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Privacy-preserving neural inference helps protect both the user input data and the model weights from being leaked to others during the inference of a deep learning model. To achieve data protection, the inference is often performed within a secure domain, and the final result is revealed in plaintext. Nevertheless, performing the computations in the secure domain incurs about a thousandfold overhead compared with the insecure version, especially when the involved operations of the entire model are mapped to the secure domain, which is the computation scheme adopted by the existing works. This work is inspired by the transfer learning technique, where the weights of some parts of the model layers are transferred from a publicly available, pre-built deep learning model, and it opens a door to further boost the execution efficiency by allowing us to do the secure computations selectively on parts of the transferred model. We have built a compiler framework, SecureTVM, to automatically translate a trained model into the secure version, where the model layers to be protected can be selectively configured by its model provider. As a result, SecureTVM outperforms the state of the art, CrypTFlow2, by a factor of 55 for the transfer learning model. We believe that this work takes a step forward toward the practical uses of privacy-preserving neural inference for real-world applications.

Preparation and fabrication of Sr@Mn3O4/GO nanocomposite towards electro-reduction of NFT in biological and environmental samples.

In empirical studies involving sensitive topics, in addition to the problem of estimating the population proportion with a sensitive characteristic, a question arises as to whether or not there is heterogeneity in the distribution of an auxiliary random variable representing the information of subjects collected from a sensitive group and a non-sensitive group. That is, it is of interest to investigate the influence of sensitive attribute on the auxiliary random variable of interest. Finite mixture models are utilised to evaluate the association. A proposed Bayesian method through data augmentation and Markov chain Monte Carlo is applied to estimate unknown parameters of interest. Deviance information criterion and marginal likelihood are employed to select a suitable model to describe the association of the sensitive characteristic with the auxiliary random variable. Simulation and real data studies are conducted to assess the performance of and illustrate applications of the proposed methodology.

Adhesion G protein-coupled receptors (aGPCRs) constitute an evolutionarily ancient family of receptors that often undergo autoproteolysis to produce α and β subunits

BACKGROUND AND AIMS Low-density lipoprotein (LDL)-cholesterol (LDL-C) promotes atherosclerotic cardiovascular disease (ASCVD), with changes in LDL electronegativity modulating its pro-atherogenic/pro-thrombotic effects. Whether such alterations associate with adverse outcomes in patients with acute coronary syndromes (ACS), a patient population at particularly high cardiovascular risk, remains unknown. METHODS This is a case-cohort study using data from a subset of 2619 ACS patients prospectively recruited at four university hospitals in Switzerland. Isolated LDL was chromatographically separated into LDL particles with increasing electronegativity (L1-L5), with the L1-L5 ratio serving as a proxy of overall LDL electronegativity. Untargeted lipidomics revealed lipid species enriched in L1 (least) vs. L5 (most electronegative subfraction). Patients were followed at 30 days and 1 year. The mortality endpoint was reviewed by an independent clinical endpoint adjudication committee. Multivariable-adjusted hazard ratios (aHR) were calculated using weighted Cox regression models. RESULTS Changes in LDL electronegativity were associated with all-cause mortality at 30 days (aHR, 2.13, 95% CI, 1.07-4.23 per 1 SD increment in L1/L5; p=.03) and 1 year (1.84, 1.03-3.29; p=.04), with a notable association with cardiovascular mortality (2.29; 1.21-4.35; p=.01; and 1.88; 1.08-3.28; p=.03). LDL electronegativity superseded several risk factors for the prediction of 1-year death, including LDL-C, and conferred improved discrimination when added to the updated GRACE score (area under the receiver operating characteristic curve 0.74 vs. 0.79, p=.03). Top 10 lipid species enriched in L1 vs. L5 were: cholesterol ester (CE) (18:2), CE (20:4), free fatty acid (FA) (20:4), phosphatidyl-choline (PC) (36:3), PC (34:2), PC (38:5), PC (36:4), PC (34:1), triacylglycerol (TG) (54:3), and PC (38:6) (all p

Additional file 2: Deterministic risk assessment results for heavy metals in TCM.

Learning to detect, characterize and accommodate novelties is a challenge that agents operating in open-world domains need to address to be able to guarantee satisfactory task performance. Certain novelties (e.g., changes in environment dynamics) can interfere with the performance or prevent agents from accomplishing task goals altogether. In this paper, we introduce general methods and architectural mechanisms for detecting and characterizing different types of novelties, and for building an appropriate adaptive model to accommodate them utilizing logical representations and reasoning methods. We demonstrate the effectiveness of the proposed methods in evaluations performed by a third party in the adversarial multi-agent board game Monopoly. The results show high novelty detection and accommodation rates across a variety of novelty types, including changes to the rules of the game, as well as changes to the agent's action capabilities.

