Manipulation of Genes Involved in Secondary Cell Wall Development During Wood Formation in Poplar

Lignins are second most abundant components of vascular plant cell walls. They provide plants with structural rigidity and are polymers of monolignols. Lignin polymerization is catalyzed by peroxidases and/or laccases. These enzymes are suggested to share functional overlap and mechanism by which they coordinate this process is not clearly understood. There are about 100 peroxidases and 50 laccase genes known in poplar genome out of which some stem differentiating xylem-specific (SDX) enzymes were selected for our study. The main objective was to genetically manipulate genes expressed in the SDX region in the cell wall to see the effects on lignin content in the wood without affecting yield of the transgenic poplar trees. We also aimed to understand the role these enzymes play during lignin polymerization. We also adopted the short tandem target mimic (STTM) strategy to manipulate miRNAs that might be involved in wood development of poplar trees. Our results indicate that peroxidases, laccases and miRNAs under investigation play some specific roles in secondary cell wall biosynthesis. Therefore, manipulation of expression of these genes may prove beneficial towards future genetic engineering of the poplar trees for improved downstream applications

Thrombocytopenia in early pregnancy predicting partial haemolysis, elevated liver enzyme and low platelet count syndrome: a case report and review of literature

The incidence of thrombocytopenia in pregnancy is 6-10% and is classically defined as a platelet count of less than 150,000/ L. Counts less than 100,000 to 150,000/L are considered mild, 50,000 to 100,000/L as moderate, and less than 50,000/L are considered as severe thrombocytopenia. It is the second most common hematological condition in pregnancy with anaemia being the leading cause. Thrombocytopenia may be related to disorders that are intrinsic to pregnancy such as gestational thrombocytopenia that is seen in three-fourths of all cases. The second common cause is hypertensive disorders in pregnancy more commonly seen in severe pre-eclampsia in 21% and in HELLP (haemolysis, elevated liver enzyme and low platelet count) that accounts for 12% of thrombocytopenia cases in pregnancy. This case report revisits the diagnosis of partial HELLP under the background of preeclampsia that warrants aggressive treatment like complete HELLP syndrome to optimize the maternal and fetal outcome

Improved Lion Optimization based Enhanced Computation Analysis and Prediction Strategy for Dropout and Placement Performance Using Big Data

Background: Predicting the undergraduate’s placement performance is vital as it impacts the credibility of educational institutions. Hence, it is significant to predict their performance based on placement in the early days of degree program. Objectives: The study intends to predict the undergraduate’s placement performance through the introduced ANN-R (Artificial Neural Network based Regression) as it is able to handle fault tolerance. For efficient prediction, relevant feature selection is needed that is performed by the proposed ILO (Improved Lion Optimization) algorithm as it has the ability to find nearest probable optimal solution. Methodology: Initially, the parameters and population are initialised. Subsequently, first best-agent is stated in accordance with fitness function. Subsequently, position of present search agent is updated. This iteration continues until all the features are selected and optimized result is attained. Here best score is computed using the proposed ILO for feature selection. Finally, the dropout analysis and placement performance of students is predicted using the introduced ANN-R through a train and test split. Results/Conclusion: Performance of the proposed system is analysed in accordance with loss metrics. Additionally, internal comparison is performed to find the extent to which the actual and predicted values correlate with one another during prediction using the existing and proposed system. The outcomes revealed that the proposed system has the ability to predict the student’s placement performance along with domain of interest with minimum errors than the traditional system. This makes the proposed system to be highly suitable for predicting student’s performance

Improved Lion Optimization based Enhanced Computation Analysis and Prediction Strategy for Dropout and Placement Performance Using Big Data

