Effect of antioxidants on post thaw microscopic, oxidative stress parameter and fertility of Boer goat spermatozoa in Tris egg yolk glycerol extender.

This study was conducted to determine the effect of antioxidants on standard semen parameters, lipid peroxidation and fertility of Boer goat semen after cryopreservation. Ejaculates from four bucks were collected, evaluated and pooled at 37. °C. The pooled semen was diluted with Tris citric acid fructose for washing. Semen samples, which were diluted with a Tris-based extender containing the antioxidant ascorbic acid (8.5. mg/ml), butylated hydroxytoluene (2. mM), cysteine (5. mM) and hypotaurine (10. mM) and an extender without antioxidant supplementation were cooled to 4. °C and frozen in 0.25 straws with programmable freezer and finally stored in liquid nitrogen. Data (10 replicates) were analyzed by one-way analysis of variance. Mean (±SEM) progressive motility was significantly higher in ascorbic acid than other supplement groups and control samples (P> 0.05). Best values were observed in ascorbic acid followed by BHT, cysteine, and hypotaurine. Antioxidant supplementation in extender showed significant (P< 0.05) better values than the control group for sperm membrane integrity, acrosome integrity and viability. The ability of antioxidants to reduce the lipid peroxidation (LPO) after freeze thawing was measured by the formation of malondialdehyde (MDA) using the thiobarbituric acid method. Results showed that addition of antioxidants significantly reduced the rate of LPO in comparison to control (P< 0.05). Ascorbic acid exhibited better values (1.27 ± 0.28), than butylated hydroxytoluene, cysteine and hypotaurine 1.32 ± 0.42, 2.27 ± 0.16 and 2.38 ± 0.17 respectively, which are significantly better than control (3.52 ± 0.54). Higher pregnancy rate was observed with ascorbic acid followed by butylated hydroxtolune, hypotaurine and cysteine. However, differences in the fertility rate were non-significant with hypotaurine, cysteine and control group

Effect of hypotaurine and cysteine on sperm cytological parameters of cooled and post thaw boer goat semen.

The purpose of this study was to determine the influence of antioxidant additives (hypotaurine and cysteine) in different concentrations to the cryopreserving media on the semen cytological parameters pre freezing and post thawing (motility, membrane integrity, morphology, acrosome integrity and viability).The experiment was done on 30 ejaculates collected by artificial vagina method from 5 boer goat bucks during April to May 2011. After collection, ejaculates qualifying standard criteria were pooled. Pooled ejaculates were washed for seminal plasma removal and then diluted in medium based on Tris in which antioxidants were added in various concentrations (hypotaurine 5, 10 and 20mM; cysteine 5, 10 and 20mM) or without antioxidants (control). The diluted semen was cooled at 4ºC, filled in 0.25ml French straws and then stored in liquid nitrogen. The results showed that semen quality did not differ (P < 0.05) in terms of morphology and acrosome integrity with antioxidants supplementation after cooling. Hypotaurine and cysteine significantly improved the characteristics of boer goat semen motility, membrane integrity, morphology, acrosome integrity and viability after cryopreservation. Addition of hypotaurine at 10mM and cysteine at 5mM concentration leads maximum improvement in liquid and frozen boer goat sperm cytological characteristics

Effect of ascorbic acid concentrations, methods of cooling and freezing on boer goat semen cryopreservation.

To improve the Boer goat semen quality during cryopreservation process, three experiments were carried out to investigate the effect of (i) different concentration of ascorbic acid supplementation (ii) rate of cooling with chilled semen characteristics and (iii) method of freezing on post-thaw Boer goat sperm using Tris-based extender. Ascorbic acid at 8.5mg/ml improved the sperm parameters (motility, integrity of membrane and acrosome, morphology and viability), compared to control in cooled samples (p<0.05). With regard to other concentrations and post-thawed parameters, ascorbic acid at 2.5-8.5mg/ml led to higher percentages of sperm motility and integrities of membrane and acrosome when compared to control (p<0.05). Slow cooling rises to higher percentages of sperm motility, acrosome integrity and viability, in comparison with fast cooling, in terms of cooled and frozen samples (p<0.05). Programmable freezing method produced the higher percentages of sperm motility, integrities of membrane and acrosome and viability when compared to the freezing method of polystyrene box during goat sperm freezing (p<0.05). In conclusion, chilled and post-thawed sperm quality of Boer goat was improved when a Tris-based extender supplemented with ascorbic acid was used at stages of different cooling rates and freezing methods. © 2012 Blackwell Verlag GmbH

