Supervised Deep Learning for Head Motion Correction in PET

Head movement is a major limitation in brain positron emission tomography (PET) imaging, which results in image artifacts and quantification errors. Head motion correction plays a critical role in quantitative image analysis and diagnosis of nervous system diseases. However, to date, there is no approach that can track head motion continuously without using an external device. Here, we develop a deep learning-based algorithm to predict rigid motion for brain PET by lever-aging existing dynamic PET scans with gold-standard motion measurements from external Polaris Vicra tracking. We propose a novel Deep Learning for Head Motion Correction (DL-HMC) methodology that consists of three components: (i) PET input data encoder layers; (ii) regression layers to estimate the six rigid motion transformation parameters; and (iii) feature-wise transformation (FWT) layers to condition the network to tracer time-activity. The input of DL-HMC is sampled pairs of one-second 3D cloud representations of the PET data and the output is the prediction of six rigid transformation motion parameters. We trained this network in a supervised manner using the Vicra motion tracking information as gold-standard. We quantitatively evaluate DL-HMC by comparing to gold-standard Vicra measurements and qualitatively evaluate the reconstructed images as well as perform region of interest standard uptake value (SUV) measurements. An algorithm ablation study was performed to determine the contributions of each of our DL-HMC design choices to network performance. Our results demonstrate accurate motion prediction performance for brain PET using a data-driven registration approach without external motion tracking hardware. All code is publicly available on GitHub: https://github.com/OnofreyLab/dl-hmc_miccai2022.

Melatonin attenuates spatial learning and memory dysfunction in developing rats by suppressing isoflurane-induced endoplasmic reticulum stress via the SIRT1/Mfn2/PERK signaling pathway

Use of the inhalation anesthetic isoflurane may increase the risk of cognitive deficiency and neurotoxicity after birth. A growing body of evidence suggests that melatonin is an effective treatment for various types of oxidative stress damage and neurodegenerative disease. In this study, we aimed to examine the effects of melatonin on isoflurane-induced endoplasmic reticulum (ER) stress, spatial learning and memory impairment during development. The rats were grouped according to whether the rats were exposed to isoflurane or a control gas and whether they were administered melatonin or phosphate buffered saline (PBS). We administered isoflurane to 7-day-old Sprague–Dawley rat pups with intraperitoneal injections of melatonin (20 mg/kg) 15 min before and 3 h after the initiation of anesthesia. Twelve hours after isoflurane anesthesia, rats were randomly selected from each group and sacrificed. The hippocampal tissue and serum were collected to determine the levels of SIRT1, Mfn2, PERK, and other proteins or cytokines related to ER stress, apoptosis, and neuroinflammation. Subsequently, all remaining rats were assessed for spatial learning and memory deficiency 31 days after birth using the Morris water maze test. We found that melatonin attenuated isoflurane-induced ER stress and neuroapoptosis in the hippocampus and decreased the level of neuroinflammatory markers in the serum of newborn rats, resulting in improved spatial learning and memory. In addition, the neuroprotective effect of melatonin was weakened after the SIRT1/Mfn2/PERK signaling pathway was suppressed by lentivirus transfection. Therefore, our findings demonstrate that melatonin ameliorates spatial learning and memory impairment after isoflurane exposure, and these beneficial effects are associated with a reduction in ER stress, neuroapoptosis, and neuroinflammation via the SIRT1/Mfn2/PERK signaling pathway.

