Non-local polarization alignment and control in fibers using feedback from correlated measurements of entangled photons.

Quantum measurements that use the entangled photons' polarization to encode quantum information require calibration and alignment of the measurement bases between spatially separate observers. Because of the changing birefringence in optical fibers arising from temperature fluctuations or external mechanical vibrations, the polarization state at the end of a fiber channel is unpredictable and time-varying. Polarization tracking and stabilization methods originally developed for classical optical communications cannot be applied to polarization-entangled photons, where the separately detected photons are statistically unpolarized, yet quantum mechanically correlated. We report here a fast method for automatic alignment and dynamic tracking of the polarization measurement bases between spatially separated detectors. The system uses the Nelder-Mead simplex method to minimize the observed coincidence rate between non-locally measured entangled photon pairs, without relying on classical wavelength-multiplexed pilot tones or temporally interleaved polarized photons. Alignment and control is demonstrated in a 7.1 km deployed fiber loop as well as in a controlled drifting scenario.

Neoadjuvant Management of Adenocarcinoma of the Esophagus and Esophagogastric Junction: Review of Randomized Evidence and Definition of Optimum Treatment Algorithm.

BACKGROUND: Neoadjuvant chemotherapy (nCT) or chemoradiotherapy (nCRT) are accepted standards of care for the management of adenocarcinoma of the esophagus and gastroesophageal junction. SUMMARY: The MRC-OEO2 study established the role of 2 cycles of neoadjuvant cisplatin/fluoropyrimidine. More recently, the FLOT-AIO4 study demonstrated the superiority of perioperative FLOT chemotherapy (5FU, oxaliplatin, and docetaxel) compared to ECX (epirubicin, cisplatin, and capecitabine) regime. The results from the pivotal CROSS study established neoadjuvant CRT as a new standard of care in OG cancer. The survival benefits observed in FLOT and CROSS studies are similar [FLOT - hazard ratio 0.75 (0.62-0.92); CROSS - 0.741 (0.55-0.98)]. KEY MESSAGES: Both nCT and nCRT have been shown to be associated with survival benefit compared to surgery alone. We have performed a comprehensive review of the available evidence to define the optimum treatment algorithm and identify specific patient sub-groups who may be appropriate for the use of one or more of these neoadjuvant options.

Visualizing screening in noble-metal clusters: static vs. dynamic.

The localized surface-plasmon resonance of metal nanoparticles and clusters corresponds to a collective charge oscillation of the quasi-free metal electrons. The polarization of the more localized d electrons opposes the overall polarization of the electron cloud and thus screens the surface plasmon. By contrast, a static electric external field is well screened, as even very small noble-metal clusters are highly metallic: the field inside is practically zero except for the effect of the Friedel-oscillation-like modulations which lead to small values of the polarization of the d electrons. In the present article, we present and compare representations of the induced densities (i) connected to the surface-plasmon resonance and (ii) resulting from an external static electric field. The two cases allow for an intuitive understanding of the differences between the dynamic and the static screening.

Computational studies indicated the effectiveness of human metabolites against SARS-Cov-2 main protease.

To fight against the devastating coronavirus disease 2019 (COVID-19), identifying robust anti-SARS-CoV-2 therapeutics from all possible directions is necessary. To contribute to this effort, we selected a human metabolites database containing waters and lipid-soluble metabolites to screen against the 3-chymotrypsin-like proteases (3CLpro) protein of SARS-CoV-2. The top 8 hits from virtual screening displayed a docking score varying between ~ - 11 and ~ - 14 kcal/mol. Molecular dynamics simulations complement the virtual screening study in conjunction with the molecular mechanics generalized Born surface area (MM/GBSA) scheme. Our analyses revealed that (HMDB0132640) has the best glide docking score, - 14.06 kcal/mol, and MM-GBSA binding free energy, - 18.08 kcal/mol. The other three lead molecules are also selected along with the top molecule through a critical inspection of their pharmacokinetic properties. HMDB0132640 displayed a better binding affinity than the other three compounds (HMDB0127868, HMDB0134119, and HMDB0125821) due to increased favorable contributions from the intermolecular electrostatic and van der Waals interactions. Further, we have investigated the ligand-induced structural dynamics of the main protease. Overall, we have identified new compounds that can serve as potential leads for developing novel antiviral drugs against SARS-CoV-2 and elucidated molecular mechanisms of their binding to the main protease. Identification of probable hits from human metabolites against SARS-CoV-2 using integrated computational approaches-Missed against MS.

