields such as clinical simulations , surgeryA few limitations of the study should be noted. First, we were able to test the device with only 2 pathologists. However, this allowed a more accurate assessment of the necropsy execution times. In our opinion, a greater number of pathologists would have determined a high variability in the time of necropsy due to the different levels of manual dexterity of each pathologist. Second, for the evaluation of the images, this study was based on subjective opinions of raters and not on objective and reproducible parameters. However, to reduce this limitation, a high number of highly qualified pathologists was selected for the evaluation of the images. In addition, the ICCs were evaluated for the 5 raters for the items region of interest, sharpness, brightness, and color discrimination. Furthermore, this study was conducted only on cats and dogs. We decided to test the device on these animals because although diagnostic necropsies are commonly performed on a broad range of animals, at present, they are the main species of forensic interest in the veterinary field ,36. HoweThese findings suggest that Google Glass is usable in the veterinary forensic pathology of pet animals, but its image quality is lower than that of a reflex camera. In particular, the image quality of Groups A and B seemed adequate for forensic photographic documentation purposes. However, in this step of development, the high frequency of poor ratings observed for the pictures of Group C suggest that the device is not suitable for taking pictures of small anatomical details or close-ups of the injuries. In our opinion, the combined use of the two devices, reflex camera for capturing images of small anatomical details and Google Glass for capturing images of the external appearance of the animals and organs, could reduce the execution times of the necropsy, lead to considerable saving of gloves, and allow acquisition of pictures useful for forensic documentation purposes. However, further studies will be needed to evaluate the application of this device to other species of veterinary interest such as wildlife or farm animals. In some of these species, the greater volume of the organs than that in pet animals could make the qualitative differences between Google Glass and the reflex camera less evident but, above all, could make the absence of the photographic zoom in Google Glass less limiting.


Quercus mongolica growing in Northeast of China. The NIR spectra of specimens were acquired by using a one-chip NIR fiber optic spectrometer whose spectral range was 900~1900 nm. The raw spectra of specimens were pretreated by multiplication scatter correlation and Savitzky-Golay smoothing and differentiation filter. To reduce the dimensions of data and complexity of modeling, the synergy interval partial least squares and successive projections algorithm were applied to extract the characteristic wavelengths, which had closing relevance with the MOE of wood, and five characteristic wavelengths were selected from full 117 variables of a spectrum. Taking the characteristic wavelengths as input values, partial least square regression (PLSR) and the propagation neural network (BPNN) were implemented to establish calibration models. The predictive ability of the models was estimated by the coefficient of determination (pr) and the root mean square error of prediction (RMSEP) and in the prediction set. In comparison with the predicted results of the models, BPNN performed better results with the higher pr of 0.91 and lower RMSEP of 0.76. The results indicate that it is feasible to accurately determine the MOE of wood by using the NIR spectroscopy technique.This article presents a non-destructive methodology to determine the modulus of elasticity (MOE) in static bending of wood through the use of near-infrared (NIR) spectroscopy. Wood specimens were obtained from  Quercus mongolica is the main secondary forest species growing in Northeast China. Quercus mongolica is a frequently used structural material, also used for manufacturing of furniture, machinery, and sports appliances. The modulus of elasticity (MOE) in static bending is one of the most important mechanical properties of Quercus mongolica. It could be used in various ways or made into different kinds of products based on its MOE, so the detection of MOE can not only achieve its best use, but also ensure safe use in engineering. However, most of the traditional methods of testing mechanical properties of wood are destructive and time-consuming [onsuming . AlthougNear-infrared (NIR) spectroscopy is a non-invasive analytical, high reliability, and pollution-free method to determine different properties of materials. NIR spectrum mainly reflects the second harmonic generation and co-frequency absorption of C-H, N-H, O-H and other hydrogen radical groups. The NIR absorption peaks of different groups have obvious differences. NIR spectra have abundant structural information and can be adapted to the determination of organic matters . NIR speThe spectral analysis methods mainly focus on pretreatment methods, feature optimization, and prediction model establishment. Andrade et al. employed multiplication scatter correlation to pretreat the spectra before the prediction of MOE and the scattering light effect in the spectra of solid samples . Liang eQuercus mongolica based on NIR spectroscopy combined with chemometrics analysis. The paper will focus on the following three aspects: (1) to reveal the relationship between NIR spectra and the MOE of the non-defect specimens of Quercus mongolica; (2) to explore an effective characteristic wavelengths extraction approach to select the closing relative information of NIR spectral data; and (3) to establish a model to predict the MOE in Static Bending of Quercus mongolica and evaluate the predictive ability of the model.Therefore, the objective of this study was to investigate and build a new approach for quantitative analysis and determination of MOE in Static Bending of Quercus mongolica timber used in the experiment was collected from Chonghe forest farm, forestry bureau of Wuchang City, Heilongjiang province, China. The geographical coordinates were of 44°37′~44°47′ N, 27°35′~127°55′ E. The average elevation was 350 m. The climate of this area was temperate continental monsoon climate with annual temperature ranging from 35° to −34°, that led to an unfreezing period of 125 days. The annual precipitation and annual evaporation capacity were 750 mm, and 340 mm, respectively. In the Quercus mongolica section of the forest farm, 3 groups of trees were collected along the topography from high to low. Each group consisted of 4 trees, and 12 sample trees were obtained. In accordance with Chinese national standards, “General requirements for physical and mechanical tests of wood (GB1927~1943–2009)”, the timber was cut into small specimens with dimensions of 300 mm (L) × 20 mm (T) × 20 mm (R). 