Discovery of new non-covalent reversible BTK inhibitors: Synthesis, in silico studies, and in vitro evaluations.

Bruton's Tyrosine Kinase (BTK) played a key role in the B cell antigen receptor (BCR) signaling pathway, and was considered a hotspot in the treatment of B cell malignant tumors and B cell immune diseases. There were 5 covalent irreversible inhibitors launched currently on the market, but C481S mutation was detected in most patients after administration. The approval of Pirtobrutinib (Jaypirca) by FDA in 2023 aroused great interest in the development of non-covalent and reversible BTK inhibitors. In order to solve the resistance of covalent irreversible inhibitors caused by C481S mutation, 11 reversible BTK inhibitors were designed based on screening in this article. The design, synthesis, in silico studies, and in vitro evaluations were performed for further verification. Among them, compound WS-11 showed best activity with IC50 of 3.9 nM for wild type, 2.2 nM for C481S mutation BTK, which was comparable to the positive control Pirtobrutinib. Furthermore, WS-11 would have a good druglikeness properties predicted by pkCSM and SwissADME, which provided a promising lead for further optimization and development.

Optimizing Soil Sampling for Accurately Prediction of the Potential Remediation-Effective Area in a Contaminated Agricultural Land.

To achieve food security in a contaminated agricultural land, the remediation areas usually need more samples to obtain accurate contamination information and implement appropriate measures. In this study, we propose an optimal encryption sampling design to instead of the detailed survey, which is determined by the variation of heavy metals and the technology capability of remediation, to guide soil sampling for accurately remediation in the potential remediation-effective areas (PRA). The coefficient of screening variation threshold (CSVT), considering spatial variation, technology capacity and acceptable error of sampling, together with the spatial cyclic statistics method of neighbourhood analysis, is introduced to identify and delineate the PRA. Both of the hypothetical analysis and application case studies are conducted to illustrate the advantages and disadvantages of the optimization. The results show that, compared with the detailed survey, the optimal design shows a lower overall accuracy due to its sparsely sampling at the clean area, but it exhibits a similar effect of accurately prediction in boundary delineation and further classification in the PRA in both simulation and application studies. This work provides an effective method for subsequent accurate remediation at the investigation stage and valuable insights into application combination of technology capacity and contaminated agricultural land investigation.

Tunneling current-controlled spin states in few-layer van der Waals magnets.

Effective control of magnetic phases in two-dimensional magnets would constitute crucial progress in spintronics, holding great potential for future computing technologies. Here, we report a new approach of leveraging tunneling current as a tool for controlling spin states in CrI3. We reveal that a tunneling current can deterministically switch between spin-parallel and spin-antiparallel states in few-layer CrI3, depending on the polarity and amplitude of the current. We propose a mechanism involving nonequilibrium spin accumulation in the graphene electrodes in contact with the CrI3 layers. We further demonstrate tunneling current-tunable stochastic switching between multiple spin states of the CrI3 tunnel devices, which goes beyond conventional bi-stable stochastic magnetic tunnel junctions and has not been documented in two-dimensional magnets. Our findings not only address the existing knowledge gap concerning the influence of tunneling currents in controlling the magnetism in two-dimensional magnets, but also unlock possibilities for energy-efficient probabilistic and neuromorphic computing.

Nonvolatile Memristive Effect in Few-Layer CrI3 Driven by Electrostatic Gating.

The potential of memristive devices for applications in nonvolatile memory and neuromorphic computing has sparked considerable interest, particularly in exploring memristive effects in two-dimensional (2D) magnetic materials. However, the progress in developing nonvolatile, magnetic field-free memristive devices using 2D magnets has been limited. In this work, we report an electrostatic-gating-induced nonvolatile memristive effect in CrI3-based tunnel junctions. The few-layer CrI3-based tunnel junction manifests notable hysteresis in its tunneling resistance as a function of gate voltage. We further engineered a nonvolatile memristor using the CrI3 tunneling junction with low writing power and at zero magnetic field. We show that the hysteretic transport observed is not a result of trivial effects or inherent magnetic properties of CrI3. We propose a potential association between the memristive effect and the newly predicted ferroelectricity in CrI3 via gating-induced Jahn-Teller distortion. Our work illuminates the potential of 2D magnets in developing next-generation advanced computing technologies.

Phytoextraction of highly cadmium-polluted agricultural soil by Sedum plumbizincicola: An eight-hectare field study.