Exploring hypoxic biology to improve radiotherapy outcomes.

Ionising radiotherapy is a well-established, effective cancer treatment modality, whose efficacy has improved with the application of newer technological modalities. However, patient outcomes are governed and potentially limited by aspects of tumour biology that are associated with radioresistance. Patients also still endure treatment-associated toxicities owed to the action of ionising radiation in normoxic tissue adjacent to the tumour mass. Tumour hypoxia is recognised as a key component of the tumour microenvironment and is well established as leading to therapy resistance and poor prognosis. In this review, we outline the current understanding of hypoxia-mediated radiotherapy resistance, before exploring targeting tumour hypoxia for radiotherapy sensitisation to improve treatment outcomes and increase the therapeutic window. This includes increasing oxygen availability in solid tumours, the use of hypoxia-activated prodrugs, targeting of hypoxia-regulated or associated signalling pathways, as well as the use of high-LET radiotherapy modalities. Ultimately, targeting hypoxic radiobiology combined with precise radiotherapy delivery modalities and modelling should be associated with improvement to patient outcomes.

Dark current and single photon detection by 1550 nm avalanche photodiodes: dead time corrected probability distributions and entropy rates.

Single photon detectors have dark count rates that depend strongly on the bias level for detector operation. In the case of weak light sources such as novel lasers or single-photon emitters, the rate of counts due to the light source can be comparable to that of the detector dark counts. In such cases, a characterization of the statistical properties of the dark counts is necessary. The dark counts are often assumed to follow a Poisson process that is statistically independent of the incident photon counts. This assumption must be validated for specific types of photodetectors. In this work, we focus on single-photon avalanche photodiodes (SPADs) made for 1550 nm. For the InGaAs detectors used, we find the measured distributions often differ significantly from Poisson due to the presence of dead time and afterpulsing with the difference increasing with the bias level used for obtaining higher quantum efficiencies. We find that when the dead time is increased to remove the effects of afterpulsing, it is necessary to correct the measured distributions for the effects of the dead time. To this end, we apply an iterative algorithm to remove dead time effects from the probability distribution for dark counts as well as for the case where light from an external weak laser source (known to be Poisson) is detected together with the dark counts. We believe this to be the first instance of the comprehensive application of this algorithm to real data and find that the dead time corrected probability distributions are Poisson distributions in both cases. We additionally use the Grassberger-Procaccia algorithm to estimate the entropy production rates of the dark count processes, which provides a single metric that characterizes the temporal correlations between dark counts as well as the shape of the distribution. We have thus developed a systematic procedure for taking data with 1550 nm SPADs and obtaining accurate photocount statistics to examine novel light sources.

Probing the charge transfer and electron-hole asymmetry in graphene-graphene quantum dot heterostructure.

In recent years, graphene-based van der Waals (vdW) heterostructures have come into prominence showcasing interesting charge transfer dynamics which is significant for optoelectronic applications. These novel structures are highly tunable depending on several factors such as the combination of the two-dimensional materials, the number of layers and band alignment exhibiting interfacial charge transfer dynamics. Here, we report on a novel graphene based 0D-2D vdW heterostructure between graphene and amine-functionalized graphene quantum dots (GQD) to investigate the interfacial charge transfer and doping possibilities. Using a combination ofab initiosimulations and Kelvin probe force microscopy (KPFM) measurements, we confirm that the incorporation of functional GQDs leads to a charge transfer induced p-type doping in graphene. A shift of the Dirac point by 0.05 eV with respect to the Fermi level (EF) in the graphene from the heterostructure was deduced from the calculated density of states. KPFM measurements revealed an increment in the surface potential of the GQD in the 0D-2D heterostructure by 29 mV with respect to graphene. Furthermore, we conducted power dependent Raman spectroscopy for both graphene and the heterostructure samples. An optical doping-induced gating effect resulted in a stiffening of theGband for electrons and holes in both samples (graphene and the heterostructure), suggesting a breakdown of the adiabatic Born-Oppenheimer approximation. Moreover, charge imbalance and renormalization of the electron-hole dispersion under the additional influence of the doped functional GQDs is pointing to an asymmetry in conduction and carrier mobility.

