Effects of Ultra-Weak Fractal Electromagnetic Signals on Malassezia furfur

Malassezia spp. are dimorphic, lipophilic fungi that are part of the normal human cutaneous commensal microbiome. However, under adverse conditions, these fungi can be involved in various cutaneous diseases. In this study, we analysed the effect of ultra-weak fractal electromagnetic (uwf-EMF) field exposure (12.6 nT covering 0.5 to 20 kHz) on the growth dynamics and invasiveness of M. furfur. The ability to modulate inflammation and innate immunity in normal human keratinocytes was also investigated. Using a microbiological assay, it was possible to demonstrate that, under the influence of uwf-EMF, the invasiveness of M. furfur was drastically reduced (d = 2.456, p < 0.001), while at the same time, its growth dynamic after 72 h having been in contact with HaCaT cells both without (d = 0.211, p = 0.390) and with (d = 0.118, p = 0.438) uwf-EM exposure, were hardly affected. Real-time PCR analysis demonstrated that a uwf-EMF exposure is able to modulate human-β-defensin-2 (hBD-2) in treated keratinocytes and at the same time reduce the expression of proinflammatory cytokines in human keratinocytes. The findings suggest that the underlying principle of action is hormetic in nature and that this method might be an adjunctive therapeutic tool to modulate the inflammatory properties of Malassezia in related cutaneous diseases. The underlying principle of action becomes understandable by means of quantum electrodynamics (QED). Given that living systems consist mainly of water and within the framework of QED, this water, as a biphasic system, provides the basis for electromagnetic coupling. The oscillatory properties of water dipoles modulated by weak electromagnetic stimuli not only affect biochemical processes, but also pave the way for a more general understanding of the observed nonthermal effects in biota.

Explore drug-like space with deep generative models

The process of design/discovery of drugs involves the identification and design of novel molecules that have the desired properties and bind well to a given disease-relevant target. One of the main challenges to effectively identify potential drug candidates is to explore the vast drug-like chemical space to find novel chemical structures with desired physicochemical properties and biological characteristics. Moreover, the chemical space of currently available molecular libraries is only a small fraction of the total possible drug-like chemical space. Deep molecular generative models have received much attention and provide an alternative approach to the design and discovery of molecules. To efficiently explore the drug-like space, we first constructed the drug-like dataset and then performed the generative design of drug-like molecules using a Conditional Randomized Transformer approach with the molecular access system (MACCS) fingerprint as a condition and compared it with previously published molecular generative models. The results show that the deep molecular generative model explores the wider drug-like chemical space. The generated drug-like molecules share the chemical space with known drugs, and the drug-like space captured by the combination of quantitative estimation of drug-likeness (QED) and quantitative estimate of protein-protein interaction targeting drug-likeness (QEPPI) can cover a larger drug-like space. Finally, we show the potential application of the model in design of inhibitors of MDM2-p53 protein-protein interaction. Our results demonstrate the potential application of deep molecular generative models for guided exploration in drug-like chemical space and molecular design.

Quantitative Estimate Index for Early-Stage Screening of Compounds Targeting Protein-Protein Interactions

Drug-likeness quantification is useful for screening drug candidates. Quantitative estimates of drug-likeness (QED) are commonly used to assess quantitative drug efficacy but are not suitable for screening compounds targeting protein-protein interactions (PPIs), which have recently gained attention. Therefore, we developed a quantitative estimate index for compounds targeting PPIs (QEPPI), specifically for early-stage screening of PPI-targeting compounds. QEPPI is an extension of the QED method for PPI-targeting drugs that models physicochemical properties based on the information available for drugs/compounds, specifically those reported to act on PPIs. FDA-approved drugs and compounds in iPPI-DB, which comprise PPI inhibitors and stabilizers, were evaluated using QEPPI. The results showed that QEPPI is more suitable than QED for early screening of PPI-targeting compounds. QEPPI was also considered an extended concept of the "Rule-of-Four" (RO4), a PPI inhibitor index. We evaluated the discriminatory performance of QEPPI and RO4 for datasets of PPI-target compounds and FDA-approved drugs using F-score and other indices. The F-scores of RO4 and QEPPI were 0.451 and 0.501, respectively. QEPPI showed better performance and enabled quantification of drug-likeness for early-stage PPI drug discovery. Hence, it can be used as an initial filter to efficiently screen PPI-targeting compounds.

CODATA Recommended Values of the Fundamental Physical Constants: 2018

We report the 2018 self-consistent values of constants and conversion factors of physics and chemistry recommended by the Committee on Data of the International Science Council. The recommended values can also be found at physics.nist.gov/constants. The values are based on a least-squares adjustment that takes into account all theoretical and experimental data available through 31 December 2018. A discussion of the major improvements as well as inconsistencies within the data is given. The former include a decrease in the uncertainty of the dimensionless fine-structure constant and a nearly two orders of magnitude improvement of particle masses expressed in units of kg due to the transition to the revised International System of Units (SI) with an exact value for the Planck constant. Further, because the elementary charge, Boltzmann constant, and Avogadro constant also have exact values in the revised SI, many other constants are either exact or have significantly reduced uncertainties. Inconsistencies remain for the gravitational constant and the muon magnetic-moment anomaly. The proton charge radius puzzle has been partially resolved by improved measurements of hydrogen energy levels.