125 specimens without defects were chosen and numbered from 1 to 125. These specimens were then stored at constant temperature and in a constant humidity incubator; the moisture content of the incubator was adjusted to 12%. The NIR spectrum scanning and mechanical properties were tested in a laboratory. The temperature was 22 ± 1 °C and the average relative humidity was 50%.The In this work, the equipment used for specimen spectrum measurements was a One-chip NIR fiber optic spectrometer, which was researched and developed by Insion Co., GmbH, Heilbronn, Germany. The spectrometer utilized two bifurcated fiber optic probes to scan the diffuse reflectance spectrum in the surface of the specimen. The wavelength ranged from 900 to 1900 nm at a spectral resolution of 9 nm. Scholars and researchers found that spectroscopy in 1100 to 1700 nm contained important information that could be used to analyze and predict the properties of wood . Before Because of the difference of growth characteristics of timber, 9 spectra were collected uniformly in each radial plane and each tangential plane by moving the probe. The 36 spectra  were averaged to one single spectrum to represent the specimen they belonged to. P was the difference of upper and lower limit load, l was the span of loading point, b was the breadth of specimen, h was the height of the specimen, and f was the deformation value in the middle of the specimen in the loading process.The experiment used the all-around mechanical testing machine of wood to determine the MOE in static bending of specimens, referring to steps and specifications in Chinese national standards, “Method for Determination of the Modulus of Elasticity in Static Bending of Wood (GB/T 1936.2-2009)”. All the samples were loaded in a tangential direction at a uniform rate (10 mm/min) until rupture (2 to 3 min per sample) to determine the MOE of the wood. The calculation of MOE was shown in Equation (1):px(j) and qx(j) were the absorbance of sample p and q at the j wavelength, respectively, m was the number of wavelengths in a spectrum, and n was the total number of samples. In order to guarantee the applicability and stability of the prediction model, the ratio of calibration set and prediction set was generally between 2:1 and 4:1. However, random set partitioning usually made the calibration set unrepresentative . AccordiIn this study, the ratio of the calibration set to the prediction set was chosen as 2:1.In the process of acquiring NIR spectrum data of the experimental samples, the noise, such as light scattering, high-frequency random noise, was inevitably caused by the spectrometer itself or the environment. The noise influenced the modeling effect and prediction accuracy. Therefore, the raw data needed to be pretreated before establishing the analysis model of the relationship between the NIR spectra and the mechanical properties of samples. The pretreatment methods studied in this work are multiplication scatter correlation (MSC), and Savitzky-Golay (SG) smoothing and differentiation filter.MSC was used to compensate the dispersion effect of spectral data and reduce the occurrence of baseline drift. MSC was also used to correct the scattering of each spectrum and obtain an ideal spectrum . SG smooThere was a lot of redundant information in the spectral data, which not only increased computational complexity, but also reduced the predicting accuracy of the model. Therefore, it was necessary to eliminate the uninformative wavelengths in the spectra that had unrelated MOE information. This process was named as characteristic spectrum extraction. The whole process included two steps: (1) optimal spectra intervals selection by synergy interval partial least squares (SiPLS); (2) characteristic wavelengths selection by successive projections algorithm (SPA).N smaller equidistant sub-intervals; second, the partial least squares (PLS) regression model was built with m sub-intervals; and lastly the combination of sub-interval spectrum, which had the lowest root mean square error of the cross-validation showed better performance [SiPLS was a further improvement on interval partial least squares (iPLS), proposed by Norgaard ,21. The formance .N to be selected were set. Starting from each variable, SPA yields M  sets of selection of N variables. The optimal initial variable and number of variable can be determined on the basis of the smallest root mean squared error of prediction in a validation set of multiple linear regression calibrations [SPA used vector projection analysis to find the variable set containing the lowest redundancy information and minimized the collinearity between variables. SPA selected a few groups of strong representative variables from the original spectrum of the experimental samples, which contained the majority of the spectral information. It also eliminated redundant and repetitive information in the spectral variables to improve the prediction ability of the model ,24. The brations .cr of calibration set, pr of prediction set), the standard error of cross-validation (SECV), the root mean square error of prediction (RMSEP), and the ratio of performance to deviation (RPD). The type of cross-validation was leave-one-out RPD, which was the quotient of standard deviation (SD) of the true value of prediction set and the RMSEP of prediction set. The selection of the final model was based on its predictability following a procedure which has already been successfully applied [cr, pr, and RPD, but a lower value of RMSEC and RMSEP.The quality of the models was assessed using several common statistical measures : the coe applied . GeneralThe MOE of the 125 specimens ranged from 10.43 GPa to 19.25 GPa. The samples were partitioned into a calibration set and a prediction set by the improved K-S method, and the K-S method was carried out by using MATLAB. The ratio of these two sets was 2:1, that was, 84 specimens were put into the calibration set and 41 into the prediction set. The partition of sets is shown in The NIR spectra of 125 specimens were collected with a wavelength ranging from 907 to 1864 nm. The number of sampling points of the wavelength variable was 117, and the raw spectra of all the specimens are as shown in The raw spectra were pretreated by MSC combined with the SG convolution smoothing and differentiation filter. The results of pretreatment are shown in In this study, the full spectrum (907~1864 nm) of specimens was divided into 5, 6, …, 15 intervals combined with 2, 3 or 4 subintervals. The optimal combination of intervals and the number of PCs was optimized by full cross-validation and determined according to the lowest RMSECV. In this study, the optimal spectra intervals were a combination of four subintervals when the spectrum was divided into 10 subintervals. After optimal spectral intervals selection by SiPLS, the number of wavelengths was reduced from 117 to 46. The 46 variables were numbered from 1 to 46. SPA was then implemented to select the characteristic wavelengths from the 46 variables. pr of 0.91 and lower RMSEP of 0.76. Furthermore, the RPD of BPNN was between 2.5 and 3.0, which suggested that the model met the needs of quantitative prediction [Quercus mongolica specimens without defects. Quercus mongolica specimens with BPNN as the prediction model. The experiments used the characteristic wavelengths in the calibration set to establish a calibration model. Partial least square regression (PLSR) was the main linear modeling method in NIR quantitative analysis, so it was used to analyze the relationship between NIR spectrum and MOE of the specimen in this work. On the other hand, the propagation neural network (BPNN), which is a widely used nonlinear model, was also