A fast ceramic mixed OH-/H+ ionic conductor for low temperature fuel cells.

Low temperature ionic conducting materials such as OH- and H+ ionic conductors are important electrolytes for electrochemical devices. Here we show the discovery of mixed OH-/H+ conduction in ceramic materials. SrZr0.8Y0.2O3-δ exhibits a high ionic conductivity of approximately 0.01 S cm-1 at 90 °C in both water and wet air, which has been demonstrated by direct ammonia fuel cells. Neutron diffraction confirms the presence of OD bonds in the lattice of deuterated SrZr0.8Y0.2O3-δ. The OH- ionic conduction of CaZr0.8Y0.2O3-δ in water was demonstrated by electrolysis of both H218O and D2O. The ionic conductivity of CaZr0.8Y0.2O3-δ in 6 M KOH solution is around 0.1 S cm-1 at 90 °C, 100 times higher than that in pure water, indicating increased OH- ionic conductivity with a higher concentration of feed OH- ions. Density functional theory calculations suggest the diffusion of OH- ions relies on oxygen vacancies and temporarily formed hydrogen bonds. This opens a window to discovering new ceramic ionic conducting materials for near ambient temperature fuel cells, electrolysers and other electrochemical devices.

Field investigation combined with modeling uncovers the ecological heterogeneity of Aedes albopictus habitats for strategically improving systematic management during urbanization.

BACKGROUND: Aedes albopictus is an invasive vector of serious Aedes-borne diseases of global concern. Habitat management remains a critical factor for establishing a cost-effective systematic strategy for sustainable vector control. However, the community-based characteristics of Ae. albopictus habitats in complex urbanization ecosystems are still not well understood. METHODS: A large-scale investigation of aquatic habitats, involving 12 sites selected as representative of four land use categories at three urbanization levels, was performed in Guangzhou, China during 2015-2017. The characteristics and dynamics of these Ae. albopictus habitats were assessed using habitat-type composition, habitat preference, diversity indexes and the Route index (RI), and the temporal patterns of these indexes were evaluated by locally weighted scatterplot smoothing models. The associations of RI with urbanization levels, land use categories and climatic variables were inferred using generalized additive mixed models. RESULTS: A total of 1994 potential habitats and 474 Ae. albopictus-positive habitats were inspected. The majority of these habitats were container-type habitats, with Ae. albopictus showing a particularly higher habitat preference for plastic containers, metal containers and ceramic vessels. Unexpectedly, some non-container-type habitats, especially ornamental ponds and surface water, were found to have fairly high Ae. albopictus positivity rates. Regarding habitats, the land use category residential and rural in Jiangpu (Conghua District, Guangzhou) had the highest number of Ae. albopictus habitats with the highest positive rates. The type diversity of total habitats (H-total) showed a quick increase from February to April and peaked in April, while the H-total of positive habitats (H-positive) and RIs peaked in May. RIs mainly increased with the monthly average daily mean temperature and monthly cumulative rainfall. We also observed the accumulation of diapause eggs in the winter and diapause termination in the following March. CONCLUSIONS: Ecological heterogeneity of habitat preferences of Ae. albopictus was demonstrated in four land use categories at three urbanization levels. The results reveal diversified habitat-type compositions and significant seasonal variations, indicating an ongoing adaptation of Ae. albopictus to the urbanization ecosystem. H-positivity and RIs were inferred as affected by climatic variables and diapause behavior of Ae. albopictus, suggesting that an effective control of overwintering diapause eggs is crucial. Our findings lay a foundation for establishing a stratified systematic management strategy of Ae. albopictus habitats in cities that is expected to complement and improve community-based interventions and sustainable vector management.

Effects of plant growth regulators and sucrose on proliferation and quality of embryogenic tissue in Picea pungens.

