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Improving composite battery electrodes requires a delicate control of active materials and electrode formulation. The electrochemically active particles fulfill their role as energy exchange reservoirs through interacting with the surrounding conductive network. We formulate a network evolution model to interpret the regulation and equilibration between electrochemical activity and mechanical damage of these particles. Through statistical analysis of thousands of particles using x-ray phase contrast holotomography in a LiNi 0.8 Mn 0.1 Co 0.1 O 2 -based cathode, we found that the local network heterogeneity results in asynchronous activities in the early cycles, and subsequently the particle assemblies move toward a synchronous behavior. Our study pinpoints the chemomechanical behavior of individual particles and enables better designs of the conductive network to optimize the utility of all the particles during operation.

Lithium-ion battery (LIB) is a broadly adopted technology for energy storage. With increasing demands to improve the rate capability, cyclability, energy density, safety, and cost efficiency, it is crucial to establish an in-depth understanding of the detailed structural evolution and cell-degradation mechanisms during battery operation. Here, we present a laboratory-based high-resolution and high-throughput X-ray micro–computed laminography approach, which is capable of in situ visualizing of an industry-relevant lithium-ion (Li-ion) pouch cell with superior detection fidelity, resolution, and reliability. This technique enables imaging of the pouch cell at a spatial resolution of 0.5 μm in a laboratory system and permits the identification of submicron features within cathode and anode electrodes. We also demonstrate direct visualization of the lithium plating in the imaged pouch cell, which is an important phenomenon relevant to battery fast charging and low-temperature cycling. Our development presents an avenue toward a thorough understanding of the correlation among multiscale structures, chemomechanical degradation, and electrochemical behavior of industry-scale battery pouch cells.

Pressure has been a facile signal readout mode for developing point-of-care testing devices due to the attractive features of portability, accessibility, rapidity, and affordability. Herein, a pressure signal readout device was designed by integrating two homemade needle-type piezoresistive transducers, a controller for a thin-film piezoresistive sensor and a smartphone. Meanwhile, a bidirectional immunochromatographic test strip was designed as an immunoreaction platform for dual-analyte detection. Using PdCuPt nanoparticles with catalase-mimic activity as signal tags, the pressure signals triggered by catalyzed aerogenous reaction were monitored by the pressure signal readout device and read on a smartphone with the Bluetooth module. In this proof-of-principle work, imidacloprid and carbendazim were detected as model analytes. The dynamic ranges for quantitating imidacloprid and carbendazim are 20 pg mL

The lithium-ion battery has demonstrated tremendous economic and social impacts. Upon battery operation under different conditions, lithium changes its chemical state and physical formation, leadin...

Nano-resolution full-field transmission X-ray microscopy has been successfully applied to a wide range of research fields thanks to its capability of non-destructively reconstructing the 3D structure with high resolution. Due to constraints in the practical implementations, the nano-tomography data is often associated with a random image jitter, resulting from imperfections in the hardware setup. Without a proper image registration process prior to the reconstruction, the quality of the result will be compromised. Here a deep-learning-based image jitter correction method is presented, which registers the projective images with high efficiency and accuracy, facilitating a high-quality tomographic reconstruction. This development is demonstrated and validated using synthetic and experimental datasets. The method is effective and readily applicable to a broad range of applications. Together with this paper, the source code is published and adoptions and improvements from our colleagues in this field are welcomed.

Technology development in spatial transcriptomics has propelled further understanding of cell types and their organization in tissues, opening new possibilities for researchers to explore transcript distributions at subcellular levels. However, current methods have limitations in either resolution, sensitivity, or speed. Here we demonstrate SPRINTseq (Spatially Resolved and signal-diluted Next-generation Targeted sequencing), a new in situ sequencing strategy that combines hybrid block coding and molecular dilution to resolve molecular crowdedness, overcoming a fundamental challenge that precluded simultaneous fast and sensitive high-resolution data acquisition. Our method recovered over 142 million transcripts in 453,843 cells from four whole mouse brain slices in less than two days. We then show how this high-resolution transcript map can be used to decipher cellular and subcellular molecular architecture of Alzheimer’s disease in an unprecedented way. This improved spatial transcriptomics technology allowed us to link abnormal cellular behaviors and their organization in diseased tissue to changes in their subcellular mRNA distribution, generating new biological insights in health and disease.

