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Testing and isolation remain a key component of public health responses to both persistent and emerging infectious diseases. Although the value of these measures have been demonstrated in combating recent outbreaks including the COVID-19 pandemic and monkeypox, their impact depends critically on the timelines of testing and start of isolation during the course of disease. To investigate this impact, we developed a delay differential model and incorporated age-since-symptom-onset as a parameter for delay in testing. We then used the model to compare the outcomes of reverse-transcription polymerase chain reaction (RT-PCR) and rapid antigen (RA) testing methods when isolation starts either at the time of testing or at the time of test result. Parameterizing the model with estimates of SARS-CoV-2 infection and diagnostic sensitivity of the tests, we found that the reduction of disease transmission using the RA test can be comparable to that achieved by applying the RT-PCR test. Given constraints and inevitable delays associated with sample collection and laboratory assays in RT-PCR testing post symptom onset, self-administered RA tests with short turnaround times present a viable alternative for timely isolation of infectious cases.

To explore the improving effect of Y2O3 on the water resistance of xY2O3-(100–x)(0.05Al2O3-0.15ZnO-0.15Na2O-0.65B2O3) (x = 0, 0.7 mol%, 1.4 mol%, 2.1 mol%, 2.8 mol%) glasses, glass structure and ion migration characteristics were respectively characterized by an infrared spectrometer and an electrochemical workstation. X-ray diffraction (XRD), scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDS) and inductively coupled plasma atomic emission spectrometry (ICP-AES) were used to comprehensively analyze the alteration products. The experimental results show that with the increase of Y2O3 content, the mass loss per unit area of the glass is significantly reduced, and pH in leaching solution has the same change trend with mass loss of the glass, which indicates the improving effect of Y2O3 addition on the studied glass. The content of [BO4] structural unit and dc conductivity of glass increase firstly and then decrease with the increase of Y2O3 content, and reach a maximum at x = 0.7 mol%. Although addition of Y2O3 plays the role in the changes of glass structure and ion migration characteristics, the more important influence of Y2O3 is on the glass surface alteration layer. Y and Zn elements are enriched on the glass surface and finally transformed into an alteration layer, moreover, the adhesion between the alteration layers and the glass matrix is enhanced with the increase of Y2O3 content, which effectively blocks the further interdiffusion between the water molecules and the glass matrix.

Air conditioners (AC) are responsible for the largest portion of energy use among all auxiliary components of vehicles. Most white-box models developed for dynamic modeling of vapor compression systems (VCS) are not adequate to model vehicle ACs due to stronger dynamics and disturbances caused by frequent door open/close, random numbers of passengers entering/leaving the vehicles as well as intermittent shading effect on roads. In this study, a novel physics-guided deep learning method is proposed for dynamic modeling of vehicle ACs based on both domain knowledge and historical operational data. To maximize the practical values of the model in control and diagnosis of ACs, this research aims at developing an integrated VCS model consisting of individual models of major AC components rather than a black-box model of the entire system. Domain knowledge guides the determination of model inputs and outputs, design of the model structure, and understanding of temporal relationship in developing dynamic heat exchanger models. A newly developed NARX-LSTM-MLP neural network is proposed for heat exchange process modelling. The compressor is modelled by a multiple-layer perception (MLP). Component models are integrated by referring to the physical system structure. Field AC operation data from a city bus collected by IoT sensors are used in this study. Validation results indicate good accordance between measurements and simulation results. The model developed is less expensive, more convenient, and more feasible for health monitoring of numerous vehicle ACs at city level in the context of IoT.

To improve the grinding performance of superalloys, a bowl-type internal-cooling grinding wheel with a phyllotactic abrasive pattern has been developed. The internal structure of the grinding wheel is optimized as a two-stage cavity with eight transitional holes by computational fluid dynamics (CFD). And the outlet of the internal channel is set at the center of the abrasive zone to enhance the heat transfer of the coolant. Innovatively, the internal channel is prepared as an independent module through 3D printing technology to explore the influence of different channel diameters (1, 2, 3 mm) on the grinding performance of the grinding wheel. The simulation results show that when the diameter of internal channel is 1 mm, the coolant will be unevenly distributed within the grinding zone. As the diameter of the internal channel increases, the fluidity of the coolant increases, and the phyllotactic abrasive pattern can promote the uniform flow of the coolant within the grinding zone. The experiments of grinding Inconel 718 (flood, internal cooling) demonstrate that the internal cooling method provided better cooling and lubrication performance. Compared to the flood cooling, the grinding temperature of the 1- and 3 mm-diameter internal channels decreased by 23.0%, 48.2%, and the surface roughness decreased by 24.6% and 58.7%, respectively. The residual compressive stress on the machined surface increased by 122.36 MPa and 279.51 MPa, respectively. Further, with the increase of internal-channel diameter, the workpiece surface morphology becomes smoother.

