Intracytoplasmic sperm injection versus in vitro fertilization in infertile women with thyroid autoimmunity.

BACKGROUND: It has been reported that intracytoplasmic sperm injection (ICSI) may be the preferred fertilization method for women with thyroid autoimmunity (TAI) seeking assisted reproduction. We compared the reproductive outcomes of women with TAI who were treated with ICSI compared to in vitro fertilization (IVF). METHODS: In this retrospective cohort study, we included women with infertility who were referred to the Reproductive Centre of Peking University Third Hospital for their first IVF/ICSI and embryo transfer treatment cycle from January, 2019 to February, 2021. In total, 2,171 and 743 women with TAI underwent IVF and ICSI, respectively, while 8,702 and 2,688 women without TAI underwent IVF and ICSI, respectively. We examined the cumulative live birth rate (primary outcome) from the initiated stimulative cycle as well as the secondary outcomes of fertilization rate, rates of clinical pregnancy and live birth after the first embryo transfer cycle. We compared the reproductive outcomes of women treated with IVF and ICSI according to TAI status. Multivariable logistic regression analyses were performed to adjust for relevant confounders. RESULTS: Women who underwent ICSI had significantly higher fertilization rates than those who underwent IVF (median [interquartile range]: 0.6 [0.5-0.8] in TAI-positive and IVF group vs. 0.7 [0.5-0.8] in TAI-positive and ICSI group vs. 0.6 [0.5-0.8] in TAI-negative and IVF group vs. 0.7 [0.5-0.8] in TAI-positive and ICSI group, p<0.001). However, the rates of cumulative live births, clinical pregnancies, and live births were significantly lower among women with TAI who underwent ICSI than those who underwent IVF (cumulative live birth: 51.8% vs. 47%, adjusted odds ratio: 0.80, 95% CI: 0.67-0.97); clinical pregnancy: 43.0% vs. 38.8%, adjusted odds ratio: 0.81, 95% confidence interval [CI]: 0.67-0.97; live birth: 36.2% vs. 32.4%, adjusted odds ratio: 0.81, 95% CI: 0.66-0.98). CONCLUSION: We observed that the use of ICSI in women with TAI was not associated with better assisted reproductive outcomes compared to IVF. Further prospective clinical trials are needed to confirm our findings.

Securing Your Airspace: Detection of Drones Trespassing Protected Areas.

Unmanned Aerial Vehicle (UAV) deployment has risen rapidly in recent years. They are now used in a wide range of applications, from critical safety-of-life scenarios like nuclear power plant surveillance to entertainment and hobby applications. While the popularity of drones has grown lately, the associated intentional and unintentional security threats require adequate consideration. Thus, there is an urgent need for real-time accurate detection and classification of drones. This article provides an overview of drone detection approaches, highlighting their benefits and limitations. We analyze detection techniques that employ radars, acoustic and optical sensors, and emitted radio frequency (RF) signals. We compare their performance, accuracy, and cost under different operating conditions. We conclude that multi-sensor detection systems offer more compelling results, but further research is required.

Contrasting benthic bacterial and fungal communities in two temperate coastal areas affected by different levels of anthropogenic pressure.

Benthic microbial communities play a crucial role in maintaining the stability and function of estuarine ecosystems. However, their organization and response to multiple stresses in severely disturbed coastal areas remains to be elucidated. In this study, we revealed the presence of contrasting benthic bacterial and fungal communities in the Liaohe (LH) and Yalujiang (YLJ) estuaries, which are located at similar latitudes and are characterized by similar climates but are subjected to different levels of anthropogenic pressure. The results showed that Firmicutes and Chloroflexi were more abundant in LH, which reflected the influence of anthropogenic pressure in this area. Functional analyses indicated that the functional genes involved in the generation of precursor metabolites and energy pathways were more enriched in the LH community, while genes regulating degradation/utilization/assimilation processes were more enriched in the YLJ community. Distance-dependent similarity analysis showed that the bacterial community in LH was more affected by environmental changes, while that in YLJ was more influenced by geographic dispersion. In contrast, no significant distance-dependent similarity was found for the fungal communities in the two areas. In addition, the network analysis showed that the bacterial-fungal network in YLJ was more complex and stable than that in the LH. Our results highlight the important roles of environmental heterogeneity in controlling microbial community composition, biogeographic patterns, and co-occurrence networks. These findings fill knowledge gaps in the understanding of the different response patterns of benthic communities under varying anthropogenic pressures.

