Giant benzenoid hydrocarbons Superphenalene resonance energy

We calculated molecular resonance energy for "superphenalene", a recently reported giant benzenoid which can be viewed as obtained from three fused "superbenzenes" Chexa-peri-hexabenzocoronenes). Using the Method of Conjugated Circuits we derived quantitative characterization of Clar's qualitative description of the considered benzenoids as composed from disjoint "pi -sextets". The calculations show the degree of differentiation between neighboring rings which decreases as we move more towards the central part of the molecule

Giant benzenoid hydrocarbons Superphenalene resonance energy

We have calculated the molecular resonance energy for "superphenalene," a recently reported giant benzenoid that can be viewed as obtained from three fused "superbenzenes" (hexa-peri-hexabenzocoronenes). Using the method of conjugated circuits we derived quantitative characterization of Clar's qualitative description of the considered benzenoids as composed from disjoint "pi-sextets." The calculations show the degree of differentiation between neighboring rings, which decreases as we progressively move towards the center of the molecule

Neuronal circuitry for pain processing in the dorsal horn

Neurons in the spinal dorsal horn process sensory information, which is then transmitted to several brain regions, including those responsible for pain perception. The dorsal horn provides numerous potential targets for the development of novel analgesics and is thought to undergo changes that contribute to the exaggerated pain felt after nerve injury and inflammation. Despite its obvious importance, we still know little about the neuronal circuits that process sensory information, mainly because of the heterogeneity of the various neuronal components that make up these circuits. Recent studies have begun to shed light on the neuronal organization and circuitry of this complex region

Empirical Relationship between Intra-Purine and Intra-Pyrimidine Differences in Conserved Gene Sequences

DNA sequences seen in the normal character-based representation appear to have a formidable mixing of the four nucleotides without any apparent order. Nucleotide frequencies and distributions in the sequences have been studied extensively, since the simple rule given by Chargaff almost a century ago that equates the total number of purines to the pyrimidines in a duplex DNA sequence. While it is difficult to trace any relationship between the bases from studies in the character representation of a DNA sequence, graphical representations may provide a clue. These novel representations of DNA sequences have been useful in providing an overview of base distribution and composition of the sequences and providing insights into many hidden structures. We report here our observation based on a graphical representation that the intra-purine and intra-pyrimidine differences in sequences of conserved genes generally follow a quadratic distribution relationship and show that this may have arisen from mutations in the sequences over evolutionary time scales. From this hitherto undescribed relationship for the gene sequences considered in this report we hypothesize that such relationships may be characteristic of these sequences and therefore could become a barrier to large scale sequence alterations that override such characteristics, perhaps through some monitoring process inbuilt in the DNA sequences. Such relationship also raises the possibility of intron sequences playing an important role in maintaining the characteristics and could be indicative of possible intron-late phenomena

QSAR studies on a number of pyrrolidin-2-one antiarrhythmic arylpiperazinyls

The activity of a number of 1-[3-(4-arylpiperazin-1-yl)propyl]pyrrolidin-2-one antiarrhythmic (AA) agents was described using the quantitative structure–activity relationship model by applying it to 33 compounds. The molecular descriptors of the AA activity were obtained by quantum chemical calculations combined with molecular modeling calculations. The resulting model explains up to 91% of the variance and it was successfully validated by four tests (LOO, LMO, external test, and Y-scrambling test). Statistical analysis shows that the AA activity of the studied compounds depends mainly on the PCR and JGI4 descriptors

Validacija topokemijskih modela za predviđanje permeabilnosti kroz krvno-moždanu barijeru

