A dual-signal ratiometric electrochemical aptasensor based on Thi/Au/ZIF-8 and catalytic hairpin assembly for ultra-sensitive detection of aflatoxin B1.

A dual-signal ratiometric electrochemical aptasensor has been developed  for AFB1 detection using thionine/Au/zeolitic imidazolate framework-8 (Thi/Au/ZIF-8) nanomaterials and catalytic hairpin assembly (CHA) reaction. Thi/Au/ZIF-8 combined with DNA hairpin 2 (H2) was used as a signal probe. [Fe(CN)6]3-/4- was served as another signal probe, and the IThi/Au/ZIF-8/I[Fe(CN)6]3-/4- ratio was for the first time utilized to quantify AFB1. AFB1-induced CHA was used to expand the ratio of electrical signals. In the presence of AFB1, H2/Thi/Au/ZIF-8 bound to the electrode via CHA, enhanced  the current signal of Thi/Au/ZIF-8. H2 contained the DNA phosphate backbone hindered [Fe(CN)6]3-/4- redox reaction and resulted in a lower [Fe(CN)6]3-/4- current signal. This aptasensor exhibited high specificity for AFB1, a linear range of 0.1 pg mL-1 to 100 ng mL-1, and a detection limit of 0.089 pg mL-1. It demonstrated favorable sensitivity, selectivity, stability, and repeatability. The aptasensor was suitable for detecting AFB1 in peanuts and black tea and holds potential for real sample applications.

Predicting maturity and identifying key factors in organic waste composting using machine learning models.

The measurement of germination index (GI) in composting is a time-consuming and laborious process. This study employed four machine learning (ML) models, namely Random Forest (RF), Artificial Neural Network (ANN), Support Vector Regression (SVR), and Decision Tree (DT), to predict GI based on key composting parameters. The prediction results showed that the coefficient of determination (R2) for RF (>0.9) and ANN (>0.9) was higher than SVR (<0.6) and DT (<0.8), suggesting that RF and ANN displayed superior predictive performance for GI. The SHapley additive exPlanations value result indicated that composting time, temperature, and pH were the important features contributing to GI. Composting time was found to have the most significant impact on GI. Overall, RF and ANN were suggested as effective tools for predicting GI in composting. This study offers the reliable approach of accurately predicting GI in composting processes, thereby enabling intelligent composting practices.

Association between amino acids and recent osteoporotic fracture: a matched incident case-control study.

Context: Osteoporotic fracture is a major public health issue globally. Human research on the association between amino acids (AAs) and fracture is still lacking. Objective: To examine the association between AAs and recent osteoporotic fractures. Methods: This age and sex matched incident case-control study identified 44 recent x-ray confirmed fracture cases in the Second Hospital of Jilin University and 88 community-based healthy controls aged 50+ years. Plasma AAs were measured by high performance liquid chromatography coupled with mass spectrometry. After adjusting for covariates (i.e., body mass index, milk intake >1 time/week, falls and physical activity), we conducted conditional logistical regression models to test the association between AAs and fracture. Results: Among cases there were 23 (52.3%) hip fractures and 21 (47.7%) non-hip fractures. Total, essential, and non-essential AAs were significantly lower in cases than in controls. In the multivariable conditional logistic regression models, after adjusting for covariates, each standard deviation increase in the total (odds ratio [OR]: 0.304; 95% confidence interval [CI]: 0.117-0.794), essential (OR: 0.408; 95% CI: 0.181-0.923) and non-essential AAs (OR: 0.290; 95%CI: 0.107-0.782) was negatively associated with recent fracture. These inverse associations were mainly found for hip fracture, rather than non-hip fractures. Among these AAs, lysine, alanine, arginine, glutamine, histidine and piperamide showed the significantly negative associations with fracture. Conclusion: There was a negative relationship between AAs and recent osteoporotic fracture; such relationship appeared to be more obvious for hip fracture.

Range-Intensity-Profile-Guided Gated Light Ranging and Imaging Based on a Convolutional Neural Network.