OPTILOD: Optimal Beacon Placement for High-Accuracy Indoor Localization of Drones.

For many applications, drones are required to operate entirely or partially autonomously. In order to fly completely or partially on their own, drones need to access location services for navigation commands. While using the Global Positioning System (GPS) is an obvious choice, GPS is not always available, can be spoofed or jammed, and is highly error-prone for indoor and underground environments. The ranging method using beacons is one of the most popular methods for localization, especially for indoor environments. In general, the localization error in this class is due to two factors: the ranging error, and the error induced by the relative geometry between the beacons and the target object to be localized. This paper proposes OPTILOD (Optimal Beacon Placement for High-Accuracy Indoor Localization of Drones), an optimization algorithm for the optimal placement of beacons deployed in three-dimensional indoor environments. OPTILOD leverages advances in evolutionary algorithms to compute the minimum number of beacons and their optimal placement, thereby minimizing the localization error. These problems belong to the Mixed Integer Programming (MIP) class and are both considered NP-hard. Despite this, OPTILOD can provide multiple optimal beacon configurations that minimize the localization error and the number of deployed beacons concurrently and efficiently.

The large-scale group consensus multi-attribute decision-making method based on probabilistic dual hesitant fuzzy sets.

We proposed a novel decision-making method, the large-scale group consensus multi-attribute decision-making method based on probabilistic dual hesitant fuzzy sets, to address the challenge of large-scale group multi-attribute decision-making in fuzzy environments. This method concurrently accounted for the membership and non-membership degrees of decision-making experts in fuzzy environments and the corresponding probabilistic value to quantify expert decision information. Furthermore, it applied to complex scenarios involving groups of 20 or more decision-making experts. We delineated five major steps of the method, elaborating on the specific models and algorithms used in each phase. We began by constructing a probabilistic dual hesitant fuzzy information evaluation matrix and determining attribute weights. The following steps involved classifying large-scale decision-making expert groups and selecting the optimal classification scheme based on effectiveness assessment criteria. A global consensus degree threshold was established, followed by implementing a consensus-reaching model to synchronize opinions within the same class of expert groups. Decision information was integrated within and between classes using an information integration model, leading to a comprehensive decision matrix. Decision outcomes for the objects were then determined through a ranking method. The method's effectiveness and superiority were validated through a case study on urban emergency capability assessment, and its advantages were further emphasized in comparative analyses with other methods.

Integrative analysis identifies oxidative stress biomarkers in non-alcoholic fatty liver disease via machine learning and weighted gene co-expression network analysis.

