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Deep Learning (DL) has been increasingly deployed in various real-world applications due to its unprecedented performance and automated capability of learning hidden representations. While DL can achieve high task performance, the training process of a DL model is both time- and resource-consuming. Therefore, current supply chains of the DL models assume the customers obtain pre-trained Deep Neural Networks (DNNs) from the third-party providers that have sufficient computing power. In the centralized setting, the model designer trains the DL model using the local dataset. However, the collected training data may contain erroneous or poisoned data points. The model designer might craft malicious training samples and inject a backdoor in the DL model distributed to the users. As a result, the user’s model will malfunction. In the federated learning setting, the cloud server aggregates local models trained on individual local datasets and updates the global model. In this scenario, the local client could poison the local training set and/or arbitrarily manipulate the local update. If the cloud server incorporates the malicious local gradients in model aggregation, the resulting global model will have degraded performance or backdoor behaviors. In this article, we present a comprehensive overview of contemporary data poisoning and model poisoning attacks against DL models in both centralized and federated learning scenarios. In addition, we review existing detection and defense techniques against various poisoning attacks.

This paper proposes AdaTest, a novel adaptive test pattern generation framework for efficient and reliable Hardware Trojan (HT) detection. HT is a backdoor attack that tampers with the design of victim integrated circuits (ICs) . AdaTest improves the existing HT detection techniques in terms of scalability and accuracy of detecting smaller Trojans in the presence of noise and variations. To achieve high trigger coverage, AdaTest leverages Reinforcement Learning (RL) to produce a diverse set of test inputs. Particularly, we progressively generate test vectors with high ‘reward’ values in an iterative manner. In each iteration, the test set is evaluated and adaptively expanded as needed. Furthermore, AdaTest integrates adaptive sampling to prioritize test samples that provide more information for HT detection, thus reducing the number of samples while improving the samples’ quality for faster exploration. We develop AdaTest with a Software/Hardware co-design principle and provide an optimized on-chip architecture solution. AdaTest’s architecture minimizes the hardware overhead in two ways: (i) Deploying circuit emulation on programmable hardware to accelerate reward evaluation of the test input; (ii) Pipelining each computation stage in AdaTest by automatically constructing auxiliary circuit for test input generation, reward evaluation, and adaptive sampling. We evaluate AdaTest’s performance on various HT benchmarks and compare it with two prior works that use logic testing for HT detection. Experimental results show that AdaTest engenders up to two orders of test generation speedup and two orders of test set size reduction compared to the prior works while achieving the same level or higher Trojan detection rate.

Large hail, with a diameter of at least 20 mm, is a hazard associated with severe convective storms (SCS) that can cause significant damage. Understanding of atmospheric environments conducive to large hail is underpinned by catalogues of past events. Because of the small footprint of hail events, these often rely on crowdsourced reports. In the UK, the relative rarity of large hail and low public awareness of SCS hazards makes obtaining a complete set of reports difficult, and in many cases the precise time of the hail is not recorded. In this study, the two major databases of UK large hail reports are merged for the first time. Composite radar reflectivity data are used to verify and enhance 260 reports since 2006. Time of the hail and the basic storm mode (isolated, clustered or linear) are visually estimated from animations. Compared to the UK’s most severe historic hailstorms (1800–2004), our quality controlled climatology of all sizes of large hail shows a diurnal cycle with a slightly broader peak. Around 55% of large hail events are associated with isolated cells, while 34% have supercellular characteristics, a much lower proportion than found in the USA. The full event set (1979–2022), comprising over 850 reports, is used to update the seasonal, spatial and size distributions of large hail in the UK. We intend that this hail event set forms part of a multi-hazard analysis of UK SCS, also including tornadoes and extreme rainfall, and its relationship to background atmospheric conditions. The effect of climate change on UK SCS will be investigated through past and future trends in these background conditions.

