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Objective Oxidative stress (OS) is a pathological process arising from the excessive production of free radicals in the body. It has the potential to alter animal gene expression and cause damage to the jejunum. However, there have been few reports of changes in the expression of long noncoding RNAs (lncRNAs) in the jejunum in piglets under OS. The purpose of this research was to examine how lncRNAs in piglet jejunum change under OS. Methods The abdominal cavities of piglets were injected with diquat (DQ) to produce OS. Raw reads were downloaded from the SRA database. RNA-seq was utilized to study the expression of lncRNAs in piglets under OS. Additionally, six randomly selected lncRNAs were verified using quantitative real-time polymerase chain reaction (qRT–PCR) to examine the mechanism of oxidative damage. Results A total of 79 lncRNAs were differentially expressed (DE) in the treatment group compared to the negative control group. The target genes of DE lncRNAs were enriched in gene ontology (GO) terms and Kyoto encyclopedia of genes and genomes (KEGG) signaling pathways. Chemical carcinogenesis-reactive oxygen species, the Foxo signaling pathway, colorectal cancer, and the AMPK signaling pathway were all linked to OS. Conclusion Our results demonstrated that DQ-induced OS causes differential expression of lncRNAs, laying the groundwork for future research into the processes involved in the jejunum’s response to OS.

Abstract Background There is mounting proof that rheumatoid arthritis (RA) and cognitive decline are related. These studies, however, have not all been uniform, and others have not discovered such a correlation. It is essential to investigate the link between RA and cognitive decline. Method We conducted a Mendelian randomization analysis utilizing three different publicly accessible RA GWAS summary datasets and a variety of meticulously verified instrumental variables. We mostly used inverse variance weighting (IVW), as well as MR-Egger, weighted median, MR-PRESSO, and several sensitivity analyses, to figure out the link between RA and cognitive impairment (CI). Results Our MR study identified the causality between RA and declining cognitive performance (β = − 0.010, 95% CI of − 0.017 to − 0.003, P = 4.33E−03) and cognitive function (β = − 0.029, 95% CI of − 0.053 to − 0.005, P = 1.93E−02). The consistent direction of the connection is revealed by sensitivity analysis utilizing the weighted median and the MR-Egger method. Furthermore, we reproduced our findings across two additional RA datasets and found identical outcomes, strengthening the validity of our findings. Conclusion This study offers proof of causality between RA and an increased risk of CI. Our findings highlight the importance of examining RA patients for cognitive ability, which may open up fresh ideas for the prevention of CI.

Microplastics (MPs) are carbon-rich polymers that are ubiquitous in the environment. With the increase of plastic production, microplastic pollution may be exacerbated and result in significant changes in microbial communities and biogeochemical processes such as carbon cycling, eventually impacting greenhouse gas emission and carbon storage in terrestrial ecosystems. However, current research on the effect of MPs on soil carbon cycling is still limited, and there is a lack of systematic review of the scattered information obtained from previous studies. Accordingly, this review provides a systematic overview of the current knowledge on the effects of MPs on soil carbon cycling and gives future research suggestions. Emerging evidence indicates that MPs could affect soil carbon stability and CO2 and CH4 emission by modifying soil physicochemical and microbiological properties; though biodegradable MPs often exhibit a greater effect than nonbiodegradable ones, the specific effects are highly dependent on plastic type, size and concentration. The specific mechanisms of MPs’ impact on soil carbon cycles remain elusive, which are discussed mainly from the perspective of microbial changes, including microbial biomass, microbial community composition, and key enzymes and functional genes associated with carbon metabolism. Further research is needed to elucidate whether MPs have a positive priming effect on soil carbon decomposition and the biotic and abiotic mechanisms involved. This review paper helps researchers gain a clearer picture of how and through which way MPs impact carbon cycling in soil ecosystems.

An important index to evaluate the process efficiency of coal preparation is the mineral liberation degree of pulverized coal, which is greatly influenced by the particle size and shape distribution acquired by image segmentation. However, the agglomeration effect of fine powders and the edge effect of granular images caused by scanning electron microscopy greatly affect the precision of particle image segmentation. In this study, we propose a novel image segmentation method derived from mask regional convolutional neural network based on deep learning for recognizing fine coal powders. Firstly, an atrous convolution is introduced into our network to learn the image feature of multi-sized powders, which can reduce the missing segmentation of small-sized agglomerated particles. Then, a new mask loss function combing focal loss and dice coefficient is used to overcome the false segmentation caused by the edge effect. The final comparative experimental results show that our method achieves the best results of 94.43% and 91.44% on AP50 and AP75 respectively among the comparison algorithms. In addition, in order to provide an effective method for particle size analysis of coal particles, we study the particle size distribution of coal powders based on the proposed image segmentation method and obtain a good curve relationship between cumulative mass fraction and particle size.

There are abundant marine carbonate rock resources in China, which are dominated by fractured-vuggy carbonate oil and gas reservoirs accounting for over two thirds of proved reserves. The fractured-vuggy carbonate condensate gas reservoir in the Tarim Basin is at a burial depth of 4500–7000 m with the characteristics of extremely strong reservoir heterogeneity, complicated and diverse reservoir seepage media, complex fluid properties and quite difficult static characterization, which brings many challenges to reserve evaluation, development plan design and performance analysis. In order to improve the recovery factor of this type of oil and gas reservoir, this paper takes the dynamic characterization to supplement the static characterization and combines each other to improve the accuracy. In addition, based on many years' of dynamic and static research and development practice, the key enhanced gas recovery (EGR) technologies for fractured-vuggy carbonate condensate gas reservoirs are innovatively developed, such as the high-accuracy dynamic characterization technology with dynamic and static iteration for fractured-vuggy body in the strongly attenuated area of desert by taking seismic inversion information as the basis and 3D numerical well test as the core, the multi-target 3D development technology in one well to improve reserve production of fractured-vuggy condensate gas reservoirs, and the gas-lift depressurization EGR technology for fractured-vuggy condensate gas reservoirs. In conclusion, this technological system better solves the key difficulties in the fine characterization of fractured-vuggy body, reserve production improvement and abandonment pressure reduction of well. What's more, it realizes the fine reservoir characterization of fractured zones and feather-shaped fractured zones and the accurate prediction of key development indexes, and increases the deployment success rate of effective wells and efficient wells by 26% compared with that in the initial stage of large-scale productivity construction. To sum up, these technologies provide powerful technical support and reference experience for the scientific and efficient development of fractured-vuggy carbonate condensate gas reservoirs.

