The Unitary Micro-Immunotherapy Medicine Interferon-γ (4 CH) Displays Similar Immunostimulatory and Immunomodulatory Effects than Those of Biologically Active Human Interferon-γ on Various Cell Types

As a cytokine, gamma-interferon (IFN-γ) is considered a key player in the fine-tuned orchestration of immune responses. The extreme cellular sensitivity to cytokines is attested by the fact that very few of these bioactive molecules per cell are enough to trigger cellular functions. These findings can, at least partially, explain how/why homeopathically-prepared cytokines, and especially micro-immunotherapy (MI) medicines, are able to drive cellular responses. We focused our fundamental research on a unitary MI preparation of IFN-γ, specifically employed at 4 CH, manufactured and impregnated onto sucrose-lactose pillules as all other MI medicines. We assessed the IFN-γ concentration in the medium after dilution of the IFN-γ (4 CH)-bearing pillules and we evaluated in vitro drug responses in a wide range of immune cells, and in endothelial cells. Our results showed that IFN-γ (4 CH) stimulated the proliferation, the activation and the phagocytic capabilities of primary immune cells, as well as modulated their cytokine-secretion and immunity-related markers’ expression in a trend that is quite comparable with the well-recognized biological effects induced by IFN-γ. Altogether, these data provide novel and additional evidences on MI medicines, and specifically when active substances are prepared at 4 CH, thus suggesting the need for more investigations.     

Improved deep learning-based macromolecules structure classification from electron cryo-tomograms

Cellular processes are governed by macromolecular complexes inside the cell. Study of the native structures of macromolecular complexes has been extremely difficult due to lack of data. With recent breakthroughs in Cellular Electron Cryo-Tomography (CECT) 3D imaging technology, it is now possible for researchers to gain accesses to fully study and understand the macromolecular structures single cells. However, systematic recovery of macromolecular structures from CECT is very difficult due to high degree of structural complexity and practical imaging limitations. Specifically, we proposed a deep learning-based image classification approach for large-scale systematic macromolecular structure separation from CECT data. However, our previous work was only a very initial step toward exploration of the full potential of deep learning-based macromolecule separation. In this paper, we focus on improving classification performance by proposing three newly designed individual CNN models: an extended version of (Deep Small Receptive Field) DSRF3D, donated as DSRF3D-v2, a 3D residual block-based neural network, named as RB3D, and a convolutional 3D (C3D)-based model, CB3D. We compare them with our previously developed model (DSRF3D) on 12 datasets with different SNRs and tilt angle ranges. The experiments show that our new models achieved significantly higher classification accuracies. The accuracies are not only higher than 0.9 on normal datasets, but also demonstrate potentials to operate on datasets with high levels of noises and missing wedge effects presented.     

An Adaptive Middleware for Context-Sensitive Communications for Real-Time Applications in Ubiquitous Computing Environments

Context-sensitivity is an important expected capability in applications in ubiquitous computing (ubicomp) environments. These applications need to use different contextual information from the user, host device, on board sensors, network, and the ambient environments to systematically adapt their actions. In addition, some context-sensitive applications may use specific contextual conditions to trigger impromptu and possibly short-lived interactions with applications in other devices. This property, referred to as context-sensitive or context-aware communications, allows applications to form short-range mobile ad hoc networks consisting of mobile and stationary devices, sensors, and other computing resources. Real-time applications, especially those having reactive behavior, running on embedded devices and requiring context-sensitive communications support, pose new challenges related to systematic representation of specific contexts, associations of contexts with real-time actions, timely context data collection and propagation, and transparent context-sensitive connection establishment. An object-based middleware can be effective to meet these challenges if such a middleware can provide a well-defined development framework as well as lightweight runtime services. In this paper, an adaptive and object-based middleware, called reconfigurable context-sensitive middleware (RCSM) is presented to facilitate context-sensitive communications in ubicomp environments. To facilitates context-sensitive communications, RCSM provides a context-aware interface definition language for specifying context-sensitive interfaces of real-time objects, an object container framework for generating interfaces-specific context-analyzers, and a context-sensitive object request broker for context-sensitive object discovery and impromptu connection management. RCSM is adaptive in the sense that depending on the context-sensitive behavior of the applications, it adapts its object discovery and connection management mechanisms.     

Promoting the charge separation and photoelectrocatalytic water reduction kinetics of Cu_2O nanowires via decorating dual-cocatalysts

Developing high activity and low-cost materials to produce hydrogen by the sustainable way of photoelectrochemical is key to social development.The abundance and inexpensive Cu₂O has been received increasing research as its suitable energy level for photocatalytic water reduction.However,the fast charge recombination rate and the sluggish catalytic kinetics are the huge challenges facing the Cu₂O photoreduction.Here,the highly reactive Cu₂O@C-MoS₂photocathode is constructed by depositing dual-cocatalysts of the carbon layer and MoS₂nanosheets on Cu₂O nanowires to realize efficient water reduction.An impressive carrier concentration of 6.59×10²³cm^-3is received,which is 2.78 times of the bare Cu₂O,resulting in remarkable enhancement in photocurrent density of 3.34 times for the Cu₂O@CMoS₂photocathode.Moreover,the applied bias photon-to-current conversion efficiency of the bare Cu₂O enhanced 4.5 times from 0.16%to 0.72%in the Cu₂O@C-MoS₂photocathode.The analysis shows that the Cu₂O as light absorber,the carbon layer as electron transfer promoter,and MoS₂nanosheets as catalytic sites,thus facilitating chrage separation and enhancing catalytic kinetics.This system paves a feasible strategy for designing other photoelectrodes to realize efficient charge separation and high catalytic activity.     

