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In this paper, drained consolidation tests of saturated remolded cohesive soil were carried out at different loading rates, and the samples at different loading stages were measured by nuclear magnetic resonance (NMR) testing. Through qualitative analysis of the relationship between the transverse relaxation time T2, peak area and porosity, the deformation micro-response of the saturated remolded cohesive soil was studied. The results showed that the transverse relaxation time T2 of the saturated remolded cohesive soil samples during the initial stage consisted of two spectral peaks, representing pores with large and small pore diameters, respectively. As loading progressed, the pore diameters in each sample group gradually shifted to smaller sizes, and the final form of the T2 spectrum was unimodal, in which the pores became denser and more uniform. In the initial stage of loading, the T2 spectrum of the soil sample with faster loading showed no obvious change trend due to the influence of drainage lag. After a sufficiently long dead load time, the final shape of the T2 spectrum was very similar regardless of loading rate, indicating that the loading rate only affected the intermediate state of the soil sample, and the final state was determined by the initial state of the sample. At the same time, after a certain dead load time, the changes in pore diameter were no longer obvious, indicating that the flowing fluid in the pore was drained and that drainage consolidation was complete. According to the positive correlation between the loading rate and maximum pore pressure, a boundary rate was obtained. We could reasonably infer that if the controlled loading rate was less than this value, drainage consolidation was complete in the main consolidation stage.

The interaction between protein and DNA plays an essential function in various critical natural processes, like DNA replication, transcription, splicing, and repair. Studying the binding affinity of proteins to DNA helps to understand the recognition mechanism of protein-DNA complexes. Since there are still many limitations on the protein-DNA binding affinity data measured by experiments, accurate and reliable calculation methods are necessarily required. So we put forward a computational approach in this paper, called PreDBA, that can forecast protein-DNA binding affinity effectively by using heterogeneous ensemble models. One hundred protein-DNA complexes are manually collected from the related literature as a data set for protein-DNA binding affinity. Then, 52 sequence and structural features are obtained. Based on this, the correlation between these 52 characteristics and protein-DNA binding affinity is calculated. Furthermore, we found that the protein-DNA binding affinity is affected by the DNA molecule structure of the compound. We classify all protein-DNA compounds into five classifications based on the DNA structure related to the proteins that make up the protein-DNA complexes. In each group, a stacked heterogeneous ensemble model is constructed based on the obtained features. In the end, based on the binding affinity data set, we used the leave-one-out cross-validation to evaluate the proposed method comprehensively. In the five categories, the Pearson correlation coefficient values of our recommended method range from 0.735 to 0.926. We have demonstrated the advantages of the proposed method compared to other machine learning methods and currently existing protein-DNA binding affinity prediction approach.

In this paper, the moisture content on the wetting front is obtained by using the Van Genuchten (VG) model of unsaturated soil, and then the distribution of moisture content in the upper part of the wetting front is simplified as a trapezoid. The Green-Ampt (G-A) infiltration model of infinite slope with unsaturated characteristics is derived. The analytical expression of safety coefficient (FOS) of infinite slope with rainfall is solved by combining the limit equilibrium method with the unsaturated soil shear strength theory phase. The results show that: 1) compared with the traditional G-A model and the combined rectangular and 1/4 oval model, the upper part of the wetting front is simplified to a trapezoidal model, which has great advantages in infiltration rate and cumulative infiltration, especially when the slope is large or the rain intensity is heavy; 2) since the distribution of soil moisture content above the wetting front is considered, the matrix suction at the wetting front is not neglected, and the safety coefficient calculated by the method proposed in this paper is closer to the actual situation than the traditional G-A model.

Dust pollution is one of the most severe environmental issues in open pit mines, hindering green mining development. Open pit mine dust has characteristics of multiple dust-generating points, is irregular, influenced by climatic conditions, and has a high degree of distribution with a wide dispersion range in three dimensions. Consequently, evaluating the quantity of dust dispersion and controlling environmental pollution are crucial for supporting green mining. In this paper, dust monitoring above the open pit mine was carried out with an unmanned aerial vehicle (UAV) on board. The dust distribution patterns above the open pit mine were studied in different vertical and horizontal directions at different heights. The results show that the temperature changes less in the morning and more at noon in winter. At the same time, the isothermal layer becomes thinner and thinner as the temperature rises, which makes it easy for dust to spread. The horizontal dust is mainly concentrated at 1300 and 1550 elevations. The dust concentration is polarized at 1350–1450 elevation. The most serious exceedance is at 1400 elevation, with TSP (the concentration of total suspended particulate), PM10 (particulates with aerodynamic diameter

