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Industrial Design education is poorly understood by laypeople but is present in Australian curricula from primary through to tertiary education levels. Designers and design researchers have long recognised the value of the broad-ranging skills, knowledge fields, and personal qualities design education imparts, but this understanding is generally not shared by the wider community who may see design as surface decoration. This research identifies indicators of value and relevance taken from the twenty-first century competences literature, then measures their presence in four different Industrial Design education settings. Two studies were undertaken. First, Industrial Design educators from primary, secondary, and tertiary levels were surveyed. Then diverse Industrial Design education stakeholders from education and non-education settings were interviewed. The studies gathered both quantitative and qualitative data on the value and relevance of current Industrial Design education in Australia. The result is a comprehensive analysis of the twenty-first century competences present in Australian Industrial Design education, which concludes with recommendations for ways Industrial Design education can benefit twenty-first century learners, as well as ways it should evolve to remain relevant.

The research shows that the blockchain has a wide range of healthcare applications including electronic medical records, drug and pharmaceutical supply chain management, biomedical research and education, remote patient monitoring, health data analysis, among others. A number of blockchain-based healthcare applications have been developed as examples based on emerging blockchain paradigms, such as smart contracts, etc. In this research, a health chain system is introduced. HealthChain is an online portal that maintains a digital copy of a patient's medical history, along with all his/her diagnoses, prescriptions, and medical receipts.

Background: Superficial fungal infections are one of the commonest human infections. Causative agents of such infections may vary from yeasts like Candida species, Trichosporon species to dermatophytes and non-dermatophyte moulds. Fungal culture therefore, holds importance in identification and characterization of a fungal isolate, so that proper diagnosis can be made and correct treatment is instituted. Our objective was to study the etiology of the superficial fungal infections in patients presenting to the dermatology department in a tertiary care hospital in New Delhi. Materials and Methods: A total of 340 skin and hair samples from patients clinically suspected to have superficial fungal infection of skin and hair were microscopically examined and cultured over a period of 2 years. The percentage and frequency distribution of etiological fungal agents was studied. Also the performance of the culture and microscopy as methods of detecting fungal agents was statistically compared using Kappa and proportions of positive and negative agreement as well as McNemar’s Chi-squared value. Corresponding p-values were also calculated for both kappa and Chi-squared values. The analysis has been done using Epitools. Results: Of these, 57.6% were positive for fungal elements by microscopic examination and the overall positivity of fungal infection was 70%. Out of 238 culture positive samples, 72.7% grew dermatophytes and 27.3% grew non-dermatophytes (including 16.8% yeasts and 10.9% non-dermatophyte moulds. Trichophyton mentagrophytes was the commonest (60.7%) dermatophyte isolated, followed by T. rubrum (17.3%), T. violaceum (7.5%), T. tonsurans (7.5%), T. verucosum (2.9%), Microsporum gypseum (1.2%) and M. canis (0.6%). Among the isolated non-dermatophytes, Candida species was the commonest (50.8%) majority of which were C. albicans , other non-dermatophytes included moulds like Fusarium spp. (6.1%), Aspergillus fumigatus (4.6%), A. flavus (3.1%), Alternaria spp. (3.1%), Acremonium spp. (3.1%), A. niger (3.1%) etc. and yeasts like Trichosporon spp. (10.8%). Conclusion: Dermatophytosis still remains the most common type of fungal infection involving skin and its appendages but non-dermatophytes are also slowly emerging as the causative agents for these infections.

