Machine learning for the diagnosis of early stage diabetes using temporal glucose profiles

Machine learning shows remarkable success for recognizing patterns in data. Here we apply the machine learning (ML) for the diagnosis of early stage diabetes, which is known as a challenging task in medicine. Blood glucose levels are tightly regulated by two counter-regulatory hormones, insulin and glucagon, and the failure of the glucose homeostasis leads to the common metabolic disease, diabetes mellitus. It is a chronic disease that has a long latent period the complicates detection of the disease at an early stage. The vast majority of diabetics result from that diminished effectiveness of insulin action. The insulin resistance must modify the temporal profile of blood glucose. Thus we propose to use ML to detect the subtle change in the temporal pattern of glucose concentration. Time series data of blood glucose with sufficient resolution is currently unavailable, so we confirm the proposal using synthetic data of glucose profiles produced by a biophysical model that considers the glucose regulation and hormone action. Multi-layered perceptrons, convolutional neural networks, and recurrent neural networks all identified the degree of insulin resistance with high accuracy above $85\%$.Comment: 4 pages, 2 figur

Ferromagnetically coupled magnetic impurities in a quantum point contact

We investigate the ground and excited states of interacting electrons in a quantum point contact using exact diagonalization method. We find that strongly localized states in the point contact appear when a new conductance channel opens due to momentum mismatch. These localized states form magnetic impurity states which are stable in a finite regime of chemical potential and excitation energy. Interestingly, these magnetic impurities have ferromagnetic coupling, which shed light on the experimentally observed puzzling coexistence of Kondo correlation and spin filtering in a quantum point contact

Self-sustained oscillations in nanoelectromechanical systems induced by Kondo resonance

We investigate instability and dynamical properties of nanoelectromechanical systems represented by a single-electron device containing movable quantum dot attached to a vibrating cantilever via asymmetric tunnel contact. The Kondo resonance in electron tunneling between source and shuttle facilitates self-sustained oscillations originated from strong coupling of mechanical and electronic/spin degrees of freedom. We analyze stability diagram for two-channel Kondo shuttling regime due to limitations given by the electromotive force acting on a moving shuttle and find that the saturation amplitude of oscillation is associated with the retardation effect of Kondo-cloud. The results shed light on possible ways of experimental realization of dynamical probe for the Kondo-cloud by using high tunability of mechanical dissipation as well as supersensitive detection of mechanical displacement

Shuttle-promoted nano-mechanical current switch

We investigate electron shuttling in three-terminal nanoelectromechanocal device built on a movable metallic rod oscillating between two drains. The device shows a double-well shaped electromechanical potential tunable by a source-drain bias voltage. Four stationary regimes controllable by the bias are found for this device: (i) single stable fixed point, (ii) two stable fixed points, (iii) two limiting cycles, and (iv) single limiting cycle. In the presence of perpendicular magnetic field the Lorentz force makes possible switching from one electromechanical state to another. The mechanism of tunable transitions between various stable regimes based on the interplay between voltage controlled electromechanical instability and magnetically controlled switching is suggested. The switching phenomenon is implemented for achieving both a reliable \emph{active} current switch and sensoring of small variations of magnetic field.Comment: 11 pages, 4 figure

A machine learning approach to discover migration modes and transition dynamics of heterogeneous dendritic cells

Dendritic cell (DC) migration is crucial for mounting immune responses. Immature DCs (imDCs) reportedly sense infections, while mature DCs (mDCs) move quickly to lymph nodes to deliver antigens to T cells. However, their highly heterogeneous and complex innate motility remains elusive. Here, we used an unsupervised machine learning (ML) approach to analyze long-term, two-dimensional migration trajectories of Granulocyte-macrophage colony-stimulating factor (GMCSF)-derived bone marrow-derived DCs (BMDCs). We discovered three migratory modes independent of the cell state: slow-diffusive (SD), slow-persistent (SP), and fast-persistent (FP). Remarkably, imDCs more frequently changed their modes, predominantly following a unicyclic SD→FP→SP→SD transition, whereas mDCs showed no transition directionality. We report that DC migration exhibits a history-dependent mode transition and maturation-dependent motility changes are emergent properties of the dynamic switching of the three migratory modes. Our ML-based investigation provides new insights into studying complex cellular migratory behavior

