Efficient optimization based thresholding technique for analysis of alzheimer MRIs.

Purpose study: Alzheimer is a type of dementia that usually affects older adults by creating memory loss due to damaged brain cells. The damaged brain cells lead to shrinkage in the size of the brain and it is very difficult to extract the grey matter (GM) and white matter (WM). The segmentation of GM and WM is a challenging task due to its homogeneous nature between the neighborhood tissues. In this proposed system, an attempt has been made to extract GM and WM tissues using optimization-based segmentation techniques.Materials and methods: The optimization method is considered for the classification of normal and alzheimer disease (ad) through magnetic resonance images (mri) using a modified cuckoo search algorithm. Gray Level Co-Occurrence Matrix (GLCM) features are calculated from the extracted GM and WM. Principal Component Analysis (PCA) is adopted for selecting the best features from the GLCM features. Support Vector Machine (SVM) is a classifier which is used to classify the normal and abnormal images. Results: The proposed optimization algorithm provides most promising and efficient level of image segmentation compared to fuzzy c means (fcm), otsu, particle swarm optimization (pso) and cuckoo search (cs). The modified cuckoo yields high accuracy of 96%, sensitivity of 97% and specificity of 94% than other methods due to its powerful searching potential for the proper identification of gray and WM tissues.Conclusions: The results of the classification process proved the effectiveness of the proposed technique in identifying alzheimer affected patients due to its very strong optimization ability. The proposed pipeline helps to diagnose early detection of AD and better assessment of the neuroprotective effect of a therapy.

Efficient Image De-Noising Technique Based on Modified Cuckoo Search Algorithm.

The image restoration has emerged as a very vital investigation technique in the domain of the image processing. The underlying motive behind the image restoration is devoted to the augmentation of the perceived visual impact of image so as to make it almost identical to the original image. A host of exploration approaches are now in vogues which are intended to steer clear of the noise, thereby regaining the images with original quality. In our earlier research, two distinct noise elimination methods like the (OGHP) and SURE shrinkage were effectively employed for the purpose of denoising, though the relative PSNR and SSIM efficiencies did not come up to the desired level. In the innovative approach envisaged in the document, at the outset, the noise is included by means of two processes like the salt and pepper and impulse noise. Subsequently, the pre-processing methods are performed with the able assistance of two novel filters such as the adaptive median filter and adaptive fuzzy switching. Thereafter, the preprocessed image is furnished to the succeeding function of noise elimination like the (OGHP) and SURE shrinkage. In the course of the OGHP noise elimination technique, the GHP constraints are optimized by employing the Cuckoo Search Algorithm. Thereafter, the noise-eliminated image is effectively estimated with the help of the Discrete Wavelet Transform (DWT). The consequential noiseless images are subjected to the image restoration procedure by efficiently employing the AGA approach. The cheering performance outcomes chant the success stories of the novel image restoration method, highlighting its superlative efficiency. Moreover, the efficacy of the innovative approach is assessed by means of a set of noise-polluted images and contrasted with the modern noiseless image restoration technique.

Influence of Branching on the Conformational Space: Case Study of Tri- sec-butyl Phosphate Using Matrix Isolation Infrared Spectroscopy and DFT Computations.

The conformational analysis of long chain phosphates poses a serious challenge due to the presence of rotationally flexible multiple alkyl groups. Tri- sec-butyl phosphate (TsBP) is an interesting example, in which branching can be expected to influence the conformational landscape. To solve the conformational problem of TsBP systematically, the conformations of model dimethyl- sec-butyl phosphate (DMsBP), a molecule possessing a single secondary butyl strand, were analyzed. On the basis of the analysis of the energy profile of DMsBP, a few conformational bunches were eliminated. The presence of branched methyl group appears to completely influence the conformational space of TsBP and as a result, the number of conformations is drastically reduced in comparison to its structural isomer, tri- n-butyl phosphate (TBP). B3LYP level of theory in association with 6-311++G(d,p) basis set was used for computing all the conformer geometries. Experimentally, the conformations of TsBP were studied using infrared spectroscopy by trapping the molecule in N2 and Ar matrixes at low temperatures, which were correlated well with the computational results.

13-Docosenamide release by bacteria in response to glucose during growth-fluorescein quenching and clinical application.

