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Abstract Cardiac arrhythmias are the leading cause of death and pose a huge health and economic burden globally. Electrocardiography (ECG) is an effective technique for the diagnosis of cardiovascular diseases because of its noninvasive and cost-effective advantages. However, traditional ECG analysis relies heavily on the clinical experience of physicians, which can be challenging and time-consuming to produce valid diagnostic results. This work proposes a new hybrid deep learning model that combines convolutional neural network (CNN) and bidirectional gated recurrent unit (BiGRU) with multi-head attention (CBGM model). Specifically, the model consists of seven convolutional layers with varying filter sizes (4, 16, 32, and 64) and three pooling layers, respectively, while the BiGRU module includes two layers with 64 units each followed by multi-head attention (8-heads). The combination of CNN and BiGRU effectively captures spatio-temporal features of ECG signals, with multi-head attention comprehensively extracted global correlations among multiple segments of ECG signals. The validation in the MIT-BIH arrhythmia database achieved an accuracy of 99.41%, a precision of 99.15%, a specificity of 99.68%, and an F1-Score of 99.21%, indicating its robust performance across different evaluation metrics. Additionally, the model’s performance was evaluated on the PTB Diagnostic ECG Database, where it achieved an accuracy of 98.82%, demonstrating its generalization capability. Comparative analysis against previous methods revealed that our proposed CBGM model exhibits more higher performance in automatic classification of arrhythmia and can be helpful for assisting clinicians by enabling real-time detection of cardiac arrhythmias during routine ECG screenings.

Improvement of cultivated cotton adaptability to extreme climate events under climate change promotes sustainable cotton production. Extreme rainfall leads to a significant decrease in cotton yield, which may be related to changes in soil water consumption (SWC) and the vertical distribution of yield, but relevant research is still scarce. Here, a two-year cotton sowing date experiment was conducted in which geostatistics, sensor technology, the spatial grid method, and principal component analysis were combined to analyze cotton utilization of soil water during extreme rainfall (2021) and normal (2022) years. The reasons for cotton yield reduction under extreme rainfall and strategies to improve cotton production adaptability to extreme rainfall were discussed. Under extreme rainfall, the morphogenesis and reproductive organ development of cotton were inhibited. The accumulation of SWC and its relationship with the biomass accumulation of cotton on different sowing dates under extreme rainfall exhibited nearly opposite characteristics to those in a normal year. Simultaneously, the two-year yield showed the opposite trend with the change in sowing date. There existed a trade-off strategy for the vertical distribution (i.e., on the upper, middle and lower fruiting branches) of cotton yield. Extreme rainfall reduced the yield at lower fruiting branches and increased the boll-forming rate of the upper fruiting branches, which was closely related to seed cotton yield, lint yield and water productivity (WP). Optimizing the cotton sowing date (early sowing should be appropriate in this study) may improve the adaptability of cotton production under extreme rainfall, but further long-term studies are needed. This study highlights the critical practice of climate-smart agriculture and has reference value for the sustainable development of cotton production.

Deficit irrigation is a common strategy to reduce water use and improve the sustainability of cotton production. However, the effects of water deficits on crop productivity and quality are subject to genotype by management by environmental interactions. This study investigated effects of water deficits and frequency of irrigation on cotton performance grown in semi-arid region, Xinjiang, the main cotton-growing area in China. Two field trials (2020 and 2021) with split experimental design, including main factors of three irrigation levels (moderate-deficit, mild-deficit and full-irrigation) and split factors of three irrigation frequencies (4, 8 and 12 days) were conducted. Results from two trials both showed little negative influence of irrigation levels on yield, and higher irrigation frequency improved yield under same irrigation level. Significant effects of irrigation levels on yield components were found in 2021, with a 22 % increase in boll number and an 18 % reduction in boll weight under moderate-deficit irrigation compared with those under full-irrigation. Interactions between irrigation levels and frequencies significantly affected harvest index (HI), showing that reduced irrigation might be beneficial for improving HI. However, decreased fibre length while increased fibre micronaire were found under deficit irrigation. A strong association between radiation use efficiency (RUE) and boll growth rate was observed, suggesting that RUE might be the driving force of yield formation. A tight correlation between both biomass and transpiration efficiency versus delta temperature between air and canopy (ΔTair-canopy) was observed, suggesting ΔTair-canopy could be used as an efficient tool to assess plant production under deficit irrigation. This study provided an improved understanding of the physiological basis of cotton yield formation and further identified a high-throughput and instantaneous method to monitor effects of deficit irrigation on crop productivity.

Abstract Background Periodic bioinformatics-based screening of wastewater for assessing the diversity of potential human viral pathogens circulating in a given community may help to identify novel or potentially emerging infectious diseases. Any identified contigs related to novel or emerging viruses should be confirmed with targeted wastewater and clinical testing. Results During the COVID-19 pandemic, untreated wastewater samples were collected for a 1-year period from the Great Lakes Water Authority Wastewater Treatment Facility in Detroit, MI, USA, and viral population diversity from both centralized interceptor sites and localized neighborhood sewersheds was investigated. Clinical cases of the diseases caused by human viruses were tabulated and compared with data from viral wastewater monitoring. In addition to Betacoronavirus, comparison using assembled contigs against a custom Swiss-Prot human virus database indicated the potential prevalence of other pathogenic virus genera, including: Orthopoxvirus, Rhadinovirus, Parapoxvirus, Varicellovirus, Hepatovirus, Simplexvirus, Bocaparvovirus, Molluscipoxvirus, Parechovirus, Roseolovirus, Lymphocryptovirus, Alphavirus, Spumavirus, Lentivirus, Deltaretrovirus, Enterovirus, Kobuvirus, Gammaretrovirus, Cardiovirus, Erythroparvovirus, Salivirus, Rubivirus, Orthohepevirus, Cytomegalovirus, Norovirus, and Mamastrovirus. Four nearly complete genomes were recovered from the Astrovirus, Enterovirus, Norovirus and Betapolyomavirus genera and viral species were identified. Conclusions The presented findings in wastewater samples are primarily at the genus level and can serve as a preliminary “screening” tool that may serve as indication to initiate further testing for the confirmation of the presence of species that may be associated with human disease. Integrating innovative environmental microbiology technologies like metagenomic sequencing with viral epidemiology offers a significant opportunity to improve the monitoring of, and predictive intelligence for, pathogenic viruses, using wastewater.