Earth fissures caused by tectonic forces, human activities, or both seriously threaten the safety of people’s lives and properties. The Taiyuan Basin, a Cenozoic downfaulted basin located in the centre of the Fen-Wei Basin tectonic belt, in northwestern China, presents the ideal study area for a hazard assessment of earth fissures. A total of 104 earth fissures have been observed in the Taiyuan Basin, with a total length of approximately 128 km. In this paper, we proposed a probabilistic method for mapping earth fissure hazards by integrating the analytic hierarchy process (AHP), the area under the curve (AUC), and the certainty factor model (CFM). Geomorphic units, geologic formations, active faults and land subsidence zones of the Taiyuan Basin were mapped in detail. Correlations between these factors and earth fissures were evaluated through spatial modelling in ArcGIS. The AUC was introduced into the AHP to weight each factor and thus, to derive an earth fissure susceptibility map. Finally, the modelled earth fissure susceptibility was compared with a digital inventory of earth fissures to develop a probability function and map the spatial variability in failure probability through the CFM. The study indicates that active faults have the greatest contribution to the generation of earth fissures. Earth fissures are prone to develop in the piedmont alluvial-diluvial clinoplain and the transitional zone near the geomorphic boundary. This mapping procedure can assist in making rational decisions regarding urban planning and infrastructure development in areas susceptible to earth fissures.

Plasmonic metamaterial absorbers are particularly important in different applications such as photodetectors, microbolometers and solar cells. In this paper, we propose a tungsten boride (WB, a refractory ceramic) based broadband metamaterial absorber whose optical properties is numerically analyzed and experimentally characterized. We have also analyzed the damage characteristics of this absorber using a femtosecond laser and compared with an ordinary Au metamaterial absorber. We observe that WB has almost the double absorption bandwidth with absorption more than 90% over the spectral range of 950 to 1400 nm when compared with the Au counterpart. Furthermore, we show that Au metamaterial is damaged at the power of around 36.4 mW whereas WB metamaterial is not damaged at that power (WB has high Tammann temperature than Au)-however the atom of WB material was knocked off by the bombardment of a femtosecond laser.

Celiac disease (CeD) is a multifactorial disease influenced by both genetic and environmental risk factors. CeD genetic components are mainly due to HLA class II genes, which account for approximately 40% of the disease heritability. The environmental factor is linked to gliadin ingestion. Despite genetic and epigenetic studies, the pathological molecular mechanism remains unclarified. The strong genetic component does not explain more than half of the hereditability; we identified several epigenetic features that contribute to the understanding of the missing hereditability. The lipid profile of infants has been proposed as a potential biomarker of CeD metabolism that can be measured before they exhibit developmental disorders and clinical symptoms. We suggest that the state of the host is a main factor for the abnormal immune response to gluten. Long before any exposure to the offending agent or any production of specific antibodies, several molecular mechanisms are differentially expressed in infants who will develop CeD compared to their peers matched for the same genetic profile. The present study explored the serum phospholipid profile of a group of infants at risk for celiac disease, followed up to 8 years to monitor the onset of CeD. We compared 30 patients who developed the disease with 20 age- and sex-matched peers with similar genetic profiles who did not develop the disease within 8 years. Serum phospholipids were analysed at 4 months, before exposure to gluten, and at 12 months of age, when none showed any marker of disease. In the 30 CeD patients, we also analysed the serum at the time of diagnosis (>24 months). The serum phospholipid profile was fairly constant across 4 and 12 months of age and, in CeD, up to 24–36 months. The phospholipid signature was dramatically different in infants who developed CeD when compared to that of control NY-CeD (Not Yet developing Celiac Disease) peers. We identified a specific serum phospholipid signature that predicts the onset of celiac disease in HLA at-risk infants years before the appearance of antibodies specific for CeD in the serum and before any clinical symptoms, even before gluten introduction into the diet at 4 months. Specifically, lysophosphatidylcholine, phosphatidylcholine, alkylacyl-phosphatidylcholine, phosphoethanolamines, phosphatidylserines, phosphatidylglycerol and phosphatidylinositol were found to be differentially represented in CeD versus NY-CeD. A set constituted by a limited number of alkylacyl-phosphatidylcholine and lyso-phosphatidylcholine, together with the duration of breast-feeding, allows the discrimination of infants who develop celiac disease before 8 years of age from those at a similar genetic risk who do not develop the disease. In addition to recent discovery, our paper unveiled a specifc phopholipid profile, able to discriminate infants who eventually develop celiac disease years before antibodies or clinical symptoms ensue.