Unraveling the Molecular Mechanism of Recognition of Human Interferon-Stimulated Gene Product 15 by Coronavirus Papain-Like Proteases: A Multiscale Simulation Study.

The papain-like protease (PLpro) of the coronavirus (CoV) family plays an essential role in processing the viral polyprotein and immune evasion. Additional proteolytic activities of PLpro include deubiquitination and deISGylation, which can reverse the post-translational modification of cellular proteins conjugated with ubiquitin or (Ub) or Ub-like interferon-stimulated gene product 15 (ISG15). These activities regulate innate immune responses against viral infection. Thus, PLpro is a potential antiviral target. Here, we have described the structural and energetic basis of recognition of PLpro by the human ISG15 protein (hISG15) using atomistic molecular dynamics simulation across the CoV family, i.e., MERS-CoV (MCoV), SARS-CoV (SCoV), and SARS-CoV-2 (SCoV2). The cumulative simulation length for all trajectories was 32.0 µs. In the absence of the complete crystal structure of complexes, protein-protein docking was used. A mutation (R167E) was introduced across all three PLpro to study the effect of mutation on the protein-protein binding. Our study reveals that the apo-ISG15 protein remains closed while it adopts an open conformation when bound to PLpro, although the degree of openness varies across the CoV family. The binding free energy analysis suggests that hISG15 binds more strongly with SCoV2-PLpro compared to SCoV or MCoV. The intermolecular electrostatic interaction drives the hISG15-PLpro complexation. Our study showed that SCoV or MCoV-PLpro binds more strongly with the C-domain of hISG15, while SCoV2-PLpro binds more favorably the N-domain of hISG15. Overall, our study explains the molecular basis of differential deISGylating activities of PLpro among the CoV family and the specificity of SCoV2-PLpro toward hISG15.

An in-silico study on selected organosulfur compounds as potential drugs for SARS-CoV-2 infection via binding multiple drug targets.

The emerging paradigm shift from 'one molecule, one target, for one disease' towards 'multi-targeted small molecules' has paved an ingenious pathway in drug discovery in recent years. We extracted this idea for the investigation of drugs for COVID-19. Perceiving the importance of organosulfur compounds, seventy-six known organosulfur compounds were screened and studied for the interaction with multiple SARS-CoV-2 target proteins by molecular dynamics simulation. Lurasidone and its derivatives displayed substantial binding affinity against five proteins (Mpro, PLpro, Spro, helicase and RdRp). The pharmacokinetics, ADMET properties and target prediction studies performed in this work further potentiates the effectiveness against SARS-CoV-2.

Advanced Methodology and Preliminary Measurements of Molecular and Mechanical Properties of Heart Valves under Dynamic Strain.

Mammalian heart valves are soft tissue assemblies with multi-scale material properties. This is because they are constructs comprising both muscle and non-contractile extracellular matrix proteins (such as collagens and proteoglycans) and transition regions where one form of tissue structure becomes another, significantly different form. The leaflets of the mitral and tricuspid valves are connected to chordae tendinae which, in turn, bind through papillary muscles to the cardiac wall of the ventricle. The transition regions between these tissue subsets are complex and diffuse. Their material composition and mechanical properties have not been previously described with both micro and nanoscopic data recorded simultaneously, as reported here. Annotating the mechanical characteristics of these tissue transitions will be of great value in developing novel implants, improving the state of the surgical simulators and advancing robot-assisted surgery. We present here developments in multi-scale methodology that produce data that can relate mechanical properties to molecular structure using scanning X-ray diffraction. We correlate these data to corresponding tissue level (macro and microscopic) stress and strain, with particular emphasis on the transition regions and present analyses to indicate points of possible failure in these tissues.

Functional Grading of a Transversely Isotropic Hyperelastic Model with Applications in Modeling Tricuspid and Mitral Valve Transition Regions.