With ever-increasing complexity in Upstream project planning, to ensure decision quality, the dynamics of subsurface resources need to be embedded into concept screening to maintain consistency between the production forecast and development plan. We developed a decision-driven subsurface surrogate model that encapsulates key reservoir dynamics into the machine augmented mathematical technologies for holistic decision recommendation in concept selection and development planning under uncertainties. The surrogate model replicates the essential subsurface dynamics by using a hybrid-approach that takes into accounts both reservoir simulation data and physical first principle. In addition to standalone usage on production forecast for rapid profile screening under resource uncertainties, the subsurface surrogate model is incorporated into mathematical optimization models that simultaneously consider surface network, commercial obligation and project economics etc. to provide alternative concepts under various uncertainties. Our subsurface surrogate model has been applied for decision making on gas gathering system design, field development optimization, field-management timing and sequencing, and field tie-back study etc. Results not only show the capability of surrogate models to enable large scale rapid decision screening, but also bear a close resemblance between the predicted production profiles and the reservoir simulation results when fed with the field operating strategy recommended by our decision models with surrogate dynamics. The study demonstrates the reliability of our surrogate modeling technology on ensuring decision quality and helps build business’ confidence on technology adoption. By further incorporating subsurface uncertainties into surrogate models, the decision makers are provided with: probabilistic analysis of the outcomes, value of information analysis, cost of optionality and flexibility, and holistic project outlook etc. Our decision-driven surrogate modeling technology incorporated in mathematical decision models is the first of its kind for holistic decision support in concept selection and development planning in oil and gas industry, and has full potentials in a variety of asset lines with reliable subsurface performance prediction under uncertainties.

Decision-making complexity in the oil and gas industry has risen dramatically in recent years, especially in consideration of uncertainties related to geopolitics, policies, marketing, subsurface resources etc. To enable decision making with the best quality opportunities and projects, we are developing an integrated suite of machine augmented mathematical technologies to recommend holistic decisions for concept selection and development planning under uncertainties. Our ongoing technology development is progressing a set of prototypes and use cases including: 1) AI-based uncertainty handling technologies aiming to detect uncertainties, quantify impacts, and translate to influence factors for decision-making (e.g., IRR, cost); 2) Decision-driven surrogate reservoir models approximating subsurface dynamics to enable rapid concept screening; 3) a set of mathematical optimization-based decision models in the form of mixed-integer linear programs (MILP) to provide solution alternatives to address different business challenges under uncertainties. We demonstrate that the use of systematic technical applications combined with human interaction can improve the decision quality significantly by considering all influence factors, searching through the entire decision space, and recommending a range of alternatives for business users to consider with minimal bias. These technologies have been designed to plug into existing processes and platforms to accelerate technology adoption and usage.

To survive in dynamic environments, animals make behavioral decisions based on innate and learnt information about the valences of sensory cues. In the brains of multiple species, innate and learnt sensory valence signals are initially encoded by distinct neural populations1–7 but then reconverge in downstream brain structures that guide behavioral choices6–9. This reconvergence hinges on the prior acquisition of learnt valence information, which in turn might depend upon innate valence signals. However, it remains unknown whether and how innate sensory valence cues shape the acquisition of learnt valence information. Here we show that in the fruit fly brain, interactions between innate and learnt sensory valence signals within interconnected short- and long-term memory units of the mushroom body jointly regulate memory formation and expression via modulation of dopamine teaching signals. By using time-lapse, in vivo optical voltage imaging to record neural spiking with millisecond-resolution in flies undergoing olfactory associative conditioning, we found that PPL1 dopamine neurons (PPL1-DANs) heterogeneously and bi-directionally encode punishment, reward, and innate and learnt odor-valence cues. Learning modulates these representations in a way that combines innate and learnt valence information and allows the PPL1-DANs to regulate memory storage in their downstream targets, mushroom body output neurons (MBONs). PPL1-γ1pedc and PPL1-γ2α’1 neurons control short-term memory formation. After repeated conditioning, feedback signals carrying short-term memory data from MBON-γ1pedc>α/β to PPL1-α’2α2 and PPL1-α3 allow these dopamine cells to encode previously learnt valences and promote long-lasting memory formation. A computational model constrained by the mushroom body connectome and our spiking data explains how dopamine signals integrate innate and learnt valence data to regulate memory storage, extinction, and the interactions between short- and long-term memory traces. The model yields non-intuitive predictions about the effects of different training protocols, which our experiments confirm. Overall, the mushroom body achieves flexible learning through dopamine-mediated integration of innate and learnt valences in parallel sets of DAN/MBON learning units with feedback interconnections. This hybrid physiologic-anatomic mechanism may be a general means by which ecological information regulates learning and memory in other species and brain structures relying on dopaminergic signaling, including the vertebrate basal ganglia10.

