Search Results (187)

Serum bactericidal assay (SBA) is a functional assay that evaluates infection- and vaccine-induced neutralizing antibodies representing the serological correlate of protection against Neisseria meningitidis. However, it is time consuming due to its readout using the enumeration of colony-forming units (CFUs), making this conventional SBA (C-SBA) difficult for large-scale use. We developed a new SBA method that takes advantage of a bioluminescence N. meningitidis serogroup B (BioLux-SBA). The assay development steps involved the human complement source validation, the setup of the optimal incubation time, and the assessment of intra-day and inter-day variability. BioLux-SBA was then compared to C-SBA using a serum collection of Norman children vaccinated in 2011 with MenBvac, an OMV meningococcal vaccine. While a conventional approach requests 48 h of work to test 24 sera per day, BioLux-SBA takes only 5 h to test 96 sera per day. The SBA titers (n = 10) correlated with R² of 0.98 (p-value < 0.0001). The deposition of terminal complement components (C5b-C9) measured by flow cytometry on the bacterial surface well correlated with BioLux SBA titers. This high-throughput method to evaluate the immunogenicity of meningococcal vaccines appears to be a reliable method for an OMV meningococcal B vaccine and requires further assessment in other laboratories and against other meningococcal vaccines. Full article

Target detection in UAV images is of great significance in fields such as traffic safety, emergency rescue, and environmental monitoring. However, images captured by UAVs usually have multi-scale features, complex backgrounds, uneven illumination, and low target resolution, which makes target detection in UAV images very challenging. To tackle these challenges, this paper introduces SPDC-YOLO, a novel model built upon YOLOv8. In the backbone, the model eliminates the last C2f module and the final downsampling module, thus avoiding the loss of small target features. In the neck, this paper proposes a novel feature pyramid, SPC-FPN, which employs the SBA (Selective Boundary Aggregation) module to fuse features from two distinct scales. In the head, the P5 detection head is eliminated, and a new detection head, Dyhead-DCNv4, is proposed, replacing DCNv2 in the original Dyhead with DCNv4 and utilizing three attention mechanisms for dynamic feature weighting. In addition, the model uses the CGB (Context Guided Block) module for downsampling, which can learn and fuse local features with surrounding contextual information, and the PPA (Parallelized Patch-Aware Attention) module replacing the original C2f module to further improve feature expression capability. Finally, SPDC-YOLO adopts EIoU as the loss function to optimize target localization accuracy. On the public dataset VisDrone2019, the experimental results show that SPDC-YOLO improves mAP₅₀ by 3.4% compared to YOLOv8n while reducing the parameters count by 1.03 M. Compared with other related methods, SPDC-YOLO demonstrates better performance. Full article

The identification of areas that are susceptible to damage due to earthquakes is of utmost importance in tectonically active regions like Northeast India. This may provide valuable inputs for seismic hazard analysis; however, it poses significant challenges. The present study emphasized the integration of Interferometric Synthetic Aperture Radar (InSAR) deformation rates with conventional geological and geophysical data to investigate earthquake damage susceptibility in the Barapani Shear Zone (BSZ) region of Northeast India. We used MintPy v1.5.1 (Miami INsar Timeseries software in PYthon) on the OpenSARLab platform to derive time series deformation using the Small Baseline Subset (SBAS) technique. We integrated geology, geomorphology, gravity, magnetic field, lineament density, slope, and historical earthquake records with InSAR deformation rates to derive earthquake damage susceptibility using the weighted overlay analysis technique. InSAR time series analysis revealed distinct patterns of ground deformation across the Barapani Shear Zone, with higher rates in the northern part and lower rates in the southern part. The deformation values ranged from 6 mm/yr to about 18 mm/yr in BSZ. Earthquake damage susceptibility mapping identified areas that are prone to damage in the event of earthquakes. The analysis indicated that about 46.4%, 51.2%, and 2.4% of the area were low, medium, and high-susceptibility zones for earthquake damage zone. The InSAR velocity rates were validated with Global Positioning System (GPS) velocity in the region, which indicated a good correlation (R² = 0.921; ANOVA p-value = 0.515). Additionally, a field survey in the region suggested evidence of intense deformation in the highly susceptible earthquake damage zone. This integrated approach enhances our scientific understanding of regional tectonic dynamics, mitigating earthquake risks and enhancing community resilience. Full article