Abstract P2080: Role Of Atg9 Proteins In Regulating Autophagosome Formation

Cardiac myocytes require large amounts of energy generated by mitochondria to sustain contraction, and there is a strong link between mitochondrial dysfunction and heart disease. Defective mitochondria are less efficient at generating ATP, produce excessive reactive oxygen species, and release pro-apoptotic proteins, all of which can lead to loss of cardiac myocytes and reduced ability to sustain contractile function. Therefore, mitochondrial quality control is critical in preventing mitochondria from causing harm to the cell. Selective autophagy of the mitochondria (mitophagy) is the primary mechanism responsible for eliminating dysfunctional mitochondria. Despite intense research focus on mitophagy over the past decade, little is still known about early events in the pathway, and the signals that orchestrate the selective recruitment of the autophagic machinery to dysfunctional mitochondria are unclear. It is known that autophagosome formation requires Atg9, which is embedded in small Golgi-derived vesicles. To date, most data on Atg9 are from yeast and our knowledge of mammalian Atg9a and Atg9b functions in cells and tissues are still limited. Here, we found that Atg9a and Atg9b are expressed in the heart and that both proteins are increased in the infarct border zone after ligation of the LAD. We also discovered that Atg9a and Atg9b are present on distinct vesicles in myocytes. Atg9b-positive vesicles are localized throughout the cytosol, whereas Atg9a-positive vesicles are associated with the mitochondria even under basal conditions. This suggests that they might have distinct functions and that Atg9a may play a more selective role at the mitochondria. Interestingly, Atg9a, but not Atg9b, is degraded by the proteasome upon activation of mitophagy, a potential indication that this may be a mechanism to prevent excessive mitophagy. Overall, our findings suggest that Atg9a and Atg9b play distinct roles in autophagy and mitophagy and that the mitochondrial localization of Atg9a allows it to initiate autophagosome formation more efficiently during mitophagy.

[Clinical validation of the 2020 diagnostic approach for pediatric autoimmune encephalitis in a single center]

Objective: To evaluate the value of the 2020 diagnostic criteria (Cellucci criteria) for pediatric autoimmune encephalitis (AE) in children with suspected AE in a single center. Methods: The clinical data of 121 children hospitalized at the First Affiliated Hospital of Zhengzhou University from October 2019 to October 2021, with a diagnosis of suspected AE, were retrospectively collected and analyzed. The children were divided into definite antibody-positive AE (dAPAE), probable antibody-negative AE (prANAE), possible AE (pAE) and non-AE groups according to the Chinese expert consensus and the Graus criteria. A new diagnosis was made according to the Cellucci criteria which was compared with the clinical diagnosis to evaluate the diagnostic value of the Cellucci criteria. The Mann-Whitney U test, Kruskal-Wallis test, and χ² test were used to compare the differences among groups. The sensitivity and specificity were used to evaluate efficacy of the Cellucci criteria. Results: Among the 121 children, 72 were males and 49 were females, with an age of 10.3 (6.5, 14.0) years at disease onset. There were 99 cases diagnosed as AE according the clinical diagnosis (58 males and 41 females), of which 43 cases were diagnosed as dAPAE, 14 cases as prANAE and 42 cases as pAE, and the other 22 cases were not AE (14 males and 8 females). The top 2 initial symptoms in the 99 children with AE were seizures (53 cases, 53.5%) and abnormal mental behaviors (35 cases, 35.4%). And the most common symptoms during the course of the disease were abnormal mental behaviors (77 cases, 77.8%) and seizures (64 cases, 64.6%). There were statistically differences in the incidence of consciousness disorders, autonomic dysfunctions during the course of the disease and the length of hospitalization among the 4 groups (χ²=21.63, 13.74, H=22.60, all P<0.05). Ninety-six of the 121 children were tested for AE-related antibodies, of which 45 cases (46.9%) were antibody-positive. According to the Cellucci criteria, 42 cases were diagnosed as dAPAE, 34 cases as prANAE and 14 cases as pAE. Compared with the clinical diagnosis, the sensitivity of the Cellucci criteria for the diagnosis of the 3 types of AE were 93.02%, 92.86% and 87.88%, and the specificity were 96.23%, 74.39% and 86.36%, respectively. Conclusions: The Cellucci criteria has a high sensitivity and specificity for the diagnosis of pAE and dAPAE in the clinical management of children with suspected AE, while a high sensitivity but low specificity for the diagnosis of prANAE. Therefore, it is recommended to apply the Cellucci criteria selectively in clinical practice according to the actual situation, especially in the diagnosis of prANAE.