Peru was one of the most affected countries during the COVID-19 pandemic. Moreover, multiple other viral diseases (enteric, respiratory, bloodborne, and vector-borne) are endemic and rising. According to Peru's Ministry of Health, various health facilities in the country were reallocated for the COVID-19 pandemic, thereby leading to reduced action to curb other diseases. Many viral diseases in the area are under-reported and not recognized. The One Health approach, in addition to clinical testing, incorporates environmental surveillance for detection of infectious disease outbreaks. The purpose of this work is to use a screening tool that is based on molecular methods, high throughput sequencing and bioinformatics analysis of wastewater samples to identify virus-related diseases circulating in Trujillo-Peru. To demonstrate the effectiveness of the tool, we collected nine untreated wastewater samples from the Covicorti wastewater utility in Trujillo-Peru on October 22, 2022. High throughput metagenomic sequencing followed by bioinformatic analysis was used to assess the viral diversity of the samples. Our results revealed the presence of sequences associated with multiple human and zoonotic viruses including Orthopoxvirus, Hepatovirus, Rhadinovirus, Parechovirus, Mamastrovirus, Enterovirus, Varicellovirus, Norovirus, Kobuvirus, Bocaparvovirus, Simplexvirus, Spumavirus, Orthohepevirus, Cardiovirus, Molliscipoxvirus, Salivirus, Parapoxvirus, Gammaretrovirus, Alphavirus, Lymphocryptovirus, Erythroparvovirus, Sapovirus, Cosavirus, Deltaretrovirus, Roseolovirus, Flavivirus, Betacoronavirus, Rubivirus, Lentivirus, Betapolyomavirus, Rotavirus, Hepacivirus, Alphacoronavirus, Mastadenovirus, Cytomegalovirus and Alphapapillomavirus. For confirmation purposes, we tested the samples for the presence of selective viruses belonging to the genera detected above. PCR based molecular methods confirmed the presence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), monkeypox virus (MPXV), noroviruses GI and GII (NoVGI and NoVGII), and rotavirus A (RoA) in our samples. Furthermore, publicly available clinical data for selected viruses confirm our findings. Wastewater or other environmental media surveillance, combined with bioinformatics methods, has the potential to serve as a systematic screening tool for the identification of human or zoonotic viruses that may cause disease. The results of this method can guide further clinical surveillance efforts and allocation of resources. Incorporation of this bioinformatic-based screening tool by public health officials in Peru and other Latin American countries will help manage endemic and emerging diseases that could save human lives and resources.

BackgroundK-complex detection plays a significant role in the field of sleep research. However, manual annotation for electroencephalography (EEG) recordings by visual inspection from experts is time-consuming and subjective. Therefore, there is a necessity to implement automatic detection methods based on classical machine learning algorithms. However, due to the complexity of EEG signal, current feature extraction methods always produce low relevance to k-complex detection, which leads to a great performance loss for the detection. Hence, finding compact yet effective integrated feature vectors becomes a crucially core task in k-complex detection.MethodIn this paper, we first extract multi-domain features based on time, spectral analysis, and chaotic theory. Those features are extracted from a 0.5-s EEG segment, which is obtained using the sliding window technique. As a result, a vector containing twenty-two features is obtained to represent each segment. Next, we explore several feature selection methods and compare their performance in detecting k-complex. Based on the analysis of the selected features, we identify compact features which are fewer than twenty-two features and deemed as relevant and proceeded to the next step. Additionally, three classical classifiers are employed to evaluate the performance of the feature selection models.ResultsThe results demonstrate that combining different features significantly improved the k-complex detection performance. The best performance is achieved by applying the feature selection method, which results in an accuracy of 93.03%±7.34, sensitivity of 93.81%±5.62%, and specificity 94.13±5.81, respectively, using a smaller number of the combined feature sets.ConclusionThe proposed method in this study can serve as an efficient tool for the automatic detection of k-complex, which is useful for neurologists or doctors in the diagnosis of sleep research.

Optimizing irrigation strategies is crucial for sustaining cotton production in the face of growing water scarcity. The three-year experimental study (2020–2022) focused on the impact of varying irrigation amounts (320, 370, and 420 mm) and frequencies (4-, 8-, and 12-day intervals) on cotton growth, yield, yield components, and fiber quality in southern Xinjiang. Employing a completely randomized design with three replications, the results indicated higher irrigation amounts resulted in numerically 2.5–7.5% higher lint yields compared to those under medium and low irrigation amounts, notably significant in 2021. Boll density emerged as the primary yield component influencing yield loss due to irrigation amount, followed by seed cotton weight boll−1. Increased boll density was associated with a greater total number of fruiting sites. Additionally, the increased seed cotton weight boll−1 could be linked to an increased seed number boll−1 and a decreased boll fraction at the lower fruiting branches with lower seed cotton weight boll−1. Higher irrigation frequency improved lint yield by increasing boll density, though a significant effect was observed only in 2020. Increased irrigation amounts generally led to longer fiber lengths but lower micronaire values, while increased irrigation frequency resulted in longer, stronger fibers and reduced micronaire values. Furthermore, the highest yield stability was observed under the condition of high irrigation amount and high frequency. This study holds certain guiding significance for water resource management in cotton production in arid regions.

BackgroundK-complex detection traditionally relied on expert clinicians, which is time-consuming and onerous. Various automatic k-complex detection-based machine learning methods are presented. However, these methods always suffered from imbalanced datasets, which impede the subsequent processing steps.New methodIn this study, an efficient method for k-complex detection using electroencephalogram (EEG)-based multi-domain features extraction and selection method coupled with a RUSBoosted tree model is presented. EEG signals are first decomposed using a tunable Q-factor wavelet transform (TQWT). Then, multi-domain features based on TQWT are pulled out from TQWT sub-bands, and a self-adaptive feature set is obtained from a feature selection based on the consistency-based filter for the detection of k-complexes. Finally, the RUSBoosted tree model is used to perform k-complex detection.ResultsExperimental outcomes manifest the efficacy of our proposed scheme in terms of the average performance of recall measure, AUC, and F10-score. The proposed method yields 92.41 ± 7.47%, 95.4 ± 4.32%, and 83.13 ± 8.59% for k-complex detection in Scenario 1 and also achieves similar results in Scenario 2.Comparison to state-of-the-art methodsThe RUSBoosted tree model was compared with three other machine learning classifiers [i.e., linear discriminant analysis (LDA), logistic regression, and linear support vector machine (SVM)]. The performance based on the kappa coefficient, recall measure, and F10-score provided evidence that the proposed model surpassed other algorithms in the detection of the k-complexes, especially for the recall measure.ConclusionIn summary, the RUSBoosted tree model presents a promising performance in dealing with highly imbalanced data. It can be an effective tool for doctors and neurologists to diagnose and treat sleep disorders.