Background: Staphylococcus aureus (S. aureus) is a well recognized nosocomial and community acquired pathogen which is implicated in causing a wide spectrum of superficial, deep pyogenic infections and toxin mediated illnesses. Localized infections sometimes progress to systemic infections, while ‘spontaneous’ bacteraemia also occur without an evident septic focus, particularly in chronic debilitated immunocompromised patients. Emergence of drug resistance to penicillins and penicillinase-resistant penicillins (i.e., oxacillin and methicillin) is a globally recognized problem. Methicillin Resistant Staphylococcus aureus isolates are also important with respect to the range of nosocomial infections it causes, leading to an increase in the hospital expenditure and mortality or morbidity rate. The increased prevalence of such resistant strains has narrowed down the list of available therapeutic options. Therefore, information regarding the prevalence of S. aureus infections in a health care setting and determining its current antibiotic resistance profile becomes crucial in selecting appropriate treatment regime. Therefore, the current study was done in the department of Microbiology to determine the prevalence of S. aureus infections and the antibiotic resistance pattern of S. aureus isolates from various clinical specimens at our tertiary care hospital in North India. Materials and Methods: A one year prospective study was carried out in the Department of Microbiology, at a tertiary care hospital in North India where non-duplicate strains of S. aureus isolated from various clinical specimens received in the Microbiology laboratory were studied. All S. aureus isolates were subjected to Antibiotic Susceptibility Testing using Kirby Bauer’s disk diffusion method on Mueller Hinton Agar plates in accordance to CLSI guidelines. The antibiotics tested included Penicillin (10U), Amoxicillin-clavulanic acid (20/10μg), Sulphamethoxazole-trimethoprim (1.25/23.75μg), Ciprofloxacin (5μg), Erythromycin (15μg), Clindamycin (2μg), Vancomycin (30μg), Teicoplanin (30μg) and Linezolid (30μg). Results: A total of 23,699 clinical specimens were processed in the laboratory while conducting this study, from which 1233 clinical isolates of S. aureus were identified and processed further. Among all clinical specimens, pyogenic samples (63.1%) yielded maximum number of S. aureus strains followed by blood samples (29.9%) and urine samples (4.8%). S. aureus infection was more evident in hospitalized 71.2% patients than in OPD patients 28.8%. Seasonal variation was also seen in isolation of S. aureus , with a higher percentage of isolates obtained during summer season than during winter season. On antibiotic susceptibility testing, 49.6% strains were Methicillin Resistant. Majority of the isolates were found resistant to Penicillin (92.1%), followed by Erythromycin (59%). Almost half of the total isolates were resistant to Sulphamethoxazole-Trimethoprim (49.3%) followed by Amoxicillin- Clavulanic acid (47.8%), Ciprofloxacin (43.4%) and Clindamycin (18.4%). Antibiotics to which all isolates showed 100% susceptibility included Vancomycin, Teicoplanin, Linezolid. Conclusion: Given the high prevalence of resistance to antibiotics seen in this study, effective treatment of infections caused by multidrug resistant Staphylococcal strains may become challenging. Drugs like Vancomycin, Teicoplanin and Linezolid promise to work as miracle drugs against the multidrug resistant MRSA strains but we need to warrant judicious use of these wonder drugs to conserve them for future use.

Water quality parameters of Holy River Ganga was analyzed at two major sites, namely, Triveni Ghat (Rishikesh) and Har ki Pauri (Haridwar) situated at Uttarakhand (India) between March 2016 and Feb...

Investigations were conducted to evaluate the adsorption of copper ions from artificially stimulated wastewater onto Egg Shells (ES), Spent Tea Leaves (STL) and their biochars. Batch studies on ES and STL were carried out at varying pH (3–8), contact time (30–150 min), solid:liquid ratio (0.25:25–2:25) and initial copper concentration (5–20 ppm). On the other hand, batch studies with biochar as adsorbent were conducted at contact time of 180 min, pH 6, solid:liquid ratio of 1:50 and 20 ppm of initial copper concentration. Kinetics and isotherm studies were taken place for evaluating the potential of ES and STL for adsorption of copper ions from artificially stimulated wastewater. Copper removal by ES and STL was 84.5% (adsorption capacity 422.5 mg/g) and 91% (adsorption capacity 447.5 mg/g) under the optimum conditions respectively. EDX-SEM analysis of ES and STL indicated that the particles were present on ES as well as STL after adsorption process. Elemental analysis showed 1.26% and 0.89% by weight were present due to copper adsorption onto ES and STL respectively. STL biochar yielded the best adsorption amongst all with more than 99% of copper removal. Its EDX-SEM analysis showed that there was the presence of particles at the surface of STL biocahr after adsorption process having 0.59% weight due to copper adsorption. Adsorption isotherm studies on ES and STL revealed multilayer adsorption with Freundlich isotherm. Study on adsorption kinetics for ES and STL revealed that ES followed pseudo-first-order kinetics while as STL followed pseudo-second-order kinetics.