Myoelectric hand prostheses offer a way for upper-limb amputees to recover gesture and prehensile abilities to ease rehabilitation and daily life activities. However, studies with prosthesis users found that a lack of intuitiveness and ease-of-use in the human-machine control interface are among the main driving factors in the low user acceptance of these devices. This paper proposes a highly intuitive, responsive and reliable real-time myoelectric hand prosthesis control strategy with an emphasis on the demonstration and report of real-time evaluation metrics. The presented solution leverages surface high-density electromyography (HD-EMG) and a convolutional neural network (CNN) to adapt itself to each unique user and his/her specific voluntary muscle contraction patterns. Furthermore, a transfer learning approach is presented to drastically reduce the training time and allow for easy installation and calibration processes. The CNN-based gesture recognition system was evaluated in real-time with a group of 12 able-bodied users. A real-time test for 6 classes/grip modes resulted in mean and median positive predictive values (PPV) of 93.43% and 100%, respectively. Each gesture state is instantly accessible from any other state, with no mode switching required for increased responsiveness and natural seamless control. The system is able to output a correct prediction within less than 116 ms latency. 100% PPV has been attained in many trials and is realistically achievable consistently with user practice and/or employing a thresholded majority vote inference. Using transfer learning, these results are achievable after a sensor installation, data recording and network training/fine-tuning routine taking less than 10 min to complete, a reduction of 89.4% in the setup time of the traditional, non-transfer learning approach.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Single-cell sequencing provides a new level of granularity in studying the heterogeneous nature of cancer cells. For some cancers, this heterogeneity is the result of copy number changes of genes within the cellular genomes. The ability to accurately determine such copy number changes is critical in tracing and understanding tumorigenesis. Current single-cell genome sequencing methodologies infer copy numbers based on statistical approaches followed by rounding decimal numbers to integer values. Such methodologies are sample dependent, have varying calling sensitivities which heavily depend on the sample’s ploidy and are sensitive to noise in sequencing data. In this paper we have demonstrated the concept of integer-counting by using a novel bioinformatic algorithm built on our library construction chemistry in order to detect the discrete nature of the genome. Supplementary material: [https://cherry.chem.bg.ac.rs/handle/123456789/4686]

In this paper, we show that anisotropic photosensitive nematic liquid crystals (PNLC) made by incorporating anisotropic absorbing dyes are promising candidates for constructing all-optical elements by virtue of the extraordinarily large optical nonlinearity of the nematic host. In particular, we have demonstrated several room-temperature ‘prototype’ PNLC-based all-optical devices such as optical diode, optical transistor and all primary logic gate operations (OR, AND, NOT) based on such optical transistor. Owing to the anisotropic absorption property and the optical activity of the twist alignment nematic cell, spatially non-reciprocal transmission response can be obtained within a sizeable optical isolation region of ~210 mW. Exploiting the same mechanisms, a tri-terminal configuration as an all-optical analogue of a bipolar junction transistor is fabricated. Its ability to be switched by an optical field enables us to realize an all-optical transistor and demonstrate cascadability, signal fan-out, logic restoration and various logical gate operations such as OR, AND and NOT. Due to the possibility of synthesizing anisotropic dyes and wide ranging choice of liquid crystals nonlinear optical mechanisms, these all-optical operations can be optimized to have much lower thresholds and faster response speeds. The demonstrated capabilities of these devices have shown great potential in all-optical control system and photonic integrated circuits.

The chloroplast is one of two organelles containing a separate genome that codes for essential and distinct cellular functions such as photosynthesis. Given the importance of chloroplasts in plant metabolism, the genomic architecture and gene content have been strongly conserved through long periods of time and as such are useful molecular tools for evolutionary inferences. At present, complete chloroplast genomes from over 4000 species have been deposited into publicly accessible databases. Despite the large number of complete chloroplast genomes, comprehensive analyses regarding genome architecture and gene content have not been conducted for many lineages with complete species sampling. In this study, we employed the genus Populus to assess how more comprehensively sampled chloroplast genome analyses can be used in understanding chloroplast evolution in a broadly studied lineage of angiosperms. We conducted comparative analyses across Populus in order to elucidate variation in key genome features such as genome size, gene number, gene content, repeat type and number, SSR (Simple Sequence Repeat) abundance, and boundary positioning between the four main units of the genome. We found that some genome annotations were variable across the genus owing in part from errors in assembly or data checking and from this provided corrected annotations. We also employed complete chloroplast genomes for phylogenetic analyses including the dating of divergence times throughout the genus. Lastly, we utilized re-sequencing data to describe the variations of pan-chloroplast genomes at the population level for P. euphratica. The analyses used in this paper provide a blueprint for the types of analyses that can be conducted with publicly available chloroplast genomes as well as methods for building upon existing datasets to improve evolutionary inference.

The quantum secret sharing is an essential and fundamental technique for sharing a secret with the all participants in quantum cryptography. It can be used to design many complex protocols such as secure multiparty summation, multiplication, sorting, voting, etc. Recently, Song et al. have discussed a quantum protocol for secret sharing, which has (t, n) threshold approach and modulo d, where t and n denote the threshold number of participants and total number of participants, respectively. Kao et al. point out that the secret in the Song et al.’s protocol cannot be reconstructed without other participants’ information. In this paper, we discuss a protocol that overcomes this problem.