selected to predict the MOE of wood. ediction . The expQuercus mongolica specimens without defects. NIR spectroscopy was used to establish a quantitative analysis model for non-destructive testing of MOE of samples. Based on the experimental results, the following conclusions can be drawn:(1)The improved K-S method can make the sample distribution uniform, and ensure that the calibration set is widely distributed(2)By pretreating with MSC and the SG smoothing and differentiation filter, the overall variation trend of spectra was more consistent, and the contour of spectra was more clear. Moreover, the absorption peak is more obvious. When the window size of SG was of 11, the effect of pretreatment was the best(3)Quercus mongolica. It reduced the dimensions of the original data, decreasing the computation and reducing the complexity of the modelling process.SiPLS combined with SPA could extract characteristic wavelengths that had the closest relevance with the MOE of (4)pr, RMSEP, and RPD of BPNN were 0.91, 0.76, and 2.93, respectively. The quantitative prediction effects of the model can meet the needs of actual industrial activities.Compared with the prediction results, BPNN was better capable of predicting the MOE of the specimens by using the characteristic wavelengths to establish the calibration model. The This study revealed the relationship between the NIR spectrum and the MOE of


Despite the large number of existing computational studies of TiO2 clusters and in spite of the higher computing power of the typical available hardware, allowing for calculations of larger systems, there are still studies that use cluster sizes that are too small and not appropriate to address particular problems or certain complex systems relevant in photovoltaic or photocatalytic applications. By means of density functional theory (DFT) calculations, we attempt to find acceptable minimal sizes of the TinOn+22H4  nanoclusters in correlation with the size of the adsorbed molecule and the rigidity of the backbone of the molecule to model systems and interface processes that occur in hybrid photovoltaics and photocatalysis. We illustrate various adsorption cases with a small rigid molecule based on coumarin, a larger rigid oligomethine cyanine dye with indol groups, and the penicillin V antibiotic having a flexible backbone. We find that the use of the n = 14 cluster to describe adsorption leads to significant distortions of both the cluster and the molecule and to unusual tridentate binding configurations not seen for larger clusters. Moreover, the significantly weaker bonding as well as the differences in the density of states and in the optical spectra suggest that the n = 14 cluster is a poor choice for simulating the materials used in the practical applications envisaged here. As the n = 24 cluster has provided mixed results, we argue that cluster sizes larger than or equal to n = 34 are necessary to provide the reliability required by photovoltaic and photocatalytic applications. Furthermore, the tendency to saturate the key quantities of interest when moving from n = 44 to n = 54 suggests that the largest cluster may bring little improvement at a significantly higher computational cost.We report the results of a computational study of TiO Nanostructured titanium dioxide is well-known for its numerous and diverse applications ,2. Examp2 nanocrystals, dictated to a large extent by their electronic structure, are influenced by the size, shape, organization, and surface properties of the nanoparticles pyridoquinoline-10-carboxylic acid (C343). The optimized geometry of C343 has been reported previously by us and other authors  indol-2(3H)-ylidene)prop-1-en-1-yl)-1,3,3-trimethyl-3H-indol-1-ium iodide, OMCD1 ,57 see . We noteComparing geometry parameters for the bare cluster and the cluster-molecule system , we note24O50H4 clusters with the adsorbed OMCD1 molecule as well as the corresponding curve for the bare Ti24O50H4 cluster is displayed in d atomic orbitals of Ti. We note that in this case too, the HOMO and the LUMO are not pure, a small contribution being present from the cluster in the HOMO and from the molecule in the LUMO.The density of states for the model system consisting of the Tip orbitals of the negative iodine ion included in the modeling of the OMVD1 molecule. They are localized just below the HOMO and are not contributing to the regeneration process of the dye in the photovoltaic applications.We noticed in the density of states of the complex system a peak at about −5.86 eV , corresponding to the ground state of OMCD1. From the conduction band, we represent the LUMO (at −3.50 eV), the band edge of the oxide, and two molecular orbitals with some charge density delocalized also on the adsorbed molecule, LUMO+2 (at −3.34 eV) and LUMO+11 (at −2.92 eV). These states can be readily identified in the density of states graph, the HOMO, and the excited states of OMCD1 being able to contribute to light absorption followed by a charge transfer in photovoltaic or photocatalytic applications.The electron density of some key orbitals is displayed in n = 14 and 24). In this section, we investigate the case of molecules with a flexible backbone and study the optimized geometry, the binding to the cluster, and the electronic spectrum. The goal is to find the optimal cluster size to model the complex system.In the previous section, we presented the results of a calculation involving two molecules with a rigid backbone but of different sizes adsorbed on small clusters -4-thia-1-azabicyclo[3.2.0]heptane-2-carboxylic acid) (PV), a member of a group of antibiotics still widely used today in human and veterinary medicine, and present in waste water from the pharmaceutical industry and animal farms. The structures of PV, in neutral as well as in deprotonated forms, are presented in Given the known adsorption tendencies of molecules onto anatase titania ,77, we pExamining n = 24, between the central O atom of the PV backbone and a Ti atom on the oxide, the peripheral phenyl group being roughly transverse to the surface. In the case of the n = 34 cluster, the additional bond involves the O atom connected to the phenyl group and the peripheral ring is roughly parallel to the surface. In the case of n = 44 and 54, there are two extra bonds to surface Ti atoms: one involves the O atom next to the phenyl ring (just as for n = 34) and one engaging the S atom. The tendency already observed for n = 34 continues, with the phenyl ring parallel to the surface, at about 2.95 Å, interacting through π–π interactions with the atoms at the surface of the oxide [The extra bond occurs, in the case of he oxide .n = 14 cluster. For the other clusters, the average distance is larger, the relative difference decreasing monotonically in absolute value with increasing n, from 0.43% to 0.11%. The standard deviation of the complex systems is significantly different from the values of the isolated cluster only for n = 14, the much wider range of distances indicating significant distortion of the structure. For the cluster width, the relative differences are less than 1% even for n = 14, whereas for the length the relative differences are larger than 2% for n = 14, 24, and 34 and smaller than 0.5% for n = 44 and 54.Comparing the data presented in The torsion angle between the PV backbone and the Ti–O bond