Three-dimensional (3D) range-gated imaging can obtain high spatial resolution intensity images as well as pixel-wise depth information. Several algorithms have been developed to recover depth from gated images such as the range-intensity correlation algorithm and deep-learning-based algorithm. The traditional range-intensity correlation algorithm requires specific range-intensity profiles, which are hard to generate, while the existing deep-learning-based algorithm requires large number of real-scene training data. In this work, we propose a method of range-intensity-profile-guided gated light ranging and imaging to recover depth from gated images based on a convolutional neural network. In this method, the range-intensity profile (RIP) of a given gated light ranging and imaging system is obtained to generate synthetic training data from Grand Theft Auto V for our range-intensity ratio and semantic network (RIRS-net). The RIRS-net is mainly trained on synthetic data and fine-tuned with RIP data. The network learns both semantic depth cues and range-intensity depth cues in the synthetic data, and learns accurate range-intensity depth cues in the RIP data. In the evaluation experiments on both a real-scene and synthetic test dataset, our method shows a better result compared to other algorithms.

Trends and prescribing patterns of antimigraine medicines in nine major cities in China from 2018 to 2022: a retrospective prescription analysis.

BACKGROUND: The objective of this study was to investigate the trends and prescribing patterns of antimigraine medicines in China. METHODS: The prescription data of outpatients diagnosed with migraine between 2018 and 2022 were extracted from the Hospital Prescription Analysis Cooperative Project of China. The demographic characteristics of migraine patients, prescription trends, and corresponding expenditures on antimigraine medicines were analyzed. We also investigated prescribing patterns of combination therapy and medicine overuse. RESULTS: A total of 32,246 outpatients who were diagnosed with migraine at 103 hospitals were included in this study. There were no significant trend changes in total outpatient visits, migraine prescriptions, or corresponding expenditures during the study period. Of the patients who were prescribed therapeutic medicines, 70.23% received analgesics, and 26.41% received migraine-specific agents. Nonsteroidal anti-inflammatory drugs (NSAIDs; 28.03%), caffeine-containing agents (22.15%), and opioids (16.00%) were the most commonly prescribed analgesics, with corresponding cost proportions of 11.35%, 4.08%, and 19.61%, respectively. Oral triptans (26.12%) were the most commonly prescribed migraine-specific agents and accounted for 62.21% of the total therapeutic expenditures. The proportion of patients receiving analgesic prescriptions increased from 65.25% in 2018 to 75.68% in 2022, and the proportion of patients receiving concomitant triptans decreased from 29.54% in 2018 to 21.55% in 2022 (both P <  0.001). The most frequently prescribed preventive medication classes were calcium channel blockers (CCBs; 51.59%), followed by antidepressants (20.59%) and anticonvulsants (15.82%), which accounted for 21.90%, 34.18%, and 24.15%, respectively, of the total preventive expenditures. Flunarizine (51.41%) was the most commonly prescribed preventive drug. Flupentixol/melitracen (7.53%) was the most commonly prescribed antidepressant. The most commonly prescribed anticonvulsant was topiramate (9.33%), which increased from 6.26% to 12.75% (both P <  0.001). A total of 3.88% of the patients received combined therapy for acute migraine treatment, and 18.63% received combined therapy for prevention. The prescriptions for 69.21% of opioids, 38.53% of caffeine-containing agents, 26.61% of NSAIDs, 13.97% of acetaminophen, and 6.03% of triptans were considered written medicine overuse. CONCLUSIONS: Migraine treatment gradually converges toward evidence-based and guideline-recommended treatment. Attention should be given to opioid prescribing, weak evidence-based antidepressant use, and medication overuse in migraine treatment.

A Global Seawater Density Distribution Model Using a Convolutional Neural Network.