Optimal state filter from sequential unknown input reconstruction: Application to distributed state filtering

This paper generalizes Kalman filtering with an intermittent unknown input problem to be left invertible discrete‐time stochastic linear systems with zero, one, or more structural delays. Contrary to the state filtering–based system inversion where the unknown input vector is reconstructed with a time delay that is equal to the structural delay of the plant, we propose an optimal state filtering by reconstructing some linear combinations of the unknown input vector with a time delay less than the structural delay. Designed under a sequential unknown input decoupling constraint, which has never been previously studied in the literature, all presented filters are very computationally efficient. The proposed state filtering is used to solve the autonomous distributed state filtering problem in large‐scale networked control systems when the unknown input vector represents interactions between subsystems and when each subsystem receives intermittent information about the interaction from unreliable networks. The stochastic stability conditions of the extended intermittent unknown input Kalman filter are established when the arrival binary sequence of packet dropouts follows a random Bernoulli process.     

Shock-Driven Endotheliopathy in Trauma Patients Is Associated with Leucocyte Derived Extracellular Vesicles

Endotheliopathy following trauma is associated with poor outcome, but the underlying mechanisms are unknown. This study hypothesized that an increased extracellular vesicle (EV) concentration is associated with endotheliopathy after trauma and that red blood cell (RBC) transfusion could further enhance endotheliopathy. In this post hoc sub study of a multicentre observational trial, 75 trauma patients were stratified into three groups based on injury severity score or shock. In patient plasma obtained at hospital admission and after transfusion of four RBC transfusions, markers for endotheliopathy were measured and EVs were labelled with anti CD41 (platelet EVs), anti CD235a (red blood cell EVs), anti CD45 (leucocyte EVs), anti CD144 (endothelial EVs) or anti CD62e (activated endothelial EVs) and EV concentrations were measured with flow cytometry. Statistical analysis was performed by a Kruskall Wallis test with Bonferroni correction or Wilcoxon rank test for paired data. In patients with shock, syndecan-1 and von Willebrand Factor (vWF) were increased compared to patients without shock. Additionally, patients with shock had increased red blood cell EV and leucocyte EV concentrations compared to patients without shock. Endotheliopathy markers correlated with leucocyte EVs (ρ = 0.263, p = 0.023), but not with EVs derived from other cells. Injury severity score had no relation with EV release. RBC transfusion increased circulating red blood cell EVs but did not impact endotheliopathy. In conclusion, shock is (weakly) associated with EVs from leucocytes, suggesting an immune driven pathway mediated (at least in part) by shock.     

Correlations between silt density index,turbidity and oxidation-reduction potential parameters in seawater reverse osmosis desalination

The reverse osmosis method is one of the most widely used methods of seawater desalination at present.Hydrophilic and desalting membranes in reverse osmosis systems are highly susceptible to the input pollutants.Various contaminants,including suspended organic and inorganic matter,result in membrane fouling and membrane degradation.Fundamental parameters such as the turbidity,the amount of chlorine injection,and silt density index(SDI)are the most predominant parameters of fouling control in the membranes.In this study,the operation system included a water intake unit,a pretreatment system,and an RO system.The pretreatment system encompassed a clarifier,a gravity sand filter,pressurized sand filters,and a cartridge filter.The correlation between the amount of chlorine injection in terms of the oxidation-reduction potential(ORP)and the SDI value of the input water was investigated at a specified site next to the Persian Gulf.The results showed that,at certain intervals of inlet turbidity,injection of a certain amount of chlorine into the raw water has a distinct effect on the decrease of SDI.     

Scattered Data Interpolation Using Cubic Trigonometric Bézier Triangular Patch

This paper discusses scattered data interpolation using cubic trigonometric Bézier triangular patches with continuity everywhere. We derive the condition on each adjacent triangle. On each triangular patch, we employ convex combination method between three local schemes. The final interpolant with the rational corrected scheme is suitable for regular and irregular scattered data sets. We tested the proposed scheme with 36,65, and 100 data points for some well-known test functions. The scheme is also applied to interpolate the data for the electric potential. We compared the performance between our proposed method and existing scattered data interpolation schemes such as Powell–Sabin (PS) and Clough–Tocher (CT) by measuring the maximum error, root mean square error (RMSE) and coefficient of determination (). From the results obtained, our proposed method is competent with cubic Bézier, cubic Ball, PS and CT triangles splitting schemes to interpolate scattered data surface. This is very significant since PS and CT requires that each triangle be splitting into several micro triangles.