The dual-configured hydrogen bonds induced by polymerized deep eutectic solvents-modified magnetic biochar enhanced the selectivity for 3,4-methylenedioxymethamphetamine.

The accurate discovering and monitoring of 3,4-methylenedioxymethamphetamine (MDMA) are especially important because of its substantial toxicity and potential harm to human and the ecological systems. Three types of polymerized deep eutectic solvents functionalized magnetic biochar (MBC@poly (AA/AAC/AAm-ChCl)) were successfully synthesized to adsorb MDMA. The isotherm and kinetic data confirmed that MBC@poly (AAm-ChCl) had the strongest adsorption capacity, and the order of adsorption capacity is as follow: MBC@poly(AAm-ChCl) > MBC@poly(AA-ChCl) > MBC@poly(MAA-ChCl), which also revealed that the adsorption was heterogeneous multi-layer chemisorption. The findings of the characterizations manifested that MBC@poly(AAm-ChCl) was the optimal adsorbent owning to its higher nitrogen content, resulting in the formation of a greater number of hydrogen bonds. Due to the strong hydrogen bonding effect of CO and -NH2 functional groups, MBC@poly(AAm-ChCl) exhibited the high selectivity towards MDMA under the coexistence of multiple chemical substances, and excellent adsorption performance over the pH range of 4-11. Urea as a hydrogen bond inhibitor further confirmed MBC@poly(AAm-ChCl) had high-density active hydrogen bonding sites. Furthermore, utilizing density functional theory (DFT) for simulating adsorption both before and after the process verified that the high selectivity of MBC@poly(AAm-ChCl) attributed to the formation of the dual-configured hydrogen bonds. This study provides support for the production of highly selective biochar for use in pretreatment during drug detection.

Efficient magnetic solid-phase extraction, UPLC-MS/MS detection, and consumption assessment of five trace psychoactive substances.

Wastewater-based epidemiology (WBE) has become an objective and updated surveillance strategy for monitoring and estimating consumption trends of psychoactive substances (PSs) in the population. Firstly, magnetic shrimp shell biochar-based adsorbent (DZMBC) was synthesized and employed for extraction trace PSs from municipal wastewater. Proper pyrolysis temperature and increased KOH activator content favored the pore structure and surface area, thus facilitating extraction. DZMBC delivered exceptional extraction performance such as pH stability, anti-interference property, fast magnetic separation ability, reusability, and reproducibility. Then, a method based on magnetic solid-phase extraction (MSPE) followed by ultra-high-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) was developed, validated, and utilized for the quantitative determination of five PSs in real wastewater samples. Methodological validation results indicated a favorable linearity, low method limits of detection (1.00-4.75 ng/L), and good precisions (intra-day and inter-day relative standard deviations < 4.8%). Finally, an objective snapshot of Chongqing drug use and consumption pattern was obtained. Methamphetamine (MAMP) and 3,4-methylenedioxymethamphetamine (MDMA) were the prevalent illegal drugs in local. Both concentrations and per capita consumption of MDMA displayed a change (P < 0.05) between July and September, while no statistical differences were observed for each week.

How hydrogen bonding and π-π interactions synergistically facilitate mephedrone adsorption by bio-sorbent: An in-depth microscopic scale interpretation.

Mephedrone (4-methylmethcathinone, MEPH) exhibited severe ecologic hazards and health detriments. A novel deep eutectic solvent functionalized magnetic ZIF-8/hierarchical porous carbon (DMZH) with excellent selectivity, interference resistance and recyclability, was developed for the rapid adsorption of MEPH. Initially, potential adsorption sites of MEPH were predicted. Then, π-π and hydrogen bonding interactions were proposed and verified from characterizations, comparative experiments and theoretical calculations. The synergistic effects of the hydrogen bonding and π-π interactions increased the adsorption energies from -15.26 kcal⋅mol-1 to -21.83 kcal⋅mol-1, enhanced the degree of π-dissociation, enlarged the π-π isosurface area, extended the van der Waals surface mutual penetration distance, achieving stronger affinity and remarkable adsorption. Furthermore, offset (parallel-displaced) π-π stacking form existed between DMZH and MEPH. DMZH acted as the hydrogen bond donor and MEPH served as the hydrogen bond acceptor to form O-H⋯O and N-H⋯O hydrogen bonding interaction. Profiting from the synergistic effects, DMZH showed satisfactory adsorption for MEPH within 20 min with a maximum adsorption capacity of 3270.11 µg∙g-1, displayed excellent performance in wide pH range of 5∼11 and in the coexistence of multi-chemicals.