Seawater density is an important physical property in oceanography that affects the accuracy of calculations such as gravity fields and tidal potentials and the calibration of acoustic and optical oceanographic sensors. In related studies, constant density values are frequently used, which can introduce significant errors. Therefore, this study employs a basic convolutional neural network model to construct a comprehensive model showing the seawater density distribution across the globe. The model takes into account depth, latitude, longitude, and month as inputs. Numerous real seawater datasets were used to train the model, and it has been shown that the model has an absolute mean error and root mean square error of less than 1 kg/m3 in 99% of the test set samples. The model effectively demonstrates the influence of input parameters on the distribution of seawater density. In this paper, we present a newly developed global model for distributing seawater density which is both comprehensive and accurate, surpassing previous models. The utilization of the model presented in this paper for estimating seawater density can minimize errors in theoretical ocean models and serve as a foundation for designing and analyzing ocean exploration systems.

A novel MARV glycoprotein-specific antibody with potentials of broad-spectrum neutralization to filovirus.

Marburg virus (MARV) is one of the filovirus species that cause deadly hemorrhagic fever in humans, with mortality rates up to 90%. Neutralizing antibodies represent ideal candidates to prevent or treat virus disease. However, no antibody has been approved for MARV treatment to date. In this study, we identified a novel human antibody named AF-03 that targeted MARV glycoprotein (GP). AF-03 possessed a high binding affinity to MARV GP and showed neutralizing and protective activities against the pseudotyped MARV in vitro and in vivo. Epitope identification, including molecular docking and experiment-based analysis of mutated species, revealed that AF-03 recognized the Niemann-Pick C1 (NPC1) binding domain within GP1. Interestingly, we found the neutralizing activity of AF-03 to pseudotyped Ebola viruses (EBOV, SUDV, and BDBV) harboring cleaved GP instead of full-length GP. Furthermore, NPC2-fused AF-03 exhibited neutralizing activity to several filovirus species and EBOV mutants via binding to CI-MPR. In conclusion, this work demonstrates that AF-03 represents a promising therapeutic cargo for filovirus-caused disease.

The security of IOT from the perspective of the observability of complex networks.

The Internet of Things (IOT) is based on the computer Internet, using RFID, wireless data communication and other technologies to construct a network covering everything in the world. It contains numerous entities such as sensors, processors, transmitters and actuators, meanwhile the interactions of which are complicated. These characteristics of IOT are consistent with those of the complex network. Motivated by this, this paper comprehends the security issue of IOT from the sight of the observability of complex network and regards the ability of reconstruction as a security threat to IOT network. We try to identify the minimum vertices whose data could reconstruct the whole data of network, in other words, we need to implement additional protective measures on these vertices to enhance the security of IOT network. By analyzing the topology of IOT network, an identification strategy is adopted and the corresponding algorithm is proposed to identify the minimum protection vertices.

Genome variation and LTR-RT analyses of an ancient peach landrace reveal mechanism of blood-flesh fruit color formation and fruit maturity date advancement.

Peach (Prunus persica) landrace has typical regional characteristics, strong environmental adaptability, and contains many valuable genes that provide the foundation for breeding excellent varieties. Therefore, it is necessary to assemble the genomes of specific landraces to facilitate the localization and utilization of these genes. Here, we de novo assembled a high-quality genome from an ancient blood-fleshed Chinese landrace Tianjin ShuiMi (TJSM) that originated from the China North Plain. The assembled genome size was 243.5 Mb with a contig N50 of 23.7 Mb and a scaffold N50 of 28.6 Mb. Compared with the reported peach genomes, our assembled TJSM genome had the largest number of specific structural variants (SVs) and long terminal repeat-retrotransposons (LTR-RTs). Among the LTR-RTs with the potential to regulate their host genes, we identified a 6688 bp LTR-RT (named it blood TE) in the promoter of NAC transcription factor-encoding PpBL, a gene regulating peach blood-flesh formation. The blood TE was not only co-separated with the blood-flesh phenotype but also associated with fruit maturity date advancement and different intensities of blood-flesh color formation. Our findings provide new insights into the mechanism underlying the development of the blood-flesh color and determination of fruit maturity date and highlight the potential of the TJSM genome to mine more variations related to agronomic traits in peach fruit.

Understanding resistant-starch formation during drying high-amylose maize kernels.

