A systematic comparison of salt removal efficiency in washing treatment by using fly ashes from 13 MSWI plants in China.

Municipal solid waste incineration (MSWI) fly ash contains large amounts of Ca, Si, and other elements, giving it the potential to be used as a raw material for cement production. However, fly ash often contains a high content of salts, which greatly limits its blending ratio during cement production. These salts are commonly removed via water washing, but this process is affected by the nature and characteristics of fly ash. To clarify the influence of the ash characteristics on salt removal, a total of 60 fly ash samples from 13 incineration plants were collected, characterized, and washed. The ash characterization and cluster analysis showed that the incinerator type and flue gas purification technology/process significantly influenced the ash characteristics. Washing removed a high percentage of salts from fly ash, but the removal efficiencies varied significantly from each other, with the chlorine removal efficiency ranging from 73.76% to 96.48%, while the sulfate removal efficiency ranged from 6.92% to 51.47%. Significance analysis further revealed that the salt removal efficiency varied not only between the ash samples from different incinerators, but also between samples collected at different times from the same incinerator. The high variance of the 60 ash samples during salt removal was primarily ascribed to their different mineralogical and chemical characteristics. Mineralogical analysis of the raw and washed ash samples showed that the mineralogical forms and proportion of these salts in each ash sample greatly influenced their removal. The presence of less-soluble and insoluble chloride salts (e.g., CaClOH, Ca2Al(OH)6(H2O)2Cl etc.) in fly ash significantly affected the chlorine removal efficiency. This study also found that Fe, Mn, and Al in fly ash were negatively correlated with the dechlorination efficiency of fly ash. In summary, the different physical and chemical properties of fly ash caused great discrepancies in salt removal. Consequently, it is suggested to consider the potential impact of the ash source and ash generation time on salt removal to ensure a reliable treatment efficiency for engineering applications.

Enhanced removal of metal-cyanide complexes from wastewater by Fe-impregnated biochar: Adsorption performance and removal mechanism.

Metal-cyanide complexes are common contaminants in industrial wastewater. Removal of these refractory contaminants is essential before their discharge into the environment. This study investigated a biochar (BC)-based sorbent material that could be applied for the efficient removal of metal-cyanide complexes from wastewater. In consideration of the strong electrostatic repulsion of the pristine BC toward anions, iron-modified BC (Fe-BC) composites were fabricated by a one-step co-pyrolysis of corn straw and FeCl3 at 600-800 °C. The adsorption performance and corresponding sorption mechanisms of representative metal-cyanide complexes (ferricyanide [Fe(CN)6]3- and tetracyanonickelate [Ni(CN)4]2-) onto the Fe-BC composites were investigated. The results indicated that the Fe-BC composites had significantly high affinity toward the metal-cyanide complexes, reaching a maximum sorption capacity of 580.96 mg/g for [Fe(CN)6]3- and 588.86 mg/g for [Ni (CN)4]2-. A mechanistic study revealed that Fe-impregnation during BC fabrication could effectively alter the negatively charged BC surface, forming more functional groups that could interact with the metal-cyanide complexes. Moreover, the transformation of carbon structure promoted the carbothermal reduction process, leading to the formation of various reductive-Fe minerals in the resulting Fe-BC composites. These modification-induced alterations to the surface and structural characteristics of BC were expected to facilitate the adsorption/precipitation of target contaminants. Different sorption mechanisms were proposed for the two metal-cyanide complexes that were the focus of this study. For [Fe(CN)6]3-, precipitation by Fe-bearing species in the Fe-BC composites was the major factor controlling [Fe(CN)6]3- removal, while for [Ni(CN)4]2- hydrogen bonding interactions between surface functional groups (especially hydroxyl (-OH) and carboxyl (-COOH)) and [Ni(CN)4]2- were the main factors controlling removal.

Pold4 is dispensable for mouse development, DNA replication and DNA repair.

The DNA polymerase delta (Pol δ), a heterotetramer of four subunits (Pol δ4), plays a pivotal role in DNA replication, as well as in DNA damage repair. Pold4, as the smallest subunit of Pol δ, is degraded in response to DNA damage or when entering into S-phase. This leads to the conversion of Pol δ4 to the trimeric complex Pol δ3. However, the contribution of Pold4 has not been fully elucidated in mammals. Cdm1, the Pold4 ortholog in Schizosaccharomyces pombe, is dispensable for cell growth and DNA damage repair, and there are no Pold4 orthologs in Saccharomyces cerevisiae. We previously generated a knockout mouse model of Pold3 and revealed its essential role in genome stability. Unexpectedly, we here found that Pold4 knockout mice are viable and fertile. In addition, Pold4 knockout mice do not exhibit any pathologic changes in the lung and spleen, tissues with the most abundant expression of Pold4. Moreover, Pold4 knockout mouse tail tip fibroblasts (TTF) exhibited normal cell growth, cell cycle, DNA replication, DNA damage and DNA repair capacity. These results suggested that Pol δ3 but not Pol δ4 may be responsible for these processes in normal cells. Interestingly, 19-month-old wild-type (WT) mice had tumors in the liver, while Pold4 knockout mice did not, and Pold4 knockout mice showed increased longevity. In further, this provided evidence suggested that Pold4 could be a potential novel target for lung carcinoma because its depletion does not affect normal cells but does affect cancer cells.