Understanding the externalities of transportation networks in the process of the agglomeration and diffusion of production factors has theoretical and practical significance for the coordinated development of China's economic growth in urban agglomerations. Therefore, the social network analysis method is introduced in this paper with the case of the Pan Pearl River Delta Urban Agglomeration to analyze the characteristics of the traffic connection network of the production factor flow within this urban agglomeration, and subsequently, an econometric panel model is adopted to quantitatively analyze the effect of the connection network on the economic growth of the urban agglomeration. The results show that (1) the traffic connection of the Pan Pearl River Delta Urban Agglomeration has network characteristics typical of a “small world”. Although the connections between cities are gradually strengthening, the regional differences are obvious, showing a core–edge pattern of eastern agglomeration and western sparseness. (2) Among the network nodes, Guangzhou, Shenzhen and other cities have obvious agglomeration and diffusion effects, stabilizing economic growth while driving the development of surrounding cities. The "polarization effect" in Chongqing and Chengdu has significantly increased, and the accumulation of factors mainly meets their own economic development but has not yet spread. (3) The Pan Pearl River Delta Urban Agglomeration's transportation network influences the region’s economic growth through the structural effect, as it strengthens the economic ties between cities, and through the action of resource factors, as the network represents the aggregation and diffusion path of factor flow. (4) Due to the different traffic connections and industrial structures across the Pan Pearl River Delta Urban Agglomeration, the factor flow of each suburban agglomeration has a differentiated impact on the regional economic growth under the traffic connection network. Therefore, to realize the coordinated economic development of the Pan Pearl River Delta Urban Agglomeration, it is necessary to "adjust measures to local conditions" and formulate accurate and precise policies.

We study quasi-spatially periodic signals (QSPSs) as a class of input signals of interest, which maintain their shapes quasi-periodically (with a phase change and a time shift) during propagation in an optical fibre. Instead of the computationally expensive nonlinear Fourier transform (NFT), the property of quasi-periodic shape invariant could be used as an alternative for decoding at the receiver. In this paper, properties of QSPSs and the signal design problem are studied, including the trade-off between various system parameters.

Ebola virus (EBOV) infection causes a high death toll, killing a high proportion of EBOV infected patients within 7 days. Comprehensive data on EBOV infection are very fragmented, hampering efforts in developing therapeutics and vaccines against EBOV. Under this circumstance, mathematical models become valuable resources to explore potential controlling strategies. In this paper, we employed experimental data of EBOV-infected nonhuman primates (NHPs) to construct a mathematical framework for determining windows of opportunity for treatment and vaccination. Considering a prophylactic vaccine based on recombinant vesicular stomatitis virus expressing the EBOV glycoprotein (VSV-EBOV), we found that the time window can be subject-specific, but vaccination could be protective if a subject is vaccinated during a period from one week to four months before infection. For the case of a therapeutic vaccine based on monoclonal antibodies (mAbs), a single dose might resolve the invasive EBOV replication even it was administrated as late as four days after infection. Our mathematical models can be used as building blocks for developing therapeutic and vaccine modalities as well as for evaluating public health intervention strategies in outbreaks. Future laboratory experiments will help to validate and refine the estimates of the windows of opportunity proposed here.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Zinc–air batteries proffer high energy density and cyclic stability at low costs but lack disadvantages like sluggish reactions at the cathode and the formation of by-products at the cathode. To resolve these issues, a new perovskite material, CaCu3Ti4O12 (CCTO), is proposed as an efficacious electrocatalyst for oxygen evolution/reduction reactions to develop zinc–air batteries (ZAB). Synthesis of this material adopted an effective oxalate route, which led to the purity in the electrocatalyst composition. The CCTO material is a proven potential candidate for energy applications because of its high dielectric permittivity (ε) and occupies an improved ORR-OER activity with better onset potential, current density, and stability. The Tafel value for CCTO was obtained out to be 80 mV dec−1. The CCTO perovskite was also evaluated for the zinc–air battery as an air electrode, corresponding to the high specific capacitance of 801 mAh g−1 with the greater cyclic efficiency and minimum variations in both charge/discharge processes. The highest power density (Pmax) measured was 127 mW cm−2. Also, the CCTO based paper battery shows an excellent performance achieving a specific capacity of 614 mAh g−1. The obtained results promise CCTO as a potential and cheap electrocatalyst for energy applications.

Control chart methods have been received much attentions in biosurvillance studies. The correlation between charting statistics or regions could be considerably important in monitoring the states of multiple outcomes or regions. In addition, the process variable distribution is unknown in most situations. In this paper, we propose a new nonparametric strategy for multivariate process monitoring when the distribution of a process variable is unknown. We discuss the EWMA control chart based on rank methods for a multivariate process, and the approach is completely nonparametric. A simulation study demonstrates that the proposed method is efficient in detecting shifts for multivariate processes. A real Japanese influenza data example is given to illustrate the performance of the proposed method.