Gas pressure is a promising signal readout mode in point-of-care testing for its merits such as rapidity, simplicity, affordability, and no need for sophisticated instrumentation. Herein, a gas pressure sensor for multiplexed detection of pathogenic bacteria was developed on a hydrogel platform. Spherical and square hydrogel pellets prepared by cross-linking of sodium alginate were functionalized with nisin and ConA for the capture of

Rapid and sensitive assay of pathogenic bacteria is critical for minimizing the risk of infectious diseases. Inspired by the interaction between bacteriophages and host bacteria, we obtained a gene sequence of tail fiber protein (TFP) from Pseudomonas aeruginosa (P. aeruginosa) bacteriophage. Then the gene sequence was used to express a recombinant TFP, which can act as a potential capture molecule for P. aeruginosa. Small ubiquitin-related modifier (SUMO) tag was fused onto the TFP fragment to overcome its unfavorable aqueous solubility. The obtained SUMO tag-fused TFP (STFP) can be uniformly distributed onto a nitrocellulose membrane to form a test line due to the improved aqueous solubility, which facilities fabrication of a lateral flow assay strip. Thus we developed a lateral flow assay method by using STFP as a capture molecule and AuCo nanoparticles-labeled aptamer as a signal tracer for point-of-care testing of P. aeruginosa. By using the test strip, P. aeruginosa can be semi quantified with color band and quantified with chemiluminescent (CL) signal catalyzed by AuCo nanoparticles. The concentration range for quantification is 3.3 × 10

Transition metal dissolution in layered cathodes is one of the most intractable issues that deteriorate the battery performance and lifetime. It not only aggravates the structure degradation in cathode but also damages the solid electrolyte interphase in anode and even induces the formation of lithium dendrites. In this work, we investigate the dissolution behaviors of polycrystalline and single-crystalline layered cathode via operando X-ray imaging techniques. The cathode particle morphology appears to have a significant impact on the evolution of the dissolution dynamics. As a mitigation strategy, we reveal that doping with a trace amount of Zr in the layered cathode could improve its robustness against the transition metal dissolution. Our finding provides valuable insights for designing the next-generation highly stable layered battery cathodes.

This study developed a method to build relationships between chemical fractionations of heavy metals in soils and their accumulations in rice and estimate the respective contribution of each geochemical speciation in the soils from the Yangtze River Delta, China. In contaminated areas, residue and humic acid-bound fractions in soils were the main phases for most heavy metals. The mobility of heavy metals was in this following order: Cd > Pb ≈ Zn > Ni > As ≈ Cr > Hg. Transfer factors calculated by the ratios of specific fractionations of heavy metals in the soil–rice system were used to assess the capability of different metal speciation transfer from soil to rice. The carbonate and Fe/Mn oxyhydroxides bound phase had significant positive correlations with total metal concentrations in rice. Hg uptake by rice might be related to the exchangeable and carbonate-bound fractions of soil Hg. Results of PCA analysis of transfer factors estimated that the labile fractions (i.e. water soluble, exchangeable and carbonate bound) contributed more than 40% of the heavy metal accumulations in rice. Effect of organic matter and residue fraction on metals transfer was estimated to be ~ 25 to ~ 30% while contribution of humic acid and Fe/Mn oxyhydroxides-bound fractions was estimated to be ~ 20 to ~ 30%. Modified risk assessment code (mRAC) and ecological contamination index (ECI) confirmed that the soil samples were polluted by heavy metals. Soil Cd contributed more than 80% of mRAC. Contrarily, the main contributors to ECI were identified as As, Hg, Pb and Zn. The average values of total target hazard quotient (TTHQ) and Risktotal were above 1 and 10–4 respectively, implying people living in the study area were exposed to both non-carcinogenic and carcinogenic risk. As and Pb were the main contributor to high TTHQ value while As, Cd and Cr in rice contributed mostly to Risktotal value. Spatial changes of ecological risk indexes and human health risk indexes showed that the samples with high TTHQ values distributed in the area with high values of mRAC. Likewise, the area with high ECI values and with high carcinogenic risk overlapped.

Increasing demand on water supplies in western US and more stringent wastewater discharge standards have made recycled water a common water source for irrigating urban green spaces. Studies are needed to evaluate nitrate-N leaching potential and phosphorus (P) movement along soil profile when recycled wastewater is used for irrigation. We collected and analyzed soil samples at the commencement and 5 and 11 years after recycled water irrigation on 2 golf courses, 5 metropolitan parks, and 1 school ground. Samples were taken at 0-20, 20-40, 40-60, 60-80, and 80-100 cm depths on golf courses and at 0-20 and 20-40 cm depths at other locations. Soil samples were tested for soil pH, soil nitrate, and AB-DTPA extracted P level. No increase in soil nitrate-N was observed over 5 and 11 years with recycled water irrigation, suggesting leaching of nitrogen to the groundwater was not a great concern. Soil P concentration at the surface soil depth was the highest 11 years after recycled water irrigation. Moreover, soil P increasing below the surface layer was observed on sites with sandy soil, suggesting long-term recycled water irrigation could impose some risk for P leaching on sandy soil.

