CoHIT: a one-pot ultrasensitive ERA-CRISPR system for detecting multiple same-site indels.

Genetic testing is crucial for precision cancer medicine. However, detecting multiple same-site insertions or deletions (indels) is challenging. Here, we introduce CoHIT (Cas12a-based One-for-all High-speed Isothermal Test), a one-pot CRISPR-based assay for indel detection. Leveraging an engineered AsCas12a protein variant with high mismatch tolerance and broad PAM scope, CoHIT can use a single crRNA to detect multiple NPM1 gene c.863_864 4-bp insertions in acute myeloid leukemia (AML). After optimizing multiple parameters, CoHIT achieves a detection limit of 0.01% and rapid results within 30 minutes, without wild-type cross-reactivity. It successfully identifies NPM1 mutations in 30 out of 108 AML patients and demonstrates potential in monitoring minimal residual disease (MRD) through continuous sample analysis from three patients. The CoHIT method is also competent for detecting indels of KIT, BRAF, and EGFR genes. Integration with lateral flow test strips and microfluidic chips highlights CoHIT's adaptability and multiplexing capability, promising significant advancements in clinical cancer diagnostics.

Potential for Recycling Metakaolin/Slag-Based Geopolymer Concrete of Various Strength Levels in Freeze-Thaw Conditions.

Geopolymer concrete (GPC) represents an innovative green and low-carbon construction material, offering a viable alternative to ordinary Portland cement concrete (OPC) in building applications. However, existing studies tend to overlook the recyclability aspect of GPC for future use. Various structural applications necessitate the use of concrete with distinct strength characteristics. The recyclability of the parent concrete is influenced by these varying strengths. This study examined the recycling potential of GPC across a spectrum of strength grades (40, 60, 80, and 100 MPa, marked as C40, C60, C80, and C100) when subjected to freeze-thaw conditions. Recycling 5-16 mm recycled geopolymer coarse aggregate (RGAs) from GPC prepared from 5 to 16 mm natural coarse aggregates (NAs). The cementitious material comprised 60% metakaolin and 40% slag, with natural gravel serving as the NAs, and the alkali activator consisting of sodium hydroxide solution and sodium silicate solution. The strength of the GPC was modulated by altering the Na/Al ratio. After 350 freeze-thaw cycles, the GPC specimens underwent crushing, washing, and sieving to produce RGAs. Subsequently, their physical properties (apparent density, water absorption, crushing index, and attached mortar content and microstructure (microhardness, SEM, and XRD) were thoroughly examined. The findings indicated that GPC with strength grades of C100, C80, and C60 were capable of enduring 350 freeze-thaw cycles, in contrast to C40, which did not withstand these conditions. RGAs derived from GPC of strength grades C100 and C80 complied with the criteria for Class II recycled aggregates, whereas RGAs produced from GPC of strength grade C60 aligned with the Class III level. A higher-strength grade in the parent concrete correlated with enhanced performance characteristics in the resulting recycled aggregates.

Simultaneous removal of calcium, phosphorus, and bisphenol A from industrial wastewater by Stutzerimonas sp. ZW5 via microbially induced calcium precipitation (MICP): Kinetics, mechanism, and stress response.

The biological treatment of complex industrial wastewater has always been a research hotspot. In this experiment, a salt-tolerant strain Stutzerimonas sp. ZW5 with aerobic denitrification and biomineralization ability was screened, and the optimum conditions of ZW5 were explored by kinetics. The removal efficiencies of nitrate (NO3--N), bisphenol A (BPA), phosphorus (PO43--P), and calcium (Ca2+) were 94.47 %, 100 %, 98.87 %, and 83.04 %, respectively. The removal mechanism of BPA was the adsorption of microbial induced calcium precipitation (MICP) and extracellular polymeric substances (EPS). Moreover, BPA could weaken the electron transfer ability and growth metabolism of microorganisms and affect the structure of biominerals. At the same time, the stress response of microorganisms would increase the secretion of EPS to promote the process of biomineralization. Through nitrogen balance experiments, it was found that the addition of BPA would lead to a decrease in the proportion of gaseous nitrogen. This experiment offers novel perspectives on the treatment of industrial effluents and microbial stress response.