Our investigations on extracellular biochemical events to find readily and sensitively detectable/measurable molecular targets for developing easier, simpler, and quicker diagnostic methods and tools for bacterial pathogens led to the observation that bacteria grown in the presence of glucose produced a compound capable of quenching fluorescein. Under the experimental conditions, among various sugars, glucose was found to induce maximum amount of the quencher when Escherichia coli was grown in presence of 50 mM glucose in rarified LB. The release of quencher closely following bacterial growth significantly from fourth hour after moderate inoculation. This fluorescein-quencher was purified using TLC and HPLC and identified using GC-MS as 13-docosenamide or erucamide, originally known as plant lipid, is a neuroactive compound in human and animals. Fluorescence and UV-absorption spectral analysis showed that the compound formed stable adduct with fluorescein in the ground state. Commercial 13-docosonamide enabled quantitation of the compound produced in micromolar quantities during glucose utilization from the medium. Twenty-seven different commonly encountered bacteria, pathogens or otherwise, could produce the quencher. A simple microplate-based growth monitoring method was developed exploiting quenching as an easily and readily measurable signal, either using a reader or an imager. While 13-docosenamide release by bacteria may be relevant in host-bacteria interactions, especially when growing under conditions that provide glucose, the new approach with inexpensive reagents can provide a new antibiogram technique.

Uranyl tris nitrato as a luminescent probe for trace water detection in acetonitrile.

Uranyl tris nitrato i.e. [UO2 (NO3 )3 ]- was formed by adding tetramethylammonium nitrate to uranyl nitrate in acetonitrile medium. The luminescence features of this complex in acetonitrile are very sensitive to water content, which could lead to the use of it as a luminescent probe for water present in acetonitrile. The luminescence intensity ratio of 507 to 467 nm peak of uranyl tris nitrato showed a linear response in the range 0-5% (v/v) water content in acetonitrile. The present method was applied for three synthetic samples of acetonitrile for water detection and the results obtained were compared using Karl Fischer titration. There was a good agreement in the values obtained by both the methods.

Nitrogen: A New Class of π-Bonding Partner in Hetero π-Stacking Interaction.

Spectroscopy under isolated conditions at low temperatures is an excellent tool to characterize the aggregates stabilized through weak interactions. Within the framework of weak interactions, the π-stacking interactions are considered unconventional with the limited experimental proofs, wherein the bonding associates are either aromatic and heterocyclic compounds or their combinations. Besides aromatic compounds, π-stacking networks can even be realized with molecules possessing electron rich π-clouds. In this work, the N2 molecule as a possible π-bonding partner is explored for the first time in which hetero π-stacking was achieved between pyrrole and N2 precursors. The matrix isolation experiments performed by seeding pyrrole and N2 mixtures in an Ar matrix at low temperatures with subsequent infrared spectral characterization revealed the generation of adducts stabilized through a π(pyrrole)···π(N2) interaction. Under identical conditions with the likelihood of two competing π-stacking and hydrogen-bonding interactions in pyrrole-N2 associates, π-stacking dominates energetically over hydrogen-bonding interaction.

Photooxidation of Trimethyl Phosphite in Nitrogen, Oxygen, and para-Hydrogen Matrixes at Low Temperatures.

Trimethyl phosphite (TMPhite) was photooxidized to trimethyl phosphate (TMP) in N2, O2, and para-H2 matrixes at low temperatures to correlate the conformational landscape of these two molecules. The photooxidation produced the trans (TGG)-rich conformer with respect to the ground state gauche (GGG) conformer of TMP in N2 and O2 matrixes, which has diverged from the conformational composition of freshly deposited pure TMP in the low-temperature matrixes. The enrichment of the trans conformer in preference to the gauche conformer of TMP during photooxidation is due to the TMPhite precursor, which exists exclusively in the trans conformer. Interestingly, whereas the photooxidized TMP molecule suffers site effects possibly due to the local asymmetry in N2 and O2 matrixes, in the para-H2 matrix owing to the quantum crystal nature the site effects were observed to be self-repaired.

PCl3-C6H6 heterodimers: evidence for Pπ phosphorus bonding at low temperatures.

A phosphorous trichloride (PCl3)-benzene (C6H6) heterodimer was generated in a low temperature N2 matrix and was characterized using infrared spectroscopy. The structure of the heterodimer produced in the matrix isolation experiment was discerned through ab initio computations. Computations disclosed that the experimentally detected dimer is stabilized through strong non-covalent phosphorus bonded Pπ interaction, considered as a class of pnicogen bonding. This experimentally unmapped Pπ interaction so far has been reconnoitered using atoms in molecules and natural bond orbital and energy decomposition analyses. The influence of substitutions on both the PCl3 and C6H6 monomeric units of the heterodimer was subsequently examined to understand the strength of Pπ interaction as a result of these substitutions.