Traditional combination therapy of photodynamic therapy (PDT) and photothermal therapy (PTT) is limited in the field of clinical cancer therapy due to activation by light with separate wavelengths, insufficient O2 supply, antioxidant ability of glutathione (GSH) in tumor cell, and low penetration depth of light. Here, a multifunctional nanoplatform composed of MoO3-x nanosheets, Ag nanocubes, and MnO2 nanoparticles was developed to overcome these drawbacks. For this nanoplatform, hyperthermia and reactive oxygen species (ROS) were simultaneously generated under single 808 nm near-infrared (NIR) light irradiation. Once this nanoplatform accumulated in the tumor region, GSH was depleted by MnO2 and intracellular H2O2 was catalyzed by MnO2 to produce O2 to relieve hypoxia. Ultrasound (US) imaging confirmed in-situ O2 generation. Magnetic resonance (MR) imaging, photoacoustic (PA) imaging, and fluorescence imaging were used to monitor in vivo biodistribution of nanomaterials. This provides a paradigm to rationally design a single NIR laser induced multimodal imaging-guided efficient PDT/PTT cancer strategy.

In this paper, the inertial migration of particles in a periodic straight channel is studied numerically based on the immersed boundary-lattice Boltzmann method in two-dimensional and three-dimensional Poiseuille flow. In the model, on considering the inertial migration of particles, the interaction of a soft particle and a hard particle that are similar in size is studied. We observe an interesting particle interaction phenomenon named the binding focusing state (BFS). That is, when the focused soft particle gets close to the hard particle, both particles will switch their original runways, and then run together on a new equilibrium position. To investigate the characteristics of the BFS, the effects of the soft particle deformability, the Reynolds numbers and the initial particle positions on the BFS are studied in detail. The soft particle deformability and Reynolds numbers can affect the formation of the BFS. Moreover, the streamline and pressure around the particles are analyzed to show the formation laws of the BFS; this is more intuitive and aids in understanding the BFS.

Ubiquitination is one of the most important post-translational modifications which involves in many biological processes. Because mass spectrometry-based ubiquitination site identification methods are costly and time consuming, computational approaches provide alternative ways to the determination of ubiquitination sites. Although machine learning based methods can effectively predict ubiquitination sites, most of them rely on feature engineering, which may lead to bias or incomplete feature. Recently, deep learning has achieved great success in prediction of post-translational modification sites. However, deep learning method has not been explored in the prediction of species-specific ubiquitination sites. In this paper, we propose a novel transfer deep learning method, named DeepTL-Ubi, for predicting ubiquitination sites of multiple species. DeepTL-Ubi enhances the performance of species-specific ubiquitination site prediction by transferring common knowledge from the large amount of human data to other species, which effectively solves the problem of insufficient training data for other species. Besides, we train and test our model by collecting ubiquitination sites for multiple species from several sources. Experiment results show that our transfer learning technique can effectively improve the predictive performance of species with small sample size, and DeepTL-Ubi is superior to existing tools in many species. The source code and training data of DeepTL-Ubi are publicly deposited at https://github.com/USTC-HIlab/DeepTL-Ubi.