Zr-containing silica residue (ZSR) is an industrial by-product of ZrOCl₂ production obtained through an alkali fusion process using zircon sand. In this study, low-cost and efficient Zr-doped mesoporous silica adsorption materials (Zr-MCM-41 and Zr-SBA-15) were prepared in one step via the hydrothermal synthesis method using ZSR as the silicon source for the removal of methylene blue (MB) from dye-contaminated wastewater. The samples were characterized using X-ray fluorescence (XRF) spectroscopy, X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), Fourier transform infrared (FT-IR) spectroscopy, thermogravimetry (TG), and N₂ adsorption–desorption measurements. The findings indicate that the synthesized Zr-MCM-41 and Zr-SBA-15 possess highly ordered mesoscopic structures with high specific surface areas of 910 and 846 m²/g, large pore volumes of 1.098 and 1.154 cm³/g, and average pore diameters of 4.18 and 5.35 nm, respectively. The results of the adsorption experiments show that the adsorbent has better adsorption properties under alkaline conditions. The adsorption process obeys the pseudo-quadratic kinetic model and the Freundlich adsorption isotherm model, indicating the coexistence of physical and chemisorption processes. The maximum adsorption capacities of Zr-MCM-41 and Zr-SBA-15 are 618.43 and 516.58 mg/g, respectively, as calculated by the Langmuir model (pH = 9, temperature of 25 °C). Compared with mesoporous silica prepared with sodium silicate as the silicon source, Zr-MCM-41 and Zr-SBA-15 have different structural properties and better adsorption properties due to Zr doping. These findings indicate that ZSR is the preferred silicon source for preparing mesoporous silica, and the mesoporous silica prepared using Zr silicon slag is a promising adsorbent and has great application potential in wastewater treatment. Full article

The Sapotaceae family comprises 65–70 genera and over 1250 species, holding significant ecological and economic value. Although previous studies have made some progress in the phylogenetic relationships and classification of Sapotaceae, many issues remain unresolved and require further in-depth research. In this study, we sequenced and assembled the complete chloroplast genomes of 21 plants from 11 genera of Sapotaceae, conducted a comparative genomic analysis, and performed a phylogenetic analysis by incorporating 16 previously published chloroplast genomes of Sapotaceae. The results showed that the chloroplast genome sizes in 21 plants of Sapotaceae range between 157,920 bp and 160,130 bp, exhibiting the typical quadripartite structure. Each genome contains 84–85 protein-coding genes, 37 tRNA genes, and 8 rRNA genes, while the ndhF gene is absent in Pouteria campechiana and Pouteria sapota. The relative synonymous codon usage (RSCU) analysis showed that isoleucine (Ile) is the most commonly used, while the codon for methionine (Met) is the least utilized. Additionally, five highly variable regions (petA-psbJ, psbI-trnS-GGA, rpl2_2-psbA, rps19-rpl2_2, and ycf4-cemA) and two coding sequences, ycf1 and matK, were identified as candidate molecular markers for species differentiation and a phylogenetic analysis within the Sapotaceae family. Phylogenetic trees were reconstructed using complete chloroplast genome sequences and analyzed using ML and BI methods, which revealed that the Sapotaceae family is divided into three distinct clades, each receiving strong statistical support (BS = 100, PP = 1). The intergeneric analysis revealed that Madhuca and Palaquium are sister groups (BS = 91, PP = 1), as are Gambeya and Chrysophyllum (BS = 91, PP = 1). Pouteria and Chrysophyllum are among the larger groups in the Sapotaceae family but the traditional classification boundaries of these genera are unstable and unfeasible, as the current genus boundaries fail to support their natural evolutionary relationships. In the phylogenetic tree, Eberhardtia aurata is placed on a separate branch. The morphological classification system shows that E. aurata has rust-colored pubescence on its branches, abaxial leaf surfaces, petioles, and other areas, which clearly distinguishes it from other genera. This study provides valuable insights into advancing phylogenetic research, population genetics, molecular breeding, and conservation strategies by comparing chloroplast genome structures and characteristics and constructing phylogenetic trees. Full article