Dietary adaptation for weight loss maintenance at Yale (DAWLY): Protocol and predictions for a randomized controlled trial

Background

Current therapies for obesity treatment are effective at producing short-term weight loss, but weight loss maintenance remains a significant challenge. Here we investigate the impact of pre-intervention dietary fat intake on the efficacy of a dietary supplement to support weight loss maintenance. Preclinical work demonstrates that a vagal afferent pathway critical for sensing dietary lipids is blunted by a high-fat diet (HFD), resulting in a reduced preference for a low-fat emulsion and severe blunting of the dopamine (DA) response to the gastric infusion of lipids. Infusion of the gut lipid messenger oleoylethanolamide (OEA), which is also depleted by HFD, immediately reverses this DA blunting and restores preference for the low-fat emulsion. Studies of OEA supplementation for weight loss in humans have had limited success. Given the strong effect of HFD on this pathway, we designed a study to test whether the efficacy of OEA as a weight loss treatment is related to pre-intervention habitual intake of dietary fat.

Methods/Design

We employed a randomized, double-blind, placebo-controlled trial in which 100 adults with overweight/obesity (OW/OB) were randomized to receive either OEA or placebo daily for 16 months. Following a baseline evaluation of diet, metabolic health, adiposity, and brain response to a palatable an energy dense food, participants in both groups underwent a 4-month behavioral weight loss intervention (LEARN^®) followed by a 1-year maintenance period. The study aims are to (1) determine if pre-intervention dietary fat intake moderates the ability of OEA to improve weight loss and weight loss maintenance after a gold standard behavioral weight loss treatment; (2) identify biomarkers that predict outcome and optimize a stratification strategy; and (3) test a model underlying OEA's effectiveness.

Discussion

Focusing on interventions that target the gut-brain axis is supported by mounting evidence for the role of gut-brain signaling in food choice and the modulation of this circuit by diet. If successful, this work will provide support for targeting the gut-brain pathway for weight loss maintenance using a precision medicine approach that is easy and inexpensive to implement.

Clinical Trial Registration

[www.ClinicalTrials.gov], identifier [NCT04614233].

Dynamic Calibration Method of Sensor Drift Fault in HVAC System Based on Bayesian Inference

Sensor drift fault calibration is essential to maintain the operation of heating, ventilation and air conditioning systems (HVAC) in buildings. Bayesian inference (BI) is becoming more and more popular as a commonly used sensor fault calibration method. However, this method focused mainly on sensor bias fault, and it could be difficult to calibrate drift fault that changes with time. Therefore, a dynamic calibration method for sensor drift fault of HVAC systems based on BI is developed. Taking the drift fault calibration of the chilled water supply temperature sensor of the chiller as an example, the performance of the proposed dynamic calibration method is evaluated. Results show that the combination of the Exponentially Weighted Moving-Average (EWMA) method with high detection accuracy and the proposed BI dynamic calibration method can effectively improve the calibration accuracy of drift fault, and the Mean Absolute Percentage Error (MAPE) value between the calibrated and normal data is less than 5%.

Photosynthetic and hydraulic traits influence forest resistance and resilience to drought stress across different biomes

Drought events lead to depressions in gross primary productivity (GPP) of forest ecosystems. Photosynthetic and hydraulic traits are important factors governing GPP variation. However, how these functional traits affect GPP responses to drought has not been well understood. We quantified the capacity of GPP to withstand changes during droughts (GPP_resistance) and its post-drought responses (GPP_resilience) using eddy covariance data from the FLUXNET2015 dataset, and investigated how functional traits of dominant tree species that comprised >80% of the biomass (or composition) influenced GPP_resistance or GPP_resilience. Light-saturated photosynthetic rate of dominant tree species was negatively related to GPP_resistance, and was positively correlated with GPP_resilience. Forests dominated by species with higher hydraulic safety margins (HSM), smaller vessel diameter (V_dia) and lower sensitivity of canopy stomatal conductance per unit land area (G_s) to droughts had a higher GPP_resistance, while those dominated by species with lower HSM, larger V_dia and higher sensitivity of G_s to droughts exhibited a higher GPP_resilience. Differences in functional traits of forests located in diverse climate regions led to distinct GPP sensitivities to droughts. Forests located in humid regions had a higher GPP_resilience while those in arid regions exhibited a higher GPP_resistance. Forest GPP_resistance was negatively related to drought intensity, and GPP_resilience was negatively related to drought duration. Our findings highlight the significant role of functional traits in governing forest resistance and resilience to droughts. Overall, forests dominated by species with higher hydraulic safety were more resistant to droughts, while forests containing species with higher photosynthetic and hydraulic efficiency recovered better from drought stress.