Differentiating Microplastics from Natural Particles in Aqueous Suspensions Using Flow Cytometry with Machine Learning.

Microplastics (MPs) in natural waters are heterogeneously mixed with other natural particles including algal cells and suspended sediments. An easy-to-use and rapid method for directly measuring and distinguishing MPs from other naturally present colloids in the environment would expedite analytical workflows. Here, we established a database of MP scattering and fluorescence properties, either alone or in mixtures with natural particles, by stain-free flow cytometry. The resulting high-dimensional data were analyzed using machine learning approaches, either unsupervised (e.g., viSNE) or supervised (e.g., random forest algorithms). We assessed our approach in identifying and quantifying model MPs of diverse sizes, morphologies, and polymer compositions in various suspensions including phototrophic microorganisms, suspended biofilms, mineral particles, and sediment. We could precisely quantify MPs in microbial phototrophs and natural sediments with high organic carbon by both machine learning models (identification accuracies over 93%), although it was not possible to distinguish between different MP sizes or polymer compositions. By testing the resulting method in environmental samples through spiking MPs into freshwater samples, we further highlight the applicability of the method to be used as a rapid screening tool for MPs. Collectively, this workflow can be easily applied to a diverse set of samples to assess the presence of MPs in a time-efficient manner.

Enhancing the Mechanical and Durability Properties of Fully Recycled Aggregate Concrete Using Carbonated Recycled Fine Aggregates.

The global construction industry is increasingly utilizing concrete prepared from recycled aggregate as a substitute for natural aggregate. However, the subpar performance of recycled fine aggregate (RFA) has resulted in its underutilization, particularly in the structural concrete exposed to challenging environments, including those involving chlorine salts and freeze-thaw climates. This study aimed to enhance the performance of RFA as a substitute for river sand in concrete as well as fulfill the present demand for fine aggregates in the construction sector by utilizing accelerated carbonation treatment to create fully recycled aggregate concrete (FRAC) composed of 100% recycled coarse and fine aggregates. The impacts of incorporating carbonated recycled fine aggregate (C-RFA) at various replacement rates (0%, 25%, 50%, 75%, and 100%) on the mechanical and durability properties of FRAC were investigated. The results showed that the physical properties of C-RFA, including apparent density, water absorption, and crushing value, were enhanced compared to that of RFA. The compressive strength of C-RFC100 was 19.8% higher than that of C-RFC0, while the water absorption decreased by 14.6%. In a comparison of C-RFC0 and C-RFC100, the chloride permeability coefficients showed a 50% decrease, and the frost resistance increased by 27.6%. According to the findings, the mechanical and durability properties, the interfacial transition zones (ITZs), and micro-cracks of the C-RFC were considerably enhanced with an increased C-RFA content.

Learning to Guide Particle Search for Dynamic Multiobjective Optimization.

Dynamic multiobjective optimization problems (DMOPs) are characterized by multiple objectives that change over time in varying environments. More specifically, environmental changes can be described as various dynamics. However, it is difficult for existing dynamic multiobjective algorithms (DMOAs) to handle DMOPs due to their inability to learn in different environments to guide the search. Besides, solving DMOPs is typically an online task, requiring low computational cost of a DMOA. To address the above challenges, we propose a particle search guidance network (PSGN), capable of directing individuals' search actions, including learning target selection and acceleration coefficient control. PSGN can learn the actions that should be taken in each environment through rewarding or punishing the network by reinforcement learning. Thus, PSGN is capable of tackling DMOPs of various dynamics. Additionally, we efficiently adjust PSGN hidden nodes and update the output weights in an incremental learning way, enabling PSGN to direct particle search at a low computational cost. We compare the proposed PSGN with seven state-of-the-art algorithms, and the excellent performance of PSGN verifies that it can handle DMOPs of various dynamics in a computationally very efficient way.

Characterization and quantification of silver complexes with dissolved organic matter by size exclusion chromatography coupled to ICP-MS.