Plastic film mulching (PFM) affects the spatiotemporal distribution of soil moisture and temperature, which in turn affects cotton growth and the spatiotemporal distribution of canopy photosynthetically active radiation (PAR). Due to the spatial heterogeneity of soil moisture, temperature and limited monitoring methods, the issues such as relatively few sampling points and long sampling intervals in most existing studies prevent the accurate quantification of spatiotemporal changes in moisture and temperature along soil profile. To investigate the effects of PFM on spatiotemporal changes in soil moisture, temperature, and canopy PAR in cotton fields, two field trials of plastic film-mulched (M) and nonmulched (NM) cultivations were performed in 2018 and 2019. The grid method was used for the soil information continuous monitoring and multiple-time fixed-site canopy PAR monitoring during the duration of cotton growth. Two-year field trial data showed that, M cultivation increased soil moisture by approximately 13.6%–25% and increased temperature by 2–4 °C in the 0–50 cm soil layer before the first irrigation (June 20) and by 1–2 °C in the 70–110 cm soil layer, compared with NM cultivation. In addition, the temperature difference between the two treatments gradually decreased with the increase in irrigation and air temperature. The M treatment reached the peak PAR interception rate 10 days earlier than the NM treatment. In 2018 and 2019, the PAR peak value under the M treatment was 4.62% and 1.8% higher than that under the NM treatment, respectively, but the PAR interception rate was decreased rapidly in the late growth stage. Overall, PFM had an effect on soil moisture retention during the whole growth period and greatly increased the soil temperature before budding stage, thus promoted the early growth of cotton. Considering this, we suggest that the irrigation quota and frequency could be appropriately decreased in the case of plastic film mulching cultivation. For nonmulching cultivation, the irrigation quota and frequency should be increased, and it is necessary to take measures to improve the soil temperature before middle July.

In this paper, the differences between two motor imagery tasks are captured through microstate parameters (occurrence, duration and coverage, and mean spatial correlation (Mspatcorr)) derived from a novel method based on electroencephalogram microstate and Teager energy operator. The results show that the significance between microstate parameters for two tasks is different (P < 0.05) with paired t-test. Furthermore, these microstate parameters are utilized as features. Support vector machine is utilized to classify the two tasks with a mean accuracy of 93.93%, which yielded superior performance compared to the other methods.

Planting density affects crop microclimate and intra-plant competition, playing an important role on yield formation and resource use, especially in areas where the cotton is grown at relatively high plant densities in Xinjiang, China. However, more studies are needed to examine how the change in planting density affects the microclimate factors such as the fraction of light intercepted (FLI), air temperature(T) and relative humidity (RH) within different canopy layers, which in turn affect the boll number per plant (BNF), boll number per unit area (BNA), boll weight (BW), and boll-setting rate (BSR) at fruiting branch (FB) positions FB1–3, FB4–6, and FB≥7 in cotton. To quantify the relationships between boll characteristics, yield, and microclimate factors, we conducted a 2-year field experiment in 2019–2020 in Xinjiang with six plant densities: 9 (P1), 12 (P2), 15 (P3), 18 (P4), 21 (P5), and 24 (P6) plants m−2. With each three plants m−2 increase in density, the average FLI and RH across different canopy layers increased by 0.37 and 2.04%, respectively, whereas T decreased by 0.64 °C. The BNF at FB≥ 7, FB4–6, and FB1–3 decreased by 0.82, 0.33, and 0.5, respectively. The highest BNA was observed in the upper and middle layers in the P4 treatment and in the lowest canopy layer with the P5. The highest BW was measured in the middle canopy layer for P3, and the highest BSR was measured in the lower layer for P3. Plant density exhibited linear or quadratic relationships with FLI, T, and RH. Microclimate factors mainly affected the boll number in each layer, but had no significant effects on the BW in any layer or the BSR in the middle and lower layers. Cotton yield was non-linearly related to plant density. The 2-year maximum yield was achieved at a plant density of 21 plants m−2, but the yield increase compared to the yield with a density of 18 plants m−2was only 0.28%. Thus, we suggest that the optimal plant density for drip-irrigated cotton in Xinjiang is 18 plants m−2, which could help farmers grow machine-harvested cotton.

The interlayer shear weakness zone (ISWZ) is a special structural plane with different widths and spacing in stratified rock masses, it has higher permeability compared with surrounding rocks which is a risk factor for the safety of the hydropower station project. The high-pressure packer test (HPPT) by step injection is always applied to characterize the permeability of ISWZ. However, the non-Darcian flow is easy to appear under high pressure, which makes the Darcy law model no longer applicable. In this study, two non-Darcian flow analytical methods for confined aquifer were proposed to investigate the non-Darcian flow permeability parameters. The equivalent permeability coefficients of different non-Darcian models were derived as well. The in situ tests were conducted on the ISWZs at the Baihetan hydropower station to verify the proposed methods. The results indicate that the flow is non-Darcian flow in the test section from integrity to destruction during the whole HPPT process. Izbash’s law has a better fit than Forchheimer’s law in this complicated test situation. The equivalent permeability coefficients after destruction are one or two orders of magnitude larger than those before. Meanwhile, it is necessary to pay attention to the increased difference of two expressions of the equivalent permeability coefficients under higher gradient (i) or velocity (v). In general, these methods can be used to evaluate the characteristic of ISWZ to analyze the impact on engineering stability.

