Channel-Aware Pretraining of Joint Encoder-Decoder Self-Supervised Model for Telephonic-Speech ASR

This paper proposes a novel technique to obtain better downstream ASR performance from a joint encoder-decoder self-supervised model when trained with speech pooled from two different channels (narrow and wide band). The joint encoder-decoder self-supervised model extends the HuBERT model with a Transformer decoder. HuBERT performs clustering of features and predicts the class of every input frame. In simple pooling, which is our baseline, there is no way to identify the channel information. To incorporate channel information, we have proposed non-overlapping cluster IDs for speech from different channels. Our method gives a relative improvement of ~4% over the joint encoder-decoder self-supervised model built with simple pooling of data, which serves as our baseline.Comment: 5 pages, 5 figure

Statin therapy and Vitamin D

Background: Statins are well-known drugs used in dyslipidemia and cardiac disorders since several years. Recently, it has been reported that long-term use of statins reduce serum vitamin D level. When statins are administered to patients with low vitamin D more muscular side effects have been reported. On the contrary, a few studies report that statins might increase vitamin D level competing with its metabolism. Hence, this study was conducted to evaluate the association between statins and vitamin D.Methods: 125 participants who fulfilled the selection criteria were enrolled in the study. 65 subjects belonged to control group and 60, statin group. The blood sample was collected for Vitamin D estimation. The results were correlated with a demographic profile, nature of statin and the muscular side effects and compared with control group.Results: The mean vitamin D level in statin group was 15.82 ng/ml±11.51 and 20.57 ng/ml±7.007 in the control group. The difference was found to be statistically significant.  13.85% in the control group and 10% in statin group had sufficient vitamin D level. 18.33% and 36.92 % had insufficient levels and 71.67% and 49.23% had a deficiency in the statin and control groups respectively.  Myalgia was reported by 30 among 60 subjects (50%) in statin group and 5 among 65 subjects (7.69%) in the control group.Conclusion: The present study has shown that statin therapy is associated with low vitamin D level and that this could contribute to the increased incidence of myalgia in the statin group

An observational clinico etiological study of Kushta with specific reference to Tinea corporis

Background: Skin diseases are among the most common health problems worldwide and are associated with considerable burden. In Ayurveda classics, skin disorders are termed in general as Kushta and are caused with involvement of Vata, Pitta, Shleshma and Krimi. Among the diversified etiological factors Krimi has a great importance. So, here an attempt is made to evaluate the clinical types, to isolate the common fungal species, etiological factors and to identify probable risk factors of Krimijanya Kushta in the present population. Objectives: To evaluate the clinical types, etiological factors and to identify probable risk factors of Krimijanya Kushta with special reference to Tinea Corporis and to isolate the prevalent causative fungal species. Materials and Methods: A total of 30 patients clinically diagnosed with tinea corporis and fulfilling the inclusion criteria were taken for the study. Results: Among 31 patients studied percentage of growth of different species of dermatophytes in culture study are given as follows, 48.4% are Trichophyton, 6.5% were Trichosporon, 3.2% were Microsporum Gypseum, 19.4% were Epidermophyton,16.1% were Candida, 3.2% were Aspergillus, 3.2% were found no growth. Conclusion: Aharaja and Viharaja Nidana mentioned in Kushta Nidana as well as Krimi Nidana were found to have Madhura, Amla, Lavana Rasa, Guru Guna, Vidahi, and Kledakaraka property which cause Swedaavarodha and is crucial for Kushta Utpathi as well as Krimi Utpatti. The prevalent causative fungal species found in the study were Trichophyton, Epidermophyton, Aspergillus, Trichosporon, Microsporum gypseum. Clinically to approach Kushta, better to be done through culture study

Antinociceptive effect of methanolic extract of Murraya koenigii leaves in swiss albino mice

Background: The objective of the study was to evaluate anti-nociceptive effect of methanolic extract of Murraya koenigii leaves on thermal and mechanical pain in swiss albino mice.Methods: Thirty adult male swiss albino mice weighing 25-30 grams were selected and allocated in to five groups. Each group consists of six animals. The control group received vehicle (10 ml/kg), standard group received morphine (10 mg/kg) and test groups received dried methanolic extract of Murraya koenigii leaves (100 mg/kg, 200 mg/kg, 400 mg/kg per oral respectively) 1 hour before placing the animal over the hot plate at temperature of 55⁰C . A cut off period of 10 sec was observed to avoid damage of the paw. The response in the form of withdrawal of paws or licking of the paws. The delay in the reaction time denotes analgesic activity. The latency was recorded before and after 15, 30, 60, 120 minutes administration of drug. After washout period of 1 month the same group of animals were utilized to evaluate the analgesic effect by tail clip method for better comparison.Results: All the doses of Murraya koenigii leaves significantly delayed reaction time in hot plate method and tail clip method. The results were comparable to that produced by standard drug morphine.Conclusions: Murraya koenigii leaves has analgesic activity which was comparable to morphine

Study to assess the prevalence of human leukocyte antigen-A*3101 allele among Indian epileptic patients and its influence on safety and efficacy of antiepileptic therapy

