ANALYTICAL STANDARDIZATION OF TRIPHALADI TAILA

Analytical study plays an important role in the standardization of the drugs. Ayurveda, the ancient system of medicine is gaining recognition throughout the world and many herbal, metal and mineral drugs are now clinically tested and accepted. However, one of the impediments in the acceptance of the ancient systems of medical preparation is the lack of standard quality control profiles. The quality of the drugs, that is, the profile of the constituents in the final product has implication in efficacy and safety.The Sneha kalpa are par excellent to other dosage forms due to their wider advantages like increased absorption and extraction of fat soluble active principles. Sneha kalpas are the only dosage from which can be administered conveniently both internally as well as externally. Triphaladi Taila is an important herbo mineral formulation mentioned in Rasaratnakara indicated for the management of the disease Palitya as Kesharanjaka. The ingredients present in the “Triphaladi Taila” are Sodhita Lauha churna, Triphala churna, Bhringaraja swarasa and Tila taila. Shodhana, Swarasa nirmana, Kalka nirmana, Churna nirmana and Snehapaka and are the main pharmaceutical procedures employed in the preparation of Triphaladi Taila. To assess the safety and to understand the physico chemical properties, organoleptic tests, moisture content, refractive index, fat, Iodine value, pH value, Saponification value, Acid value,  ash value, weight/ml, viscosity, iron, volatile oil and microbial tests  as well as chromatographically (TLC) for developing standards

A Context Aware Approach for Generating Natural Language Attacks

We study an important task of attacking natural language processing models in a black box setting. We propose an attack strategy that crafts semantically similar adversarial examples on text classification and entailment tasks. Our proposed attack finds candidate words by considering the information of both the original word and its surrounding context. It jointly leverages masked language modelling and next sentence prediction for context understanding. In comparison to attacks proposed in prior literature, we are able to generate high quality adversarial examples that do significantly better both in terms of success rate and word perturbation percentage.Comment: Accepted as Student Poster at AAAI 202

La protein binding at the GCAC site near the initiator AUG facilitates the ribosomal assembly on the hepatitis C virus RNA to influence internal ribosome entry site-mediated translation

Human La autoantigen has been shown to influence internal initiation of translation of hepatitis C virus (HCV) RNA. Previously, we have demonstrated that, among the three RRMs of La protein, the RRM2 interacts with HCV internal ribosome entry site (IRES) around the GCAC motif near the initiator AUG present in the stem region of stem-loop IV (SL IV) (Pudi, R., Abhiman, S., Srinivasan, N., and Das S. (2003) J. Biol. Chem. 278, 12231-12240). Here, we have demonstrated that the mutations in the GCAC motif, which altered the binding to RRM2, had drastic effect on HCV IRES-mediated translation, both in vitro and in vivo. The results indicated that the primary sequence of the stem region of SL IV plays an important role in mediating internal initiation. Furthermore, we have shown that the mutations also altered the ability to bind to ribosomal protein S5 (p25), through which 40 S ribosomal subunit is known to contact the HCV IRES RNA. Interestingly, binding of La protein to SL IV region induced significant changes in the circular dichroism spectra of the HCV RNA indicating conformational alterations that might assist correct positioning of the initiation complex. Finally, the ribosome assembly analysis using sucrose gradient centrifugation implied that the mutations within SL IV of HCV IRES impair the formation of functional ribosomal complexes. These observations strongly support the hypothesis that La protein binding near the initiator AUG facilitates the interactions with ribosomal protein S5 and 48 S ribosomal assembly and influences the formation of functional initiation complex on the HCV IRES RNA to mediate efficient internal initiation of translation

Generating Natural Language Attacks in a Hard Label Black Box Setting

We study an important and challenging task of attacking natural language processing models in a hard label black box setting. We propose a decision-based attack strategy that crafts high quality adversarial examples on text classification and entailment tasks. Our proposed attack strategy leverages population-based optimization algorithm to craft plausible and semantically similar adversarial examples by observing only the top label predicted by the target model. At each iteration, the optimization procedure allow word replacements that maximizes the overall semantic similarity between the original and the adversarial text. Further, our approach does not rely on using substitute models or any kind of training data. We demonstrate the efficacy of our proposed approach through extensive experimentation and ablation studies on five state-of-the-art target models across seven benchmark datasets. In comparison to attacks proposed in prior literature, we are able to achieve a higher success rate with lower word perturbation percentage that too in a highly restricted setting.Comment: Accepted at AAAI 2021 (Main Conference

Esterification of Glycerol with Acetic Acid over Highly Active and Stable Alumina-based Catalysts: A Reaction Kinetics Study