Signal Property Information-Based Target Detection with Dual-Output Neural Network in Complex Environments.

The performance of traditional model-based constant false-alarm ratio (CFAR) detection algorithms can suffer in complex environments, particularly in scenarios involving multiple targets (MT) and clutter edges (CE) due to an imprecise estimation of background noise power level. Furthermore, the fixed threshold mechanism that is commonly used in the single-input single-output neural network can result in performance degradation due to changes in the scene. To overcome these challenges and limitations, this paper proposes a novel approach, a single-input dual-output network detector (SIDOND) using data-driven deep neural networks (DNN). One output is used for signal property information (SPI)-based estimation of the detection sufficient statistic, while the other is utilized to establish a dynamic-intelligent threshold mechanism based on the threshold impact factor (TIF), where the TIF is a simplified description of the target and background environment information. Experimental results demonstrate that SIDOND is more robust and performs better than model-based and single-output network detectors. Moreover, the visual explanation technique is employed to explain the working of SIDOND.

Enhanced adsorption of 3,4-methylenedioxymethamphetamine by magnetic graphene oxide-polydopamine nanohybrid modified zeolitic imidazolate framework-67 and its micro-mechanism: Experiments and calculations.

Exploring the structure-dependent adsorption mechanism of contaminants in wastewater is beneficial to high-efficiency adsorbents design and environmental remediation. In this study, emerging porous material of zeolitic imidazolate framework-67 (ZIF-67) has been modified by the magnetic graphene oxide-polydopamine nanohybrid (mGOP) to obtain three-dimensional ZIF-67/mGOP through an in-situ growth strategy, which was applied to adsorb 3,4-methylenedioxymethamphetamine (MDMA, "ecstasy") in wastewater. A combination of characterizations, experiments (pH, humic acid and ion strength effect) and quantum chemical calculations revealed the microscopic adsorption mechanism involves each single component, of which the hydrogen bond (O/N…HO) and π-π electron donor acceptor (π-π EDA) interactions of mGOP endowed favourable adsorption of ZIF-67/mGOP, and mechanisms of the pore filling and Co-O chelation of ZIF-67 played synergistic effect. Such nanocomposite as a ZIFs-based adsorbent exhibited ultra-high porosity (total pore volume = 0.4033 cm3/g) and specific surface area (995.22 m2/g), revealed the heterogeneity and multilayer adsorption properties, and obtained a theoretical maximum adsorption capacity of 159.845 µg/g which higher than that of mZIF-67 alone. Overall, this work provided an effective strategy for rationally modulate ZIFs-based composites and exploration of adsorption mechanism.

Deep insight into selective adsorption behavior and mechanism of novel deep eutectic solvent functionalized bio-sorbent towards methcathinone: Experiments and DFT calculation.

This work designed and synthesized novelly selective, highly efficient and friendly environmental biochar nanomaterial (ZMBC@ChCl-EG) by screening suitable deep eutectic solvent (DES) as the functional monomer via Density Functional Theory (DFT). The prepared ZMBC@ChCl-EG achieved the highly efficient adsorption of methcathinone (MC) and exhibited excellent selectivity as well as good reusability. Selectivity analysis concluded that the distribution coefficient value (KD) of ZMBC@ChCl-EG towards MC was 3.247 L/g, which was about 3 times higher than that of ZMBC, corresponding to stronger selective adsorption capacity. The studies of isothermal and kinetics indicated that ZMBC@ChCl-EG had an excellent adsorption capacity towards MC and the adsorption was mainly chemically controlled. In addition, DFT was used to calculate the binding energies between MC and each component. The binding energies were -10.57 kcal/mol for ChCl-EG/MC, -3.15∼-9.51 kcal/mol for BCs/MC, -2.33 kcal/mol for ZIF-8/MC, respectively, suggesting that DES played a major role in enhancing methcathinone adsorption. Lastly, the adsorption mechanisms were revealed by variables experiment combined with characterizations and DFT calculation. The main mechanisms were hydrogen bonding and π-π interaction.

Development and validation of nomogram of peritoneal metastasis in gastric cancer based on simplified clinicopathological features and serum tumor markers.