Groundwater flow in an aquifer has frequently been found to be non-Darcian by performing in situ tests. A novel analytic model is proposed in this study for describing the unsteady non-Darcian flow in a confined aquifer by taking advantage of the observed flow rate and injection pressure during the constant head packer test. A linearization approximation of the Izbash equation is used to approximate the nonlinear term in the governing equation. This analytic model is applied to describe the non-Darcian flow in the interlayer staggered zone at the Baihetan hydropower station, China. The test results inversed by the genetic algorithm show that non-Darcian flow happened during the test under the injection pressure 0.3 MPa with the power index n is 1.278, non-Darcian hydraulic conductivity k1 is 1.613×10−5 cm/s and the specific storage Ss is 9.757×10−5 m-1, respectively. The sensitivity analysis indicated that when the power index n or the specific storage Ss is larger, and the hydraulic head will increase more slowly and needs longer to stabilize, but the non-Darcian hydraulic conductivity k1 shows the opposite trend. Moreover, the hydraulic head is more sensitive to the power index n compared to other parameters at late times. The findings of this study reveal the non-Darcian flow during the constant head packer test and provide a simple and fast way to estimate parameters for more accurate seepage field simulation.

Abstract Background Within-canopy interception of photosynthetically active radiation (PAR) impacts yield and other agronomic traits in cotton (Gossypium hirsutum L.). Field experiments were conducted to investigate the influence of 6 cotton varieties (they belong to 3 different plant types) on yield, yield distribution, light interception (LI), LI distribution and the relationship between yield formation and LI in Anyang, Henan, in 2014 and 2015. Result The results showed that cotton cultivars with long branches (loose-type) intercepted more LI than did cultivars with short branches (compact-type), due to increased LI in the middle and upper canopy. Although loose-type varieties had greater LI, they did not yield significantly higher than compact-type varieties, due to decreased harvest index. Therefore, improving the harvest index by adjusting the source-to-sink relationship may further increase cotton yield for loose-type cotton. In addition, there was a positive relationship between reproductive organ biomass accumulation and canopy-accumulated LI, indicating that enhancing LI is important for yield improvement for each cultivar. Furthermore, yield distribution within the canopy was significantly linearly related to vertical LI distribution. Conclusion Therefore, optimizing canopy structure of different plant type and subsequently optimizing LI distribution within the cotton canopy can effectively enhance the yield.

Abstract Background Accurate functional diagnosis of coronary stenosis is vital for decision making in coronary revascularization. With recent advances in computational fluid dynamics (CFD), fractional flow reserve (FFR) can be derived non-invasively from coronary computed tomography angiography images (FFRCT) for functional measurement of stenosis. However, the accuracy of FFRCT is limited due to the approximate modeling approach of maximal hyperemia conditions. To overcome this problem, a new CFD based non-invasive method is proposed. Methods Instead of modeling maximal hyperemia condition, a series of boundary conditions are specified and those simulated results are combined to provide a pressure-flow curve for a stenosis. Then, functional diagnosis of stenosis is assessed based on parameters derived from the obtained pressure-flow curve. Results The proposed method is applied to both idealized and patient-specific models, and validated with invasive FFR in six patients. Results show that additional hemodynamic information about the flow resistances of a stenosis is provided, which cannot be directly obtained from anatomy information. Parameters derived from the simulated pressure-flow curve show a linear and significant correlations with invasive FFR (r > 0.95, P < 0.05). Conclusion The proposed method can assess flow resistances by the pressure-flow curve derived parameters without modeling of maximal hyperemia condition, which is a new promising approach for non-invasive functional assessment of coronary stenosis.

Wheat-cotton double cropping has improved crop productivity and economic benefits per unit land area in many countries, including China. However, relay intercropping of full-season cotton and wheat, the most commonly adopted mode, is labor-intensive and unconducive to mechanization. The direct sowing of short-season cotton after wheat (CAW) has been successful, but cotton yields and economic benefits are greatly reduced. Whether the relay intercropping of short-season cotton before the wheat harvest increases cotton yields remains unclear, as does the earliness and fiber quality relative to those for CAW. Therefore, we directly planted short-season cotton after wheat harvest on 15 June (CAW) as the control and interplanted short-season cotton in wheat on 15 May (S1), 25 May (S2) and 5 June (S3), which were 30, 20 and 10 days prior to wheat harvest, respectively, from 2016 to 2018. The crop growth, yield, yield components, boll distribution, and earliness of the cotton were evaluated. The yields and earliness of short-season cotton under relay intercropping were 26.7–30.6% and 20.4–42.9% higher than those under CAW, respectively. Compared with CAW, relay intercropping treatments increased the boll density, boll weight and lint percentage by 5.6–13.1%, 12.5–24.5% and 5.8–12.7%, respectively. The dry matter accumulation and harvest index under the relay intercropping treatments were also greater than those under CAW, which might be attributed to the greater partitioning of dry matter to the seed cotton than to the boll shells. Among the relay intercropping treatments (S1, S2 and S3), the lint yield did not differ, but S1 and S2 were considerably better than S3 based on earliness and fiber quality. The analysis of the within-plant spatial boll distribution showed that more bolls were formed on the lower to middle fruiting branches and at the first fruiting sites for S1 and S2 than for S3 and CAW. Therefore, the increased earliness and fiber quality induced through early relay intercropping (S1 and S2) could be attributed to an improved spatial boll distribution compared to late relay intercropping (S3) or CAW. Conclusively, compared to late relay intercropping and CAW, early relay intercropping considerably increased the lint yield, fiber quality, and earliness by improving the yield components, boll distribution, and dry matter accumulation and partitioning. The relay intercropping of short-season cotton 20 to 30 days before wheat harvest represents a promising alternative to CAW in wheat-cotton double-cropping systems in the Yellow River Basin of China and other regions with similar conditions.

