Search Results (16)

The issues related to soil environmental contamination caused by heavy metals have garnered increasing attention. In particular, the soil pollution risk in the eastern coastal regions of China has attracted widespread concern. This study surveyed heavy metals in the soils near the Jiahe River Basin of Yantai City in Shandong Province, China. A total of 213 soils were sampled and analyzed for 12 items: Cr, Hg, As, Pb, Cd, Cu, Ni, Zn, Co, V, Mn, and pH. The 11 heavy metals were evaluated using the national standard GB15618-2018, with three risk levels of background, screening, and intervention, and using pollution indices, including the contamination factor (C_f), ecological risk factor (Er), enrichment factor (EF), and index of geo-accumulation (I_geo), with different respective risk levels. The results indicate a strong consistency between the evaluations both for the index I_geo and for GB15618-2018 on five metals (i.e., Cr, Hg, As, Pb, and Cd). Therefore, the index I_geo may serve as a supplementary indicator for assessing the pollution risks of heavy metals in agricultural soils regarding samples of Cu, Ni, and Zn that exceed the screening values in GB15618-2018, as well as for Co, V, and Mn, which have not yet been established in GB15618-2018. According to the three-level classification of risk in GB15618-2018, the seven commonly used levels of the index I_geo are also incorporated into the three levels of background, screening, and intervention. The overall pollution risk of 11 heavy metals in the soils of the Jiahe River Basin of Yantai City belongs to the background level. Specifically, Hg and Pb in the total area are classified at the background level. Manganese, V, Co, Zn, Ni, and Cr are recognized at the screening level sporadically, while Cu, As, and Cd are found at the screening level in small areas. No areas within the region are classified at the intervention level. Full article

Effective noise reduction strategies are crucial for improving the precision of Ground-Based Synthetic Aperture Radar (GB-SAR) technology in bridge deflection measurement, particularly in mitigating the signal noise introduced by complex environmental factors, and thereby ensuring reliable structural health assessments. This study presents an innovative gradual de-noising method that integrates an Improved Second-Order Blind Identification (I-SOBI) algorithm with Fast Fourier Transform (FFT) featuring Adaptive Cutoff Frequency Selection (A-CFS) for reducing the complex environmental noises. The novel method is a two-stage process. The first stage employs the proposed I-SOBI to preserve the contribution of effective information in separated signals as much as possible and to recover pure signals from noisy ones that have nonlinear characteristics or are non-Gaussian in distribution. The second stage utilizes the FFT with the A-CFS method to further deal with the residual high-frequency noises still within the signals, which is conducted under a proper cutoff frequency to ensure the quality of de-noised outputs. Through meticulous simulation and practical experiments, the effectiveness of the proposed de-noising method has been comprehensively validated. The experimental results state that the method performs better than the traditional Second-Order Blind Identification (SOBI) method in terms of noises reduction capabilities, achieving a higher accuracy of bridge deflection measurement using GB-SAR. Additionally, the method is particularly effective for de-noising nonlinear time-series signals, making it well-suited for handling complex signal characteristics. It significantly contributes to the provision of reliable bridge dynamic-behavior information for infrastructure assessment. Full article