BACKGROUND: Peritoneal metastasis (PM) is not uncommon in patients with gastric cancer(GC), which affects clinical treatment decisions, but the relevant examination measures are not efficiently detected. Our goal was to develop a clinical radiomics nomogram to better predict peritoneal metastases. METHODS: A total of 3480 patients from 2 centers were divided into 1 training, 1 internal validation, and 1 external validation cohort(1949 in the internal training set, 704 in the validation set, and 827 in the external validation cohort) with clinicopathologically confirmed GC. We recruited 11 clinical factors, including age, sex, smoking status, tumor size, differentiation, Borrmann type, location, clinical T stage, and serum tumor markers (STMs) comprising carbohydrate antigen 19-9 (CA19-9), carbohydrate antigen 72-4 (CA72-4), and carcinoembryonic antigen (CEA), to develop the radiomics nomogram. For clinical predictive feature selection and the establishment of clinical models, statistical methods of analysis of variance (ANOVA), relief and recursive feature elimination (RFE) and logistic regression analysis were used. To develop combined predictive models, tumor diameter, type, and location, clinical T stage and STMs were finally selected. The discriminatory ability of the nomogram to predict PM was evaluated by the area under the receiver operating characteristic curve(AUC), and decision curve analysis (DCA) was conducted to evaluate the clinical usefulness of the nomogram. RESULTS: The AUC of the clinical models was 0.762 in the training cohorts, 0.772 in the internal validation cohort, and 0.758 in the external validation cohort. However, when combined with STMs, the AUC was improved to 0.806, 0.839 and 0.801, respectively. DCA showed that the combined nomogram was of good clinical evaluation value to predict PM in GC. CONCLUSIONS: The present study proposed a clinical nomogram with a combination of clinical risk factors and radiomics features that can potentially be applied in the individualized preoperative prediction of PM in GC patients.

Neural Network-Based Autonomous Search Model with Undulatory Locomotion Inspired by Caenorhabditis Elegans.

Caenorhabditis elegans (C. elegans) exhibits sophisticated chemotaxis behavior with a unique locomotion pattern using a simple nervous system only and is, therefore, well suited to inspire simple, cost-effective robotic navigation schemes. Chemotaxis in C. elegans involves two complementary strategies: klinokinesis, which allows reorientation by sharp turns when moving away from targets; and klinotaxis, which gradually adjusts the direction of motion toward the preferred side throughout the movement. In this study, we developed an autonomous search model with undulatory locomotion that combines these two C. elegans chemotaxis strategies with its body undulatory locomotion. To search for peaks in environmental variables such as chemical concentrations and radiation in directions close to the steepest gradients, only one sensor is needed. To develop our model, we first evolved a central pattern generator and designed a minimal network unit with proprioceptive feedback to encode and propagate rhythmic signals; hence, we realized realistic undulatory locomotion. We then constructed adaptive sensory neuron models following real electrophysiological characteristics and incorporated a state-dependent gating mechanism, enabling the model to execute the two orientation strategies simultaneously according to information from a single sensor. Simulation results verified the effectiveness, superiority, and realness of the model. Our simply structured model exploits multiple biological mechanisms to search for the shortest-path concentration peak over a wide range of gradients and can serve as a theoretical prototype for worm-like navigation robots.

Neural model generating klinotaxis behavior accompanied by a random walk based on C. elegans connectome.

Klinotaxis is a strategy of chemotaxis behavior in Caenorhabditis elegans (C. elegans), and random walking is evident during its locomotion. As yet, the understanding of the neural mechanisms underlying these behaviors has remained limited. In this study, we present a connectome-based simulation model of C. elegans to concurrently realize realistic klinotaxis and random walk behaviors and explore their neural mechanisms. First, input to the model is derived from an ASE sensory neuron model in which the all-or-none depolarization characteristic of ASEL neuron is incorporated for the first time. Then, the neural network is evolved by an evolutionary algorithm; klinotaxis emerged spontaneously. We identify a plausible mechanism of klinotaxis in this model. Next, we propose the liquid synapse according to the stochastic nature of biological synapses and introduce it into the model. Adopting this, the random walk is generated autonomously by the neural network, providing a new hypothesis as to the neural mechanism underlying the random walk. Finally, simulated ablation results are fairly consistent with the biological conclusion, suggesting the similarity between our model and the biological network. Our study is a useful step forward in behavioral simulation and understanding the neural mechanisms of behaviors in C. elegans.

Diabetes mellitus promoted lymph node metastasis in gastric cancer: a 15-year single-institution experience.

