Association between blood pressure levels and premature atrial contractions in patients with hypertension.

BACKGROUND: High blood pressure (BP) induces left atrial structural and functional remodeling that increases susceptibility to atrial arrhythmia. We hypothesized that lower systolic BP (SBP) levels are associated with a lower prevalence of premature atrial contractions (PACs) in patients with hypertension. METHODS: This analysis included 4,697 participants (mean age 62±13.1 years, 50% women, 25.6% blacks) with hypertension from the Third National Health and Nutrition Examination Survey who did not have a prior history of cardiovascular disease (CVD). Multivariable logistic regression was used to examine the cross-sectional association between SBP and prevalence of PACs ascertained from 12-lead resting electrocardiograms. Multivariable Cox proportional hazard analysis was used to examine the association between baseline PACs and CVD mortality. RESULTS: Approximately 1.6% (n=74) of participants had baseline PACs. Patients with SBP ≤140 mmHg had a lower prevalence of PACs than those with SBP ≥140 mmHg (1.1% vs. 1.9%, p-value=0.01). In a multivariable logistic regression model, each 10 mmHg decrease in SBP was associated with a 12% lower odds of PACs (OR (95%CI): 0.88 (0.77-0.99)). During 14 years of follow-up, 645 CVD deaths occurred. In a multivariable-adjusted Cox model, presence of PACs was associated with a 78% increased risk of CVD mortality (HR (95%CI): 1.78 (1.23-2.60)). CONCLUSIONS: In patients with hypertension, lower SBP levels are associated with a lower prevalence of PACs, and presence of PACs is associated with a higher risk of CVD mortality risk. These findings highlight the potential role of BP lowering in the management of cardiac arrhythmias.

Bioinformatics-based screening tool identifies a wide variety of human and zoonotic viruses in Trujillo-Peru 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.

Racial differences in prevalence and impact of electrocardiographic subclinical myocardial injury risk factors.

BACKGROUND: We explored whether the reported racial differences in subclinical myocardial injury (SCMI) are due to variations in the prevalence or differential impact of the SCMI risk factors. METHODS: This analysis included 3074 Whites, 1337 Blacks, and 1441 Mexican Americans from the Third National Health and Nutrition Examination Survey who were free of cardiovascular disease. SCMI was defined from standard electrocardiograms as a cardiac infarction/injury score ≥ 10 points. Multivariable logistic regression analysis was used to assess the association of SCMI with its risk factors stratified by race. Multiplicative interaction between each risk factor and race was also examined. RESULTS: Overall prevalence of SCMI was 20.3%, with Mexican Americans exhibiting a lower prevalence than Whites and Blacks (16.5%, 20.4%, and 20.7%, respectively). Whites had more prevalence of dyslipidemia and smoking. Mexican Americans had more diabetes, while Blacks had more hypertension, obesity, and left ventricular hypertrophy. Significant risk factors for SCMI were older age, lower income (<20 K), smoking, diabetes, and no regular exercise. The association of SCMI with age was more pronounced in Mexican Americans (p-value for interaction 0.03), whereas the associations of SCMI with smoking, no-regular exercise, and diabetes were stronger in Whites (p-value for interaction 0.04, 0.001, 0.007, respectively). CONCLUSIONS: Heterogeneity in the racial differences in the prevalence of SCMI risk factors exists, but they do not explain racial differences in SCMI. The stronger associations of smoking, diabetes, and no regular exercise with SCMI partially explain the higher prevalence of SCMI in Whites.

A broad wastewater screening and clinical data surveillance for virus-related diseases in the metropolitan Detroit area in Michigan.

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.

Fractional derivative method for anomalous aquitard flow in a leaky aquifer system with depth-decaying aquitard hydraulic conductivity.