Cross-project defect prediction (CPDP) aims to predict software defects in a target project domain by leveraging information from different source project domains, allowing testers to identify defective modules quickly. However, CPDP models often underperform due to different data distributions between source and target domains, class imbalances, and the presence of noisy and irrelevant instances in both source and target projects. Additionally, standard features often fail to capture sufficient semantic and contextual information from the source project, leading to poor prediction performance in the target project. To address these challenges, this research proposes Smote Correlation and Attention Gated recurrent unit based Long Short-Term Memory optimization (SCAG-LSTM), which first employs a novel hybrid technique that extends the synthetic minority over-sampling technique (SMOTE) with edited nearest neighbors (ENN) to rebalance class distributions and mitigate the issues caused by noisy and irrelevant instances in both source and target domains. Furthermore, correlation-based feature selection (CFS) with best-first search (BFS) is utilized to identify and select the most important features, aiming to reduce the differences in data distribution among projects. Additionally, SCAG-LSTM integrates bidirectional gated recurrent unit (Bi-GRU) and bidirectional long short-term memory (Bi-LSTM) networks to enhance the effectiveness of the long short-term memory (LSTM) model. These components efficiently capture semantic and contextual information as well as dependencies within the data, leading to more accurate predictions. Moreover, an attention mechanism is incorporated into the model to focus on key features, further improving prediction performance. Experiments are conducted on apache_lucene, equinox, eclipse_jdt_core, eclipse_pde_ui, and mylyn (AEEEM) and predictor models in software engineering (PROMISE) datasets and compared with active learning-based method (ALTRA), multi-source-based cross-project defect prediction method (MSCPDP), the two-phase feature importance amplification method (TFIA) on AEEEM and the two-phase transfer learning method (TPTL), domain adaptive kernel twin support vector machines method (DA-KTSVMO), and generative adversarial long-short term memory neural networks method (GB-CPDP) on PROMISE datasets. The results demonstrate that the proposed SCAG-LSTM model enhances the baseline models by 33.03%, 29.15% and 1.48% in terms of F1-measure and by 16.32%, 34.41% and 3.59% in terms of Area Under the Curve (AUC) on the AEEEM dataset, while on the PROMISE dataset it enhances the baseline models’ F1-measure by 42.60%, 32.00% and 25.10% and AUC by 34.90%, 27.80% and 12.96%. These findings suggest that the proposed model exhibits strong predictive performance. Full article

Glioblastoma (GB) is the most aggressive and common type of cancer within the central nervous system (CNS). Despite the vast knowledge of its physiopathology and histology, its etiology at the molecular level has not been completely understood. Thus, attaining a cure has not been possible yet and it remains one of the deadliest types of cancer. Usually, GB is diagnosed when some symptoms have already been presented by the patient. This diagnosis is commonly based on a physical exam and imaging studies, such as computed tomography (CT) and magnetic resonance imaging (MRI), together with or followed by a surgical biopsy. As these diagnostic procedures are very invasive and often result only in the confirmation of GB presence, it is necessary to develop less invasive diagnostic and prognostic tools that lead to earlier treatment to increase GB patients’ quality of life. Therefore, blood-based biomarkers (BBBs) represent excellent candidates in this context. microRNAs (miRNAs) are small, non-coding RNAs that have been demonstrated to be very stable in almost all body fluids, including saliva, serum, plasma, urine, cerebrospinal fluid (CFS), semen, and breast milk. In addition, serum-circulating and exosome-contained miRNAs have been successfully used to better classify subtypes of cancer at the molecular level and make better choices regarding the best treatment for specific cases. Moreover, as miRNAs regulate multiple target genes and can also act as tumor suppressors and oncogenes, they are involved in the appearance, progression, and even chemoresistance of most tumors. Thus, in this review, we discuss how dysregulated miRNAs in GB can be used as early diagnosis and prognosis biomarkers as well as molecular markers to subclassify GB cases and provide more personalized treatments, which may have a better response against GB. In addition, we discuss the therapeutic potential of miRNAs, the current challenges to their clinical application, and future directions in the field. Full article

