GDV images: Current research and results

We use statistical analysis and machine learning to interpret the GDV coronas of fruits and human’s fingers in order to verify two hypotheses: (A) the GDV images contain useful information about the object/patient and (B) the human bioelectromagnetic field can be influenced by some outside factors. We performed several independent studies, three of which we here briefly describe: (a) recording coronas of berries of different grapevines, (b) detecting the influence of drinking the tap water from ordinary glass and energetic glass K2000, and (c) detecting the influence of natural energy source in Tunjice near Kamnik, Slovenia on the human bioelectromagnetic field. All three studies, as well as some other studies described elsewhere, gave significant results and therefore support both hypotheses

Machine learning and GDV images: current research and results

We use machine learning to interpret the GDV coronas of human's fingers in order to verify three hypotheses: 1. The GDV images contain useful information about the object/patient, 2. The map of coronas of fingers according to Chinese medicine does make sense, and 3. The human bioelectric field can be influenced by some outside factors, such as special T-shirts. We performed three independent studies: (a) recording coronas of apple skin, in order to verify if we can obtain any useful information for distinguishing the sort, age and the sun/shadow part of the apple, (b) detecting the state of menstrual cycle for females, and (c) detecting of the influence of different T-shirts on human's bioelectric field

Multiple plasmid origin-of-transfer regions might aid the spread of antimicrobial resistance to human pathogens

Antimicrobial resistance poses a great danger to humanity, in part due to the widespread horizontal gene transfer of plasmids via conjugation. Modeling of plasmid transfer is essential to uncovering the fundamentals of resistance transfer and for the development of predictive measures to limit the spread of resistance. However, a major limitation in the current understanding of plasmids is the incomplete characterization of the conjugative DNA transfer mechanisms, which conceals the actual potential for plasmid transfer in nature. Here, we consider that the plasmid-borne origin-of-transfer substrates encode specific DNA structural properties that can facilitate finding these regions in large datasets and develop a DNA structure-based alignment procedure for typing the transfer substrates that outperforms sequence-based approaches. Thousands of putative DNA transfer substrates are identified, showing that plasmid mobility can be twofold higher and span almost twofold more host species than is currently known. Over half of all putative mobile plasmids contain the means for mobilization by conjugation systems belonging to different mobility groups, which can hypothetically link previously confined host ranges across ecological habitats into a robust plasmid transfer network. This hypothetical network is found to facilitate the transfer of antimicrobial resistance from environmental genetic reservoirs to human pathogens, which might be an important driver of the observed rapid resistance development in humans and thus an important point of focus for future prevention measures

Parallel Factor Analysis Enables Quantification and Identification of Highly Convolved Data-Independent-Acquired Protein Spectra

The latest high-throughput mass spectrometry-based technologies can record virtually all molecules from complex biological samples, providing a holistic picture of proteomes in cells and tissues and enabling an evaluation of the overall status of a person\u27s health. However, current best practices are still only scratching the surface of the wealth of available information obtained from the massive proteome datasets, and efficient novel data-driven strategies are needed. Powered by advances in GPU hardware and open-source machine-learning frameworks, we developed a data-driven approach, CANDIA, which disassembles highly complex proteomics data into the elementary molecular signatures of the proteins in biological samples. Our work provides a performant and adaptable solution that complements existing mass spectrometry techniques. As the central mathematical methods are generic, other scientific fields that are dealing with highly convolved datasets will benefit from this work

Toward learning the principles of plant gene regulation

Advanced machine learning (ML) algorithms produce highly accurate models of gene expression, uncovering novel regulatory features in nucleotide sequences involving multiple cis-regulatory regions across whole genes and structural properties. These broaden our understanding of gene regulation and point to new principles to test and adopt in the field of plant science

Evidence-based clinical knowledge assistance towards supplementing patient referral letters for evidence informed decision making

Referral letters are common means by which healthcare practitioners exchange information relevant to patient care. It has been argued that information contained in letters of referral and reply often does not meet the information needs of letter receipting.GPs (general practitioners) and specialist require knowledge and information at point of care that is related to patient specification to narrow the information gap and to assist towards better interpretation of referral letters.Given this problem at hand we present, in this paper ECKA (Evidence based clinical knowledge assistance) framework that provides evidence based clinical knowledge assistance from clinical practice guidelines and medical explicit knowledge in terms of medical literature from Medline/Pubmed pertaining to referral letter.This will help narrow the information gap at point of care and to provide better interpretation of referral letters