BACKGROUND: Previous studies have revealed that diabetes mellitus (DM) promotes disease progress of gastric cancer (GC). This study aimed to further investigating whether DM advanced lymph nodes (LNs) metastasis in GC. METHODS: The clinicopathologic data of GC patients with >15 examined LN (ELN) between October 2004 and December 2019 from a prospectively maintained database were included. The observational outcomes included the number (N3b status) and anatomical distribution (N3 stations) of metastatic LN (MLN). RESULTS: A total of 2142 eligible patients were included in the study between October 2004 and December 2019. N3 stations metastasis (26.8% in DM vs. 19.3% in non-DM, P  = 0.026) and N3b status (18.8% in DM vs. 12.8% in non-DM, P  = 0.039) were more advanced in the DM group, and multivariate logistic regression analyses confirmed that DM was an independent factor of developing N3 stations metastasis (odds ratio [OR] = 1.771, P  = 0.011) and N3b status (OR = 1.752, P  = 0.028). Also, multivariate analyses determined DM was independently associated with more MLN (ß = 1.424, P  = 0.047). The preponderance of N3 stations metastasis (DM vs. non-DM, T1-2: 2.2% vs. 4.9%, T3: 29.0% vs. 20.3%, T4a: 38.9% vs. 25.8%, T4b: 50.0% vs. 36.6%; ELN16-29: 8.6% vs. 10.4%, ELN30-44: 27.9% vs. 20.5%, ELN ≥ 45: 37.7% vs. 25.3%), N3b status (DM vs. non-DM, T1-2: 0% vs. 1.7%, T3: 16.1% vs. 5.1%, T4a: 27.8% vs. 19.1%, T4b: 44.0% vs. 28.0%; ELN16-29: 8.6% vs. 7.9%, ELN30-44: 18.0% vs. 11.8%, ELN ≥ 45: 26.4% vs. 17.3%), and the number of MLN (DM vs. non-DM, T1-2: 0.4 vs. 1.1, T3: 8.6 vs. 5.2, T4a: 9.7 vs. 8.6, T4b: 17.0 vs. 12.8; ELN16-29: 3.6 vs. 4.6, ELN30-44: 5.8 vs. 5.5, ELN ≥ 45: 12.0 vs. 7.7) of DM group increased with the advancement of primary tumor depth stage and raising of ELN. CONCLUSIONS: DM was an independent risk factor for promoting LN metastasis. The preponderance of LN involvement in the DM group was aggravated with the advancement of tumor depth.

Multiple Power Allocation Game Schemes for Spectrum Coexistence Model Between Multistatic MIMO Radar Sensors and MU Communication.

The normal operations of radar systems and communication systems under the condition of spectrum coexistence are facing a huge challenge. This paper uses game theory to study power allocation problems between multistatic multiple-input multiple-output (MIMO) radars and downlink communication. In the case of spectrum coexistence, radars, base station (BS) and multi-user (MU) have the working state of receiving and transmitting signals, which can cause unnecessary interferences to different systems. Therefore, when they work together, they should try to suppress mutual interferences. Firstly, the signal from BS is considered as interference when radar detects and tracks targets. A supermodular power allocation game (PAG) model is established and the existence and uniqueness of the Nash equilibrium (NE) in this game are proved. In addition, the power allocation problem from BS to MU is also analyzed, and two Stackelberg PAG models are constructed. It is proved that the NE of each game exists and is unique. Simultaneously, two Stackelberg power allocation iterative algorithms converge to the NEs. Finally, numerical results verify the convergence of the proposed PAG algorithms.

Frequency analysis of light field sampling for texture information.

Light field sampling (LFS) theory can properly reduce minimum sampling rate while ensuring that novel views are not distorted for image-based rendering (IBR). The minimum sampling rate is determined by spectral support of light field. The spectral support of light field has studied the influence of the following factors: the minimum depth and the maximum depth, non-Lambertian reflections, whether the scene surfaces are flat, maximum frequency of painted signals. In this paper, we further perfect the light field spectrum analysis from the quantitative description of scene texture information based on the existing spectrum analysis theory. The quantification of texture information can be interactively refined via detected regional entropy. Thus, we can derive a spectral analytical function of light field with respect to texture information. The new function allows the spectral support of light field to be analyzed and estimated for different texture information associated with scene objects. In this way, we limit the spectral analysis problems of light field to those of a simpler signal. We show that this spectral analysis approach can be easily extended to arbitrary scene complexity levels, as we simplify the LFS of complex scenes to a plane. Additionally, the spectral support of light field broadens as the plane texture information becomes more complex. We present experimental results to demonstrate the performance of LFS with texture information, verify our theoretical analysis, and extend our conclusions on the optimal minimum sampling rate.