Surgical simulators and injury-prediction human models require a combination of representative tissue geometry and accurate tissue material properties to predict realistic tool-tissue interaction forces and injury mechanisms, respectively. While biological tissues have been individually characterized, the transition regions between tissues have received limited research attention, potentially resulting in inaccuracies within simulations. In this work, an approach to characterize the transition regions in transversely isotropic (TI) soft tissues using functionally graded material (FGM) modeling is presented. The effect of nonlinearities and multi-regime nature of the TI model on the functional grading process is discussed. The proposed approach has been implemented to characterize the transition regions in the leaflet (LL), chordae tendinae (CT) and the papillary muscle (PM) of porcine tricuspid valve (TV) and mitral valve (MV). The FGM model is informed using high resolution morphological measurements of the collagen fiber orientation and tissue composition in the transition regions, and deformation characteristics predicted by the FGM model are numerically validated to experimental data using X-ray diffraction imaging. The results indicate feasibility of using the FGM approach in modeling soft-tissue transitions and has implications in improving physical representation of tissue deformation throughout the body using a scalable version of the proposed approach.

Topological Control of Synchronization Patterns: Trading Symmetry for Stability.

Symmetries are ubiquitous in network systems and have profound impacts on the observable dynamics. At the most fundamental level, many synchronization patterns are induced by underlying network symmetry, and a high degree of symmetry is believed to enhance the stability of identical synchronization. Yet, here we show that the synchronizability of almost any symmetry cluster in a network of identical nodes can be enhanced precisely by breaking its structural symmetry. This counterintuitive effect holds for generic node dynamics and arbitrary network structure and is, moreover, robust against noise and imperfections typical of real systems, which we demonstrate by implementing a state-of-the-art optoelectronic experiment. These results lead to new possibilities for the topological control of synchronization patterns, which we substantiate by presenting an algorithm that optimizes the structure of individual clusters under various constraints.

Laminar Chaos in Experiments: Nonlinear Systems with Time-Varying Delays and Noise.

A new type of dynamics called laminar chaos was recently discovered through a theoretical analysis of a scalar delay differential equation with time-varying delay. Laminar chaos is a low-dimensional dynamics characterized by laminar phases of nearly constant intensity with periodic durations and a chaotic variation of the intensity from one laminar phase to the next laminar phase. This is in stark contrast to the typically observed higher-dimensional turbulent chaos, which is characterized by strong fluctuations. In this Letter we provide the first experimental observation of laminar chaos by studying an optoelectronic feedback loop with time-varying delay. The noise inherent in the experiment requires the development of a nonlinear Langevin equation with variable delay. The results show that laminar chaos can be observed in higher-order systems, and that the phenomenon is robust to noise and a digital implementation of the variable time delay.

Delayed dynamical systems: networks, chimeras and reservoir computing.

We present a systematic approach to reveal the correspondence between time delay dynamics and networks of coupled oscillators. After early demonstrations of the usefulness of spatio-temporal representations of time-delay system dynamics, extensive research on optoelectronic feedback loops has revealed their immense potential for realizing complex system dynamics such as chimeras in rings of coupled oscillators and applications to reservoir computing. Delayed dynamical systems have been enriched in recent years through the application of digital signal processing techniques. Very recently, we have showed that one can significantly extend the capabilities and implement networks with arbitrary topologies through the use of field programmable gate arrays. This architecture allows the design of appropriate filters and multiple time delays, and greatly extends the possibilities for exploring synchronization patterns in arbitrary network topologies. This has enabled us to explore complex dynamics on networks with nodes that can be perfectly identical, introduce parameter heterogeneities and multiple time delays, as well as change network topologies to control the formation and evolution of patterns of synchrony. This article is part of the theme issue 'Nonlinear dynamics of delay systems'.

Using machine learning to assess short term causal dependence and infer network links.

We introduce and test a general machine-learning-based technique for the inference of short term causal dependence between state variables of an unknown dynamical system from time-series measurements of its state variables. Our technique leverages the results of a machine learning process for short time prediction to achieve our goal. The basic idea is to use the machine learning to estimate the elements of the Jacobian matrix of the dynamical flow along an orbit. The type of machine learning that we employ is reservoir computing. We present numerical tests on link inference of a network of interacting dynamical nodes. It is seen that dynamical noise can greatly enhance the effectiveness of our technique, while observational noise degrades the effectiveness. We believe that the competition between these two opposing types of noise will be the key factor determining the success of causal inference in many of the most important application situations.

Harvesting entropy and quantifying the transition from noise to chaos in a photon-counting feedback loop.

