Miracle-dz/UniFAR-FTUs huggingface dataset:
Isolation of Indigenous Selenium Tolerant Yeast and Investigation of the Relationship Between Growth and Selenium Biotransformation

Purpose: Organic selenium compound such as selenomethionine plays a significant function in the response to oxidative stress. Saccharomyces cerevisiae have the ability to accumulate selenium and selenium biotransformation. Selection of indigenous selenium tolerant yeast is our goals. The relationship between cell growth and selenium biotransformation was also investigated. Methods: The screening of the yeast cell was carried out at two steps in order to select yeast with high capacity for resistance and accumulation of selenium. The isolates were selected according to produced high biomass at different concentrations of selenium. Secondly, best yeast strains from previous step were grown in presence of 25 mg/L of sodium selenite and organic selenium content was measured. Results: The S17 isolate showed had maximum organic selenium accumulation (2515 ppm) and biomass production (2.73 g/L) compared to the other isolates. The biomass production and organic selenium accumulation of the S17 during 120 hours was shown a direct relationship between growth and biotransformation. Conclusion: This increase in organic selenium content was achieved with yeast screening. It is interesting to know that organic selenium has high bioavailability and low toxicity compared with inorganic selenium. Therefore, finding yeast strains which are resistant to selenium can be very helpful in cancer prevention.
Exploring ImageJ toolbox for Oceanography : A case study in Malacca Straits

The oil discharges in marinas and coastal areas has adversely affected the marine environment that has become the driving force for the early detection of oil spills to allow timely intervention. Synthetic Aperture Radar (SAR) data represents an effective tool that is extensively used for the detection and identification of oil spills in the marine environment. This paper aims at exploring the plugins available in ImageJ, a popular public domain Java image processing and analysis program at the National institute of Health (NIH), in the detection of oil spills for oceanography. As the popularity of the ImageJ open-source comes from being extensively used in biomedical and medical image processing applications, this study showed that ImageJ is a good choice for the detection of oil spills in oceanography applications throughout the extensibility capability supported by ImageJ framework.
Primary lymphoma of the female genital system : a report of four cases

Female patients with primary lymphoma of the female genital system (PLFGT) are extremely rare, accounting for only 1-3% of all cases of extra-nodal lymphoma. Given its rarity, there is still no standard consensus regarding the optimal management strategy for this disease. The current study presents four cases of PLFGT. All patients were postmenopausal women admitted to the hospital for pelvic mass or postmenopausal vaginal bleeding, with two of them harboring c-myc and bcl-2/bcl-6 co-expression, termed as “double-expressor” (DE) lymphomas. To the best of our best knowledge, this is the second article describing DE diffuse large B-cell lymphoma (DLBCL) confined to the female genital tract. One of the patients developed an early relapse in the central nervous system within three months. The aim of this study was to spread awareness among clinicians about the rarity of this disease and, especially, the aggressive nature of this neoplasm. Therefore, reporting these new cases may help in understanding clinical characteristics and prognosis of this disease. In a clinical setting, for elderly postmenopausal women complaining abdominal mass, clinicians and pathologists should suspect the possibility of PLFGT, making a more timely and accurate diagnosis.
Benzene from traffic Fuel content and ambient air concentrations