Population Density of Grey Francolin (Franclinus Pondicrianus L.) in District Tando Allahyar, Sindh, Pakistan

The population density of Grey Francolin (Franclinus Pondicrianus L.) is distributed throughout the Baluchistan, KKPK, Punjab, and Indus plains of Sindh Province, Pakistan. The grey francolin is a prime game bird of our country. This species has been declared threatened worldwide according to the Red Data Book, also published by the International Union for Conservation of Nature in 2018. There is no information available regarding the density of the population of Grey francolin in different populations in the district of Tando Allahyar, Sindh, Pakistan. Keeping this in mind, the present study was carried out to find the density population of Grey francolin birds in the habitat area to observe the conservation measurements. The study was conducted through direct sighting with the help of local residents of the particular areas using the Visual Encounter Method. The observations were recorded at three fixed transects, with a length of 300 to 350 m and a width of 30 to 60 m laid down at every site for recording the birds by nearest line. In Tando Allahyar, Sindh, Pakistan, the population density of Grey francolin varies significantly based on breeding practices and habitat characteristics. The observed densities were 0.90 birds per hectare in cultivated open land and 0.16 birds per hectare in wetland and associated natural vegetation. This suggests a notable influence of habitat and breeding practices on Grey francolin population

Milepost GCC: Machine Learning Enabled Self-tuning Compiler

International audienceTuning compiler optimizations for rapidly evolving hardwaremakes porting and extending an optimizing compiler for each new platform extremely challenging. Iterative optimization is a popular approach to adapting programs to a new architecture automatically using feedback-directed compilation. However, the large number of evaluations required for each program has prevented iterative compilation from widespread take-up in production compilers. Machine learning has been proposed to tune optimizations across programs systematically but is currently limited to a few transformations, long training phases and critically lacks publicly released, stable tools. Our approach is to develop a modular, extensible, self-tuning optimization infrastructure to automatically learn the best optimizations across multiple programs and architectures based on the correlation between program features, run-time behavior and optimizations. In this paper we describeMilepostGCC, the first publicly-available open-source machine learning-based compiler. It consists of an Interactive Compilation Interface (ICI) and plugins to extract program features and exchange optimization data with the cTuning.org open public repository. It automatically adapts the internal optimization heuristic at function-level granularity to improve execution time, code size and compilation time of a new program on a given architecture. Part of the MILEPOST technology together with low-level ICI-inspired plugin framework is now included in the mainline GCC.We developed machine learning plugins based on probabilistic and transductive approaches to predict good combinations of optimizations. Our preliminary experimental results show that it is possible to automatically reduce the execution time of individual MiBench programs, some by more than a factor of 2, while also improving compilation time and code size. On average we are able to reduce the execution time of the MiBench benchmark suite by 11% for the ARC reconfigurable processor.We also present a realistic multi-objective optimization scenario for Berkeley DB library using Milepost GCC and improve execution time by approximately 17%, while reducing compilatio

Crowdtuning : towards practical and reproducible auto-tuning via crowdsourcing and predictive analytics

