Document Based Clustering For Detecting Events in Microblogging Websites

Social media has a great in?uence in our daily lives. People share their opinions, stories, news, and broadcast events using social media. This results in great amounts of information in social media. It is cumbersome to identify and organize the interesting events with this massive volumes of data, typically browsing, searching, monitoring events becomes more and more challenging. A lot of work has been done in the area of topic detection and tracking (TDT). Most of these methods are based on single-modality (e.g., text, images) information or multi-modality information. In the single-modality analysis, many existing methods adopt visual information (e.g., images and videos) or textual information (e.g., names, time references, locations, title, tags, and description) in isolation to model event data for event detection and tracking. This problem can be resolved by a novel multi-model social event tracking and an evolutionary framework not only effectively capturing the events, but also generates the summary of these events over time. We proposed a novel method works with mmETM, which can effectively model the social documents, which includes the long text along with the images. It learns the similarities between the textual and visual modalities to separate the visual and non-visual representative topics. To incorporate our method to social tracking, we adopted an incremental learning technique represented as mmETM, which gives informative textual and visual topics of event in social media with respect to the time. To validate our work, we used a sample data set and conducted various experiments on it. Both subjective and quantitative assessments show that the proposed mmETM technique performs positively against a few best state-of-the art techniques

Validated gradient Stability-Indicating UPLC Method for the Determination of Lidocaine and its Degradation Impurities in Pharmaceutical Dosage Form

Objective: Aim of the present work was to develop a stability indicating ultra performance liquid chromatography (UPLC) method to determine Lidocaine and its degradation impurities in pharmaceutical dosage forms. Method: Chromatographic separation was achieved by gradient elution on Agilent eclipse plus C18 (100x4.6) mm, and 1.8m column with potassium dihydrogen phosphate buffer (pH 4.50) and acetonitrile within a short runtime of 14.0 min. The eluted compounds were monitored at 230 nm using photodiode array (PDA) detector, the flow rate was 1.0 mL/min, and the column oven temperature was maintained at 40 ?C. Result: The resolution of Lidocaine and six (potential, bi-products and degradation) impurities was greater than 2.0 for all pairs of components. The repeatability and intermediate precision, expressed by the RSD, were less than 1.0%. The accuracy and validity of the method were further ascertained by performing recovery studies. The specificity of the method was investigated under different stress conditions including hydrolytic, oxidative, photolytic and thermal as recommended by ICH guidelines. Relevant degradation was found to take place under oxidative condition. Conclusion: Method was Robustness against small modification in pH, column oven temperature, flow rate and percentage of the mobile phase composition was ascertained. All these results provide that the method has stability indicating properties being fit for its intended purpose; it may find application for the routine analysis of the related substances of Lidocaine formulations

A detailed approach on multiple myeloma and its treatment

Multiple myeloma is a cancer formed by malignant plasma cells. Normal plasma cells are found in the bone marrow and are an important part of the immune system. The immune system is composed of several types of cells that work together to fight infections and other diseases. Lymphocytes are the main cell type of the immune system. There are 2 major types of lymphocytes: T cells and B cells. When B cells respond to an infection, they mature and change into plasma cells. Plasma cells make the antibodies that help the body attack and kill germs. Lymphocytes can be found in many areas of the body, such as lymph nodes, the bone marrow, the intestines, and the bloodstream. Plasma cells, however, are mainly found in the bone marrow. Bone marrow is the soft tissue inside some hollow bones. In addition to plasma cells, normal bone marrow has cells that make the different normal blood cells. When plasma cells become cancerous and grow out of control they can produce a tumor called a plasmacytoma. These tumors generally develop in a bone, but they are also rarely found in other tissues. If there is only a single plasma cell tumor, it is called an isolated plasmacytoma. When there is more than one plasma cell tumor, it is called ‘multiple myeloma’. There are lot of plasma cell abnormalities, but multiple myeloma causes severe bone pain, so here we covered detailed notes on the disease and its treatment strategies

FORMULATION AND EVALUATION OF ACECLOFENAC PRONIOSOME LOADED ORABASE FOR MANAGEMENT OF DENTAL PAIN

Objective: The main motive is to develop proniosomes loaded orabase for enhanced permeation and prolonged release of aceclofenac for oro dental conditions.Methods: Various aceclofenac (ACL) proniosomal gels were formulated employing various surfactants, span 60 was superior and significant for loading into orabase. The formulations were scrutinized for entrapment efficiency, optical microscopy, in vitro diffusion and release studies, mucoadhesive strength, ex-vivo permeation studies and drug-excipient interactions were determined by FTIR spectroscopy.Results: Considering best entrapment efficiency with span 60 (97.60±1.85) and optimum vesicle shape, along with prolonged drug permeation (45% for 24 h) the formulation F(ACL)1 was selected and optimized for loading into orabase. The F(ACL)1 loaded orabase exhibited significant prolonged release over 14 h, and permeation profiles exhibited nearly two-fold increased flux in comparison with control. Good mucoadhesive strength was observed for proniosomal orabase 6370 dynes/cm2. No evidence of incompatibility amongst formulation components from FTIR studies. SEM images revealed the particle size range from 136 µm to 236 µm for proniosomal orabase.Conclusion: Orabase can be an effective carrier for proniosomes with enhanced permeation and prolonged release for oro-dental conditions