Skeletal muscle grows in response to a combination of genetic and environmental factors, and its growth and development influence the quality of pork. Elucidating the molecular mechanisms regulating the growth and development of skeletal muscle is of great significance to both animal husbandry and farm management. The Jiangquan black pig is an excellent pig breed based on the original Yimeng black pig, importing the genes of the Duroc pig for meat traits, and cultivated through years of scientific selection and breeding. In this study, full-length transcriptome sequencing was performed on three growth stages of Jiangquan black pigs, aiming to study the developmental changes in Jiangquan black pigs at different developmental stages at the molecular level and to screen the key genes affecting the growth of skeletal muscle in Jiangquan black pigs. We performed an enrichment analysis of genes showing differential expression and constructed a protein–protein interaction network with the aim of identifying core genes involved in the development of Jiangquan black pigs. Notably, genes such as TNNI2, TMOD4, PLDIM3, MYOZ1, and MYH1 may be potential regulators of muscle development in Jiangquan black pigs. Our results contribute to the understanding of the molecular mechanisms of skeletal muscle development in this pig breed, which will facilitate molecular breeding efforts and the development of pig breeds to meet the needs of the livestock industry.

Dissolved organic matter (DOM) serves as a critical link in the migration and transformation of heavy metals at the soil–solid interface, influencing the migration behaviour and transformation processes of Cu2+ in soil. There have been studies on the combination mechanisms between DOM and Cu2+ in paddy soils. However, the adsorption/complexation and redox processes between DOM and Cu2+ in other agricultural soil types (such as dry farmland and vegetable fields) are unclear. In order to reveal the combination process of DOM with Cu in different agricultural soil types and the dynamic changes in chemical behaviour that occur, this study analysed the variability of DOM components and structure in three soils using three-dimensional fluorescence spectroscopy and X-ray photoelectron spectroscopy. In addition, the priority order of different DOM compounds in combination with Cu and the change process in relation to the Cu valence state in the soil of Lujiang County, Anhui Province, was revealed based on laboratory experiments. The results showed that the composition of soil DOM was mainly composed of humic-like and fulvic-like substances with a clear terrestrial origin and that the organic matter showed a high degree of decomposition characteristics. The results indicated that the composition of soil DOM is mainly composed of humic and fulvic acid-like substances, and they have obvious characteristics of terrestrial origin. In addition, the soil organic matter showed high decomposition characteristics. The complex stability constants (lgKM) of humic acid-like substances with Cu2+ follow the order of forest land (lgKM = 5.21), vegetable land (lgKM = 4.90), and dry farmland (lgKM = 4.88). The lgKM of fulvic acid-like substances with Cu2+ is in the order of dry farmland (lgKM = 4.51) and vegetable land (lgKM = 4.39). Humic acid-like substances in soil DOM combine preferentially with Cu2+, showing a stronger chelating affinity than fulvic acid-like substances. Cu2+ complexes mainly include hydroxyl, phenolic hydroxyl and amino functional groups are included in soil DOM, accompanied by redox reactions. In comparison to dry farmland, the soil DOM in forest and vegetable fields undergoes more intense redox reactions simultaneously with the chelation of Cu2+. Therefore, the application of organic fertilisers to vegetable and forest soils may lead to uncertainties concerning the fate of heavy metals with variable chemical valence. These results contribute to a deeper understanding of the interaction mechanisms between DOM and Cu2+ in agricultural soils.

Objective The objective of our study was to systematically evaluate the clinical efficacy and safety of manual therapy in the treatment of gastroesophageal reflux disease (GERD), providing a reference value for clinical decision-making.

The extensive utilization of organophosphate esters (OPEs) has resulted in their widespread presence in the environment, raising concerns about potential human health risks. In this study, 13 OPEs were analyzed in both gas and particle phases as well as in indoor and outdoor atmospheric environments. Moreover, human exposure to OPEs were investigated within a university environment, focusing on forehead contact and individual PM2.5 inhalation. The results showed similar distribution patterns of OPEs indoors and outdoors, although higher concentrations were found indoors. The average atmospheric concentration of ∑OPEs (combining particle and gaseous OPEs) was 1575 pg/m3 in the outdoor environment and 6574 pg/m3 ∑OPEs in the indoor microenvironments. The overwhelming majority of OPEs exhibit a pronounced propensity to adsorb onto PM2.5 particles. Notably, the concentration of OPEs on the forehead differed significantly from that in the atmospheric environment, whereas individual PM2.5 exposure was consistent with the concentration of indoor PM2.5. Intriguingly, some OPEs with high octanol–water partition coefficient (log Kow) were not detected in the environment but found on human foreheads. Gas-particle partitioning was predicted using the Harner–Bidleman and Li–Ma–Yang models and the results were in agreement with the monitoring data for approximately half of the OPE monomers. Correlations between OPEs exposure and gas-particle partitioning were found to be more significant for novel OPEs. No non-cancer risk to humans through individual exposure to OPEs was identified via forehead exposure or inhalation. The previously unreported relationship between individual exposure and the environmental occurrence of traditional and novel OPEs demonstrated in this study highlights the importance of evaluating the potential health risks associated with actual OPE exposure.

