131,056 research outputs found
The role of Vitamin D in metabolic and reproductive disturbances of polycystic ovary syndrome: A narrative mini-review
No full textVitamin D is a secosteroid hormone that plays a pivotal role in several metabolic and reproductive pathways in humans. Increasing evidence supports the role of Vitamin D deficiency in metabolic disturbances and infertility in women with polycystic ovary syndrome (PCOS). Indeed, supplementation with Vitamin D seems to have a beneficial role on insulin resistance and endometrial receptivity. On the other hand, exceedingly high levels of Vitamin D appear to play a detrimental role on oocytes development and embryo quality. In the current review, we summarize the available evidence about the topic, aiming to suggest the best supplementation strategy in women with PCOS or, more generally, in those with metabolic disturbances and infertility. Based on the retrieved data, Vitamin D seems to have a beneficial role on IR, insulin sensitivity and endometrial receptivity, but high levels and incorrect timing of administration seem to have a detrimental role on oocytes development and embryo quality. Therefore, we encourage a low dose supplementation (400-800 IU/day) particularly in Vitamin D deficient women that present metabolic disturbances like PCOS. As far as the reproductive health, we advise Vitamin D supplementation in selected populations, only during specific moments of the ovarian cycle, to support the luteal phase. However, ambiguities about dosage and timing of the supplementation still emerge from the clinical studies published to date and further studies are required
DIVERSITY AND DYNAMICS OF CULTIVABLEPOPULATION OF ACETIC ACID BACTERIA ANDYEASTS IN KOMBUCHA
No full textIn recent years functional foods promoted with healthclaims have attracted increasing attention on the market.Among them kombucha is a fermented beverage widelyconsumed in Eastern Asian countries, but little is knownabout its constituent microbial communities. In this studytwo 12 days benchmark kombucha fermentations fromgreen and black tea were carried out. A culture-dependentapproach was applied both on exopolysaccharidic andliquid phases to monitor dynamics and diversity of aceticacid bacteria (AAB) and yeasts community. Among AABone main profile was observed (86% of strains); remainingstrains were grouped in 4 profiles by 16S/RFLPbasedanalysis. Whereas by (GTG)5/PCR typing sixteenclusters were obtained. 16S rRNA gene sequencing confirmedthe occurrence of Gluconacetobacter xylinus as predominantboth in green and black samples at 0, 6 and 12days of fermentation. Mainly on ACB medium minor bacterialgroups often colonizating tea leaves (Paenibacillusspp.), plants (Plantibacter spp.) and moisturing environments(Williamsia spp.), were detected starting from 6thfermentation day. Yeast population consisted of a restrictednumber of dominant species: Dekkera sp., Schizo -saccharomyces sp., Zygosaccharomyces sp., Dekkera sp.and Pichia sp. D. anomala was prevailing in both phasesthough all black and green kombucha fermentation times.Sc. pombe was detected only within 6 days of both greenand black tea and it was not isolated after 9 days, when the high ethanol-producing species D. bruxellensis was detected.Z. bailii was isolated from exopolysaccharidic ofblack tea from 6 to 12 days. Finally P. membranifacienswas detected occasionally at the end of the fermentativeprocess. For each species the degree of diversity wasdetermined by combined M13 and OPA20-based RAPDmethod. Cluster analysis showed that one to two prevailingbiotypes occurred through all the process
Correlation Clustering with Global Weight Bounds
No full textGiven a set of objects and nonnegative real weights expressing “positive” and “negative” feeling of clustering any two objects together, min-disagreement correlation clustering partitions the input object set so as to minimize the sum of the intra-cluster negative-type weights plus the sum of the inter-cluster positive-type weights. Min-disagreement correlation clustering is APX -hard, but efficient constant-factor approximation algorithms exist if the weights are bounded in some way. The weight bounds so far studied in the related literature are mostly local, as they are required to hold for every object-pair. In this paper, we introduce the problem of min-disagreement correlation clustering with global weight bounds, i.e., constraints to be satisfied by the input weights altogether. Our main result is a sufficient condition that establishes when any algorithm achieving a certain approximation under the probability constraint keeps the same guarantee on an input that violates the constraint. This extends the range of applicability of the most prominent existing correlation-clustering algorithms, including the popular Pivot, thus providing benefits, both theoretical and practical. Experiments demonstrate the usefulness of our approach, in terms of both worthiness of employing existing efficient algorithms, and guidance on the definition of weights from feature vectors in a task of fair clustering
Correlation Clustering: From Local to Global Constraints
No full textGiven a set of data objects, consider that object pairs are assigned two weights expressing the advantage of putting those objects in the same cluster or in separate clusters, respectively. Correlation clustering partitions the input object set so as to minimize the sum of the intra-cluster negative-type weights plus the sum of the inter-cluster positive-type weights. Existing approximation algorithms provide quality guarantees if the weights are bounded in some way. Regardless of the type, the weight bounds that have been so far studied are local bounds, i.e., constraints that are required to hold for every object pair in isolation. In this paper, we discuss global weight bounds in correlation clustering, and in particular, we derive bounds on edge weights' aggregate functions that are sufficient to lead to proved quality guarantees. Our formulation extends the range of applicability of the most prominent existing correlationclustering algorithms thus providing benefits, both theoretical and practical. Also, we showcase our results in a real-world scenario of feature selection for fair clustering