Evidence for phosphorus bonding in phosphorus trichloride-methanol adduct: a matrix isolation infrared and ab initio computational study.

The weak interaction between PCl3 and CH3OH was investigated using matrix isolation infrared spectroscopy and ab initio computations. In a nitrogen matrix at low temperature, the noncovalent adduct was generated and characterized using Fourier transform infrared spectroscopy. Computations were performed at B3LYP/6-311++G(d,p), B3LYP/aug-cc-pVDZ, and MP2/6-311++G(d,p) levels of theory to optimize the possible geometries of PCl3-CH3OH adducts. Computations revealed two minima on the potential energy surface, of which, the global minimum is stabilized by a noncovalent P···O interaction, known as a pnictogen bonding (phosphorus bonding or P-bonding). The local minimum corresponded to a cyclic adduct, stabilized by the conventional hydrogen bonding (Cl···H-O and Cl···H-C interactions). Experimentally, 1:1 P-bonded PCl3-CH3OH adduct in nitrogen matrix was identified, where shifts in the P-Cl modes of PCl3, O-C, and O-H modes of CH3OH submolecules were observed. The observed vibrational frequencies of the P-bonded adduct in a nitrogen matrix agreed well with the computed frequencies. Furthermore, computations also predicted that the P-bonded adduct is stronger than H-bonded adduct by ∼1.56 kcal/mol. Atoms in molecules and natural bond orbital analyses were performed to understand the nature of interactions and effect of charge transfer interaction on the stability of the adducts.

Unraveling the conformational landscape of triallyl phosphate: matrix isolation infrared spectroscopy and density functional theory computations.

The conformations of triallyl phosphate (TAP) were studied using matrix isolation infrared spectroscopy and density functional theory (DFT) calculations. TAP was trapped in N2, Ar, and Xe matrixes at 12 K using an effusive source and the resultant infrared spectra recorded. The computational analysis on conformers of TAP is a challenging problem due to the presence of the large number of conformations. To simplify this problem, conformational analysis was performed on prototypical molecules such as dimethyl allyl phosphate (DMAP) and diallyl methyl phosphate (DAMP), to systematically arrive at the conformations of TAP. The above methodology discerned 131 conformations for TAP, which were found to contribute to the room temperature population. The computations were performed using B3LYP/6-311++G(d,p) level of theory. Vibrational wavenumber calculations were performed for the various conformers to assign the experimental infrared features of TAP, trapped in solid N2, Ar, and Xe matrixes.

In situ separation of ethanol with aqueous two-phase system and assessment of KLa for yeast growth in batch cultivation.

In the fermentation process, the separation of product and its purification is the most difficult and exigent task in the ground of biochemical engineering. Another major problem that is encountered in the fermentation is product inhibition, which leads to low conversion and low productivities. Extractive fermentation is a technique that helps in the in situ removal of product and better performance of the fermentation. An aqueous two-phase system was employed for in situ ethanol separation since the technique was biofriendly to the Saccharomyces cerevisiae and the ethanol produced. The two-phase system was obtained with polyethylene glycol 4000 (PEG 4000) and ammonium sulfate in water above critical concentrations, with the desire that the ethanol moves to the top phase while cells rest at the bottom. The overall mass transfer coefficient (KLa) was also estimated for the yeast growth at different rpm. The concentration and yield of ethanol were determined for conventional fermentation to be around 81.3% and for extractive fermentation around 87.5% at the end of the fermentation. Based on observation of both processes, extractive fermentation was found to be the best.

Including a diverse set of voices to address biological invasions.

Inclusivity is fundamental to progress in understanding and addressing the global phenomena of biological invasions because inclusivity fosters a breadth of perspectives, knowledge, and solutions. Here, we report on how the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) assessment on invasive alien species (IAS) prioritized inclusivity, the benefits of this approach, and the remaining challenges.

Bacterial lipid modification enhances immunoprophylaxis of filarial abundant larval transcript-2 protein in Mastomys model.