The photosynthetically active radiation (PAR) of crop canopy is highly related to yield formation, but how it relates to yield and yield distribution is not well understood. The focus of this study was to explore the relationship between light competition under different densities and yield distributions of cotton. The experiment was conducted in 2019 and 2020 at the Cotton Research Institute of the Chinese Academy of Agricultural Sciences in Anyang city, Henan Province, China. A randomized block design was employed, with a total of three repeats. Each repeat had six density treatments: D1: 15,000; D2: 33,000; D3: 51,000; D4: 69,000; D5: 87,000; and D6: 105,000 plants·ha−1. As predicted, the results showed that the canopy light interception, leaf area index, plant height, and biomass of high-density cotton were higher than those of low-density cotton. The aboveground biomass produced by D6 was the highest, and was 12.9, 19.5, 25.4, 46.3, and 69.2% higher in 2019 and 14.3, 19.9, 32.5, 53.7, and 109.9% higher in 2020 than D5, D4, D3, D2, and D1, respectively. Leaf area, plant height, biomass, boll number, and boll weight were significantly correlated with the light interception rate. D5 (87,000 plants·ha−1) had a higher light interception rate and the highest yield. The highest lint yields produced by D5 were 1673.5 and 1375.4 kg·ha−1 in two years, and was 3.2, 4.3, 5.6, 9.7, and 24.7% higher in 2019, and 6.8, 10.6, 13.5, 21.5, and 34.4% higher in 2020 than D6, D4, D3, D2, and D1, respectively. The boll retention of the lower fruit branch under D5 reached 0.51 and 0.57 in two years, respectively. The shedding rate of the upper fruit branch decreased with the increase in cotton density in two years. The boll retention rate and shedding rate in the lower part of cotton plants were most closely related to light interception, with R2 values of 0.91 and 0.96, respectively. Our study shows cotton yield could be improved through higher light interception by optimizing planting density and canopy structure.

GASFLOW-MPI is a widely used scalable computational fluid dynamics numerical tool to simulate the fluid turbulence behavior, combustion dynamics, and other related thermal–hydraulic phenomena in nuclear power plant containment. An efficient scalable linear solver for the large-scale pressure equation is one of the key issues to ensure the computational efficiency of GASFLOW-MPI. Several advanced Krylov subspace methods and scalable preconditioning methods are compared and analyzed to improve the computational performance. With the help of the powerful computational capability, the large eddy simulation turbulent model is used to resolve more detailed turbulent behaviors. A backward-facing step flow is performed to study the free shear layer, the recirculation region, and the boundary layer, which is widespread in many scientific and engineering applications. Numerical results are compared with the experimental data in the literature and the direct numerical simulation results by GASFLOW-MPI. Both time-averaged velocity profile and turbulent intensity are well consistent with the experimental data and direct numerical simulation result. Furthermore, the frequency spectrum is presented and a –5/3 energy decay is observed for a wide range of frequencies, satisfying the turbulent energy spectrum theory. Parallel scaling tests are also implemented on the KIT/IKET cluster and a linear scaling is realized for GASFLOW-MPI.

Abstract To identify countermeasures for the impacts of climate change on crop production, exploring the changes in crop phenology and their relationship to climate change is required. This study was based on cotton phenology and climate data collected from 13 agro-meteorological experimental stations and 13 meteorological stations on the North China Plain from 1981 to 2012. Spatiotemporal trends in the cotton phenology data, lengths of the different growing phases, mean temperatures, and rainfall were analyzed. These results indicated that warming accelerated cotton growth, advanced cotton phenology, and shortened the growing period of cotton. However, harvest dates were significantly delayed at 8 (61.5%) stations, the length of both the flowering-boll opening and boll opening-harvest periods increased at 10 (77.0%) stations, and a positive correlation was found between the mean temperature and the length of the whole growing period at 10 (77.0%) stations. Therefore, cotton practices and cultivars on the North China Plain should be adjusted accordingly. The response of cotton phenology to climate change, as shown here, can further guide the development of options for the adaptation of cotton production in the near future.

Different cotton (Gossypium hirsutum L.)-wheat (Triticum aestivum) planting patterns are widely applied in the Yellow River Valley of China, and crop yield mainly depends on light interception. However, little information is available on how cotton canopy light capturing and yield distribution are affected by planting patterns. Hence, field experiments were conducted in 2016 and 2017 to study the response of cotton canopy light interception, square and boll distribution, the leaf area index (LAI) and biomass accumulation to three planting patterns: a cotton monoculture (CM, planted on 15 May) system, a cotton/wheat relay intercropping (CWI, planted on 15 May) system, in which three rows of wheat rows were intercropped with one row of cotton, and a system in which cotton was directly seeded after wheat (CWD, planted on 15 June). The following results were obtained: 1) greater light capture capacity was observed for cotton plants in the CM and CWI compared with the CWD, and the light interception of the CM was 22.4% and 51.4% greater than that of the CWI and CWD, respectively, at 30 days after sowing (DAS) in 2016; 2) more bolls occurred at the first sympodial position (SP) than at other SPs for plants in the CM; 3) based on the LAI and biomass accumulation, the cotton growth rate was the greatest in CWD, followed by CM and CWI; and 4) the CM produced significantly greater yields than did the other two treatments because it yielded more bolls and greater boll weight. Information on the characteristics of cotton growth and development in response to different planting patterns would be helpful for understanding the response of cotton yields to planting patterns and would facilitate the improvement of cotton productivity.

The growth and development of cotton are closely related to climatic variables such as temperature and solar radiation. Adjusting planting density is one of the most effective measures for maximizing cotton yield under certain climatic conditions. The objectives of this study were (1) to determine the optimum planting density and the corresponding leaf area index (LAI) and yield under the climatic conditions of Henan Province, China, and (2) to learn how climatic conditions influence cotton growth, yield, and yield components. A three-year (2013-2015) field experiment was conducted in Anyang, Henan Province, using cultivar SCRC28 across six planting density treatments: 15,000, 33,000, 51,000, 69,000, 87,000, and 105,000 plants ha-1. The data showed that the yield attributes, including seed cotton yield, lint yield, dry matter accumulation, and the LAI, increased as planting density increased. Consequently, the treatment of the maximum density with 105,000 plants ha-1 was the highest-yielding over three years, with the LAIs averaged across the three years being 0.37 at the bud stage, 2.36 at the flower and boll-forming stage, and 1.37 at the boll-opening stage. Furthermore, the correlation between the cotton yield attributes and meteorological conditions indicated that light interception (LI) and the diurnal temperature range were the climatic factors that most strongly influenced cotton seed yield. Moreover, the influence of the number of growing degree days (GDD) on cotton was different at different growth stages. These observations will be useful for determining best management practices for cotton production under the climatic conditions of Henan Province, China.

