Efficient Catalytic Degradation of Methyl Orange by Various ZnO-Doped Lignin-Based Carbons.

Herein, a series of ZnO-doped lignin-based carbons (LC/ZnO) were successfully prepared from different types of lignin and used for methyl orange (MO) photocatalytic degradation. The apparent morphology, internal structure, and photoelectric properties of prepared LC/ZnO composites and their effects on subsequent MO photocatalytic degradation were investigated by various characterization techniques. The results showed that the LC/ZnO composites that were prepared in this work mainly consisted of highly dispersed ZnO nanoparticles and lignin-based carbon nano-sheets, which were beneficial for subsequent photogenerated electrons and holes formation, dispersion, and migration. The MO could be significantly degraded with various ZnO-doped lignin-based carbons, especially over the LCSL/ZnO, and the maximum degradation rate was 96.9% within 30 min under the simulated 300w sunlight exposure. The experiments of free radical elimination showed that the photocatalytic degradation of MO over LC/ZnO were a result of the co-action of multiple free radicals, and h+ might play the predominant roles in MO degradation. In addition, the pH of the solution had little effect on MO degradation, and the MO could be effectively degraded even in an alkaline solution of pH = 12.0. The cycling experiments showed that the prepared LC/ZnO had a good stability for MO photodegradation, especially for LCSL/ZnO, even after 5 times recycling, and the degradation rate of MO only dropped from 97.0% to 93.0%. The research not only provided a fundamental theory for the efficient photocatalytic degradation of MO by LC/ZnO composites, but also offered a new insight into lignin valorization.

Lower limit characterization of segregation degree for large-size low-alloy steel.

Based on the original position statistic distribution analysis technique, the characterization method of segregation for large-size metal materials gives significant guidance to the research of material properties and production. However, random errors are inevitably brought into the calculation of segregation degree for materials characterization by the Spark Mapping Analysis for Large Sample (SMALS) technique, resulting in a misguide of the segregation degree. In this paper, we present the lower limit of segregation degree (Ds(L)) method to distinguish the random error from metal material segregation for large-size samples over the SMALS method. The random error of standard material in the 95% confidence interval was utilized as Ds(L) and the method has been applied for macro-segregation quantitative analysis. The precision correlation between Spark Atomic Emission Spectrometry (Spark-AES) and SMALS was established. Furthermore, the functional relationship between the Ds(L) and element content C can be obtained in the SMALS method. The Ds(L) method as the criterion can be used to not only characterize the minimum limit of the segregation degree but also the segregation existence for large-size samples. Applying to low-alloy steel can illustrate the effective performance of the Ds(L) method. Results on both spark mapping analysis and Spark-AES verify the substantial consistency.

Explainable survival analysis with uncertainty using convolution-involved vision transformer.

Image-based precision medicine research is able to help doctors make better decisions on treatments. Among all kinds of medical images, a special form is called Whole Slide Image (WSI), which is used for diagnosing patients with cancer, aiming to enable more accurate survival prediction with its high resolution. However, One unique challenge of the WSI-based prediction models is processing the gigabyte-size or even terabyte-size WSIs, which would make most models computationally infeasible. Although existing models mostly use a pre-selected subset of key patches or patch clusters as input, they might discard some important morphology information, making the prediction inferior. Another challenge is improving the prediction models' explainability, which is crucial to help doctors understand the predictions given by the models and make faithful decisions with high confidence. To address the above two challenges, in this work, we propose a novel explainable survival prediction model based on Vision Transformer. Specifically, we adopt dual-channel convolutional layers to utilize the complete WSIs for more accurate predictions. We also introduce the aleatoric uncertainty into our model to understand its limitation and avoid overconfidence in using the prediction results. Additionally, we present a post-hoc explainable method to identify the most salient patches and distinct morphology features as supporting evidence for predictions. Evaluations of two large cancer datasets show that our proposed model is able to make survival predictions more effectively and has better explainability for cancer diagnosis.

The Overview of Cognitive Aging Models.

To understand the cause of the age-related decline in cognitive function and its underlying mechanism, the cognitive aging model can provide us with important insights. In this section, we will introduce behavioral and neural models about age-related cognitive changes. Among behavioral models, several aging theories were discussed from the perspectives of educational, biological, and sociological factors, which could explain parts of the aging process. With the development of imaging technology, many studies have discussed the neural mechanism of aging and successively proposed neural models to explain the aging phenomenon. Behavioral models and neural mechanism models supplement each other, gradually unveiling the mystery of cognitive aging.

A novel stable nitritation process: Treating sludge by alternating free nitrous acid/heat shock.