Many physical processes, including the intensity fluctuations of a chaotic laser, the detection of single photons, and the Brownian motion of a microscopic particle in a fluid are unpredictable, at least on long timescales. This unpredictability can be due to a variety of physical mechanisms, but it is quantified by an entropy rate. This rate, which describes how quickly a system produces new and random information, is fundamentally important in statistical mechanics and practically important for random number generation. We experimentally study entropy generation and the emergence of deterministic chaotic dynamics from discrete noise in a system that applies feedback to a weak optical signal at the single-photon level. We show that the dynamics transition from shot noise to chaos as the photon rate increases and that the entropy rate can reflect either the deterministic or noisy aspects of the system depending on the sampling rate and resolution.

Experiments with arbitrary networks in time-multiplexed delay systems.

We report a new experimental approach using an optoelectronic feedback loop to investigate the dynamics of oscillators coupled on large complex networks with arbitrary topology. Our implementation is based on a single optoelectronic feedback loop with time delays. We use the space-time interpretation of systems with time delay to create large networks of coupled maps. Others have performed similar experiments using high-pass filters to implement the coupling; this restricts the network topology to the coupling of only a few nearest neighbors. In our experiment, the time delays and coupling are implemented on a field-programmable gate array, allowing the creation of networks with arbitrary coupling topology. This system has many advantages: the network nodes are truly identical, the network is easily reconfigurable, and the network dynamics occur at high speeds. We use this system to study cluster synchronization and chimera states in both small and large networks of different topologies.

Experimental observation of chimera and cluster states in a minimal globally coupled network.

A "chimera state" is a dynamical pattern that occurs in a network of coupled identical oscillators when the symmetry of the oscillator population is broken into synchronous and asynchronous parts. We report the experimental observation of chimera and cluster states in a network of four globally coupled chaotic opto-electronic oscillators. This is the minimal network that can support chimera states, and our study provides new insight into the fundamental mechanisms underlying their formation. We use a unified approach to determine the stability of all the observed partially synchronous patterns, highlighting the close relationship between chimera and cluster states as belonging to the broader phenomenon of partial synchronization. Our approach is general in terms of network size and connectivity. We also find that chimera states often appear in regions of multistability between global, cluster, and desynchronized states.

Robot-Guided Atomic Force Microscopy for Mechano-Visual Phenotyping of Cancer Specimens.

Atomic force microscopy (AFM) and other forms of scanning probe microscopy have been successfully used to assess biomechanical and bioelectrical characteristics of individual cells. When extending such approaches to heterogeneous tissue, there exists the added challenge of traversing the tissue while directing the probe to the exact location of the targeted biological components under study. Such maneuvers are extremely challenging owing to the relatively small field of view, limited availability of reliable visual cues, and lack of context. In this study we designed a system that leverages the visual topology of the serial tissue sections of interest to help guide robotic control of the AFM stage to provide the requisite navigational support. The process begins by mapping the whole-slide image of a stained specimen with a well-matched, consecutive section of unstained section of tissue in a piecewise fashion. The morphological characteristics and localization of any biomarkers in the stained section can be used to position the AFM probe in the unstained tissue at regions of interest where the AFM measurements are acquired. This general approach can be utilized in various forms of microscopy for navigation assistance in tissue specimens.

Towards an Automated MEMS-based Characterization of Benign and Cancerous Breast Tissue using Bioimpedance Measurements.

Micro-Electro-Mechanical-Systems (MEMS) are desirable for use within medical diagnostics because of their capacity to manipulate and analyze biological materials at the microscale. Biosensors can be incorporated into portable lab-on-a-chip devices to quickly and reliably perform diagnostics procedure on laboratory and clinical samples. In this paper, electrical impedance-based measurements were used to distinguish between benign and cancerous breast tissues using microchips in a real-time and label-free manner. Two different microchips having inter-digited electrodes (10 µm width with 10 µm spacing and 10 µm width with 30 µm spacing) were used for measuring the impedance of breast tissues. The system employs Agilent E4980A precision impedance analyzer. The impedance magnitude and phase were collected over a frequency range of 100 Hz to 2 MHz. The benign group and cancer group showed clearly distinguishable impedance properties. At 200 kHz, the difference in impedance of benign and cancerous breast tissue was significantly higher (3110 Ω) in the case of microchips having 10 µm spacing compared to microchip having 30 µm spacing (568 Ω).