Embryogenic tissue (ET) is important for genetic modification and plant re-generation. The proliferation ability and vigor of ET are crucial for plant propagation via somatic embryogenesis. In this study, ET was induced from mature zygotic embryos in blue spruce (Picea pungens Engelm.). There were significant differences in ET induction between two provenances, i.e. 78.8 ± 12.5% and 62.50 ± 12.8% respectively. Effects of 2,4-Dichlorophenoxy acetic acid (2,4-D), 6-Benzyl amino-purine (6-BA) and/or sucrose on ET proliferation and somatic embryo (SE) maturation were further investigated with four cell lines. The highest ET proliferation rate reached 1473.7 ± 556.0% biweekly. Concentrations of 2,4-D or 6-BA applied at tissue proliferation stage impacted SE maturation among the cell lines, whereas sucrose showed less effects. The highest rate, 408 ± 230 mature SEs/g FW, was achieved in SE maturation cultures. This research demonstrated that the culture conditions, i.e. the specific concentrations of 2,4-D and BA, at ET proliferation stage affected not only ET growth, but also the quality of ET for SE maturation. This study revealed the necessity and benefit in developing both the general and the genotype-specific protocols for efficient production of mature SEs, or somatic plants in blue spruce.

Real-time Spread Burst Detection in Data Streaming.

Data streaming has many applications in network monitoring, web services, e-commerce, stock trading, social networks, and distributed sensing. This paper introduces a new problem of real-time burst detection in flow spread, which differs from the traditional problem of burst detection in flow size. It is practically significant with potential applications in cybersecurity, network engineering, and trend identification on the Internet. It is a challenging problem because estimating flow spread requires us to remember all past data items and detecting bursts in real time requires us to minimize spread estimation overhead, which was not the priority in most prior work. This paper provides the first efficient, real-time solution for spread burst detection. It is designed based on a new real-time super spreader identifier, which outperforms the state of the art in terms of both accuracy and processing overhead. The super spreader identifier is in turn based on a new sketch design for real-time spread estimation, which outperforms the best existing sketches.

A Hierarchical Spatial Transformer for Massive Point Samples in Continuous Space.

Transformers are widely used deep learning architectures. Existing transformers are mostly designed for sequences (texts or time series), images or videos, and graphs. This paper proposes a novel transformer model for massive (up to a million) point samples in continuous space. Such data are ubiquitous in environment sciences (e.g., sensor observations), numerical simulations (e.g., particle-laden flow, astrophysics), and location-based services (e.g., POIs and trajectories). However, designing a transformer for massive spatial points is non-trivial due to several challenges, including implicit long-range and multi-scale dependency on irregular points in continuous space, a non-uniform point distribution, the potential high computational costs of calculating all-pair attention across massive points, and the risks of over-confident predictions due to varying point density. To address these challenges, we propose a new hierarchical spatial transformer model, which includes multi-resolution representation learning within a quad-tree hierarchy and efficient spatial attention via coarse approximation. We also design an uncertainty quantification branch to estimate prediction confidence related to input feature noise and point sparsity. We provide a theoretical analysis of computational time complexity and memory costs. Extensive experiments on both real-world and synthetic datasets show that our method outperforms multiple baselines in prediction accuracy and our model can scale up to one million points on one NVIDIA A100 GPU. The code is available at https://github.com/spatialdatasciencegroup/HST.

Training Medical-Diagnosis Neural Networks on the Cloud with Privacy-Sensitive Patient Data from Multiple Clients.

Artificial neural networks (ANNs) are changing the paradigm in medical diagnosis. However, it remains an open problem how to outsource the model training operations to the cloud while protecting the privacy of distributed patient data. Homomorphic encryption suffers from high overhead over data independently encrypted from numerous sources, differential privacy introduces a high level of noise which drastically increases the number of patient records needed to train a model, while federated learning requires all participants to perform synchronized local training that counters our goal of outsourcing all training operations to the cloud. This paper proposes to use matrix masking for outsourcing all model training operations to the cloud with privacy protection. After outsourcing their masked data to the cloud, the clients do not need to coordinate and perform any local training operations. The accuracy of the models trained by the cloud from the masked data is comparable to the accuracy of the optimal benchmark models that are trained directly from the original raw data. Our results are confirmed by experimental studies on privacy-preserving cloud training of medical-diagnosis neural network models based on real-world Alzheimer's disease data and Parkinson's disease data.