The degradation of nickel-rich cathode materials for lithium-ion batteries upon prolonged electrochemical cycling features a complicated interplay among electronic structure, lattice configuration,...

Lithium-ion batteries (LIBs) feature structural and chemical complexities across a broad range of length scales. It is the hierarchy of the battery structure that determines its functionality. An in-depth understanding of the battery function, degradation, and failure mechanisms requires a thorough and systematic investigation from structural, chemical, mechanical, and dynamic perspectives. Here we present a macro-to-nano zoom through the hierarchy of a commercial 18650 type LIB using a suite of state-of-the-art X-ray microscopy techniques. Damage, deformation, and heterogeneity at different length scales are visualized and are associated with different degradation phenomena and mechanisms. Our results highlight the importance of the mechanical properties of the cathode material, which could impact both the immediate and the long-term cell behaviors significantly. While this study focuses on a commercial lithium cell with a standard configuration, our findings can be extrapolated and are applicable to the development of next-generation energy storage technology, e.g. solid-state batteries.

Defocus blur often degrades the performance of image understanding, such as object recognition and image segmentation. Restoring an all-in-focus image from its defocused version is highly beneficial to visual information processing and many photographic applications, despite being a severely ill-posed problem. We propose a novel convolutional neural network architecture AIFNet for removing spatially-varying defocus blur from a single defocused image. We leverage light field synthetic aperture and refocusing techniques to generate a large set of realistic defocused and all-in-focus image pairs depicting a variety of natural scenes for network training. AIFNet consists of three modules: defocus map estimation, deblurring and domain adaptation. The effects and performance of various network components are extensively evaluated. We also compare our method with existing solutions using several publicly available datasets. Quantitative and qualitative evaluations demonstrate that AIFNet shows the state-of-the-art performance.

Defocus deblurring is a challenging task due to the spatially varying nature of defocus blur. While deep learning approach shows great promise in solving image restoration problems, defocus deblurring demands accurate training data that consists of all-in-focus and defocus image pairs, which is difficult to collect. Naive two-shot capturing cannot achieve pixel-wise correspondence between the defocused and all-in-focus image pairs. Synthetic aperture of light fields is suggested to be a more reliable way to generate accurate image pairs. However, the defocus blur generated from light field data is different from that of the images captured with a traditional digital camera. In this paper, we propose a novel deep defocus deblurring network that leverages the strength and overcomes the shortcoming of light fields. We first train the network on a light field-generated dataset for its highly accurate image correspondence. Then, we fine-tune the network using feature loss on another dataset collected by the two-shot method to alleviate the differences between the defocus blur exists in the two domains. This strategy is proved to be highly effective and able to achieve the state-of-the-art performance both quantitatively and qualitatively on multiple test sets. Extensive ablation studies have been conducted to analyze the effect of each network module to the final performance., CVPR 2022 Oral

Candida auris is an emerging yeast pathogen with a remarkable ability to develop antifungal resistance, in particular to fluconazole and other azoles. Azole resistance in C. auris was shown to result from different mechanisms, such as mutations in the target gene ERG11 or gain-of-function (GOF) mutations in the transcription factor TAC1b and overexpression of the drug transporter Cdr1. The roles of the transcription factor Mrr1 and of the drug transporter Mdr1 in azole resistance is still unclear. Previous works showed that deletion of MRR1 or MDR1 had no or little impact on azole susceptibility of C. auris. However, an amino acid substitution in Mrr1 (N647T) was identified in most C. auris isolates of clade III that were fluconazole resistant. This study aimed at investigating the role of the transcription factor Mrr1 in azole resistance of C. auris. While the MRR1(N647T) mutation was always concomitant to hot spot ERG11 mutations, MRR1 deletion in one of these isolates only resulted in a modest decrease of azole MICs. However, introduction of the MRR1(N647T) mutation in an azole-susceptible C. auris isolate from another clade with wild-type MRR1 and ERG11 alleles resulted in significant increase of fluconazole and voriconazole MICs. We demonstrated that this MRR1 mutation resulted in reduced azole susceptibility via upregulation of the drug transporter MDR1 and not CDR1. In conclusion, this work demonstrates that the Mrr1-Mdr1 axis may contribute to C. auris azole resistance by mechanisms that are independent from ERG11 mutations and from CDR1 upregulation.

