A bacteriological study of the natural flora of edible oyster, Crassostrea madrasensis

The total viable bacterial populations in the oysters and the sea water from the edible oyster farm at Tuticorin were in the range of 10 super(3) to 10 super(4) per ml and 1 super(2) to 10 super(3) per ml respectively. The maximum most probable number of faecal coliform recorded during the one year period of study of both the oysters and seawater were 33 per 100 ml. Pathogenic bacteria (Salmonella sp., Vibrio cholerae, coagulase positive staphylococci and faecal streptococci were absent in oysters and farm water. Study of 197 (98 taken from oyster liquid and 99 from oyster farm water) randomly isolated cultures indicated that gram negative asporogenus rod-like bacteria of the Vibrio, Flavobacterium, Achromobacter and Pseudomonas groups were the dominant flora of the oyster liquid as well as seawater.

Bacteria from seawater used in Penaeus monodon larval cultures

Bacteria in the seawater used in P. monodon hatchery operations were isolated on Bachmann's agar. The total plate counts in 25 isolations ranged from 1.0 - 5.0 x 10² to 5.1 -10.0 x 10⁵ cells per ml. Out of 124 isolates, 98 (79 percent) were Gram-positive and 26 (21 percent) were Gram-negative. Micrococcus and Staphylococcus were dominant in the former group, while Acinetobacter, Moraxella, Flavobacterium and Alcaligenes were most numerous in the latter. Twenty-nine of the Gram-positive isolates closely resembled Peptostreptococcus, Planococcus, and Pediococcus.

Organoleptic and microbiological quality changes of catla (Catla catla) in immediate and delayed ice storage and at ambient temperature

An investigation was carried out on the quality changes of Catla (Catla calla) stored immediately (0 h) in ice, after 6 hours in ice and at ambient temperature. The samples were examined for organoleptic and microbiological parameters in summer. Organoleptically, the acceptability of fish varied between 16-20 days in both the iced storage conditions and 12-13.5 hours at ambient temperature (28°C). When fish were organoleptically just acceptable on the 16th day of storage, bacterial load were 6.23 and 6.17 log10 cfu/g, respectively for 0 hour and after 6 hours iced fish. But on the 20th day of storage, when fish were just unacceptable SPC were 6.51 and 6.62 log10 cfu/g. In case of ambient temperature storage condition standard plate count was 8.36 log10 cfu/g on 13.5 hours, when fish were organoleptically just unacceptable. At the time of rejection for fish stored in ice (0 hour and after 6 hours) on 20th day, gram negative and gram positive values were 55.45%, 44.55% and 44.52%, 55.48% respectively. While fish were rejected after 13.5 hours at ambient temperature gram negative and gram positive bacteria were found as 43.02% and 56.98%. The differences in SPC, gram positive and gram negative bacteria between the storage times were statistically significant (p<0.05).

Application of whey protein incorporation with Cinnamon oil as a preservative in Huse huso fillet under chilled storage condition

Biodegradable protein-based film was developed by incorporating cinnamon essential oil (CEO) into whey protein concentrate (WPC) at level of 0.8% and 1.5% v/v. Then physical and mechanical properties of the films were evaluated. Adding CEO to the WPC matrix decreased the water vapour permeability of the films and water solubility. Films containing CEO showed significant antibacterial activity both gram-positive and gram-negative strains and exhibited significant inhibitory effect on the studied fungi. In continue, the effect of whey coating and whey coating incorporated with 1.5% CEO on quality and shelf life of Huso huso fillet during refregrated (4±1°C) storage period were also investigated. The control and treated fish samples were analyzed for microbiological (total viable count, psychrophilic counts), chemical (PV, TBA, FFA, pH, TVB-N), and sensory characteristics in 4-day intervals up of microbial, chmical and sensoy analyses indicated lower levels of PV, TBA, FFA, pH, TVB-N in coasted sampels and specially, those with CEO while were kept in refrigerator. Based on results, whey protein edible coating contain 1.5% cinnamon essential oil could enhance preserving ability Huso huso during storage cold.

Gross clinical signs and haematological changes associated with artifical infection of Edwardsiella tarda in Koi Carp

The occurrence of diseases is a significant setback for successful aquafarming. One of the common fish bacterial disease syndromes, Edwardsiellosis is caused by Edwardsiella tarda, a gram-negative, rod shaped bacterium associated with several diseases of marine and fresh water fish. In this study, an attempt was made to observe and analyze the onset of clinical symptoms and certain haematological parameters in Koi Carp, Cyprinus carpio L., following artificial infection with Edwardsiella tarda. The disease progress was observed and the clinical symptoms were monitored over a period of 15 days following infection. Fish were sampled at three day intervals to analyse the haematological parameters: total erythrocyte counts (RBC), total leucocyte counts (WBC), haemoglobin content and differential leucocyte count. Clinical symptoms observed included: erratic swimming behaviour, loss of appetite, haemorrhages, dropsy and exophthalmia. There was a significant decrease in the total RBC and haemoglobin levels by the 3rd and 6th day post infection, and an increase thereafter. WBC counts were higher in all infected groups in comparison to the control group. A significant increase in the number of neutrophils was found in the infected group up to the 9th day and a decrease thereafter. The lymphocyte number was significantly less up to the 12th day while the monocyte counts were significantly higher up to the 12th day post infection. The results showed that the bacterium, E. tarda, is pathogenic to Koi Carp. The hematological changes and clinical signs in infected fish reported in this paper will be helpful in the identification and the control of this infection.