Poultry farming plays a significant role in ensuring food security and economic growth in many countries. However, various factors such as feeding management practices, environmental conditions, and diseases lead to poultry mortality (dead birds). Therefore, regular monitoring of flocks and timely veterinary assistance is crucial for maintaining poultry health, well-being, and the success of poultry farming operations. However, the current monitoring method relies on manual inspection by farm workers, which is time-consuming. Therefore, developing an automatic early mortality detection (MD) model with higher accuracy is necessary to prevent the spread of infectious diseases in poultry. This study aimed to develop, evaluate, and test the performance of YOLOv5-MD and YOLOv6-MD models in detecting poultry mortality under various cage-free (CF) housing settings, including camera height, litter condition, and feather coverage. The results demonstrated that the YOLOv5s-MD model performed exceptionally well, achieving a high mAP@0.50 score of 99.5%, a high FPS of 55.6, low GPU usage of 1.04 GB, and a fast-processing time of 0.4 h. Furthermore, this study also evaluated the models’ performances under different CF housing settings, including different levels of feather coverage, litter coverage, and camera height. The YOLOv5s-MD model with 0% feathered covering achieved the best overall performance in object detection, with the highest mAP@0.50 score of 99.4% and a high precision rate of 98.4%. However, 80% litter covering resulted in higher MD. Additionally, the model achieved 100% precision and recall in detecting hens’ mortality at the camera height of 0.5 m but faced challenges at greater heights such as 2 m. These findings suggest that YOLOv5s-MD can detect poultry mortality more accurately than other models, and its performance can be optimized by adjusting various CF housing settings. Therefore, the developed model can assist farmers in promptly responding to mortality events by isolating affected birds, implementing disease prevention measures, and seeking veterinary assistance, thereby helping to reduce the impact of poultry mortality on the industry, ensuring the well-being of poultry and the overall success of poultry farming operations. Full article

Additive manufacturing (AM) is often used for prototyping; however, in recent years, there have been several final product applications, namely the development of polymer-metal hybrid (PMH) components that have emerged. In this paper, the objective is to characterize the adhesion of single-lap joints between two different materials: aluminium and a polymer-based material manufactured by fused filament fabrication (FFF). Single-lap joints were fabricated using an aluminium substrate with different surface treatments: sandpaper polishing (SP) and grit blasting (GB). Three filaments for FFF were tested: acrylonitrile butadiene styrene (ABS), polyamide (PA), and polyamide reinforced with short carbon fibers (PA + CF). To characterize the behaviour of these single-lap joints, mechanical tension loading tests were performed. The analysis of the fractured surface of the joints aimed to correlate the adhesion performance of each joint with the occurred failure mode. The obtained results show the impact of surface roughness (0.16 < Ra < 1.65 µm) on the mechanical properties of the PMH joint. The ultimate lap shear strength (ULSS) of PMH single-lap joints produced by FFF (1 < ULSS < 6.6 MPa) agree with the reported values in the literature and increases for substrates with a higher surface roughness, remelting of the primer (PA and PA + CF), and higher stiffness of the polymer-based adherent. Full article

In QTAIM dual-functional analysis, H_b(r_c) is plotted versus H_b(r_c) − V_b(r_c)/2 for the interactions, where H_b(r_c) and V_b(r_c) are the total electron energy densities and potential energy densities, respectively, at the bond critical points (BCPs) on the interactions in question. The plots are analyzed by the polar (R, θ) coordinate representation for the data from the fully optimized structures, while those from the perturbed structures around the fully optimized structures are analyzed by (θ_p, κ_p). θ_p corresponds to the tangent line of the plot, and κ_p is the curvature; θ and θ_p are measured from the y-axis and y-direction, respectively. The normal and inverse behavior of interactions is proposed for the cases of θ_p > θ and θ_p < θ, respectively. The origin and the mechanism for the behavior are elucidated. Interactions with θ_p < θ are typically found, although seldom for [F–I-∗-F]⁻, [MeS-∗-TeMe]²⁺, [HS-∗-TeH]²⁺ and CF₃SO₂N-∗-IMe, where the asterisks emphasize the existence of BCPs in the interactions and where [Cl–Cl-∗-Cl]⁻ and CF₃SO₂N-∗-BrMe were employed as the reference of θ_p > θ. The inverse behavior of the interactions is demonstrated to arise when H_b(r_c) − V_b(r_c)/2 and when the corresponding G_b(r_c), the kinetic energy densities at BCPs, does not show normal behavior. Full article