Cavity Control of Excitons in Two-Dimensional Materials

We propose a robust and efficient way of controlling the optical spectra of two-dimensional materials and van der Waals heterostructures by quantum cavity embedding. The cavity light-matter coupling leads to the formation of exciton-polaritons, a superposition of photons and excitons. Our first-principles study demonstrates a reordering and mixing of bright and dark excitons spectral features and in the case of a type II van-der-Waals heterostructure an inversion of intra- and interlayer excitonic resonances. We further show that the cavity light-matter coupling strongly depends on the dielectric environment and can be controlled by encapsulating the active two-dimensional (2D) crystal in another dielectric material. Our theoretical calculations are based on a newly developed nonperturbative many-body framework to solve the coupled electron-photon Schrödinger equation in a quantum-electrodynamical extension of the Bethe-Salpeter approach. This approach enables the ab initio simulations of exciton-polariton states and their dispersion from weak to strong cavity light-matter coupling regimes. Our method is then extended to treat van der Waals heterostructures and encapsulated 2D materials using a simplified Mott-Wannier description of the excitons that can be applied to very large systems beyond reach for fully ab initio approaches.

Thermoelectric Inversion in a Resonant Quantum Dot-Cavity System in the Steady-State Regime

We theoretically investigate thermoelectric effects in a quantum dot system under the influence of a linearly polarized photon field confined to a 3D cavity. A temperature gradient is applied to the system via two electron reservoirs that are connected to each end of the quantum dot system. The thermoelectric current in the steady state is explored using a quantum master equation. In the presence of the quantized photons, extra channels, the photon replica states, are formed generating a photon-induced thermoelectric current. We observe that the photon replica states contribute to the transport irrespective of the direction of the thermal gradient. In the off-resonance regime, when the energy difference between the lowest states of the quantum dot system is smaller than the photon energy, the thermoelectric current is almost blocked and a plateau is seen in the thermoelectric current for strong electron–photon coupling strength. In the resonant regime, an inversion of thermoelectric current emerges due to the Rabi-splitting. Therefore, the photon field can change both the magnitude and the sign of the thermoelectric current induced by the temperature gradient in the absence of a voltage bias between the leads.

High-dimensional similarity searches using query driven dynamic quantization and distributed indexing

The concept of similarity is used as the basis for many data exploration and data mining tasks. Nearest neighbor (NN) queries identify the most similar items, or in terms of distance the closest points to a query point. Similarity is traditionally characterized using a distance function between multi-dimensional feature vectors. However, when the data is high-dimensional, traditional distance functions fail to significantly distinguish between the closest and furthest points, as few dissimilar dimensions dominate the distance function. Localized similarity functions, i.e. functions that only consider dimensions close to the query, quantize each dimension independently and only compute similarity for the dimensions where the query and the points fall into the same bin. These quantizations are query-agnostic and there is potential to improve accuracy when a query-dependent quantization is used. In this work we propose a query dependent equi-depth (QED) on-the-fly quantization method to improve high-dimensional similarity searches. The quantization is done for each dimension at query time and localized scores are generated for the closest p fraction of the points while a constant penalty is applied for the rest of the points. QED not only improves the quality of the distancemetric,but also improves query time performance by filtering out non relevant data. We propose a distributed indexing and query algorithm to efficiently compute QED. Our experimental results show improvements in classification accuracy as well as query performance up to one order of magnitude faster than Manhattan-based sequential scan NN queries over datasets with hundreds of dimensions. Furthermore, similarity searches with QED show linear or better scalability in relation to the number of dimensions, and the number of compute nodes.

Water's quantum structures and life

This article discusses several clues pointing to the spontaneous quantum origin of the recently discovered dissipative structures induced in liquid water by low-energy physical perturbations. These structures show an astonishing permanence, so much that large ponderal quantities of supramolecular aggregates of water - at ambient pressure and temperature - subsist even in the solid phase, strongly suggesting the possibility that these structures are the matrix itself of life.

[The Gradual Formation of the Concept of ‘Light Quanta']

The Gradual Formation of the Concept of 'Light Quanta'. The complex concept of 'light quanta' which made its first appearance in Albert Einstein's 1905 paper on a "heuristic point of view" to cope with the photoelectric effect and other forms of interaction of light and matter, has a rich history both before and after 1905. Some of its semantic layers lead as far back as Newton and Kepler, others are only fully espoused several decades later, yet others initially increased, then diminished in importance and finally vanished. Two historiographic approaches are discussed and exemplified: a) my own model of conceptual development as a series of semantic accretions, and b) Mark Turner's model of 'conceptual blending'. Both of these models are shown to be useful and will be further explored in my own efforts to come to grips with the complex process of concept formation.