Adipose tissue has many important functions including metabolic energy storage, endocrine functions, thermoregulation and structural support. Given these varied functions, the microvascular characteristics within the tissue will have important roles in determining rates/limits of exchange of nutrients, waste, gases and molecular signaling molecules between adipose tissue and blood. Studies on skeletal muscle have suggested that tissues with higher aerobic capacity contain higher microvascular density (MVD) with lower diffusion distances (DD) than less aerobically active tissues. However, little is known about MVD in adipose tissue of most vertebrates; therefore, we measured microvascular characteristics (MVD, DD, diameter and branching) and cell size to explore the comparative aerobic activity in the adipose tissue across diving tetrapods, a group of animals facing additional physiological and metabolic stresses associated with diving. Adipose tissues of 33 animals were examined, including seabirds, sea turtles, pinnipeds, baleen whales and toothed whales. MVD and DD varied significantly (P < 0.001) among the groups, with seabirds generally having high MVD, low DD and small adipocytes. These characteristics suggest that microvessel arrangement in short duration divers (seabirds) reflects rapid lipid turnover, compared to longer duration divers (beaked whales) which have relatively lower MVD and greater DD, perhaps reflecting the requirement for tissue with lower metabolic activity, minimizing energetic costs during diving. Across all groups, predictable scaling patterns in MVD and DD such as those observed in skeletal muscle did not emerge, likely reflecting the fact that unlike skeletal muscle, adipose tissue performs many different functions in marine organisms, often within the same tissue compartment.

Electrochemical oxygen reduction is a promising method for in situ H2O2 production. Its important precondition is that dissolved oxygen molecules have to diffuse to and arrive at the cathode surface for reacting with electrons. Obviously, shortening the diffusion distance is beneficial to improve the reaction efficiency. In this study, a novel microchannel aeration mode was proposed to confine the diffusion distance of O2 to the micrometer level. For this mode, an aeration cathode was fabricated from a carbon block with microchannel arrays. The diameter of each channel was only 10-40 μm. Oxygen will be pumped into every microchannel from the top entry, while an aqueous solution will permeate into microchannels through the bottom entry and pores in the channel wall. This microchannel aeration cathode exhibited a H2O2 yield of up to 4.34 mg h-1 cm-2, about eight times higher than that of the common bubbling aeration mode. The corresponding energy consumption was only 7.35 kWh kg-1, which was superior to most reported results. In addition to H2O2, this aeration cathode may also produce •OH via a one-electron reduction of H2O2. In combination with H2O2 and •OH, phenol, sulfamethoxazole, and atrazine were degraded effectively. We expect that this microchannel aeration cathode may inspire researchers focused on H2O2 production, water pollutant control, and other multiphase interfacial reactions.

Small interfering RNA (siRNA) is regarded as one of the most powerful tools for the treatment of various diseases by downregulating the expression of aberrant proteins. Delivery vehicle is often necessary for getting siRNA into the cells. Nanocomplex using polyamidoamine (PAMAM) is regarded a promising approach for the delivery of siRNA. The size of siRNA nanocomplexes is a critical attribute in order to achieve high gene silencing efficiency in vivo. Microfluidics provides advantages in the preparation of siRNA nanocomplexes due to better reproducibility and a potential for more robust process control. The mixing efficiency of siRNA and PAMAM is different in microfluidics systems with different geometries, therefore, resulting in nanocomplexes with varying size attributes. In this study, hydrodynamic flow focusing microfluidic chips with different channel designs, i.e. diameters/widths, channel shapes (cylindrical/rectangular) and inter-channel spacings were optimized in silico and rapidly prototyped using 3D printing and finally, used for production of siRNA nanocomplexes. The fluid mixing inside the microfluidic chips was simulated using the finite element method (FEM) with the single-phase laminar flow interface in connection with the transport of diluted species interface. The digital design and optimization of microfluidic chips showed consistency with experimental results. It was concluded that the size of siRNA nanocomplexes can be controlled by adjusting the channel geometry of the microfluidic chips and the simulation with FEM could be used to facilitate the design and optimization of microfluidic chips in order to produce nanocomplexes with desirable attributes.

In wastewater treatment, oxygen effective diffusion coefficient (D eff ) is a key parameter in the study of oxygen diffusion-reaction process and mechanism in biofilms. Almost all the reported methods for estimating the D eff rely on other biokinetic parameters, such as substrate consumption rate and reaction rate constant. Then, the estimation was complex. In this study, a method independent of other biokinetic parameters was proposed for estimating the dissolved oxygen (DO) D eff in biofilms. It was based on the dynamic DO microdistribution in a non-steady-state inactive biofilm, which was measured by the oxygen transfer modeling device (OTMD) combining with an oxygen microelectrode system. A pure DO diffusion model was employed, and the expression of the DO D eff was obtained by applying the analytical solution of the model to a selected critical DO concentration. DO D eff in the biofilm from the bioreactor was calculated as (1.054 ± 0.041) × 10-9 m2/s, and it was in the same order of magnitude with the reported results. Therefore, the method proposed in this study was effective and feasible. Without measurement of any other biokinetic parameters, this method was convenient and will benefit the study of oxygen transport-reaction process in biofilms and other biofouling deposits. Graphical abstract ᅟ.

Epilepsy is a chronic neurological disorder characterized by sudden and apparently unpredictable seizures. A system capable of forecasting the occurrence of seizures is crucial and could open new therapeutic possibilities for human health. This paper addresses an algorithm for seizure prediction using a novel feature - diffusion distance (DD) in intracranial Electroencephalograph (iEEG) recordings. Wavelet decomposition is conducted on segmented electroencephalograph (EEG) epochs and subband signals at scales 3, 4 and 5 are utilized to extract the diffusion distance. The features of all channels composing a feature vector are then fed into a Bayesian Linear Discriminant Analysis (BLDA) classifier. Finally, postprocessing procedure is applied to reduce false prediction alarms. The prediction method is evaluated on the public intracranial EEG dataset, which consists of 577.67[Formula: see text]h of intracranial EEG recordings from 21 patients with 87 seizures. We achieved a sensitivity of 85.11% for a seizure occurrence period of 30[Formula: see text]min and a sensitivity of 93.62% for a seizure occurrence period of 50[Formula: see text]min, both with the seizure prediction horizon of 10[Formula: see text]s. Our false prediction rate was 0.08/h. The proposed method yields a high sensitivity as well as a low false prediction rate, which demonstrates its potential for real-time prediction of seizures.

The purpose of this study is to introduce diffusion methods as a tool to label CT scan images according to their position in the human body. A comparative study of different methods based on a k-NN search is carried out and we propose a new, simple and efficient way of applying diffusion techniques that is able to give better location forecasts than methods that can be considered the current state-of-the-art.

Approximately 1% of the world\'s population suffers from epilepsy. An automatic seizure detection system is of great significance in the monitoring and diagnosis of epilepsy. In this paper, a novel method is proposed for automatic seizure detection in intracranial EEG recordings. The EEG recordings are divided into 4-s epochs, and then wavelet decomposition with five scales is performed to the EEG epochs. Detail signals at scales 3, 4, and 5 are selected to form a signal distribution. The diffusion distances are extracted as features, and Bayesian linear discriminant analysis (BLDA) is used as the classifier. A total of 193.75h of intracranial EEG recordings from 21 patients having 87 seizures are employed to evaluate the system, and the average sensitivity of 94.99%, specificity of 98.74%, and false-detection rate of 0.24/h are achieved. The seizure detection system based on diffusion distance yields a high sensitivity as well as a low false-detection rate for long-term EEG recordings.