Nearly ~ 108 types of High entropy alloys (HEAs) can be developed from about 64 elements in the periodic table. A major challenge for materials scientists and metallurgists at this stage is to predict their crystal structure and, therefore, their mechanical properties to reduce experimental efforts, which are energy and time intensive. Through this paper, we show that it is possible to use machine learning (ML) in this arena for phase prediction to develop novel HEAs. We tested five robust algorithms namely, K-nearest neighbours (KNN), support vector machine (SVM), decision tree classifier (DTC), random forest classifier (RFC) and XGBoost (XGB) in their vanilla form (base models) on a large dataset screened specifically from experimental data concerning HEA fabrication using melting and casting manufacturing methods. This was necessary to avoid the discrepancy inherent with comparing HEAs obtained from different synthesis routes as it causes spurious effects while treating an imbalanced data—an erroneous practice we observed in the reported literature. We found that (i) RFC model predictions were more reliable in contrast to other models and (ii) the synthetic data augmentation is not a neat practice in materials science specially to develop HEAs, where it cannot assure phase information reliably. To substantiate our claim, we compared the vanilla RFC (V-RFC) model for original data (1200 datasets) with SMOTE-Tomek links augmented RFC (ST-RFC) model for the new datasets (1200 original + 192 generated = 1392 datasets). We found that although the ST-RFC model showed a higher average test accuracy of 92%, no significant breakthroughs were observed, when testing the number of correct and incorrect predictions using confusion matrix and ROC-AUC scores for individual phases. Based on our RFC model, we report the development of a new HEA (Ni25Cu18.75Fe25Co25Al6.25) exhibiting an FCC phase proving the robustness of our predictions.

During microbial electrosynthesis (MES) driven CO2 reduction, cathode plays a vital role by donating electrons to microbe. Here, we exploited the advantage of reduced graphene oxide (RGO) paper as novel cathode material to enhance electron transfer between the cathode and microbe, which in turn facilitated CO2 reduction. The acetate production rate of Sporomusa ovata-driven MES reactors was 168.5 ± 22.4 mmol m−2 d−1 with RGO paper cathodes poised at −690 mV versus standard hydrogen electrode. This rate was approximately 8 fold faster than for carbon paper electrodes of the same dimension. The current density with RGO paper cathodes of 2580 ± 540 mA m−2 was increased 7 fold compared to carbon paper cathodes. This also corresponded to a better cathodic current response on their cyclic voltammetric curves. The coulombic efficiency for the electrons conversion into acetate was 90.7 ± 9.3% with RGO paper cathodes and 83.8 ± 4.2% with carbon paper cathodes, respectively. Furthermore, more intensive cell attachment was observed on RGO paper electrodes than on carbon paper electrodes with confocal laser scanning microscopy and scanning electron microscopy. These results highlight the potential of RGO paper as a promising cathode for MES from CO2.

Dielectric-graphene-dielectric (DGD) structure has been widely used to construct optical devices at infrared region with features of small footprint and low-energy dissipation. The optical properties of graphene can be manipulated by changing its chemical potential by applying a biased voltage onto graphene. However, the excitation efficiency of surface wave on graphene by end-fire method is very low because of large wavevector mismatch between infrared light and surface wave. In this paper, a dielectric-semiconductor-dielectric (DSD) tapered waveguide with magnetic tunability for efficient excitation of surface waves on DGD at infrared region is proposed and analyzed. Efficient excitation of surface waves on DGD with various chemical potentials in graphene layer and incident frequencies can be attained by merely changing the external magnetic field applied onto the DSD tapered waveguide. The electromagnetic simulations verify the design of the proposed structure. More importantly, the constituent materials used in the proposed structure are available in nature. This work opens the door toward various applications in the field of using surface waves.

Cell cutting is a significant task in biology study, but the highly productive non-embedded cell cutting is still a big challenge for current techniques. This paper proposes a vision-based nano robotic system and then realizes automatic non-embedded cell cutting with this system. First, the nano robotic system is developed and integrated with a nanoknife inside an environmental scanning electron microscopy (ESEM). Then, the positions of the nanoknife and the single cell are recognized, and the distance between them is calculated dynamically based on image processing. To guarantee the positioning accuracy and the working efficiency, we propose a distance-regulated speed adapting strategy, in which the moving speed is adjusted intelligently based on the distance between the nanoknife and the target cell. The results indicate that the automatic non-embedded cutting is able to be achieved within 1–2 mins with low invasion benefiting from the high precise nanorobot system and the sharp edge of nanoknife. This research paves a way for the high-throughput cell cutting at cell’s natural condition, which is expected to make significant impact on the biology studies, especially for the in-situ analysis at cellular and subcellular scale, such as cell interaction investigation, neural signal transduction and low invasive cell surgery.

