Join and Meet Operations for Type-2 Fuzzy Sets With Nonconvex Secondary Memberships

In this paper, we will present two theorems for the join and meet operations for general type-2 fuzzy sets with arbitrary secondary memberships, which can be nonconvex and/or nonnormal type-1 fuzzy sets. These results will be used to derive the join and meet operations of the more general descriptions of interval type-2 fuzzy sets presented in a paper by Bustince Sola et al. ('Interval type-2 fuzzy sets are generalization of interval-valued fuzzy sets: Towards a wider view on their relationship,' IEEE Trans. Fuzzy Syst., vol. 23, pp. 1876-1882, 2015), where the secondary grades can be nonconvex. Hence, this study will help to explore the potential of type-2 fuzzy logic systems which use the general forms of interval type-2 fuzzy sets which are not equivalent to interval-valued fuzzy sets. Several examples for both general type-2 and the more general forms of interval type-2 fuzzy sets are presented

Targeted Disruption of the α-Amylase Gene in the Hyperthermophilic Archaeon \u3ci\u3eSulfolobus solfataricus\u3c/i\u3e

Sulfolobus solfataricus secretes an acid-resistant _-amylase (amyA) during growth on starch as the sole carbon and energy source. Synthesis of this activity is subject to catabolite repression. To better understand _-amylase function and regulation, the structural gene was identified and disrupted and the resulting mutant was characterized. Internal _-amylase peptide sequences obtained by tandem mass spectroscopy were used to identify the amyA coding sequence. Anti-_-amylase antibodies raised against the purified protein immunoprecipitated secreted _-amylase activity and verified the enzymatic identity of the sequenced protein. A new gene replacement method was used to disrupt the amyA coding sequence by insertion of a modified allele of the S. solfataricus lacS gene. PCR and DNA sequence analysis were used to characterize the altered amyA locus in the recombinant strain. The amyA::lacS mutant lost the ability to grow on starch, glycogen, or pullulan as sole carbon and energy sources. During growth on a non-catabolite-repressing carbon source with added starch, the mutant produced no detectable secreted amylase activity as determined by enzyme assay, plate assay, or Western blot analysis. These results clarify the biological role of the α-amylase and provide additional methods for the directed genetic manipulation of the S. solfataricus genome

Lung tumorspheres as a drug screening platform against cancer stem cells

Treatment resistance and metastasis are linked to cancer stem cells (CSCs). This population represents a promising target, but remains unexplored in lung cancer. The main objective of this study was to characterize lung CSCs and discover new therapeutic strategies

Selecting Negative Samples for PPI Prediction Using Hierarchical Clustering Methodology

Protein-protein interactions (PPIs) play a crucial role in cellular processes. In the present work, a new approach is proposed to construct a PPI predictor training a support vector machine model through a mutual information filter-wrapper parallel feature selection algorithm and an iterative and hierarchical clustering to select a relevance negative training set. By means of a selected suboptimum set of features, the constructed support vector machine model is able to classify PPIs with high accuracy in any positive and negative datasets

Automatic system for personalised exercise recommendation in breast cancer care using mobile technologies and machine learning

[ES] Aliviar las secuelas del cáncer en general, y en particular del cáncer de mama, es uno de los mayores retos de nuestros tiempos, y precisamente el ejercicio terapéutico se plantea como una solución para paliar los efectos secundarios del cáncer y su tratamiento a corto y largo plazo. No obstante, para que las intervenciones del ejercicio físico sean más efectivas estas deben estar adaptadas a cada paciente según sus capacidades y necesidades de entrenamiento específicas. Dicha adaptación al entrenamiento utilizando tecnologías de salud móvil (mSalud) ya se ha llevado a cabo con éxito en entornos deportivos, y en este trabajo se plantea una aproximación similar para pacientes con cáncer de mama, donde se pretende ajustar de forma individual las dosis de entrenamiento a las necesidades de cada paciente. Para ello, se ha diseñado y desarrollado un sistema completo de mSalud que ha permitido extraer un conjunto de datos longitudinal con mediciones de la carga del ejercicio de pacientes de cáncer de mama. A partir de dichos datos se están utilizando técnicas de ciencia de datos y aprendizaje automático para extraer los diferentes estados de recuperación de las pacientes a lo largo de una intervención en ejercicio físico, lo cual nos permitirá plantear un sistema de ayuda a la toma de decisiones para prescribir dosis individualizadas de ejercicio terapéutico.[EN] Alleviating the sequelae of cancer in general, and breast cancer in particular, is one of the greatest challenges of our times, and therapeutic exercise is precisely one solution to alleviate the side effects of cancer and its treatment in the short and long term. However, in order to make exercise interventions more effective, they must be adapted to each patient according to their specific training needs and abilities. Such adaptation to training using mobile health technologies (mHealth) has already been successfully carried out in sports settings, and this work proposes a similar approach for breast cancer patients, where the aim is to individually adjust the training doses to the needs of each patient. To this end, a complete mHealth system has been designed and developed to extract a longitudinal dataset of exercise load measurements from breast cancer patients. To leverage these data, data science and machine learning techniques are being used to extract the different states of recovery of patients throughout a physical exercise intervention, which will allow us to propose a decision support system to prescribe individualized doses of therapeutic exercise

From Ideas to Practice, Pilots to Strategy: Practical Solutions and Actionable Insights on How to Do Impact Investing

This report is the second publication in the World Economic Forum's Mainstreaming Impact Investing Initiative. The report takes a deeper look at why and how asset owners began to include impact investing in their portfolios and continue to do so today, and how they overcame operational and cultural constraints affecting capital flow. Given that impact investing expertise is spread among dozens if not hundreds of practitioners and academics, the report is a curation of some -- but certainly not all -- of those leading voices. The 15 articles are meant to provide investors, intermediaries and policy-makers with actionable insights on how to incorporate impact investing into their work.The report's goals are to show how mainstream investors and intermediaries have overcome the challenges in the impact investment sector, and to democratize the insights and expertise for anyone and everyone interested in the field. Divided into four main sections, the report contains lessons learned from practitioner's experience, and showcases best practices, organizational structures and innovative instruments that asset owners, asset managers, financial institutions and impact investors have successfully implemented

Targeted Disruption of the α-Amylase Gene in the Hyperthermophilic Archaeon \u3ci\u3eSulfolobus solfataricus\u3c/i\u3e

Sulfolobus solfataricus secretes an acid-resistant _-amylase (amyA) during growth on starch as the sole carbon and energy source. Synthesis of this activity is subject to catabolite repression. To better understand _-amylase function and regulation, the structural gene was identified and disrupted and the resulting mutant was characterized. Internal _-amylase peptide sequences obtained by tandem mass spectroscopy were used to identify the amyA coding sequence. Anti-_-amylase antibodies raised against the purified protein immunoprecipitated secreted _-amylase activity and verified the enzymatic identity of the sequenced protein. A new gene replacement method was used to disrupt the amyA coding sequence by insertion of a modified allele of the S. solfataricus lacS gene. PCR and DNA sequence analysis were used to characterize the altered amyA locus in the recombinant strain. The amyA::lacS mutant lost the ability to grow on starch, glycogen, or pullulan as sole carbon and energy sources. During growth on a non-catabolite-repressing carbon source with added starch, the mutant produced no detectable secreted amylase activity as determined by enzyme assay, plate assay, or Western blot analysis. These results clarify the biological role of the α-amylase and provide additional methods for the directed genetic manipulation of the S. solfataricus genome