Federated Learning (FL) is a collaborative machine learning approach allowing participants to jointly train a model without having to share their private, potentially sensitive local datasets with others. Despite its benefits, FL is vulnerable to so-called backdoor attacks, in which an adversary injects manipulated model updates into the federated model aggregation process so that the resulting model will provide targeted false predictions for specific adversary-chosen inputs. Proposed defenses against backdoor attacks based on detecting and filtering out malicious model updates consider only very specific and limited attacker models, whereas defenses based on differential privacy-inspired noise injection significantly deteriorate the benign performance of the aggregated model. To address these deficiencies, we introduce FLAME, a defense framework that estimates the sufficient amount of noise to be injected to ensure the elimination of backdoors. To minimize the required amount of noise, FLAME uses a model clustering and weight clipping approach. This ensures that FLAME can maintain the benign performance of the aggregated model while effectively eliminating adversarial backdoors. Our evaluation of FLAME on several datasets stemming from application areas including image classification, word prediction, and IoT intrusion detection demonstrates that FLAME removes backdoors effectively with a negligible impact on the benign performance of the models., Comment: To appear in the 31st USENIX Security Symposium, August 2022, Boston, MA, USA

Depression is one of the most fatal mental diseases, and there is currently a lack of efficient drugs for the treatment of depression. Emerging evidence has indicated oxidative stress as a key pathological feature of depression. We targeted reactive oxygen species (ROS) and synthesized CeO

This study evaluated the subacute toxicity and toxicokinetics of a potential anti-cancer drug candidate, pterostilbene, in rats. Animals were orally administered at two repeated doses of 200 and 500 mg/kg for 28 days. No mortality was observed during the 28 days of continuous administration of pterostilbene. Body weight and food consumption in each group increased steadily, while no significant difference was found. Liver weight in the 500 mg/kg female, but not male group increased with mild cytoplasmic vacuoles observed in histopathological study. Toxicokinetics was assessed by measuring plasma concentrations of pterostilbene on the first and 28th day of administration using UPLC-MS/MS. Toxicokinetic parameters showed that AUC

Flood modelling can provide useful information to support flood risk assessment and management. The accuracy of flood simulation results is highly dependent on the quality of input data. In particular, digital elevation models (DEMs) may directly influence the performance of flood predictions and improper representation of complex urban features including buildings and bridges may lead to incorrect prediction of flooding paths and extents, and consequently miscalculate flood risk. In this work, a geographic information system (GIS)-based correction method is proposed to make modifications in high-resolution DEMs by adding building complexes and removing unphysical representations of bridges for a more realistic description of flood paths in considering the flow connectivity in intensely urbanized areas and with the objective of obtaining more accurate flood simulation results. The proposed DEM correction method is applied to support large-scale urban flood modelling in Fuzhou City, China, using an established hydrodynamic flood model known as High-Performance Integrated hydrodynamic Modelling System (HiPIMS). Comparisons are made to the simulation results with and without the DEM improvements using the proposed correction method. The results demonstrate that correct representation of the artificial structures in the urban DEM can significantly improve the flood simulation results.

The high concentration of OVs and active sites of S0.9TF promote its high NRR activity. The proton-conducting solid oxide electrolysis cells (PCECs) with S0.9TF material as a cathode showed a high output (bar) and durability (line).

Urban flooding has become one of the most common natural hazards threatening people’s lives and assets globally due to climate change and rapid urbanization. Hydraulic structures, e.g., sluicegates and pumping stations, can directly influence flooding processes and should be represented in flood modeling and risk assessment. This study aims to present a robust numerical model by incorporating a hydraulic structure simulation module to accurately predict the highly transient flood hydrodynamics interrupted by the operation of hydraulic structures to support object-level risk assessment. Source-term and flux-term coupling approaches are applied and implemented to represent different types of hydraulic structures in the model. For hydraulic structures such as a sluicegate, the flux-term coupling approach may lead to more accurate results, as indicated by the calculated values of NSE and RMSE for different test cases. The model is further applied to predict different design flood scenarios with rainfall inputs created using Intensity-Duration-Frequency relationships, Chicago Design Storm, and surveyed data. The simulation results are combined with established vehicle instability formulas and depth-damage curves to assess the flood impact on individual objects in an urbanized case study area in Zhejiang Province, China.