Pearson's patterns correlational of clinical risks at admissions with hospitalization outcomes during initial COVID-19 outbreak

COVID-19 outbreaks have crushed our healthcare systems, which requires clinical guidance for the healthcare following the outbreaks. We conducted retrospective cohort studies with Pearson's pattern-based analysis of clinical parameters of 248 hospitalized patients with COVID-19. We found that dysregulated neutrophil densities were correlated with hospitalization duration before death (p = 0.000066, r = -0.45 for % neutrophil; p = 0.0001, r = -0.47 for neutrophil count). As such, high neutrophil densities were associated with mortality (p = 4.23 × 10^-31 for % neutrophil; p = 4.14 × 10^-27 for neutrophil count). These findings were further illustrated by a representative "second week crash" pattern and validated by an independent cohort (p = 5.98 × 10^-11 for % neutrophil; p = 1.65 × 10^-7 for neutrophil count). By contrast, low aspartate aminotransferase (AST) or lactate dehydrogenase (LDH) levels were correlated with quick recovery (p ≤ 0.00005). Collectively, these correlational at-admission findings may provide healthcare guidance for patients with COVID-19 in the absence of targeted therapy.

Perinatal Supplementation of Egg Yolk Enhances Cognition and Alters Brain Network Function in Offspring

Objectives

Egg yolks are a nutrient dense food often recommended to pregnant women, as they contain bioactive compounds that enhance brain development in offspring. We examined the effects of perinatal intake of egg yolks on cognition and brain functional network activity in offspring using a translational sow/piglet dyad model.

Methods

Sows were fed a control diet (n = 6) or a diet containing egg yolks (n = 5,350 mg egg yolk powder/kg BW/day, equivalent to ∼3 eggs/day for humans) from late gestation throughout lactation. At weaning, piglet offspring (n = 4/sow, total n = 44) performed object recognition testing (ORT) to assess hippocampal-dependent learning and memory outcomes. A subset of the piglets (n = 2/sow, total n = 22) underwent magnetic resonance imaging (MRI) to acquire diffusion tensor imaging (DTI) and resting-state functional MRI. Correlation analysis of outcomes from ORT and MRI was performed.

Results

Piglets from egg yolk fed sows spent more time with novel objects than familiar objects (p = 0.039), suggesting that maternal intake of egg yolk may improve hippocampal-dependent cognitive outcomes. DTI analysis showed that perinatal intake of egg yolks tended to increase fiber length in the hippocampus (p = 0.073) compared to control. Moreover, intake of egg yolk resulted in a 14.3% increase in functional connectivity in the cerebellar network (p = 0.025) and a 15.6% trending increase in the executive network (p = 0.089) compared to control. Interestingly, the visual network was positively correlated with proportional frequency (p = 0.043) and time (p = 0.078) spent with novel objects and negatively correlated with proportional frequency (p = 0.007) and time (p = 0.016) spent with familiar objects, suggesting that the activation of visual network may influence hippocampal-dependent cognitive outcomes at weaning.

Conclusions

Perinatal intake of egg yolk enhanced hippocampal-dependent learning and memory, increased fiber length in the hippocampus and altered brain functional connectivity in offspring at weaning. The findings from this study support that egg yolk altered activation of specific brain networks are that associated with cognitive outcomes in weaning piglets.

Funding Sources

This work was supported by grants to H.J.P from the USDA-National Institute of Food and Agriculture and the Egg Nutrition Center.

DeepDRiD: Diabetic Retinopathy-Grading and Image Quality Estimation Challenge

We described a challenge named "Diabetic Retinopathy (DR)-Grading and Image Quality Estimation Challenge" in conjunction with ISBI 2020 to hold three sub-challenges and develop deep learning models for DR image assessment and grading. The scientific community responded positively to the challenge, with 34 submissions from 574 registrations. In the challenge, we provided the DeepDRiD dataset containing 2,000 regular DR images (500 patients) and 256 ultra-widefield images (128 patients), both having DR quality and grading annotations. We discussed details of the top 3 algorithms in each sub-challenges. The weighted kappa for DR grading ranged from 0.93 to 0.82, and the accuracy for image quality evaluation ranged from 0.70 to 0.65. The results showed that image quality assessment can be used as a further target for exploration. We also have released the DeepDRiD dataset on GitHub to help develop automatic systems and improve human judgment in DR screening and diagnosis.