Interests in using high-amylose maize (HAM) flour and starch for low glycemic index foods continue to grow. The objective of this work was to understand resistant-starch formation during drying the HAM kernels. Freshly harvested HAM kernels with 28.2 % initial moisture were subjected to sun drying (~30 °C) or hot-air drying at 50 °C, 70 °C, 90 °C, or 110 °C. The enzymatic digestibility of HAM flour decreased from 63.6 % to 41.1 % as the drying temperature increased from 30 °C to 110 °C. The swelling power, solubility, and overall viscosity of HAM flours milled from kernels dried at 110 °C decreased, whereas the peak and conclusion gelatinization temperatures, enthalpy change, and relative crystallinity increased compared to those of flours from kernels dried at 30 °C, 50 °C, 70 °C, and 90 °C. Light microscopic and scanning electron microscopic images showed that starch granule aggregation in HAM flour increased with increasing drying-temperatures. The aggregates remained after 16 h enzymatic hydrolysis of cooked HAM flours. These results suggested that the increase of enzymatic resistance of HAM flour resulted from the formation of high temperature-resistant ordered structures in starch granules and the starch aggregates less accessible to enzymatic hydrolysis.

Superconductivity of cubic MB6 (M = Na, K, Rb, Cs).

Previous studies have shown that NaB6, KB6, and RbB6 adopting Pm3̄m are superconductors with a relatively high Tc under ambient conditions. In this paper, we conducted systematic structural and related properties research on CsB6 through a genetic evolution algorithm and total energy calculations based on density functional theory between 0 and 20 GPa. Our results reveal a cubic Pm3̄m CsB6, which is dynamically stable under the pressures we studied. We systematically calculated the formation enthalpies, electronic properties, and superconducting properties of Pm3̄m MB6 (M = Na, K, Rb, Cs). They all exhibit metallic features, and boron has high contributions to band structures, density of states, and electron-phonon coupling (EPC). The calculated results about the Helmholtz free energy difference of Pm3̄m CsB6 at 0, 10, and 20 GPa indicate that it is stable upon chemical decomposition (decomposition to simple substances Cs and B) from 0 to 400 K. The phonon density of states indicates that boron atoms occupy the high frequency area. The EPC results show that Pm3̄m CsB6 is a superconductor with Tc = 11.7 K at 0 GPa, close to NaB6 (13.1 K), KB6 (11.7 K), and RbB6 (11.3 K) at 0 GPa in our work, which indicates that boron atoms play an essential role in superconductivity: vibrations of B6 regular octagons lead to the high Tc of Pm3̄m MB6. Our work about Pm3̄m hexaborides provides a supplementary study on the borides of the group IA elements (without Fr and Li) and has an important guiding significance for the experimental synthesis of CsB6.

Unraveling EGFR-TKI resistance in lung cancer with high PD-L1 or TMB in EGFR-sensitive mutations.

BACKGROUND: Although EGFR-TKI resistance mechanisms in non-small cell lung cancer (NSCLC) have been extensively studied, certain patient subgroups remain with unclear mechanisms. This retrospective study analysed mutation data of NSCLC patients with EGFR-sensitive mutations and high programmed death-ligand 1 (PD-L1) expression or high TMB to identify primary resistance mechanisms. METHODS: Hybrid capture-based next-generation sequencing (NGS) was used to analyse mutations in 639 genes in tumor tissues and blood samples from 339 NSCLC patients. PD-L1 immunohistochemical staining was also performed on the same cell blocks. Molecular and pathway profiles were compared among patient subgroups. RESULTS: TMB was significantly higher in lung cancer patients with EGFR-sensitive mutations and high PD-L1 expression. Compared with the high-expression PD-L1 or high TMB and low-expression or TMB groups, the top 10 genes exhibited differences in both gene types and mutation rates. Pathway analysis revealed a significant mutations of the PI3K signaling pathway in the EGFR-sensitive mutation group with high PD-L1 expression (38% versus 12%, p < 0.001) and high TMB group (31% versus 13%, p < 0.05). Notably, PIK3CA and PTEN mutations emerged as the most important differentially mutated genes within the PI3K signaling pathway. CONCLUSIONS: Our findings reveal that the presence of PI3K signaling pathway mutations may be responsible for inducing primary resistance to EGFR-TKIs in NSCLC patients with EGFR-sensitive mutations along with high PD-L1 expression or high TMB. This finding is of great significance in guiding subsequent precision treatments in NSCLC.