Background: Colorectal cancer (CRC) is one of malignant tumors that seriously threatening human health. β-catenin is a central hub in Wnt pathway, aberrant activation of Wnt/β-catenin signaling pathway promotes the tumorigenesis/progression of CRC. Methods and Results: Here we found a β-catenin interactor, UXT-V1, could modulate Wnt signaling. The expression of UXT-V1 mRNA was increased in CRC tissues. Overexpression of UXT-V1 increased the canonical Wnt signaling, as evidenced by Wnt reporter systems and the up-regulation of marker genes including Axin, CyclinD1 and c-Myc. While, knockdown of UXT-V1 impaired the expression of these genes and attenuated Wnt signaling. Mechanistically, overexpression of UXT-V1 could inhibit GSK3β mediated β-catenin phosphorylation and degradation. Knockout of UXT-V1 increased β-catenin phosphorylation, prevented CRC cell growth, and inhibited tumorigenesis in NOD-SCID mice. Conclusions: Taken together, our findings revealed that UXT-V1 could control Wnt signaling through targeting GSK3β mediated β-catenin phosphorylation and degradation, providing a molecular basis for CRC treatment.

Cranes are widely deployed for lifting and moving heavy objects in dynamic environments with human coexistence. Suddenly appeared workers, vehicles, and robots can affect the safety of the cranes. To avoid possible collisions, the cranes must have prediction ability to know how dangerous the situation is. In this paper, we address the safety issues of bridge cranes based on its online physical states and control model. Due to the swing of the payload, the safe braking distance cannot be a constant value. Therefore, we here propose a model prediction control (MPC)-based anti-swing method for non-zero initial states, where a new reference trajectory and a new cost function for optimization are proposed, such that the proposed MPC method can control the crane to follow the proposed reference trajectory and achieve a stable stop state with anti-swing. Furthermore, an offline learning mechanism is introduced to learn a statistical model between the velocity of the crane and the safe braking distance achieved by using the proposed MPC braking control method. In this way, we can predict how far the crane would require to safely stop without swing based on its current velocity, which is the safe distance prediction to evaluate the dangerous level of the dynamic obstacle. Experiments using both a simulated crane and a real crane demonstrate that the proposed safe braking distance prediction method is effective for safe braking control of the bridge cranes.

Background Vanishing white matter (VWM) is an autosomal recessive disorder characterized by childhood ataxia with central hypomyelination. Adult-onset VWM should be considered as a differential diagnosis for suspected cases of multiple sclerosis (MS). Methods Targeted region sequencing (TRS) and Sanger sequencing validation were performed to identify and validate the likely pathogenic mutations in a family with VWM. Results The main clinical manifestations of the proband included decreased vision and sleepiness accompanied by atrophy of the corpus callosum, affected inner rim of the corpus callosum, decreased apparent diffusion coefficient value or persistent hyperintensity-diffusion-weighted imaging, atrophied optic nerve, and no recordable visual evoked potentials. Due to the slow development and atypical VWM image features, MS was initially suspected. After prednisone was administered, the patient's condition did not improve significantly, and other diseases were considered. The TRS and Sanger sequencing identified compound heterozygous mutations of EIF2B3 in the proband; c.965C > G /p.Ala322Gly in exon 8 and c.130G > A/p.Glu44Lys in exon 2 were missense mutations inherited from the mother and father, respectively. The proband's oldest brother had the same compound heterozygous mutations but showed no symptoms. Conclusion This is the first report of adult-onset VWM in a Chinese family. Initially, MS was suspected, and genetic testing confirmed the diagnosis of VWM. This study may further broaden the clinical spectrum of EIF2B3, thus providing a foundation for further research on the pathogenesis and genetic therapy for VWM.