is about 160° for the smallest cluster and around 130° for all others, consistent with the bending of the molecule observed in The adsorption energy, determined as the difference between the calculated energies of the entire system and of the separated constituents, is displayed in nOn+22H4 to examine the role of the size of the cluster. An examination of Next, we examined the density of states for the PV molecule adsorbed on TiIn the case of the smallest cluster, when the PV molecule is only slightly distorted, the numerous such states that occur are similar to those of the isolated molecule. As the cluster size increases and the energy is lowered by extra bonding and π–π interactions, the additional states in the gap suffer significant changes in their position.d atomic orbitals.The electron density of the key molecular orbitals of the complex systems, shown in The states in the gap having an obvious dominant PV contribution are in fact mixed, with some small weight from the oxide. This mixture allows, with a small oscillator strength, for transitions that otherwise would have been forbidden. To understand the effect of the cluster size on the optical properties of the system, the electronic absorption spectra of three complex structures are displayed in The adsorbed PV molecule introduces states in the gap that move the absorption band from the UV towards the visible range of the spectrum. This behaviour is important for both photovoltaic and photocatalytic applications. A careful examination of nOn+22H4 clusters  revealed the existence of some distortions with respect to the bulk, which decrease with the increase of n. As the cluster size increases the surface-to-bulk ratio decreases and, as expected, the properties of the model cluster mimic better and better those of the nanoparticles used in typical photovoltaic and photocatalytic applications . Our cl2, with n from 1 to 384, and for n up to 80 the geometry optimization being finalized with DFT at the B3LYP/DZVP2 level. They report similar tendencies of the TiO2 nanoclusters to display distortions from the bulk, particularly for clusters of smaller sizes. We note that our n = 14 and 24 clusters have a similar structure with their 14a and 24b clusters; for the other sizes, the numbers of Ti atoms are different, similarities being identified between our n = 34 and their 36b as well as our n = 44 and their 48a [n−1/2).Our results for the cluster structures compare well, particularly with those obtained by Persson et al. ,37,38,39heir 48a . Moreovei) the surface-to-bulk ratio, (ii) the binding energy per atom, (iii) the DFT calculated HOMO–LUMO band gap as well as (iv) the TD-DFT determined optical gap, that the cluster with n = 54 brings little improvement compared to the one with n = 44, but increases significantly the computational cost. Our findings are consistent with previous reports, particularly those of Persson and co-workers, who used clusters with n = 38 [n = 38 [n = 82 [Our calculations indicated, through the tendency to saturate of 6 [6O21H18 [2)9 [2)15 [2)16 [As our results contradict those of several previous studies, which used very small clusters, we argue that the TiO authors ,24 canno authors . Moreove[5O20H22 , (TiO2)6H22 [2)6 ,29,30, T[6O21H18  (TiO2)9 H18 [2)9 ,84, and )9 [2)15  or (TiO215 [2)16 , may notn = 24 cluster, we observed relatively small distortions of either the cluster or the molecule. The binding configuration was the expected bidentate bridge, with the adsorbed molecule almost perpendicular to the surface. The density of states showed that, although the large molecule adsorbed on the relatively small cluster affected some of the states in the valence and conduction bands, the key orbitals at the edges of the bands were not altered significantly.When attaching the larger but still rigid OMCD1 molecule onto the n = 14 cluster left the molecule almost unaffected. However, that situation proved to be deceiving, as attaching the molecule onto increasingly larger clusters demonstrated that the energy of the system can be lowered substantially by distorting the PV. The bending and twisting of the backbone allowed for energy minimization due to extra bonds and π–π interactions. The tendency to saturate of the adsorption energy indicated that the benefits brought about by the cluster with n = 54 are not significant compared to the one with n = 44. The same tendency to saturate comes from the analysis of the density of states, obtained by DFT calculations, and of the electronic absorption spectra, determined by TD-DFT methods. Our results compare well with the pioneering work of Persson et al. [n of at least 38.The adsorption of a molecule with a flexible backbone on the different clusters showed important differences, which cannot be ignored. The anchoring of the PV molecule on the n et al. ,37 and Dn et al. ,44, as wn et al. ,87,88,892 in dye-sensitized solar cells (DSSC) [p orbitals of O and the d orbitals of Ti, for a sizeable electron transfer probability.Now, to link the results of our calculations, summarized above, to practical applications, such as in hybrid photovoltaics or in photocatalytic degradation of pollutants under visible light, we need to address two key processes: light absorption and charge transfer from the molecule to the oxide. For that reason, it is useful to recall the typical approach used to investigate whether a molecule is a good candidate as a dye to sensitize TiOs (DSSC) ,51. One n = 34 and 44 being sufficient for most practical purposes (given the molecules studied here), whereas n = 54 brought little further improvement.A second criterion is the matching of the absorption spectrum of the dye with the solar irradiation spectrum . Our res2. The push→pull effect can be studied by analyzing how the electron density is localized in the ground and excited states of the dye adsorbed on the cluster. A proper description of the charge delocalization is crucial for this purpose, which, as our results demonstrated, requires a suitable account of the structure, by an adequate choice of the TiO2 cluster.Another condition for the dye is the charge injection into the oxide. The high rate of charge injection for typical organic sensitizers is achieved if the molecule exhibits the “push→pull” effect . This efThe charge injection also depends on the proper alignment of the excited state of the dye with respect to the conduction band edge of the oxide. Additionally, another criterion is the dye regeneration, by returning the electron to the dye after passing through and performing work in the external circuit. In DSSCs, this process is performed by the electrolyte  if the redox level of the electrolyte is situated above the ground state of the dye. The calculations leading to the density of states reveal the position of the ground state and the excited state resulting after light absorption, allowing for the analysis of the energy level alignment. Moreover, the use of TD-DFT calculations providing the optical gaps lead to the conduction band oxidation potential see , which cnOn+22H4 systems, where they are small but significant, but more so for the PV-TinOn+22H4 structures, where they become important. Under these circumstances, the influence of the cluster size on the proper description of the energy level alignment has to be taken into account when