A bivalent vaccine containing D614G and BA.1 spike trimer proteins or a BA.1 spike trimer protein booster shows broad neutralizing immunity.

The newly emerged severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant, sublineages BA.1 and BA.2, recently became the dominant variants of concern (VOCs) with significantly higher transmissibility than any other variant appeared and markedly greater resistance to neutralization antibodies and original ancestral WA1 spike-matched vaccine. Therefore, it is urgent to develop vaccines against VOCs like Omicron. Unlike the new booming messenger RNA (mRNA) vaccine, protein vaccines have been used for decades to protect people from various kinds of viral infections and have advantages with their inexpensive production protocols and their relative stability in comparison to the mRNA vaccine. Here, we show that sera from BA.1 spike protein vaccinated mice mainly elicited neutralizing antibodies against BA.1 itself. However, a booster with BA.1 spike protein or a bivalent vaccine composed of D614G and BA.1 spike protein-induced not only potent neutralizing antibody response against D614G and BA.1 pseudovirus, but also against BA.2, other four SARS-CoV-2 VOCs (Alpha, Beta, Gamma, and Delta) and SARS-CoV-2-related coronaviruses (pangolin CoV GD-1 and bat CoV RsSHC014). The two recombinant spike protein vaccines method described here lay a foundation for future vaccine development for broad protection against pan-sarbecovirus.

Homologous or heterogenous vaccination boosters enhance neutralizing activities against SARS-CoV-2 Omicron BA.1 variant.

The SARS-CoV-2 Omicron BA.1 variant of concern contains more than 30 mutations in the spike protein, with half of these mutations localized in the receptor-binding domain (RBD). Emerging evidence suggests that these large number of mutations impact the neutralizing efficacy of vaccines and monoclonal antibodies. We investigated the relative contributions of spike protein and RBD mutations in Omicron BA.1 variants on infectivity, cell-cell fusion, and their sensitivity to neutralization by monoclonal antibodies or vaccinated sera from individuals who received homologous (CoronaVac, SinoPharm) or heterologous (CoronaVac-BNT162b2, BioNTech) and nonhuman primates that received a recombinant RBD protein vaccine. Our data overall reveal that the mutations in the spike protein reduced infectivity and cell-cell fusion compared to the D614G variant. The impaired infectivity and cell-cell fusion were dependent on non-RBD mutations. We also find reduced sensitivity to neutralization by monoclonal antibodies and vaccinated sera. However, our results also show that nonhuman primates receiving a recombinant RBD protein vaccine show substantial neutralization activity. Our study sheds light on the molecular differences in neutralizing antibody escape by the Omicron BA.1 variant, and highlights the promise of recombinant RBD vaccines in neutralizing the threat posed by the Omicron BA.1 variant.

First report of the optrA-carrying multidrug resistance genomic island in Campylobacter jejuni isolated from pigeon meat.

Campylobacter spp., such as Campylobacter jejuni and Campylobacter coli, are important zoonotic Gram-negative pathogens that cause acute intestinal diseases in humans. The optrA gene, encoding an ATP-binding cassette F (ABC-F) protein that confers resistance to oxazolidinones and phenicols, has been found in C. coli in China. In this study, the optrA gene was first identified in C. jejuni collected from retail meat in China from 2013 to 2016. Nine strains, isolated from a pigeon meat sample, carry the optrA gene. The molecular characteristics of the optrA-positive strains were determined by whole genome sequencing. Pulsed-field gel electrophoresis, multilocus sequence typing, and single nucleotide polymorphism analyses demonstrated that the nine optrA-positive isolates were genetically homogeneous. Phylogenetic characteristics and sequence comparison revealed that optrA was located on a chromosome-borne multidrug resistance genomic island. The optrA gene along with the tet(O) gene formed two different translocatable units (TUs), thereby supporting the transmission of TU-associated resistance genes. The emergence and spread of such TUs and strains are of great concern in terms of food safety, and measures must be implemented to avoid their dissemination in other Gram-negative bacteria and food chains.