Is There a group effect? It depends on how you ask the question: Intraclass correlations for california psychotherapy alliance scale-group items
No full textCalifornia Psychotherapy Alliance Scale-Group (CALPAS-G) data were collected from 1,138 group sessions attended by 248 group members in 16 counseling groups. Hierarchical linear modeling (HLM) was used to derive between-groups, between-member, and between-session variance components and intraclass correlation coefficients (ICCs) for the 12 CALPAS-G items. Using Ledermann and Kenny's (2012) descriptions of variable types, we examined differences in between-groups variance for the 6 CALPAS-G items classified as "Individual" items and the 6 CALPAS-G items classified as "Group" items. A Related-Samples Wilcoxon's Signed Ranked Test showed that the ICCs for the Group items were significantly larger than the ICCs for the Individual items. The results show the importance of how items are worded. If researchers want to accurately examine the between-groups component of the group therapy relationship they should develop measures that ask clients to describe their perceptions of the group, not the members own experience of the group
In and Out: Optimizing Overall Interaction in Probabilistic Graphs under Clustering Constraints
No full textWe study two novel clustering problems in which the pairwise interactions between entities are characterized by probability distributions and conditioned by external factors within the environment where the entities interact. This covers any scenario where a set of actions can alter the entities' interaction behavior. In particular, we consider the case where the interaction conditioning factors can be modeled as cluster memberships of entities in a graph and the goal is to partition a set of entities such as to maximize the overall vertex interactions or, equivalently, minimize the loss of interactions in the graph. We show that both problems are NP-hard and they are equivalent in terms of optimality. However, we focus on the minimization formulation as it enables the possibility of devising both practical and efficient approximation algorithms and heuristics. Experimental evaluation of our algorithms, on both synthetic and real network datasets, has shown evidence of their meaningfulness as well as superiority with respect to competing methods, both in terms of effectiveness and efficiency
Il riparto di competenze tra Stato, Regioni ed enti locali (artt. 59 e 60, D. Lgs. 31.3.1998, n. 112)
No full textA combinatorial multi-armed bandit approach to correlation clustering
No full textGiven a graph whose edges are assigned positive-type and negative-type weights, the problem of correlation clustering aims at grouping the graph vertices so as to minimize (resp. maximize) the sum of negative-type (resp. positive-type) intra-cluster weights plus the sum of positive-type (resp. negative-type) inter-cluster weights. In correlation clustering, it is typically assumed that the weights are readily available. This is a rather strong hypothesis, which is unrealistic in several scenarios. To overcome this limitation, in this work we focus on the setting where edge weights of a correlation-clustering instance are unknown, and they have to be estimated in multiple rounds, while performing the clustering. The clustering solutions produced in the various rounds provide a feedback to properly adjust the weight estimates, and the goal is to maximize the cumulative quality of the clusterings. We tackle this problem by resorting to the reinforcement-learning paradigm, and, specifically, we design for the first time a Combinatorial Multi-Armed Bandit (CMAB) framework for correlation clustering. We provide a variety of contributions, namely (1) formulations of the minimization and maximization variants of correlation clustering in a CMAB setting; (2) adaptation of well-established CMAB algorithms to the correlation-clustering context; (3) regret analyses to theoretically bound the accuracy of these algorithms; (4) design of further (heuristic) algorithms to have the probability constraint satisfied at every round (key condition to soundly adopt efficient yet effective algorithms for correlation clustering as CMAB oracles); (5) extensive experimental comparison among a variety of both CMAB and non-CMAB approaches for correlation clustering
Graph Query Reformulation with Diversity
No full textWe study a problem of graph-query reformulation enabling explorative query-driven discovery in graph databases. Given a query issued by the user, the system, apart from returning the result patterns, also proposes a number of specializations (i.e., supergraphs) of the original query to facilitate the exploration of the results.
We formalize the problem of finding a set of reformulations of the input query by maximizing a linear combination of coverage (of the original query's answer set) and diversity among the specializations. We prove that our problem is hard, but also that a simple greedy algorithm achieves a-approximation guarantee.
The most challenging step of the greedy algorithm is the computation of the specialization that brings the maximum increment to the objective function. To efficiently solve this step, we show how to compute the objective-function increment of a specialization linearly in the number of its results and derive an upper bound that we exploit to devise an efficient search-space visiting strategy.
An extensive evaluation on real and synthetic databases attests high efficiency and accuracy of our proposal
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