The catalytic activity of Cu- or Ni monometallic and Cu-Ni bimetallic (Cu/Ni ratio = 3, 1, 0.33) catalysts supported on γ-Al2O3 and SO42–/γ-Al2O3 catalysts were evaluated for esterification of glycerol. The reactions were performed in a batch reactor under reflux at standard reaction conditions: temperature 110 °C, atmospheric pressure, glycerol to acetic acid molar ratio 1:9, and catalyst loading 0.25 g. The best catalytic activity was observed over 2 M SO42–/γ-Al2O3 catalyst, which showed the glycerol conversion of 97 % within 2 hours of reaction. At this condition, the selectivity to glyceryl monoacetate (MAG), glyceryl diacetate (DAG), and glyceryl triacetate (TAG) were 27.0 %, 49.9 % and 23.1 %, respectively, after 5 h of reaction. After three consecutive runs, the 2 M SO42–/γ-Al2O3 catalysts showed superior performance and no loss in activity was observed. The reaction kinetics results over 2 M SO42–/γ-Al2O3 catalyst showed that the dependence on the reaction rate to glycerol concentration was of pseudo-second order, while the activation energy was found to be 106 kJ mol–1

Assessment and Enhancement of Power System Security using Soft Computing and Data Mining Approaches

The power system is a complex network with numerous equipment’s interconnected for it’s reliable operation. These power system networks are forced to operate under highly stressed conditions closer to their limits. One of the key objective of the power system operators is to provide safe, economic and reliable power to it’s consumers. However, such network experiences perturbations due to many factors. These perturbations may lead to system collapse or even black out, which impacts the reliability of the system. Thus, one of the major aspect for the secure operation of the system can be achieved through security assessment. In this context, the power system static security assessment is necessary to evaluate the security status under contingency scenario. One of the approach for the security assessment is by contingency ranking, where the severity of a specific contingency is computed and ranked with highest severity to the lowest one. Initially, this approach is implemented using several load flow methods in order to identify the limit violations. However, these approaches are complex, time consuming and not feasible for real time implementation. These approaches are applied to a specific system operating condition. Thus in this context, this thesis focusses to implement soft computing and data mining approaches for security assessment by contingency ranking and classification approach. Along with the security assessment, this thesis also focusses on a control mechanism approach for the security enhancement under contingency scenario using evolutionary computing techniques. In this thesis, the various aspects of the power system security such as it’s assessment, and it’s enhancement are studied. The conventional contingency ranking approach by NRLF method is presented for the security assessment. In order to predict the system severity, a ranking module is designed with two neural network models namely, MFNN and RBFN for security assessment under different load conditions. Both neural network models are quite accurate in predicting the performance indices in less time. Another aspect of power system static security assessment is by classification approach, where the security states are classified into secure, critically secure, insecure and highly insecure. This approach helps in proper security monitoring. Thus, this thesis also presents the design and implementation of two security pattern classifier models namely the decision tree and the random forest classifiers. The classifiers are trained and tested with several security patterns generated in an offline mode. The proposed models are compared with MLP, RBFN and SVM classifier models in order to prove their efficiency in classifying the security levels. Further, this thesis work also focusses on a control mechanism for security enhancement under N-1 line outage contingency scenario. Initially contingency analysis is carried out under N-1 line outage case and critical contingencies are identified. The objective is to reschedule the generators with minimum fuel cost in such a way that the overloaded lines are relieved from stress. In order to enhance the power system security, an evolutionary computing algorithm, namely an enhanced cuckoo search algorithm is proposed for the contingency constrained economic load dispatch. To study the robustness and effectiveness of the proposed algorithm, the results are compared with CS, BA and PSO algorithms

Hepatitis C virus internal ribosome entry site-mediated translation is stimulated by specific interaction of independent regions of human La autoantigen

The human La autoantigen has been shown to interact with the internal ribosome entry site (IRES) of hepatitis C virus (HCV) in vitro. Using a yeast three-hybrid system, we demonstrated that, in addition to full-length La protein, both N- and C-terminal halves were able to interact with HCV IRES in vivo. The exogenous addition of purified full-length and truncated La proteins in rabbit reticulocyte lysate showed dose-dependent stimulation of HCV IRES-mediated translation. However, an additive effect was achieved adding the terminal halves together in the reaction, suggesting that both might play critical roles in achieving full stimulatory activity of the full-length La protein. Using computational analysis, three-dimensional structures of the RNA recognition motifs (RRM) of the La protein were independently modeled. Of the three putative RRMs, RRM2 was predicted to have a good binding pocket for the interaction with the HCV IRES around the GCAC motif near the initiator AUG and RRM3 binds perhaps in a different location. This observation was further investigated by the filter-binding and toe-printing assays. The results presented here strongly suggest that both the N- and C-terminal halves can interact independently with the HCV IRES and are involved in stimulating internal initiation of translation