Macrophages are crucial effectors of innate immunity against the pathogenic bacterium Listeria monocytogenes. The pro-inflammatory cytokine tumour necrosis factor-α (TNF) has been shown to be crucial for resistance to L. monocytogenes and mice deficient in TNF signalling succumb quickly after infection. However, the mechanisms underlying TNF-mediated defence against L. monocytogenes infection have not been completely elucidated. Here, we demonstrate that TNF concurrently functions to support a pro-inflammatory M1 phenotype while actively blocking macrophage polarization to the M2 phenotype. Compared to WT mice, peritoneal macrophages in TNF-deficient mice inoculated with L. monocytogenes respond with M2 polarization by upregulating Arg1. Consistently, TNF blockade in vitro resulted in M2 polarization in peritoneal macrophages during L. monocytogenes infection. Additionally, TNF promotes the transition from M2 to M1 polarization in peritoneal macrophages. Further investigation of peritoneal macrophage polarization suggested the NF-κB pathway is involved in the TNF-dependent M2 to M1 shift. Conversely, treatment of peritoneal macrophage with a PPARγ agonist blunted the expression of M1 genes induced by TNF and reduced NF-κB signalling pathway activation. Competing signalling mechanisms therefore play an essential role in the ability of peritoneal macrophage to resolve L. monocytogenes infections with TNF playing an essential role in driving M1 polarization.Abbreviations: LPM: large peritoneal macrophage; SPM: small peritoneal macrophage; LLO: listeriolysin O; iNOS: inducible nitric oxide synthase; DCs: dendritic cells.

The practical application of the state-of-art graphite anode materials for potassium ion batteries (KIBs) is currently frustrated by their poor cycling life and unsatisfactory rate performance. Her...

Diplolaimella is a ubiquitous cosmopolitan genus, but information on the life cycles of its species is limited. Here, we describe the life cycle of a free-living bacterivorous nematode, Diplolaimella stagnosa, and report the effects of temperature and food availability on its population dynamics. Specimens were primarily collected from the intertidal zone of Hangzhou Bay Wetland, China and culture experiments were conducted in nutrient agar media with habitat water at 20°C. The nematode primarily fed on an unidentified bacterium that it carried. Under these conditions, both males and females matured in 16 days. Reproduction was by gamogenesis and gravid females normally carried 7–8 eggs. Embryogenesis was completed in 58 h, and the entire life cycle (egg to adult) was completed in 16–18 days. During juvenile development, body lengths of worms increased linearly up to the 16th day, and then remained constant. Body lengths of males and females were 898.1 ± 6.0 μm and 1039.7 ± 14.7 μm, respectively. Nematodes kept at 25°C had a greater population increase than those at 20°C, and the population growth of nematodes was substantially higher in microcosms with abundant bacteria supplied by leaves of Phragmites australis than in microcosms without plant litter. Based on its life strategy and the influence of food availability and temperature on population growth, D. stagnosa was allocated to group c-p 2, suggesting its potential use as a model organism in toxicological studies.

Background Both the protein domains and transcript structures influence protein functional variation. The genomic location of both protein domains and transcript structural features can be described using the genomic coordinates of their encoded sequences. However, the coordinates of protein domains and transcriptional features often differ greatly, and it is difficult to view them in combination at the genome-wide level. In this paper, we describe the development of a new tool that allows users to visualize domains and transcript features together, using either built-in or uploaded genome datasets, and export publication-ready figures. Results We developed a user-friendly, independent R package and Shiny web application named “VisProDom”. VisProDom consists of a genome-wide database containing entire annotated transcripts merged with annotated protein domains from the Pfam database. The built-in dataset includes 82 files, which merge genome general feature format (GFF) annotations with rpsblast tabular outputs from protein sequence searches in the Pfam database. Multiple genomes can be simultaneously screened for protein domains or transcript names. VisProDom includes step-by-step introductions and clickable elements for ease of use. Conclusion VisProDom can display hundreds of transcripts alongside protein domains and export figures in a publication-ready format. This makes it a valuable tool for molecular evolution and comparative genomics.

Insects used as food and feed has attracted considerable attention for their nutritional profiles in recent years. Fatty acid profile, such as unsaturated fatty acid content, ratio of unsaturated to saturated fatty acids, determines the quality of insect products. Multiple previous studies have used spectroscopy technologies and machine learning algorithms to predict fatty acid content in various foods and feeds. However, these approaches were not applied for predicting fatty acid content in insects before. In this study, 50 insect samples containing 9 commercial insect species were collected. Machine learning methods were applied to build the calibration models to predict fatty acid content from Fourier-transform infrared spectroscopy spectra. For all fatty acids, partial least square regression, regression trees and neural network based methods were among the best machine learning methods. For the best performing model, a coefficient of determination of 0.98, a root mean square error of prediction of 3.19%, and a ratio of performance of 3.91 were achieved using regression tree to predict linoleic acid. The high model performance indicates the potential of applying FTIR and such machine learning methods for fast and non-destructive prediction of fatty acid of insect oil products.