Opinion summarization provides an important solution for summarizing opinions expressed among a large number of reviews. However, generating aspect-specific and general summaries is challenging due to the lack of annotated data. In this work, we propose two simple yet effective unsupervised approaches to generate both aspect-specific and general opinion summaries by training on synthetic datasets constructed with aspect-related review contents. Our first approach, Seed Words Based Leave-One-Out (SW-LOO), identifies aspect-related portions of reviews simply by exact-matching aspect seed words and outperforms existing methods by 3.4 ROUGE-L points on SPACE and 0.5 ROUGE-1 point on OPOSUM+ for aspect-specific opinion summarization. Our second approach, Natural Language Inference Based Leave-One-Out (NLI-LOO) identifies aspect-related sentences utilizing an NLI model in a more general setting without using seed words and outperforms existing approaches by 1.2 ROUGE-L points on SPACE for aspect-specific opinion summarization and remains competitive on other metrics., Comment: EACL 2023 Findings

Additional file 3: Table S1. HBGV of heavy metals from different sources.

Methotrexate (MTRX) is an anticancer drug that is also used for several chronic illnesses; however, a high dosage of MTRX can cause adverse effects, so it is necessary to monitor the MTRX level in blood and urine samples. This work reports the electrochemical determination of MTRX based on Sb2O3 and Bi2O3 composite material (Sb2O3@Bi2O3)modified electrodes. The one-pot sonohydrolysis synthesis method is employed for the preparation of the Sb2O3@Bi2O3 composite, which reveals high crystallinity with mixed phases of monoclinic Bi2O3 and cubic Sb2O3. Furthermore, the phases are identified by XRD, Raman spectroscopy, XPS, and HRTEM analysis. This composite reveals nanoneedle and nanocube morphologies. The Sb2O3@Bi2O3modified GCE shows excellent electrochemical activity for the detection of the anticancer drug methotrexate (MTRX). The activity is compared with the individual Sb2O3 and Bi2O3 compounds. Sb2O3@Bi2O3/GCE shows good electroanalytical characteristics in chronoamperometric analysis. Sb2O3@Bi2O3/GCE exhibits a linear range of approximately 0.01 μM to 174.6 μM, a sensitivity of 1.46 μA cm-2μM-1, and an LOD of 2.9 nM. Moreover, Sb2O3@Bi2O3/GCE delivered high selectivity among highly interfering compounds in blood and urine samples.

In this study, we designed a CrNiCo-P/GCN composite for use as a high-performance multifunctional catalyst for the oxygen evolution reaction (OER), electrochemical determination, and photodegradation of roxarsone (ROX). CrNiCo-P/GCN demonstrates favorable charge resistance and electrical conductance due to its intrinsic properties. It exhibits an admirable OER overpotential of 290 mV with a lower Tafel plot value of 125 mV dec

Nitrogen-doped carbon quantum dot decorated gadolinium oxide nanoseeds as an electrode modifier for the sensitive electrochemical detection of the antibiotic drug clioquinol in urine samples.

In the present study, the influence of phase purity and crystallinity on the electrochemical properties of well-designed GdBiVO4@rGO nanocomposite has been fabricated by the facile hydrothermal method for the detection of chloramphenicol (Cp) were reported. The surface morphology and electrochemical sensing property of as prepared samples had characterized by X-ray diffraction spectroscopy (XRD), Fourier Transform Infrared spectroscopy (FT-IR), Scanning Electron Microscope (SEM), Transmission Electron Microscope (TEM), X-ray photoelectron microscopy (XPS), UV-Visible diffuse reflectance spectroscopy (UV-Vis DRS) and Electron Impedance Spectroscopy (EIS) techniques. The electrochemical property of synthesized GdBiVO4@rGO nanocomposite had carried out for the detection of hazardous antiprotozoal chloramphenicol (CP) drug in food had scrutinized by Cyclic Voltammetry (CV) and Differential Pulse Voltammetry (DPV). The GdBiVO4@rGO nanocomposite on electrode showed an excellent and quick response towards CP drug. Under an optimized condition, the proposed electrochemical sensor displayed a low detection limit of up to 0.0229 μM and broad linear range of 0.05–36.55 & 42.55-2336.55 μM for CP. In addition, the GdBiVO4@rGO electrodes exhibited good performance characteristics of repeatability and reproducibility and proved suitable for CP analysis in food samples.