The measurements of benzene showed very clear decreasing trends in the air concentrations and the emissions since 1994. At the same time the measurements of CO and NOx also showed a decreasing trend, but not so strong as for benzene. The general decreasing trend is explained by the increasing number of petrol vehicles with three way catalysts, 60-70% in 1999. The very steep decreasing trend for benzene at the beginning of the period from 1994 was explained by the combination of more catalyst vehicles and reduced benzene content in Danish petrol. The total amount of aromatics in petrol, including toluene, increased only weakly. The analyses of air concentrations were confirmed by analyses of petrol sold in Denmark. The concentration of benzene at Jagtvej in Copenhagen is still in 1998 above the expected new EU limit value, 5 μg/m as annual average. However, the reduced content of benzene in petrol from 1998 and the increasing number of vehicles with catalysts will probably lead to compliance with this limit value. Editing complete: January 2000 ISBN: 87-7772-527-1 ISSN: 0905-815X Paper quality: Cyclus Offset, 100% recycled paper Printed by: Hvidovre Kopi ApS. Number of pages: 44 Circulation: 250 Price: DKK 60,(incl. 25% VAT, excl. freight) For sale at: National Environmental Research Institute P.O. Box 358 Frederiksborgvej 399 DK-4000 Roskilde Denmark Tel: +45 46 30 12 00 Fax: +45 46 30 12 14 Miljøbutikken Information and Books Læderstræde 1 DK-1201 Copenhagen K Denmark Tel. : +45 33 95 40 00 Fax: +45 33 92 76 90 E-mail: butik@mem.dk URL: www.mem.dk/butik
Diamond sources beneath the Hall Peninsula , Baffin Island , Nunavut : an update of the preliminary assessment

The physical description, scanning electron microscope imaging and initial results on nitrogen content and aggregation state (determined by Fourier-transform infrared spectroscopy) of the Chidliak diamonds were presented in CNGO’s 2012 Summary of Activities. Here we provide an update on the diamond residence history based on nitrogen concentration-aggregation relationships, and carbon and nitrogen isotopic characteristics.
Challenges in the Verification of Reinforcement Learning Algorithms

Machine learning (ML) is increasingly being applied to a wide array of domains from search engines to autonomous vehicles. These algorithms, however, are notoriously complex and hard to verify. This work looks at the assumptions underlying machine learning algorithms as well as some of the challenges in trying to verify ML algorithms. Furthermore, we focus on the speciﬁc challenges of verifying reinforcement learning algorithms. These are highlighted using a speciﬁc example. Ultimately, we do not oﬀer a solution to the complex problem of ML veriﬁcation, but point out possible approaches for veriﬁcation and interesting research opportunities.
Analysis of Influential Factors of Members ’ Evaluation of the Cooperatives : Based on the survey of 408 Cooperative Members

This paper analyzes the factors that affect members’ evaluation of the cooperatives using Ordered Probit Model, on the basis of the survey of 408 members in four provinces. The results show that the evaluation of the members with higher education level is relatively high. Members’ evaluation of Cooperatives led and operated by the country able persons and governments is higher than that of those by leading enterprises. The larger the cooperatives’ scale is and the more the members of enterprises and public institutions or social groups are, the higher the evaluation is. Cooperative members’ evaluation is relatively high of those that pay attention to extend industrial chains. The quantity of the service provided by the cooperatives before, during and after production is also one influential factor. The cooperatives’ internal governance also affects their members’ evaluation.
Load Rebalancing with Security for Hadoop File System in Cloud 1

[1]A file system is used for the organization, storage,[1]retrieval, naming, sharing, and protection of files. Distributed file system has certain degrees of transparency to the user and the system such as access transparency,[2] location transparency, failure transparency, heterogeneity, replication transparency etc. [1][3]NFS (Network File System), RFS (Remote File Sharing), Andrew File System (AFS) are examples of Distributed file system. Distributed file systems are generally used for cloud computing applications based on [4] the MapReduce programming model. A MapReduce program consist of a Map () procedure that performs filtering and a Reduce () procedure that performs a summary operation. However, in a cloud computing environment, sometimes failure is occurs and nodes may be upgraded, replaced, and added in the system. Therefore load imbalanced problem arises. To solve this problem, load rebalancing algorithm is implemented in this paper so that central node should not overloaded. The implementation is done in hadoop distributed file system. As apache hadoop is used, security issues are arises. To solve these security issues and to increase security, [20] Kerberos authentication protocol is implemented to handle multiple nodes. This paper shows real time implementation experiment on cluster with result.
Predicting loss reserves using quantile regression