A sulfidogenic reactor was established employing biological sulfate reduction process for treating industrial wastewater, Acid Mine Drainage (AMD) and leachate produced from E-waste landfill sites containing heavy metals. Heavy metal sulfide precipitation was carried out by using sulfide rich effluent from sulfidogenic reactor as well as through chemical precipitation using Na2S. A methanogenic Continuously Stirred Tank Reactor (CSTR) operating on synthetic jaggery wastewater was converted into sulfidogenic reactor by gradually increasing S O 4 - - concentration in the influent. The process steps involved (i) Establishing and operating methanogenic reactor (ii) Converting of methanogenic reactor into sulfidogenic reactor and (iii) Utilizing sulfide rich effluent for heavy metal precipitation. CSTR methanogenic reactor was first established and operated at loading rate of 0.5 kg COD/m3d. COD removal of 80% was achieved. Volatile fatty acid (VFA) in the reactor was 1900–1950 mg/l with biogas production of 1–1.2 l/d. Subsequently, methanogenic reactor was converted into a sulfidogenic reactor by gradually increasing COD: S O 4 - - from 20:1 to 5:1. Sulfate removal efficiency of 80% demonstrated that methanogenic population in the reactor was successfully replaced by Sulfate Reducing Bacteria (SRB) population. Sulfate in the influent was converted into sulfide under anaerobic conditions prevailing in sulfidogenic reactor and sulfide rich effluent from the reactor was then used to precipitate Copper from wastewater. Copper removal efficiency upto 60% was achieved by biogenic sulfide precipitation as compared to 90% by chemical precipitation.

This paper demonstrates machine learning based approach to retrieve soil moisture (SM) and its validation over India using CYGNSS data. CYGNSS mission is mainly designed and dedicated for monitoring the tropical cyclones over ocean.However, recent developments has highlighted the potential of GNSS-Reflectometry for land applications, specially for SM with high spatio-temporal frequency over traditional satellite data sets. It can be directly utilized to retrieve SM as complementary data to fill the spatial and temporal gaps in satellite microwave radiometer derived SM, like from SMAP and SMOS mission to meet the requirements of high spatial and temporal frequency data sets for agricultural applications. In this work, we developed an Artificial Neural Network (ANN) framework to derive SM and validated at selected agricultural sites over India. SMAP derived vegetation and roughness parameters were also used as inputs for training of ANN model to add the effect of vegetation and roughness. Detailed spatial and temporal correlation analyses of CYGNSS SM were performed to test the proposed ANN model using SMAP SM and in-situ observations from hydra probe station data from 2018 to 2019. It was observed from temporal correlation analysis that CYGNSS and SMAP SM follow a good trend with high correlation using in-situ data. Spatial correlation also shows high correlation with Pearson correlation coefficient of 0.69 and RMSD of 0.057 m3/m3during pre-monsoon and 0.65 and 0.053 m3/m3in post monsoon periods, respectively.

The ongoing Coronavirus Disease-2019 (COVID-19) pandemic caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) can cause illnesses that vary from asymptomatic or paucisymptomatic conditions to Acute Respiratory Distress Syndrome (ARDS) or respiratory failure requiring ventilator support or sepsis and multiple organ dysfunction syndromes. Timely treatment and appropriate management of COVID-19 diseased patients is crucial to curb the morbidity and mortality rates. Much of the research is focused on formulating preventive and therapeutic strategies based on the complex pathophysiology of this disease. New drugs targeting viral entry into host cells, inhibiting viral Proteases, viral RNA Polymerases (RdRp), transport channels, and the drugs with multicentric action, immunomodulators and adjuvants are being researched upon. Other management modalities like regenerative medicine and the use of convalescent plasma therapy have been explored. This review is aimed at the therapeutic aspects of the treatment and management modalities.

Polyhydroxyalkanoates (PHAs) are the biopolymer of choice if we look for a substitute of petroleum-based non-biodegradable plastics. Microbial production of PHAs as carbon reserves has been studied for decades and PHAs are gaining attention for a wide range of applications in various fields. Still, their uneconomical production is the major concern largely attributed to high cost of organic substrates for PHA producing heterotrophic bacteria. Therefore, microalgae/cyanobacteria, being photoautotrophic, prove to have an edge over heterotrophic bacteria. They have minimal metabolic requirements, such as inorganic nutrients (CO2, N, P, etc.) and light, and they can survive under adverse environmental conditions. PHA production under photoautotrophic conditions has been reported from cyanobacteria, the only candidate among prokaryotes, and few of the eukaryotic microalgae. However, an efficient cultivation system is still required for photoautotrophic PHA production to overcome the limitations associated with (1) stringent management of closed photobioreactors and (2) optimization of monoculture in open pond culture. Thus, a hybrid system is a necessity, involving the participation of microalgae/cyanobacteria and bacteria, i.e., both photoautotrophic and heterotrophic components having mutual interactive benefits for each other under different cultivation regime, e.g., mixotrophic, successive two modules, consortium based, etc. Along with this, further strategies like optimization of culture conditions (N, P, light exposure, CO2 dynamics, etc.), bioengineering, efficient downstream processes, and the application of mathematical/network modeling of metabolic pathways to improve PHA production are the key areas discussed here. Conclusively, this review aims to critically analyze cyanobacteria as PHA producers and proposes economically sustainable production of PHA from microbial autotrophs and heterotrophs in “hybrid biological system.”