Seroprevalence of SARS-CoV-2-specific antibodies and vaccination-related adverse events in systemic lupus erythematosus and rheumatoid arthritis

Background

This study aimed to investigate the seroreactivity of Coronavirus disease 2019 (COVID-19) vaccination and its adverse events among systemic lupus erythematosus (SLE) patients, rheumatoid arthritis (RA) patients, and healthy controls (HCs).

Methods

A total of 60 SLE patients, 70 RA patients and 35 HCs, who received a complete inactivated COVID-19 vaccine (Vero cells) regimen, were recruited in the current study. Serum IgG and IgM antibodies against Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) were determined by using chemiluminescent microparticle immunoassay (CMIA).

Results

There were no significant differences regarding the seroprevalences of IgG and IgM antibodies against SARS-CoV-2, and the self-reported vaccination-related adverse events among SLE patients, RA patients and HCs. The inactivated COVID-19 vaccines appeared to be well-tolerated and moderately immunogenic. In addition, case-only analysis indicated that in SLE patients, the disease manifestation of rash and anti-SSA autoantibody were associated with seroprevalence of IgG antibody against SARS-CoV-2, whereas the uses of ciclosporin and leflunomide had influence on the seroprevalence of IgM antibody against SARS-CoV-2. In RA patients, rheumatoid factor (RF) appeared to be associated with the seroprevalence of IgG antibody against SARS-CoV-2.

Conclusion

Our study reveals that the seroprevalences of IgG and IgM antibodies against SARS-CoV-2 and vaccination-related adverse effects are similar among SLE, RA and HCs, suggesting that COVID-19 vaccine is safe and effective for SLE and RA patients to prevent from the pandemic of COVID-19.

Spin in the Abstracts of Randomized Controlled Trials in Operative Dentistry: A Cross-sectional Analysis

OBJECTIVE

To assess the presence and characteristics of spin in recently published RCT abstracts in operative dentistry and to investigate potential factors associated with the presence of spin.

METHODS AND MATERIALS

The PubMed database was searched to identify parallel-group RCTs published between 2015 and 2019 in the field of operative dentistry, which compared two or more groups and had nonsignificant results for the primary outcome. Two authors evaluated independently the presence and characteristics of spin among these abstracts. Multivariable logistic regression analyses were conducted to identify factors associated with the presence of spin in the Results and the Conclusions sections, respectively.

RESULTS

A total of 77 RCT abstracts were included, among which 58 (75.3%) showed at least one type of spin. Spin was identified in the Results and Conclusions sections of 32 (41.6%) and 45 (58.4%) abstracts, respectively. 19 RCTs (24.7%) presented spin in both the Results and the Conclusions section of abstracts. The presence of spin in the Results section of abstracts was significantly associated with source of funding (OR=8.10; p=0.025) and number of treatment arms was associated with the presence of spin in the Conclusions section of abstracts (OR=5.66; p=0.005).

CONCLUSION

The occurrence rate of spin in the sample of operative dentistry RCTs abstracts is high.

The Drivers of the Martian Bow Shock Location: A Statistical Analysis of Mars Atmosphere and Volatile EvolutioN and Mars Express Observations