Simultaneous electrochemical determination of antibiotic drugs such as acetaminophen and levofloxacin using SrV NPs decorated graphitic carbon nitride (SrV/GCN) nanocomposite.

This review expresses recent trends in electrocatalysts towards hydrogen production and water oxidation. The recent and future trends with different strategies for the development of electrocatalysts for fuel cells are highlighted.

An electrochemical sensor was developed based on the pyrochlore-type binary metal oxide samarium stannate nanoparticles (Sm2Sn2O7 NPs). The Sm2Sn2O7 NPs were prepared using the co-precipitation technique, and they were subjected to physiochemical characterizations such as X-ray diffraction (XRD), Raman spectroscopy, and X-ray photoelectron spectroscopy (XPS). The morphological and elemental information was interpreted by field-emission scanning electron microscopy (FESEM) and high-resolution transmission electron microscopy (HRTEM) analysis. The electrocatalytic sensing of the pharmaceutical drug azathioprine (AZN) was exhibited using cyclic voltammetry (CV) and differential pulse voltammetry (DPV) over the Sm2Sn2O7/GCE (GCE - glassy carbon electrode). The Sm2Sn2O7/GCE had high electrocatalytic behavior for AZN determination with a limit of detection of 4 nM and superior sensitivity of 0.27 μA μM–1 cm–2, and it has a linear range from 0.01 to 948 μM. Furthermore, the Sm2Sn2O7/GCE exhibited excellent selectivity, long-term storage stability, good repeatability, and reproducibility for AZN detection. Additionally, the Sm2Sn2O7/GCE sensor possessed considerable importance for the sensing of AZN in biological fluids for practical applications. We believe that this pyrochlore-based Sm2Sn2O7 NP electrocatalyst can act as a prospective electrode material for the electrochemical determination of AZN in clinical biological applications.

Serotonin (SRT), one among the neurotransmitters, is mainly responsible for physiological functions in human body. The effect of changes and intake of SRT can influence higher impact of peoples, thus sensing such compound with more effective and simple technique will be an extreme significance. Zeolite imidazole framework among MOF was constructed with the correlation of multiwall carbon nanotubes (ZIF-67/MWCNT) as sensor material via simple co-precipitation method. The rationally designed ZIF-67/MWCNT was evaluated for physico-chemical properties analyzed with XRD, XPS and the morphological structures were identified with FESEM. Cyclic voltammetry (CV) and amperometric (IT) technique was initiated for electrochemical detection of SRT. The ZIF-67/MWCNT exhibited a dodecahedron shape and larger surface area as agreed from BET analysis. The electrochemical results obtained at the interfacial site of ZIF-67/MWCNT proved to be more competent with excellent selectivity. SRT was electrochemically detected in human blood serum and urine as real sample analysis. The linear range was 0.049 μM to 800 μM with a lower LOD-7 nM. Moreover, the attained sensitivity was 5.613 µA µM−1 cm2 with excellent repeatability and reproducible behavior. The highly conducting matrix ZIF-67/MWCNT with abundantly accessible sites and higher surface area has gained significant results towards SRT detection.

The cytotoxic dosage of exposed drug waste is frequently discharging on aquatic surfaces via wastewater treatment plants and pharma industries. Early detection of these drug wastes with a reliable and accurate are in great need. In this report, a noble metal effectively doped on oxidized one-dimensional (1D) single-walled carbon nanotube and abundant oxygen functional groups presenting two-dimensional (2D) graphene oxide on its surface, and hydrophobic cavities are the promising selector towards electrochemical oxidation of Naproxen (NPZ). A hybrid nanocomposite Au@f-CNT/GO was engineered by the ultrasound-assist sonication method and employed for the ultra-quantification of the therapeutic drug NPZ. In addition, various physicochemical and analytical techniques were utilized to confirm the successful formation of composite and principal detection of NPZ. Au@f-CNT/GO fabricated glassy carbon electrode exhibits a low resistance and higher electrocatalytic activity toward NPZ sensing. In addition, under the optimized condition, the sensor demonstrates an acceptable linear range of 100 nM − 15.6 µM, a lower limit of detection of 14 nM (S/N = 3), and higher sensitivity of 1.7484 μA μM−1 cm2. Furthermore, the proposed sensor has high selectivity, stability, reproducibility, and repeatability. Moreover, real sample analysis shows appreciable recovery results of 96 to 99% which strongly suggests that the proposed sensor has potential applicability for the sensitive detection of NPZ in the various water and biological samples.