Insulin modulates the frequency of Ca2+ oscillations in mouse pancreatic islets

Pancreatic islets can adapt to oscillatory glucose to produce synchronous insulin pulses. Can islets adapt to other oscillatory stimuli, specifically insulin? To answer this question, we stimulated islets with pulses of exogenous insulin and measured their Ca2+ oscillations. We observed that sufficiently high insulin (>500 nM) with an optimal pulse period (similar to 4 min) could make islets to produce synchronous Ca2+ oscillations. Glucose and insulin, which are key stimulatory factors of islets, modulate islet Ca2+ oscillations differently. Glucose increases the active-to-silent ratio of phases, whereas insulin increases the period of the oscillation. To examine the dual modulation, we adopted a phase oscillator model that incorporated the phase and frequency modulations. This mathematical model showed that out-of-phase oscillations of glucose and insulin were more effective at synchronizing islet Ca2+ oscillations than in-phase stimuli. This finding suggests that a phase shift in glucose and insulin oscillations can enhance inter-islet synchronization.111Ysciescopu

Probing the Importance of Charge Balance and Noise Current in WSe2/WS2/MoS(2)van der Waals Heterojunction Phototransistors by Selective Electrostatic Doping

Heterojunction structures using 2D materials are promising building blocks for electronic and optoelectronic devices. The limitations of conventional silicon photodetectors and energy devices are able to be overcome by exploiting quantum tunneling and adjusting charge balance in 2D p–n and n–n junctions. Enhanced photoresponsivity in 2D heterojunction devices can be obtained with WSe2 and BP as p-type semiconductors and MoS2 and WS2 as n-type semiconductors. In this study, the relationship between photocurrent and the charge balance of electrons and holes in van der Waals heterojunctions is investigated. To observe this phenomenon, a p-WSe2/n-WS2/n-MoS2 heterojunction device with both p–n and n–n junctions is fabricated. The device can modulate the charge carrier balance between heterojunction layers to generate photocurrent upon illumination by selectively applying electrostatic doping to a specific layer. Using photocurrent mapping, the operating transition zones for the device is demonstrated, allowing to accurately identify the locations where photocurrent generates. Finally, the origins of flicker and shot noise at the different semiconductor interfaces are analyzed to understand their effect on the photoresponsivity and detectivity of unit active area (2.5 µm2, λ = 405 nm) in the p-WSe2/n-WS2/n-MoS2 heterojunction device. © 2020 The Authors. Published by Wiley-VCH GmbH1

Tripartite cell networks for glucose homeostasis

Controlling the excess and shortage of energy is a fundamental task for living organisms. Diabetes is a representative metabolic disease caused by the malfunction of energy homeostasis. The islets of Langerhans in the pancreas release long-range messengers, hormones, into the blood to regulate the homeostasis of the primary energy fuel, glucose. The hormone and glucose levels in the blood show rhythmic oscillations with a characteristic period of 5-10 min, and the functional roles of the oscillations are not clear. Each islet has alpha and beta cells that secrete glucagon and insulin, respectively. These two counter-regulatory hormones appear sufficient to increase and decrease glucose levels. However, pancreatic islets have a third cell type, delta cells, which secrete somatostatin. The three cell populations have a unique spatial organization in islets, and they interact to perturb their hormone secretions. The mini-organs of islets are scattered throughout the exocrine pancreas. Considering that the human pancreas contains approximately a million islets, the coordination of hormone secretion from the multiple sources of islets and cells within the islets should have a significant effect on human physiology. In this review, we introduce the hierarchical organization of tripartite cell networks, and recent biophysical modeling to systematically understand the oscillations and interactions of alpha, beta, and delta cells. Furthermore, we discuss the functional roles and clinical implications of hormonal oscillations and their phase coordination for the diagnosis of type II diabetes.11Nsciescopu

Accelerated continuous time quantum Monte Carlo method with machine learning

An acceleration of continuous time quantum Monte Carlo (CTQMC) methods is a potentially interesting branch of work as they are matchless as impurity solvers of a dynamical mean field theory (DMFT) approach for the description of strongly correlated systems. The inversion of the k x k matrix with k(2) operations given by the diagram expansion order k in the CTQMC fast update and the multiplication of the k x k matrix, and the noninteracting properties with k x omega(m-1)(nmax) operations to measure the in-point correlators, are computationally time consuming. Here we propose the CTQMC method in combination with a machine learning technique, which eliminates the k x omega(nmax) and k x omega(3)(nmax) operations for the two-point impurity Green's functions G(sigma) (i omega(n)) and four-point vertices chi(sigma), ((sigma) over bar) (i omega(n1), i omega(n2), i omega(n3), i omega(n4)), respectively. This method not only predicts the accurate physical properties at low temperature, but also dramatically decreases the computational times of chi(sigma), ((sigma) over bar) (i omega(n1), i omega(n2), i omega(n3), i omega(n4)) for the nonlocal extension of DMFT approximation.11Nsciescopuskc