Rainfall-triggered landslides generally pose a high risk due to their sudden initiation, massive impact force, and energy. It is, therefore, necessary to perform accurate and timely hazard prediction for these landslides. Most studies have focused on the hazard assessment and verification of landslides that have occurred, which were essentially back-analyses rather than predictions. To overcome this drawback, a framework aimed at forecasting landslide hazards by combining UAV remote sensing and numerical simulation was proposed in this study. A slow-moving landslide identified by SBAS-InSAR in Tianjin city of northern China was taken as a case study to clarify its application. A UAV with laser scanning techniques was utilized to obtain high-resolution topography data. Then, extreme rainfall with a given return period was determined based on the Gumbel distribution. The Particle Flow Code (PFC), a discrete element model, was also applied to simulate the runout process after slope failure under rainfall and earthquake scenarios. The results showed that the extreme rainfall for three continuous days in the study area was 151.5 mm (P = 5%), 184.6 mm (P = 2%), and 209.3 mm (P = 1%), respectively. Both extreme rainfall and earthquake scenarios could induce slope failure, and the failure probabilities revealed by a seepage–mechanic interaction simulation in Geostudio reached 82.9% (earthquake scenario) and 92.5% (extreme rainfall). The landslide hazard under a given scenario was assessed by kinetic indicators during the PFC simulation. The landslide runout analysis indicated that the landslide had a velocity of max 23.4 m/s under rainfall scenarios, whereas this reached 19.8 m/s under earthquake scenarios. In addition, a comparison regarding particle displacement also showed that the landslide hazard under rainfall scenarios was worse than that under earthquake scenarios. The modeling strategy incorporated spatial and temporal probabilities and runout hazard analyses, even though landslide hazard mapping was not actually achieved. The present framework can predict the areas threatened by landslides under specific scenarios, and holds substantial scientific reference value for effective landslide prevention and control strategies. Full article

Mesoporous titania nanoparticles (NPs) can be used for encapsulation polyphenols, with applications in the food industry, cosmetics, or biomedicine. TiO₂ NPs were synthesized using the sol-gel method combined with solvothermal treatment. TiO₂ NPs were characterized through X-ray diffraction, FTIR spectroscopy, the N₂ adsorption method, scanning and transmission electron microscopy, and thermal analysis. The sample prepared using Pluronic F127 presented a higher surface area and less agglomerated NPs than the samples synthesized with Pluronic P123. Grape marc (GM), a by-product from wine production, can be exploited for preparing extracts with good antioxidant properties. In this regard, we prepared hydroethanolic and ethanolic GM extracts from two cultivars, Feteasca Neagra (FN) and Pinot Noir. The extract components were determined by spectrometric analyses and HPLC. The extract with the highest radical scavenging activity, the hydroethanolic FN extract, was encapsulated in titania (FN@TiO₂) and compared with SBA-15 silica support. Both resulting materials showed biocompatibility on the NCTC fibroblast cell line in a 50–300 µg/mL concentration range after 48 h of incubation and even better radical scavenging potential than the free extract. Although titania has a lower capacity to host polyphenols than SBA-15, the FN@TiO₂ sample shows better cytocompatibility (up to 700 µmg/mL), and therefore, it could be used for skin-care products. Full article

The development of advanced photocatalysts for air pollution removal is essential to improve indoor air quality. TiO₂/mesoporous silica SBA-15 nanocomposites were synthesized using an organometallic decoration method, which leverages the high reactivity of Ti precursors to be hydrolyzed on the surface water groups of silica supports. Both lab-made Ti(III) amidinate and commercial Ti(IV) amino precursors were utilized to react with water-rich SBA-15, obtained through a hydration process. The hydrated SBA-15 and the TiO₂/SBA-15 nanocomposites were characterized using TGA, FTIR, ¹H and ²⁹Si NMR, TEM, SEM, N₂ physisorption, XRD, and WAXS. This one-step TiO₂ decoration method achieved a loading of up to 51.5 wt.% of approximately 9 nm anatase particles on the SBA-15 surface. This structuring provided excellent accessibility of TiO₂ particles for photocatalytic applications under pollutant gas and UV-A light exposure. The combination with the high specific surface area of SBA-15 resulted in the efficient degradation of 400 ppb of NO pollutant gas. Due to synergistic effects, the best nanocomposite in this study demonstrated a NO abatement performance of 4.0% per used mg of TiO₂, which is 40% more efficient than the reference photocatalytic material TiO₂ P-25. Full article