Automatic Indoor as-Built Building Information Models Generation by Using Low-Cost RGB-D Sensors.

To generate indoor as-built building information models (AB BIMs) automatically and economically is a great technological challenge. Many approaches have been developed to address this problem in recent years, but it is far from being settled, particularly for the point cloud segmentation and the extraction of the relationship among different elements due to the complicated indoor environment. This is even more difficult for the low-quality point cloud generated by low-cost scanning equipment. This paper proposes an automatic as-built BIMs generation framework that transforms the noisy 3D point cloud produced by a low-cost RGB-D sensor (about 708 USD for data collection equipment, 379 USD for the Structure sensor and 329 USD for iPad) to the as-built BIMs, without any manual intervention. The experiment results show that the proposed method has competitive robustness and accuracy, compared to the high-quality Terrestrial Lidar System (TLS), with the element extraction accuracy of 100%, mean dimension reconstruction accuracy of 98.6% and mean area reconstruction accuracy of 93.6%. Also, the proposed framework makes the BIM generation workflows more efficient in both data collection and data processing. In the experiments, the time consumption of data collection for a typical room, with an area of 45-67 m 2 , is reduced to 4-6 min with an RGB-D sensor from 50-60 min with TLS. The processing time to generate BIM models is about half minutes automatically, from around 10 min with a conventional semi-manual method.

A Range-Independent Disparity-Based Calibration Model for Structured Light Pattern-Based RGBD Sensor.

Consumer-grade RGBD sensors that provide both colour and depth information have many potential applications, such as robotics control, localization, and mapping, due to their low cost and simple operation. However, the depth measurement provided by consumer-grade RGBD sensors is still inadequate for many high-precision applications, such as rich 3D reconstruction, accurate object recognition and precise localization, due to the fact that the systematic errors of RGB sensors increase exponentially with the ranging distance. Most existing calibration models for depth measurement must be carried out with different distances. In this paper, we reveal the mechanism of how an infrared (IR) camera and IR projector contribute to the overall non-centrosymmetric distortion of a structured light pattern-based RGBD sensor. Then, a new two-step calibration method for RGBD sensors based on the disparity measurement is proposed, which is range-independent and has full frame coverage. Three independent calibration models are used for the calibration for the three main components of the RGBD sensor errors: the infrared camera distortion, the infrared projection distortion, and the infrared cone-caused bias. Experiments show the proposed calibration method can provide precise calibration results in full-range and full-frame coverage of depth measurement. The offset in the edge area of long-range depth (8 m) is reduced from 86 cm to 30 cm, and the relative error is reduced from 11% to 3% of the range distance. Overall, at far range the proposed calibration method can improve the depth accuracy by 70% in the central region of depth frame and 65% in the edge region.

Fast and Automatic Reconstruction of Semantically Rich 3D Indoor Maps from Low-quality RGB-D Sequences.

Semantically rich indoor models are increasingly used throughout a facility's life cycle for different applications. With the decreasing price of 3D sensors, it is convenient to acquire point cloud data from consumer-level scanners. However, most existing methods in 3D indoor reconstruction from point clouds involve a tedious manual or interactive process due to line-of-sight occlusions and complex space structures. Using the multiple types of data obtained by RGB-D devices, this paper proposes a fast and automatic method for reconstructing semantically rich indoor 3D building models from low-quality RGB-D sequences. Our method is capable of identifying and modelling the main structural components of indoor environments such as space, wall, floor, ceilings, windows, and doors from the RGB-D datasets. The method includes space division and extraction, opening extraction, and global optimization. For space division and extraction, rather than distinguishing room spaces based on the detected wall planes, we interactively define the start-stop position for each functional space (e.g., room, corridor, kitchen) during scanning. Then, an interior elements filtering algorithm is proposed for wall component extraction and a boundary generation algorithm is used for space layout determination. For opening extraction, we propose a new noise robustness method based on the properties of convex hull, octrees structure, Euclidean clusters and the camera trajectory for opening generation, which is inapplicable to the data collected in the indoor environments due to inevitable occlusion. A global optimization approach for planes is designed to eliminate the inconsistency of planes sharing the same global plane, and maintain plausible connectivity between the walls and the relationships between the walls and openings. The final model is stored according to the CityGML3.0 standard. Our approach allows for the robust generation of semantically rich 3D indoor models and has strong applicability and reconstruction power for complex real-world datasets.

Geometric Integration of Hybrid Correspondences for RGB-D Unidirectional Tracking.