Synthetic aerial image generation for miniature aerial system

This paper presents a computer vision based method which generates a synthetic image of the earth as would be viewed by an aerial camera. The method takes geo-referenced, ortho-rectified aerial image database as the source and using a pinhole perspective camera model generates the synthetic image. The method requires the position and attitude of the camera, which act as the extrinsic parameters for the camera model. The intrinsic parameters are chosen to emulate a real camera. Finally, the results of synthetic aerial image generation implemented on MATLAB are presente

Modification of lipid membrane compressibility induced by an electric field

Changes in membrane deformation and compressibility, induced by an external electric field, are investigated using coarse-grained martini force field simulations in a salt-free environment. We observe changes in the area of the membrane above a critical electric field. Below this value, the membrane compressibility modulus is found to decrease monotonically. For higher electric fields, the membrane projected area remains constant while the net interfacial area increases, with the corresponding compressibility moduli, show the opposite behavior. We find that the mechanical parameters, surface tension and bending modulus, of a freely floating membrane in the absence of explicit ions, are unaffected by the presence of the electric field. We believe these results have a bearing on our understanding of the electroformation of uncharged lipids in a salt-free environment

Prospective study of primary amenorrhoea and its management in a rural tertiary centre

Background: Amenorrhoea (absence of menstruation) is a symptom of varied causes. It results from dysfunction of hypothalamic-pituitary ovarian axis, uterus and vagina. It is a major concern for pubertal girls and their family members. It has a major impact on the physical, mental, psychological and social life of the girl and her family. The objective of the present study was to evaluate the aetiology and management of primary amenorrhoea in young adolescent girls.Methods: It was a prospective study conducted for a period of 2 years from August 2016 to July 2018 at Rajarajeswari medical college and hospital. Patients presenting with history of amenorrhoea that is, absence of menses by the age of 13 years with no visible development of secondary sexual characteristics or by 15 years of age with the presence of normal secondary sexual characteristics were included in our study. Cases of secondary amenorrhoea were excluded. Detailed history, examination, investigations and management was documented and analysed.Results: A total of 25 patients of primary amenorrhea were studied during the study period. In our study outflow tract anomalies were the commonest cause of amenorrhoea accounting for 84%, of which imperforate hymen (32%) and Mayer Rokitansky Küster Hauser syndrome (MRKH) 36% were the two most common Mullerian anomaly causing primary amenorrhoea. Gonadal dysgenesis accounted for 12% of the cases. Amenorrhoea was the commonest complaint patients presented with accounting to 76%, followed by cyclical pain abdomen accounting for 16% of cases.Conclusions: Primary amenorrhoea is multifactorial and is of major concern among adolescent girls. Early diagnosis and intervention has an impact on the physical and psychological wellbeing of the girl

Parasitic Strip Loaded Dual Band Notch Circular Monopole Antenna with Defected Ground Structure

In this article a parasitic strip loaded monopole antennas are designed to notch dual and triple bands. The designed models are constructed on one side of the substrate material and on the other end defected ground structures are implemented. The basic antenna comprises a tuning stub and a ground plane with tapered shape slot as DGS. Another model is constructed with circular monopole radiating element on front side and similar kind of ground structure used in the basic rectangular tuning stub antenna. To create notched bands with tuning stubs, two symmetrical parasitic slits are placed inside the slot of the ground plane. The basic model is of the rectangular stub notching triple band and the circular tuning stub antenna notching dual band. Dual band notched circular tuning stub antenna is prototyped on FR4 substrate and measured results from vector network analyzer are compared with simulation results of HFSS for validation

Application of machine learning constructs to predict post-operative complications and adverse events following shoulder hemiarthroplasty

Background: Artificial intelligence (AI) constructs and machine learning (ML) algorithms have demonstrated utility in predicting various clinical, surgical, and financial outcomes. In this study, we applied AI to shoulder hemiarthroplasty (HA) to predict various post-operative complications. Methods: The sample was queried from the American college of surgeons-national surgical quality improvement program (ACS-NSQIP) database for all shoulder HA cases from 2008-2018. Six ML algorithms-random forest classifier, gradient boosting classifier, decision tree classifier, SVM classifier-tuned model, Gaussian Naïve Bayes classifier, multi-layer perception-analyzed the sample dataset. Postoperative complications included extended length of stay, non-home discharge destination, transfusion, and any adverse event. Each ML model was compared to logistic regression (LR), and model strength was evaluated. Results: We identified a total of 1585 shoulder HA cases. Mean age, BMI, operative time, and length of stay were 66±12 years, 31±8 kg/m2, 114±61 minutes, and 2.93±6.61 days. Preop hematocrit, longer operative time, and older age were most predictive of extended length of stay. Preop hematocrit, operative time, and ASA class had the highest importance in any adverse events (AAE) prediction. ML models outperformed traditional comorbidity indices, LR, for predicting extended length of stay (79% vs. 66%), non-home discharge destination (79% vs. 65%), any adverse event (78% vs. 66%), and transfusion requirement (82% vs. 63%).  Conclusions: ML algorithms predicted post-surgical outcomes of interest following shoulder HA at a higher rate to conventional LR and can assist orthopedic surgeons in decision making.