An Efficient Symmetric Electrolyzer Based On Bifunctional Perovskite Catalyst for Ammonia Electrolysis.

Ammonia is a natural pollutant in wastewater and removal technique such as ammonia electro-oxidation is of paramount importance. The development of highly efficient and low-costing electrocatalysts for the ammonia oxidation reaction (AOR) and hydrogen evolution reaction (HER) associated with ammonia removal is subsequently crucial. In this study, for the first time, the authors demonstrate that a perovskite oxide LaNi0.5 Cu0.5 O3-δ after being annealed in Ar (LNCO55-Ar), is an excellent non-noble bifunctional catalyst towards both AOR and HER, making it suitable as a symmetric ammonia electrolyser (SAE) in alkaline medium. In contrast, the LNCO55 sample fired in air (LNCO55-Air) is inactive towards AOR and shows very poor HER activity. Through combined experimental results and theoretical calculations, it is found that the superior AOR and HER activities are attributed to the increased active sites, the introduction of oxygen vacancies, the synergistic effect of B-site cations and the different active sites in LNCO55-Ar. At 1.23 V, the assembled SAE demonstrates ≈100% removal efficiency in 2210 ppm ammonia solution and >70% in real landfill leachate. This work opens the door for developments towards bifunctional catalysts, and also takes a profound step towards the development of low-costing and simple device configuration for ammonia electrolysers.

N,N-Dimethylacetamide-Diluted Nitrate Electrolyte for Aqueous Zn//LiMn2O4 Hybrid Ion Batteries.

N,N-Dimethylacetamide (DMA) cooperated with LiNO3 salt has previously shown to be a promising electrolyte for a Li//O2 battery, showing good stability against both the O2 electrode reaction and Li stripping/plating. In this work, DMA is hybridized with a concentrated nitrate electrolyte [2.5 m Zn(NO3)2 + 13 m LiNO3 aqueous solution] for better electrochemical stability while using less dissolved salts. The widest electrochemical stability window for this DMA-diluted electrolyte is determined as 3.1 V, the negative critical stability potential of which is -1.6 V versus Ag/AgCl, indicating desirable stability against hydrogen evolution and Zn deposition. The findings can be attributed to the weakened Li+/Zn2+ solvation sheath caused by low permittivity of DMA, as revealed through Raman spectra characterization and molecular dynamics simulation. A Zn//Zn symmetrical cell and Zn//LiMn2O4 hybrid ion batteries are assembled in air directly, attributed to the stability of DMA toward O2. Zn stripping/plating with a dendrite-free morphology is delivered for 110 h and 200 charge/discharge cycles under 1 C rate, achieving 99.0% Coulombic efficiency. The maximum capacity of the battery is 121.0 mA h·g-1 under 0.2 C rate (based on the mass of LiMn2O4), delivering an energy density of 165.8 W h·kg-1 together with 2.0 V working voltage. This work demonstrates the feasibility and validity of utilizing a relatively dilute electrolyte dissolved in oxygen for a highly stable aqueous rechargeable battery.

Somatic embryogenesis in mature zygotic embryos of Picea pungens.

This study developed somatic embryogenesis protocols for Picea pungens (Engelm), an important ornamental species, including initiation, proliferation, maturation, germination, and acclimation. Somatic embryogenic tissues were induced from mature zygotic embryos of five families, with a frequency of [Formula: see text] 22% for each. Embryogenic tissues (ET) from 13 clones of three families were proliferated for one week, achieving an average rate of 179.1%. The ET of 38 clones of three families were cultured in maturation medium for six weeks; 188 mature embryos on average were counted per gram ET cultured, of which [Formula: see text] 81.1% appeared normal, and each clone developed at least 28 normally matured embryos. A total of 69.9% or more of cotyledonary somatic embryos germinated normally and developed into normal emblings. The experiment of transplanting the emblings into a greenhouse had an average survival rate of 68.5%. Considerable variation among and within families during initiation and proliferation was observed, but this variation decreased in the maturation and germination. Changing the concentration of plant growth regulator of the initiation medium did not significantly change the initiation frequency. We recommend incorporating these protocols into the current Picea pungens practical programs, although further research is essential to increase efficiencies and reduce cost.

