Narrow Results

The present study is devoted to modelling the onset and the spread of epidemics. The mathematical approach is based on the generalized kinetic theory for active particles. The modelling includes virus mutations and the role of the immune system. Moreover, the heterogeneous distribution of patients is also taken into account. The structure allows the derivation of specific models and of numerical simulations related to real systems.

The paper presents a model of virus mutations and evolution of epidemics in a system of interacting individuals, where the intensity of the pathology, described by a real discrete positive variable, is heterogeneously distributed, and the virus is in competition with the immune system or therapeutical actions. The model is developed within the framework of the Kinetic Theory of Active Particles. The paper also presents a qualitative analysis developed to study the well-posedness of the mathematical problem associated to the general framework. Finally, simulations show the ability of the model to predict some interesting emerging phenomena, such as the mutation to a subsequent virus stage, the heterogeneous evolution of the pathology with the co-presence of individual carriers of the virus at different levels of progression, and the presence of oscillating time phases with either virus prevalence or immune system control.

Complex systems, as interwoven miscellaneous interacting entities that emerge and evolve through self-organization in a myriad of spiraling contexts, exhibit subtleties on global scale besides steering the way to understand complexity which has been under evolutionary processes with unfolding cumulative nature wherein order is viewed as the unifying framework. Indicating the striking feature of non-separability in components, a complex system cannot be understood in terms of the individual isolated constituents’ properties per se, it can rather be comprehended as a way to multilevel approach systems behavior with systems whose emergent behavior and pattern transcend the characteristics of ubiquitous units composing the system itself. This observation specifies a change of scientific paradigm, presenting that a reductionist perspective does not by any means imply a constructionist view; and in that vein, complex systems science, associated with multiscale problems, is regarded as ascendancy of emergence over reductionism and level of mechanistic insight evolving into complex system. While evolvability being related to the species and humans owing their existence to their ancestors’ capability with regards to adapting, emerging and evolving besides the relation between complexity of models, designs, visualization and optimality, a horizon that can take into account the subtleties making their own means of solutions applicable is to be entailed by complexity. Such views attach their germane importance to the future science of complexity which may probably be best regarded as a minimal history congruent with observable variations, namely the most parallelizable or symmetric process which can turn random inputs into regular outputs. Interestingly enough, chaos and nonlinear systems come into this picture as cousins of complexity which with tons of its components are involved in a hectic interaction with one another in a nonlinear fashion amongst the other related systems and fields. Relation, in mathematics, is a way of connecting two or more things, which is to say numbers, sets or other mathematical objects, and it is a relation that describes the way the things are interrelated to facilitate making sense of complex mathematical systems. Accordingly, mathematical modeling and scientific computing are proven principal tools toward the solution of problems arising in complex systems’ exploration with sound, stimulating and innovative aspects attributed to data science as a tailored-made discipline to enable making sense out of voluminous (-big) data. Regarding the computation of the complexity of any mathematical model, conducting the analyses over the run time is related to the sort of data determined and employed along with the methods. This enables the possibility of examining the data applied in the study, which is dependent on the capacity of the computer at work. Besides these, varying capacities of the computers have impact on the results; nevertheless, the application of the method on the code step by step must be taken into consideration. In this sense, the definition of complexity evaluated over different data lends a broader applicability range with more realism and convenience since the process is dependent on concrete mathematical foundations. All of these indicate that the methods need to be investigated based on their mathematical foundation together with the methods. In that way, it can become foreseeable what level of complexity will emerge for any data desired to be employed. With relation to fractals, fractal theory and analysis are geared toward assessing the fractal characteristics of data, several methods being at stake to assign fractal dimensions to the datasets, and within that perspective, fractal analysis provides expansion of knowledge regarding the functions and structures of complex systems while acting as a potential means to evaluate the novel areas of research and to capture the roughness of objects, their nonlinearity, randomness, and so on. The idea of fractional-order integration and differentiation as well as the inverse relationship between them lends fractional calculus applications in various fields spanning across science, medicine and engineering, amongst the others. The approach of fractional calculus, within mathematics-informed frameworks employed to enable reliable comprehension into complex processes which encompass an array of temporal and spatial scales notably provides the novel applicable models through fractional-order calculus to optimization methods. Computational science and modeling, notwithstanding, are oriented toward the simulation and investigation of complex systems through the use of computers by making use of domains ranging from mathematics to physics as well as computer science. A computational model consisting of numerous variables that characterize the system under consideration allows the performing of many simulated experiments via computerized means. Furthermore, Artificial Intelligence (AI) techniques whether combined or not with fractal, fractional analysis as well as mathematical models have enabled various applications including the prediction of mechanisms ranging extensively from living organisms to other interactions across incredible spectra besides providing solutions to real-world complex problems both on local and global scale. While enabling model accuracy maximization, AI can also ensure the minimization of functions such as computational burden. Relatedly, level of complexity, often employed in computer science for decision-making and problem-solving processes, aims to evaluate the difficulty of algorithms, and by so doing, it helps to determine the number of required resources and time for task completion. Computational (-algorithmic) complexity, referring to the measure of the amount of computing resources (memory and storage) which a specific algorithm consumes when it is run, essentially signifies the complexity of an algorithm, yielding an approximate sense of the volume of computing resources and seeking to prove the input data with different values and sizes. Computational complexity, with search algorithms and solution landscapes, eventually points toward reductions vis à vis universality to explore varying degrees of problems with different ranges of predictability. Taken together, this line of sophisticated and computer-assisted proof approach can fulfill the requirements of accuracy, interpretability, predictability and reliance on mathematical sciences with the assistance of AI and machine learning being at the plinth of and at the intersection with different domains among many other related points in line with the concurrent technical analyses, computing processes, computational foundations and mathematical modeling. Consequently, as distinctive from the other ones, our special issue series provides a novel direction for stimulating, refreshing and innovative interdisciplinary, multidisciplinary and transdisciplinary understanding and research in model-based, data-driven modes to be able to obtain feasible accurate solutions, designed simulations, optimization processes, among many more. Hence, we address the theoretical reflections on how all these processes are modeled, merging all together the advanced methods, mathematical analyses, computational technologies, quantum means elaborating and exhibiting the implications of applicable approaches in real-world systems and other related domains.