Aquitards significantly affect groundwater flow in multi-aquifer systems through adjacent aquifer leakage. Despite this, studies focusing on their heterogeneity and the non-conventional diffusion patterns of their flow are still limited. In this study, a factional derivative approach was first extended to explore the time-dependent behavior of flow transport in the aquitard. Two analytical solutions were derived for specific discharges in independent aquitards under different boundary conditions. The findings revealed that aquitard flow exhibits obvious anomalous diffusion behaviors, characterized by slower decay and heavy-tailed specific discharge data. The fractional derivative model provided a more accurate representation of this behavior than traditional models, as evidenced by its superior agreement with experimental data. Moreover, a transient model for pumping tests in a leaky aquifer system was developed, incorporating the memory effect of anomalous flow and vertical heterogeneity in aquitards. Relevant semi-analytical solutions were derived to explore the impacts of memory factor ß and decay exponent of aquitard hydraulic conductivity (K) on the leakage aquifer system. Theoretical results demonstrated that stronger memory effect reduces drawdowns in the aquitard and confined aquifer during mid-to-late times. A larger dimensionless decay exponent (Ad) decreases aquitard drawdown and increases aquifer drawdown at late times. Sensitivity analysis showed aquitard drawdown experiences two peaks in sensitivity to ß and Ad at early- or mid-times, affected by memory effect and decay exponent of aquitard K, signifying maximal impact at these specific intervals. This study provides a practical model to effectively manage groundwater resources by accurately reflecting aquitard memory and heterogeneity effects.

Toward High-Performance Mg/S Batteries with M4-Assisted Mg(AlCl4 )2 /PYR14TFSI/DME Electrolyte and MoS2 @CMK/S Cathode.

Rechargeable magnesium batteries (RMBs) are considered as one of the most promising candidates for next-generation batteries. However, the popularization of RMBs is seriously plagued due to the lack of suitable non-nucleophilic electrolytes and the passivation of Mg anode. Herein, a novel non-nucleophilic electrolyte is developed by introducing (s)-1-methoxy-2-propylamine (M4) into themagnesium aluminum chloride complex (MACC)-like electrolyte. The as-synthesizes Mg(AlCl4 )2 -IL-DME-M4 electrolyte enables robust reversible cycling of Mg plating/stripping with low overpotential, high anodic stability, and ionic conductivity (8.56 mS cm-1 ). These features should be mainly attributed to the in situ formation of an MgF2 containing Mg2+ -conducting interphase, which dramatically suppresses the passivation and parasitic reaction of Mg anode with electrolyte. Remarkably, the Mg/S batteries assemble with as-synthesize electrolyte and a new type MoS2 @CMK/S cathode deliver unprecedented electrochemical performance. Specifically, the Mg/S battery exhibited the highest reversible capacity up to 1210 mAh g-1 at 0.1 C, excellent rate capability and satisfactory long-term cycling stability with a reversible capacity of 370 mAh g-1 (coulombic efficiency of ≈100%) at 1.0 C for 600 cycles. The study findings provide a novel strategy and inspiration for designing efficient non-nucleophilic Mg electrolyte and suitable sulfur-host materials for practical Mg/S battery applications.

Electrocardiographic left atrial abnormality and risk of heart failure.

INTRODUCTION: The association and the racial differences of the electrocardiographic markers of left atrial abnormality (ECG-LAA) with heart failure (HF) are unclear. METHODS: We examined the cross-sectional association of ECG-LAA, defined as deep terminal negativity of P wave in V1 (DTNPV1) with HF in 8460 participants (51.5% women, 60.3 ± 13.5 age and 49.8% Whites) from the US Third National Health and Nutrition Examination Survey. We excluded participants without P-wave in their ECG or with ECG findings that interfere with measurements of P-wave. DTNPV1 was automatically measured from ECGs processed at a central lab. Values of DTNPV1 ≥ 100 µV were considered abnormal. Past medical history of HF was identified through health interviews. Multivariable logistic regression analysis was used to examine the associations of DTNPV1 with HF. RESULTS: Abnormal DTNPV1 was detected in 3.2% (n = 271) of the participants. HF was significantly more common in individuals with abnormal, compared to those with normal, DTNPV1 (14.7% vs. 4.8%, respectively; p-value <0.001). In a model adjusted for socio-demographics and cardiovascular risk factors, ECG-LAA was associated with 98% increased odds of HF (OR (95% CI): 1.98 (1.30-3.01), p < 0.001). This association was stronger in non-White (vs. White) participants (OR (95% CI): 3.14 (1.82-5.43) vs. 1.01 (0.51-1.97), respectively; interaction p-value =0.01), but consistent in subgroups stratified by age and sex. CONCLUSIONS: ECG-LAA, defined as abnormal DTNPV1, is associated with an increased risk of HF, underscoring the role of atrial disease in developing HF. Racial differences in this association exist, possibly suggesting considering ECG-LAA in personalized assessments of HF risk.