Bioenergy crops provide enormous renewable biomass resources convertible for biofuel production, but lignocellulose recalcitrance fundamentally determines its enzymatic saccharification at high cost and low efficiency. In this study, total 30 diverse Miscanthus lignocellulose substrates were incubated with T. reesei strain to secret lignocellulose-degradation enzymes, and their major wall polymers features (cellulose crystallinity, hemicellulose arabinose and lignin H-monomer) were meanwhile examined with distinct impacts on the enzyme activities. Using characteristic Miscanthus (Msi62) de-lignin residue as inducing substrate with the reesei strain, this study detected that the Msi62-induced enzymes were of consistently higher enhancements on enzymatic saccharification of various lignocellulose residues examined in 17 grassy and woody bioenergy crops, particularly for the hemicellulose hydrolyses, compared to other two reesei-secreted cellulases and three commercial enzymes. Notably, based on SDS-gel protein separation profiling and LC-MS/MS analysis, the Msi62-induced enzymes consist of distinct cellulases (CBHI, BG, EGII) compositions and high-activity xylanases. Therefore, this study has demonstrated an applicable approach to achieve the optimal cellulases and xylanases cocktails that enable for low-costly and high-efficient enzymatic saccharification of diverse lignocellulose sources, providing a potential strategy for large-scale biofuel production in all major bioenergy crops.

Simultaneous Localization and Mapping (SLAM) systems are proposed to estimate robot poses and reconstruct 3-D map of surrounding environment. Since most of the existing SLAM systems rely on the static-world assumption, to perform them in dynamic environments still remains challenging. In this paper, we propose a novel sparse feature based visual SLAM, named as DP-SLAM, which is based on a moving probability propagation model for dynamic keypoints detection. The probability indicates the likelihood of one keypoint being located on the moving objects. Our approach combines the results of geometry constraints and semantic segmentation to track the dynamic keypoints in a Bayesian probability estimation framework. We integrate our method into the front-end of the ORB-SLAM2 system, which acts as a pre-processing stage to filter out keypoints that are associated with moving objects. Furthermore, we inpaint the frame background that has been occluded by the detected dynamic objects, which benefit some applications such as virtual and augmented reality. Experimental results on the TUM RGB-D dataset and our own sequences demonstrate that our approach can improve performance of state-of-the-art SLAM system in various challenging scenarios.

Malignant soft tissue tumors (STTs) are often mistaken for benign tumors, leading to inappropriate treatment including unplanned resection. Elastography, as a non-invasive measurement of tissue mechanical properties, makes use of the different soft tissue elasticity in diverse pathologies to generate information that can be used for diagnostic purposes. Elastography for STTs carries important information that is helpful in differentiating malignant and benign masses. The present study was undertaken to systematically review existing trials on the reliability of elastography in assessment of malignant STTs. A comprehensive literature exploration of the PubMed, EMbase and China National Knowledge Infrastructure databases was conducted for published articles involving the application of elastography in distinguishing malignant STTs. The diagnostic performance of elastography was evaluated with pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio and area under the summary receiver operating characteristic curve. Publication bias was also evaluated. This meta-analysis enrolled 18 eligible studies with a total of 1420 patients. The overall number of reported STTs was 1569, of which 478 were classified as positive and 1091 as negative at elastography. The pooled sensitivity, specificity, positive likelihood ratio and negative likelihood ratio of elastography were 0.82 (95% confidence interval: 0.74-0.87), 0.80 (0.71-0.86), 3.99 (2.65-6.01) and 0.23 (0.15-0.34), respectively. The diagnostic odds ratio and area under the curve were 17.36 (8.28-36.38) and 0.88 (0.84-0.90), respectively (Glas et al. 2003). The results of meta-regression analysis revealed that the total number of patients and prevalence of malignant STTs were significant factors in sensitivity, and the year of publication, total number of patients and index test were significant factors affecting study heterogeneity for specificity (p0.05). No significant publication bias was observed. This meta-analysis indicates that ultrasound elastography achieves relatively good performance in discriminating between malignant and benign STTs. Nevertheless, further research is needed to verify this finding.

The instability of plasma waves is influenced by many factors, such as quantum effects and electrical thermal motion. However, in sufficiently small electronic devices, viscous electron flows can be generated from the electronic interactions and govern electronic transport. The strong influence of the viscous electron fluid on plasma wave instability in field effect transistors(FET) was analyzed in this study. The theoretical results show that the instability increment and radiation frequency are functions of the Mach number, and the instability increment. Further, the computer simulative data show that the radiation frequencies increase within a certain range and the instability increment decreases owing to the presence of viscous electron flows. Therefore, it can be concluded that the viscous electron flow FETs exerts considerable influence on the characteristics of the terahertz wave.