choosing the cluster size used in the simulation.Our results show that both the conduction band edge and the ground state of the dye are influenced, to various extents, by the size of the cluster. The differences can be observed from the density of states calculations for the C343-Ti2. We performed calculations of the C343-Ti24O50H4 system with the B3LYP functional and various basis sets for determining the optimized geometry, the electronic structure, and the UV–Vis spectra. Our calculations are consistent with the early work of Persson and co-workers [As a final point, Illas and co-workers  showed t-workers , emphasi2 clusters and complex systems consisting of the cluster with an adsorbed molecule to study the influence of the cluster size on the electrical and optical properties of materials/interfaces, having in mind hybrid photovoltaic and photocatalytic applications.We reported DFT calculations of several TiOnOn+22H4 clusters , which revealed the existence of some distortions with respect to the bulk, which are larger for smaller clusters. The tendency of the cluster with n = 54 to saturate the surface-to-bulk ratio, the binding energy per atom, the electronic (DFT) band gap, as well as the optical (TD-DFT) gap brings about little improvement compared to the one with n = 44, but increases significantly the computational cost.We performed calculations for different Tin = 14 case significant distortions of both the cluster and the molecule and unusual tridentate binding configurations. Such behaviour is not seen for larger clusters, not even for n = 24. The adsorption energy comparisons showed for n = 14 significantly weaker bonding, differences in the density of states , and in the optical spectra (both in terms of peak wavelength and intensity). Under these circumstances, we conclude that the n = 14 cluster is a poor choice for simulating the materials used in the practical applications envisaged here.The study of the complex systems, consisting of the cluster with the adsorbed molecule, demonstrated for the n = 24 cluster has provided mixed results. First, the inner symmetry of the cluster structure causes stronger electron localization of the electron density at the centre of the cluster, causing the HOMO to move higher in energy and split away from the immediately lower energy states. This anomaly leads to a smaller DFT band gap and TD-DFT optical gap. The cluster showed some distortions but described qualitatively well the binding of the rigid molecules, even for those of a larger size. For flexible molecules, the n = 24 cluster indicated correctly the bending and twisting of the adsorbed molecule but, due to its small size, was not able to describe the correct geometry and the π–π interactions. Consequently, we conclude that the n = 24 cluster can be used well for qualitative calculations, particularly when the adsorbed molecule is rigid, or to obtain some initial information to set up further more elaborate, computational experiments. However, if accuracy is important or the molecule is flexible, the structure is unlikely to provide the precision required by such applications as hybrid photovoltaics or photocatalytic degradation of pollutants under visible light.The use of the n = 34, the structure and the electronic and optical properties are described in a more reliable way.When the molecule is adsorbed on the larger clusters, starting from 2 sensitizers in DSSCs or the likelihood of the degradation of organic pollutants under visible light irradiation using anatase titania as a photocatalyst. We emphasized the influence of the cluster size on the proper description of the anchoring to the oxide, ruling out the n = 14 cluster for all systems and using with high caution the n = 24 cluster when molecules are flexible.We reviewed the important criteria used in computational studies to explain or predict the suitability of various dyes as TiOn = 24 cluster, and more accurate results being obtained for larger systems.The analysis of the matching between the absorption spectrum of the dye with the solar irradiation spectrum relies on quality UV–Vis simulated spectra. Our results showed significant changes in both wavelength and intensity of the electronic transitions in the visible range, the spectra being qualitatively described by the The charge injection into the oxide is influenced by the anchoring, by the push→pull effects determined by the charge distribution of the ground and excited states of the dye, and by the energy level alignment between the excited state of the dye and the conduction band edge of the oxide. Hence, the accuracy of the geometry optimization, of the density of states, and of the optical spectrum calculations are very important. Such accuracy is obtained with larger clusters.The dye regeneration, by passing the electron from the electrolyte back to the dye, can happen only if the redox level of the electrolyte lies higher than the ground state of the dye. A proper approach to check this criterion requires a reliable calculation of the density of states, which, again, is high for larger clusters.n = 34 are necessary to provide the reliability required by photovoltaic and photocatalytic applications. The tendency to saturation of the key quantities of interest when moving from n = 44 to n = 54 suggests that the largest cluster may bring little improvement at a significantly higher computational cost. Probably, for many molecules of practical interest, sizes of n = 34 and particularly n = 44 may be an appropriate choice.In summary, our results lead us to the conclusion that cluster sizes larger than or equal to 2 cluster.Finally, an important word of caution is mandatory. Consistency in calculations may be an even more important rule in the simulation of practical systems, as the functionals and the basis sets used in the DFT and TD-DFT computations may have an even stronger influence on the final results than the size of the TiO


The preparation of complex architectures has inspired the search for new methods and new processes in organic synthesis. Multicomponent reactions have become an interesting approach to achieve such molecular diversity and complexity. This review intends to illustrate important gold-catalyzed examples for the past ten years leading to interesting skeletons involved in biologically active compounds. Multicomponent reactions (MCRs) have been defined as processes in which three or more reagents are added to a single vessel at the same time affording final products containing most of the atoms from the starting materials ,2. ThereThis importance is reflected in the large number of publications reported in this field over the last decade ,5. Moreo3 or AuBr3 ..23, alco23, alkyne 24, and alcohol 25 reaction partners. The resulting β-alkoxyketones 26 were obtained from moderate to good yields.With the best reaction conditions, the scope of this approach was explored by varying the aldehyde 24 and the hemiacetal 27, generated in situ by the reaction of the aldehyde with one equivalent of the alcohol 25, could provide 28, which could subsequent split into gold(I) enol 29 and oxocarbenium ion 30 . Regeneration of the gold catalyst and release of the FB(OH)2 species would afford the final alcohol 38  dipyridine complex immobilized on SBA-15 as nano catalysts at room temperature  remaining after the seventh