A study on feature selection using multi-domain feature extraction for automated k-complex detection.

Background: K-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. Method: In 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. Results: The 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. Conclusion: The 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.

To explore the change of motor cognitive function in pituitary tumor rats after operation.

To study the changes of motor and cognitive function of pituitary tumor rats after the operation. Methods: The experiment was divided into three groups: control group, model group and operation group (30 animals for each group). Female Fischer344 rats were selected. Model group rats were subcutaneously embedded with 10 mg estrogen sustained-release pump to induce a pituitary tumor model, and control group rats were subcutaneously embedded with a normal saline sustained-release pump as control. The operation group was successfully treated by microsurgery after the model was established. The quantitative expressions of PTTG, FGF-2 and VEGF were detected by Western blot. Morris test was used to detect the spatial learning and memory ability of rats. Western blot results showed that compared with the model group, the expression of the operation group was decreased, but still higher than that of the control group (p < 0.05). The water maze test results showed that the incubation period of searching the safe island in the model group was significantly longer than that in the control group on the 8th and 9th day after the injury, and the difference was statistically significant (p < 0.05). The incubation period of searching the safe island on the 8th and 9th day after injury in the operation group was significantly shorter than that in the control group. Through the detection of behavioral-related experimental and protein, the motor memory of rats after pituitary tumor surgery can be improved to some extent.

A RUSBoosted tree method for k-complex detection using tunable Q-factor wavelet transform and multi-domain feature extraction.

Background: K-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 method: In 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. Results: Experimental 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 methods: The 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. Conclusion: In 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.

Competition for Light Interception in Different Plant Canopy Characteristics of Diverse Cotton Cultivars.

Identifying the ideal plant nature and canopy structure is of great importance for improving photosynthetic production and the potential action of plants. To address this challenge, an investigation was accomplished in 2018 and 2019 at the Institute of Cotton Research (ICR) of the Chinese Academy of Agricultural Science (CAAS), Henan Province, China. Six cotton varieties with diverse maturities and plant canopy structures were used to evaluate the light interception (LI) in cotton, the leaf area index (LAI), the biomass, and the yield throughout the two years of study. The light spatial distribution in the plant canopy was evaluated using a geographic statistical method, following the increasing quantity of radiation intercepted, which was determined using the rules of Simpson. Compared to the cotton plants with a compact structure, varieties with both a loose and tower design captured a comparatively higher amount of light (average 31.3%) and achieved a higher LAI (average 32.4%), eventually achieving a high yield (average 10.1%). Furthermore, the polynomial correlation revealed a positive relationship between the biomass accumulation in the reproductive parts and canopy-accrued light interception (LI), signifying that light interception is critical for the yield development of cotton. Furthermore, when the leaf area index (LAI) was peaked, radiation interception and biomass reached the highest during the boll-forming stage. These findings will provide guidance on the light distribution in cotton cultivars with an ideal plant structure for light capture development, providing an important foundation for researchers to better manage light and canopies.

Effects of plastic film mulching on the spatiotemporal distribution of soil water, temperature, and photosynthetic active radiation in a cotton field.

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.

Scaling up ultrathin boat-graphane with the non-classical stiffness relation to macroscopic metamaterials.

Scaling up ultrathin nanosheets with unusual mechanical properties to macroscopic metamaterials is an intriguing topic considering the significant gap of their characteristic scales. In this regard, we investigate the relation between the in- and out-of-plane stiffness of monolayer boat-graphane in two principal axis directions by quantum mechanical calculations. A non-classical relation between the two types of stiffness is found, in opposition to the classical one for orthotropic thin plates. Analytical lattice dynamics models are proposed and suggest that the two kinds of stiffness stem from different covalent interaction sources, giving rise to the discrepancy. Guided by the geometry of boat-graphane and the model predictions, the non-classical relation successfully scales to macroscopic metamaterial plates, as justified by finite element simulations. The key structural parameters for successful scaling are also explored. The present work not only enriches our understanding of the nanomechanics of ultrathin nanosheets but also suggests a novel approach to construct mechanical metamaterials.