Ultraflexible Monolithic Three-Dimensional Static Random Access Memory.

Flexible static random access memory (SRAM) plays an important role in flexible electronics and systems. However, achieving SRAM with a small footprint, high flexibility, and high thermal stability has always been a big challenge. In this work, an ultraflexible six-transistor SRAM with high integration density is realized based on a monolithic three-dimensional (M3D) design. In this design, vertical stacked n-type indium gallium zinc oxide thin film transistors and p-type carbon nanotube transistors share common gate and drain electrodes, respectively, saving interlayer vias used in traditional M3D designs. This compact architecture reduces the footprint of the SRAM cell from a six-transistor to a four-transistor area, saving 33% of the area, and significantly enables the SRAM to have the highest flexibility among the reported ones, withstanding a harsh deforming process (6000 cycles of bending at a radius of 500 µm) without performance degradation. Moreover, this design facilitates the thermal stability of the SRAM under high temperature (333 K). It also exhibits great static and dynamic performance, with the highest normalized hold noise margin of 73.6%, a maximum gain of 151.2, and a minimum static power consumption of 3.15 µW in hold operation among the reported flexible SRAMs. This demonstration provides possibilities for SRAMs to be used in advanced wearable system applications.

Functional characterization of AF-04, an afucosylated anti-MARV GP antibody.

Marburg virus (MARV), one member of the Filoviridae family, cause sporadic outbreaks of hemorrhagic fever with high mortality rates. No countermeasures are currently available for the prevention or treatment of MARV infection. Monoclonal antibodies (mAbs) are promising candidates to display high neutralizing activity against MARV infection in vitro and in vivo. Recently, growing evidence has shown that immune effector function including antibody-dependent cell-mediated cytotoxicity (ADCC) is also required for in vivo efficacy of a panel of antibodies. Glyco-engineered methods are widely utilized to augment ADCC function of mAbs. In this study, we generated a fucose-knockout MARV GP-specific mAb named AF-04 and showed that afucosylation dramatically increased its binding affinity to polymorphic FcγRIIIa (F176/V176) compared with the parental AF-03. Accordingly, AF-04-mediated NK cell activation and NFAT expression downstream of FcγRIIIa in effector cells were also augmented. In conclusion, this work demonstrates that AF-04 represents a novel avenue for the treatment of MARV-caused disease.

De-Pinning Fermi Level and Accelerating Surface Kinetics with an ALD Finish Boost the Fill Factor of BiVO4 Photoanodes to 44.

With the rapid development of performance and long-term stability, bismuth vanadate (BiVO4 ) has emerged as the preferred photoanode in photoelectrochemical tandem devices. Although state-of-the-art BiVO4 photoanodes realize a saturated photocurrent density approaching the theoretical maximum, the fill factor (FF) is still inferior, pulling down the half-cell applied bias photon-to-current efficiency (HC-ABPE). Among the major fundamental limitations are the Fermi level pinning and sluggish surface kinetics at the low applied potentials. This work demonstrates that the plasma-assisted atomic layer deposition technique is capable of addressing these issues by seamlessly installing an angstrom-scale FeNi-layer between BiVO4 and electrolyte. Not only this ultrathin FeNi layer serves as an efficient OER cocatalyst, more importantly, it also effectively passivates the surface states of BiVO4 , de-pins the surface Fermi level, and enlarges the built-in voltage, allowing the photoanode to make optimal use of the photogenerated holes for achieving high FF up to 44% and HC-ABPE to 2.2%. This study offers a new approach for enhancing the FF of photoanodes and provides guidelines for designing efficient unassisted solar fuel devices.

Bacteria-derived nanovesicles enhance tumour vaccination by trained immunity.