The feasibility of stable nitritation of sludge alternately treated by free nitrous acid (FNA) and heat shock in a sequencing batch reactor (SBR) was investigated in this study. The linear regression method was used to determine the optimal treatment conditions. Results revealed that an FNA concentration of 2.20 mg HNO2-N/L, exposure time of 24 h, and treatment ratio of 20% could inhibit nitrite oxidizing bacteria (NOB) activity to the greatest extent while maintaining the maximum ammonium oxidizing bacteria (AOB) activity; after heat shock at 60 °C for 20 min, NOB were inhibited while AOB still had certain activity. In the long-term continuous-flow experiment, the single FNA or heat shock treatments easily allowed adapt NOB to affect the stability of nitritation. The alternating FNA/heat shock treatment can achieve long-term stability of nitritation. Microbial community analysis revealed that the alternating FNA/heat shock treatment could inhibit NOB while maintaining high AOB abundance.

Effects of interactions between quorum sensing and quorum quenching on microbial aggregation characteristics in wastewater treatment: A review.

Due to the increasingly urgent demand for effective wastewater denitrification and dephosphorization systems, there is a need to improve the performance of existing biological treatment technologies. As a bacteria-level communication mechanism, quorum sensing (QS) synchronizes gene expression in a density-dependent manner and regulates bacterial physiological behavior. On this basis, the QS-based bacterial communication mechanism and environmental factors affecting QS are discussed. This paper reviews the influence of QS on sludge granulation, biofilm formation, emerging contaminants (ECs) removal, and horizontal gene transfer in sewage treatment system. Furthermore, the QS inhibition strategies are compared. Based on the coexistence and balance of QQ and QS in the long-term operation system, QQ, as an effective tool to regulate the growth density of microorganisms, provides a promising exogenous regulation strategy for residual sludge reduction and biofilm pollution control. This paper reviews the potential of improving wastewater treatment efficiency based on QS theory and points out the feasibility and prospect of exogenous regulation strategy. PRACTITIONER POINTS: The mechanism of bacterial communication based on QS and the environmental factors affecting QS were discussed. The application of QS and QQ in improving the sludge performance of biological treatment systems was described. The significance of QS and QQ coexistence in sewage treatment process was described.

Comparing the effects of dexmedetomidine and dexamethasone as perineural adjuvants on peripheral nerve block: A PRISMA-compliant systematic review and meta-analysis.

BACKGROUND: Dexmedetomidine (Dexm), a selective alpha-2 adrenoceptor agonist, and dexamethasone (Dexa), a very potent and highly selective glucocorticoid, have both been proven effectively to prolong the duration of local anesthetics (LA) in regional anesthesia. However, data comparing the efficacy of Dexm and Dexa as perineural adjuvants are inconsistent. Therefore, this systematic review and meta-analysis of randomized and quasi-randomized controlled trials (RCTs) was conducted to compare the effects of Dexm and Dexa when used as LA adjuvants on peripheral nerve block (PNB). METHODS: We systematically searched PubMed, Cochrane Library, EMBASE, Web of Science, and ScienceDirect databases up to October, 2020. The primary outcome was the duration of analgesia. Secondary outcomes included incidence of rescue analgesia, cumulative opioid consumption, time required for onset of sensory and motor blockades, duration of sensory and motor blockades, incidence of postoperative nausea and vomiting (PONV), and side effect-associated outcomes (e.g., bradycardia, sedation, hypotension, rates of infection, and neurological complications). The study was registered on PROSPERO, number CRD42020188796. RESULTS: After screening of full-text relevant articles, 13 RCTs that met the inclusion criteria were retrieved for this systematic review. It was revealed that perineural Dexm provided equivalent analgesic duration to perineural Dexa. Besides, the intake of Dexm increased the incidence of rescue analgesia in limbs surgery, as well as the cumulative opioid consumption, and decreased the time required for onset of sensory and motor blockades for long-acting LA (all P < .05). Other analysis revealed insignificant difference between the 2 groups in terms of the incidence of PONV (P > .05). Additionally, 2 studies demonstrated that Dexm possesses more sedative properties than Dexa (P < .05). CONCLUSIONS: This meta-analysis indicated that the analgesic duration of Dexm and Dexa as LA adjuvants in PNB is the same. Meanwhile, the effects of perineural Dexm and Dexa on some secondary outcomes, including the incidence of rescue analgesia, cumulative opioid consumption, and time required for onset of sensory and motor blockades, are associated with the surgical site and type of LA.

[An algorithm of corneal reconstruction based on precise location of corneal center].

Placido disk is widely used in corneal topography. In order to solve the problem that the convex of the corneal can not be precisely located in the Placido corneal topography system, an algorithm of corneal reconstruction based on the Placido disk was introduced. The key of this method is the calculation of radius of corneal convex by using the innermost ring data. Based on image analysis result, we precisely calculated the radius of corneal convex iteratively by connecting the convex and the first ring using a circle, and then calculated the location of all the reflect point and its power. At last we created the pseudo color map of the human corneal. The corneal was simulated by using standard steel sphere, and the calculating errors of the result were all below 0.25D. It showed that the algorithm used in this work could get relatively accurate powers and would have fair stability.