Surveillance of SARS-CoV-2 in nine neighborhood sewersheds in Detroit Tri-County area, United States: Assessing per capita SARS-CoV-2 estimations and COVID-19 incidence.

Wastewater-based epidemiology (WBE) has been suggested as a useful tool to predict the emergence and investigate the extent of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In this study, we screened appropriate population biomarkers for wastewater SARS-CoV-2 normalization and compared the normalized SARS-CoV-2 values across locations with different demographic characteristics in southeastern Michigan. Wastewater samples were collected between December 2020 and October 2021 from nine neighborhood sewersheds in the Detroit Tri-County area. Using reverse transcriptase droplet digital polymerase chain reaction (RT-ddPCR), concentrations of N1 and N2 genes in the studied sites were quantified, with N1 values ranging from 1.92 × 102 genomic copies/L to 6.87 × 103 gc/L and N2 values ranging from 1.91 × 102 gc/L to 6.45 × 103 gc/L. The strongest correlations were observed with between cumulative COVID-19 cases per capita (referred as COVID-19 incidences thereafter), and SARS-CoV-2 concentrations normalized by total Kjeldahl nitrogen (TKN), creatinine, 5-hydroxyindoleacetic acid (5-HIAA) and xanthine when correlating the per capita SARS-CoV-2 and COVID-19 incidences. When SARS-CoV-2 concentrations in wastewater were normalized and compared with COVID-19 incidences, the differences between neighborhoods of varying demographics were reduced as compared to differences observed when comparing non-normalized SARS-CoV-2 with COVID-19 cases. This indicates when studying the disease burden in communities of different demographics, accurate per capita estimation is of great importance. The study suggests that monitoring selected water quality parameters or biomarkers, along with RNA concentrations in wastewater, will allow adequate data normalization for spatial comparisons, especially in areas where detailed sanitary sewage flows and contributing populations in the catchment areas are not available. This opens the possibility of using WBE to assess community infections in rural areas or the developing world where the contributing population of a sample could be unknown.

Radiomics Model Based on Enhanced Gradient Level Set Segmentation Algorithm to Predict the Prognosis of Endoscopic Treatment of Sinusitis.

Methods: Computed tomography (CT) images of sinusitis in 91 patients were collected. By introducing boundary gradient information into the edge detection function, the sensitivity of the level set model to the boundary of different intensities of lesions was adjusted to obtain accurate segmentation results. After that, the segmented CT image was imported into Mazda texture analysis software for feature extraction. Three dimensionality reduction methods were used to screen the best texture features. Four analysis methods in the B11 module were used to calculate the misclassified rate (MCR). Results: The segmentation algorithm based on an enhanced gradient level set has good segmentation results for sinusitis lesions. The radiomics results show that the raw data analysis method under the Fisher dimensionality reduction method has a low MCR (25.27%). Conclusion: The enhanced gradient level set segmentation algorithm can segment sinusitis lesions accurately. The radiomics model effectively predicts the prognosis of endoscopic treatment of sinusitis.

Digital versus conventional full-arch impressions in linear and 3D accuracy: a systematic review and meta-analysis of in vivo studies.

OBJECTIVES: The purpose of this systematic review was to compare the accuracy of digital and conventional full-arch impressions in vivo. MATERIALS AND METHODS: This systematic review was conducted according to the PRISMA and registered at the PROSPERO (CRD42021232736). Electronic and hand searches were performed to identify in vivo studies comparing the linear or 3D accuracy of digital and conventional impressions. The risk of bias (ROB) of included studies was assessed by QUADAS-2, and the overall quality of evidence was assessed by GRADE. RESULTS: Twenty-two studies met the inclusion criteria, and 13 studies were included in the meta-analysis. There was no significant difference between digital and conventional impressions in the linear measurements of tooth width, anterior Bolton ratio, overall Bolton ratio, intercanine distance (ICD), and intermolar distance (IMD). The repeated measurement mean errors (RMEs) were less than 0.1 mm, the intra-examiner intraclass correlation coefficient (ICC) values were more than 0.9, and the inter-examiner ICC values were more than 0.87 for both impression techniques. The 3D deviation between digital and alginate impressions was 0.09 mm. The 3D precision of both impression techniques was less than 0.1 mm. CONCLUSIONS: The trueness of digital and alginate full-arch impressions was similar, and both impression techniques showed high precision. More research was needed to compare digital impressions and other conventional impression materials. CLINICAL RELEVANCE: For patients with completely natural dentition, the digital impressions obtained directly from intraoral scanning can be considered a viable alternative to alginate impressions.