Modification of the cotton canopy results in shade avoidance and competition for light, which shows that density and spatial arrangement of cotton have a great impact on light interception. This experiment was conducted in 2018 and 2019 in the experimental field at the Institute of Cotton Research of Chinese Academy of Agricultural Science in Anyang city, Henan Province, China. Six plant densities of cotton variety SCRC28 were used to assess spatial competition for light in cotton populations during the whole growing period. Light interception data were collected and analyzed according to the spatial grid method and the extension of Simpson’s 3/8 rule. The results showed that at the bottom of the canopy, greater light interception was observed at high densities than at low densities, while in the external part of the layer of the canopy in the horizontal direction, low light interception was recorded at low densities. Leaf area, aboveground biomass and plant height were obviously correlated with light interception, and the cotton population with a higher density (8.7 plants m−2) performed best at the light interception competition, and with the highest yield. The results will provide guidance on light management through the optimization of the structure of the canopy to provide more solar radiation and a significant basis by which to improve the management of light and canopy architecture.

Background: Non-alcoholic fatty liver disease (NAFLD) is closely associated with metabolic disorders including hepatic lipid accumulation and inflammation. Alisol A 24-acetate, a triterpene from Alismatis rhizome, has multiple biologic activities such as hypolipidemic, anti-inflammatory and anti-diabetic. Thus we hypothesized that Alisol A 24 -acetate would have effect on NAFLD. The present study was conducted to investigate the therapeutic effects and potential mechanisms of Alisol A 24-acetate against hepatic steatosis in a free fatty acids (FFAs) induced NAFLD cell model. Methods: This study was divided into four groups including Control group, Model group (FFA group), Alisol A 24-acetate (FFA+A) group, Fenofibrate (FFA+F) group. Preventive role of Alisol A 24-acetate was evaluated using 10µM Alisol A 24-acetate plus 1 mM FFA (oleate:palmitate=2:1) incubated with HepG2 cells for 24 h, which was determined by Oil Red O Staining, Oil Red O based colorimetric assay and intracellular triglyceride (TG) content. Besides, the inflammatory cytokines tumor necrosis factor (TNF)- α, interleukin (IL)-6 levels as well as the protein and mRNA expressions that were involved in fatty acid synthesis and oxidation including Adiponectin, AMP-activated protein kinase (AMPK) α, peroxisome proliferator-activated receptor (PPAR) α, sterol regulatory element binding protein 1c (SREBP-1c), acetyl-CoA carboxylase (ACC), fatty acid synthase (FAS), carnitine palmitoyltransferase 1 (CPT1) and acyl coenzyme A oxidase 1 (ACOX1) were detected. Results: Alisol A 24-acetate significantly decreased the numbers of lipid droplets, Oil Red O lipid content, and intracellular TG content. Besides, inflammatory cytokines TNF-α, IL-6 levels were markedly inhibited by Alisol A 24-acetate. Furthermore, Alisol A 24-acetate effectively increased the protein and mRNA expressions of Adiponectin, the phosphorylation of AMPKα, CPT1 and ACOX1, whereas decreased SREBP-1c, the phosphorylation of ACC and FAS at both protein and mRNA levels. However, there was no significant effect on the protein and mRNA expressions of PPARα by Alisol A 24-acetate. Conclusions: These results demonstrated that Alisol A 24-acetate effectively ameliorated hepatic steatosis likely through Adiponectin, which activated AMPKα signaling pathways via down-regulating SREBP-1c, ACC, FAS and up-regulating CPT1 and ACOX1, and inhibited inflammation. Thereby, Alisol A 24-acetate could be a promising candidate for the treatment of NAFLD.

The number of cotton plants grown per unit area has recently gained attention due to technology expense, high input, and seed cost. Yield consistency across a series of plant populations is an attractive cost-saving option. Field experiments were conducted to compare biomass accumulation, fiber quality, leaf area index, yield and yield components of cotton planted at various densities (D1, 1.5; D2, 3.3; D3, 5.1; D4, 6.9; D5, 8.7; and D6, 10.5 plants m−2). High planting density (D5) produced 21% and 28% more lint yield as compared to low planting density (D1) during both years, respectively. The highest seed cotton yield (4662 kg/ha) and lint yield (1763 kg/ha) were produced by high plant density (D5) while the further increase in the plant population (D6) decreased the yield. The increase in yield of D5 was due to more biomass accumulation in reproductive organs as compared to other treatments. The highest average (19.2 VA gm m−2 d−1) and maximum (21.8 VM gm m−2 d−1) rates of biomass were accumulated in reproductive structures. High boll load per leaf area and leaf area index were observed in high planting density as compared to low, while high dry matter partitioning was recorded in the lowest planting density as compared to other treatments. Plants with low density had 5% greater fiber length as compared to the highest plant density, while the fiber strength and micronaire value were 10% and 15% greater than the lowest plant density. Conclusively, plant density of 8.7 plants m−2 is a promising option for enhanced yield, biomass, and uniform fiber quality of cotton.

Background: Peguero electrocardiographic left ventricular hypertrophy (ECG-LVH) criteria are newly developed criteria that have shown better diagnostic performance than the traditional Cornell-voltage and Sokolow-Lyon criteria. However, prediction of poor outcomes rather than detection of increased left ventricular mass is becoming the primary use for ECG-LVH criteria which requires investigating any new ECG-LVH criteria in terms of prediction.Aims: To examine the prognostic significance of the newly developed Peguero ECG-LVH criteria.Methods: We compared the prognostic significance of Peguero ECG-LVH with Cornell-voltage and Sokolow-Lyon ECG-LVH criteria in 7,825 participants (age 59.8 ± 13.4 years; 52.7% women) from the third National Health and Nutrition Examination Survey who were free of major intraventricular conduction defects. ECG-LVH criteria were derived from digital ECG tracings processed at a central core laboratory.Results: At baseline, ECG-LVH was detected in 11.8% by Peguero; in 4.3% by Cornell voltage and in 6.4% by Sokolow-Lyon. During a median follow up of 13.8 years, 2,796 all-cause mortality events occurred. In multivariable models adjusted for demographics and cardiovascular risk factors, presence of Peguero ECG-LVH was associated with increased risk of all-cause mortality [HR (95% CI): 1.29 (1.16, 1.44)]. This association was not significantly different from the associations of Cornell voltage-LVH or Sokolow-Lyon LVH with all-cause mortality [HR (95%CI): 1.32 (1.12, 1.55) and 1.24 (1.07, 1.43), respectively; p-values for comparisons of these HRs with the HR of Peguero ECG-LVH 0.817 and 0.667, respectively]. Similar patterns of associations were observed with cardiovascular, ischemic heart disease and heart failure mortalities.Conclusion: Peguero ECG-LVH is predictive of increased risk of death similar to the traditional ECG-LVH criteria.