Background Chronic obstructive pulmonary disease (COPD) has been considered a disease of the elderly, but it could also occur in young people aged 20–50 years. However, the characteristics and prognosis of COPD in such young people remain unclear.Methods Our retrospective cohort study was based on the National Health and Nutrition Examination Survey (NHANES). Participants who 20–50 years old at baseline and completed the pulmonary function test were enrolled in our study cohort. These participants were followed up to 31 December 2019. The sample weight and Taylor Linearization Procedures were adapted to make representative estimations of prevalence and baseline characteristics. The weighted logistic regression model was used to assess the risk factors. The propensity score method and Cox proportional hazard models were applied to calculate the risk of mortality.Results The weighted prevalence of COPD in young people in the USA was 1.64% and it increased with age, with a higher prevalence in males than females (2.59% vs 0.72%, p

Objective To determine the association between psoriasis vulgaris and hyperuricemia. Methods From September 2021 to May 2022, 100 patients with psoriasis vulgaris were enrolled at our outpatient and inpatient clinics, and 100 age-, sex- and body mass index (BMI) -matched individuals without psoriasis were enrolled at physical examination center. The circulating levels of uric acid were compared between the two groups. Single factor analysis and multi-factor analysis were applied to explore the influencing factors, such as sex, age of onset, disease duration, BMI, smoking and alcohol consumption. Results The prevalence of hyperuricemia in the observation group and the control group was 40.00% and 26.00%, respectively (χ2=4.43, P=0.035). There was no correlation between the PASI and serum uric acid levels (P>0.05). Increased serum uric acid levels in patients with psoriasis vulgaris were associated with gender, duration of disease≥10 years, high BMI, smoking and alcohol consumption (P0.05). Duration of disease, BMI, and alcohol consumption were independent risk factors for concomitant hyperuricemia in patients with psoriasis vulgaris. Conclusions The prevalence of hyperuricaemia is higher in patients with psoriasis vulgaris than in the healthy controls. Gender, disease duration, BMI and history of smoking and alcohol consumption may be associated with hyperuricaemia in patients with psoriasis vulgaris.

Research on combined phytotoxicity of perfluorooctanoic acid (PFOA) and nanoparticles is very important for the remediation of PFOA contaminated soil and further assessment for the potential of nano-enhanced phytoremediation. Here, joint effects of PFOA and CuO nanoparticles (nCuO) in plants were studied by exposing radish (Raphanus sativus L.) to PFOA (4 mg/kg) and nCuO (200 and 400 mg/kg) for 30 days, and measuring for contaminant accumulation, radish biomass, photosynthesis profiles and nutrient contents. Results showed that PFOA accumulated highly in radish organs but showed limited effects on radish biomass. nCuO could increase the transfer rate of PFOA from root to shoot and reduce PFOA accumulation in edible root part, but higher nCuO lead to decreased radish biomass. Reversely, PFOA alleviated the adverse effects of nCuO on leaf photosynthesis and root metabolism of vitamins and amino acids. These results provided basics for exploring possibility of nano-enhanced phytoremediation for PFOA soil pollution.

Objective The growth of pigs involves multiple regulatory mechanisms, and modern molecular breeding techniques can be used to understand the skeletal muscle growth and development to promote the selection process of pigs. This study aims to explore candidate lncRNAs and mRNAs related to skeletal muscle growth and development among Duroc pigs with different average daily gain (ADG). Methods A total of 8 pigs were selected and divided into two groups: H group (high-ADG) and L group (low-ADG). And followed by whole transcriptome sequencing to identify differentially expressed (DE) lncRNAs and mRNAs. Results In RNA-seq, 703 DE mRNAs (263 up-regulated and 440 down-regulated) and 74 DE lncRNAs (45 up-regulated and 29 down-regulated) were identified. In addition, 1,418 Transcription factors (TFs) were found. Compared with mRNAs, lncRNAs had fewer exons, shorter transcript length and open reading frame length. DE mRNAs and DE lncRNAs can form 417 lncRNA-mRNA pairs (antisense, cis and trans). DE mRNAs and target genes of lncRNAs were enriched in cellular processes, biological regulation, and regulation of biological processes. In addition, quantitative trait locus (QTL) analysis was used to detect the functions of DE mRNAs and lncRNAs, the most of DE mRNAs and target genes of lncRNAs were enriched in QTLs related to growth traits and skeletal muscle development. In single-nucleotide polymorphism/insertion-deletion (SNP/INDEL) analysis, 1,081,182 SNP and 131,721 INDEL were found, and transition was more than transversion. Over 60% of percentage were skipped exon events among alternative splicing events. Conclusion The results showed that different ADG among Duroc pigs with the same diet maybe due to the DE mRNAs and DE lncRNAs related to skeletal muscle growth and development.

Orthogonal frequency division multiplexing (OFDM) in 5G has many advantages; however, one of the disadvantages is that the superposition of a large number of subcarriers leads to a high peak-to-average power ratio (PAPR) of the transmit signal. A high PAPR results in high-power amplifier distortion and performance degradation. The partial transmit sequence (PTS) algorithm is commonly used for PAPR reduction. It enumerates all combinations of phase factors, weighs the signal using each phase factor combination, and finds the set of phase factors that minimizes the PAPR value of the OFDM signal. The advantage of the PTS is that it determines the optimal solution through enumeration; however, its major drawback is the higher complexity caused by the use of enumeration. Some studies have introduced the discrete particle swarm optimization (DPSO) algorithm instead of enumeration to determine the optimal solution of the PTS algorithm. As an excellent optimization method, the DPSO algorithm represents each individual as a solution during the optimization. Through iterative updates of the initial population, individuals in the population continuously move closer to the optimal solution. This approach significantly reduces complexity compared with the exhaustive enumeration used in the traditional PTS algorithm. However, the disadvantage of the general DPSO algorithm is that it can result in premature and early convergence, which leads to degradation of the PAPR reduction performance. In this study, we propose an improved method based on the general DPSO-based PTS algorithm, and the improved algorithm MDPSO-PTS adopts dynamic time-varying learning factors, which can find the optimal combination of phase factors more efficiently. The MDPSO-PTS algorithm expands the search space when seeking the optimal combination of phase factors. This avoids the drawback of premature convergence commonly observed in general DPSO-PTS algorithms, preventing early consideration of local optima as global optima. A comparative simulation of the improved MDPSO-PTS algorithm with the general DPSO-PTS algorithm shows that the improved algorithm has stronger PAPR reduction, whereas the complexity remains basically unchanged. A comparative simulation with the traditional PTS algorithm shows a significant reduction in complexity, with only a slight, acceptable loss of reduction performance.