Trained immunity enhances the responsiveness of immune cells to subsequent infections or vaccinations. Here we demonstrate that pre-vaccination with bacteria-derived outer-membrane vesicles, which contain large amounts of pathogen-associated molecular patterns, can be used to potentiate, and enhance, tumour vaccination by trained immunity. Intraperitoneal administration of these outer-membrane vesicles to mice activates inflammasome signalling pathways and induces interleukin-1ß secretion. The elevated interleukin-1ß increases the generation of antigen-presenting cell progenitors. This results in increased immune response when tumour antigens are delivered, and increases tumour-antigen-specific T-cell activation. This trained immunity increased protection from tumour challenge in two distinct cancer models.

Prompt Hole Extraction Suppresses V5+ Dissolution and Sustains Large-Area BiVO4 Photoanodes for Over 2100 h Water Oxidation.

Nanostructured bismuth vanadate (BiVO4) is at the forefront of emerging photoanodes in photoelectrochemical tandem devices for solar water splitting owing to the suitable band edge position and efficient charge separation capability. However, the (photo)chemical corrosion involving V5+ dissolution limits the long-term stability of BiVO4. Herein, guided by DFT calculations, we introduce an ALD-derived NiOx catalyst layer on BiVO4 to stabilize the surface Bi-O bonds, facilitate hole extraction, and thus suppress the V5+ dissolution. At the same time, the ALD NiOx catalyst layer could efficiently suppress the surface recombination and accelerate the surface OER kinetics, boosting the half-cell applied bias photon-to-current efficiency of BiVO4 to 2.05%, as well as a fill factor of 47.1%. By adding trace NaVO3 to the electrolyte, the NiOx/BiVO4 photoanode with an illumination area of 10.5 cm2 shows a record operational stability of more than 2100 h.

Investigation of Acid Tolerance Mechanism of Acetobacter pasteurianus under Different Concentrations of Substrate Acetic Acid Based on 4D Label-Free Proteomic Analysis.

Acetobacter pasteurianus is always used to brew vinegar because of its ability of producing and tolerating a high concentration of acetic acid. During vinegar fermentation, initial acetic acid contributes to acetic acid accumulation, which varies with initial concentrations. In this study, to investigate the mechanisms of tolerating and producing acetic acid of Acetobacter pasteurianus under different concentrations of substrate acetic acid, four-dimensional label-free proteomic technology has been used to analyze the protein profiles of Acetobacter pasteurianus at different growth stages (the lag and exponential phases) and different substrate acetic acid concentrations (0%, 3%, and 6%). A total of 2093 proteins were quantified in this study. The differentially expressed proteins were majorly involved in gene ontology terms of metabolic processes, cellular metabolic processes, and substance binding. Under acetic acid stress, strains might attenuate the toxicity of acetic acid by intensifying fatty acid metabolism, weakening the tricarboxylic acid cycle, glycerophospholipid and energy metabolism during the lag phase, while strains might promote the assimilation of acetic acid and inter-conversion of substances during the exponential phase by enhancing the tricarboxylic acid cycle, glycolysis, pyruvate, and energy metabolism to produce and tolerate acid. Besides, cell cycle regulation and protein translation might be potential acid tolerance pathways under high acid stress. The result contributes to the exploration of new potential acid tolerance mechanisms in Acetobacter pasteurianus from four-dimensional label-free relative quantitative proteomics analysis.

Quantitatively mapping local quality of super-resolution microscopy by rolling Fourier ring correlation.

In fluorescence microscopy, computational algorithms have been developed to suppress noise, enhance contrast, and even enable super-resolution (SR). However, the local quality of the images may vary on multiple scales, and these differences can lead to misconceptions. Current mapping methods fail to finely estimate the local quality, challenging to associate the SR scale content. Here, we develop a rolling Fourier ring correlation (rFRC) method to evaluate the reconstruction uncertainties down to SR scale. To visually pinpoint regions with low reliability, a filtered rFRC is combined with a modified resolution-scaled error map (RSM), offering a comprehensive and concise map for further examination. We demonstrate their performances on various SR imaging modalities, and the resulting quantitative maps enable better SR images integrated from different reconstructions. Overall, we expect that our framework can become a routinely used tool for biologists in assessing their image datasets in general and inspire further advances in the rapidly developing field of computational imaging.