On Outsourcing Artificial Neural Network Learning of Privacy-Sensitive Medical Data to the Cloud.

Machine learning and artificial neural networks (ANNs) have been at the forefront of medical research in the last few years. It is well known that ANNs benefit from big data and the collection of the data is often decentralized, meaning that it is stored in different computer systems. There is a practical need to bring the distributed data together with the purpose of training a more accurate ANN. However, the privacy concern prevents medical institutes from sharing patient data freely. Federated learning and multi-party computation have been proposed to address this concern. However, they require the medical data collectors to participate in the deep-learning computations of the data users, which is inconvenient or even infeasible in practice. In this paper, we propose to use matrix masking for privacy protection of patient data. It allows the data collectors to outsource privacy-sensitive medical data to the cloud in a masked form, and allows the data users to outsource deep learning to the cloud as well, where the ANN models can be trained directly from the masked data. Our experimental results on deep-learning models for diagnosis of Alzheimer's disease and Parkinson's disease show that the diagnosis accuracy of the models trained from the masked data is similar to that of the models from the original patient data.

Investigation of Perovskite Oxide SrCo0.8 Cu0.1 Nb0.1 O3-δ as a Cathode Material for Room Temperature Direct Ammonia Fuel Cells.

Single-phase perovskite oxide SrCo0.8 Cu0.1 Nb0.1 O3-δ was synthesized using a Pechini method. X-ray diffraction (XRD) analysis indicated a cubic structure with a=3.8806(7) Å. The oxide material was combined with active carbon, forming a composite electrode to be used as the cathode in a room temperature ammonia fuel cell based on an anion membrane electrolyte and NiCu/C anode. An open circuit voltage (OCV) of 0.19 V was observed with dilute 0.02 m (340 ppm) ammonia solution as the fuel. The power density and OCV were improved upon the addition of 1 m NaOH to the fuel, suggesting that the addition of NaOH, which could be achieved through the introduction of alkaline waste to the fuel stream, could improve performance when wastewater is used as the fuel. It was found that the SrCo0.8 Cu0.1 Nb0.1 O3-δ cathode was converted from irregular shape into shuttle-shape during the fuel cell measurements. As the key catalysts for electrode materials for this fuel cell are all inexpensive, after further development, this could be a promising technology for removal of ammonia from wastewater.

Threshold-Based Widespread Event Detection.

Widespread event detection is a fundamental network function that has many important applications in cybersecurity, traffic engineering, and distributed data mining. This paper introduces a new probabilistic threshold-based event detection problem, which is to find all events that appear in any w-out-of-a monitors with probabilistic guarantee on false positives, where a is the total number of monitors and the threshold w(≤ a) is a positive integer parameter that can be arbitrarily set, according to specific application requirements. We develop an efficient threshold filter solution and its improved versions, which combine Bloom filters, counting Bloom filter, threshold filter and compressed filters in a series of encoding and filtering steps, providing tradeoff between detection accuracy and communication overhead. We theoretically optimize the system parameters in the proposed solutions to minimize the communication overhead under the constraint of probabilistic detection guarantee. Extensive simulations demonstrate the practical viability of the proposed solutions in their ability of finding widespread events in a large network with few false positives and low communication overhead.

Application of antibodies against Borna disease virus phosphoprotein and nucleoprotein on paraffin sections.

In order to study the application of antibodies against recombinant proteins for detecting Borna disease virus (BDV) phosphoprotein (p24) and nucleoprotein (p40) (BDV­p24/p40) on paraffin sections by immunohistochemistry. The purified fusion p24 and p40 proteins were used for the preparation of polyclonal and monoclonal anti­p24 and anti­40 antibodies, which were confirmed by ELISA and western blotting. Paraffin sections were made from BDV­infected Sprague­Dawley (SD) rats (n=20), PBS­injected SD rats (n=20), normal SD rats (n=20) and normal C57 mice (n=20). Immunohistochemical staining was performed according to the EnVision™ two­step protocol. Heat­mediated antigen retrieval was performed using the retrieval buffer sodium citrate (1 mM; pH 6.0). All the antibodies against recombinant proteins exhibited good sensitivity and specificity. There were significant differences between the BDV­infected group and the BDV­uninfected group for poly­ and monoclonal anti­p24 and ­p40 antibodies. These antibodies against recombinant proteins may be used effectively to detect BDV p24 and p40 in paraffin sections.