cycle was of about 2.1%. This value suggested good stability of the heterogeneous gold catalyst under the reaction conditions employed.A series of compounds containing an indole ,81,82,8350 and 51 mediated by a gold(III) catalyst  derivatives 50 and 51 . The authors extended the protocol to another reactant acenaphthoquinone 52, instead of isatins 47, under the same reaction conditions to prepare a variety of spiroacenaphthyleneone derivatives 53a–f with excellent yields . In contrast, path B would start with the attack of the dimedone 49a to the isatin 47a coordinated with HAuCl4 to afford the aldol adduct III followed by a subsequent dehydration of III and nucleophilic attack of 48a to afford the intermediate VI. In both reaction pathways, the intermediate II would involve the cycloaddition between the hydroxyl group and the cyano moiety to form the desired spirooxindole 50a  chloride catalyst, avoiding its recyclability.57 and 59 (The butenolide scaffold is also found in many natural products . Moreove7 and 59  101]..57 and 557 or 59 via electrophilic trapping along with a 1,2-hydride shift pathway or deprotonation-protonation sequence (The authors proposed a plausible reaction mechanism based on their experimental results and previous studies  102,103103,104. sequence . This go3 coupling reaction. However, here it has been demonstrated that new processes have been developed, for which there is a preponderance of alkynes, carbonyls and amines as components for the coupling reactions. The plausible catalytic cycles to understand the role of gold catalyst in addition of each reagent involved have also been discussed in this work, and they clearly state the high Lewis acid capacity of gold catalysts to activate alkyne or carbonyl groups. The increasing interest in both gold catalysis and the development of more sustainable an economical procedures, make that a significant progress in the next few years is expected, with the use of gold catalysts for the discovery of new coupling reactions involving novel components that will allow the preparation of more complex scaffolds with potentially interesting properties.The preparation of complex architectures has inspired the search for new methods and new processes in organic synthesis. In this sense, multicomponent reactions have become interesting protocols to achieve such molecular diversity and complexity. This objective, joined to the interest in the chemistry of gold, has allowed the synthesis of model structures using new efficient approaches. This review has illustrated these important and scarce examples in order to give a general vision related to the application and scope of gold catalysts in multicomponent reactions, until now dominated by the A


The developed models were found to be useful for the estimation of pIC50 of CXCR2 receptors for which no experimental data is available.The CXCR2 receptors play a pivotal role in inflammatory disorders and CXCR2 receptor antagonists can in principle be used in the treatment of inflammatory and related diseases. In this study, quantitative relationships between the structures of 130 antagonists of the CXCR2 receptors and their activities were investigated by the partial least squares (PLS) method. The genetic algorithm (GA) has been proposed for improvement of the performance of the PLS modeling by choosing the most relevant descriptors. The results of the factor analysis show that eight latent variables are able to describe about 86.77% of the variance in the experimental activity of the molecules in the training set. Power prediction of the QSAR models developed with SMLR, PLS and GA-PLS methods were evaluated using cross-validation, and validation through an external prediction set. The results showed satisfactory goodness-of-fit, robustness and perfect external predictive performance. A comparison between the different developed methods indicates that GA-PLS can be chosen as supreme model due to its better prediction ability than the other two methods. The applicability domain was used to define the area of reliable predictions. Furthermore, the  The chemokine receptors CXCR2/CXCR1 were cloned and identified and are activated by IL-8 (CXCL8) [The chemokine CXCR2 receptor, a seven-transmembrane G-protein-coupled receptor, was cloned and identified in the early 1990s . Chemoki (CXCL8) ,5. Inter (CXCL8) . In the  (CXCL8) .During the past decades, different approaches have been used for the development of QSAR models. The major differences between these approaches are in the structural parameters (descriptors) used to characterize molecules and/or in the mathematical methods used to establish a correlation between the descriptor values and the biological activities. One of the most successful approaches for the prediction of the chemical properties based on the molecular structural information is modeling of quantitative structure-activity/property relationships (QSAR/QSPR). The main goal of QSAR/QSPR is to predict complex physical, chemical and biological properties of the compounds from molecular structures ,9. The c50  of a diverse set of compounds seems to be interesting. QSAR analysis employs statistical methods to drive quantitative mathematical relationships between chemical structure and biological activity. Thus, the use of the QSAR for the development of a theoretical model for calculation of the ICThe strategy used in the QSAR models includes the following steps; (1) selection of a data set; (2) generation of the data molecular structures; (3) optimization of the geometry of the molecular structures by appropriate method; (4) generation of various structural descriptors; (5) application of variable selection or/and data reduction methods on the calculated descriptors; (6) regression analysis; and finally (7) evaluation of the validity and predictability of the developed QSAR models.in silico screening is adopted to the QSAR model in order to predict the structure of new potentially active compounds.In the past, QSAR models have been built in the general field of chemokine antagonists including CCR1 , CCR5 1,16, CXCRN,N’-diphenylureas, nicotinamide N-oxides, quinoxalines, triazolethiols, acylsulfonamide carboxylic acid bioisosteres, N-linked sulfonylurea, and furyl-3,4-diamino-3-cyclobut-3-ene-1,2-dione. The general structure and biological activities of the CXCR2 antagonists are provided in The biological and chemical data of 130 CXCR2 antagonists, taken from literatures were selected for QSAR study ,21,22,2350s in the range of 4.96–9.00 was used for building up the QSAR model, whereas the prediction set containing 22 compounds  with pIC50s in the range of 5.70–8.70 was used for evaluation of the model’s predictive ability. The distribution of pIC50 values of 130 essential CXCR2 antagonist receptors are demonstrated in 50 values cover a wide range from 4.96 to 9.00.In order to guarantee that training and prediction sets cover the total space occupied by the original data set, it was divided into two parts of training and predication set according to the Kennard-Stones algorithm . The KenFurthermore, in order to detect the homogeneities in the data set and to recognize the potential outliers in all of the molecules under study, the principal components analysis (PCA)  was perf® Vista operating system was used. HyperChem Release 7 software  was