Neural architecture search (NAS) finds favorable network topologies for better task performance. Existing hardware-aware NAS techniques only target to reduce inference latency on single CPU/GPU systems and the searched model can hardly be parallelized. To address this issue, we propose ColocNAS, the first synchronization-aware, end-to-end NAS framework that automates the design of parallelizable neural networks for multidevice systems while maintaining a high task accuracy. ColocNAS defines a new search space with elaborated connectivity to reduce device communication and synchronization. ColocNAS consists of three phases: 1) offline latency profiling that constructs a lookup table of inference latency of various networks for online runtime approximation; 2) differentiable latency-aware NAS that simultaneously minimizes inference latency and task error; and 3) reinforcement-learning-based device placement fine-tuning to further reduce the latency of the deployed model. Extensive evaluation corroborates ColocNAS's effectiveness to reduce inference latency while preserving task accuracy.

Background The outbreak of COVID-19 (caused by SARS-Cov-2) is very serious, and no effective antiviral treatment has yet been confirmed. The adage “old drug, new trick” in this context may suggest the important therapeutic potential of existing drugs. We found that the lopinavir/ritonavir treatment recommended in the fifth edition of the Treatment Plan of China can only help to improve a minority of throat-swab nucleic-acid results (3/15) in hospitals. Our previous use of chloroquine to treat patients with COVID-19 infection showed an improvement in more throat-swab nucleic-acid results (5/10) than the use of lopinavir/ritonavir. Methods/design This is a prospective, open-label, randomized controlled, multicenter clinical study. The study consists of three phases: a screening period, a treatment period of no more than 10 days, and a follow-up period for each participant. Participants with COVID-19 infection who are eligible for selection for the study will be randomly allocated to the trial group or the control group. The control group will be given lopinavir/ritonavir treatment for no more than 10 days. The trial group will be given chloroquine phosphate treatment for no more than 10 days. The primary outcome is the clinical recovery time at no more than 28 days after the completion of therapy and follow-up. The secondary outcomes include the rate of treatment success after the completion of therapy and follow-up, the time of treatment success after no more than 28 days, the rate of serious adverse events during the completion of therapy and follow-up, and the time to return to normal temperature (calculated from the onset of illness) during the completion of therapy and follow-up. Comparisons will be performed using two-sided tests with a statistical significance level of 5%. Discussion This experiment should reveal the efficacy and safety of using chloroquine versus lopinavir/ritonavir for patients with mild/general COVID-19 infection. If the new treatment including chloroquine shows a higher rate of throat-swab SARS-CoV-2 real-time fluorescent reverse transcription polymerase chain reaction (RT-PCR) negativity and is safe, it could be tested as a future COVID-19 treatment. Trial registration Chinese Clinical Trial Registry, ID: ChiCTR2000029741. Registered on 11 February 2020.

Coking on Ni surfaces limits the direct application of methane-based fuels in SOFCs with Ni-cermet anodes. Loading an anodic catalytic layer with a high catalytic activity for CH4 conversion can effectively protect the Ni-based anode from coking and increase the cell durability. In this work, a Ni–Fe alloy composite catalyst was prepared by reducing perovskite La0.7Sr0.3Fe0.8Ni0.2O3-δ (LSFN) and then evaluating its catalytic activity in the partial oxidation of CH4. The catalyst was applied on a conventional Ni–8 mol.% Y-stabilized ZrO2 (YSZ) anode for methane SOFCs using two methane-containing fuels (97% CH4–3% H2O and 30% CH4–70% air). The catalyst-modified cells showed much higher performances and durability than the conventional cell using a Ni–YSZ anode, indicating the potential application for direct-methane SOFCs.