Diurnal rhythms in physiological functions contribute to homeostasis in many organisms. Although relationships between molecular biology and diurnal rhythms have been well studied in model organisms like higher plants, those in harmful algal bloom species are poorly understood. Here we measured several physiological parameters and the expression patterns of photosynthesis-related and antioxidant-enzyme genes in the Chattonella marina complex to understand the biological meaning of diurnal rhythm. Under a light–dark cycle, Fv/Fm and expression of psbA, psbD, and 2-Cys prx showed significant increases in the light and decreases during the dark. These rhythms remained even under continuous dark conditions. DCMU suppressed the induction of psbA, psbD, and 2-Cys prx expression under both light regimes. Oxidative stress levels and H₂O₂ scavenging activities were relatively stable, and there was no significant correlation between H₂O₂ scavenging activities and antioxidant-enzyme gene expression. These results indicate that the Chattonella marina complex has developed mechanisms for efficient photosynthetic energy production in the light. Our results showed that this species has a diurnal rhythm and a biological clock. These phenomena are thought to contribute to the efficiency of physiological activities centered on photosynthesis and cell growth related to the diurnal vertical movement of this species. Full article

Cephaleuros species are well-known as plant pathogens that cause red rust or algae spot diseases in many economically cultivated plants that grow in shady and humid environments. Despite their prevalence, the adaptive evolution of these pathogens remains poorly understood. We sequenced and characterized three Cephaleuros (Cephaleuros lagerheimii, Cephaleuros diffusus, and Cephaleuros virescens) chloroplast genomes, and compared them with seven previously reported chloroplast genomes. The chloroplast sequences of C. lagerheimii, C. diffusus, and C. virescens were 480,613 bp, 383,846 bp, and 472,444 bp in length, respectively. These chloroplast genomes encoded 94 genes, including 27 tRNA genes, 3 rRNA genes, and 64 protein-coding genes. Comparative analysis uncovered that the variation in genome size was principally due to the length of intergenic spacer sequences, followed by introns. Furthermore, several highly variable regions (trnY-GTA, trnL-TAG, petA, psbT, trnD-GTC, trnL-TAA, ccsA, petG, psaA, psaB, rps11, rps2, and rps14) were identified. Codon bias analysis revealed that the codon usage pattern of Cephaleuros is predominantly shaped by natural selection. Additionally, six chloroplast protein-coding genes (atpF, chlN, psaA, psaB, psbA, and rbcL) were determined to be under positive selection, suggesting they may play a vital roles in the adaptation of Cephaleuros to low-light intensity habitats. Full article

Stand basal area (SBA) is an important variable in the prediction of forest growth and harvest yield. However, achieving the additivity of SBA models for multiple tree species in the complex structure of broad-leaved mixed forests is an urgent scientific issue in the study of accurately predicting the SBA of mixed forests. This study used data from 58 sample plots (30 m × 30 m) for Populus davidiana × Betula platyphylla broad-leaved mixed forests to construct the SBA basic model based on nonlinear least squares regression (NLS). Adjustment in proportion (AP) and nonlinear seemingly unrelated regression (NSUR) were used to construct a multi-species additive basal area prediction model. The results identified the Richards model (M₆) and Korf model (M₁) as optimal for predicting the SBA of P. davidiana and B. platyphylla, respectively. The SBA models incorporate site quality, stand density index, and age at 1.3 m above ground level, which improves the prediction accuracy of basal area. Compared to AP, NSUR is an effective method for addressing the additivity of basal area in multi-species mixed forests. The results of this study can provide a scientific basis for optimizing stand structure and accurately predicting SBA in multi-species mixed forests. Full article