Background: Non-alcoholic fatty liver disease (NAFLD) is the most common chronic liver disease globally, with the potential to progress to non-alcoholic steatohepatitis (NASH), cirrhosis, and even hepatocellular carcinoma. Given the absence of effective treatments to halt its progression, novel molecular approaches to the NAFLD diagnosis and treatment are of paramount importance. Methods: Firstly, we downloaded oxidative stress-related genes from the GeneCards database and retrieved NAFLD-related datasets from the GEO database. Using the Limma R package and WGCNA, we identified differentially expressed genes closely associated with NAFLD. In our study, we identified 31 intersection genes by analyzing the intersection among oxidative stress-related genes, NAFLD-related genes, and genes closely associated with NAFLD as identified through Weighted Gene Co-expression Network Analysis (WGCNA). In a study of 31 intersection genes between NAFLD and Oxidative Stress (OS), we identified three hub genes using three machine learning algorithms: Least Absolute Shrinkage and Selection Operator (LASSO) regression, Support Vector Machine - Recursive Feature Elimination (SVM-RFE), and RandomForest. Subsequently, a nomogram was utilized to predict the incidence of NAFLD. The CIBERSORT algorithm was employed for immune infiltration analysis, single sample Gene Set Enrichment Analysis (ssGSEA) for functional enrichment analysis, and Protein-Protein Interaction (PPI) networks to explore the relationships between the three hub genes and other intersecting genes of NAFLD and OS. The distribution of these three hub genes across six cell clusters was determined using single-cell RNA sequencing. Finally, utilizing relevant data from the Attie Lab Diabetes Database, and liver tissues from NASH mouse model, Western Blot (WB) and Reverse Transcription Quantitative Polymerase Chain Reaction (RT-qPCR) assays were conducted, this further validated the significant roles of CDKN1B and TFAM in NAFLD. Results: In the course of this research, we identified 31 genes with a strong association with oxidative stress in NAFLD. Subsequent machine learning analysis and external validation pinpointed two genes: CDKN1B and TFAM, as demonstrating the closest correlation to oxidative stress in NAFLD. Conclusion: This investigation found two hub genes that hold potential as novel targets for the diagnosis and treatment of NAFLD, thereby offering innovative perspectives for its clinical management.

AIM2 regulates autophagy to mitigate oxidative stress in aged mice with acute liver injury.

The cytoplasmic pattern recognition receptor, absent in melanoma 2 (AIM2), detects cytosolic DNA, activating the inflammasome and resulting in pro-inflammatory cytokine production and pyroptotic cell death. Recent research has illuminated AIM2's contributions to PANoptosis and host defense. However, the role of AIM2 in acetaminophen (APAP)-induced hepatoxicity remains enigmatic. In this study, we unveil AIM2's novel function as a negative regulator in the pathogenesis of APAP-induced liver damage in aged mice, independently of inflammasome activation. AIM2-deficient aged mice exhibited heightened lipid accumulation and hepatic triglycerides in comparison to their wild-type counterparts. Strikingly, AIM2 knockout mice subjected to APAP overdose demonstrated intensified liver injury, compromised mitochondrial stability, exacerbated glutathione depletion, diminished autophagy, and elevated levels of phosphorylated c-Jun N-terminal kinase (JNK) and extracellular signal-regulated kinase (ERK). Furthermore, our investigation revealed AIM2's mitochondrial localization; its overexpression in mouse hepatocytes amplified autophagy while dampening JNK phosphorylation. Notably, induction of autophagy through rapamycin administration mitigated serum alanine aminotransferase levels and reduced the necrotic liver area in AIM2-deficient aged mice following APAP overdose. Mechanistically, AIM2 deficiency exacerbated APAP-induced acute liver damage and inflammation in aged mice by intensifying oxidative stress and augmenting the phosphorylation of JNK and ERK. Given its regulatory role in autophagy and lipid peroxidation, AIM2 emerges as a promising therapeutic target for age-related acute liver damage treatment.

A SARS-CoV-2 Nanobody Displayed on the Surface of Human Ferritin with High Neutralization Activity.

Purpose: COVID-19 is rampant throughout the world, which has caused great damage to human lives and seriously hindered the development of the global economy. Aiming at the treatment of SARS-CoV-2, in this study, we proposed a novel fenobody strategy based on ferritin (Fe) self-assembly technology. Methods: The neutralizing nanobody H11-D4 of SARS-CoV-2 fused to the C-terminus of end-modified human ferritin was expressed in E. coli and silkworm baculovirus expression systems. A large number of nanoparticles were successfully self-assembled in silkworms, while relatively few nanoparticles can be observed in the treated products from E. coli by electron microscopy. Subsequently, the fenobody's expression level and neutralizing activity were then evaluated. Results: The results showed that the IC50 of H11-D4 and fenobody Fe-H11-D4 expressed in E. coli were 171.1 nmol L-1 and 20.87 nmol L-1, respectively. However, the IC50 of Fe-HD11-D4 expressed in silkworms was 1.46 nmol L-1 showing better neutralization activity. Conclusion: Therefore, fenobodies can be well self-assembled in silkworm baculovirus expression system, and ferritin self-assembly technology can effectively improve nanobody neutralization activity.