Sida cordifolia is a medicinal shrub that is conventionally used in the Indian system of medicine;however, the genes contributing to its medicinal properties have been minimally explored, thus limiting its application. High-throughputsequencing and Liquid Chromatography with tandem mass spectrometry(LC-MS/MS) technologies were applied to unravel the medicinally important bioactive compounds. As a result, transcriptomic sequencing generated more than 12 GB of clean data, and 187,215 transcripts were obtained by de novoassembly. These transcripts were broadly classified into 20 classes, based on the gene ontology classification, and 6551 unigenes were annotated using Kyoto Encyclopedia of Genes and Genomes (KEGG) database with more than 142 unigenes involved in the biosynthesis of secondary metabolites. LC-MS/MS analysis of three tissues of Sida cordifolia revealed that acacetin and procyanidin are some important metabolites identified thatcontribute to its medicinal value. Several key enzymes witha crucial role in phenylpropanoid and flavonoid biosynthetic pathways were identified, especially phenylalanine ammonia lyase, which might be an important rate-limiting enzyme. Real-Time Quantitative Reverse Transcription Polymerase chain reaction (qRT-PCR) analysis revealed enzymes, such as Phenylalanine ammonia lyase (PAL), Cinnamyl alcohol dehydrogenase 1 (CAD), Cinnamoyl-CoA reductase 1 (CF1) and Trans cinnamate 4-monooxygenase(TCM), which were predominantly expressed in root compared to leaf and stem tissue. The study provides a speculative insight for the screening of active metabolites and metabolic engineering in Sida cordifolia. Full article

Fagopyrum dibotrys, being native to southwest China, is widely distributed in Yunnan, Guizhou Provinces and Chongqing City. However, the quality of medicinal materials growing in different origins varies greatly, and cannot meet the market demand for high-quality F. dibotrys. In this study, 648 metabolites were identified, and phenolic compounds of F. dibotrys from different origins were clearly separated by principal component analysis (PCA). Our results suggested that the medicinal differences of F. dibotrys from different origins can be elucidated via the variations in the abundance of the phenolic and flavonoid compounds. We found that the epicatechin, total flavonoids and total tannin content in Yunnan Qujing (YQ) and Yunnan Kunming (YK) were higher than those in Chongqing Shizhu (CS), Chongqing Fuling (CF) and Guizhou Bijie (GB), suggesting that Yunnan Province can be considered as one of the areas that produce high-quality medicinal materials. Additionally, 1,6-di-O-galloyl-β-D-glucose, 2,3-di-O-galloyl-D-glucose and gallic acid could be used as ideal marker compounds for the quality control of F. dibotrys from different origins caused by metabolites, and the F. dibotrys planted in Yunnan Province is well worth exploiting. Full article

The middle sub-member (Es3z) within the third member (Es3) of the Eocene Shahejie formation is the main source of the generation and accumulation of hydrocarbons in the lacustrine deltas of Bonan depression. Exploration and research work in different blocks is carried out separately. Types of sedimentary facies, and their vertical and lateral evolution in Es3z are not studied in detail. To fill this knowledge gap, we did a detailed analysis of facies and lithological characteristics through integrative studies of cores, well logs and seismic data. Identification of sedimentary structures and lithology of the reservoir zone from cores are calibrated with high-quality well logs and seismic data. Depositional facies in Es3z reservoirs are identified through analysis of sedimentary structures, grain size, log’s trends and seismic sections. Es3z was deposited in the fan delta front setting where five facies associations are found, among them distributary channels consisting of MCS, CSg, PCSs, MS, RCL, WCS, PBSs, RCS and GBS lithofacies, natural levee containing DFs, and furthermore, sheet sand are associated to CBS and SSM lithofacies. GM, GGM and DGM lithofacies are related to inter-distributary deposits, whereas mouth bars consist of PLS, CS and CFS. Depositional history, flow direction of the sediments, and facies distribution are investigated through detailed facies mapping and cross-section profiling to show that the sediments were sourced from southeast to northwest. We found thicker succession of sedimentary profiles towards north and north-west directions. Belt distributary channel deposits, covering a wide range of areas, act as potential reservoirs along with mouth bar deposits, while mudstones in interdistributary channels act as a good source and seal rocks. The methodology adopted has great potential to explore the reservoirs of fan delta front in lacustrine deltas. Full article