The incompatibilities among complex data formats and various schema used by biological databases that house these data are becoming a bottleneck in biological research. For example, biological data format varies from simple words (e.g. gene name), numbers (e.g. molecular weight) to sequence strings (e.g. nucleic acid sequence), to even more complex data formats such as taxonomy trees. Some information is embedded in narrative text, such as expert comments and publications. Some other information is expressed as graphs or images (e.g. pathways networks). The confederation of heterogeneous web databases has become a crucial issue in today's biological research. In other words, interoperability has to be archieved among the biological web databases and the heterogeneity of the web databases has to be resolved. This paper presents a biological ontology, BAO, and discusses its advantages in supporting the semantic integration of biological web databases are discussed.

INDIA – Bioven starts BV-NSCLC-001 Phase III trial in NSCLC.

INDIA – Initiative in Chemical Biology and Therapeutics.

PHILLIPPINES – Asia–Pacific Analysis: The slow road to green energy.

SINGAPORE – Takeda progressing well in Asia with New Drug Applications.

SINGAPORE – NTU and University of Warwick boost brainpower in global neuroscience research.

THAILAND – Thai PhD. student awarded Monsanto's Beachell–Borlaug International Scholarship for rice improvement research.

EUROPE – Open access will change the world, if scientists want it to.

UNITED STATES & CANADA – Verisante places Aura Beta Units for safety, verification testing in B.C., Alberta and Ontario clinics.

UNITED STATES & CANADA – Life Technologies sets new worldwide standard for criminal forensic testing with introduction of GlobalFilerTM Express Kit.

UNITED STATES & CANADA – How immune cells can nudge nerves to regrow.

UNITED STATES & CANADA – Improved Genomic Target Selection Using IDT Oligos.

UNITED STATES & CANADA – US team uncover non-invasive method for diagnosing epilepsy.

Agilent Technologies supports Professor's work developing transformative NMR applications for structural biology.

Alchemia announces multi-target drug discovery collaboration with AstraZeneca on VAST discovery platform.

DSM and DecImmune Therapeutics sign agreement to develop N2 pathway blocking antibody.

Novel technology from Columbia University for Neurobiological Research.

A new EU-funded industry-academia drug discovery partnership targets challenging kinases.

Avian Influenza A (H7N9) threatens, GenScript supports researchers in impending battle.

The International Peptide Symposium Held in Singapore for the First Time.

Inspirations from 2015 FWIS L'Oréal Winners.

Emerging Opportunities in Myanmar's Diagnostic Imaging and In Vitro Diagnostics.

Scanning the Future of Medical Imaging

Putting Numbers into Biology: The Combination of Light Sheet Fluorescence Microscopy

and Fluorescence Spectroscopy

Abyss Processing – Exploring the Deep in Medical Images

Geoffrey Ball and his Innovation: VIBRANT SOUNDBRIDGE Hearing Implant.