The Martian interaction with the solar wind leads to the formation of a bow shock upstream of the planet. The shock dynamics appear complex, due to the combined influence of external and internal drivers. The extreme ultraviolet fluxes and magnetosonic Mach number are known major drivers of the shock location, while the influence of other possible drivers is less constrained or unknown such as crustal magnetic fields, solar wind dynamic pressure, or the Interplanetary Magnetic Field (IMF) intensity, and orientation. In this study, we compare the influence of the main drivers of the Martian shock location, based on several methods and published datasets from Mars Express (MEX) and Mars Atmosphere Volatile EvolutioN (MAVEN) missions. We include here the influence of the crustal fields, extreme ultraviolet fluxes, solar wind dynamic pressure, as well as (for MAVEN, thanks to magnetic field measurements) magnetosonic Mach number and Interplanetary Magnetic Field parameters (intensity and orientation angles). The bias due to the cross-correlations among the possible drivers is investigated with a partial correlations analysis. Several model selection methods (Akaike Information Criterion and Least Absolute Shrinkage Selection Operator regression) are also used to rank the relative importance of the physical parameters. We conclude that the major drivers of the shock location are extreme ultraviolet fluxes and magnetosonic Mach number, while crustal fields and solar wind dynamic pressure are secondary drivers at a similar level. The IMF orientation also plays a significant role, with larger distances for perpendicular shocks rather than parallel shocks.

Barth Syndrome Cardiomyopathy: An Update

Barth syndrome (BTHS) is an X-linked mitochondrial lipid disorder caused by mutations in the TAFAZZIN (TAZ) gene, which encodes a mitochondrial acyltransferase/transacylase required for cardiolipin (CL) biosynthesis. Cardiomyopathy is a major clinical feature of BTHS. During the past four decades, we have witnessed many landmark discoveries that have led to a greater understanding of clinical features of BTHS cardiomyopathy and their molecular basis, as well as the therapeutic targets for this disease. Recently published Taz knockout mouse models provide useful experimental models for studying BTHS cardiomyopathy and testing potential therapeutic approaches. This review aims to summarize key findings of the clinical features, molecular mechanisms, and potential therapeutic approaches for BTHS cardiomyopathy, with particular emphasis on the most recent studies.

Effects of Stores’ Environmental Components on Chinese Consumers’ Emotions and Intentions to Purchase Luxury Brands: Integrating Partial Least Squares-Structural Equation Modeling and Fuzzy-Set Qualitative Comparative Analysis Approaches

Purpose

Drawing upon the stimulus-organism-response (S-O-R) model, this paper aims to investigate the effects of stores’ environmental components (i.e., lighting, cleanliness, scent, design, layout, music, and employee interaction) on Chinese consumers’ emotions and intentions to purchase luxury brands.

Design/Methodology/Approach

Data were collected from Chinese consumers who have purchased luxury brands from retail stores. Partial least squares-structural equation modeling (PLS-SEM) and fuzzy-set qualitative comparative analysis (fsQCA) were employed for data analysis.

Findings

The results of PLS-SEM indicated that three dimensions of the store environment (i.e., music, scent, and employee interaction) directly and significantly influenced Chinese consumers’ emotions. However, fsQCA revealed greater heterogeneity among respondents by highlighting stores’ environmental components and Chinese consumers’ emotions.

Originality/Value

This study makes significant contributions to the marketing literature by examining the S-O-R model alongside fsQCA approach to conditionally predict outcomes in a luxury brand context. The present study may be one of the first to examine the effects of stores’ environmental components on Chinese consumers’ emotions and purchase intentions, applying both symmetrical test (PLS-SEM) and asymmetrical test (fsQCA) approaches to determine significant components of the store environment as well as component combinations that predict Chinese consumers’ emotions and purchase intentions.

Utility Metrics for Evaluating Synthetic Health Data Generation Methods: Validation Study

Background

A regular task by developers and users of synthetic data generation (SDG) methods is to evaluate and compare the utility of these methods. Multiple utility metrics have been proposed and used to evaluate synthetic data. However, they have not been validated in general or for comparing SDG methods.

Objective

This study evaluates the ability of common utility metrics to rank SDG methods according to performance on a specific analytic workload. The workload of interest is the use of synthetic data for logistic regression prediction models, which is a very frequent workload in health research.

Methods

We evaluated 6 utility metrics on 30 different health data sets and 3 different SDG methods (a Bayesian network, a Generative Adversarial Network, and sequential tree synthesis). These metrics were computed by averaging across 20 synthetic data sets from the same generative model. The metrics were then tested on their ability to rank the SDG methods based on prediction performance. Prediction performance was defined as the difference between each of the area under the receiver operating characteristic curve and area under the precision-recall curve values on synthetic data logistic regression prediction models versus real data models.