Summary: The electrification revolution in the automobile and other industries demands annual production capacity of batteries of at least 102 GWh, which presents a twofold challenge: supply of key materials such as cobalt and nickel and recycling when batteries are retired from use. Pyrometallurgical and hydrometallurgical recycling are currently used in industry but suffer from complexity, high costs, and secondary pollution. Here we report a molten-salt-based method for direct recycling (MSDR) that is environmentally benign and creates value on the basis of a techno-economic analysis using real-world data and price information. We also experimentally demonstrate the feasibility of MSDR by upcycling a low-nickel polycrystalline LiNi0.5Mn0.3Co0.2O2 (NMC) cathode material into Ni-rich (Ni > 65%) single-crystal NMCs with increased energy density (>10% increase) and outstanding electrochemical performance (>94% capacity retention after 500 cycles). This work may open opportunities for closed-loop recycling of electric vehicle batteries and manufacturing of next-generation NMC cathode materials.

As a "superbug", methicillin-resistant Staphylococcus aureus (MRSA) has long been one of the most ubiquitous drug-resistant bacteria inducing numerous nosocomial infections. To achieve effective diagnosis and following treatment decision of infectious diseases induced by MRSA, it is highly desired to establish rapid analysis and antibiotic susceptibility test methods for this pathogen. In this study, we successfully expressed a bifunctional protein by fusing green fluorescent protein and cellular wall-binding domain of bacteriophage P108. The bifunctional protein can be employed as a signal probe for broad-spectrum fluorimetry of MRSA strains because it can both bind with the target pathogen and emit intensive fluorescence. By using it as the signal probe and porcine IgG as the capture agent, MRSA can be analyzed within a dynamic range of 1.0 × 10

Candida auris is an emerging yeast pathogen of candidemia with the ability to develop resistance to all current antifungal drug classes. Novel antifungal therapies against C. auris are warranted. NSC319726 is a thiosemicarbazone with an inhibitory effect on fungal ribosome biogenesis that has demonstrated some antifungal activity. In this study, we assessed the in vitro activity and in vivo efficacy of NSC319726 against C. auris. NSC319726 was active in vitro against 22 C. auris isolates from different clades, with MICs ranging from 0.125 to 0.25 mg/liter. Despite complete visual growth inhibition, the effect was described as fungistatic in time-kill curves. Interactions with fluconazole, amphotericin B, and micafungin, as tested by the checkerboard dilution method, were described as indifferent. NSC319726 demonstrated significant effects in rescuing G. mellonella larvae infected with two distinct C. auris isolates, compared to the untreated group. In conclusion, NSC319726 demonstrated in vitro activity against C. auris and in vivo efficacy in an invertebrate model of infection. Its potential role as a novel antifungal therapy in humans should be further investigated. IMPORTANCE Candida auris is emerging as a major public health threat because of its ability to cause nosocomial outbreaks of severe invasive candidiasis. Management of C. auris infection is difficult because of its frequent multidrug-resistant profile for currently licensed antifungals. Here, we show that the thiosemicarbazone NSC319726 was active in vitro against a large collection of C. auris isolates from different clades. Moreover, the drug was well tolerated and effective for the treatment of C. auris infection in an invertebrate model of Galleria mellonella. We conclude that NSC319726 might represent an interesting drug candidate for the treatment of C. auris infection.

The development of cloud native technologies and web technologies is constantly innovating all aspects of web applications. As the scale of web applications continues to expand, the complexity of design, development, deployment and maintenance is getting higher and higher. The industry urgently needs a new web development paradigm to meet the upcoming challenge, so the concept of microservices comes into being, and soon becomes the preferred solution for large companies and individual developers. The emergence of public and private clouds further reduces the difficulty of implementing microservices-based applications. This article analyzes the idea of microservices architecture and its key components, then presents a microservices-based cloud native application Online experiment platform. As a complete product, its life cycle well demonstrates the advantages of microservices architecture and the practical significance of cloud native concept.

Rationale: Alteration of human respiratory microbiota had been observed in COVID-19. How the microbiota is associated with the prognosis in COVID-19 is unclear. Objectives: To characterize the feat...

Summary The manufacturing of commercial lithium-ion batteries (LIBs) involves a number of sophisticated production processes. Various cell defects can be induced, and, depending on their structural and chemical characteristics, they could lead to acute failure and/or chronic degradation. Although tremendous efforts have been devoted to develop a robust quality control (QC) procedure, the functional role of the cell defects is not well understood. Here, we address this question through a systematic experimental study of commercial 18650-type LIBs that have failed the QC inspection due to a self-discharging effect. We identify and recover the defective regions from the cell and conduct a comprehensive investigation from the chemical, structural, and morphological perspectives. Our results reveal how the structural defects affect the cell performance, which is highly important to industry-scale battery production.