With the advancement of system miniaturization and automation, Lab-on-a-Chip (LoC) technology has revolutionized traditional experimental procedures. Microfluidic Biochip (MFB) is an emerging branch of LoC with wide medical applications such as DNA sequencing, drug delivery, and point of care diagnostics. Due to the critical usage of MFBs, their security is of great importance. In this article, we exploit the vulnerabilities of two types of MFBs: Flow-based Microfluidic Biochip (FMFB) and Digital Microfluidic Biochip (DMFB). We propose a systematic framework for applying Reverse Engineering (RE) attacks and Hardware Trojan (HT) attacks on MFBs as well as for practical countermeasures against the proposed attacks. We evaluate the attacks and defense on various benchmarks where experimental results prove the effectiveness of our methods. Security metrics are defined to quantify the vulnerability of MFBs. The overhead and performance of the proposed attacks as well as countermeasures are also discussed.

This paper reports a way to utilize low-concentration coal-bed gas(LC-CBG) to generate power using SOFC technology. Ni–BaO–CeO2@SiO2 (@NBC) porous nanoparticles with a core-shell structure were synthesized and applied as a protective layer on an anode consisting of Ni–yttria stabilized zirconia (Ni–YSZ). Catalytic activity of @NBC relative to methane partial oxidation was evaluated. Electrochemical performance and durability of the @NBC-modified cells (@NBC//Ni–YSZ) were tested fed with simulated LC-CBG (30% CH4–70% air). A conventional cell without a protective layer (Ni–YSZ) and a bare NBC-modified cell (NBC//Ni–YSZ) were used for a comparison. The pre- and post-mortem microstructures of cells were analyzed. Influence of porosity of the anode layer on polarization resistance was also investigated. The loading mode of catalysts was discussed as well. Using simulated LC-CBG as a fuel, peak power density of @NBC//Ni–YSZ showed 50% increase at 800 °C comparing with Ni–YSZ at the same conditions. The aging test in galvano-static mode demonstrated good durability of @NBC//Ni–YSZ lasting for over 160 h when exposed to LC-CBG. For comparison, NBC//Ni–YSZ showed rapid voltage drop after 60 h. Voltage of the Ni–YSZ dropped after 10 h. No coking was observed on the @NBC//Ni–YSZ anode surface after the durability tests.

SrCo0.8Fe0.2O3−δ (SCF) exhibits high ionic–electronic conductivity. However, its instability in the presence of CO2 restricts its application in electrochemical devices. In this study, 5 mol% zirconium was introduced into SCF to improve its structural stability and CO2 tolerance. SrCo0.8Fe0.15Zr0.05O3−δ (SCFZ) showed metallic electrical conductivity above 500 °C. Furthermore, X-ray diffraction and CO2-temperature programmed desorption experiments indicated that SCFZ possessed adequate structural stability and CO2 tolerance. The oxygen reduction activity and CO2 tolerance were studied using a symmetrical cell, SCFZ|BaZr0.1Ce0.7Y0.1Yb0.1O3−δ (BZCYYb)|SCFZ. The electrochemical performances of the SCFZ material as a cathode were investigated systematically for proton-conducting solid oxide fuel cells (H+-SOFCs) with a configuration of NiO–BZCYYb|BZCYYb|SCFZ–BZCYYb. The output of 712 mW cm−2 at 700 °C and a durability test of over 300 h indicated that SCFZ had significant structural stability and CO2-tolerance.