Reed flower (RF)-derived porous carbon-based electrodes are produced with KOH as the activating agent and diethanolamine (DEA) as the additive via a facile method without precarbonization. DEA with...

In this report, 3D flower-like cobalt phosphate (Co3(PO4)2, CPO) was prepared through a facile co-precipitation method towards voltammetric sensing of rutin (RU), quercetin (QCT) and also employed for supercapacitor application. The structural morphologies were analyzed by various analytical techniques. The Brunauer–Emmett–Teller analysis of CPO displays a specific surface area of 40.885 m2 g−1 and a mesopore volume of 0.0108 cm3 g−1, which provides more catalytic sites, and a good electron transfer pathway that leading to promote enhanced electrochemical performance for detection of RU, and QCT. The oxidation peak current demonstrated low detection limits of 0.009 μM for RU and 0.003 μM for QCT with broad linear concertation of RU and QCT were 0.02 → 121.62 μM, and 0.08 → 129.8 μM, respectively. The fabricated electrode shows a higher sensitivity of 1.352 and 3.847 μA μM−1 cm−2 for RU and QCT detection. The modified CPO/SPCE offers excellent selectivity, repeatability, reproducibility, and further monitoring for RU and QCT in spiked fruit samples showing applicable recoveries. The electrochemical supercapacitor was explored using a CPO/NF electrode that revealed high specific capacitance of 680 F g−1 at 1.0 A g−1, and good stability of 89.3% capacitance retention after 10000 cycles at 6.0 A g−1.

For the photocatalytic degradation of Direct Blue 15 (DB15), tin oxide/polyaniline (SnO2/PANI) nanocomposites were prepared using the co-precipitation process. SEM, FTIR, XRD, and UV–Vis analysis were used to characterize the as-prepared SnO2/PANI nanocomposites. The XRD pattern confirms the presence of SnO2 reflection planes in both bare and SnO2/PANI. The changes in planes of SnO2 are owing to the incorporation of PANI in the SnO2. The metal oxide and the hydroxyl group of SnO2 are altered slightly with the substitution of PANI. The inclusion of PANI in SnO2 reduces the crystallite size and increases surface area compared to bare SnO2 nanoparticles. The reflectance and bandgap energy (3.1–2.7 eV) of the SnO2 decreased after the substitution of PANI. The photocatalytic dye removal efficiency of the SnO2/PANI nanocomposite was significantly improved compared to bare SnO2. The photocatalytic mechanism of SnO2/PANI nanocomposites for the degradation of DB15 is systematically explained, as well as the synergetic effect of SnO2/PANI nanocomposites. Toxicity of treated DB15 prompts a lesser impact on Artemia salina than untreated DB15. This experiment clarified that SnO2/PANI nanocomposites produced non-toxic by-products and they are lesser toxic to aquatic food web components.

Nitrofurantoin (NFT) is mainly used in humans for the treatment of urinary tract infections. NFT is used as feed additives in animals, due to its broad antimicrobial activity. However, it shows more side effects on human health and the environment. Therefore low-cost, portable, and rapid sensors are necessary for the detection of NFT in real samples. Herein, we successfully developed an electrochemical sensor using a glassy carbon electrode (GCE) modified with gadolinium orthoferrite (GdFeO3) decorated on reduced graphene oxide (RGO) nanocomposite for the detection of NFT. The facile hydrothermal method was used to synthesis a novel GdFeO3/RGO nanocomposite, the morphological and structural characterization was confirmed by the FESEM, HRTEM, EDX, XRD, Raman, and XPS techniques. The formation mechanism of GdFeO3/RGO nanocomposite had been discussed. The effective intercalation of the nanostructured GdFeO3 to the RGO sheets leads to the significant enhancement in physicochemical properties such as electrical conductivity, electro-active surface area, structural stability, and electrochemical activity, which was observed from the EIS and CV experimental results. The electrochemical studies established that the developed GdFeO3/RGO sensor was highly sensitive and selective to NFT. Moreover, the GdFeO3/RGO sensor exhibits good sensitivity of 4.1985 μA μM−1 cm−2, a low detection limit (LOD) of 0.0153 µM and a linear range from 0.001 to 249 µM for NFT detection under optimized experimental conditions. In addition, the investigation of storage time on the CV response of the GdFeO3/RGO sensor indicates superior stability. Owing to these extraordinary analytical advantages, the as-fabricated sensor was applied to detect the NFT levels in human urine and river water samples with satisfactory results.