used to draw the molecular structures. Dragon software  was empi.e., one of the any two descriptors with an inter-correlation greater than 0.95 was removed to reduce the redundant and useless information.The theoretical molecular descriptors were derived from the chemical structure of the compounds. The 3D-structures of all the compounds were drawn using the HyperChem software. The resulting geometries were further refined by means of the semiempirical AM1 method and the molecular structures were optimized using the Polak-Rebiere algorithm until the root mean square gradient reached 0.1 kJ (mol Å). Then they were transferred into the Dragon program package (version 3)  to obtai2 or R2cv), relative error percent of prediction sets (REPPred), the root mean square error of prediction (RMSEP), root mean square error of cross-validation (RMSECV), validation through an external prediction set and Y-randomization. However, it should be noted that a high Q2 does not necessarily mean a high predictability of the developed model [2 is a necessary condition, but not sufficient for a developed model to have high predictability. Evaluation of a model’s stability and predictive ability is another key step in QSAR modeling. Different statistical parameters have been used for the evaluation of the suitability of the developed models for prediction of the activity of the studied compounds  this inced model . In othe50 of an external set that was not used in the development of the model. The predictive powers of the developed regression models on the training set were evaluated by predicted values of the prediction set. These parameters are listed in In order to assess the predictive ability and to check the statistical significance of the developed models, the proposed models were applied to predict the values of pICi, yi, n are the predicted value, the experimental value, the mean of the experimental value in the prediction set and the number of samples, respectively.The REP is calculated according to the following equation:i, iy and n are the prediction value, the measured value and the number of measurements, respectively. The RMSECV is a measure of a model’s ability to predict new samples. The RMSECV is calculated via a leave one out cross-validation, where each sample is left out of the model formulation and then is predicted. The RMSEP is defined as a measure of the average difference between the predicated and experimental values at the predication stage. The RMSEP is calculated by applying Eq. (2) to the predication set. The root mean square error cross validation (RMSECV) is a frequently used measure of the differences between the predicted values by a model or an estimator and the actually observed values from the objects being modeled or estimated. The RMSECV is defined as follows:2 or R2CV) which is used as the criteria of both robustness and the predictive ability of the model. Cross-validated coefficient of R2CV (LOO-Q2) is calculated according to the following formula:i and yi are the predicted value, the experimental value (over the prediction set), respectively, and Most QSAR modeling methods implement the leave-one-out (LOO) or leave-some-out (LSO) cross-validation procedure . The outR2 is the correlation coefficient of regression between the experimental values and the prediction activities of the compounds on the training and prediction sets. R2o, R’2o, are mathematically defined as the regression of the experimental activities against predicted activities and regression of the predicted activities against experimental activities, respectively; where as, k and k’ are the slopes of these equations [In these equations, quations . When thR2 and Q2 values; the results are shown in Furthermore, in order to assess the robustness of the model, the Y-randomization test was applied. The dependent variable vector (inhibitory activity) was randomly shuffled and a new QSAR model was developed using the original independent variable matrix. As was expected the new QSAR models  have low The predictive ability of QSAR/QSPR models is affected by two factors: the descriptors, which must carry enough of the molecular structure information for the interpretation of the activity/property; and the employed modeling method. However, with too many descriptors, there is the possibility of over fitting of the statistical methods. Thus, in QSAR/QSPR studies the identification and selection of descriptors which provide maximum information in activity variations and have minimum co-linearity is important. On the other hand, the use of PLS usually results in well fitted stable models which have high predictive ability, but the estimation is not always very accurate and stable over the time. Therefore, a genetic algorithm (GA)  with a Pj represents the mean effect for the descriptor j, βj is the coefficient of the descriptor j, dij is the value of the interested descriptors for each molecule and m is the number of descriptors in the model. The MF value shows the relative importance of each descriptor in compare to the other descriptors. The MF of the descriptor MATS5v, GATS8p, MATS2m and BEHp2 are also shown in 50 of the compound. The effect of MATS5v, GATS8p, MATS2m and BEHp2 for the QSAR study of CXCR2 receptors and the standardized regression coefficient on the significance of an individual descriptor in the model is shown in On the basis of Kennard-Stones algorithm, 108 compounds out of 130 were selected as the training set and the remaining 22 were selected as the test set. Stepwise regression was used on the training data set to select the significant descriptors and it was found that between 733 calculated descriptors the MATS5v , GATS8P (Moran autocorrelation-lag8/weighted by atomic polarizabilites), MATS2m (Moran autocorrelation-lag2/weighted by atomic masses) and BEHp2  construct the best model and there was no significant correlation between these descriptors  were inserted as weighting components. As a result, these descriptors address the topology of the structure or parts thereof in association with a specific physicochemical property. Bearing in mind this aspect, the interpretation of 2D autocorrelation descriptors was uneasy. The binding of a ligand to a target depends on the shape of the ligand and on a variety of factors such as molecular electrostatic potential, polarizability, hydrophobicity, and lipophobicity. Therefore, in a QSAR study the strategy for encoding molecular information, either explicitly or implicitly, should account for these physicochemical effects. Furthermore, since the data sets usually include molecules of different size with different numbers of atoms, the structural encoding schemes must allow comparison between such molecules. The descriptors, MATS5v, GATS8p and MATS2m are BCTU descriptors were designed to encode atomic properties relevant to intermolecular interactions. The three standard BCUT descriptor types–atomic charge, polarizability and hydrogen bonding properties—that are relevant to intermolecular interactions are supported. The BCUT  descriptors are the eigenvalues of a modified connectivity matrix known as the Burden matrix [BEHp2.n matrix . The BCUThe general purpose of the linear regression method is to quantify the relationship between several independent or predictive variables and a dependent variable. Independent or