The real-world datasets in many domains like medical, intrusion detection, fraud transactions and bioinformatics are highly imbalanced. In classification problems, imbalanced datasets negatively affect the accuracy of class predictions. This skewness can be handled either by oversampling minority class examples or by undersampling majority class. In this paper, a new undersampling approach, namely Neighbours on Line (NOL) has been proposed to improve class distribution. The approach performed better than the existing Condensed Nearest Neighbour approach of undersampling.

With e-commerce becoming mainstream and a manifold increase in online transactions, security risks associated with these have become crucial concerns. In this chapter, we focus on the security issues arising out of online credit card usage. Literature in the last two and half decades has been reviewed to analyze the changing attack vectors and solution approaches to this problem. Most common attributes and open datasets of credit card transactions have been compiled to provide a starting point for new researchers. Existing fraud detection methods have been scrutinized for efficacy in addressing key challenges of fraud detection like real-time detection, concept drift, imbalanced datasets, and classifier adaptability. New directions in credit card fraud detection research have also been proposed.

This paper demonstrates the relationship of surface radar reflectivity derived from Cyclone Global Navigation Satellite System (CYGNSS) measurements with the Soil Moisture (SM) derived from Soil Moisture Active Passive (SMAP) over India. CYGNSS is an operational eight-satellite constellation, each with a Delay Doppler Mapping Instrument (DDMI) for deriving wind speed in tropical cyclones over oceans. However, these reflections from the land surface, acquired by the DDMI instrument, can also be used to retrieve Soil Moisture (SM). Surface radar reflectivity was derived from Delay Doppler Map (DDM) at specular points generated by CYGNSS measurements as coherent specular reflections over land using radar range equations. From the analysis carried out for selected data sets, a strong linear relationship between CYGNSS derived radar reflectivity and SMAP derived SM has been demonstrated.

Real-world datasets in many domains like medical, intrusion detection, fraud transactions and bioinformatics are highly imbalanced. In classification problems, imbalanced datasets negatively affect the accuracy of class predictions. This skewness can be handled either by oversampling minority class examples or by undersampling majority class. In this work, popular methods of both categories have been evaluated for their capability of improving the imbalanced ratio of five highly imbalanced datasets from different application domains. Effect of balancing on classification results has been also investigated. It has been observed that adaptive synthetic oversampling approach can best improve the imbalance ratio as well as classification results. However, undersampling approaches gave better overall performance on all datasets.

Credit card transactions have become common place today and so is the frauds associated with it. One of the most common modus operandi to carry out fraud is to obtain the card information illegally and use it to make online purchases. For credit card companies and merchants, it is in-feasible to detect these fraudulent transactions among thousands of normal transactions. If sufficient data is collected and made available, machine learning algorithms can be applied to solve this problem. In this work, popular supervised and unsupervised machine learning algorithms have been applied to detect credit card frauds in a highly imbalanced dataset. It was found that unsupervised machine learning algorithms can handle the skewness and give best classification results.

The present study integrates the technology acceptance model (TAM) and the innovation diffusion theory (IDT) for analysing consumers’ intention to make use of Paytm services. It is a cross-sectional study; the data was gathered from 259 Internet consumers. The statistical tools, including structural equation modelling (SEM), are applied to understand the inter-construct relationships. The intention to use Paytm is positively influenced by a perceived ease of use, perceived usefulness and social influence. Moreover, the moderating effects of perceived risk (PR) are found to be significant on the associations among social influence, perceived ease of use and Paytm usage intentions. This study will help digital wallet service providers (DWSP), who focus on identifying the important factors for accelerating the use of mobile wallets. The findings will also facilitate the government to devise ways for augmenting cashless fund movement in the economy. Besides explaining the phenomenon of Paytm usage intentions, the study reveals the moderating role of PR with respect to various antecedents, thereby filling the gap in the existing literature.

