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Currently, around 50% (~178 million) of all Americans are missing at least 1 tooth, with 12% (~40 million) of Americans missing all of their teeth. Dental implants are a common solution to this issue; titanium is the leading material of choice. Zirconia ceramic dental implants are a suitable replacement for titanium, but are often criticized for their brittleness. By developing a tantalum functional gradient coating, we hope to increase the viability of zirconia ceramic materials for dental implants. Two sets of material were chosen to test the proprietary BruxZir tantalum ion solution: yttria stabilized zirconia (3Y-TZP) and alumina toughened zirconia (ATZ). Solutions with varying concentrations of 0%, 10%, 15%, and 30% weight percent tantalum were prepared, and the ceramic samples were coated and sintered. Each sample was analyzed through various characterization methods, including SEM, EDS, XRD, nanoindentation, and Raman spectroscopy to observe how the solution impacted each sample’s microstructure and mechanical properties. Although our results are still in progress, we found evidence of an inverse relationship between tantalum concentration and grain size.

TDI-like multi-slit hyperspectral imaging for enhanced throughput via Kalman filter: supplemental document

In this paper, a novel approach was set up to analyze and discriminate propolis from different regions based on GC-MS and multivariate statistical analysis. A number of Chinese and Brazilian green propolis samples were dealt with based on this method, and a set of data were processed with partial least squares-discriminant analysis (PLS-DA). A clear differences between the two groups were shown in score plot. The chemical markers for the differentiation were selected through loading plot. Based on the comparison between the reference and/or NIST database and mass fragments in the publication, chemical markers were tentatively identified. Lauric acid, 2(3H)-naphthalenone, spathulenol and benzenebutanoic acid were taken as chemical markers based on the above strategy. This research could provide some valuable information to the quality control of propolis from different origins.

ObjectivesThis study aimed at establishing a mouse model of immune-related adverse in humanized BALB/c-hPD1/hCTLA4 mice to investigate their potential pathogenesis and explore therapeutic targets for immune-related arthritis and pneumonitis.MethodsHumanized BALB/c-hPD1/hCTLA4 mice were injected with vehicle or collagen-specific antibodies (CA) and immune checkpoint inhibitors (ICI, ipilimumab, anti-human CTLA-4; and nivolumab, anti-human PD-1), and some mice were treated with anti-TNF-α antibody, leading to the control, collagen antibody-induced arthritis (CAIA), CAIA+ICI and treatment groups. The severity of clinical arthritis and pneumonitis in mice was monitored longitudinally and the pathological changes in the joints and lungs were histologically analyzed and the contents of lung hydroxyproline were measured. The frequency of different subsets of T cells was analyzed by flow cytometry and multiplex immunofluorescency.ResultsCompared with the control, the ICI group of mice developed the delayed onset of moderate degrees of arthritis while the CAIA+ICI group of mice exhibited the early onset of severe arthritis. Treatment with ICI caused severe pneumonitis, especially in the mice with CA. Flow cytometry analysis indicated a significantly higher frequency of splenic TNF-α+CD4+and TNF-α+CD8+T cells, but not other subsets of T cells tested, in the CAIA+ICI group of mice, relative to that in other groups of mice. Treatment with anti-TNF-α significantly mitigated the severity of arthritis and pneumonitis as well as deposition of collagen in lung of mice. The treatment also decreased the frequency of TNF-α+CD4+and TNF-α+CD8+T cells as well as effector memory T cells in the periphery lymph orangs and lungs of mice.ConclusionsWe successfully established a humanized mouse model of ICI-related severe arthritis and pneumonitis with a higher frequency of TNF-α+T cells, which were significantly mitigated by anti-TNF-α treatment. Conceptually, ICI treatment can induce multiple autoimmune-like diseases in autoimmune-prone individuals and TNF-α+T cells may be therapeutic targets for intervention of immune-related arthritis and pneumonitis.