Pengaruh interval Booster terhadap produksi antibodi pada lele dumbo (Clarias gariepinus) yang divaksin debris aeroromonas hydrophila=The effect of booster intervals on antibody production in African catfish (Clarias gar

Vaccination is the one method to control disease of Motile Aeromonas Septicemia on African catfish (Clarias gariepinus). The aim of this research was to determine the effect of booster intervals using Aeromonas hydrophila debris on immune response of African catfish. The research was constructed as CRD, c.i. 95 %. Firstly, thirty five catfish were reinfected and reisolated (three times) with A. hydrophila. The virulent A. hydrophila was broken by sonication into debris. Thirty catfish were divided into six treated groups, i.e.(1) control [Kj](2) placebo control [K2] with debris vaccination(3) without bosster [Pi](4) once booster [132](5) twice boosters [133](6) three times boosters [N. Booster interval was once a week. Data were taken for ten weeks. The parameter assessed was antibody titer were analyzed by ANOVA and Duncan\u27s Multiple Range Test. The research showed that the highest antibody titer was reached on the 2nd â 3 rd week, and significan differences among all treatments and controls. The vaccination could increase the adaptive response immunity through the increase of antibody titer. Debris vaccine with three times boosters [134] was the most effective vaccination. Key words: Aeromonas hydrophila, antibody, booster, debris

Implementasi dan Analisis Unsupervised Feature Selection pada Artikel Berita Berbahasa Indonesia

ABSTRAKSI: Meningkatnya penggunaan internet telah memicu pertumbuhan dan pertukaran informasi menjadi jauh lebih pesat dibandingkan era sebelumnya. Volume berita elektronik berbahasa Indonesia semakin bertambah besar dan menyimpan informasi yang berharga di dalamnya. Pengelompokkan berita berbahasa Indonesia merupakan salah satu solusi yang dapat digunakan untuk mempermudah mencerna informasi penting yang ada di dalamnya. Clustering dapat digunakan untuk membantu menganalisis berita dengan mengelompokkan secara otomatis berita yang memiliki kesamaanPada text clustering terdapat suatu permasalahan yaitu adanya fitur – fitur yang berdimensi tinggi. Diperlukan metode Feature selection untuk mengurangi dimensi fitur ini. Feature selection memiliki kemampuan mengurangi dimensionalitas suatu data sehingga dapat meningkatkan performansi clustering. Ada beberapa pendekatan sebagai teknik dari implementasi feature selection, salah satunya adalah filter based feature selection.Pada tugas akhir ini, dilakukan analisis perbandingan metode feature selection antara Term contribution dan Document Frequency. Metode-metode feature selection tersebut diterapkan secara filter feature selection. Pada akhir pengujian, dapat dibuktikan bahwa metode Term contribution lebih baik daripada Document Frequency karena memperhitungkan frekuensi kemunculan term pada suatu dokumen dan jumlah dokumen yang dimiliki term tersebut, sehingga term yang terpilih adalah term yang khas atau bersifat diskriminator. Hal ini dapat meningkatkan performansi clustering dokumen berdasarkan precision dan entropy.Kata Kunci : : clustering, filter feature selection, Term contribution, Document Frequency.ABSTRACT: The increasing of internet\u27s using has made the growth and exchanging of informations become higher than before. The volume of Indonesian electronic news become bigger and its save valuable information in it. The grouping of Indonesian news is one of solution which can be used to catch valuable information easier. Clustering can be used to help analizing news by grouping news which have the similarity automaticallyText clustering has a problem, that is high dimension of features. Feature selection\u27s method is needed to reduce this problem. Feature selection has the ability to reduce data dimension so it can improve clustering\u27s performance. There are some approaches as the technique of feature selection\u27s implementation, one of them is filter based feature selecion.On this final project, the analysis of feature selection\u27s method between Document Frequency and Term contribution is done. These methods are implemented by filter feature selection. At the end of testing, can be proved that Term contribution is better than Document Frequency, because it considers term frequency in a document and the amount of document frequency, so the choosen terms are unique or discriminatory. It can improve clustering’s performance with precision and entropy as the points to measure the performanceKeyword: : clustering, filter feature selection, Term contribution, Document Frequency