Sketch-based measurement has emerged as a promising alternative to the traditional sampling-based network measurement approaches due to its high accuracy and resource efficiency. While there have been various designs around sketches, they focus on measuring one particular flow key, and it is infeasible to support many keys based on these sketches. In this work, we take a significant step towards supporting arbitrary partial key queries, where we only need to specify a full range of possible flow keys that are of interest before measurement starts, and in query time, we can extract the information of any key in that range. We design CocoSketch, which casts arbitrary partial key queries to the subset sum estimation problem and makes the theoretical tools for subset sum estimation practical. To realize desirable resource-accuracy tradeoffs in software and hardware platforms, we propose two techniques: (1) stochastic variance minimization to significantly reduce per-packet update delay, and (2) removing circular dependencies in the per-packet update logic to make the implementation hardware-friendly. We implement CocoSketch on four popular platforms (CPU, Open vSwitch, P4, and FPGA) and show that compared to baselines that use traditional single-key sketches, CocoSketch improves average packet processing throughput by 27.2x and accuracy by 10.4x when measuring six flow keys.

Item batch denotes a consecutive sequence of identical items that are close in time in a data stream. It is a useful data stream pattern in cache, burst detection, APT detection, \etc Basic item batch measurement tasks include membership, cardinality, time span and size. Currently, there is no algorithm tailored for item batch measurement. The greatest challenge lies in accurately estimating the time gap between two consecutive identical items. In this paper, we propose Clock-sketch, a framework that introduces the well-known CLOCK algorithm into item batch measurement. The methodology of Clock-sketch is to clean outdated information as much as possible, while guaranteeing that the information of all items visited within the time window $\mathcalT $ is preserved. We conduct experiments on three real-world datasets that feature in item batch pattern. We compare the accuracy and throughput performance of our Clock-sketch against the state-of-the-art and two naive approaches without using Clock-sketch technique. Results of item batch activeness show that Clock-sketch outperforms the state-of-the-art SWAMP in generating 50 times less false positive rate when memory is small. All source codes are open-sourced and released at Github.

In order to investigate the transfer and accumulation pathways of heavy metals in cropland ecosystems, an investigation of the geochemical behaviors of heavy metals in soil and rice plants was carried out in the Yangtze River Delta. Soil is one of the biggest reservoirs of heavy metals and affects food safety at the beginning of the food chain. The results of this study demonstrate that heavy metal levels in soil decreased with increasing soil pH, while rice shoots accumulated heavy metals more readily under low soil pH conditions. The non-carcinogenic hazard quotients (HQ) of heavy metals show that health risks for humans were primarily due to Pb and As. Furthermore, cancer risk (Risk) results suggested that ~76% and ~15.7% of cancer risk was caused by Cd and As levels, respectively. Decreasing soil pH enhanced the non-carcinogenic and carcinogenic health risks for the human body. Through exponential change between transfer factor (TFgrain/soil) and soil metals, HQ, a direct monitoring method for rice plants, was built using regression curves. It is proposed that besides condition of soil with high heavy metal concentration, for rice grown with surface soil metals, the safety of the rice product should be monitored when soil metals are under the following levels after harvest: non-carcinogenic risk, As

Candida auris is a novel Candida species that has spread in all continents causing nosocomial outbreaks of invasive candidiasis. C. auris has the ability to develop resistance to all antifungal drug classes. Notably, many C. auris isolates are resistant to the azole drug fluconazole, a standard therapy of invasive candidiasis.Azole resistance in C. auris can result from mutations in the azole target gene ERG11 and/or overexpression of the efflux pump Cdr1. TAC1 is a transcription factor controlling CDR1 expression in C. albicans The role of TAC1 homologs in C. auris (TAC1a and TAC1b) remains to be better defined.In this study, we compared sequences of ERG11, TAC1a and TAC1b between a fluconazole-susceptible and five fluconazole-resistant C. auris isolates of clade IV. Among four of the resistant isolates, we identified a similar genotype with concomitant mutations in ERG11 (F444L) and TAC1b (S611P). The simultaneous deletion of tandemly arranged TAC1a/TAC1b resulted in a decrease of minimal inhibitory concentration (MIC) for fluconazole. Introduction of the ERG11 and TAC1b mutations separately and/or combined in the wild-type azole susceptible isolate resulted in a significant increase of azole resistance with a cumulative effect of the two combined mutations. Interestingly, CDR1 expression was not significantly affected by TAC1a/TAC1b deletion or by the presence of the TAC1b S611P mutation, suggesting the existence of Tac1-dependent and Cdr1-independent azole resistance mechanisms.We demonstrated the role of two previously unreported mutations responsible for azole resistance in C. auris, which were a common signature among four azole-resistant isolates of clade IV.