Traditional loss reserves models focus on the mean of the conditional loss distribution. If the factors driving high claims differ systematically from those driving medium to low claims, alternative models that differentiate such differences are required. We propose quantile regression model loss reserving as the model offers potentially different solutions at distinct quantiles so that the effects of risk factors are differentiated at different points of the conditional loss distribution. Due to its nonparametric nature, quantile regression is free of the model assumptions for traditional mean regression models, including homogeneous variance across risk factors and symmetric and light tails, etc. These model assumptions have posed a great barrier in applications as they are often not met in the claim data. Using two sets of run-off triangle claim data from Israel and Queensland, Australia, we present the quantile regression approach that illustrates the sensitivity of claim size to risk factors, namely the trend pattern and initial claim level, in different quantiles. Trained models are applied to predict future claims in the lower run-off triangle. Findings suggest that reliance on standard loss reserves techniques gives rise to misleading inferences and that claim size is not homogeneously driven by the same risk factors across quantiles.
WWC Quick Review of the Report “ Efficacy of a Theory-Based Abstinence-Only Intervention Over 24 Months

Quick reviews assess whether a study’s design is consistent with WWC evidence standards. They are based on the evidence published in the report cited and rely on effect sizes and significance levels as reported by study authors. The WWC rating refers only to the results summarized above and not necessarily to all results presented in the study. The WWC does not confirm study authors’ findings or contact authors for additional information about the study. Eight hours of instruction over two Saturday sessions
A Systematic Review on Entrepreneurial Marketing : Three Decade Research on Entrepreneurial Marketing

Marketing and Entrepreneurship research streams have flourished in the three decade. Entrepreneurial marketing (EM), born out of the practice of firms operating in conditions of uncertainty, is emerging as a powerful alternative to cope with the decreasing effectiveness associated with traditional marketing. In this article, the authors provide a comprehensive review of definition, development, dimension, models and frameworks of EM construct. Also this article offer the theoretical foundations (lens) of entrepreneurial marketing as well as the constructs considered in entrepreneurial marketing research. In total, three type of interest are outlined: First typology of research related to raised and emergent of entrepreneurial marketing construct (include, definition, Interface, difference between EM versus traditional marketing .), Second typology related to development domain of EM construct (include, dimension of EM, models, frameworks, marketing mix, use theoretical lens, levels of abstract, and EM as a formative construct), and finally typology related to EM as a robust construct (include, categories of EM, school of thought, contingency approach, EM as a reflective construct and .).
ANALYZING RADAR-MEASURED RAINFALL VS . RAIN GAUGES IN GIS

Rainfall data are traditionally collected at discrete point locations in space, at meteorological stations (rain gauges). Values at any other point must be interpolated or can be remotely sensed by ground-based radar, which can detect the areal distribution of precipitation at more detailed spatial scale. Nevertheless, radar measurements are affected by various types of errors and the transformation of measured radar reflectivity into rain rates is far from accurate. This study provides a deeper analysis of the influence of topography on radar measured precipitation. By the means of linear regression analysis residuals between 134 rain gauges and corresponding radar estimated rainfalls were calculated, and then studied using residual regression analysis with the following independent variables: altitude, longitude, latitude, aspect, slope, curvature, distance from the radar antenna, aspect perpendicular to the radar beam referred to as directional difference, mean air temperature, and solar radiation. The independent variables were derived from the 90 m SRTM DEM in ArcGIS. A multivariate second order polynomial regression model was developed with three topographic and locational variables as the best predictors: altitude, distance, and latitude, which can explain up to 74% of variance of the residual errors. This means that radar measurement errors are not only a cause of random variation, but can be partially predicted, which may allow for some type of correction and improvement in radar’s accuracy.
Visual Primitives for Abductive Reasoning