Oroxylin A, the main component of

Logic locking is a circuit obfuscation technique that inserts additional key gates to the original circuit in order to prevent potential threats such as circuit overproduction, piracy, and counterfeiting. The encrypted circuit generates desired outputs only when the correct keys are applied to the key gates. Previous works have identified the vulnerability of logic locking to satisfiability (SAT)-based attacks. However, SAT attacks are unscalable and have limited effectiveness on circuits with SAT-hard structures. To address the above constraints, we propose GALU, the first genetic algorithm-based logic unlocking framework that is parallelizable and significantly faster than the conventional SAT-based counterparts. GALU works by formulating circuit deobfuscation (i.e., identifying the correct keys) as a combinatorial optimization problem and approaches it using genetic algorithms (GAs). We consider key sequences as individuals in distinct populations and propose an adaptive, diversity-guided GA framework consisting of four main steps: circuit fitness evaluation, population selection, crossover, and mutation. In each iteration, the key sequences with high fitness scores are selected and transformed into the offspring key sequences. As a result of evolutionary key searching, GALU is highly scalable, effective, and efficient. To optimize the runtime overhead of logic unlocking, we integrate the design of GALU’s algorithm, software and hardware in a closed loop. In particular, we identify circuit fitness evaluation as the performance bottleneck and employ hardware emulation on programmable hardware for runtime optimization. To this end, GALU framework automatically constructs customized auxiliary circuitry to pipeline the computation in constraints checking, sorting, crossover, and mutation. GALU is the first adaptive and scalable attack framework that provides the flexibility/trade-off between runtime overhead and key usability. This is achieved by producing a group of approximate keys with improving quality over time. We perform a comprehensive evaluation of GALU’s performance on various benchmarks and demonstrate that GALU achieves up to 1089.2 × speedup and 4268.6 × more energy-efficiency compared to the state-of-the-art SAT attacks for circuit logic unlocking.

Objective. To investigate the effect of three-dimensional intensity-modulated radiation therapy (IMRT) that accurately target delineation on quality of life (Qol) in patients with low-grade gliomas. Methods. From February 2015 to December 2019, 100 patients with low-grade gliomas were randomly divided into three-dimensional conformal radiotherapy control group ( n = 50 ) and three-dimensional IMRT research group ( n = 50 ). The scores of the Mini-Cog Assessment (Mini-Cog) and Montreal Cognitive Assessment Scale (MoCA), the self-care ability score (BI), and the effect of symptom improvement and the quality of life SF-36 score were compared between the two groups. Results. After radiotherapy, the self-care ability of patients in the two groups was significantly improved, and the improvement of study group was better than that of the control group ( P < 0.05 ). The Mini-Cog and MOCA scores in the study group were significantly higher than those in the control group ( P < 0.05 ). In addition, the symptom improvement effect and quality of life of the patients in the study group were also significantly better than those in the control group ( P < 0.05 ). The scores of SDS and SAS of patients who underwent three-dimensional conformal IMRT were significantly lower than those of the control group. There was no significant difference in mortality between the two groups. Conclusion. Three-dimensional conformal intensity-modulated radiation therapy can delineate the target volume more accurately, regulate the intensity of radiation, and improve the symptoms and quality of life of patients with low-grade gliomas.

Hemin, a stable form of heme iron, is a potential iron supplement for the treatment of iron deficiency. To date, the pharmacokinetics and in vivo ADME properties of hemin are to be elucidated.In this study, a rapid, sensitive, and validated inductively coupled plasma mass spectrometry (ICP-MS) method was used in combination withResults showed that the ICP-MS method is accurate and sensitive enough to quantitatively determine the in vivo disposition process ofIn conclusion, the present study validated the work-flow of preclinical pharmacokinetic studies of iron-containing drug candidates with using ICP-MS and stable (trace) isotope labeling strategy. It also provided useful information to support the further development of hemin as a drug/nutrition supplement candidate.

Copyright © The Author(s) 2021. Increasing resilience to natural hazards and climate change is critical for achieving many Sustainable Development Goals (SDGs). In recent decades, China has experienced rapid economic development and became the second-largest economy in the world. This rapid economic expansion has led to large-scale changes in terrestrial (e.g., land use and land cover changes), aquatic (e.g., construction of reservoirs and artificial wetlands) and marine (e.g., land reclamation) environments across the country. Together with climate change, these changes may significantly influence flood risk and, in turn, compromise SDG achievements. The Luanhe River Basin (LRB) is one of the most afforested basins in North China and has undergone significant urbanisation and land use change since the 1950s. However, basin-wide flood risk assessment under different development scenarios has not been considered, although this is critically important to inform policy-making to manage the synergies and trade-offs between the SDGs and support long-term sustainable development. Using mainly open data, this paper introduces a new framework for systematically assessing flood risk under different social and economic development scenarios. A series of model simulations are performed to investigate the flood risk under different land use change scenarios projected to 2030 to reflect different development strategies. The results are systematically analysed and compared with the baseline simulation based on the current land use and climate conditions. Further investigations are also provided to consider the impact of climate change and the construction of dams and reservoirs. The results potentially provide important guidance to inform future development strategies to maximise the synergies and minimise the trade-offs between various SDGs in LRB. Natural Environment Research Council (NERC) of the UK Research and Innovation (UKRI) through the Towards a Sustainable Earth (TaSE) programme (NE/S012427/1).