Different inhibitory effects on the proliferation and apoptosis of human and laboratory Borna disease virus­infected human neuroblastoma SH­SY5Y cells in vitro.

Borna disease virus (BDV) is a neurotropic and non­cytolytic virus, which causes behavioral disorders in a wide range of warm­blooded species. It is well established that BDV induces neurodegeneration by impairing neurogenesis and interfering with neuronal functioning in the limbic system. In the present study, the potential role of BDV infection in SH­SY5Y cells was identified, and comparisons of two original BDV strains (the human Hu­H1 and the laboratory Strain V) were performed to further elucidate the phenotypes of BDV pathogenesis with strain differences. Cell Counting Kit­8 and flow cytometric analyses revealed that the two BDV strain­infected groups exhibited marked anti­proliferation and cell cycle arrest compared with the control group, and the Hu­H1 strain caused more evident effects. However, the Hu­H1 strain did not exert effects on the apoptosis of SH­SH5Y cells, while Strain V led to a marked increase in apoptosis upon initial infection. Western blot analysis confirmed the upregulation of apoptosis regulator BAX protein and the downregulation of apoptosis regulator Bcl­2 protein caused by the two BDV strains. The results of the present study provided evidence that infection with BDV suppressed SH­SY5Y cellular functioning and exhibited divergent antiproliferative and apoptotic roles in cells between the two strains. The present study provided an insight for future investigation of strain differences and underlying pathomechanisms.

Inhibition of corticotropin-releasing hormone receptor 1 and activation of receptor 2 protect against colonic injury and promote epithelium repair.

Maternal separation (MS) in neonates can lead to intestinal injury. MS in neonatal mice disrupts mucosal morphology, induces colonic inflammation and increases trans-cellular permeability. Several studies indicate that intestinal epithelial stem cells are capable of initiating gut repair in a variety of injury models but have not been reported in MS. The pathophysiology of MS-induced gut injury and subsequent repair remains unclear, but communication between the brain and gut contribute to MS-induced colonic injury. Corticotropin-releasing hormone (CRH) is one of the mediators involved in the brain-gut axis response to MS-induced damage. We investigated the roles of the CRH receptors, CRHR1 and CRHR2, in MS-induced intestinal injury and subsequent repair. To distinguish their specific roles in mucosal injury, we selectively blocked CRHR1 and CRHR2 with pharmacological antagonists. Our results show that in response to MS, CRHR1 mediates gut injury by promoting intestinal inflammation, increasing gut permeability, altering intestinal morphology, and modulating the intestinal microbiota. In contrast, CRHR2 activates intestinal stem cells and is important for gut repair. Thus, selectively blocking CRHR1 and promoting CRHR2 activity could prevent the development of intestinal injuries and enhance repair in the neonatal period when there is increased risk of intestinal injury such as necrotizing enterocolitis.

Achieving Strong Privacy in Online Survey.

Thanks to the proliferation of Internet access and modern digital and mobile devices, online survey has been flourishing into data collection of marketing, social, financial and medical studies. However, traditional data collection methods in online survey suffer from serious privacy issues. Existing privacy protection techniques are not adequate for online survey for lack of strong privacy. In this paper, we propose a practical strong privacy online survey scheme SPS based on a novel data collection technique called dual matrix masking (DM2), which guarantees the correctness of the tallying results with low computation overhead, and achieves universal verifiability, robustness and strong privacy. We also propose a more robust scheme RSPS, which incorporates multiple distributed survey managers. The RSPS scheme preserves the nice properties of SPS, and further achieves robust strong privacy against joint collusion attack. Through extensive analyses, we demonstrate our proposed schemes can be efficiently applied to online survey with accuracy and strong privacy.