Interviews with Nobel Laureates in Physiology or Medicine.

Talk about Over-the-Counter (OTC) Medicines and Self-Care.

Passionate in Science.

Making a difference.

Women in Science.

How to make value-based care a reality?

Impactful interventions to maximise healthspan.

Viral hepatitis in Arkhangai: How Asia’s first micro-treatment program’s successes can be leveraged to treat a “silent killer”.

The long-suffering challenge of vaccination.

A brief retrospective of the evolution of mechanics and its reciprocal impacts on medicine and biology is offered, from the limited viewpoint of an early contributor to some aspects of biomechanics. The development of the field after World War II, and particularly in the nineteen sixties and seventies, set the foundation for today's remarkable achievements. Looking ahead, the expanding complexity and challenges of the interaction of mechanics with biology and medicine, together with the loss of centrality of the mechanistic view in the physical sciences, compel a reexamination of the role, potential and limits of mechanics in this context. Future advances call for a broader metamechanics conception encompassing forces, energies, fields, information, network and systems theory, as well as for models spanning the range of scales from atom and molecule to cell, organ and organism.

Among Esther Thelen's most important contributions to developmental theory is that there is no single factor that has priority in driving development. In this paper, we discuss how this notion influenced our research on perceptual-motor development. We show that multiple factors constrain perceptual-motor development, but that a relatively minor change in one of them may lead to significant changes in the observed perceptual-motor behavior.

We present significantly advanced studies of the previously introduced physical growth mechanism and unite it with biochemical growth factors. Obtained results allowed formulation of the general growth law which governs growth and evolutional development of all living organisms, their organs and systems. It was discovered that the growth cycle is predefined by the distribution of nutritional resources between maintenance needs and biomass production. This distribution is quantitatively defined by the growth ratio parameter, which depends on the geometry of an organism, phase of growth and, indirectly, the organism's biochemical machinery. The amount of produced biomass, in turn, defines the composition of biochemical reactions. Changing amount of nutrients diverted to biomass production is what forces organisms to proceed through the whole growth and replication cycle. The growth law can be formulated as follows: the rate of growth is proportional to influx of nutrients and growth ratio. Considering specific biochemical components of different organisms, we find influxes of required nutrients and substitute them into the growth equation; then, we compute growth curves for amoeba, wild type fission yeast, and fission yeast's mutant. In all cases, predicted growth curves correspond very well to experimental data. Obtained results prove validity and fundamental scientific value of the discovery.

An environment friendly, economic and maneuverable hydrothermal method was proposed for fabrication of nitrogen and chlorine co-doped carbon quantum dots (N,Cl-CQDs). D-Glucosamine hydrochloride as the only precursor offered source of carbon, nitrogen and chlorine. As a consequent N,Cl-CQDs can emit blue luminescence and detect Fe${}^{3+}$ by fluorescence response with high selectivity and sensitivity. There is a linear semilogarithmic correlation between the quenching efficiency $F_0∕F$ and the concentration of Fe${}^{3+}$ with a detection limit of 0.167 $\mu$M. The N,Cl-CQDs exhibit a high quantum yield of 16.8% along with the fluorescence lifetime of 2.2ns. It is worth noting that the prepared N,Cl-CQDs show excellent biocompatibility and they are promising materials for sensing and biology.

The article introduces a mathematical model of the physical growth mechanism which is based on the relationships of the physical and geometrical parameters of the growing object, in particular its surface and volume. This growth mechanism works in cooperation with the biochemical and other growth factors. We use the growth equation, which mathematically describes this mechanism, and study its adequacy to real growth phenomena. The growth model very accurately fits experimental data on growth of Amoeba, Schizosaccharomyces pombe, E.coli. Study discovered a new growth suppression mechanism created by certain geometry of the growing object. This result was proved by experimental data. The existence of the growth suppression phenomenon confirms the real workings and universality of the growth mechanism and the adequacy of its mathematical description. The introduced equation is also applicable to the growth of multicellular organisms and tumors. Another important result is that the growth equation introduces mathematical characterization of geometrical forms that can biologically grow. The material is supported by software application, which is released to public domain.

This short paper traces some convergences between the ontologies of mind and AI presented in Carlos Montemayor’s Precis for The Prospect of Humanitarian Artificial Intelligence and Enactivism, with a focus on autonomy, embeddedness, perspective, and social interaction. I argue that it is surprising that Montemayor does not engage with any of the traditional 4e ontologies here, especially enactivism, considering his strong reliance on biology and autonomy in carving up intelligence and consciousness.