Traditionally, visual-based RGB-D SLAM systems only use correspondences with valid depth values for camera tracking, thus ignoring the regions without 3D information. Due to the strict limitation on measurement distance and view angle, such systems adopt only short-range constraints which may introduce larger drift errors during long-distance unidirectional tracking. In this paper, we propose a novel geometric integration method that makes use of both 2D and 3D correspondences for RGB-D tracking. Our method handles the problem by exploring visual features both when depth information is available and when it is unknown. The system comprises two parts: coarse pose tracking with 3D correspondences, and geometric integration with hybrid correspondences. First, the coarse pose tracking generates the initial camera pose using 3D correspondences with frame-by-frame registration. The initial camera poses are then used as inputs for the geometric integration model, along with 3D correspondences, 2D-3D correspondences and 2D correspondences identified from frame pairs. The initial 3D location of the correspondence is determined in two ways, from depth image and by using the initial poses to triangulate. The model improves the camera poses and decreases drift error during long-distance RGB-D tracking iteratively. Experiments were conducted using data sequences collected by commercial Structure Sensors. The results verify that the geometric integration of hybrid correspondences effectively decreases the drift error and improves mapping accuracy. Furthermore, the model enables a comparative and synergistic use of datasets, including both 2D and 3D features.

CT45A1 siRNA silencing suppresses the proliferation, metastasis and invasion of lung cancer cells by downregulating the ERK/CREB signaling pathway.

The aim of the present study was to investigate the role of the cancer­testis antigen family 45 member A1 (CT45A1) in the proliferation, apoptosis, invasion and metastasis of lung cancer cells, and the associated molecular mechanisms. Western blotting determined that the expression of CT45A1 in normal lung cells was far lower than that observed in lung cancer cells. Following the transfection of CT45A1 small (or short) interfering (si)RNA and its negative control into A549 cells using Lipofectamine 2000, the CT45A1 protein and mRNA levels were determined further by western blotting and reverse transcription­polymerase chain reaction. Following CT45A1 siRNA transfection, the levels of CT45A1 in lung cancer cells were markedly reduced (P<0.01). Then, cell viability and apoptosis were investigated with a methyl thiazolyl tetrazolium assay and Annexin V­FITC/propidium iodide staining, respectively. Transwell assays were employed to evaluate the migration and invasion of A549 cells. When compared with the negative control, the viability, migration and invasion of lung cancer cells treated with CT45A1 siRNA were suppressed and apoptosis was promoted (all P<0.01). In addition, the levels of B­cell lymphoma­2 (Bcl­2), Bcl­2 associated X (Bax), survivin, matrix metalloproteinase 2 (MMP2), MMP9, extracellular signal­regulated kinase 1/2 (ERK1/2), phosphorylated ERK1/2 (p­ERK1/2), cyclic AMP response element binding protein (CREB) and p­CREB were assessed by western blotting. Following CT45A1 silencing, the expressions of Bcl­2, survivin, MMP2, MMP9, p­ERK1/2 and p­CREB were downregulated and the expression of Bax was upregulated (all P<0.01). It was concluded that CT45A1 siRNA silencing suppressed the proliferation, metastasis and invasion of lung cancer cells by downregulating the ERK/CREB signalling pathway.

Enhanced RGB-D Mapping Method for Detailed 3D Indoor and Outdoor Modeling.

RGB-D sensors (sensors with RGB camera and Depth camera) are novel sensing systems that capture RGB images along with pixel-wise depth information. Although they are widely used in various applications, RGB-D sensors have significant drawbacks including limited measurement ranges (e.g., within 3 m) and errors in depth measurement increase with distance from the sensor with respect to 3D dense mapping. In this paper, we present a novel approach to geometrically integrate the depth scene and RGB scene to enlarge the measurement distance of RGB-D sensors and enrich the details of model generated from depth images. First, precise calibration for RGB-D Sensors is introduced. In addition to the calibration of internal and external parameters for both, IR camera and RGB camera, the relative pose between RGB camera and IR camera is also calibrated. Second, to ensure poses accuracy of RGB images, a refined false features matches rejection method is introduced by combining the depth information and initial camera poses between frames of the RGB-D sensor. Then, a global optimization model is used to improve the accuracy of the camera pose, decreasing the inconsistencies between the depth frames in advance. In order to eliminate the geometric inconsistencies between RGB scene and depth scene, the scale ambiguity problem encountered during the pose estimation with RGB image sequences can be resolved by integrating the depth and visual information and a robust rigid-transformation recovery method is developed to register RGB scene to depth scene. The benefit of the proposed joint optimization method is firstly evaluated with the publicly available benchmark datasets collected with Kinect. Then, the proposed method is examined by tests with two sets of datasets collected in both outside and inside environments. The experimental results demonstrate the feasibility and robustness of the proposed method.