Background: Predicting the undergraduate’s placement performance is vital as it impacts the credibility of educational institutions. Hence, it is significant to predict their performance based on placement in the early days of degree program. Objectives: The study intends to predict the undergraduate’s placement performance through the introduced ANN-R (Artificial Neural Network based Regression) as it is able to handle fault tolerance. For efficient prediction, relevant feature selection is needed that is performed by the proposed ILO (Improved Lion Optimization) algorithm as it has the ability to find nearest probable optimal solution. Methodology: Initially, the parameters and population are initialised. Subsequently, first best-agent is stated in accordance with fitness function. Subsequently, position of present search agent is updated. This iteration continues until all the features are selected and optimized result is attained. Here best score is computed using the proposed ILO for feature selection. Finally, the dropout analysis and placement performance of students is predicted using the introduced ANN-R through a train and test split. Results/Conclusion: Performance of the proposed system is analysed in accordance with loss metrics. Additionally, internal comparison is performed to find the extent to which the actual and predicted values correlate with one another during prediction using the existing and proposed system. The outcomes revealed that the proposed system has the ability to predict the student’s placement performance along with domain of interest with minimum errors than the traditional system. This makes the proposed system to be highly suitable for predicting student’s performance

Magnetization reversal in a site-dependent anisotropic Heisenberg ferromagnet under electromagnetic wave propagation

Abstract Information density and switching of magnetization offers an interesting physical phenomenon which invoke magneto-optical techniques employed on the magnetic medium. In this paper, we explore the soliton assisted magnetization reversal in the nanosecond regime in the theoretical framework of the Landau–Lifshitz–Maxwell (LLM) model. Starting from the Landau–Lifshitz equation, we employ the reductive perturbation method to derive an inhomogeneous nonlinear Schrodinger equation, governing the nonlinear spin excitations of a site-dependent anisotropic ferromagnetic medium under the influence of electromagnetic (EM) field in the classical continuum limit. From the results, it is found that the soliton undergoes a flipping thereby indicating the occurrence of magnetization reversal behavior in the nanoscale regime due to the presence of inhomogeneity in the form of a linear function. Besides, the spin components of magnetization are also evolved as soliton spin excitations

Serum adenosine deaminase as oxidative stress marker in type 2 diabetes mellitus

Background: Oxidative stress markers are increased in type 2 diabetes mellitus and its estimation helps in predicting the long term complications. In present study comparison and correlation of the levels of serum adenosine deaminase, serum malondialdehyde, and serum total antioxidant capacity in type 2 diabetes mellitus and in age and sex matched healthy controls.Methods: Study group consisted of 100 individuals between the age group of 35-65 years of age. Of which 50 individuals with type 2 diabetes mellitus were considered as cases. The control group consisted of 50 age and sex matched healthy individuals. Study was approved by institutional ethical committee. By aseptic precautions 2 ml of venous blood was collected in a plain vacutainer tube, after 8-12 hours of fasting. Serum adenosine deaminase, serum malondialdehyde, and serum total antioxidant capacity were estimated in all groups.Results: The study observed an increased level of serum adenosine deaminase, malondialdehyde and decreased levels of total antioxidant capacity in type 2 diabetes mellitus compared to controls. Serum adenosine deaminase levels in type 2 diabetics were 50.77 ± 6.95 and in controls was 17.86 ± 4.04. Serum Malondialdehyde levels in type 2 diabetics was 512.13 ± 70.15 and in controls was 239.32 ± 23.97. Serum total antioxidant levels in type 2 diabetics was 0.39±0.15 and in controls was 1.66±0.25. Positive correlation was seen between serum adenosine deaminase and malondialdehyde and it was statistically significant. Statistically significant negative correlation was seen between serum adenosine deaminase and total antioxidant capacity.Conclusion: Adenosine deaminase can be used as oxidative stress marker. Their increased levels indicate oxidative stress in type 2 diabetes mellitus. Therefore, estimation of serum adenosine deaminase levels help in early prediction and prevention of long term complications occurring due to oxidative stress in diabetics, thereby decreasing the mortality and morbidity in them.