Remove elemental mercury from simulated flue gas by CeO2-modified MnOx/HZSM-5 adsorbent.

In this study, we synthesized a Ce-modified Mn/HZSM-5 adsorbent via the ultrasound-assisted impregnation for Hg0 capture. Given the addition of 15% CeO2, ~ 100% Hg0 efficiency was reached at 200 °C, suggesting its promotional effect on Hg0 removal. The doped Ce introduced additional chemisorbed oxygen species onto the adsorbent surfaces, which facilitated the oxidation of Hg0 to HgO. Even though adding CeO2 led to a weakened adsorbent acidity, yet it appeared that this negative affect could be completely overcome by the enhanced oxidative ability, which finally endowed Ce-modified Mn/HZSM-5 with a satisfactory Hg0 removal performance within the whole investigated temperature range. During the Hg0 capture process, chemisorption was predominant with Mn4+operating as the main active site for oxidizing Hg0 to Hg2+. Finally, the 15Ce-Mn/HZSM-5 adsorbent exhibited good recyclability and stability. However, its tolerance to H2O and SO2 appeared relatively weak, suggesting that some modification should be conducted to improve its practicality.

Recurrent hypoglycemia induced by clopidogrel: A case report and mini review.

This report describes a patient who developed recurrent hypoglycemia episodes after 23 days of antiplatelet therapy with clopidogrel for left subclavian artery stent implantation. The patient suffered from palpitation, profuse sweating and weakness on the 23rd day of clopidogrel treatment. The minimum plasma glucose was 2.2 mmol/L, and the hypoglycemia was associated with significantly elevated levels of insulin. A diagnosis of insulin autoimmune syndrome (IAS) was made with the presence of insulin autoantibody and a comprehensive differential diagnosis of other conditions related with hypoglycemia. Clopidogrel was stopped, and the patient was treated with acarbose and had frequent low-carbohydrate meals; his hypoglycemia did not occur within 10 days. To date, seven cases of IAS induced by clopidogrel have been reported. Most reported cases were male aged in their 70s, and the hypoglycemic attack appeared 1-4 weeks after exposure to clopidogrel, characterized by severe hyperinsulinemia hypoglycemia with high titers of insulin autoantibodies. Most IAS cases can resolve spontaneously when they stop using the trigger medicine. Severe cases had been treated with drugs that reduce pancreatic insulin secretion (such as somatostatin and diazoxide), immunosuppressants (glucocorticoids, azathioprine and rituximab) and even immunoadsorption to remove the insulin autoantibody from the body. Considering the hypoglycemic attack might increase the risk of cardiovascular events among patients taking clopidogrel, we recommend that doctors should be aware of IAS as a rare severe adverse effect of clopidogrel, and be vigilant for the symptoms related with hypoglycemia in clopidogrel users.

Dual-Proton Conductor for Fuel Cells with Flexible Operational Temperature.

The properties of proton conductors determine the operating temperature range of fuel cells. Typically, phosphoric acid (PA) proton conductors exhibit excellent proton conductivity owing to their high proton dissociation and self-diffusion abilities. However, at low temperatures or high current densities, water-induced PA loss causes rapid degradation of cell performance. Maintaining efficient and stable proton conductivity within a flexible temperature range can significantly reduce the start-up temperature of PA-doped proton exchange membrane fuel cells. In this study, a dual-proton conductor composed of an organic phosphonic acid (ethylenediamine tetramethylene phosphonic acid, EDTMPA) and an inorganic PA is developed for proton exchange membranes. The proposed dual-proton conductor can operate within a flexible temperature range of 80-160 °C, benefiting from the strong interaction between EDTMPA and PA, and the enhanced proton dissociation. Fuel cells with the EDTMPA-PA dual-proton conductor showed excellent cell stability at 80 °C. In particular, under the high current density of 1.5 A cm-2 at 160 °C, the voltage decay rate of the fuel cell with the dual-proton conductor is one-thousandth of that of the fuel cell with PA-only proton conductor, indicating excellent stability.