Background: Onychomycosis is the fungal infec­tion of nail of which the incidence varies from 0.5-12% in India and around 5% worldwide.Onychomycosis is considered to be gender and age-related disease, being commoner in males and older adults in both genders. Apart from dermatophytes, other fungal agents like yeasts and non-dermatophyte moulds are increasingly being acknowledged as important etiological agents for the same. Our aim was to study the etiology of the nail infections in patients presenting to a dermatology department in a tertiary care hospital in central Delhi.Methods: This study included nail samples from patients of various age groups with suspected onychomycosis attending dermatology clinics in our hospital over a period of 2 years. For all samples, KOH wet mount microscopic examination followed by culture on Sabouraud’s dextrose agar was done. Growth of filamentous fungi and yeast obtained on SDA were identified using conventional microbiology techniques. Statistical analysis was performed using Epitools.Results: Of the total 1061 nail samples received, maximum prevalence seen in patients of age group 21 to 50 years. The positivity of onychomycosis was 57.02%. Out of culture positive samples, 24.62% grew dermatophytes and 75.37% grew non-dermatophytes (31.40% yeasts and 43.97% non-dermatophyte moulds). Trichophyton mentagrophytes was the most common (59.73%) dermatophyte followed by T. rubrum (23.49%) while among non-dermatophyte moulds; Aspergillus niger (20.68%) was the most common followed by A. flavus (16.54%) and others. Common yeasts included Candida spp. (65.79%) and Trichosporon spp. (28.95%).Conclusions: Non- dermatophytes are emerging as major etiological agents of onychomycosis which may be accounted various host factors. So, it becomes important to correctly identify the causative fungi to ensure appropriate treatment.

Type 2 diabetes (T2D) is a wide-spread, chronic metabolic disorder, affecting millions of people worldwide. The epidemic of diabetes has placed a huge strain on public health, longevity and economy. T2D occurs as a result of both genetic and environmental factors and is heterogeneous in its presentation across individuals. This review gives an overview of the genetic variations identified by genome-wide association studies which predispose individuals to T2D and those which are responsible for variable drug response across patients, and the necessity to adopt a personalized approach to diabetes management. We also include a perspective on diabetes in Arabs, given the high incidence of T2D and consanguineous marriages, and the need to understand associated genetic components in this vulnerable population.

Gram-negative, rod-shaped bacterial strain ptl-2T, was isolated from soil of the Himalayan valley, India. The bacterial strain ptl-2T has been characterized using a polyphasic taxonomic approach including morphological characterization, fatty acid analysis, biochemical tests, 16S rRNA and nifH gene sequence analysis. 16S rRNA gene sequence analysis showed that the strain ptl-2T belonged to the genus Pseudacidovorax and is closely related to Pseudacidovorax intermedius (99.3 % similarity). It showed

Gram-negative, free-living bacterial strain ptl-3T was isolated from Himalayan valley soil, India. Polyphasic taxonomy was performed including morphological characterization, fatty acid analysis, biochemical tests, 16S rRNA and nifH gene sequence analyses. 16S rRNA gene sequence analysis showed that the strain ptl-3T belonged to the genus Azospirillum and was closely related to A. brasilense (98.7 % similarity) and A. rugosum (97 % similarity). 16S rRNA gene sequence similarity (96–95 %) was shown with other members of the genus Azospirillum. Major fatty acid 18:1ω7c was also similar to the genus Azospirillum. DNA–DNA relatedness value between strain ptl-3T and A. brasilense was found to be 47 %. Various biochemical tests showed that the strain ptl-3T differed from its closely related species A. brasilense. On the basis of phenotypic, chemotaxonomic and molecular genetics evidence, a bacterium with the type strain ptl-3T is proposed as a novel species of the genus Azospirillum. The name of bacterial strain ptl-3T has been proposed as Azospirillum himalayense sp. nov. The type strain of ptl-3T (CCUG 58760T, KCTC 23189T) has been submitted to two culture collection centres. The accession numbers for 16S rRNA and nifH gene are GQ 284588 and GQ 249665. respectively.

Food production is affected by rise in population, infertile soil, and water scarcity. Wheat and legumes are the major crops globally, but often encounter reduced growth due to various chemical and environmental stresses. The 5-enolpyruvylshikimate-3-phosphate (EPSP) synthase is a key enzyme in the biosynthetic pathway of aromatic amino acid in plants and microbes. Glyphosate is a broad spectrum, non-selective herbicide, which acts as an inhibitor of EPSP. In contrast, reactive oxygen species (ROS) generated by hydrogen peroxide (H2O2) in soil is thought to provide growth stimulation in plants. It has been postulated that the oxidation of glyphosate may occur through ROS generated by H2O2 that acts as electron acceptor and may work via the modulation of EPSP and its degradation. Therefore, in this work in silico molecular docking studies were conducted showing blockade in the active cavity of EPSP by H2O2 that prevented the binding of glyphosate. The key amino acid residues of EPSP involved in the binding...