Evaluation of Ketorolac Tromethamine Microspheres by Chitosan/Gelatin B Complex Coacervation

Microspheres (MS) of Ketorolac Tromethamine (KT) for oral delivery were prepared by complex coacervation (method-1) and simple coacervation (method-2) methods without the use of chemical cross–linking agent (glutaraldehyde) to avoid the toxic reactions and other undesirable effects of the chemical cross-linking agents. Alternatively, ionotropic gelation was employed by using sodium-tripolyphosphate (Na-TPP) as cross linking agent. Chitosan and gelatin B were used as polymer and copolymer respectively. All the prepared microspheres were subjected to various physico-chemical studies, such as drug-polymer compatibility by Thin Layer Chromatography (TLC) and Fourier Transform Infra Red Spectroscopy (FTIR), surface morphology by Scanning Electron Microscopy (SEM), frequency distribution, encapsulation efficiency, in-vitro drug release characteristics and release kinetics. The physical state of drug in the microspheres was determined by Differential Scanning Calorimetry (DSC) and X-ray powder Diffractometry (XRD). TLC and FTIR studies indicated no drug-polymer incompatibility. All the MS showed release of drug by a fickian diffusion mechanism. DSC and XRD analysis indicated that the KT trapped in the microspheres existed in an amorphous or disordered-crystalline status in the polymer matrix. It is possible to design a controlled drug delivery system for the prolonged release of KT, improving therapy by possible reduction of time intervals between administrations

NOVA INFORMACIJSKA TEHNOLOGIJA PROCJENE KORISTI IZDVAJANJA CESTA POMOĆU SATELITSKIH SNIMKI VISOKE REZOLUCIJE TEMELJENE NA PCNN I C-V MODELU

Road extraction from high resolution satellite images has been an important research topic for analysis of urban areas. In this paper road extraction based on PCNN and Chan-Vese active contour model are compared. It is difficult and computationally expensive to extract roads from the original image due to presences of other road-like features with straight edges. The image is pre-processed using median filter to reduce the noise. Then road extraction is performed using PCNN and Chan-Vese active contour model. Nonlinear segments are removed using morphological operations. Finally the accuracy for the road extracted images is evaluated based on quality measures.Izdvajanje cesta pomoću satelitskih slika visoke rezolucije je važna istraživačka tema za analizu urbanih područja. U ovom radu ekstrakcije ceste se uspoređuju na PCNN i Chan-Vese aktivnom modelu. Teško je i računalno skupo izdvojiti ceste iz originalne slike zbog prisutnosti drugih elemenata ravnih rubova sličnih cestama. Slika je prethodno obrađena korištenjem filtera za smanjenje smetnji. Zatim se ekstrakcija ceste izvodi pomoću PCNN i Chan-Vese aktivnog modela konture. Nelinearni segmenti su uklonjeni primjenom morfoloških operacija. Konačno, točnost za ceste izdvojene iz slika se ocjenjuje na temelju kvalitativnih mjera

Incidence of ventilator associated pneumonia and its risk factors

Hospitals are intended to heal the sick; but they are also sources of infection. Ironically, the advances in medicine are partly responsible for the fact that today; hospital infections are the leading cause of death worldwide. Newer technology and latest surgical and medical diagnostic methods and treatment procedures have increased the number of invasive techniques leading to higher chances of nosocomial infection. Pneumonia is the leading cause of death due to nosocomial infections. Intubation & mechanical ventilation greatly increases the risk for ventilator-associated pneumonia (VAP). In developing country like India, such hospital-acquired infections have a significant impact on patient’s morbidity, mortality, hospital stay and on financial concerns of the patient, hospital and community. The present investigation was aimed to determine the incidence of ventilator associated pneumonia in the neurosurgery intensive care unit of a tertiary care centre and to determine the risk factors of ventilator associated pneumonia. A total of 30 samples belonging to the age group of 15 to 75 years who where on mechanical ventilator for more than 48 hours in the neurosurgery intensive care unit of a tertiary care centre were selected using convenience sampling. The incidence of VAP was estimated to be 30%. The risk factors identified for the development of VAP was found to be combined head and cervical spine injury (P=0.001), associated injuries (P=0.035), additional surgeries (P=0.025), nasogastric feeding (P=0.001), intake of immuno suppressive drugs (P=0.004), pre operative antibiotics (p=0.000) and duration of mechanical ventilation >5 days (P=0.000). The mortality among patients with VAP was found to be higher than patients without VAP (88.9% than non VAP patients)