Assembly mechanisms of microbial communities in plastisphere related to species taxonomic types and habitat niches.

A lot of plastic floats are presented in the kelp cultivation zone, enabling us to effectively evaluate the differences between surface water (SW) and plastic-attached (PA) microbial communities. In this study, we explored the microbial communities (both bacteria and protists) in SW and PA niches during the kelp cultivation activities. Effects of habitat niches on the diversity and composition of microbial communities were found. Beta partitioning and core taxa analyses showed species turnover and local species pool governed the microbial community assembly, and they contributed more to bacteria and protists, respectively. Based on the results of null model, bacterial communities presented a more deterministic and homogeneous assembly compared to protistan communities. Moreover, microbial communities in PA niche had higher species turnover and homogenizing assembly compared to the SW niche. The results of this study supplemented the theory of microbial community assembly and expanded our understanding of protists in plastisphere.

Engineering the Crystal Facet of Monoclinic NiO for Efficient Catalytic Ozonation of Toluene.

Modulating oxygen vacancies of catalysts through crystal facet engineering is an innovative strategy for boosting the activity for ozonation of catalytic volatile organic compounds (VOCs). In this work, three kinds of facet-engineered monoclinic NiO catalysts were successfully prepared and utilized for catalytic toluene ozonation (CTO). Density functional theory calculations revealed that Ni vacancies were more likely to form preferentially than O vacancies on the (110), (100), and (111) facets of monoclinic NiO due to the stronger Ni-vacancy formation ability, further affecting O-vacancy formation. Extensive characterizations demonstrated that Ni vacancies significantly promoted the formation of O vacancies and thus reactive oxygen species in the (111) facet of monoclinic NiO, among the three facets. The performance evaluation showed that the monoclinic NiO catalyst with a dominant (111) facet exhibits excellent performance for CTO, achieving a toluene conversion of ∼100% at 30 °C after reaction for 120 min under 30 ppm toluene, 210 ppm ozone, 45% relative humidity, and a space velocity of 120 000 h-1. This outperformed the previously reported noble/non-noble metal oxide catalysts used for CTO at room temperature. This study provided novel insight into the development of highly efficient facet-engineered catalysts for the elimination of catalytic VOCs.

Learning geometric Jensen-Shannon divergence for tiny object detection in remote sensing images.

Tiny objects in remote sensing images only have a few pixels, and the detection difficulty is much higher than that of regular objects. General object detectors lack effective extraction of tiny object features, and are sensitive to the Intersection-over-Union (IoU) calculation and the threshold setting in the prediction stage. Therefore, it is particularly important to design a tiny-object-specific detector that can avoid the above problems. This article proposes the network JSDNet by learning the geometric Jensen-Shannon (JS) divergence representation between Gaussian distributions. First, the Swin Transformer model is integrated into the feature extraction stage as the backbone to improve the feature extraction capability of JSDNet for tiny objects. Second, the anchor box and ground-truth are modeled as two two-dimensional (2D) Gaussian distributions, so that the tiny object is represented as a statistical distribution model. Then, in view of the sensitivity problem faced by the IoU calculation for tiny objects, the JSDM module is designed as a regression sub-network, and the geometric JS divergence between two Gaussian distributions is derived from the perspective of information geometry to guide the regression prediction of anchor boxes. Experiments on the AI-TOD and DOTA datasets show that JSDNet can achieve superior detection performance for tiny objects compared to state-of-the-art general object detectors.

Machine Learning Study on Microwave-Assisted Batch Preparation and Oxygen Reduction Performance of Fe-N-C Catalysts.