Expression of insecticidal protein in transgenic Bacillus thuringiensis (Bt) is lower in cotton reproduction organs, especially during boll development period. The current study investigated the effects of nitrogen fertilization under nitrogen deficit on boll development and Bt toxin content in boll shell, which is the first target of boll worm harm. The protein synthesis and degradation in this process was also studied to uncover the underlying mechanism. Five nitrogen levels (under nitrogen deficiency) were imposed on two cultivars, Sikang3 (hybrid) and Sikang1 (conventional), at the Yangzhou University Farm, Yangzhou, China during 2015 to 2016 cotton growth seasons. Under nitrogen deficiency, enhanced nitrogen dose increased the boll number per plant, boll volume, boll weight, boll shell amino acid content, protease, and peptidase activities, but reduced boll shell Bt protein content, soluble protein content, glutamic pyruvic transaminase (GPT) and glutamate oxaloacetate transaminase (GOT) activities. There was a significant negative correlation between boll growth and boll shell insecticidal protein content under nitrogen deficiency, which was a result of uneven distribution of nitrogen in cotton bolls. Under increased nitrogen application, most nitrogen was transported and assimilated in boll seed instead of boll shell in developing cotton bolls, which resulted in decreased protein synthesis and increased protein degradation, and thus declined Bt protein content in boll shell.

In this study, transplanting cotton seedlings grown in artificial substrate is considered due to recent increased interest in cotton planting labor saving approaches. The nursery methods used for growing cotton seedlings affect root growth. However, the underlying functional responses of root growth to variations in cotton seedling transplanting methods are poorly understood. We assessed the responses of cotton (Gossypium hirsutum L.) roots to different planting methods by conducting cotton field experiments in 2012 and 2013. A one-factor random block design was used with three replications and three different cotton planting patterns (substrate seedling transplanted cotton (SSTC), soil-cube seedling transplanted cotton (ScSTC) and directly sown cotton (DSC). The distributions and variances of the root area density (RAD) and root length density (RLD) at different cotton growing stages and several yield components were determined. Overall, the following results were observed: 1) The RAD and RLD were greatest near the plants (a horizontal distance of 0 cm) but were lower at W20 and W40 cm in the absence of film mulching than at E20 and E40 cm with film mulching. 2) The roots were confined to shallow depths (20-40 cm), and the root depths of SSTC and DSC were greater than the root depths of ScSTC. 3) Strong root growth was observed in the SSTC at the cotton flowering and boll setting stages. In addition, early onset root growth occurred in the ScSTC, and vigorous root growth occurred throughout all cotton growth stages in DSC. 4) The SSTC plants had more lateral roots with higher root biomass (RB) than the ScSTC, which resulted in higher cotton yields. However, the early onset root growth in the ScSTC resulted in greater pre-frost seed cotton (PFSC) yields. These results can be used to infer how cotton roots are distributed in soils and capture nutrients.

Plant population density (PPD) and nitrogen (N) application rate (NAR) are two controllable factors in cotton production. We conducted field experiments to investigate the effects of PPD, NAR and their interaction (PPD × NAR) on yield, N uptake and N use efficiency (NUE) of cotton using a split-plot design in the North China Plain during 2013 and 2014. The main plots were PPDs (plants m-2) of 3.00 (low), 5.25 (medium) and 7.50 (high) and the subplots were NARs of 0 (N-free), 112.5 (low), 225.0 (moderate) and 337.5 (high). During both 2013 and 2014, biological yield and N uptake of cotton increased significantly, but harvesting index decreased significantly with NAR and PPD increasing. With NAR increasing, internal nitrogen use efficiency(NUE) decreased significantly under three PPDs and agronomical NUE, physiologilal NUE, nitrogen recovery efficiency(NRE) and partial factor productivity from applied nitrogen (PFPN) also decreased significantly under high PPD between two years. Lint yield increment varied during different PPDs and years, but NAR enhancement showed less function under higher PPD than lower PPD in general. Taken together, moderate NAR under medium PPD combined higher lint yield with higher agronomic NUE, physiological NUE, and NRE, while low NAR with high PPD would achieve a comparable yield with superior NRE and PFPN and high NAR under high PPD and medium PPD produced higher biological yield but lower harvest index, lint yield and NUE compared to moderate NAR with medium PPD. Our overall results indicated that, in this region, increasing PPD and decreasing NAR properly would enhance both lint yield and NUE of cotton.

BackgroundThe electrocardiogram (ECG) is an objective tool for cardiovascular disease (CVD) risk assessment. Methods and ResultsWe evaluated distribution of ECG abnormalities and risk factors for developing new abnormalities in 1314 patients with type 1 diabetes (T1D) from the Epidemiology of Diabetes Interventions and Complications (EDIC) study. Annual ECGs were centrally read. ECG abnormalities were classified as major and minor according to the Minnesota ECG Classification. At EDIC year 1 (baseline), 356 (27.1%) of the participants had at least 1 ECG abnormality (major or minor) whereas 26 (2%) had at least one major abnormality. During 16 years of follow‐up, 1016 (77.3%) participants developed at least 1 new ECG abnormality (major or minor), whereas 172 (13.1%) developed at least 1 new major abnormality. Independent risk factors for developing new major ECG abnormalities were: age, current smoking, increased systolic blood pressure, and higher glycosylated hemoglobin (hazard ratio [HR] [95% CI]: 1.04 [1.02–1.06] per 1‐year increase, 1.75 [1.22–2.53], 1.03 [1.01–1.05] per 1 mm Hg increase, and 1.16 [1.04–1.29] per 10% increase, respectively). Independent risk factors for developing any new ECG abnormalities (major or minor) were age and systolic blood pressure (HR [95% CI]: 1.02 [1.01–1.03] per 1‐year increase and 1.01 [1.00–1.02] per 1 mm Hg increase, respectively). ConclusionsNew ECG abnormalities commonly occur in the course of T1D, consistent with the recognized increasing risk for CVD as patients age. Advanced age, increased systolic blood pressure, smoking, and higher HbA1c are independent risk factor for developing major ECG abnormalities, which underscores the importance of tight glucose control in T1D in addition to management of common CVD risk factors.

