Absolute convergence of the twisted Arthur-Selberg trace formula

We show that the distributions occurring in the geometric and spectral side of the twisted Arthur-Selberg trace formula extend to non-compactly supported test functions. The geometric assertion is modulo a hypothesis on root systems proven when the group is split. The result extends the work of Finis-Lapid (and M\"uller, spectral side) to the twisted setting. We use the absolute convergence to give a geometric interpretation of sums of residues of certain Rankin-Selberg L-functions.Comment: Accepted to be published in Mathematische Zeitschrift. Removed proof of RCL for base change; Section 8 now requires Assumption 8.1. Also, minor correction

A Strategic Analysis of Eloquent Systems

Eloquent develops software for library, records, archives, and collections management. Circa 2002, it developed web-enabled software products (WebLibrary, WebRecords, WebArchives, and WebMuseum), and since then targeted its existing DOS-based customers. Now, it plans to target new customers with a low-investment marketing strategy. The library management software industry is saturated with many big and niche players. The records management software industry is expected to grow exponentially due to the recent enactment of the Sarbanes Oxley Act in the United States. The archives and collections management software industries are relatively stable with reasonable level of competition. Considering competition, industry growth, and product strengths, Eloquent should focus on two products: WebLibrary targeted at schools and WebArchives targeted at government archival institutions. It should use direct emails to make itself visible in the marketplace. However, rather than sending spam messages, it should build its own database of potential customers using advertisements and its website

Exploring Sensibility in Modern Indian English Drama

The Indian English Drama has developed as an important and versatile body of English Literature and has caught attention of the global audiences. It has made a substantial progress by encapsulating various issues that India has been facing from time to time. It finds its impetus from Indian sensibility, philosophy, myths and religious beliefs and attracted attention of the people beyond boundaries. When one goes through the history of Indian English Drama, one comes to know that it has made a little progress than Indian English Fiction and Poetry. Though Indian English Drama came to the scene before these above-mentioned genres but failed to keep pace with them because of some reasons. Unlike Fiction and Poetry, Drama cannot be restricted to reading only. It needs a theatre, an encouraging audience, effective dialogues, efficient actors and other stagecraft. Indian English Drama passes many phases and at last comes to a whole new range of playwrights who have left no stone unturned to give it its due place. The present paper studies Indian English Drama with all its flaws and highlights the contribution of Modern Indian English Playwrights

Study of primary endoscopic dacryocystorhinostomy without lacrimal stent

OBJECTIVES/HYPOTHESIS: To evaluate the surgical outcome of primary endoscopic dacryocystorhinostomy (DCR) in nasolacrimal duct obstruction (NLDO) in our set up. STUDY DESIGN: Retrospective non randomized study of consecutive 571 DCR performed in MIMER Medical College, Talegaon-Dabhade, Pune, India during January 2006 to December 2013 with a follow up period of minimum 1 year.METHODS: Retrospective analysis of 571 consecutive primary endoscopic DCR procedures performed without stenting was done. Nasolacrimal system irrigation was used for preoperative assessment. Success was defined as absence of symptoms and patent nasolacrimal system.RESULTS: The overall success rate was 87.39% (499/571).CONCLUSIONS: Our study suggests that the surgical outcomes of primary endoscopic DCR in nasolacrimal duct obstruction are good

Outcomes Associated with the Utilization of Antidepressants and Psychotherapy Amongst Cancer Patients with Depression

Objective: To determine patterns of use, prescription medicine costs, office-based visit costs and quality of life (QOL) across classes of antidepressants/psychotherapy in elderly cancer patients. Methods: Medical Expenditure Panel Survey data files from 2005-2015 for cancer patients with depression aged 18 years or older were used for the study. Frequencies of patients under specific classes of antidepressants/psychotherapy were identified. Costs and QOL scores were compared using Analysis of Variance (ANOVA). Generalized linear models, linear/multinomial logistic regression were used for analyses adjusted for demographics, overall health status, number and type of comorbidities. Results: The study sample consisted of 17,671 cancer patients with depression. 32.08% patients had an antidepressant prescribed whereas 15.30% reported psychotherapy. SSRI (62.44%) was the most frequently prescribed class. The prescription and office-based visits costs were adjusted for demographics, overall health status, number and type of comorbidities. These adjusted prescription costs were the highest for SNRI (Mean = $112.92), adjusted office-based (psychotherapy) visit costs were the highest for those receiving psychotherapy only without any antidepressant (Mean =$166.39/visit). QOL scores were higher amongst patients who had combinations of antidepressants prescribed, specifically SSRI with either a TCA or SNRI as compared to those who were prescribed an individual class or those who did not receive any treatment at all. Conclusion: Antidepressants were prescribed more often than psychotherapy amongst cancer patients with depression in the United States. The prescription costs and associated QOL scores were higher amongst those with antidepressants prescribed as compared to those receiving psychotherapy with or without an antidepressant for cancer patients with depression in the United States