Humans organize the world into objects that adhere to specific conceptual rules. For example, most adults would agree that it is impossible for two individual objects to occupy the same physical space at the same time, without one being contained in the other. Similarly, it is impossible for one object to exist in some location B, after existing in another location A, without having traversed a path between the two locations [3]. The ability to quickly learn intuitive theories that organize our surroundings into relatable concepts is a trait that enables humans to build intricate mental models of the world. Such a capacity also allows humans to reason about and explain situations they encounter in the world and is thus a key component of the foundational knowledge we call intuitive physics. It is not surprising then that infants even as young as 4-6 months exhibit the beginnings of physical reasoning [10, 47]. Seminal work in Cognitive Science and Developmental Psychology has shown that infants are able to reason about basic physical phenomena—including object individuation by relative motion [26, 51], object permanence [3], spatiotemporal continuity [48]—despite their limited perceptual capabilities compared to grown adults. Presented with a complex and largely uninterpretable environment, infants seek structure by developing simple physical rules and building upon them. Despite successes in statistical pattern recognition tasks, modern deep learning systems have not yet matched the reasoning power of a 4-6 month old infant. Finding, testing, and revising possible explanations for observations in a given environment—abductive inference—can inform how an agent builds and refines models of the world. Explanation through abduction enables a robot to build such models in a taskagnostic manner, mirroring the world-modeling capabilities of the developing infant. We posit that task-agnostic models of intuitive physics will enable more robust robotic intelligence by bridging the gap between the experiential learning capabilities of model-free algorithms and the explanatory power of model-based learning. An effective physical reasoning system must address two problems. The first problem is transforming raw sensory information into a set of concepts, preferably ones that are compositional. The second problem involves reasoning over those concepts in an interpretable way. The former is a problem broached by representation learning [8], where the goal is to extract visual concepts from an observed sensory input [18, 20]. In this paper we focus on the latter, proposing Fig. 1: Sample frames from the PhysSprite dataset. Top-toBottom: Spatially separated blocks, a single-object occlusion event, a box-and-rod partial occlusion. Left-to-Right: frames progress through time.
Effect of shade and associated environmental conditions resulting from exposure of poultry sheds to two levels of sun light on the performance of two broiler strains .

The objective of the experiment was to test the effect of reducing temperature in poultry sheds resulting from exposure to sun light, on the performance of two broiler strains. It was a 2 (shades) x 2 (broiler hybrids) factorial arranged in a completely randomized design with 3 replicates. Two hybrids (Cobb and Hubbard) were initially reared at normal brooding temperatures for seven days and subsequently transferred to twelve different sheds. Each hybrid was exposed to high shade or low sunlight (308.73 Lux with 28.5°C and 77.74% relative humidity:) and low shade or high sunlight ( 2902.73 Lux with 28.82°C and 80.42% relative humidity). Data were recorded using a random sample of 30 birds from 200 birds in each replicate. Light intensity, temperature and relative humidity were daily recorded while body weights were recorded at weekly intervals. Birds were weighed and slaughtered at 40 days and the FCR and dressing percentages calculated. Irrespective of high or low shade conditions, Cobb hybrid as compared with Hubbard hybrid had significantly superior (P <0.05)final body weights,(1887.2 and 1721.0 for Cobb vs1651.8and 1632.4g for Hubbard) carcass weight (1655.2 and 1502.3 vs 1459.1 and 1256.0 g Iand FCR (1.80 and 1.98 vs 2.11 and 2.05 ) respectively. High shade resulted in superior (P <0.05) body and carcass weights in both hybrids and FCR in Cobb hybrid. Carcass recovery percentages were also higher for both hybrids with high shade conditions(90 vs 84% for Cobb and 92 vs 76% for Hubbard) while it significantly ( P<0.05) increased the dressing percentage only of Hubbard (94.3 vs 68.6%). It can be concluded that Cobb had a better overall performance compared to Hubbard. High compared to low shade which resulted in a lower ambient temperature improved the performance of both strains with a greater effect on the better performing Cobb strain.
The determinants of trust transfer on mobile shopping decision: flow experience as a moderator