Phytoextraction with Sedum plumbizincicola is an in-situ, environmentally friendly and highly efficient remediation technique for slightly Cd-polluted soils but it remains a challenge to remediate highly Cd-polluted soils under field conditions. Here, an 8-ha field experiment was conducted to evaluate the feasibility of repeated phytoextraction by S. plumbizincicola of a highly Cd-polluted acid agricultural soil (pH 5.61, [Cd] 2.58 mg kg-1) in Yunnan province, southwest China. Mean shoot dry biomass production, Cd concentration and Cd uptake were 1.95 t ha-1, 170 mg kg-1 and 339 g ha-1 at the first harvest, and 0.91 t ha-1, 172 mg kg-1 and 142 g ha-1 at the second harvest. After two seasons of phytoextraction, soil total and CaCl2-extractable Cd concentrations decreased from 2.58 ± 0.69 to 1.53 ± 0.43 mg kg-1 and 0.22 ± 0.12 to 0.14 ± 0.07 mg kg-1, respectively. Stepwise multiple linear regression analysis shows that the shoot Cd concentration and uptake of S. plumbizincicola were positively related to soil CaCl2-extractable Cd concentrations, especially in the first crop. A negative relationship indicates that soil organic matter content played an important role in soil Cd availability and shoot Cd concentration in the first crop. In addition, the rhizosphere effect on soil CaCl2-extractable Cd concentration was negatively correlated with soil pH in the first crop. The accuracy of the calculation of soil Cd phytoextraction efficiency at field scale depends on all of the following factors being considered: shoot Cd uptake, cropping pattern, standardized sampling points, and the leaching and surface runoff of Cd. Phytoextraction with S. plumbizincicola is a feasible technique for efficient Cd removal from highly polluted soils and wide variation in soil properties can influence phytoextraction efficiency at the field scale.

Selenium-enriched Polysaccharides from Pyracantha fortuneana (SePFP) Ameliorated Hepatic Lipid Accumulation through Enhancing Mitochondrial Fatty Acid ß-Oxidation in Aging Mice.

Selenium-enriched polysaccharides from Pyracantha fortuneana (SePFP) has many beneficial physiological activities, but how it improves the aging associated abnormal lipid metabolism is still unclear. Therefore, we explored the mechanisms of the regulatory role of SePFP on liver lipid accumulation in aging mice. METHODS: 60 naturally aged C57BL/6J male mice were divided into 6 groups: adult group, aging group (21-month-old mice), aging mice treated with low-, medium- and high-doses of SePFP (SePFP-L, SePFP-M, SePFP-H), and aging mice treated with resveratrol (RSV). SePFP and RSV were administrated daily via oral gavage from 16 to 21 months old. The parameters of energy metabolism were measured in all mice before sacrifice, and liver tissues were collected to determine the levels of metabolism-related enzymes by real-time PCR and Western blot. RESULTS: We found that SePFP significantly reduced the body weight, liver to bodyweight ratio, and white fat to body weight ratio in aging mice. SePFP also down-regulated the triglycerides and cholesterol levels in liver and serum, and decreased respiratory quotient in aging mice. The mechanism of SePFP regulating lipid metabolism was mainly through promoting fatty acid transportation to mitochondria and enhancing mitochondrial ß-oxidation and ketone body production. CONCLUSION: SePFP attenuates liver lipid deposition in aging mice by enhancing hepatic mitochondrial ß-oxidation.

Carbon responsibility allocation method based on complex structure carbon emission flow theory.

A carbon responsibility allocation method based on the complex structure carbon emission flow theory is proposed to address the problem posed by the unclear carbon responsibility allocation of each link in the low-carbon development of electric power. First, the calculation method, distribution characteristics, and mechanism of carbon emission flow were analyzed. The "carbon potential of complex structure" concept was introduced to track "carbon trajectory" and "green trajectory" by harnessing the ability of complex structures to retain two-dimensional information. Subsequently, the carbon responsibility allocation methods for network loss and users' electricity consumption behavior were developed to realize the accurate carbon responsibility allocation of each system link. Finally, the effectiveness and advancement of the proposed carbon responsibility allocation method were verified using the improved IEEE 6-bus and 30-bus test systems. The application of the proposed complex structure carbon potential in the carbon emission flow theory expands the research dimension of electric power carbon emission for low-carbon development from the "carbon perspective," provides a novel optimization space for the operation of the distribution network and realizes the carbon emission flow theory, which serves as a bridge from calculation evaluation to optimization decision.

CPACK: An Intelligent Cyber-Physical Access Control Kit for Protecting Network.