It is well-known that cell adhesion is important in many biological processes such as cell migration and proliferation. A better understanding of the cell adhesion process will shed insight into these cellular biological responses as well as cell adhesion-related diseases treatment. However, there is little research which has attempted to investigate the process of cell adhesion and its mechanism. Thus, this paper aims to study the time-dependent adhesion properties of single living chondrocytes using an advanced coupled experimental-numerical approach. Atomic Force Microscopy (AFM) tips will be used to apply lateral forces to detach chondrocytes that are seeded for three different periods. An advanced Finite Element Analysis (FEA) model combining porohyperelastic (PHE) constitutive model and cohesive zone formulation is developed to explore the mechanism of adhesion. The results revealed that the cells can resist normal traction better than tangential traction in the beginning of adhesion. This is when the cell adhesion molecules establish early attachment to the substrates. After that when the cells are spreading, stress fiber bundles generate tangential traction on the substrate to form strong adhesion. Both simulation and experimental results agree well with each other, providing a powerful tool to study the cellular adhesion process.

The most distinctive characteristic of synthetic aperture radar (SAR) is that it can acquire data under all weather conditions and at all times. However, its coherent imaging mechanism introduces a great deal of speckle noise into SAR images, which makes the segmentation of target and shadow regions in SAR images very difficult. This paper proposes a new SAR image segmentation method based on wavelet decomposition and a constant false alarm rate (WD-CFAR). The WD-CFAR algorithm not only is insensitive to the speckle noise in SAR images but also can segment target and shadow regions simultaneously, and it is also able to effectively segment SAR images with a low signal-to-clutter ratio (SCR). Experiments were performed to assess the performance of the new algorithm on various SAR images. The experimental results show that the proposed method is effective and feasible and possesses good characteristics for general application.

Microbial fuel cell (MFC) technology is a simple way to accelerate the treatment of the oily sludge which is a major problem affecting the quality of oil fields and surrounding environment while generating electricity. To investigate the oil removal and the characteristics of changes in the composition of bacteria, sediment microbial fuel cells (SMFCs) supplemented with oily sludge was constructed. The results showed that the degradation efficiency of total petroleum hydrocarbon (TPH) of SMFC treatment was 10.1 times higher than the common anaerobic degradation. In addition, the degradation rate of n-alkanes followed the order of high carbon number > low carbon number > medium carbon number. The odd–even alkane predominance (OEP) increased, indicating that a high contribution of even alkanes whose degradation predominates. The OUT number, Shannon index, AEC index, and Chao1 index of the sludge treated with SMFC (YN2) are greater than those of the original sludge (YN1), showing that the microbial diversity of sludge increased after SMFC treatment. After SMFC treatment the relative abundance of Chloroflexi, Bacteroidia and Pseudomonadales which are essential for the degradation of the organic matter and electricity production increased significantly in YN2. These results will play a crucial role in improving the performance of oily sludge MFC.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

This paper explores the phylogeny of the delphacid subfamily Delphacinae based on nuclear ribosomal and mitochondrial DNA sequences of four genetic loci (16S rDNA, 28S rDNA, Cytochrome oxidase I and Cytochrome b). Maximum likelihood and Bayesian analyses yield robust phylogenetic trees. The topologies support the monophyly of Delphacinae and its basal split into three tribes, and provisionally support subdividing Delphacini into three clades, including a more broadly defined Numatina. The tribe Tropidocephalini is divided into two clades. In addition, Paranectopia is transferred from Tropidocephalini to Delphacini and Harmalia syn. nov. is regarded as a junior synonym of Opiconsiva. The genera Bambusiphaga, Megadelphax and Muirodelphax are found to be paraphyletic. The estimated time to the most recent common ancestor of Delphacinae is roughly at 90 million years ago in Late Cretaceous.

Recently, public displays such as liquid crystal displays (LCDs) are often used in urban green spaces, however, the display devices can spoil green landscape of urban green spaces because they look like artificial materials. We previously proposed a green landscape-friendly grass animation display method by controlling a pixel-by-pixel grass color dynamically. The grass color can be changed by moving a green grass length in yellow grass, and the grass animation display can play simple animations using grayscale images. In the previous research, the color scale was mapped to the green grass length subjectively, however, this method has not achieved displaying the grass colors corresponding to the color scale based on objective evaluations. Here, we introduce a dynamic grass color scale display technique based on a grass length. In this paper, we developed a grass color scale setting procedure to map the grass length to the color scale with five levels through image processing. Through the outdoor experiment of the grass color scale setting procedure, the color scale can correspond to the green grass length based on a viewpoint. After the experiments, we demonstrated a grass animation display to show the animations with the color scale using the experiment results., 17 pages