In this paper, void growth and EM performance under directional current (DC) reversal have been studied by PLR EM. It's found that without DC reversal, void growth mode has one category (void increasing) and statistical void growth rate is positively linearly correlated with MTTF. With DC reversal, void growth mode has three categories (void increasing, void transition, and void refilling) whose proportions have not significant difference with each other (proportions are around 30%), MTTF of void refilling is about one time higher than forward, and overall EM performance with DC reversal shows better than that without DC reversal (MTTF increased by ~16%) for the same cumulative electric resistance shift.

Electromigration performance is a key index for BEOL process reliability, in this paper, 2-terminal and 4-terminal kelvin structure EM tests are performed in both WLR IsoEM and PLR EM method, it's found 2-terminal WLR IsoEM test time is faster than that of 4 terminal kelvin structure under the same desired temperature stress condition, the TTF ratio is about 0.6 and 0.5 times for narrow metal (MxN) and wide metal (MxW) respectively. Besides, Iso-EM TTF increases with the rising of the chuck temperature and lower layer metal shows worse than the upper. While on the contrary, 2 terminal PLR EM TTF is bigger than that of 4 terminal. This distinct observation is investigated and its mechanism is discussed in the paper.

Package-level electro-migration (EM) performance of AlCu metallization was investigated in this paper. The metal line width & length dependence of J max were evaluated. Relationships were achieved between reliability performance and structural size. For width dependence, J max plunged seriously as width increased from sub-micro to about 2 um; Unexpected greater J max was discovered when width approached 5 um; For length dependence, J max was increased sharply as metal line became shorter when length was below 100 urn; when length was above 400 um, J max showed obvious rise with metal line became longer, rather than saturation in previous studies.

Glyphosate-based herbicides (GBHs) are a group of widely used broad-spectrum agricultural pesticides. Due to the recalcitrance of GBH, it has been found in food and environment as a contaminant, posing a threat to public health. The health risks associated with GBH have been indicated by reporting acute toxicity data (an acute exposure of GBH at a 0.5% dose), which primarily discuss toxicity in relation to accidental high-rate exposure. Currently, there is little information regarding the toxicity of GBH at environmentally relevant levels. In this study, we used mature mouse oocytes to study the toxic effects of low-dose GBH exposure in vitro (0.00001%-0.00025%) and in vivo (0.0005%, orally administered through daily drinking water) during meiotic maturation. GBH exposure led to meiotic maturation failure with spindle defects and chromosome misalignment. In addition, GBH treatment severely reduced sperm-binding ability and disrupted early embryo cleavage. Moreover, GBH exposure significantly increased the reactive oxygen species (ROS) levels and apoptotic rates. Evidence indicates that such effects in GBH-exposed oocytes are likely due to overexpression of the G-protein estrogen receptor (GPER/GPR30). Remarkably, we found that melatonin administration elicited significant protection against GBH-induced oocyte deterioration via preserving the expression of GPR30, along with activation of its downstream signaling event (pERK/ERK). Taken together, these results revealed that low-dose glyphosate has a certain adverse effect on oocyte maturation and early embryo cleavage, and highlight the protective roles of melatonin.

With the rapid emergence and spread of SARS-CoV-2 variants, development of vaccines with broad and potent protectivity has become a global priority. Here, we designed a lipid nanoparticle-encapsulated, nucleoside-unmodified mRNA (mRNA-LNP) vaccine encoding the trimerized receptor-binding domain (RBD trimer) and showed its robust capability in inducing broad and protective immune responses against wild-type and major variants of concern (VOCs) in the mouse model of SARS-CoV-2 infection. The protectivity was correlated with RBD-specific B cell responses especially the long-lived plasma B cells in bone marrow, strong ability in triggering BCR clustering, and downstream signaling. Monoclonal antibodies isolated from vaccinated animals demonstrated broad and potent neutralizing activity against VOCs tested. Structure analysis of one representative antibody identified a novel epitope with a high degree of conservation among different variants. Collectively, these results demonstrate that the RBD trimer mRNA vaccine serves as a promising vaccine candidate against SARS-CoV-2 variants and beyond.