The relationship between wage difference and relative employment is a very important issue in the field of economic structural change. An M-sector economic growth model is constructed in this paper to investigate the relationship between wage difference, price difference, technology gap, relative employment and sectoral dynamic change from the perspective of technologies. Labor flow is regarded as a decision-making behavior to maximize the benefits of economic agents. The benefits of labor flow mainly come from sectoral wage difference, and the costs of labor flow mainly come from the social resource expenditure during labor flow process. Our model illustrates that: (i) the relative employment is positively correlated with the real wage difference and technology gap; (ii) the sectoral dynamic changes coexist with the aggregate economic growth; (iii) increases in technology gap, and price and wage differences will stimulate labor migration, exacerbate the unbalanced growth of sectoral economies, and lead to faster and more effective aggregate economic growth.

Thousands of items are stored in libraries, archives and supermarkets, it usually takes a long time to find what we want in thousands of items. How to achieve precise localization is particularly important. We present a fine-grained Radio Frequency Identification (RFID) positioning method to help us find the items we want. In order to achieve high-precision positioning of RFID tags, we propose Hyperbolic and Hologram Composite (HHC) localization algorithm, which determines the positioning candidate set by hyperbolic intersection, thereby reducing the positioning delay, and then use the holographic positioning to filter the candidate set. We use multi-frequency holography positioning technology to reduce the influence of the reader’s own inaccurate positioning, and use multipath suppression technology to reduce the interference of multipath in the environment, finally achieves higher precision positioning. The experimental results show that the average calculation time of one positioning of our HHC positioning system is 10.3 ms, and 90% of the positioning error margin is below 4.8 cm, which realizes high-precision and low-delay positioning.

In order to maximize the working potential of a steam ejector, aside from experiment testing of various design parameters, it is also important to understand the involved thermal fluid mixing behaviors. In this study, an ejector refrigeration experimental system was established, and a numerical model was developed to comprehend the complex and non-linear flow characteristic using an ideal gas model which is more consistent with the experimental data compared with the wet steam model. Herein, it was discovered that the choking flow and the occurrence locations of this phenomenon played an essential role in the system efficiency. For the first time, three choke behaviors modes were characterized by means of numerical investigations: “fit-choked flow mode”, “sub-choked flow mode” and “super-choked flow mode”. The relationship between the choke and the normal shock wave was revealed to analyze the two mixing fluids flow conditions. The influence of primary fluid pressure and the back pressure on the ejector performance under three choke flow modes were comprehensively discussed. The simulation results indicated that the choking flow was a critical factor in affecting the performance of the steam ejector, and the performance optimizes at “fit-choked flow mode” when the primary fluid pressure was 0.36 MPa with a remarkable entrainment ratio of 0.525.

Various studies suggest that the performances of TMDs are largely thickness dependent. In this paper, we develop a chemical vapor deposition method to synthesis monolayer and bilayer MoS2 flakes with a solution precursor. The MoS2 phototransistors were prepared to investigate their optoelectronic performance. The MoS2 photodetectors exhibit high detectivity of 2.44 × 1011 and a fast response/recovery time of 97 ms/291 ms. The photoresponsivity of bilayer MoS2 flakes was found up to 7160 A W−1. Our research will pave a pathway to control the layer numbers of other TMDs nanostructures, expand the application of high performance 2D materials.

As a typical organism of platyhelminth, Dugesia japonica attracts more and more attention for its strong regenerative ability. Protein arginine methyltransferase (PRMT) family is composed of a class of enzymes with methyltransferase activities, which play fundamental roles in vivo in many important physiological processes. PRMT1 is a predominant type Ⅰ PRMT, which has been reported to be expressed in Schmidtea mediterranea. Nevertheless, the existence and the specific biological functions of PRMT1 in Dugesia japonica need further investigation. In this study, we acquired the full-length sequence of DjPRMT1 and confirmed it was a conserved protein. Thereafter, whole-mount in situ hybridization results showed DjPRMT1 was mainly expressed in neoblasts of adult worms, and obvious aggregation of DjPRMT1 was observed at the wound site in early stages of regeneration. Silencing of the DjPRMT1 gene retarded the movement of planarians with decreased DjPIWI-A expression, and DjPRMT1 knockdown also affected planarian regeneration with slightly attenuated proliferation around the blastema of posterior-facing wounds regeneration. In summary, these preliminary results demonstrated DjPRMT1 was involved in the regeneration of planarian.