Access Control Lists (ACL) are critical to protecting network and cyber-physical systems. Traditional firewalls mostly use reactive methods to enforce ACLs, so that new ACL updates cannot take effect immediately. In this paper, based on our previous work, we propose CPACK, an intelligent cyber-physical access control kit, which uses a smart algorithm to upgrade the ACL list. CPACK adopts a proactive way to enforce ACL and reacts to a new ACL update and network view update in real time. We implement CPACK on both Floodlight and ONOS controller. We then conduct a large number of experiments to compare CPACK with the Floodlight firewall application. The experimental results show that CPACK has a better performance than the existing Floodlight firewall application. CPACK is also integrated into the new version of Floodlight and ONOS controller.

Cadmium isotope fractionation during transport processes within agricultural soil profiles in a mining area: Implications for source tracing.

Cadmium (Cd) isotope fractionation patterns within soil profiles and the underlying mechanisms remain unclear and poorly documented. Here, Cd concentrations and isotope compositions of metal ore, surface soils and soil profile samples around a lead-zinc mine in southwest China were determined, and the relationships between soil properties and Cd isotope fractionation within the soil profiles were investigated. Cadmium concentrations of eleven surface soil samples were 0.49-66.1 mg kg-1 and the samples with high Cd concentrations had Cd isotope compositions similar to the metal ore (δ114/110Cd = 0.02‰), indicating that mining activity was the main Cd source at the study areas. Within three soil profiles with different Cd pollution levels the δ114/110Cd values gradually increased with increasing depth from 0 to 40 cm (Δ114/110Cd = 0.08-0.18‰), reaching a maximum at 30-40 cm depth, and then remained fairly constant or decreased with increasing soil depth below 40 cm. Soil δ114/110Cd values were negatively correlated with free iron and manganese oxides contents, which decreased at 0-40 cm depth then increased below 40 cm. This indicates that light Cd isotopes within 0-40 cm depth preferentially migrated downward with free iron and manganese oxides, leaving the soils at a depth of 0-40 cm enriched in heavy Cd isotopes. At 40-90 cm depth the preferential retention of heavy Cd isotopes by hydroxides may be responsible for the gradual decrease in δ114/110Cd values with increasing soil depth. These observations demonstrate that the vertical migration of Cd can induce detectable isotope fractionation within soil profiles and alter the δ114/110Cd values including those of the surface soils. Our study highlights the need to consider Cd mobilization and transport in soil profiles when tracing metal sources using isotope techniques.

Legacy of contamination with metal(loid)s and their potential mobilization in soils at a carbonate-hosted lead-zinc mine area.

Chemical weathering of carbonate-hosted Pb-Zn mines via acid-promoted or oxidative dissolution generates metal-bearing colloids at neutral mine drainage sites. However, the mobility and bioavailability of the colloids associated with metals in nearby soils are unknown. Here, we monitored the mobility of metal(loid)s in soils affected by aeolian deposition and river transport in the vicinity of a carbonate-hosted Pb-Zn mine. Using chemical extraction, ultrafiltration, and microscopic and spectroscopic analysis of metals we find that contamination levels of the soil metals cadmium (Cd), lead (Pb) and zinc (Zn) were negatively correlated with metal extractability. However, nano-scale characterization indicates that colloid-metal(loid) interactions induced potential mobilization and increased risk from metal(loid)s. Dynamic light scattering (DLS) and HRTEM-EDX-SAED analysis further indicate that organic matter (OM)-rich nano-colloids associated with calcium (Ca), silicon (Si) and iron (Fe) precipitates accounted for the majority of the dissolved metal fractions in carbonate-hosted Pb-Zn mine soils. More stable nano-crystals (ZnS, ZnCO3, Zn-bearing sulfates, hematite and Al-Si-Fe compounds) were present in the pore water of aeolian-impacted upland soils rather than in river water-impacted soils. Our results suggest that future work should consider the possibility that potential mobilization of metal(loid)s induced by the weathering and transformation of these metal-bearing nano-crystals to metal-bearing amorphous colloids, potentially elevating metal mobility and/or bioavailability in river water-impacted agricultural soils.

Preparation of a bis-Schiff base immobilized mesoporous SBA-15 nanosensor for the fluorogenic sensing and adsorption of Cu2.