Background: The pandemic of Coronavirus disease 2019 (COVID-19) is ongoing globally, which is a big challenge for public health. Alteration of human microbiota had been observed in COVID-19. However, it is unknown how the microbiota is associated with the fatality in COVID-19. Methods: We conducted metatranscriptome sequencing on 588 longitudinal oropharyngeal swab specimens collected from 192 COVID-19 patients recruited in the LOTUS clinical trial (Registration number: ChiCTR2000029308) (including 39 deceased patients), and 95 healthy controls from the same geographic area. Findings: The upper respiratory tract (URT) microbiota in COVID-19 patients differed from that in healthy controls, while deceased patients possessed a more distinct microbiota. Streptococcus was enriched in recovered patients, whereas potential pathogens, including Candida and Enterococcus, were more abundant in deceased patients. Moreover, the microbiota dominated by Streptococcus was more stable than that dominated by other species. In contrast, the URT microbiota in deceased patients showed a more significant alteration and became more deviated from the norm after admission. The abundance of Streptococcus on admission, particularly that of S. parasanguis, was identified as a strong predictor of fatality by Cox and L1 regularized logistic regression analysis, thus could be used as a potential prognostic biomarker of COVID-19. Interpretation Alteration of the URT microbiota was observed in COVID-19 patients and was associated with the fatality rate. A higher abundance of Streptococcus, especially S. parasanguis, on admission in oropharyngeal swabs predicts a better outcome. The generalization of the results in other populations and underlying mechanisms need further investigations. Trial Registration: Participants were enrolled in ChiCTR2000029308. Funding: This study was funded in part by the National Major Science & Technology Project for Control and Prevention of Major Infectious Diseases in China (2017ZX10103004, 2018ZX10301401), the Chinese Academy of Medical Sciences (CAMS) Innovation Fund for Medical Sciences (2019-I2M-2-XX, 2016-I2M-1-014, 2018-I2M-1-003), The Non-profit Central Research Institute Fund of CAMS (2020HY320001, 2019PT310029), Beijing Advanced Innovation Center for Genomics (ICG), and Beijing Advanced Innovation Center for Structural Biology (ICSB). Declaration of Interests: All authors declare no competing interests. Ethics Approval Statement: The study was approved by the Institutional Review Board of Jin Yin-Tan Hospital (KY2020-02.01). Written informed consent was obtained from all patients or their legal representatives if they were too unwell to provide consent.

Immunochromatographic test strip (ITS) for point-of-care testing (POCT) has attracted prominent attention due to the advantages including rapid response, low cost and good portability. Here, we developed a sensitive ITS for detecting aflatoxin B

The comprehensive energy-saving potential evaluation method of the energy-saving schemes of a distribution network considering the power uncertainty of source and load is studied in this paper. The K-means clustering method is firstly employed to extract typical scenarios of distribution network, and the forward-push back method is used to calculate the power flow in typical scenarios, then energy-saving reconstruction schemes are formulated according to the power flow calculation results. A comprehensive energy saving potential evaluation index system that consists the improvement rate of network loss, line loss rate, transformer loss rate, annual electricity saving cost, annual equipment investment cost, annual maintenance cost and voltage quality improvement rate is built, and the comprehensive evaluation method based on DEMATEL-ANP-TOPSIS mixed decision model is used to evaluate the comprehensive energy-saving potential of the energy-saving reconstruction schemes regarding the index system. Finally, the optimal reconstruction scheme is selected based on the evaluation results of energy saving potential in multiple scenarios. The effectiveness of the proposed method is verified by an example in IEEE-33 node distribution network.

Finding top-k items in data streams is a fundamental problem in data mining. Existing algorithms that can achieve unbiased estimation suffer from poor accuracy. In this paper, we propose a new sketch, WavingSketch, which is much more accurate than existing unbiased algorithms. WavingSketch is generic, and we show how it can be applied to four applications: finding top-k frequent items, finding top-k heavy changes, finding top-k persistent items, and finding top-k Super-Spreaders. We theoretically prove that WavingSketch can provide unbiased estimation, and then give an error bound of our algorithm. Our experimental results show that, compared with the state-of-the-art, WavingSketch has 4.50 times higher insertion speed and up to 9 x 106 times (2 x 104 times in average) lower error rate in finding frequent items when memory size is tight. For other applications, WavingSketch can also achieve up to 286 times lower error rate. All related codes are open-sourced and available at Github anonymously.

Echinocandins represent the first-line therapy of candidemia. Echinocandin resistance among Candida spp. is mainly due to acquired FKS mutations. In this study, we report the emergence of FKS-mutant Candida albicans/glabrata in Switzerland and provide the microbiological and clinical characteristics of 9 candidemic episodes. All patients were previously exposed to echinocandins (median 26 days; range 15–77). Five patients received initial echinocandin therapy with persistent candidemia in 4 of them. Overall mortality was 33%.