Recently published topochemical models for permeability through the blood-brain barrier were validated and cross-validated in the present study. Five models based on three topochemical indices, Wiener’s topochemical index - a distance-based topochemical descriptor, molecular connectivity topochemical index - an adjacency-based topochemical descriptor and eccentric connectivity topochemical index - an adjacency-cum-distance based topochemical descriptor, for permeability of structurally and chemically diverse molecules through blood-brain barrier were used in the present investigation. A data set comprising 62 structurally and chemically diverse compounds was selected. This data set was divided into two sets of 31 compounds each - one to serve as the validation set and other as the cross-validation set. The values of all the three-topochemical indices in the original as well as in the normalized form for each of the 31 compounds of the validation set were computed using an in house computer program. Resultant data was analyzed and each compound was assigned a permeability characteristic using topochemical models, which was then compared with the reported permeability through the blood-brain barrier. Accuracy of prediction of these models was calculated. The same procedure was similarly followed for the cross-validation set. Studies revealed accuracy of prediction of the order of 7080% during validation. Surprisingly, very high predictability of the order of 7791% was observed during cross-validation. High predictability observed during validation as well as cross-validation authenticates topochemical models for prediction of permeability through the blood-brain barrier.U ovom radu su validirani i unakrsno validirani nedavno objavljeni topokemijski modeli za permeabilnost kroz krvno-moždanu barijeru. Predviđanje prolaska kroz krvno-moždanu barijeru strukturno i kemijski različitih molekula provedeno je na pet modela koji se temelje na tri topološka indeksa, Wienerovom topološkom indeksu, topološkom indeksu molekularne povezanosti i topološkom indeksu ekscentrične povezanosti. Ukupno 62 spoja podijeljena su u dva seta koji su sadržavali 31 spoj. Jedan set upotrebljen je za validaciju, a drugi za unakrsnu validaciju. Vrijednosti svih triju topoloških indeksa u početnom setu i u normaliziranom setu su računate pomoću kompjutorskog programa. Rezultati su analizirani i svakom spoju je pridružena teorijska vrijednost permeabilnosti, koja je zatim uspoređivana s objavljenim eksperimentalnim podacima za permeabilnost kroz krvno-moždanu barijeru. Točnost predviđanja bila je između 70 i 80%. Isti postupak je proveden za unakrsno validacijski set, a točnost je bila iznenađujeće velika (7791%), što ukazuje da se upotrebljeni topokemijski modeli mogu upotrijebiti za predviđanje permeabilnsot kroz krvno-moždanu barijeru

A New Method for Species Identification via Protein-Coding and Non-Coding DNA Barcodes by Combining Machine Learning with Bioinformatic Methods

Species identification via DNA barcodes is contributing greatly to current bioinventory efforts. The initial, and widely accepted, proposal was to use the protein-coding cytochrome c oxidase subunit I (COI) region as the standard barcode for animals, but recently non-coding internal transcribed spacer (ITS) genes have been proposed as candidate barcodes for both animals and plants. However, achieving a robust alignment for non-coding regions can be problematic. Here we propose two new methods (DV-RBF and FJ-RBF) to address this issue for species assignment by both coding and non-coding sequences that take advantage of the power of machine learning and bioinformatics. We demonstrate the value of the new methods with four empirical datasets, two representing typical protein-coding COI barcode datasets (neotropical bats and marine fish) and two representing non-coding ITS barcodes (rust fungi and brown algae). Using two random sub-sampling approaches, we demonstrate that the new methods significantly outperformed existing Neighbor-joining (NJ) and Maximum likelihood (ML) methods for both coding and non-coding barcodes when there was complete species coverage in the reference dataset. The new methods also out-performed NJ and ML methods for non-coding sequences in circumstances of potentially incomplete species coverage, although then the NJ and ML methods performed slightly better than the new methods for protein-coding barcodes. A 100% success rate of species identification was achieved with the two new methods for 4,122 bat queries and 5,134 fish queries using COI barcodes, with 95% confidence intervals (CI) of 99.75–100%. The new methods also obtained a 96.29% success rate (95%CI: 91.62–98.40%) for 484 rust fungi queries and a 98.50% success rate (95%CI: 96.60–99.37%) for 1094 brown algae queries, both using ITS barcodes