Single-nucleotide polymorphisms (SNPs), as disease-related biogenetic markers, are crucial in elucidating complex disease susceptibility and pathogenesis. Due to computational inefficiency, it is difficult to identify high-dimensional SNP interactions efficiently using combinatorial search methods, so the spherical evolutionary multi-objective (SEMO) algorithm for detecting multi-locus SNP interactions was proposed. The algorithm uses a spherical search factor and a feedback mechanism of excellent individual history memory to enhance the balance between search and acquisition. Moreover, a multi-objective fitness function based on the decomposition idea was used to evaluate the associations by combining two functions, K2-Score and LR-Score, as an objective function for the algorithm’s evolutionary iterations. The performance evaluation of SEMO was compared with six state-of-the-art algorithms on a simulated dataset. The results showed that SEMO outperforms the comparative methods by detecting SNP interactions quickly and accurately with a shorter average run time. The SEMO algorithm was applied to the Wellcome Trust Case Control Consortium (WTCCC) breast cancer dataset and detected two- and three-point SNP interactions that were significantly associated with breast cancer, confirming the effectiveness of the algorithm. New combinations of SNPs associated with breast cancer were also identified, which will provide a new way to detect SNP interactions quickly and accurately.

BackgroundParkinson’s disease (PD) is a progressive neurodegenerative disease with characteristic pathological abnormalities, including the loss of dopaminergic (DA) neurons, a dopamine-depleted striatum, and microglial activation. Lipid accumulation exhibits a close relationship with these pathologies in PD.MethodsHere, 6-hydroxydopamine (6-OHDA) was used to construct a rat model of PD, and the lipid profile in cerebrospinal fluid (CSF) obtained from model rats was analyzed using lipidomic approaches.ResultsEstablishment of this PD model was confirmed by apomorphine-induced rotation behaviors, loss of DA neurons, depletion of dopamine in the striatum, and microglial activation after 6-OHDA-induced lesion generation. Unsupervised and supervised methods were employed for lipid analysis. A total of 172 lipid species were identified in CSF and subsequently classified into 18 lipid families. Lipid families, including eicosanoids, triglyceride (TG), cholesterol ester (CE), and free fatty acid (FFA), and 11 lipid species exhibited significantly altered profiles 2 weeks after 6-OHDA administration, and significant changes in eicosanoids, TG, CE, CAR, and three lipid species were noted 5 weeks after 6-OHDA administration. During the period of 6-OHDA-induced lesion formation, the lipid families and species showed concentration fluctuations related to the recovery of behavior and nigrostriatal abnormalities. Correlation analysis showed that the levels of eicosanoids, CE, TG families, and TG (16:0_20:0_18:1) exhibited positive relationships with apomorphine-induced rotation behaviors and negative relationships with tyrosine hydroxylase (TH) expression in the midbrain.ConclusionThese results revealed that non-progressive nigrostriatal degeneration induced by 6-OHDA promotes the expression of an impairment-related lipidomic signature in CSF, and the level of eicosanoids, CE, TG families, and TG (16:0_20:0_18:1) in CSF may reveal pathological changes in the midbrain after 6-OHDA insult.

BackgroundThe neutralizing antibodies (NAbs) response after COVID-19 vaccination after liver dysfunction is unclear. In this study, we evaluated the NAbs response after COVID-19 vaccination in hospitalized patients suffering from liver dysfunction.MethodsIn this cross-sectional study with longitudinal follow-up, we enrolled eligible patients with liver dysfunction and healthy volunteers with full-course COVID-19 vaccination. Blood samples were collected for the NAbs testing at the time of admission and after treatment. Multiple regression analysis to assess independent risk factors affecting NAbs response.ResultsA total of 137 patients and 134 healthy controls (HC) were enrolled. Both seropositivity (65.7% vs 80.6%, p

Based on the analysis of geological characteristics of ultra-deep fault-controlled fracture-cavity carbonate reservoirs and division of reservoir units, two physical models were made, and physical simulations of oil displacement by water injection were carried out to find out water flooding mechanism in the fault-controlled fracture-cavity carbonate reservoir under complex flow state. On this basis, a mathematical model of fault-controlled carbonate reservoir with coexisting seepage and free flow has been established. Pilot water injection tests have been carried out to evaluate the effects of enhancing oil recovery by water injection. The results show that: fault-controlled fracture-cavity carbonate reservoir units can be divided into three types: the strong natural energy connected type, the weak natural energy connected type and the weak natural energy isolated type; the fault-fracture activity index of the fault-controlled fractured-cavity body can effectively characterize the connectivity of the reservoir and predict the effective direction of water injection; the mathematical model of fault-controlled carbonate reservoir with coexisting seepage and free flows can quantitatively describe the fluid flow law in the fracture-cavity body; the water injected into the fault-controlled fracture-cavity body is weakly affected by the capillary force of the lithologic body, and the oil-water movement is mainly dominated by gravity. The development modes of single well water injection, unit water injection, and single well high pressure water injection proposed based on the connection structure of fracture- cavity space and well storage space configuration are confirmed effective by pilot tests, with obvious water injection gravity flooding effect.

The widespread use of scanning electron microscopy (SEM) has increased the requirements for SEM image quality. SEM images obtained by electron beam feedback have more complex texture features than natural images obtained by optical imaging, and this condition results in poor performance of algorithms used for assessing natural image quality on SEM datasets,meanwhile,the field of SEM image quality assessment(IQA) is mostly aimed at specific distortion types. In order to solve the above two problems,to address the rich texture, few edges, and extreme sensitivity to the distortion degree of SEM images, we propose a texture and semantic IQA (TSIQA) method for SEM images based on sparse mask and information entropy increase. First, we construct a neural network containing sparse mask module (SMM), which is used to extract intuitive texture features in the spatial and channel domains. Simultaneously, information growth attention (IGA) is introduced into SMM to detect the difference between current and past features of the network for extracting deep semantic information. The quality assessment experiments on SEM image datasets show that compared with the state-of-the-art IQA methods, including popular no-reference techniques adapted to the SEM-IQA, the TSIQA has superiority in typical criteria.