The domestic rabbit (Oryctolagus cuniculus f. domesticus) is a very important variety in biomedical research and agricultural animal breeding. Due to the different geographical areas in which rabbit breeds originated, and the long history of domestication/artificial breeding, rabbits have experienced strong selection pressure, which has shaped many traits of most rabbit varieties, such as color and weight. An efficient genome-wide single-nucleotide polymorphism (SNP) detection strategy is genotyping-by-sequencing (GBS), which has been widely used in many organisms. This study attempted to explore bi-allelic SNPs associated with fur color and weight-related traits using GBS in five rabbit breeds. The data consisted of a total 831,035 SNPs in 150 individuals from Californian rabbits (CF), German Zika rabbits (ZK), Qixing rabbits (QX), Sichuan grey rabbits (SG), and Sichuan white rabbits (SW). In addition, these five breeds of rabbits were obviously independent populations, with high genetic differentiation among breeds and low genetic diversity within breeds. A total of 32,144 SNP sites were identified by selective sweep among the different varieties. The genes that carried SNP loci in these selected regions were related to important traits (fur color and weight) and signal pathways, such as the MAPK/ERK signaling pathway and the Hippo signaling pathway. In addition, genes related to fur color and weight were identified, such as ASIPs, MITFs and KITs, ADCY3s, YAPs, FASs, and ACSL5s, and they had more SNP sites. The research offers the foundation for further exploration of molecular genetic markers of SNPs that are related to traits. Full article

Understanding the extent of contamination, sources and various carcinogenic and non-carcinogenic risks associated with different heavy metals in soil-crop systems is crucial for the prevention of heavy metal pollution. A survey was undertaken to determine heavy metal concentrations and degree of pollution in soil-crop systems (rice, wheat, and corn) using various indices such as pollution factor (CF), geo-accumulation index (I_geo), enrichment coefficients and transfer coefficient, and to determine the source of heavy metals pollution in the Wanjiang Economic Zone, Anhui Province, China. A total of 308 pairs of soil-crop samples were collected in this study, comprising 245 pairs of soil-rice samples, 53 pairs of soil-wheat samples, and 10 pairs of soil-corn samples. The concentrations of cadmium (Cd) and nickel (Ni) in the soil of the study area exceeded the national limitation of heavy metals in the soil of China (GB 15618-2018, Soil Environmental Quality: Risk Control Standard for Soil Contamination of Agricultural Land. Ministry of Environmental Protection of China. Beijing. China). The concentrations of copper (Cu), zinc (Zn) and lead (Pb) were also above the national limits to a lesser extent. All eight heavy metals (Cd, Cu, Ni, Pb Zn, arsenic (As), chromium (Cr), and mercury (Hg)) exceeded the background values in the study area. The enrichment coefficients of rice, wheat and maize to Cd, Cu and Zn were higher than those to other elements. On the basis of I_geo, it can be indicated that the rhizosphere soil of rice was slightly polluted by Cd and Hg, while the concentrations of the other heavy metals were below the safety limits. The CF and pollution load index (PLI) indicated that the soil in the study area was heavily contaminated with heavy metals. A principal component analysis identified different sources of soil heavy metal pollution, that is, Cu, Pb, Zn and Cd from industrial sources, Cr and Ni from natural sources, and As and Hg from agricultural sources. The carcinogenic risk of heavy metals was related to the intake of crops. Residents in the study area ingest rice, wheat, and corn on a daily basis. On the basis this study, it is suggested that local governments should pay attention to the carcinogenic risk of heavy metals in rice. Full article