Tuning general compiler optimization heuristics or optimizing software for rapidly evolving hardware has become intolerably complex, ad-hoc, time consuming and error prone due to enormous number of available design and optimization choices, complex interactions between all software and hardware components, and multiple strict requirements placed on performance, power consumption, size, reliability and cost. Iterative feedback-directed compilation, auto-tuning and machine learning have been showing a high potential to solve above problems. For example, we successfully used them to enable the world's first machine learning based self-tuning compiler, Milepost GCC, which automatically learns the best optimizations across multiple programs, data sets and architectures based on static and dynamic program features. Unfortunately, its practical use was very limited by very long training times and lack of representative benchmarks and data sets. Furthermore, "black box" machine learning models alone could not get full insight into correlations between features and best optimizations. In this thesis, we present the first to our knowledge methodology and framework, called Collective Mind (cM), to let the community share various benchmarks, data sets, compilers, tools and other artifacts while formalizing and crowdsourcing optimization and learning in reproducible way across many users (platforms). Our open-source framework and public optimization repository helps make auto-tuning and machine learning practical. Furthermore, cM let the community validate optimization results, share unexpected run-time behavior or model mispredictions, provide useful feedback for improvement, customize common auto-tuning and learning modules, improve predictive models and find missing features. Our analysis and evaluation of the proposed framework demonstrates that it can effectively expose, isolate and collaboratively identify the key features that contribute to the model prediction accuracy. At the same time, formalization of auto-tuning and machine learning allows us to continuously apply standard complexity reduction techniques to leave a minimal set of influential optimizations and relevant features as well as truly representative benchmarks and data sets. We released most of the experimental results, benchmarks and data sets at http://c-mind.org while validating our techniques in the EU FP6 MILEPOST project and during HiPEAC internship at STMicroelectronics.Le réglage des heuristiques d'optimisation de compilateur pour de multiples cibles ou implémentations d’une même architecture est devenu complexe. De plus, ce problème est généralement traité de façon ad-hoc et consomme beaucoup de temps sans être nécessairement reproductible. Enfin, des erreurs de choix de paramétrage d’heuristiques sont fréquentes en raison du grand nombre de possibilités d’optimisation et des interactions complexes entre tous les composants matériels et logiciels. La prise en compte de multiples exigences, comme la performance, la consommation d'énergie, la taille de code, la fiabilité et le coût, peut aussi nécessiter la gestion de plusieurs solutions candidates. La compilation itérative avec profil d’exécution (profiling feedback), le réglage automatique (auto tuning) et l'apprentissage automatique ont montré un grand potentiel pour résoudre ces problèmes. Par exemple, nous les avons utilisés avec succès pour concevoir le premier compilateur qui utilise l'apprentissage pour l'optimisation automatique de code. Il s'agit du compilateur Milepost GCC, qui apprend automatiquement les meilleures optimisations pour plusieurs programmes, données et architectures en se basant sur les caractéristiques statiques et dynamiques du programme. Malheureusement, son utilisation en pratique, a été très limitée par le temps d'apprentissage très long et le manque de benchmarks et de données représentatives. De plus, les modèles d'apprentissage «boîte noire» ne pouvaient pas représenter de façon pertinente les corrélations entre les caractéristiques des programme ou architectures et les meilleures optimisations. Dans cette thèse, nous présentons une nouvelle méthodologie et un nouvel écosystème d’outils(framework) sous la nomination Collective Mind (cM). L’objectif est de permettre à la communauté de partager les différents benchmarks, données d’entrée, compilateurs, outils et autres objets tout en formalisant et facilitant la contribution participative aux boucles d’apprentissage. Une contrainte est la reproductibilité des expérimentations pour l’ensemble des utilisateurs et plateformes. Notre cadre de travail open-source et notre dépôt (repository) public permettent de rendre le réglage automatique et l'apprentissage d’optimisations praticable. De plus, cM permet à la communauté de valider les résultats, les comportements inattendus et les modèles conduisant à de mauvaises prédictions. cM permet aussi de fournir des informations utiles pour l'amélioration et la personnalisation des modules de réglage automatique et d'apprentissage ainsi que pour l'amélioration des modèles de prévision et l'identification des éléments manquants. Notre analyse et évaluation du cadre de travail proposé montre qu'il peut effectivement exposer, isoler et identifier de façon collaborative les principales caractéristiques qui contribuent à la précision de la prédiction du modèle. En même temps, la formalisation du réglage automatique et de l'apprentissage nous permettent d'appliquer en permanence des techniques standards de réduction de complexité. Ceci permet de se contenter d'un ensemble minimal d'optimisations pertinentes ainsi que de benchmarks et de données d’entrée réellement représentatifs. Nous avons publié la plupart des résultats expérimentaux, des benchmarks et des données d’entrée à l'adresse http://c-mind.org tout en validant nos techniques dans le projet EU FP6 Milepost et durant un stage de thèse HiPEAC avec STMicroelectronics