Isolating and recognizing crudeoiklegrading [sic] bacteria of Queshm Island shores for biodegradation of crude oil pollutions

This research was carried out for recognizing Natural Flora Bacteria of oil pollution in the coasts of Queshm island. In The First steps, The coasts of this Island were scrutinized as a Field of research and For knowing whether oil stains exist or not. It gets obvious That southern coasts of Queshm have got oil pollution which is created by oil tankers which carry oil of Iran continental shelf. Them oil stains were sampled from to certain stations. In The First step, primary isolation of exisiting bacteria in every oil sample was done and then purification of each bacterium was carried out. Then each purified bacterium that has got strong, recognized, typic growth was enriched oil sample of T5 station. And Bacterium C4 (gram—negative coccobacillus) was chosen as the second priority From oil sample of TA station and Bacterium B1 (gram—positive coccus) was chosen as The third priority From oil sample of TI station. All The above mentioned bacteria were biochemically, physiologically and morphologically experimented For specking The species. According To The tests done and comparing with The tests done and comparing with the reference Berge y' s, bacterium A5 Pelongs to the species pseudomonas sp and becterium C4 belongs to the species Aeromonas sp and bacterium BI belongs to The species micrococcus sp. In The Last stage, bacterium with The First priority (TA5 pseudomonas sp) was used in the planned microcosm. The sake of optimum and adapting to Laboratory conditions Each enriched and purified bacterium was given a code for station and a code For itself . Then This bacterium was studied and it was proved that it has potentiality For using oil as a source of carbon. From oil samples of 10 stations, 30 various Colonies of bacterium were Isolated, of which 20 bacteria had the highest potentiality of growth. And the other bacteria that has no typic growth were omitted From being studied. Since all of These 20 bacterium are able to use oil, a bacterium with maximum rate of growth in the presence of crude oil and Lack of other hydrocarbonic sources and with The code A5 ( gram — negative Bacillus ) was chosen as First priority From The mentioned microcosm contains sea water , suspension oil degrading bacterium , crude oil, azote and various concentrations of carbon and Incubated in 30°` and shook 150 PRA1 According to the results , index oil degrading bacterium (pseudomonas sp) belongs oil sample of T5 stations (east of sheeb draz Gulf) which growth best and have the potentiality of degrading oil in 25 glli malas and 50 glli cheese water and with 5 gill urea .

Meiothermus rosaceus sp nov isolated from Tengchong hot spring in Yunnan, China

A rosy-pigmented Gram-negative, thermophilic bacterium with an optimum growth temperature of about 55degreesC was isolated from Tengchong hot springs in Yunnan province, China. Its growth scarcely occurred below 40degreesC or above 70degreesC. Phylogenetic and secondary structural analyses of 16S rRNA and DNA-DNA hybridization showed that the organism represented a new species of the genus Meiothermus. This new species could be distinguished easily from other species of the genus Meiothermus by the following phenotypic characteristics: rosy pigment, expanded body, sucrose and maltose were not utilized, gelatin and starch were not hydrolyzed. On the basis of the above data, the name Meiothermus rosaceus sp. nov. was proposed for the species represented by the strain RH9901(T)(CCTCC-AB200291). (C) 2002 Federation of European Microbiological Societies. Published by Elsevier Science B.V. All rights reserved.

Morphological decomposition in Arabic ASR systems

In recent years, the use of morphological decomposition strategies for Arabic Automatic Speech Recognition (ASR) has become increasingly popular. Systems trained on morphologically decomposed data are often used in combination with standard word-based approaches, and they have been found to yield consistent performance improvements. The present article contributes to this ongoing research endeavour by exploring the use of the 'Morphological Analysis and Disambiguation for Arabic' (MADA) tools for this purpose. System integration issues concerning language modelling and dictionary construction, as well as the estimation of pronunciation probabilities, are discussed. In particular, a novel solution for morpheme-to-word conversion is presented which makes use of an N-gram Statistical Machine Translation (SMT) approach. System performance is investigated within a multi-pass adaptation/combination framework. All the systems described in this paper are evaluated on an Arabic large vocabulary speech recognition task which includes both Broadcast News and Broadcast Conversation test data. It is shown that the use of MADA-based systems, in combination with word-based systems, can reduce the Word Error Rates by up to 8.1 relative. © 2012 Elsevier Ltd. All rights reserved.