Results

The utility metric best able to rank SDG methods was the multivariate Hellinger distance based on a Gaussian copula representation of real and synthetic joint distributions.

Conclusions

This study has validated a generative model utility metric, the multivariate Hellinger distance, which can be used to reliably rank competing SDG methods on the same data set. The Hellinger distance metric can be used to evaluate and compare alternate SDG methods.

Assessing the drinking water quality in the Inner Mongolia Autonomous Region from 2014 to 2018

The objective of this study was to understand the drinking water quality state in the Inner Mongolia Autonomous Region from 2014 to 2018 and to derive information that will provide a basis for improving the drinking water quality in the region. Monitoring data for drinking water from the Inner Mongolia Autonomous Region for 2014 to 2018 were analyzed and the results were compared with GB 5749-2006, the Standard Test Method for Drinking Water, and GB 5749-2006, the Drinking Water Quality Standards. Data for a total of 30,613 water samples were assessed. Of the data for the microbiological index, sensory trait and general chemical index, and toxicological index, 89, 80, and 69% were qualified, respectively. For the toxicological index, the fluoride and nitrate nitrogen data were the least compliant. The water quality in all the cities was generally very suitable for drinking. However, there were marked differences in the qualified rates of drinking water in different areas and the qualified rates of the data for the three indexes were lower in rural areas than in urban areas. Given the varied issues with the drinking water quality, the relevant departments of League cities should implement appropriate and effective treatment measures to improve the drinking water quality and ensure it is safe for residents.

Discrete Aurora at Mars: Dependence on Upstream Solar Wind Conditions

Discrete aurora at Mars, characterized by their small spatial scale and tendency to form near strong crustal magnetic fields, are emissions produced by particle precipitation into the Martian upper atmosphere. Since 2014, Mars Atmosphere and Volatile EvolutioN's (MAVEN's) Imaging Ultraviolet Spectrograph (IUVS) has obtained a large collection of UV discrete aurora observations during its routine periapsis nightside limb scans. Initial analysis of these observations has shown that, near the strongest crustal magnetic fields in the southern hemisphere, the IUVS discrete aurora detection frequency is highly sensitive to the interplanetary magnetic field (IMF) clock angle. However, the role of other solar wind properties in controlling the discrete aurora detection frequency has not yet been determined. In this work, we use the IUVS discrete aurora observations, along with MAVEN observations of the upstream solar wind, to determine how the discrete aurora detection frequency varies with solar wind dynamic pressure, IMF strength, and IMF cone angle. We find that, outside of the strong crustal field region (SCFR) in the southern hemisphere, the aurora detection frequency is relatively insensitive to the IMF orientation, but significantly increases with solar wind dynamic pressure, and moderately increases with IMF strength. Interestingly however, although high solar wind dynamic pressures cause more aurora to form, they have little impact on the brightness of the auroral emissions. Alternatively, inside the SCFR, the detection frequency is only moderately dependent on the solar wind dynamic pressure, and is much more sensitive to the IMF clock and cone angles. In the SCFR, aurora are unlikely to occur when the IMF points near the radial or anti-radial directions when the cone angle (arccos(B x /|B|)) is less than 30° or between 120° and 150°. Together, these results provide the first comprehensive characterization of how upstream solar wind conditions affect the formation of discrete aurora at Mars.

First measurement of 25Al+p resonant scattering relevant to the astrophysical reaction 22Mg(α,p)25Al

Type I X-ray bursts (XRBs) are the most frequently observed thermonuclear explosions in nature. The 22Mg(α,p)25Al reaction plays a critical role in XRB models. However, experimental information is insufficient to deduce a precise 22Mg(α,p)25Al reaction rate for the respective XRB temperature range. A new measurement of 25Al+p resonant scattring was performed up to the astrophysically interested energy region of 22Mg(α,p)25Al. Several resonances were observed in the excitation functions, and their level properties have been determined based on an R-matrix analysis. In particular, proton widths and spin-parities of four natural-parity resonances above the α threshold of 26Si, which can contribute the reaction rate of 22Mg(α,p)25Al, were first experimentally determined.