Collusion Resistant Multi-Matrix Masking for Privacy-Preserving Data Collection.

An integral part of any social or medical research is the availability of reliable data. For the integrity of participants' responses, a secure environment for collecting sensitive data is required. This paper introduces a novel privacy-preserving data collection method: collusion resistant multi-matrix masking (CRM3). The CRM3 method requires multiple masking service providers (MSP), each generating its own random masking matrices. The key step is that each participant's data is randomly decomposed into the sum of component vectors, and each component vector is sent to the MSPs for masking in a different order. The CRM3 method publicly releases two sets of masked data: one being right multiplied by random invertible matrices and the other being left multiplied by random orthogonal matrices. Both MSPs and the released data may be hosted on cloud platforms. Our data collection and release procedure is designed so that MSPs and the data collector are not able to derive the original participants' data hence providing strong privacy protection. However, statistical inference on parameters of interest will produce exactly the same results from the masked data as from the original data, under commonly used statistical methods such as general linear model, contingency table analysis, logistic regression, and Cox proportional hazard regression.

Identification of suitable reference genes for BDV-infected primary rat hippocampal neurons.

Borna disease virus (BDV) is a neurotropic RNA virus that infects the limbic system of mammals and results in behavioral disorders. The hippocampus is a core region in the limbic system, which contributes to memory and learning and is important in the regulation of emotion. However, no validated microRNA housekeeping genes have yet been identified in BDV­infected rat primary hippocampal neurons. Proper normalization is key in accurate miRNA expression analysis. The present study used reverse transcription­quantitative polymerase chain reaction (RT­qPCR) to evaluate the expression stability of 10 commonly used reference genes [miR­92a, 5S, U6, miR­103, miR­101a, miR-let-7a, miR­16, E2 small nucleolar RNA (snoRNA), U87 and miR­191] in BDV­infected rat hippocampal neurons and non­infected controls across 12 days post­infection. The data was analyzed by four statistical algorithms: geNorm, NormFinder, BestKeeper, and the comparative Δ­Ct method. Subsequently, the most suitable reference genes (miR­101a and U87) and the least suitable (snoRNA) were determined by the RankAggreg package. miR­155 was selected as a standard by which to evaluate the most and least suitable reference genes. When normalized to the most stable reference gene there were significant differences between the two groups. However, when the data were normalized to the less stably expressed gene, the results were not significant. miR­101a was recommended as a suitable reference gene for BDV-infected rat primary hippocampal neurons.

Redox-Reversible Iron Orthovanadate Cathode for Solid Oxide Steam Electrolyzer.

A redox-reversible iron orthovanadate cathode is demonstrated for a solid oxide electrolyser with up to 100% current efficiency for steam electrolysis. The iron catalyst is grown on spinel-type electronic conductor FeV2O4 by in situ tailoring the reversible phase change of FeVO4 to Fe+FeV2O4 in a reducing atmosphere. Promising electrode performances have been obtained for a solid oxide steam electrolyser based on this composite cathode.

Applying Triple-Matrix Masking for Privacy Preserving Data Collection and Sharing in HIV Studies.

BACKGROUND: Many HIV research projects are plagued by the high missing rate of selfreported information during data collection. Also, due to the sensitive nature of the HIV research data, privacy protection is always a concern for data sharing in HIV studies. METHODS: This paper applies a data masking approach, called triple-matrix masking [1], to the context of HIV research for ensuring privacy protection during the process of data collection and data sharing. RESULTS: Using a set of generated HIV patient data, we show step by step how the data are randomly transformed (masked) before leaving the patients' individual data collection device (which ensures that nobody sees the actual data) and how the masked data are further transformed by a masking service provider and a data collector. We demonstrate that the masked data retain statistical utility of the original data, yielding the exactly same inference results in the planned logistic regression on the effect of age on the adherence to antiretroviral therapy and in the Cox proportional hazard model for the age effect on time to viral load suppression. CONCLUSION: Privacy-preserving data collection method may help resolve the privacy protection issue in HIV research. The individual sensitive data can be completely hidden while the same inference results can still be obtained from the masked data, with the use of common statistical analysis methods.