Crowdtuning: systematizing auto-tuning using predictive modeling and crowdsourcing

International audienceSoftware and hardware co-design and optimization of HPC systems has become intolerably complex, ad-hoc, time consuming and error prone due to enormous number of available design and optimization choices, complex interactions between all software and hardware components, and multiple strict requirements placed on performance, power consumption, size, reliability and cost. We present our novel long-term holistic and practical solution to this problem based on customizable, plugin-based, schema-free, heterogeneous, open-source Collective Mind repository and infrastructure with unified web interfaces and on-line advise system. This collaborative framework distributes analysis and multi-objective off-line and on-line auto-tuning of computer systems among many participants while utilizing any available smart phone, tablet, laptop, cluster or data center, and continuously observing, classifying and modeling their realistic behavior. Any unexpected behavior is analyzed using shared data mining and predictive modeling plugins or exposed to the community at cTuning.org for collaborative explanation, top-down complexity reduction, incremental problem decomposition and detection of correlating program, architecture or run-time properties (features). Gradually increasing optimization knowledge helps to continuously improve optimization heuristics of any compiler, predict optimizations for new programs or suggest efficient run-time (online) tuning and adaptation strategies depending on end-user requirements. We decided to share all our past research artifacts including hundreds of codelets, numerical applications, data sets, models, universal experimental analysis and auto-tuning pipelines, self-tuning machine learning based meta compiler, and unified statistical analysis and machine learning plugins in a public repository to initiate systematic, reproducible and collaborative R\&D with a new publication model where experiments and techniques are validated, ranked and improved by the community

HiPEAC Internship report: Machine Learning for Compilation and Architecture

This is a report about HiPEAC-funded internship at STMicroelectronics to systematize optimization and co-design of computer systems using machine learning

Allelopathic Impact of Sorghum and Sunflower on Germinability and Seedling Growth of Cotton (Gossypium hirsutum L.)

Sorghum and sunflower are considered as highly allelopathic plants with inhibitory efficacy on plants of other species. In a pot study, the phytotoxic potential of sorghum and sunflower shoot and root on germination and seedling growth of cotton was evaluated through soil incorporation of powders and spray of water extracts. The experiment was conducted at | department of Agronomy, Sindh Agriculture University Tandojam during Kharif (summer) 2010 and 2011. The analysis of pooled data suggested that all the powders and water extracts of both allelopathic crops caused substantial suppression of germination and related traits of cotton seedlings as compared to control (untreated). Sorghum shoot powder (10 g kg-1 soil) caused highest allelopathic effects and reduced cotton seed germination by 12.8%, root length by 45.4%, shoot length by 51.9%, fresh weight seedling-1 by 41.7% and dry weight seedling-1 by 36.7%, followed by sunflower shoot powder (10 g kg-1 soil) in phytotoxic efficiency for inhibiting seed germination, seedling growth and weight in contrast to control (untreated). Sorghum showed superiority over sunflower in allelopathic efficiency. Powder of both crops was found more allelopathic in contrast to water extract. Among plant parts phytotoxic potential, shoot proved higher in inhibitory effect than root. However, it was concluded from the results of present study that both sorghum and sunflower possess allelopathic compounds with growth suppressing ability which could be utilized for effective weed management in cotton under field conditions as eco-friendly low-cost alternate of herbicides with wise strategy