Soybean agglutinin (SBA) is a primary antinutritional factor in soybeans that can inhibit the growth of humans and mammals, disrupt the intestinal environment, and cause pathological changes. Therefore, detecting and monitoring SBA in foods is essential for safeguarding human health. In this paper, M13 phage-displayed nanobodies against SBA were isolated from a naive nanobody library. An M13 phage-displayed nanobody-based competitive enzyme-linked immunosorbent assay (P-cELISA) was then established for SBA analysis using biotinylated anti-M13 phage antibody (biotin-anti-M13) and streptavidin poly-HRP conjugate (SA-poly-HRP). The biotin-anti-M13@SA-poly-HRP probe can easily amplify the detection signal without the chemical modifications of phage-displayed nanobodies. The established P-cELISA presented a linear detection range of 0.56–250.23 ng/mL and a limit of detection (LOD) of 0.20 ng/mL, which was 12.6-fold more sensitive than the traditional phage-ELISA. Moreover, the developed method showed good specificity for SBA and acceptable recoveries (78.21–121.11%) in spiked wheat flour, albumen powder, and whole milk powder. This study proposes that P-cELISA based on biotin-anti-M13@SA-poly-HRP may provide a convenient and effective strategy for the sensitive detection of SBA. Full article

Monitoring ground displacements identifies potential geohazard risks early before they cause critical damage. Interferometric synthetic aperture radar (InSAR) is one of the techniques that can monitor these displacements with sub-millimeter accuracy. However, using the InSAR technique is challenging due to the need for high expertise, large data volumes, and other complexities. Accordingly, the development of an automated system to indicate ground displacements directly from the wrapped interferograms and coherence maps could be highly advantageous. Here, we compare different machine learning algorithms to evaluate the feasibility of achieving this objective. The inputs for the implemented machine learning models were pixels selected from the filtered-wrapped interferograms of Sentinel-1, using a coherence threshold. The outputs were the same pixels labeled as fast positive, positive, fast negative, negative, and undefined movements. These labels were assigned based on the velocity values of the measurement points located within the pixels. We used the Parallel Small Baseline Subset service of the European Space Agency’s GeoHazards Exploitation Platform to create the necessary interferograms, coherence, and deformation velocity maps. Subsequently, we applied a high-pass filter to the wrapped interferograms to separate the displacement signal from the atmospheric errors. We successfully identified the patterns associated with slow and fast movements by discerning the unique distributions within the matrices representing each movement class. The experiments included three case studies (from Italy, Portugal, and the United States), noted for their high sensitivity to landslides. We found that the Cosine K-nearest neighbor model achieved the best test accuracy. It is important to note that the test sets were not merely hidden parts of the training set within the same region but also included adjacent areas. We further improved the performance with pseudo-labeling, an approach aimed at evaluating the generalizability and robustness of the trained model beyond its immediate training environment. The lowest test accuracy achieved by the implemented algorithm was 80.1%. Furthermore, we used ArcGIS Pro 3.3 to compare the ground truth with the predictions to visualize the results better. The comparison aimed to explore indications of displacements affecting the main roads in the studied area. Full article

Heat shock protein 90 (HSP90) is a critical target for anticancer and anti-fungal-infection therapies due to its central role as a molecular chaperone involved in protein folding and activation. In this study, we developed in vitro Förster Resonance Energy Transfer (FRET) assays to characterize the binding of C. albicans HSP90 to its co-chaperone Sba1, as well as that of the homologous human HSP90α to p23. The assay for human HSP90α binding to p23 enables selectivity assessment for compounds aimed to inhibit the binding of C. albicans HSP90 to Sba1 without affecting the physiological activity of human HSP90α. The combination of the two assays is important for antifungal drug development, while the assay for human HSP90α can potentially be used on its own for anticancer drug discovery. Since ATP binding of HSP90 is a prerequisite for HSP90-Sba1/p23 binding, ATP-competitive inhibitors can be identified with the assays. The specificity of binding of fusion protein constructs—HSP90-mNeonGreen (donor) and Sba1-mScarlet-I (acceptor)—to each other in our assay was confirmed via competitive inhibition by both non-labeled Sba1 and known ATP-competitive inhibitors. We utilized the developed assays to characterize the stability of both HSP90–Sba1 and HSP90α–p23 affinity complexes quantitatively. K_d values were determined and assessed for their precision and accuracy using the 95.5% confidence level. For HSP90-Sba1, the precision confidence interval (PCI) was found to be 70–120 (100 ± 20) nM while the accuracy confidence interval (ACI) was 100–130 nM. For HSP90α-p23, PCI was 180–260 (220 ± 40) nM and ACI was 200–270 nM. The developed assays were used to screen a nucleoside-mimetics library of 320 compounds for inhibitory activity against both C. albicans HSP90-Sba1 and human HSP90α-p23 binding. No novel active compounds were identified. Overall, the developed assays exhibited low data variability and robust signal separation, achieving Z factors > 0.5. Full article