predictive variables could be various physicochemical descriptors of the molecules, their principle components or latent variables. The partial least squares (PLS) method is used to establish relationships between the dependent variables of the Y matrix and the descriptors of the X matrix  . The pro50 in training set (R2 = 0.74 and RMSECV = 0.6).As it is shown in 2 value and the external validation method were performed. A high Q2 and R2 values (Q2 > 0.5) were considered as a proof of high predictive ability of the model. The external validation method was performed by dividing the original data set randomly into two parts, training and prediction set, and the values of pIC50 of molecules in the prediction set were predicted by the developed model. The results of the calculated R2, Q2, REP%, RMSECV and etc. for prediction set are reported in Finally, for the evaluation of the predictive ability of the developed model, the Q2 (Q2) and regression coefficient R2 for a predictive set with known values of biological activities, the validated model can be used for predicting activities/ properties of new chemicals [It should be noted that even when there is no correlation between the LOO- cross-validated Rhemicals ,36. Furt2) by the leave-one-out procedure in cross-validation is used as the internal predictivity [As mentioned before, one of the problems in choosing the set of molecular descriptors is the co-linearity within them. To overcome this problem some workers tried to combine the genetic algorithms (GA) with PLS ,38,39. Gictivity ; (3) RepRogers and Hopfinger first applied GA-PLS method in QSAR analysis and stated that it is very effective and superior to PLS method. In this paper, to find the more convenient set of descriptors, a GA-PLS analysis was performed ,42,43.R2, REP%, RMSEP and Q2 for prediction set of GA-PLS study are also reported in 2 and R2 value of the GA-PLS were improved in compare to the MLR and PLS methods. However, the interpretations of the chemical properties of these descriptors are difficult as their definition is based on mathematics. The details are described in the handbook and literature of Dragon software [All descriptors were preprocessed by auto scaling before performing the GA-PLS was performed. The GA was optimized by variation and selection of the fitness values. The fitness function is defined as:software . Furtherin silico screening procedure is a useful tool for predicting and identifying new biologically active compounds with improved characteristics prior to their actual synthesis [in silico procedure can be applied as a physico-chemical filter to reduce the number of compounds to be tested experimentally for hit/lead generation. In other words, the in silico procedure minimizes the time and cost associated with identifying new leads. A virtual screening was performed by insertion, deletion and substitution of different substitutes on the original molecules [i.e., whether a new chemical will lie within the structural model domain or outside of it. Furthermore, the leverage approach along with the Williams plot is used to determine the applicability domain in all QSAR models. The ynthesis ,45. Thusolecules ,47 and tolecules , but theolecules  which usih for each chemical compound, in which QSAR model was used to predict its activity, was calculated according to the following equation:ix is the descriptor vector of the considered compound and X is the descriptor matrix derived from the training set descriptor values and the warning leverage (h*) was determined as [n is the number of training compounds, p is the number of predictor variables. The defined applicability domain (AD) was then visualized via a Williams plot, the plot of the standardized residuals versus the leverage values (h). A compound with hi > h* seriously influences the regression performance and may be excluded from the applicability domain, but it doesn’t appear to be an outlier because its standardized residual may be small. Moreover, a value of 3 for standardized residuals is commonly used as a cut-off value for accepting predictions, because points that lie within ±3 standardized residual from the mean cover 99% of the normally distributed data [To construct the William plot, the leverage mined as :h∗=3, was found to be 0.25 for the developed QSAR model. The chemicals that had a standardized residual more than three times of the standard deviation units were considered to be outliers while chemicals with a leverage value higher than h* were considered to be influential or high leverage chemicals. Based on the leverages (h > 0.25), the one compound were found to be outside of the defined AD (h > h*).The Williams plot for the QSAR is illustrated in in silico screening was applied to the design of new structures with potential CXCR2 inhibitors according to the developed QSAR model and was validated by the developed GA-PLS model. For this purpose, compound 66 of the N,N’-diphenylurea derivatives listed in 50 = 8.22) was selected as a template due to its good inhibition. The molecule was modified in such a way that its synthesis was experimentally possible. Then, the in silico screen was applied by substituting different groups in the X and Y positions of the ring; the results of this investigation are given in N,N’-diphenylurea substituents since all of the studied derivatives were within the applicability domain. Among different molecules designed, the compound 10c showed the best activity (pIC50 = 8.50). Thus, in order to clarify the relation between the activities of the compounds with different functional group, this compound was selected for further structural modification. So, in the next step the oxygen of the amide group of compound 10c was substituted by different function groups, the results are demonstrated in 10c, and the best predicted activity was found for the compound 9d (where X = S). Thus, it is demonstrating that using a simple QSAR model, it is possible to simultaneously identify compounds with improved activity and to determine the structural modifications that don’t fall within the applicability domain. Finally, this result confirms the reliability of the models and it shows that with the construction of the QSAR model and use of in silico screening it is possible to identify new synthetic targets for drug discovery.Next, the Pred), the root mean square error of prediction (RMSEP), root mean square error of cross-validation (RMSECV), validation through and Y-randomization. It was also shown that the proposed method is a useful aid for reduction of the time and cost of synthesis and activity determination of CXCR2 receptor antagonists. Furthermore, the results confirm that among the construction models used, the GA-PLS is superior for prediction of the IC50 of CXCR2 antagonists. Our future work will focus on validation for putative CXCR2 antagonists for virtual screening. In this study, three different modeling methods, SMLR, PLS and GA-PLS were used in the construction of a QSAR model for CXCR2 antagonists and the resulting models were compared. It was shown that performing GA prior to the calibration, yields a regression model with improved predictive power. The accuracy and predictability of the proposed models were illustrated by various criteria, including cross-validation, relative error percent of prediction sets (REP