Mine Internet of Things (MIoT) devices in intelligent mines often face substantial signal attenuation due to challenging operating conditions. The openness of wireless communication also makes it susceptible to smart attackers, such as active eavesdroppers. The attackers can disrupt equipment operations, compromise production safety, and exfiltrate sensitive environmental data. To address these challenges, we propose an intelligent reflecting surface (IRS)-assisted secure transmission system for an MIoT device which enhances the security and reliability of wireless communication in challenging mining environments. We develop a joint optimization problem for the IRS phase shifts and transmit power, with the goal of enhancing legitimate transmission while suppressing eavesdropping. To accommodate time-varying channel conditions, we propose a reinforcement learning (RL)-based IRS-assisted secure transmission scheme that enables MIoT device to optimize both the IRS reflecting coefficients and transmit power for optimal transmission policy in dynamic environments. We adopt the deep deterministic policy gradient (DDPG) algorithm to explore the optimal transmission policy in continuous space. This can reduce the discretization error caused by traditional RL methods. The simulation results indicate that our proposed scheme achieves superior system utility compared with both the IRS-free (IF) scheme and the IRS randomly configured (IRC) scheme. These results demonstrate the effectiveness and practical relevance of our contributions, proving that implementing IRS in MIoT wireless communication can enhance safety, security, and efficiency in the mining industry.

Porcine skeletal muscle is a highly heterogeneous tissue type, and the Longissimus Dorsi muscle (LDM), as the most economical and physiologically metabolized skeletal muscle in pigs, has always been the focus of research and improvement in pig molecular breeding. Circular RNA, as an important new member of regulatory non-coding RNA after microRNA, has become a frontier hot spot in life science research. This study aims to explore candidate circRNAs related to growth, meat quality, and skeletal muscle development among Duroc pigs with different average daily gain (ADG). Eight pigs were selected and divided into two groups: H group (high-ADG) and L group (low-ADG), followed by RNA-Seq analysis to identify circRNAs. The results showed that backfat at 6-7 rib (BF) and Intramuscular fat (IMF) content in the H group was significantly lower than L group, but ribeye area (REA) in the H group was higher than in the L group. In RNA-seq, 296 Differentially expressed (DE) circRNAs (157 upregulated and 139 downregulated) were identified and exons flanking long introns are easier to circularize to produce circRNAs. Most of the DE circRNAs were enriched in Quantitative trait locus (QTL) regions related to meat quality and growth traits. In addition, a gene can produce one or more circRNA transcripts. It was also found that the source genes of DE circRNAs were enriched in MAPK, FoXO, mTOR, PI3K-Akt, and Wnt signaling pathways. The results showed that different ADG, carcass, and meat quality traits among half-sibling Duroc pigs with the same diet may be due to the DE circRNAs related to skeletal muscle growth and development.

Nanoparticles have been applied to remediate heavy metal pollutants (e.g., arsenic) in groundwater and soil, where the migration mechanism of pollutants in this multi-component system has not been fully understood. To better understand how nano-iron oxide affects arsenic migration under multivariate influence, this study prepared arsenic loaded composite colloids using arsenic (As(V)), humic acid, and nano iron oxide (n-α-Fe2O3). Then we explored the migration behavior of the composite colloids (HA-n-α-Fe2O3) loaded As(V) in a quartz sand column affected by n-α-Fe2O3 under various humic acid concentrations, pH values, ionic strengths, and ferric oxide contents. Two migration models, including a classical advection-dispersion equation (ADE) and a Hausdorff fractal advection-dispersion equation (HADE), were used to quantify the observed co-migration of arsenic conveyed by HA-n-α-Fe2O3 colloids loaded As(V) in the column. Our analysis indicates that the adsorption capability of nano-iron oxide on arsenic decreases with pH, humic acid and iron oxide concentrations, thus accelerating arsenic movement. While, the increasement of ionic strength enhances the adsorption force of nano-iron oxide on arsenic and suppresses arsenic migration. Furthermore, simulation results suggest that the HADE model outperforms the traditional ADE model in characterizing arsenic migration, where the time derivative index is an indicator of anomalous diffusion.

Abstract It is of great importance to power the nodes of the rechargeable wireless sensor network to detect events continuously in the area of interest. This paper proposes a joint data transmission and on‐demand charging algorithm based on the space correlation. The new algorithm optimises the event detection, data forwarding and node charging jointly to improve the charging efficiency. First, the active nodes participating in the event detection are selected using an improved iterative node selection method to reduce the number of nodes working concurrently. Then, the greedy data transmission scheme based on grid partition is proposed to transmit the observed data to the sink node. Finally, the nodes in the networks are charged using the on‐demand charging method based on grid partition, which greatly decreases the charging frequency and energy loss of the mobile charger. The simulation results demonstrate that the proposed method has superior performance in the distance travelled by the mobile charger, the energy utilisation, the average energy consumption of the mobile charger and the node charging latency.

Low-coverage whole genome sequencing is a low-cost genotyping technology. Combined with genotype imputation approaches, it is likely to become a critical component of cost-effective genomic selection programs in agricultural livestock. Here, we used the low-coverage sequence data of 617 Dezhou donkeys to investigate the performance of genotype imputation for low-coverage whole genome sequence data and genomic prediction based on the imputed genotype data. The specific aims were as follows: 1) to measure the accuracy of genotype imputation under different sequencing depths, sample sizes, minor allele frequency (MAF), and imputation pipelines and 2) to assess the accuracy of genomic prediction under different marker densities derived from the imputed sequence data, different strategies for constructing the genomic relationship matrixes, and single-vs. multi-trait models. We found that a high imputation accuracy (>0.95) can be achieved for sequence data with a sequencing depth as low as 1x and the number of sequenced individuals ≥400. For genomic prediction, the best performance was obtained by using a marker density of 410K and a G matrix constructed using expected marker dosages. Multi-trait genomic best linear unbiased prediction (GBLUP) performed better than single-trait GBLUP. Our study demonstrates that low-coverage whole genome sequencing would be a cost-effective approach for genomic prediction in Dezhou donkey.