Background: The objective was to study the prevalence of human leukocyte antigen (HLA)-A*3101 allele among epileptic patients and to assess the safety and efficacy of antiepileptic therapy.Methods: 295 subjects were selected and divided into two groups, Group I had 192 epileptic patients and Group II had 103 normal healthy controls. After written informed consent, 30 ml of mouthwash sample was collected from each subject and DNA was extracted by standard salting-out technique and used for HLA-A*3101 genotyping by two-step nested allele-specific polymerase chain reaction amplification and agarose gel electrophoresis.Results: In Group I, 12 (6.25%) of the 192 patients were tested positive for HLA-A*3101 allele and all were taking carbamazepine (CBZ). Among them, 56 (30%) subjects had developed less severe adverse effects such as headache and giddiness, skin rashes and memory disturbances, and HLA-A*3101 was present in 8 of them while 136 had no adverse effects in which 4 of them were tested positive for the allele. In Group II, 3 (2.9%) of the 103 healthy controls were tested positive for the allele. No difference was found in response to antiepileptic therapy between allele positive and negative patients.Conclusion: The present study had shown that HLA-A*3101 is prevalent in 6.25% of the Indian epileptic population under study. The presence of this allele has a significant association with the development of mild cutaneous reactions like skin rashes. However, no difference was observed in allele positive patients in response to antiepileptic therapy in comparison with allele negative patients

COSMECEUTICALSâ€: AN OPINION IN THE DIRECTION OF PHARMACEUTICALS

 Cosmeceuticals are the latest addition to the health industry and are described as cosmetic products with drug-like activities. Cosmetics are productsthat are used to cleanse and beautify the skin (Millikan, 2001). Pharmaceuticals are essentially drug products and are defined as products that prevent,mitigate, treat or cure disease and/or affect the structure or function of the body (Vermeer and Gilchrest, 1996). Cosmeceuticals is a deliberateportmanteau of these two terms and is intended to connote drug-like benefits from an otherwise cosmetic product. While the food, drug, and cosmeticact does not recognize the term cosmeceutical,†the cosmetic industry has begun to use this word to refer to cosmetic products that have drug-likebenefits. The term cosmeceutical was coined by Kilgman, but these lines of product became popular in 1996 and have an expanding market that hasrapidly reached Africa. Many scientists and health consumers in Africa may not be conversant with this line of products. They may, therefore, be underresearchedor over-utilized. In the cosmetic arena, many materials are used commercially. Cosmetic ingredients previously considered inert†havethe potential to provide a biologic effect to the skin. In a cosmeceutical formulation, the boundary between an active†and inert†ingredient may beobscured. There is most common names of the different ingredients used in cosmeceutical products such as antioxidants, the binding agent, emollients,emulsions, humectants, lubricants, preservatives, solvents, surfactants, vehicle, etc. Potential for cosmeceutical ingredients in the United States aloneis SI00 million and includes such products as skin peelers, wrinkle creams, emollients, hair growth stimulants, skin lighteners and darkeners, andbotanicals. The 75 million baby boomers are the major market for cosmeceuticals. Cosmeceuticals claims are largely unsubstantiated and the term,though misleading, has probably come to stay. The term and the target consumers appear flamboyant enough to with stand Government regulations.In a free trade world, the benefits and adverse effects of cosmeceuticals are probably optimized by frequent review to inform the clinical and publicstake-holders of their uses and limitations. This comprehensive review attempts to briefly, expand the recent knowledge about cosmeceuticals.Keywords: Cosmetics, Formulation/stability, Safety testing, Claim substantiation

NEIGHBORHOOD-BASED APPROACH OF COLLABORATIVE FILTERING TECHNIQUES FOR BOOK RECOMMENDATION SYSTEM

Recommendation System or Recommender System help the user to predict the "rating" or "preference" a user would give to an item. Recommender systems in general helps the users to find content, products, or services (such as digital products, books, music, movie, TV programs, and web sites) by combining and analyzing suggestions from other users, which mean rating from various people, and users. These recommendation systems use analytic technology to calculate the results that a user is willing to purchase, and the users will receive recommendations to a product of their interest. The aim of the System is to provide a recommendation based on users likes or reviews or ratings. Recommendation system comprises of content based and collaborative based filtering techniques. In this paper, collaborative based filtering has been used to get the expected outcome. The expected outcome has been achieved through collaborative filtering with the help of correlation techniques which in turn comprises of Pearson correlation, cosine similarity, Kendall’ s Tau correlation, Jaccard similarity, Spearman Rank Correlation, Mean-squared distance, etc. This paper tells about which similarity metrics such us Pearson correlation (PC), constrained Pearson correlation (CPC), spearman rank correlation (SRC) which is good in the context of book recommendation system and then applied with neighborhood algorithm

NEIGHBORHOOD-BASED APPROACH OF COLLABORATIVE FILTERING TECHNIQUES FOR BOOK RECOMMENDATION SYSTEM

Recommendation System or Recommender System help the user to predict the "rating" or "preference" a user would give to an item. Recommender systems in general helps the users to find content, products, or services (such as digital products, books, music, movie, TV programs, and web sites) by combining and analyzing suggestions from other users, which mean rating from various people, and users. These recommendation systems use analytic technology to calculate the results that a user is willing to purchase, and the users will receive recommendations to a product of their interest. The aim of the System is to provide a recommendation based on users likes or reviews or ratings. Recommendation system comprises of content based and collaborative based filtering techniques. In this paper, collaborative based filtering has been used to get the expected outcome. The expected outcome has been achieved through collaborative filtering with the help of correlation techniques which in turn comprises of Pearson correlation, cosine similarity, Kendall’ s Tau correlation, Jaccard similarity, Spearman Rank Correlation, Mean-squared distance, etc. This paper tells about which similarity metrics such us Pearson correlation (PC), constrained Pearson correlation (CPC), spearman rank correlation (SRC) which is good in the context of book recommendation system and then applied with neighborhood algorithm