AA7075 alloys are high strength alloys and are used as an important material for making engineering parts. Forged AA7075 alloys showed significantly decreased toughness when the material was hot deformed at a high temperature. This study investigated the effects of forging parameters on the tensile properties and the microstructure of AA7075 forgings. The tensile properties and corrosion resistance of different forgings were determined to be correlated with their microstructures. The experiment annealed and hot-deformed sample bars at 633 K, cold-deformed them at room temperature (RF), and at sub-zero temperatures (CF). After T73 heat treatment, the microstructures depended on the deformation temperature. This varied significantly, from elongated grains for hot-forged samples to equiaxial grains for cold-deformed samples. The hot-deformed samples had a tensile strength of 592 MPa for UTS, 538 MPa for YS, and 13.4% for elongation. These were stronger but less elongated than the cold-deformed samples. All hot-deformed (HF), RF, and CF samples exhibited mechanical properties that exceeded UTS > 505 MPa, YS > 435 MPa, and an elongation > 13%, and showed moderate corrosion resistance if samples were in contact with a 3.5 wt.% NaCl solution. The toughness of the forgings could be significantly improved by decreasing the forging temperatures. The corrosion resistance of AA7075-T73 forgings was affected by the total grain boundary (GB) lengths per unit area and the 2nd phase particle counts per unit area. Increasing the high-angle grain boundary lengths (HAGBs) per unit area accelerated corrosion and increased the Icorr value. Full article

The Himalayas are one of earth’s hotspots of biodiversity. Among its many cryptic and undiscovered organisms, including vertebrates, this complex high-mountain ecosystem is expected to harbour many species with adaptations to life in high altitudes. However, modern evolutionary genomic studies in Himalayan vertebrates are still at the beginning. Moreover, in organisms, like most amphibians with relatively high DNA content, whole genome sequencing remains bioinformatically challenging and no complete nuclear genomes are available for Himalayan amphibians. Here, we present the first well-annotated multi-tissue transcriptome of a Greater Himalayan species, the lazy toad Scutiger cf. sikimmensis (Anura: Megophryidae). Applying Illumina NextSeq 500 RNAseq to six tissues, we obtained 41.32 Gb of sequences, assembled to ~111,000 unigenes, translating into 54362 known genes as annotated in seven functional databases. We tested 19 genes, known to play roles in anuran and reptile adaptation to high elevations, and potentially detected diversifying selection for two (TGS1, SENP5) in Scutiger. Of a list of 37 genes, we also identify 27 candidate genes for sex determination or sexual development, all of which providing the first such data for this non-model megophryid species. These transcriptomes will serve as a valuable resource for further studies on amphibian evolution in the Greater Himalaya as a biodiversity hotspot. Full article

This work studies the relationship between the energy allocated for transmitting a pair of correlated Gaussian sources over a two-user Gaussian broadcast channel with noiseless channel output feedback (GBCF) and the resulting distortion at the receivers. Our goal is to characterize the minimum transmission energy required for broadcasting a pair of source samples, such that each source can be reconstructed at its respective receiver to within a target distortion, when the source-channel bandwidth ratio is not restricted. This minimum transmission energy is defined as the energy-distortion tradeoff (EDT). We derive a lower bound and three upper bounds on the optimal EDT. For the upper bounds, we analyze the EDT of three transmission schemes: two schemes are based on separate source-channel coding and apply encoding over multiple samples of source pairs, and the third scheme is a joint source-channel coding scheme that applies uncoded linear transmission on a single source-sample pair and is obtained by extending the Ozarow–Leung (OL) scheme. Numerical simulations show that the EDT of the OL-based scheme is close to that of the better of the two separation-based schemes, which makes the OL scheme attractive for energy-efficient, low-latency and low-complexity source transmission over GBCFs. Full article