In this paper, we propose new inner and outer bounds of the capacity region for multiple access channels in visible light communication (VLC) networks under both peak and average optical power constraints. Specifically, the proposed inner bounds are established by employing the single-user capacity achieving input distribution for each user. The proposed outer bounds are derived by determining single-user capacities for each user and calculating a sum capacity upper bound by relaxing the input constraints. Numerical results show that the proposed new bounds are extremely tight and outperform existing bounds over wide ranges of SNRs.

The quadrature angular diversity aperture (QADA) receiver, consisting of a quadrant photodiode (QPD) and an aperture placed above the QPD, has been investigated for pose estimation for visible light systems. Current work on pose estimation for the QADA receiver uses classical camera sensor algorithms well known in computer vision. To this end, however, the light spot center first has to be obtained based on the RSS. However, this is less straightforward than for camera sensors, as in contrast to such sensors where the relationships are linear, the RSS output from the QADA is a non-linear function of the light spot position. When applying closed form solutions or iterative methods for cameras on a QADA, the non-linearity will degrade their performance. Furthermore, since in practice the aperture is not always perfectly aligned with the QPD, a procedure to calibrate the receiver is needed. Current work on calibration requires additional sophisticated equipment to measure the pose during calibration, which increases the difficulty of implementation. In this paper, we target the above problems for pose estimation and calibration of the QADA receiver. To this end, we first study the effect of the strategy of differencing and normalization on the probability density function (PDF), a commonly applied strategy for the QPD’s robustness against RSS variation, and it is shown that the applied strategy results in a complex PDF, which makes an effective and efficient estimation hard to achieve. Therefore, we derive an approximated PDF in a simple closed-form, based on which the calibration and the pose estimation algorithms using the least squares principle are proposed. The proposed calibration does not require any information about the pose of the receiver and is robust to variation of the received power and imperfect knowledge of the radiation pattern of the LED, making it easy to implement. We also derive the corresponding Cramér-Rao lower bound on the misalignment to benchmark the performance of the misalignment and to serve as an indicator to determine the required signal-to-noise ratio (SNR) or number of LEDs to obtain a desired accuracy. The calibration and pose estimation are evaluated by means of a Monte Carlo simulation. Computer simulations show that this theoretical bound is close to the RMSE of the proposed estimator and that the proposed pose estimator outperforms the PnP algorithm.

In this paper, we investigate the design and implementation of machine learning (ML)-based demodulation methods in the physical layer of visible light communication (VLC) systems. We build a flexible hardware prototype of an end-to-end VLC system, from which the received signals are collected as the real data. The dataset is available online, which contains eight types of modulated signals. Then, we propose three ML demodulators based on convolutional neural network (CNN), the deep belief network (DBN), and adaptive boosting (AdaBoost), respectively. Specifically, the CNN-based demodulator converts the modulated signals to images and recognizes the signals by the image classification. The proposed DBN-based demodulator contains three restricted Boltzmann machines to extract the modulation features. The AdaBoost method includes a strong classifier that is constructed by the weak classifiers with the k-nearest neighbor algorithm. These three demodulators are trained and tested by our online open dataset. The experimental results show that the demodulation accuracy of the three data-driven demodulators drops as the transmission distance increases. A higher modulation order negatively influences the accuracy for a given transmission distance. Among the three ML methods, the AdaBoost modulator achieves the best performance.

In this paper, we investigate deep learning (DL)-enabled signal demodulation methods and establish the first open dataset of real modulated signals for wireless communication systems. Specifically, we propose a flexible communication prototype platform for measuring real modulation dataset. Then, based on the measured dataset, two DL-based demodulators, called deep belief network (DBN)-support vector machine (SVM) demodulator and adaptive boosting (AdaBoost)-based demodulator, are proposed. The proposed DBN-SVM based demodulator exploits the advantages of both DBN and SVM, i.e., the advantage of DBN as a feature extractor and SVM as a feature classifier. In DBN-SVM based demodulator, the received signals are normalized before being fed to the DBN network. Furthermore, an AdaBoost-based demodulator is developed, which employs the k-nearest neighbor as a weak classifier to form a strong combined classifier. Finally, the experimental results indicate that the proposed DBN-SVM based demodulator and AdaBoost-based demodulator are superior to the single classification method using DBN, SVM, and maximum likelihood-based demodulator.

In this paper, we consider the two key problems in the physical-layer security of nonorthogonal multiple access (NOMA) visible light communication (VLC) networks: investigating a closed-form achievable security rate and studying the optimal security beamforming design. Specifically, under the dimming control, practical power, and successive interference cancellation constraints, we derive both the outer and inner bounds of the security capacity region with closed-form expressions, which are evaluated via numerical results. Then, based on the proposed security-rate expression, we investigated the optimal security beamforming design to minimize the total LED power, and to maximize the minimum secrecy rate, respectively. Both the problems are nonconvex. We apply different relaxation techniques to efficiently solve them. The simulation results demonstrate the efficacy of the proposed security beamforming design schemes in the NOMA VLC networks.

The localization algorithm for mobile robots working in narrow space needs to handle the scenario that the geometric shape of reference nodes tends to a line, which results in the matrix of least squares localization approaches ill-conditioned. Estimator bias becomes an important factor that can degrade the localization performance. In this paper, we present a fast unbiased range-based localization algorithm to resist the ill-conditioned problem. The main strategy is to augment objective function in the resultant optimization formulations via introducing a measurement distance into the locating model, which forms a least squares problem with cone constrained. The proposed model decouples the measurement distances from the matrix of least squares, which avoids the ill-conditioned problem when the target is around the geometric center. The closed-form expression of locating position ensures that the proposed algorithm is unbiased and low computation burden in the presence of zero-mean disturbance. Moreover, the robustness improvement of the augmented objective function is analyzed. Numerical simulations are used to corroborate the analytic results which demonstrate the good performance, robustness, and fastness of the proposed method.