Rapid histopathological evaluation of fresh, unfixed human tissue using optical sectioning microscopy would have applications to intraoperative surgical margin assessment. Microscopy with ultraviolet surface excitation (MUSE) is a low-cost optical sectioning technique using ultraviolet illumination which limits fluorescence excitation to the specimen surface. In this paper, we characterize MUSE using high incident angle, water immersion illumination to improve sectioning. Propidium iodide is used as a nuclear stain and eosin yellow as a counterstain. Histologic features of specimens using MUSE, nonlinear microscopy (NLM) and conventional hematoxylin and eosin (H&E) histology were evaluated by pathologists to assess potential application in Mohs surgery for skin cancer and lumpectomy for breast cancer. MUSE images of basal cell carcinoma showed high correspondence with frozen section H&E histology, suggesting that MUSE may be applicable to Mohs surgery. However, correspondence in breast tissue between MUSE and paraffin embedded H&E histology was limited due to the thicker optical sectioning in MUSE, suggesting that further development is needed for breast surgical applications. We further demonstrate that the transverse image resolution of MUSE is limited by the optical sectioning thickness and use co-registered NLM to quantify the improvement in MUSE optical sectioning from high incident angle water immersion illumination.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

We present a simple, facile method to micropattern planar metal electrodes defined by the geometry of a microfluidic channel network template. By introducing aqueous solutions of metal into reversibly adhered PDMS devices by desiccation instead of flow, we are able to produce difficult to pattern “dead end” or discontinuous features with ease. We characterize electrodes fabricated using this method and perform electrical lysis of mammalian cancer cells and demonstrate their use as part of an antibody capture assay for GFP. Cell lysis in microwell arrays is achieved using the electrodes and the protein released is detected using an antibody microarray. We show how the template channels used as part of the workflow for patterning the electrodes may be produced using photolithography-free methods, such as laser micromachining and PDMS master moulding, and demonstrate how the use of an immiscible phase may be employed to create electrode spacings on the order of 25–50 μm, that overcome the current resolution limits of such methods. This work demonstrates how the rapid prototyping of electrodes for use in total analysis systems can be achieved on the bench with little or no need for centralized facilities.

Realistic neurons may hold complex anatomical structure, for example, autapse connection to some internuncial neurons, which this specific synapse can connect to its body via a close loop. Continuous exchanges of charged ions across the membrane can induce complex distribution fluctuation of intracellular and extracellular charged ions of cell, and a time-varying electromagnetic field is set to modulate the membrane potential of neuron. In this paper, an autapse-modulated neuron model is presented and the effect of electromagnetic induction is considered by using magnetic flux. Bifurcation analysis and sampled time series for membrane potentials are calculated to investigate the mode transition in electrical activities and the biological function of autapse connection is discussed. Furthermore, the Gaussian white noise and electromagnetic radiation are considered on the improved neuron model, it is found appropriate setting and selection for feedback gain and time delay in autapse can suppress the bursting in neuronal behaviors. It indicates the formation of autapse can enhance the self-adaption of neuron so that appropriate response to external forcing can be selected, this biological function is helpful for encoding and signal propagation of neurons. It can be useful for investigation about collective behaviors in neuronal networks exposed to electromagnetic radiation.

The universal linguistic colour categorisation pattern as evident in the World Colour Survey (WCS) has been a principal focus of investigations on the relationship between language and cognition, yet most existing studies have failed to clarify whether this universality resulted primarily from individual perceptual constraints and/or socio-cultural transmissions. This paper designed an agent-based, unsupervised learning model to address the relative importance of these two aspects to linguistic colour categorisation. By directly comparing with the empirical data in the WCS, our study demonstrated that: the physical colour stimuli that reflect human perceptual constraints on colours trigger a categorisation pattern quantitatively resembling the WCS data, the randomised stimuli that distort such constraints lead to distinct categorisation patterns, and the processes of linguistic categorisation in both cases follow similar dynamics. These results reveal how perceptual and socio-cultural factors interact with each other to trigger linguistic universality, and serve as decisive evidence that human perceptual constraints induce the universality in linguistic categorisation, yet socio-cultural transmissions, though imperative, play an auxiliary role of transcribing perceptual constraints into common linguistic categories with slight variations.

Depressive disorder (DD) is a mental illness affecting more than 300 million people worldwide, whereas social stigma and subtle, variant symptoms impede diagnosis. Psychomotor retardation is a common component of DD with a negative impact on motor function, usually reflected on patients’ routine activities, including, nowadays, their interaction with mobile devices. Therefore, such interactions constitute an enticing source of information towards unsupervised screening for DD symptoms in daily life. In this vein, this paper proposes a machine learning-based method for discriminating between subjects with depressive tendency and healthy controls, as denoted by self-reported Patient Health Questionnaire-9 (PHQ-9) compound scores, based on typing patterns captured in-the-wild. The latter consisted of keystroke timing sequences and typing metadata, passively collected during natural typing on touchscreen smartphones by 11/14 subjects with/without depressive tendency. Statistical features were extracted and tested in univariate and multivariate classification pipelines to reach a decision on subjects’ status. The best-performing pipeline achieved an AUC = 0.89 (0.72–1.00; 95% Confidence Interval) and 0.82/0.86 sensitivity/specificity, with the outputted probabilities significantly correlating (>0.60) with the respective PHQ-9 scores. This work adds to the findings of previous research associating typing patterns with psycho-motor impairment and contributes to the development of an unobtrusive, high-frequency monitoring of depressive tendency in everyday living.