An inorganic-organic chemosensing material (MS-NSP) was developed by anchoring the bis-Schiff base fluorophore onto the channel surface of a SBA-15 mesoporous silica surface with a quaternary ammonium linker. The mesostructure, morphology, and spectral features of MS-NSP were systematically described. The nanohybrid could be implemented as a multifunctional fluorescent nanosensor for Cu2+ ions. A good linearity was observed over the concentration range from 0 to 4 mM, and the lower Cu2+ detection limit by MS-NSP was found to be 0.19 µM. Additionally, the fluorescence response of MS-NSP was remarkably specific for Cu2+ ions compared to other competitive ionic species. Moreover, the MS-NSP can also be utilized as an adsorbent for the effective elimination of Cu2+ from an aqueous solution. The kinetic features of adsorption were well described by the pseudo-second-order model and the sorption isotherm was in agreement with the Langmuir model. The theoretical maximum adsorption amount of Cu2+ ions was determined at 58.5 mg g-1. Finally, the interaction of MS-NSP and Cu2+ was investigated using theoretical calculations. Overall, the material in the present study is useful for both the sensitive detection and effective extraction of Cu2+ ions.

Soil Metal Immobilization in Agricultural Land Contaminated with Cadmium and Lead: A Case Study of Effectiveness Evaluation in Lanping, Southwest China.

The growth of edible crops on land that is highly polluted with potentially toxic elements is prohibited in many developed countries, but the growth of fiber or energy crops may be permitted. Here, we have evaluated metal immobilization in a maize field polluted with cadmium (Cd) and lead (Pb) to determine the thresholds of soil CaCl2-extractable Cd and Pb and to assess management options designed to maximize food safety. Based on geographical and statistical methods we found that when the soil pH was increased from 5.24 to 6.24, the soil CaCl2-extractable Cd and Pb values decreased by 47.8 and 74.7%, respectively. Soil CaCl2-extractable Pb concentrations need to be < 2.14 mg kg-1 in order to comply with the Chinese maximum permissible grain Pb concentration (< 0.2 mg kg-1). Immobilization increased the percentage of samples that were below permissible levels from 77.4% to 96.2% (grain Cd) and 90.6% to 96.2% (grain Pb) during the period 2017 to 2019. To avoid excessive or inadequacy immobilization, the spatial distribution of correlation coefficients of soil pH, CaCl2-extractable or grain Cd/Pb may be helpful in the precise management of immobilization for long-term remediation.

Application of Improved Asynchronous Advantage Actor Critic Reinforcement Learning Model on Anomaly Detection.

Anomaly detection research was conducted traditionally using mathematical and statistical methods. This topic has been widely applied in many fields. Recently reinforcement learning has achieved exceptional successes in many areas such as the AlphaGo chess playing and video gaming etc. However, there were scarce researches applying reinforcement learning to the field of anomaly detection. This paper therefore aimed at proposing an adaptable asynchronous advantage actor-critic model of reinforcement learning to this field. The performances were evaluated and compared among classical machine learning and the generative adversarial model with variants. Basic principles of the related models were introduced firstly. Then problem definitions, modelling processes and testing were detailed. The proposed model differentiated the sequence and image from other anomalies by proposing appropriate neural networks of attention mechanism and convolutional network for the two kinds of anomalies, respectively. Finally, performances with classical models using public benchmark datasets (NSL-KDD, AWID and CICIDS-2017, DoHBrw-2020) were evaluated and compared. Experiments confirmed the effectiveness of the proposed model with the results indicating higher rewards and lower loss rates on the datasets during training and testing. The metrics of precision, recall rate and F1 score were higher than or at least comparable to the state-of-the-art models. We concluded the proposed model could outperform or at least achieve comparable results with the existing anomaly detection models.

Two-Dimensional 2M-WS2 Nanolayers for Superconductivity.

Recently, a newly discovered VIB group transition metal dichalcogenide (TMD) material, 2M-WS2, has attracted extensive attention due to its interesting physical properties such as topological superconductivity, nodeless superconductivity, and anisotropic Majorana bound states. However, the techniques to grow high-quality 2M-WS2 bulk crystals and the study of their physical properties at the nanometer scale are still limited. In this work, we report a new route to grow high-quality 2M-WS2 single crystals and the observation of superconductivity in its thin layers. The crystal structure of the as-grown 2M-WS2 crystals was determined by X-ray diffraction (XRD) and scanning tunneling microscopy (STM). The chemical composition of the 2M-WS2 crystals was determined by energy dispersive X-ray spectroscopy (EDS) analysis. At 77 K, we observed the spatial variation of the local tunneling conductance (dI/dV) of the 2M-WS2 thin flakes by scanning tunneling spectroscopy (STS). Our low temperature transport measurements demonstrate clear signatures of superconductivity of a 25 nm-thick 2M-WS2 flake with a critical temperature (T C) of ∼8.5 K and an upper critical field of ∼2.5 T at T = 1.5 K. Our work may pave new opportunities in studying the topological superconductivity at the atomic scale in simple 2D TMD materials.