The future of mobile communication systems is evolving rapidly toward being more intelligent, while also having the ability to interconnect with everything and be aware of the current wireless environment. For complex scenarios, the asymmetry features of channel state information (CSI) will seriously restrict the performance of the communication system. With its accurate positioning technology and high-speed communication rate, ultra-wideband (UWB) has a promising future as a solution to integrate communication and positioning functions. The traditional CSI channel estimation usually requires channel training, which will greatly increase the overhead of the system. This paper proposes a location information-assisted beamforming to replace the traditional channel training process. First, we use the user location parameter information to reconstruct the channel model and derive the CSI error distribution based on the location distribution. Second, considering the uncertainty of the user positioning error, we model the robust beamforming optimization problem that minimizes the total transmit power. To solve this non-convex problem effectively, we design a new beamforming algorithm by using semidefinite relaxation (SDR) and Bernstein-type inequalities. Finally, simulation results verify the robustness of the proposed robust beamforming compared to the worst-case robust beamforming.

The largest ultra−deep (>7000 m) strike−slip fault−controlled oilfield was found in the tight Ordovician carbonates in the Tarim Basin. Because oil production wells in the Fuman Oilfield generally have bottom water, a reasonable production design is of significant importance for extending the water−free oil recovery period and improving oil recovery. However, there is no economical and effective quantitative method to determine a reasonable production capacity and a corresponding reasonable work system for stable−production wells in fault−controlled fractured carbonate reservoirs. In this contribution, we integrated the dynamic and static data of different types of wells in the Fuman Oilfield. It was found that there is a positive relationship between well−controlled dynamic reserves and reasonable productivity, and the productivity corresponding to the upper limit of the reserve can be defined as reasonable productivity. Further, we proposed a correlation equation between well−controlled dynamic reserves and reasonable productivity that shows the reasonable productivity of a stable well from the well−controlled dynamic reserve, which was obtained by combining the well productivity test and analogous well productivity test methods. The relationship between the reasonable productivity per unit nozzle and the rational productivity was obtained according to the determined matching relationship between the reasonable productivity of the stable−production well and the nozzle. The application results show that it can not only quickly determine the reasonable production capacity of stable−production wells but also deepen the understanding of the quantitative characteristics of the production capacity of the Fuman Oilfield. It also provides insight in the rational production allocation of new wells and optimize the development design, which supports the 1500 × 104 bbl/year oil production from the ultra−deep fractured carbonate reservoirs along the strike−slip fault zones in the Tarim Basin.

The largest carbonate condensate field has been found in the Tarim Basin, NW China. Different from sandstone condensate gas reservoirs, however, the conventional gas injection for pressure maintenance development is not favorable for Ordovician fracture-cave reservoirs. Based on this, in this paper, 21 sets of displacement experiments in full-diameter cores and a pilot test in 11 boreholes were carried out to study enhanced oil recovery (EOR) in complicated carbonate reservoirs. The experimental results show that the seepage channels of the gas condensate reservoirs are fractures, which are quite different from sandstone pore-throat structures. Condensate oil recovery using water injection was up to 57–88% in unfilled fractured caves and at ca. 52–80% in sand-filled fractured caves. These values are much higher than the 14–46% and 17–58% values obtained from the depletion and gas injection experiments, respectively. The water injection in 11 wells showed that the condensate oil recovery increased by 0–17.7% (avg. 3.1%). The effective EOR for residual oil replacement using water injection may be attributed to fractures, as the gas channel leads to an ineffective gas circulation and pipe flow in fracture-cave reservoirs, which is favorable for waterflood development. The complicated fracture network in the deep subsurface may be the key element in the varied and lower oil recovery rates obtained from the wells than from the experiments. This case study provides new insights for the exploitation of similar condensate gas reservoirs.

Based on cores, well logging and karstic paleogeomorphology, the identification method of multi-period paleokarstic water tables and their distribution and control on reservoirs were researched of Yingshan Formation in Tazhong Area, Tarim Basin. Three periods' water tables numbered 1 to 3 have been identified from bottom to top of Yingshan Formation in Tazhong Area. The corresponding typical horizontal undercurrent zones are characterized by single karst fabrics such as sack-like dissolved vugs or subhorizontal channels, combination of various karst fabrics like pre-existing high-angle fractures intersected by small caves and subhorizontal channels, and combination of multi-layered caves and different karst fabrics of large pores. On the plane, periodical rising sea levels caused the development of No.1, No.1 and 2, No.1, 2 and 3 water tables in karst depressions, slopes and highlands respectively. As the result of the nearly horizontal occurrence of Yingshan Formation, submembers of Yingshan Formation had tiny angles with fracture-cave bodies of each period's water table, so the karst reservoirs are characterized by “crossed strata regionally and developed along the stratum locally”. The distance between water table and unconformity, thickness of horizontal undercurrent zone and average thickness of reservoir all tended to increase with the rise of karst landforms. The karst highland and slope had 2-3 periods' water tables and thicker horizontal undercurrent zones, so they had thicker and better quality reservoirs, making them favorable exploration areas. Key words: paleokarstic water table identification, stages and distribution, reservoir-controlled pattern, Ordovician Yingshan Formation, Tazhong area, Tarim Basin

Speech enhancement in a vehicle environment remains a challenging task for the complex noise. The paper presents a feature extraction method that we use interchannel attention mechanism frame by frame for learning spatial features directly from the multichannel speech waveforms. The spatial features of the individual signals learned through the proposed method are provided as an input so that the two-stage BiLSTM network is trained to perform adaptive spatial filtering as time-domain filters spanning signal channels. The two-stage BiLSTM network is capable of local and global features extracting and reaches competitive results. Using scenarios and data based on car cockpit simulations, in contrast to other methods that extract the feature from multichannel data, the results show the proposed method has a significant performance in terms of all SDR, SI-SNR, PESQ, and STOI.