As in many other parasitic diseases, efficacious vaccine for lymphatic filariasis has been elusive for want of new approaches leaving billions of people either debilitated or at risk. With multiple B- and T-cell epitopes, the abundant larval transcript-2 (ALT-2) of the filarial worm, Brugia malayi, has been shown to be a promising immunoprophylactic target. To enhance its efficacy, it was lipid modified using our recently developed protein engineering tool, which then offered 30% more immunoprotection (49 vs. 79%) in Mastomys coucha model. Sustained high levels of IFN-γ (about 100 times) and high antibody titres (10-fold) elicited by lipid-modified ALT-2, as compared to the native form, indicated the maintenance of Th1/Th2 balance that is impaired in filariasis. Thus, this study provides the basis for developing efficacious vaccines for filariasis and other parasitic diseases by exploiting bacterial lipid modification.

Neonatal outcomes of small for gestational age preterm infants in Canada.

To compare the effect of small for gestational age (SGA) on mortality, major morbidity and resource utilization among singleton very preterm infants (<33 weeks gestation) admitted to neonatal intensive care units (NICUs) across Canada. Infants admitted to participating NICUs from 2003 to 2008 were divided into SGA (defined as birth weight <10th percentile for gestational age and sex) and non-small gestational age (non-SGA) groups. The risk-adjusted effects of SGA on neonatal outcomes and resource utilization were examined using multivariable analyses. SGA infants (n = 1249 from a cohort of 11,909) had a higher odds of mortality (adjusted odds ratio [AOR] 2.46; 95% confidence interval [CI], 1.93-3.14), necrotizing enterocolitis (AOR 1.57; 95% CI, 1.22-2.03), bronchopulmonary dysplasia (AOR 1.78; 95% CI, 1.48-2.13), and severe retinopathy of prematurity (AOR 2.34; 95% CI, 1.71-3.19). These infants also had lower odds of survival free of major morbidity (AOR 0.50; 95% CI, 0.43-0.58) and respiratory distress syndrome (AOR 0.79; 95% CI, 0.68-0.93). In addition, SGA infants had a more prolonged stay in the NICU, and longer use of ventilation continuous positive airway pressure, and supplemental oxygen (p < 0.01 for all). SGA infants had a higher risk of mortality, major morbidities, and higher resource utilization compared with non-SGA infants.

Efficient and optimized communication in biomedical sensor networks based on bioinspired particle swarm optimization for medical applications.

Healthcare, sports, the military, location monitoring and wireless body networks are emerging as technology of major relevance. As a result of the widespread usage of biomedical sensor networks in medical applications, it is essential that data packets containing vital signs be reliably and effectively supplied to the medical center. Because of its mobility, real-time monitoring, cheap cost, and real-time feedback, it may be used in a broad variety of applications. Effective data transport and a limited energy supply are the main challenges in WBAN. Uses genetic heuristics to enhance routing algorithm efficiency. Low-energy adaptive clustering hierarchy (LEACH) and distributed energy efficiency clustering (DEC) are two kinds of clustering algorithms (DEEC). A clustering-based routing protocol may be optimized using this study's optimization approach so that the network's lifetime can be extended.. The cluster heads (CHs) in sensor nodes are picked with the least amount of overhead grading possible. The cluster is being balanced. Passive clustering based on Bioinspired Particle Swarm Optimization (BPSO) should be used for clustering purposes. Routing messages efficiently means sending them quickly and efficiently without using a lot of bandwidth. Using constraints such as distance and residual power, the optimal path for the cluster may be determined with the help of iterative and heuristic chicken swarm optimization (IHCSO) for short. An evaluation of the packet distribution allocation, capacity, and average end-to-end latency illustrates the practicability of the proposed system in research concerning its efficiency. According to the findings of the research, following the technique that was proposed leads to much better outcomes.

Effective Image Processing and Segmentation-Based Machine Learning Techniques for Diagnosis of Breast Cancer.

Breast cancer is the second leading cause of death among women, behind only heart disease. However, despite the high incidence and mortality rates associated with breast cancer, it is still unclear as to what is responsible for its development in the first place. The prevention of breast cancer is not possible with any of the current available methods. Patients who are diagnosed and treated for breast cancer at an early stage have a better chance of having a successful treatment and recovery. In the field of breast cancer detection, digital mammography is widely acknowledged to be a highly effective method of detecting the disease early on. We may be able to improve early detection of breast cancer with the use of image processing techniques, thereby boosting our chances of survival and treatment success. This article discusses a breast cancer image processing and machine learning framework that was developed. The input data set for this framework is a sequence of mammography images, which are used as input data. The CLAHE approach is then utilized to improve the overall quality of the photographs by means of image processing. It is called contrast restricted adaptive histogram equalization (CLAHE), and it is an improvement on the original histogram equalization technique. This aids in the removal of noise from photographs while simultaneously improving picture quality. The segmentation of images is the next step in the framework's development. An image is divided into distinct portions at this point because the pixels are labeled at this step. This assists in the identification of objects and the delineation of boundaries. To categorize these preprocessed images, techniques such as fuzzy SVM, Bayesian classifier, and random forest are employed, among others.