Supplemental document for the submitted manuscript

Additional file 1. The total ion current in the negative and positive ion mode in light-induced AgNPs (Figure S1); Primary and secondary mass spectra of 20 chemical substances detected in green-synthesized AgNPs (Figure S2); SEM and EDS patterns of seeds cross-sections treated with different concentrations (Figure S3); SEM images of seeds (Figure S4); Influence of different nanopriming treatment on alfalfa seed in petri dishes (Figure S5); Calculation of pink AgNPs’ crystal size by using Debye-Scherrer formula (Table S1); Effect of AgNPs nanopriming on growth parameters of 14 d seedlings (Table S2); Raw data for all growth parameters of 14 d seedlings under AgNPs treatments (Table S3).

With the rapid development of the scale of the power grid, problems such as the changing operation mode of the distribution network and a large amount of monitoring data have become increasingly prominent. At present, the centralized sensing system based on synchronous phasor measurement has been researched, but there are still problems such as synchronization data delay mismatch in the implementation process, and the sampling data caused by clock differences are not timely and asynchronous, which is a problem in the construction of the power distribution IoT. The emergence of Beidou satellite timing, voltage-controlled crystal oscillator and other technologies provide new solutions to the above problems. This paper proposes a wide-area synchronous sampling technology for power distribution IoT terminal equipment that combines Beidou satellite timing with automatic frequency control. This technology can effectively solve the problems of too long time delay and too large synchronization error, and is an important technology to improve the real-time performance of synchronization data in the distribution network. The paper firstly compares various clock synchronization technologies, and then focuses on analyzing the causes of crystal oscillator errors, and then supplements the error control algorithm. Finally, the Beidou satellite timing and automatic frequency control combination of sampling synchronization software and hardware design methods are proposed, and the experimental data is analyzed. Experimental verification shows that the above method can control the clock synchronization error within one hundred nanoseconds, and realize the wide-area high-precision synchronous sampling of the power distribution IoT terminal equipment.

Power distribution network management and operation are facing challenges due to the increasing distributed generation penetration and electric vehicle adoption. Traditional distribution transformer terminals are designed to provide specific control and monitoring functions for distribution transformers, thus have limitations in aspects such as inadequate scalability of software and hardware, non-standardized design and proprietary communication which usually requires high cost for upgrading and operation. To address these issues, the smart distribution transformer terminal is proposed and defined with a combination of communication gateway function and distribution network monitoring and control capability. As the core part of power distirbution network management, the functions and design philosophy of smart distribution transformer terminal are presented. The demonstration projects and applications are introduced in the following part.

Additional file 1: Figures.

In order to adapt to the energy revolution and digitization, a new design for the distribution network monitoring system is proposed to achieve data lifecycle management. Firstly, the current practices and limitations of existing monitoring systems are presented. According to the development trend, the reconstruction strategy is presented involving three feedback local-computing loops and data interaction between distributed intelligent agents and cloud-based master station. To support the huge scale of sensed data integration and transmission, the reference framework of data interaction is established, and the main monitoring information is presented. Then some technical evolution trends towards the new architecture are described in the fields of unified model, new generation of intelligent terminal, network interconnection, edge-cloud computing and security system.

The surface structure of bimetallic Pd/Au catalyst, accompanied by the presence of potassium acetate (KOAc) are critical for synthesis of vinyl acetate (VAc). In this study, different configurations of the Pd/Au (1 1 1) surface were constructed. The density functional theory (DFT) was used to investigate the adsorption of ethylene and acetate on the Pd/Au (1 1 1) surface. The reaction mechanism of VAc formation was studied by following the Samanos pathway. The existence of potassium ion (K+) enhanced the adsorption of ethylene and acetate on the Pd/Au (1 1 1) surface, and promoted the dehydrogenation of acetic acid. The Pd/Au (1 1 1) surface configuration, with two adjacent palladium atoms embedded on the gold surface, is the most favorable surface structure for VAc synthesis. The introduction of K+ was obstructive to the formation of ethyl acetate-like intermediates, but facilitated β-H elimination during the formation of VAc. This work provides a deeper mechanistic understanding of VAc synthesis on the Pd/Au (1 1 1) surface, which could shed some light on the catalysts design and the industrial control of VAc synthesis.