Compared with the line-of-sight (LOS) condition, the multipath effect is more serious in the non-line-of-sight (NLOS) condition. Therefore, the LOS and NLOS identification is necessary for the multipath analysis of signal propagation. The commonly used method is the support vector machine (SVM) method with high computational complexity. To tackle this problem, this paper adopts the SVM classifier based on fewer selected features of the normalized power delay profile (PDP). Therein, the PDP can be obtained using the sliding correlation method. The results show that the SVM-based classifier can achieve high accuracy on LOS and NLOS identification. We then analyze the impact of the signal-to-noise ratio (SNR) and transmitting-receiving (Tx-to-Rx) distance on distinguishable multipaths under LOS and NLOS conditions. According to statistical measurement results, a function of distinguishable multipath numbers is established. Finally, we investigate the multipath power and delay parameters of average delay spread and root mean square (RMS) delay spread based on multipath results. The outcomes of this paper provide a useful support for analyzing signal propagation characteristics.

Abstracts Stress, including both psychological and physical stimulation, can cause changes in the microbiota and mucosal function of the gastrointestinal system. There are few research studies available about the faecal microbiota changes after stress, such as water immersion restraint stress (WIRS). Therefore, in this study, we focused on analysing the composition changes of faecal microbiota in WIRS mice. The WIRS model, in which Blab/c mice were immersed in 21 ± 2 °C water for 4 h each day for 14 days, was established. Behavioural changes, the serum levels of corticosterone, IFN-γ and IL-17 and gastric mucosal injury were also assessed. Ten faecal microbiota samples were detected by Illumina Miseq sequencing of the 16S rRNA genes from 367205 characterised sequences. Finally, we find significant differences in the faecal microbiota composition between the control and the WIRS groups. There was an obvious increase in Lachnospiraceae in the WIRS mice (p = 0.0286, p

The increasing severity of arsenic pollution has progressively threatened human life and attracted much attention. One of the important topics in environmental sciences is to accurately describe the inorganic arsenic transport in heterogeneous porous media, occurring anomalous diffusion phenomenon, which ultimately benefits the control of arsenic pollution. In this paper, we re-evaluate the dataset of the inorganic arsenic transport in porous media in previous work by using a time-Hausdorff fractal model (HADE). Transport experiments of arsenic-carrying (As(V)) ferric humate complex colloids through a quartz sand column were carried out under varying dissolved organic matter (humic acid) concentrations, pH values, ionic strengths, and ferric concentrations. The results show that under our experimental settings, arsenic migration is promoted with the increase of concentrations of HA, ferric ion and sodium ion, and pH to varying degrees. The intensity of arsenic sub-diffusion behavior is opposite to that of arsenic transport. The HADE model can describe the migration behavior of arsenic well, and the value of the time fractal derivative can reflect the diffusion intensity of arsenic migration to a certain extent. By comparing the HADE model, ADE model, and time-fractional model (fADE) to the experimental data, the HADE model can significantly improve all the simulation results of capturing As(V) breakthrough curves (BTCs).

The aim of this study was to evaluate the effects of different Se sources on the meat quality and shelf life of fattening pigs. The control diet was supplemented with 0.3 mg/kg of Se from sodium selenite (SS), and experimental diets included 0.3, 0.3 and 0.15 + 0.15 mg/kg of Se from Se-enriched yeast (SY), selenomethionine (Se-Met) and SS + Se-Met, respectively. The results showed that using organic Se or Se + Se-Met in fattening pigs’ diet could increase average daily gain (ADG) (p < 0.05), decrease F/G (p < 0.05), reduce (p < 0.01) moisture, drip loss and cooking loss of longissimus thoracis, as well as increase (p < 0.05) protein and fat contents of longissimus thoracis. Diet supplementation with SY or Se + Se-Met could increase (p < 0.01) back fat thickness and skin thickness, and SY could increase (p < 0.01) belly fat rat. Adding SY or Se + Se-Met could reduce (p < 0.01) L value (45 min, 24 h). Adding Se-Met could decrease (p < 0.01) b value (45 min, 24 h), adding Se + Se-Met could reduce b value (45 min), and adding SY could reduce the b value (24 h). However, there were no (p < 0.05) significant effects on dressing percentage, carcass sloping length, eye muscle area, pH, a value (45 min) and a value (24 h) of longissimus thoracis. Moreover, the TVB-N contents of longissimus thoracis on the first and fifth days, the numbers of Lactobacillus on the third to seventh days and the numbers of E. coli in in the fifth to seventh days of longissimus thoracis were reduced (p < 0.01) by diet supplementation with organic Se. In conclusion, all the results indicate that replacing inorganic Se in diet with organic Se could improve meat quality of fattening pigs. In addition, organic Se could reduce the total volatile basic nitrogen (TVB-N) contents of longissimus thoracis and reduce the numbers of E. coli and Lactobacillus in longissimus thoracis, prolonging the shelf life of pork. These results demonstrated that organic Se supplementation was more effective than SS supplementation for meat quality and the shelf life of fattening pigs.

Understanding the stability of chloropyromorphite (CPY) is of considerable benefit for improving risk assessment and remediation strategies in contaminated water and soil. The stability of CPY in the rhizosphere of phosphorus-deficient ryegrass was evaluated to elucidate the role of root-secreted low molecular weight organic acids (LMWOAs) on the dissolution of CPY. Results showed that CPY treatments significantly reduced the ryegrass biomass and rhizosphere pH. The presence of calcium nitrate extractable lead (Pb) and phosphorus (P) suggested that CPY in the rhizosphere could be bioavailable, because P and Pb uptake by ryegrass potentially provided a significant concentration gradient that would promote CPY dissolution. Pb accumulation and translocation in ryegrass was found to be significantly higher in P-sufficient conditions than in P-deficient conditions. CPY treatments significantly enhanced root exudation of LMWOAs irrigated with P-nutrient solution or P-free nutrient solution. Oxalic acid was the dominant species in root-secreted LMWOAs of ryegrass under P-free nutrient solution treatments, suggesting that root-secreted oxalic acid may be the driving force of root-induced dissolution of CPY. Hence, our work, provides clarifying hints on the role of LMWOAs in controlling the stability of CPY in the rhizosphere.