Graphone model interpolation and Arabic pronunciation generation

This paper extends n-gram graphone model pronunciation generation to use a mixture of such models. This technique is useful when pronunciation data is for a specific variant (or set of variants) of a language, such as for a dialect, and only a small amount of pronunciation dictionary training data for that specific variant is available. The performance of the interpolated n-gram graphone model is evaluated on Arabic phonetic pronunciation generation for words that can't be handled by the Buckwalter Morphological Analyser. The pronunciations produced are also used to train an Arabic broadcast audio speech recognition system. In both cases the interpolated graphone model leads to improved performance. Copyright © 2011 ISCA.

Improving LVCSR system combination using neural network language model cross adaptation

State-of-the-art large vocabulary continuous speech recognition (LVCSR) systems often combine outputs from multiple subsystems developed at different sites. Cross system adaptation can be used as an alternative to direct hypothesis level combination schemes such as ROVER. The standard approach involves only cross adapting acoustic models. To fully exploit the complimentary features among sub-systems, language model (LM) cross adaptation techniques can be used. Previous research on multi-level n-gram LM cross adaptation is extended to further include the cross adaptation of neural network LMs in this paper. Using this improved LM cross adaptation framework, significant error rate gains of 4.0%-7.1% relative were obtained over acoustic model only cross adaptation when combining a range of Chinese LVCSR sub-systems used in the 2010 and 2011 DARPA GALE evaluations. Copyright © 2011 ISCA.

Syllable language models for Mandarin speech recognition: exploiting character language models.

Mandarin Chinese is based on characters which are syllabic in nature and morphological in meaning. All spoken languages have syllabiotactic rules which govern the construction of syllables and their allowed sequences. These constraints are not as restrictive as those learned from word sequences, but they can provide additional useful linguistic information. Hence, it is possible to improve speech recognition performance by appropriately combining these two types of constraints. For the Chinese language considered in this paper, character level language models (LMs) can be used as a first level approximation to allowed syllable sequences. To test this idea, word and character level n-gram LMs were trained on 2.8 billion words (equivalent to 4.3 billion characters) of texts from a wide collection of text sources. Both hypothesis and model based combination techniques were investigated to combine word and character level LMs. Significant character error rate reductions up to 7.3% relative were obtained on a state-of-the-art Mandarin Chinese broadcast audio recognition task using an adapted history dependent multi-level LM that performs a log-linearly combination of character and word level LMs. This supports the hypothesis that character or syllable sequence models are useful for improving Mandarin speech recognition performance.

Discriminative spoken language understanding using word confusion networks

Current commercial dialogue systems typically use hand-crafted grammars for Spoken Language Understanding (SLU) operating on the top one or two hypotheses output by the speech recogniser. These systems are expensive to develop and they suffer from significant degradation in performance when faced with recognition errors. This paper presents a robust method for SLU based on features extracted from the full posterior distribution of recognition hypotheses encoded in the form of word confusion networks. Following [1], the system uses SVM classifiers operating on n-gram features, trained on unaligned input/output pairs. Performance is evaluated on both an off-line corpus and on-line in a live user trial. It is shown that a statistical discriminative approach to SLU operating on the full posterior ASR output distribution can substantially improve performance both in terms of accuracy and overall dialogue reward. Furthermore, additional gains can be obtained by incorporating features from the previous system output. © 2012 IEEE.

Americium nuclear battery for space power applications

The work presents simplified242mAm fueled nuclear battery concept design featuring direct fission products energy conversion and passive heat rejection. The performed calculations of power conversion efficiency under thermal and nuclear design constraints showed that 14 W/kg power density can be achieved, which corresponds to conversion efficiency of about 6%. Total power of the battery scales linearly with its surface area. 144 kW of electric power can be produced by a nuclear battery with an external radius of about 174 cm and total mass of less than 10300 kg. The mass of242m Am fuel for such a system is 3200 gram.

Paraphrastic language models

In natural languages multiple word sequences can represent the same underlying meaning. Only modelling the observed surface word sequence can result in poor context coverage, for example, when using n-gram language models (LM). To handle this issue, this paper presents a novel form of language model, the paraphrastic LM. A phrase level transduction model that is statistically learned from standard text data is used to generate paraphrase variants. LM probabilities are then estimated by maximizing their marginal probability. Significant error rate reductions of 0.5%-0.6% absolute were obtained on a state-ofthe-art conversational telephone speech recognition task using a paraphrastic multi-level LM modelling both word and phrase sequences.

Paraphrastic language models and combination with neural network language models

In natural languages multiple word sequences can represent the same underlying meaning. Only modelling the observed surface word sequence can result in poor context coverage, for example, when using n-gram language models (LM). To handle this issue, paraphrastic LMs were proposed in previous research and successfully applied to a US English conversational telephone speech transcription task. In order to exploit the complementary characteristics of paraphrastic LMs and neural network LMs (NNLM), the combination between the two is investigated in this paper. To investigate paraphrastic LMs' generalization ability to other languages, experiments are conducted on a Mandarin Chinese broadcast speech transcription task. Using a paraphrastic multi-level LM modelling both word and phrase sequences, significant error rate reductions of 0.9% absolute (9% relative) and 0.5% absolute (5% relative) were obtained over the baseline n-gram and NNLM systems respectively, after a combination with word and phrase level NNLMs. © 2013 IEEE.