This study tended to develop a trust transfer model and examine the effect of trust transference among trust in physical stores, e-service and mobile shopping (m-shopping) service on m-shopping decisions. Characteristics of trustworthiness (ability, integrity and benevolence) were tested as antecedents, and flow experience was adopted as a moderator in the model. 419 valid questionnaires were collected from e-survey. The analytical results indicated that ability, integrity and benevolence increased customer trust in physical stores and e-service, and trust transference existed among different shopping channels; all three types of trust positively influenced m-shopping decision; flow experience significantly moderated the relationship between trust in physical stores and m-shopping decisions. The study suggested that managers should equally value all the shopping channels and improve its e-service quality by providing an aesthetic, secure and usable website, and dedicating their efforts to environmentally-friendly and community activities to show corporate social responsibility (CSR).
ELIISA MANNERMAA In vitro Model of Retinal Pigment Epithelium for Use in Drug Delivery

The posterior location and the blood-retinal barrier (BRB) make drug delivery in diseases affecting retina and vitreous challenging. The outer part of BRB is composed of retinal pigment epithelium (RPE), which restricts drug entry to the retina from the systemic circulation and from the periocular space. In this study, filter grown ARPE-19 cells have been characterized as a potential in vitro model of the human RPE for use in drug delivery studies. ARPE-19 barrier properties were evaluated in different culture conditions. The ARPE-19 model was 3-17 times more permeable than isolated bovine RPE-choroid tissue, but the ARPE-19 model efficiently separated test compounds based on their lipophilicity and molecular sizes. Expression of RPE related genes, RPE65, CRALBP, TRP1, tyrosinase and Mitf-A and OTX2 transcription factors was greatly enhanced by filter culture. It has become evident that transporters play an important role in pharmacokinetics. The expression of efflux proteins, p-glycoprotein (P-gp), multidrug resistance associated proteins 1-6 (MRP) and breast cancer related protein (BCRP), in various RPE cell lines was studied. As with primary RPE cells, ARPE-19 cells express MRP1, MRP4 and MRP5 efflux proteins. Efflux protein activity was evaluated in cellular uptake studies using calcein-AM and carboxydichorofluorescein as probe molecules, and by bi-directional permeability studies. The studies indicate MRP1 and MRP5 activity in ARPE-19 cell line. Active transport in the ARPE-19 cell model was qualitatively, though not quantitatively similar, with isolated RPE-choroid tissue. Furthermore, non-viral gene transfer was studied in the ARPE-19 cell model. Prolonged gene expression was achieved by liposomal carriers. In conclusion, the ARPE-19 cell model can be used to screen drug molecules with different physicochemical properties and in gene delivery studies. The greater passive permeability may lead to an underestimation of active transport in this model. Several membrane transporters are expressed and active in the ARPE-19 cell model. National Library of Medicine Classification: QS 525, QU 55.7, QW 38, QZ 52, WW 103 Medical Subject Headings: Blood-Retinal Barrier; Cell Culture Techniques; Drug Delivery Systems; Gene Therapy; Gene Transfer Techniques; Membrane Transport Proteins; Pharmacokinetics; Retinal Pigment Epithelium
Environment Generalization in Deep Reinforcement Learning

A key challenge in deep reinforcement learning (RL) is environment generalization: a policy trained to solve a task in one environment often fails to solve the same task in a slightly different test environment. A common approach to improve inter-environment transfer is to learn policies that are invariant to the distribution of environments. However, we argue that instead of being invariant, the policy should identify the specific nuances of an environment and exploit them to achieve better performance. In this work, we propose the “Environment-Probing” Interaction (EPI) policy, which allows the agent to probe a new environment to extract an implicit understanding of that environment’s behavior. Once this environment-specific information is obtained, it is used as an additional input to a task-specific policy that can now perform environment-conditioned actions to solve a task. To learn these EPI-policies, we present a reward function based on transition predictability. Specifically, a higher reward is given if the trajectory generated by the EPI-policy can be used to better predict transitions. We experimentally show that EPI-conditioned task-specific policies significantly outperform commonly used environment generalization methods on novel testing environments.