Aiming at the fact of the heavy workload and high cost in the construction, operation and maintenance of power distribution system, the definition, technical focus and implementation of the plug and play technology for power distribution terminals are analyzed based on the value brought by the Internet of Things (IoT) which can improve the automation level of power distribution system and reduce the workload of system construction and daily maintenance. The plug and play solution for power distribution terminal management was explored in some cities, which has been proved efficiently.

Dengue, a mosquito-borne infectious disease caused by the dengue viruses, is present in many parts of the tropical and subtropical regions of the world. All four serotypes of dengue viruses are endemic in Singapore, an equatorial city-state. Frequent outbreaks occur, sometimes leading to national epidemics. However, few studies have attempted to characterize breakpoints which precede large rises in dengue case counts. In this paper, Bayesian regime switching (BRS) models were employed to infer epidemic and endemic regimes of dengue transmissions, each containing regime specific autoregressive processes which drive the growth and decline of dengue cases, estimated using a custom built multi-move Gibbs sampling algorithm. Posterior predictive checks indicate that BRS replicates temporal trends in Dengue transmissions well and nowcast accuracy assessed using a post-hoc classification scheme showed that BRS classification accuracy is robust even under limited data with the AUC-ROC at 0.935. LASSO-based regression and bootstrapping was used to account for plausibly high dimensions of climatic factors affecting Dengue transmissions, which was then estimated using cross-validation to conduct statistical inference on long-run climatic effects on the estimated regimes. BRS estimates epidemic and endemic regimes of dengue in Singapore which are characterized by persistence across time, lasting an average of 20 weeks and 66 weeks respectively, with a low probability of transitioning away from their regimes. Climate analysis with LASSO indicates that long-run climatic effects up to 20 weeks ago do not differentiate epidemic and endemic regimes. Lastly, by fitting BRS to simulated disease data generated from a stochastic Susceptible-Infected-Recovered model, mechanistic links between infectivity and regimes classified using BRS were provided. The model proposed could be applied to other localities and diseases under minimal data requirements where transmission counts over time are collected., Author summary Dengue, a mosquito-borne infectious disease caused by the dengue viruses, is present in many parts of the tropical and subtropical regions of the world. All four serotypes of dengue viruses are endemic in Singapore, an equatorial city-state. Frequent outbreaks occur, sometimes leading to national epidemics. However, few studies have attempted to characterize breakpoints which precede large rises in dengue case counts. In this paper, Bayesian regime switching (BRS) models were employed to infer epidemic and endemic regimes of dengue transmissions, each containing regime specific processes which drive the growth and decline of dengue cases, estimated using a custom built multi-move Gibbs sampling algorithm. Assessments against various baseline showed that BRS performs better in characterizing dengue transmissions. The dengue regimes estimated by BRS are characterized by their persistent nature. Next, climate analysis showed no short nor long term associations between classified regimes with climate. Lastly, fitting BRS to simulated disease data generated from a mechanistic model, we showed links between disease infectivity and regimes classified using BRS. The model proposed could be applied to other localities and diseases under minimal data requirements where transmission counts over time are collected.

In this paper, a novel approach using HPLC-MS/MS (high performance liquid chromatography-tandem mass spectrometry) was established for the identification of Pericarpium Citri Reticulatae (PCR). The identities of the components were researched by comparing the mass spectra and retention times with those of reference compounds and mass fragments with those of the known compounds published in literature. Based on the proposed strategy, ten constituents were identified. The established HPLC-MS/MS method could identify the major constituents in PCR with high efficiency and sensitivity. The new method would be suitable for the chemical identification of main components from Pericarpium Citri Reticulatae.

Regarding the dynamic trust measurement issues, a trust measurement model for a software system written in C language by combining static and dynamic measurement is designed. To gain an initial way to abstract the behaviors model, the softwareias behavior model based on API calls is constructed firstly, then the method to monitor and measure the real behaviors during the target software runtime is given. The experiment results show that the model can reflect software behavior better and enhance the system security.