Automatic Calibration of Modified FM Synthesis to Harmonic Sounds Using Genetic Algorithms

Many audio synthesis techniques have been successful inreproducing the sounds of musical instruments. Several of these techniques require parameters calibration. However, this task can be difficult and time-consuming especially when there is not intuitive correspondence between a parameter value and the change in the produced sound. Searching the parameter space for a given synthesis technique is, therefore, a task more naturally suited to an automatic optimization scheme. Genetic algorithms (GA) have been used rather extensively for this purpose, and in particular for calibrating Classic FM (ClassicFM) synthesis to mimic recorded harmonic sounds. In this work, we use GA to further explore its modified counterpart, Modified FM (ModFM), which has not been used as widely, and its ability to produce musical sounds not as fully explored. We completely automize the calibration of a ModFM synthesis model for the reconstruction of harmonic instrument tones using GA. In this algorithm, we refine parameters and operators such as crossover probability or mutation operator for closer match. As an evaluation, we show that GA system automatically generates harmonic musical instrument sounds closely matching the target recordings, a match comparable to the application of GA to ClassicFM synthesis

Some Perspectives on Network Modeling in Therapeutic Target Prediction

Drug target identification is of significant commercial interest to pharmaceutical companies, and there is a vast amount of research done related to the topic of therapeutic target identification. Interdisciplinary research in this area involves both the biological network community and the graph algorithms community. Key steps of a typical therapeutic target identification problem include synthesizing or inferring the complex network of interactions relevant to the disease, connecting this network to the disease-specific behavior, and predicting which components are key mediators of the behavior. All of these steps involve graph theoretical or graph algorithmic aspects. In this perspective, we provide modelling and algorithmic perspectives for therapeutic target identification and highlight a number of algorithmic advances, which have gotten relatively little attention so far, with the hope of strengthening the ties between these two research communities

On Information Granulation via Data Filtering for Granular Computing-Based Pattern Recognition: A Graph Embedding Case Study

Granular Computing is a powerful information processing paradigm, particularly useful for the synthesis of pattern recognition systems in structured domains (e.g., graphs or sequences). According to this paradigm, granules of information play the pivotal role of describing the underlying (possibly complex) process, starting from the available data. Under a pattern recognition viewpoint, granules of information can be exploited for the synthesis of semantically sound embedding spaces, where common supervised or unsupervised problems can be solved via standard machine learning algorithms. In this companion paper, we follow our previous paper (Martino et al. in Algorithms 15(5):148, 2022) in the context of comparing different strategies for the automatic synthesis of information granules in the context of graph classification. These strategies mainly differ on the specific topology adopted for subgraphs considered as candidate information granules and the possibility of using or neglecting the ground-truth class labels in the granulation process and, conversely, to our previous work, we employ a filtering-based approach for the synthesis of information granules instead of a clustering-based one. Computational results on 6 open-access data sets corroborate the robustness of our filtering-based approach with respect to data stratification, if compared to a clustering-based granulation stage

Digest of Russian Space Life Sciences, issue 33

This is the thirty-third issue of NASA's USSR Space Life Sciences Digest. It contains abstracts of 55 papers published in Russian journals. The abstracts in this issue have been identified as relevant to the following areas of space biology and medicine: biological rhythms, body fluids, botany, cardiovascular and respiratory systems, developmental biology, endocrinology, equipment and instrumentation, gastrointestinal system, genetics, hematology, human performance, metabolism, microbiology, musculoskeletal system, neurophysiology, nutrition, operational medicine, psychology, radiobiology, and reproductive system

W. M. Keck Foundation 2010 Annual Report

Contains board chair's message, 2010 program highlights and grantee profiles, grants list, financial statements, and lists of board members and committee members

Critical Transitions In a Model of a Genetic Regulatory System

We consider a model for substrate-depletion oscillations in genetic systems, based on a stochastic differential equation with a slowly evolving external signal. We show the existence of critical transitions in the system. We apply two methods to numerically test the synthetic time series generated by the system for early indicators of critical transitions: a detrended fluctuation analysis method, and a novel method based on topological data analysis (persistence diagrams).Comment: 19 pages, 8 figure

Method for finding metabolic properties based on the general growth law. Liver examples. A General framework for biological modeling

We propose a method for finding metabolic parameters of cells, organs and whole organisms, which is based on the earlier discovered general growth law. Based on the obtained results and analysis of available biological models, we propose a general framework for modeling biological phenomena and discuss how it can be used in Virtual Liver Network project. The foundational idea of the study is that growth of cells, organs, systems and whole organisms, besides biomolecular machinery, is influenced by biophysical mechanisms acting at different scale levels. In particular, the general growth law uniquely defines distribution of nutritional resources between maintenance needs and biomass synthesis at each phase of growth and at each scale level. We exemplify the approach considering metabolic properties of growing human and dog livers and liver transplants. A procedure for verification of obtained results has been introduced too. We found that two examined dogs have high metabolic rates consuming about 0.62 and 1 gram of nutrients per cubic centimeter of liver per day, and verified this using the proposed verification procedure. We also evaluated consumption rate of nutrients in human livers, determining it to be about 0.088 gram of nutrients per cubic centimeter of liver per day for males, and about 0.098 for females. This noticeable difference can be explained by evolutionary development, which required females to have greater liver processing capacity to support pregnancy. We also found how much nutrients go to biomass synthesis and maintenance at each phase of liver and liver transplant growth. Obtained results demonstrate that the proposed approach can be used for finding metabolic characteristics of cells, organs, and whole organisms, which can further serve as important inputs for many applications in biology (protein expression), biotechnology (synthesis of substances), and medicine.Comment: 20 pages, 6 figures, 4 table