Molecular characterization of Iranian Dracocephalum (Lamiaceae) species based on RAPD data

Taxonomic relationship and genetic diversity among 17 accessions belonging to six Iranian Dracocephalum L. species and one accession of Lallemantia as closely related genus was analyzed using RAPD markers. Forty RAPD markers were used and only twelve of them gave reproducible polymorphic bands among the accessions studied. In total 262 bands were produced out of which 10 bands were monomorphic and 252 bands were polymorphic. Among the taxa investigated Dracocephalum kotschyi (Damavand population) showed the highest number of RAPD bands (144), while D. multicaule (Zanjan population) showed the lowest number (95). UPGMA cluster analysis showed efficacy of RAPD data to differentiate the species at molecular level. Dracocephalum polychaetum and D. surmandinum as two different species in Flora of Iranica revealed a close relationship with Dracocephalum kotschyi and formed a mixed subcluster. The RAPD analysis offered rapid and reliable tools for the estimation of inter- and intra specific variability in Dracocephalum

Microlending 2017 Data Set

This dataset contains anonymized lending transactions from the crowdsources microlending site Kiva Microloans. The data has been transformed to make it suitable for recommender systems experimentation and research. See the attached README file and datasheet document for additional information.&nbsp;Copyright 2022 Robin Burke</p

Controlling the Fairness / Accuracy Tradeoff in Recommender Systems

Recommender systems are one of the most pervasive applications of machine learning. They play a pivotal role in helping users find items tailored to their taste. Although these systems intend to assist people in their information needs, they can cause implicit or explicit discrimination against individuals or groups. There are several ways that different biases can creep into recommender systems. Reflection of societal and historical prejudices in datasets and during the data collection process, lack of sufficient data on minority groups, lack of suitable evaluation methods and model designs to detect these biases and lessen the unfairness caused by them are among the many reasons for unfairness in these systems. A system needs to defend against the biases in recommendation output to prevent harm and unfairness. However, integrating the goal of fairness with accuracy in recommender systems is challenging, primarily because of this goal's significant trade-offs with accuracy. Accuracy in recommender systems is the ability of that system to predict users' needs and interests accurately. On the other hand, fairness is a complicated concept with a variety of definitions. To use fairness as an objective, we need to define it based on the application area and the context of a problem. Additionally, we need to specify the fairness concerns of the different stakeholders involved in the recommender systems and the fairness priorities of a system. Any of these aspects might disagree with the goal of accuracy. For example, if fairness for content providers is more exposure to users, increasing it might cause a reduction in accuracy. Therefore, controlling the trade-off between accuracy and fairness becomes essential. Throughout this dissertation, several recommendation models and re-ranking approaches are presented that aim to address this problem using in- and post- processing methods. These approaches show promising results, but it is worth mentioning that they have intrinsic limitations and, therefore, shouldn't be considered ultimate solutions

The Multisided Complexity of Fairness in Recommender Systems

Recommender systems are poised at the interface between stakeholders: for example, job applicants and employers in the case of recommendations of employment listings, or artists and listeners in the case of music recommendation. In such multisided platforms, recommender systems play a key role in enabling discovery of products and information at large scales. However, as they have become more and more pervasive in society, the equitable distribution of their benefits and harms have been increasingly under scrutiny, as is the case with machine learning generally. While recommender systems can exhibit many of the biases encountered in other machine learning settings, the intersection of personalization and multisidedness makes the question of fairness in recommender systems manifest itself quite differently. In this article, we discuss recent work in the area of multisided fairness in recommendation, starting with a brief introduction to core ideas in algorithmic fairness and multistakeholder recommendation. We describe techniques for measuring fairness and algorithmic approaches for enhancing fairness in recommendation outputs. We also discuss feedback and popularity effects that can lead to unfair recommendation outcomes. Finally, we introduce several promising directions for future research in this area

Evaluation of the antinociceptive and anti-inflammatory effects of essential oil of Nepeta pogonosperma Jamzad et Assadi in rats

BACKGROUND AND THE PURPOSE OF STUDY: Concerning the different effects of essential oils from Nepeta genus on the central nervous system including pain killing effect, this study was designed to evaluate the antinociceptive and anti-inflammatory effects of essential oil of Nepeta pogonosperma Jamzad et Assadi (NP), a recently identified species. METHODS: Air-dried aerial parts of NP were hydrodistillated and GC-MS analysis of obtained essential oil was conducted. Total 24 male Wister rats weighing 225 ± 25 gm were studied. Essential oil of NP was administered intraperitoneally at the doses of 50 mg/kg, 100 mg/kg and 200 mg/kg for the experimental groups. Control rats received equal volume (2 ml/kg) of normal saline. Antinociception was assessed by tail flick test (after 30 minutes) and formalin test (for further 60 minutes). Then the animal was sacrificed and the paw edema was measured using a water plethysmometer. RESULTS: 4aα,7α,7aβ-nepetalactone and 1,8-cineole were found as the main concentrated components of NP essential oil. All the doses of NP showed antinociception. NP 200 mg/kg reduced the pain sensation in tail flick (p <0.01) and formalin test (p <0.001 in both phases). In paw edema test, NP 100 and 200 mg/kg significantly reduced the inflammation (p <0.01 and p <0.05). CONCLUSION: This study reveals that the essential oil of NP may minimize both the acute and chronic forms of nociception and may have potent role against inflammation, but the dose should be maintained precisely to obtain the intended effect