Background: Measures of lumbar lordosis (LL) and elliptical modeling variables have been shown to discriminate between normal and chronic low back pain (CLBP) patients. Pelvic morphology influences an individual’s sagittal lumbar alignment. Our purpose is to investigate the sensitivity and specificity of lumbar sagittal radiographic alignment and modeling variables to identify if these can discriminate between normal controls and CLBP patients. Methods: We conducted a computer analysis of digitized vertebral body corners on lateral lumbar radiographs of normal controls and CLBP patients. Fifty normal controls were attained from a required pre-employment physical examination (29 men; 21 women; mean age of 27.7 ± 8.5 years), with no history of low back pain, a normal spinal examination, no pathologies, anomalies, or instability. Additionally, 50 CLBP patients (29 men; 29.5 ± 8 years of age) were randomly chosen and matched to the characteristics of the controls. The inclusion criteria required no abnormalities on lumbar spine radiographs. The parameters included the following: ARA L1-L5 lordosis, ARA T12-S1 lordosis, Cobb T12-S1, b/a elliptical modelling ratio, sacral base angle (SBA), and S1 posterior tangent to vertical (PTS1). Two measures of pelvic morphology were determined for each person—the angle of pelvic incidence (API) and posterior tangent pelvic incidence angle (PTPIA)—and the relationships between API − ARA T12-S1, API − Cobb T12-S1, and API − ARA L1-5 was determined. Descriptive statistics and correlations among the primary variables were determined. The receiver operating characteristic curves (ROC curves) for primary variables were analyzed. Results: The mean values of LL were statistically different between the normal and CLBP groups (p < 0.001), indicating a hypo-lordotic lumbar spine for the CLBP group. The mean b/a ratio was lower in the chronic pain group (p = 0.0066). The pelvic morphology variables were similar between the groups (p > 0.05). API had a stronger correlation to the SBA and Cobb T12-S1 than PTPIA did, while PTPIA had a stronger correlation to the S1 tangent and ARA T12-S1 than API did. While CLBP patients had a stronger correlation of ARA T12-S1 and Cobb T12-S1 relative to the pelvic morphology, they also had a reduced correlation of ARA L1-L5 lordosis relative to their SBA and pelvic morphology measures. API − T12-S1, API − L1-L5, and API − Cobb T12-S1 were statistically different between the groups, p < 0.001. Using ROC curve analyses, it was identified that ARA L1-L5 lordosis of 36° and ARA T12-S1 of 68° have a good sensitivity and specificity to discriminate between normal and CLBP patients. ROC curve analyses identified that lordosis ARAT12-S1 < 68° (AUC = 0.83), lordosis ARAL1-L5 < 36° (AUC = 0.78), API − ARA T12-S1 < −18° (AUC = 0.75), API − ARAL1-L5 > 35° (AUC = 0.71), and API − Cobb T12-S1 < −5° (AUC = 0.69) had moderate to good discrimination between groups (AUC = 0.83, 0.78, 0.75, and 0.72). Conclusions: Pelvic morphology is similar between normal and CLBP patients. CLBP patients have an abnormal ‘fit’ of their API − ARAT12-S1 and L1-L5 lumbar lordosis relative to their pelvic morphology and sacral tilt shown as a hypolordosis. Full article