Development of Machine Learning and Medical Enabled Multimodal for Segmentation and Classification of Brain Tumor Using MRI Images.

The improper and excessive growth of brain cells may lead to the formation of a brain tumor. Brain tumors are the major cause of death from cancer. As a direct consequence of this, it is becoming more challenging to identify a treatment that is effective for a specific kind of brain tumor. The brain may be imaged in three dimensions using a standard MRI scan. Its primary function is to examine, identify, diagnose, and classify a variety of neurological conditions. Radiation therapy is employed in the treatment of tumors, and MRI segmentation is used to guide treatment. Because of this, we are able to assess whether or not a piece that was spotted by an MRI is a tumor. Using MRI scans, this study proposes a machine learning and medically assisted multimodal approach to segmenting and classifying brain tumors. MRI pictures contain noise. The geometric mean filter is utilized during picture preprocessing to facilitate the removal of noise. Fuzzy c-means algorithms are responsible for segmenting an image into smaller parts. The identification of a region of interest is facilitated by segmentation. The GLCM Grey-level co-occurrence matrix is utilized in order to carry out the process of dimension reduction. The GLCM algorithm is used to extract features from photographs. The photos are then categorized using various machine learning methods, including SVM, RBF, ANN, and AdaBoost. The performance of the SVM RBF algorithm is superior when it comes to the classification and detection of brain tumors.

1-Diphenyl-methyl-ene-2-(9H-fluoren-9-yl-idene)hydrazine.

In the title mol-ecule, C(26)H(18)N(2), the 9H-fluorene unit is almost planar, as the cyclo-penta-diene ring makes dihedral angles of 1.12 (6) and 1.46 (6)° with the fused benzene rings. The dihedral angle between the two phenyl rings of the diphenyl-methyl-ene residue is 61.78 (6)°.

(E)-1-Diphenyl-methyl-idene-2-[(1H-indol-3-yl)methyl-idene]hydrazine.

In the title compound, C(22)H(17)N(3), the 1H-indole unit is essentially planar, with a dihedral angle of 0.95 (10)° between the pyrrole ring and the fused benzene ring. The dihedral angle between the two phenyl rings is 65.09 (10)°. In the crystal, an inter-molecular N-H⋯N hydrogen bond forms an infinite chain in the b-axis direction.

Narcotics and sedatives use in the mechanical ventilation in preterm infants: Predictors and outcome.

BACKGROUND: Mechanical ventilation (MV) causes discomfort but whether it causes pain remains controversial. Around the world neonatal intensive care units (NICU) often utilize narcotics and/or sedatives during MV of vulnerable infants yet the association with adverse neonatal outcomes has not been adequately addressed. OBJECTIVE: Test for associations between the use of narcotics/sedatives during MV and mortality/morbidity in preterm infants in a large infant cohort in Canada. DESIGN/METHODS: Preterm infants born <35 weeks gestational age (GA) requiring MV for >24 hrs were identified retrospectively from the Canadian Neonatal Network database, 2010-12. Infants were categorized according to whether they received narcotics/sedatives for greater than 24 hours concurrently with MV. Infants were excluded if moribund on admission, had major congenital anomalies, diagnoses where narcotic administration is routine and suspected seizures. Multivariable logistic and linear regression analysis tested for association of narcotics/sedatives use during MV with mortality/morbidity (nosocomial infections, BPD, ROP, IVH) and length of MV. RESULTS: After exclusions the cohort included 2672 infants; 467(17%) exposed only to narcotics 101(4%) only to sedatives and 299(11%) to both. All models were adjusted for GA, gender, small for GA, SNAP-II score >20, multiple births, delivery mode, outborn, PDA status, MV type, use of high flow, muscle relaxant use, indwelling lines, caffeine and surfactant therapy. The composite mortality/morbidity, and MV days were significantly higher for MV infants exposed to narcotics, sedatives or both compared to infants not exposed. CONCLUSION: Mounting evidence of the adverse short and long-term impacts of narcotics/sedatives during MV supports the need for further work in alternative therapies.