Background and Objectives: Esophageal squamous cell carcinoma (ESCC) is one of the most common malignancies. Anti-tumor associated antigen autoantibodies (TAAbs) can be used as biomarkers for tumor detection. The aim of this study was to identify a reliable TAAb as the diagnostic marker for ESCC. Materials and Methods: The Cancer Genome Atlas (TCGA) database was used to screen candidate genes. The mRNA expression of the key gene was then verified by micro array dataset GSE44021 from the Gene Expression Omnibus (GEO) database and the diag nostic value of the corresponding autoantibody to the key gene in ESCC was detected by enzyme-linked im muno sorbent assay (ELISA). Results: CXCL8 was identified as the key gene. The dataset GSE44021 showed that CXCL8 mRNA expression was prominently over-expressed in ESCC tissues compared with normal tissues. ELISA results showed that the level of anti-CXCL8 autoantibody in ESCC patients was significantly higher than in normal controls and the receiver operating char ac teristic (ROC) curve indicated that anti-CXCL8 autoantibody could discriminate ESCC patients from normal controls, with the area under the ROC curve (AUC) for the verification cohort, and the validation cohort were 0.713 and 0.751, respectively. Conclusions: Our study illustrated that anti-CXCL8 autoantibody had good diagnostic value, and may become a candidate biomarker for ESCC.

Ni-based anodes of SOFCs are susceptible to coking, which greatly limits practical application of direct methane-based fuels. An indirect internal reformer is an effective way to convert methane-based fuels into syngas before they reach anode. In this work, catalytic activity of a redox-stable perovskite La0.7Sr0.3Cr0.8Fe0.2O3-δ (LSCrFO) for methane conversion was evaluated. The catalyst was fabricated as an anodic protective layer to improve coking resistance of a Ni cermet anode. Using wet CH4 as a fuel, the LSCrFO-modified cell showed excellent power output and good coking resistance with peak power density of 1.59 W cm−2 at 800 °C. The cell demonstrated good durability lasting for at least 100 h. While the bare cell without the protective layer showed poor durability with the cell voltage fast dropped from 0.75 V to 0.4 V within 30 min. Under wet coal bed methane (CBM) operation, obvious performance degradation within 35 h (1.7 mV h−1) was observed due to the influence of heavy carbon compounds in CBM. The pre- and post-mortem microstructures and carbon analysis of the anode surface and catalyst surface were further conducted.

The development of technologies used to prepare thin electrolyte films will stimulate the application of electrolyte supported SOFCs since thin electrolyte films typically have low ohmic resistances and good electrochemical performance. This paper presents a novel method for the preparation of thin electrolyte films for yttria-stabilized zirconia (YSZ)-supported solid oxide fuel cells (SOFCs) via dry pressing/heating/quenching/calcining. The thicknesses of the as-prepared YSZ films were as low as 78 mu m, which is significantly thinner than those prepared using a traditional method (greater than 200 mu m) via dry pressing/calcining/polishing. More importantly, the preparation process was quicker. Using this novel method, a YSZ-supported cell with a configuration of (La0.6Sr0.4)(0.9)Co0.8Fe0.2O3-delta (LSCF)-Ce0.8Sm0.2O2-delta(SDC)/SDC/YSZ/SDC/Ba0.5Sr0.5Co0.8Fe0.2O3-delta(BSCF)-SDC was fabricated and tested. The results showed promising electrochemical performance and a peak power density of 0.64 W cm(-2) at 850 degrees C was obtained, which was much higher than the cell fabricated using the traditional method (0.29 W cm(-2)). The ohmic resistance (R-O) at 850 degrees C is 0.19 Omega cm(2), which is much lower than that of the cell fabricated using the traditional method (0.33 Omega cm(2)) at an identical temperature. The modified method described in this work is shown to be a promising technique to prepare thin electrolyte films for high-performance, electrolyte-supported SOFCs.