Identifying the characteristics of light interception and utilization is of great significance for improving the potential photosynthetic activity of plants. The present research investigates the differences in absorbing and converting photosynthetically active radiation (PAR) among various cotton cultivars. Field experiments were conducted in 2012, 2013 and 2014 in Anyang, Henan, China. Ten cultivars with different maturity and plant architectures were planted at a density of 60,000 plants ha-1 in randomized blocks, with three replicates. The spatial distribution of light in canopy was measured and quantified with a geo-statistical method, according to which the cumulative amount of intercepted radiation was calculated by Simpson 3/8 rules. Finally, light interception was analyzed in association with the biomass accumulation of different cultivars. The key results were: (1) late-maturing varieties with an incompact plant architecture captured more solar radiation throughout the whole growth period than middle varieties with columnar architecture and even more than early varieties with compact architecture, and they produced more biomass; (2) the highest PAR interception ratio and the maximum biomass accumulation rate occurred during the blossoming and boll-forming stage, when leaf area index (LAI) reached its peak; (3) the distribution within the canopy presented a significant spatial heterogeneity, and at late growing stage, the PAR was mainly intercepted by upper canopies in incompact-type plant communities, but was more homogeneous in columnar-type plants; however, the majority of radiation was transmitted through the canopy in compact-type colonies; (4) there was not a consistent variation relationship between the cumulative intercepted PAR (iPAR) and biomass among these cultivars over the three years of the study. Based on these results, we attempted to clarify the distinction in light spatial distribution within different canopies and the patterns of PAR interception in diverse cotton cultivars with different hereditary characters, thereby providing a significant basis for researchers to select cultivars with appropriate growth period and optimal plant architecture for improvement of light interception and utilization.

The partitioning of light is very difficult to assess, especially in discontinuous or irregular canopies. The aim of the present study was to analyze the spatial distribution of photosynthetically active radiation (PAR) in a heterogeneous cotton canopy based on a geo-statistical sampling method. Field experiments were conducted in 2011 and 2012 in Anyang, Henan, China. Field plots were arranged in a randomized block design with the main plot factor representing the plant density. There were 3 replications and 6 densities used in every replicate. The six plant density treatments were 15,000, 33,000, 51,000, 69,000, 87,000 and 105,000 plants ha(-1). The following results were observed: 1) transmission within the canopy decreased with increasing density and significantly decreased from the top to the bottom of the canopy, but the greatest decreases were observed in the middle layers of the canopy on the vertical axis and closing to the rows along the horizontal axis; 2) the transmitted PAR (TPAR) of 6 different cotton populations decreased slowly and then increased slightly as the leaves matured, the TPAR values were approximately 52.6-84.9% (2011) and 42.7-78.8% (2012) during the early cotton developmental stage, and were 33.9-60.0% (2011) and 34.5-61.8% (2012) during the flowering stage; 3) the Leaf area index (LAI) was highly significant exponentially correlated (R(2) = 0.90 in 2011, R(2) = 0.91 in 2012) with the intercepted PAR (IPAR) within the canopy; 4) and a highly significant linear correlation (R(2) = 0.92 in 2011, R(2) = 0.96 in 2012) was observed between the accumulated IPAR and the biomass. Our findings will aid researchers to improve radiation-use efficiency by optimizing the ideotype for cotton canopy architecture based on light spatial distribution characteristics.

At present, global energy consumption and emissions are largely dominated by non-renewable resources, particularly fossil fuels. As a result, there is a growing focus on investing in and researching alternative energy sources across various sectors, including residential buildings. Effective energy management in these structures can track real-time energy demand and appliance usage, leading to reduced energy bills and enhanced overall efficiency. This research examined the efficient functioning of appliances within smart homes, emphasizing the role of demand response (DR) in energy management for electrical grids. The efficient operation of these appliances is modeled based on household usage patterns, the capacity for demand flexibility in demand-side management (DSM), and energy pricing. The operation of the appliances is managed through a two-tier energy optimization process. In the first tier, energy usage is adjusted through DR and load shifting. The second tier focuses on reducing both consumption costs and consumer discomfort, taking into account the optimized energy usage established in the first tier. Finally, the efficiency of the two-layer energy optimization is confirmed by testing the proposed case studies in the numerical simulation. [ABSTRACT FROM AUTHOR]

In this paper, Caputo boundary value problems of order 3 < ζ ≤ 4 are investigated on the interval [0, 1]. By Guo-Krasnoselskii fixed point theorem, some criteria of existence and multiplicity of positive and decreasing solutions are established. The main novelty of the paper lies in its capability to achieve positive solutions while the corresponding Green’s function changes sign. Finally, two examples are provided to illustrate the application of these results. [ABSTRACT FROM AUTHOR]

Abstract Idiopathic pulmonary fibrosis (IPF) is a severe interstitial lung disease characterized by a grim prognosis, in which various forms of cell death are significant contributors to its development. The objective of this study is to explore diagnostic biomarkers and molecular subtypes associated with PANoptosis in IPF, and to develop reliable diagnostic models based on PANoptosis-related mechanisms. The peripheral blood transcriptomic data of IPF from the Gene Expression Omnibus (GEO) database and PANoptosis-related genes from the GeneCards database were utilized to conduct differential gene expression analysis and weighted gene co-expression network analysis (WGCNA), identifying PANoptosis-related differentially expressed genes (PDEGs). We yielded 9 PDEGs and employed machine learning algorithms to identify 3 key diagnostic biomarkers for PANoptosis in IPF: MMP9, FCMR, NIBAN3. Consensus clustering algorithm was applied to recognize two PANoptosis-related subtypes. Cluster 1 exhibited higher abundance of adaptive immune response cells and significant enrichment in DNA damage and repair-related pathways. Cluster 2 exhibited greater prevalence of innate immune response cells and predominant enhancement in pathways related to lipid cholesterol metabolism and vascular remodeling. Diagnostic models were developed with the aid of clinical decision-making and a novel approach to the diagnosis and treatment for IPF.