Repeat expansion has been implicated in 10 out of 17 candidate genes identified for autosomal dominant cerebellar ataxias (ADCAs)—commonly referred as spinocerebellar ataxias (SCAs). Though genetically distinct, the SCAs share a large number of features that confound their clinical classification. In addition, there is a difference in the prevalence and phenotypic expression of ataxias between different ethnic groups. We have created a new SCA-locus-specific variation database (LSVD) that aims to catalog and integrate information on SCAs associated with trinucleotide repeat expansion (SCA1, SCA 2, SCA 3, SCA 6, SCA 7, SCA 8, SCA 12, SCA 17, Friedreich's ataxia [FRDA], and dentatorubral-pallidoluysian atrophy [DRPLA]) from all over the world. The database has been developed using the Leiden Open (source) Variation Database (LOVD) software (Leiden University Medical Center, Leiden, the Netherlands). The database houses detailed information on clinical features, such as age and symptom at onset, mode of inheritance, and genotype information, pertaining to the SCA patients from more than 400 families across India. All the compiled genotype data conforms to the HGVS Nomenclature guidelines. This would be a very useful starting point for understanding the molecular correlates of phenotypes in ataxia—a multilocus disease in which related molecular mechanisms converge to overlapping phenotypes. The database is accessible online at http://miracle.igib.res.in/ataxia. Hum Mutat 30:1–6, 2009. © 2009 Wiley-Liss, Inc.

Background: Fungal infections are an emerging group of infections particularly affecting immunocompromised individuals and those with indwelling medical devices. Spectrum of fungal pathogens is wide which include yeasts like Candida and Cryptococcus and filamentous fungi like Aspergillus and Zygomycetes. Materials and methods: A retrospective study was conducted over a period of five years in the department of Microbiology in a tertiary care hospital in North India. Various clinical samples were collected from the patients presenting with clinically suspected fungal infections. Direct microscopy with potassium hydroxide (KOH) was done to visualize the presence of fungal elements and Gram staining was done for any suspected yeast infection. Fungal cultures of all samples were inoculated on Sabourauds dextrose agar (SDA). Results: A total of 5724 clinical samples with suspected fungal etiology were received in the Microbiology laboratory during the study period. From a total of 689 isolates which came positive, Candida sp. was the most common isolate accounting for 520 (75.4 %), followed by Aspergillus species 110 (15.9 %), Mucor species 34 (4.9 %), Cryptococcus species 9 (1.3 %), Penicillium species 8 (1.2 %), Dermatophytes 4 (0.6 %), Fusarium sp. 3 (0.4%) and Acremonium sp. 1 (0.1%). Conclusions: Fungal infections share a good amount in the burden of increased morbidity and mortality in the diseased. Regular surveillance studies are important to determine the prevalence of different fungal infections in various centers.

Spinocerebellar ataxias (SCAs) are often caused by expansions of CTG/ CAG trinucleotide repeat in the genome. Expansions at the SCA1, 2 and 3 loci are the most frequent, but differences in their relative proportion in regions occur across the world. We carried out this study to assess the occurrence of SCA1, 2 and 3, at a tertiary neuro-psychiatric center in Bangalore, Karnataka.Probands (N=318) who were diagnosed to have an ataxia syndrome (progressive degenerative ataxia of unknown cause) attending the clinical services of the National Institute of Mental Health and Neuro Sciences (NIMHANS), Bangalore, were evaluated over a period of three years. Standard protocols were used for both clinical and molecular diagnosis.Genotyping established that SCA1, 2 and 3 accounted for more than one third of the ataxia cases seen in the clinic. In the cases with established family history and autosomal dominant inheritance SCA1 was most prevalent followed by SCA2 and SCA3.Our findings suggested SCA1 rather than SCA2 to be the more common mutation in southern India. Large numbers of SCA3 probands were also identified. Differences in prevalence of these syndromes within India need to be explored further for founder effects, correlations with phenotype, and patterns of outcome. Family history was not apparent in almost a fifth of those tested positive, highlighting the value of testing even in the absence of family history. Molecular testing should be extended to cover the other forms of ataxia, of which a large number are now known. Combined efforts to confirm the presence of these less common forms, as well as family studies to detect novel mutations, are necessary in this context in India.