Five-week warning of COVID-19 peaks prior to the Omicron surge in Detroit, Michigan using wastewater surveillance.

Wastewater-based epidemiology (WBE) is useful in predicting temporal fluctuations of COVID-19 incidence in communities and providing early warnings of pending outbreaks. To investigate the relationship between SARS-CoV-2 concentrations in wastewater and COVID-19 incidence in communities, a 12-month study between September 1, 2020, and August 31, 2021, prior to the Omicron surge, was conducted. 407 untreated wastewater samples were collected from the Great Lakes Water Authority (GLWA) in southeastern Michigan. N1 and N2 genes of SARS-CoV-2 were quantified using RT-ddPCR. Daily confirmed COVID-19 cases for the City of Detroit, and Wayne, Macomb, Oakland counties between September 1, 2020, and October 4, 2021, were collected from a public data source. The total concentrations of N1 and N2 genes ranged from 714.85 to 7145.98 gc/L and 820.47 to 6219.05 gc/L, respectively, which were strongly correlated with the 7-day moving average of total daily COVID-19 cases in the associated areas, after 5 weeks of the viral measurement. The results indicate a potential 5-week lag time of wastewater surveillance preceding COVID-19 incidence for the Detroit metropolitan area. Four statistical models were established to analyze the relationship between SARS-CoV-2 concentrations in wastewater and COVID-19 incidence in the study areas. Under a 5-week lag time scenario with both N1 and N2 genes, the autoregression model with seasonal patterns and vector autoregression model were more effective in predicting COVID-19 cases during the study period. To investigate the impact of flow parameters on the correlation, the original N1 and N2 gene concentrations were normalized by wastewater flow parameters. The statistical results indicated the optimum models were consistent for both normalized and non-normalized data. In addition, we discussed parameters that explain the observed lag time. Furthermore, we evaluated the impact of the omicron surge that followed, and the impact of different sampling methods on the estimation of lag time.

Carbon footprint of cotton production in China: Composition, spatiotemporal changes and driving factors.

Analyzing the carbon footprint of crop production and proposing low-carbon emission reduction production strategies can help China develop sustainable agriculture under the goal of 'carbon peak and carbon neutrality'. Cotton is an economically important crop in China, but few reports have systematically quantified the carbon footprint of China's cotton production and analyzed its spatiotemporal changes and driving factors. This study used a life cycle approach to analyze the spatiotemporal changes and identify the main components and driving factors of the carbon footprint of cotton production in China between 2004 and 2018 based on statistical data. The results showed that the carbon footprint per unit area of cotton in Northwest China, the Yellow River Basin and the Yangtze River Basin reached 6220.13 kg CO2eq·ha-1, 3528.14 kg CO2eq·ha-1 and 2958.56 kg CO2eq·ha-1, respectively. From 2004 to 2018, the CFa in the Yellow River Basin and Northwest China increased annually, with average increases of 59.87 kg CO2eq·ha-1 and 260.70 kg CO2eq·ha-1, respectively, while the CFa in the Yangtze River Basin decreased by an average of 21.53 kg CO2eq·ha-1 per year. The ridge regression and Logarithmic Mean Divisia Index (LMDI) model showed that fertilizer, irrigation electricity and agricultural film were the main influences on carbon emission growth at the micro level and that the economic factor was the key factor at the macro level. Improving the efficiency of cotton fertilization and electricity use and ensuring the high-quality development of the cotton industry are effective strategies to reduce the carbon footprint of cotton cultivation in the future. This study comprehensively uses statistical data and mathematical modeling to provide theoretical support for accounting and in-depth analysis of cotton carbon emissions. The results are valuable for policy making related to sustainable development and the low-carbon development of the Chinese cotton industry.

Boll characteristics and yield of cotton in relation to the canopy microclimate under varying plant densities in an arid area.

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.

Exploring differences for motor imagery using Teager energy operator-based EEG microstate analyses.

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.