Blockchain-Federated-Learning and Deep Learning Models for COVID-19 Detection Using CT Imaging.

With the increase of COVID-19 cases worldwide, an effective way is required to diagnose COVID-19 patients. The primary problem in diagnosing COVID-19 patients is the shortage and reliability of testing kits, due to the quick spread of the virus, medical practitioners are facing difficulty in identifying the positive cases. The second real-world problem is to share the data among the hospitals globally while keeping in view the privacy concerns of the organizations. Building a collaborative model and preserving privacy are the major concerns for training a global deep learning model. This paper proposes a framework that collects a small amount of data from different sources (various hospitals) and trains a global deep learning model using blockchain-based federated learning. Blockchain technology authenticates the data and federated learning trains the model globally while preserving the privacy of the organization. First, we propose a data normalization technique that deals with the heterogeneity of data as the data is gathered from different hospitals having different kinds of Computed Tomography (CT) scanners. Secondly, we use Capsule Network-based segmentation and classification to detect COVID-19 patients. Thirdly, we design a method that can collaboratively train a global model using blockchain technology with federated learning while preserving privacy. Additionally, we collected real-life COVID-19 patients' data open to the research community. The proposed framework can utilize up-to-date data which improves the recognition of CT images. Finally, we conducted comprehensive experiments to validate the proposed method. Our results demonstrate better performance for detecting COVID-19 patients.

An Integration of blockchain and AI for secure data sharing and detection of CT images for the hospitals.

Deep learning, for image data processing, has been widely used to solve a variety of problems related to medical practices. However, researchers are constantly struggling to introduce ever efficient classification models. Recent studies show that deep learning can perform better and generalize well when trained using a large amount of data. Organizations such as hospitals, testing labs, research centers, etc. can share their data and collaboratively build a better learning model. Every organization wants to retain the privacy of their data, while on the other hand, these organizations want accurate and efficient learning models for various applications. The concern for privacy in medical data limits the sharing of data among multiple organizations due to some ethical and legal issues. To retain privacy and enable data sharing, we present a unique method that combines locally learned deep learning models over the blockchain to improve the prediction of lung cancer in health-care systems by filling the defined gap. There are several challenges involved in sharing that data while maintaining privacy. In this paper, we identify and address such challenges. The contribution of our work is four-fold: (i) We propose a method to secure medical data by only sharing the weights of the trained deep learning model via smart contract. (ii) To deal with different sized computed tomography (CT) images from various sources, we adopted the Bat algorithm and data augmentation to reduce the noise and overfitting for the global learning model. (iii) We distribute the local deep learning model wights to the blockchain decentralized network to train a global model. iv) We propose a recurrent convolutional neural network (RCNN) to estimate the region of interest (ROI) in theCT images. An extensive empirical study has been conducted to verify the significance of our proposed method for better prediction of cancer in the early stage. Experimental results of the proposed model can show that our proposed technique can detect the lung cancer nodules and also achieve better performance.

Time Series Forecasting and Classification Models Based on Recurrent with Attention Mechanism and Generative Adversarial Networks.

Time series classification and forecasting have long been studied with the traditional statistical methods. Recently, deep learning achieved remarkable successes in areas such as image, text, video, audio processing, etc. However, research studies conducted with deep neural networks in these fields are not abundant. Therefore, in this paper, we aim to propose and evaluate several state-of-the-art neural network models in these fields. We first review the basics of representative models, namely long short-term memory and its variants, the temporal convolutional network and the generative adversarial network. Then, long short-term memory with autoencoder and attention-based models, the temporal convolutional network and the generative adversarial model are proposed and applied to time series classification and forecasting. Gaussian sliding window weights are proposed to speed the training process up. Finally, the performances of the proposed methods are assessed using five optimizers and loss functions with the public benchmark datasets, and comparisons between the proposed temporal convolutional network and several classical models are conducted. Experiments show the proposed models' effectiveness and confirm that the temporal convolutional network is superior to long short-term memory models in sequence modeling. We conclude that the proposed temporal convolutional network reduces time consumption to around 80% compared to others while retaining the same accuracy. The unstable training process for generative adversarial network is circumvented by tuning hyperparameters and carefully choosing the appropriate optimizer of "Adam". The proposed generative adversarial network also achieves comparable forecasting accuracy with traditional methods.