In heterogeneous wireless networks, random packet loss and high latency lead to conventional TCP variants performing unsatisfactorily in the case of competing communications. Especially on high-latency wireless links, conventional TCP variants are unable to estimate congestion degrees accurately for fine-grained congestion control because of the effects of random packet loss and delay oscillations. This paper proposes a TCP variant at the sender side to identify congestion degrees, namely TCP-WBQ, which quickly responses to the real congestion and effectively shields against random packet loss and oscillations of latency time. The proposed algorithm of congestion control firstly constructs a backlog-queue model based on the dynamics of the congestion window, and deduces the two bounds of the model which delimit oscillations of the backlog queue for non-congestion and random packet loss respectively. TCP-WBQ detects congestion degrees more accurately and thus implements the corresponding schemes of adjusting the congestion window, maintaining a tradeoff between high throughputs and congestion avoidance. The comprehensive simulations show that TCP-WBQ works efficiently in bandwidth utilization with single and multiple bottleneck scenarios, and achieves high performance and competitive fairness in heterogeneous wireless networks.

With the presence of novel coronavirus disease at the end of 2019, several approaches were proposed to help physicians detect the disease, such as using deep learning to recognize lung involvement based on the pattern of pneumonia. These approaches rely on analyzing the CT images and exploring the COVID-19 pathologies in the lung. Most of the successful methods are based on the deep learning technique, which is state-of-the-art. Nevertheless, the big drawback of the deep approaches is their need for many samples, which is not always possible. This work proposes a combined deep architecture that benefits both employed architectures of DenseNet and CapsNet. To more generalize the deep model, we propose a regularization term with much fewer parameters. The network convergence significantly improved, especially when the number of training data is small. We also propose a novel Cost-sensitive loss function for imbalanced data that makes our model feasible for the condition with a limited number of positive data. Our novelties make our approach more intelligent and potent in real-world situations with imbalanced data, popular in hospitals. We analyzed our approach on two publicly available datasets, HUST and COVID-CT, with different protocols. In the first protocol of HUST, we followed the original paper setup and outperformed it. With the second protocol of HUST, we show our approach superiority concerning imbalanced data. Finally, with three different validations of the COVID-CT, we provide evaluations in the presence of a low number of data along with a comparison with state-of-the-art.

Considering cell migration is essential for understanding physiological processes and diseases. The vertical migration of cells in three dimensions is vital, but most previous studies on cell migration have only focused on two-dimensional horizontal migration. In this paper, cell migration in the vertical direction was studied. Barriers with a height of 1, 5, 10, and 25 µm with grating and arrows in channels as guiding patterns were fabricated. The effects of barrier height and guiding patterns on the vertical migration of MC3T3 cells were explored. The study revealed that taller barriers hinder vertical migration of MC3T3 cells, whereas grating and arrows in channels promote it. The time-lapse and micrograph images showed that as the barrier height increased, the cell climbing angle along the barrier sidewall decreased, and the time taken to climb over the barrier increased. These results indicate that taller barriers increase the difficulty of vertical migration by MC3T3 cells. To promote the vertical migration of MC3T3 cells, 10 µm tall barriers with 18° and 40° sloped sidewalls were fabricated. For barriers with 18° sloped sidewalls, the probability for MC3T3 cells to climb up and down the 10 µm tall barriers was 40.6% and 20.3%, respectively; this is much higher than the migration probability over vertical barriers. This study shows topographic guidance on the vertical migration of MC3T3 cells and broadens the understanding of cell migration in the vertical direction.

Epidemiological studies support a protective role of lycopene against stroke occurrence or mortality, but the results have been conflicting. We conducted a meta-analysis to assess the relationship between dietary or circulating lycopene and stroke risk (including stroke occurrence or mortality). Relevant papers were collected by screening the PubMed database through October 2013. Only prospective studies providing relative risk estimates with 95% confidence intervals for the association between lycopene and stroke were included. A random-effects model was used to calculate the pooled estimate. Subgroup analysis was conducted to investigate the effects of various factors on the final results. The pooled analysis of seven prospective studies, with 116,127 participants and 1,989 cases, demonstrated that lycopene decreased stroke risk by 19.3% (RR = 0.807, 95% CI = 0.680–0.957) after adjusting for confounding factors. No heterogeneity was observed (p = 0.234, I2 = 25.5%). Circulating lycopene, not dietary lycopene, was associated with a statistically significant decrease in stroke risk (RR = 0.693, 95% CI = 0.503–0.954). Lycopene could protect European, or males against stroke risk. Duration of follow-up had no effect on the final results. There was no evidence of publication bias. Lycopene, especially circulating lycopene, is negatively associated with stroke risk.