The fate and behavior of silver in aquatic systems is intricately determined by its interactions with dissolved organic matter (DOM). In this study, we have introduced a method for identification and quantification of silver-DOM complexes using size exclusion chromatography-inductively coupled plasma mass spectrometry (SEC-ICP-MS). Our findings revealed that silver(I) was weakly bound to Suwannee River humic acid, fulvic acid, and natural organic matter (SRHA, SRFA, and SRNOM) in various media, resulting in facile dissociation during chromatographic separation. Suitable chromatographic conditions were determined for the elution of Ag-DOM complexes, involving the use of 0.5 mM ammonium acetate (pH 7) as the mobile phase and silver-aged column (pre-absorbing 0.1-0.7 µg silver(I)). SEC-UV and SEC-ICP-MS chromatograms revealed that Ag-binding fractions of DOM were dominated by its aromatic compounds. The quantification of silver-DOM complexes was achieved by SEC-ICP-MS combination with on-line isotope dilution. Silver at concentrations below 20 µg L-1 was mainly present in the form of organic complexes in low salinity water. These measurements aligned well with the results obtained using the equilibrium dialysis method. Species analyses of Ag-DOM complexes provide a deeper understanding of the reactivity, transport, and fate of silver in aquatic environments. ENVIRONMENTAL IMPLICATION: Ionic silver is highly toxic to aquatic organisms such as fish and zooplankton. The complexation of silver with binding sites within DOM significantly influences its speciation, mobility, and toxicity. Despite the complex and unknown structure of silver-DOM complexes, this study provided a SEC-ICP-MS method to identify and quantify these complexes in a range of media. By uncovering the formation of silver-DOM complexes across diverse media, this work enhances the comprehension of silver transformation processes and associated environmental risks in aquatic environments.

Generation of SARS-CoV-2 spike receptor binding domain mutants and functional screening for immune evaders using a novel lentivirus-based system.

The emergence of rapid and continuous mutations of severe acute respiratory syndrome 2 (SARS-CoV-2) spike glycoprotein that increased with the Omicron variant points out the necessity to anticipate such mutations for conceiving specific and adaptable therapies to avoid another pandemic. The crucial target for the antibody treatment and vaccine design is the receptor binding domain (RBD) of the SARS-CoV-2 spike. It is also the site where the virus has shown its high ability to mutate and consequently escape immune response. We developed a robust and simple method for generating a large number of functional SARS-CoV-2 spike RBD mutants by error-prone PCR and a novel nonreplicative lentivirus-based system. We prepared anti-RBD wild type (WT) polyclonal antibodies and used them to screen and select for mutant libraries that escape inhibition of virion entry into recipient cells expressing human angiotensin-converting enzyme 2 and transmembrane serine protease 2. We isolated, cloned, and sequenced six mutants totally bearing nine mutation sites. Eight mutations were found in successive WT variants, including Omicron and other recombinants, whereas one is novel. These results, together with the detailed functional analyses of two mutants provided the proof of concept for our approach.

Development of an Alternative In Vitro Rumen Fermentation Prediction Model.

The aim of this study is to identify an alternative approach for simulating the in vitro fermentation and quantifying the production of rumen methane and rumen acetic acid during the rumen fermentation process with different total mixed rations. In this experiment, dietary nutrient compositions (neutral detergent fiber (NDF), acid detergent fiber (ADF), crude protein (CP), and dry matter (DM)) were selected as input parameters to establish three prediction models for rumen fermentation parameters (methane and acetic acid): an artificial neural network model, a genetic algorithm-bp model, and a support vector machine model. The research findings show that the three models had similar simulation results that aligned with the measured data trends (R2 ≥ 0.83). Additionally, the root mean square errors (RMSEs) were ≤1.85 mL/g in the rumen methane model and ≤2.248 mmol/L in the rumen acetic acid model. Finally, this study also demonstrates the models' capacity for generalization through an independent verification experiment, as they effectively predicted outcomes even when significant trial factors were manipulated. These results suggest that machine learning-based in vitro rumen models can serve as a valuable tool for quantifying rumen fermentation parameters, guiding the optimization of dietary structures for dairy cows, rapidly screening methane-reducing feed options, and enhancing feeding efficiency.