WatchPose: A View-Aware Approach for Camera Pose Data Collection in Industrial Environments.

Collecting correlated scene images and camera poses is an essential step towards learning absolute camera pose regression models. While the acquisition of such data in living environments is relatively easy by following regular roads and paths, it is still a challenging task in constricted industrial environments. This is because industrial objects have varied sizes and inspections are usually carried out with non-constant motions. As a result, regression models are more sensitive to scene images with respect to viewpoints and distances. Motivated by this, we present a simple but efficient camera pose data collection method, WatchPose, to improve the generalization and robustness of camera pose regression models. Specifically, WatchPose tracks nested markers and visualizes viewpoints in an Augmented Reality- (AR) based manner to properly guide users to collect training data from broader camera-object distances and more diverse views around the objects. Experiments show that WatchPose can effectively improve the accuracy of existing camera pose regression models compared to the traditional data acquisition method. We also introduce a new dataset, Industrial10, to encourage the community to adapt camera pose regression methods for more complex environments.

A Secure Mutual Batch Authentication Scheme for Patient Data Privacy Preserving in WBAN.

The current advances in cloud-based services have significantly enhanced individual satisfaction in numerous modern life areas. Particularly, the recent spectacular innovations in the wireless body area networks (WBAN) domain have made e-Care services rise as a promising application field, which definitely improves the quality of the medical system. However, the forwarded data from the limited connectivity range of WBAN via a smart device (e.g., smartphone) to the application provider (AP) should be secured from an unapproved access and alteration (attacker) that could prompt catastrophic consequences. Therefore, several schemes have been proposed to guarantee data integrity and privacy during their transmission between the client/controller (C) and the AP. Thereby, numerous effective cryptosystem solutions based on a bilinear pairing approach are available in the literature to address the mentioned security issues. Unfortunately, the related solution presents security shortcomings, where AP can with ease impersonate a given C. Hence, this existing scheme cannot fully guarantee C's data privacy and integrity. Therefore, we propose our contribution to address this data security issue (impersonation) through a secured and efficient remote batch authentication scheme that genuinely ascertains the identity of C and AP. Practically, the proposed cryptosystem is based on an efficient combination of elliptical curve cryptography (ECC) and bilinear pairing schemes. Furthermore, our proposed solution reduces the communication and computational costs by providing an efficient data aggregation and batch authentication for limited device's resources in WBAN. These additional features (data aggregation and batch authentication) are the core improvements of our scheme that have great merit for limited energy environments like WBAN.

Eliminating redundant spatial variation to better understand the variance of interest of soil potentially toxic elements at different sampling scales in different soil types south of Nanjing, China.

Sampling scale and prediction of spatial distribution are essential in surveys of soil metal pollution. Sufficient sampling density encompassing the principal spatial sources of variance and prediction of polluted areas with the help of soil maps makes pollution evaluation more reliable and subsequent soil remediation assessment more efficient. Two soil sampling schemes, using 232 points at 2-km intervals in 2002 for sampling at county scale and 109 points at 200-1000-m intervals in 2012 at town scale, were used to study the potentially toxic metals Cu, Cd, Cr, Hg, Ni, Pb, Zn, and the metalloid As in an urban-rural hinge area. We focused on finding characteristics of the explanatory power of soil type toward different sampling scales from 200 to 2000 m, a routine sampling scale in practice for remediation of soil potentially toxic elements (PTEs). We also attempted to eliminate the redundant spatial variation to better understand the variance of soil PTEs. Spatial variation of PTEs at different scales was compared and estimated using soil map units based on geostatistical methods. The explanatory power of the soil map units selected at different scales was significantly different at P < 0.01 and the smaller scales better explained the spatial variance. Anthropic activities profoundly affected the contents of PTEs in soils and the amounts of anthropogenic pollutants released often exceed the contribution from natural sources. Variances of interest of Cr and Cu were underestimated by 72.4 and 32.8%, respectively, due to soil type as a factor but were overestimated for other elements by percentages following the sequence Zn (45.4%) > Hg (28.6%) > Pb (28.8%) > Ni (26.73%) > As (13.7%) > Cd (10.5%). Eliminating variances of zero interest would be helpful in increasing the effectiveness of remediation of metal-contaminated soils.