Development and Validation of Predictive Risk and Multiple Criteria Decision Analysis Models to Evaluate Cardiovascular Outcomes Among Cancer Patients

Objective: The objectives of our study were to characterize the study population with cancer and cardiovascular diseases (CVD) both as compared to those without and to build a predictive model using machine learning (ML) algorithms that can predict the risk of CVD in cancer patients. In addition, our objective was also to evaluate characteristics associated with cardiotoxic adverse events of breast cancer therapies and develop a multiple criteria decision analysis (MCDA) model to conduct benefit-risk assessment of breast cancer therapy regimens. Methods: We used Medical Expenditure Panel Survey (MEPS) and FDA Adverse Events Reporting System (FAERS) 2005-2015 files along with literature evidence for our study. We used MEPS database to train our predictive models using ML algorithms such as random forest (RF), gradient boosting and deep learning and compared these to standard regression models. Separate predictive models were built for chronic and acute CVD. We characterized the population with both cancer and CVD and those with cancer therapy associated cardiotoxic adverse events using multinomial logistic models . FAERS and literature evidence were also used to build the MCDA model to rank the breast cancer therapy regimens given the benefits and the risks involved in the treatment alternatives. Results: Our study sample consisted of 44,217 cancer patients identified using MEPS 2005-2015 files out of which 12,339 (28.7%) patients were also diagnosed with CVD. Age, marital status, education and employment status were the sociodemographic characteristics that differed significantly across cancer patients with and without CVD. We observed that most of the ML models for chronic (RF c-statistic: 0.9872, gradient boosting c-statistic: 0.7608, deep learning c-statistic: 0.7662) and acute CVD (RF c-statistic: 0.9738, gradient boosting c-statistic: 0.7853, deep learning c-statistic: 0.8267) were more accurate than the standard regression models for chronic (standard regression model c-statistic: 0.7641, GLM net model c-statistic: 0.7349) and acute (standard regression model c-statistic: 0.7534, GLM net model c-statistic: 0.7853) CVD. We then used the most accurate RF model to build a web-based application that could predict CVD risk. We then identified 35,630,544 breast cancer patients using FAERS dataset. Our findings suggest that breast cancer patients receiving targeted therapies were more likely to be diagnosed with CVD as compared to those who were receiving conventional therapies (OR = 1.213, 95% CI = 1.180, 1.247). On conducting MCDA, we found that the breast cancer therapy regimen 3 with trastuzumab, cyclophosphamide/ carboplatin and a taxane (paclitaxel/ docetaxel) was the most preferred therapy alternative given the benefits and the risks associated with each of the alternatives. Conclusion: Our study thus evaluated the use of newer analytical techniques such as ML algorithms and MCDA to evaluate certain outcomes. Our study suggests that ML algorithms were more accurate in predicting CVD risk in cancer patients. In addition, our MCDA model suggested that the breast cancer therapy regimen with trastuzumab, cyclophosphamide/ carboplatin and a taxane was the most preferred alternative considering the survival and adverse events benefits and risks

Growth enhancement of agricultural crops using seaweed liquid fertilizer

Extensive use of chemical fertilizer is leading to infertility of soil and various hazardous effect on the ecosystem. Using biofertilizer reduces these harmful effects. Many are commercially available, being a poor match to chemical fertilizer they are not used on a large scale. Near coastal regions seaweeds are used as biofertilizers. Recent studies show seaweeds have multiple growth regulators, macro and micronutrients, polysaccharides which are necessary for plant growth. The study is carried out to check the bioactivity of Seaweed liquid fertilizer (SLF) as a growth stimulant. Sargassum cinereum, Ulva rigida and Ahnfeltia plicata found on the Indian coastline were used. SLFs were prepared from each of three species in varying concentration (0.02%, 0.04%, 0.06%, 0.08% and 0.1% v/v) with water and Urea as control. The test plant selected was Vigna radiata. Agronomic characters like germination rate, shoot length, flowering and fruiting period of test plants were studied. Phyto-hormones from SLF were detected by thin-layer chromatography. Elemental analysis of micronutrients in SLF was carried by ICP-AES. Along with Chlorophyll and Protein estimation of grown plants were checked. SLF shelf life was studied using chemical and biological preservatives. The effect of 0.06% concentration of all SLFs showed enhancement in growth and phytochemicals in plants. Plants treated with SLF showed flowering earlier than control plants. Thus, using SLF bio-fertilizer can become an alternative method to reduce the use of harmful chemical fertilizers