The Fe-N-C catalyst represents one of the most promising candidates for replacing platinum-based catalysts toward the oxygen reduction reaction. The pivotal factor in the successful integration of Fe-N-C catalysts within applications is the attainment of a large-scale production capability. Microwave-assisted pyrolysis offers various advantages, including enhanced energy and time efficiency, uniform heating, and high yield in single-batch processes. These characteristics render it exceptionally suitable for the mass production of catalysts. Through a synergistic approach involving machine learning techniques and microscopic characterization, we discerned performance trends and underlying mechanisms within batch-synthesized Fe-N-C catalysts under microwave-assisted preparation conditions. Machine learning analysis revealed that the precursor mass exerts the most substantial influence on product performance. Furthermore, microscopic characterization unveiled that these influencing factors impact catalyst performance by modulating the degree of agglomeration. Our research introduces an efficacious machine learning model for prognosticating performance and dissecting the influencing factors pertinent to Fe-N-C catalyst synthesis within a microwave system.

Construction of Zr-Metal-Organic Frameworks-Based Composite Materials toward Anhydrous Proton Conduction and Photocatalytic CO2 Reduction.

Metal-organic frameworks constructed from Zr usually possess excellent chemical and physical stability. Therefore, they have become attractive platforms in various fields. In this work, two families of hybrid materials based on ZrSQU have been designed and synthesized, named Im@ZrSQU and Cu@ZrSQU, respectively. Im@ZrSQU was prepared through the impregnation method and employed for proton conduction. Im@ZrSQU exhibited terrific proton conduction performance in an anhydrous environment, with the highest proton conduction value of 3.6 × 10-2 S cm-1 at 110 °C. In addition, Cu@ZrSQU was synthesized via the photoinduction method for the photoreduction of CO2, which successfully promoted the conversion of CO2 into CO and achieved the CO generation rate of up to 12.4 µmol g-1 h-1. The photocatalytic performance of Cu@ZrSQU is derived from the synergistic effect of Cu NPs and ZrSQU. Based on an in-depth study and discussion toward ZrSQU, we provide a versatile platform with applications in the field of proton conduction and photocatalysis, which will guide researchers in their further studies.

Uptake of the core outcome set on polycystic ovary syndrome before and after its publication.

STUDY QUESTION: Does the core outcome set (COS) on polycystic ovary syndrome (PCOS) impact the selection of research outcomes? SUMMARY ANSWER: Following the publication of the COS on PCOS, an increasing number of trials are reporting both the generic domain and body mass index; however, the uptake of this COS has not been as extensive as expected. WHAT IS KNOWN ALREADY: The COS on PCOS included 33 core outcomes in the following seven domains: the generic (3), metabolic (8), reproductive (7), pregnancy (10), psychological (3), oncological (1), and long-term (1). This was done to improve consistency in outcome selection and definition. However, thus far, no studies have investigated the effectiveness of this COS in the above-mentioned tasks. STUDY DESIGN, SIZE, DURATION: A methodological study based on the trial registries, including 395 eligible clinical trials registered between 1 January 2018 and 21 September 2022. PARTICIPANTS/MATERIALS, SETTING, METHODS: A total of 1258 registered clinical studies on PCOS were retrieved from the World Health Organization International Clinical Trials Registry Platform. Of those, 395 were selected according to the inclusion and exclusion criteria, and divided into two groups based on the publication date of the COS on PCOS (4 February 2020): pre-publication and post-publication. The practical uptake of this COS was explored after data collation, assessment, comparison of the uptake of core outcomes or domains before and after the publication of this COS, and correlation analysis between the domains. MAIN RESULTS AND THE ROLE OF CHANCE: There were 26 out of 33 core outcomes and five out of seven domains reported in the 395 trials. The highest uptake was observed for the reproductive domain and the reproductive hormonal profile (63.0% and 38.7%, respectively). After the publication of the COS on PCOS, the uptake of the generic domain and body mass index increased from 24.1% to 35.8% (P = 0.011) and 17.8% to 26.5% (P = 0.039), respectively. The total number of reported core outcomes in the generic domain met statistical significance (P = 0.012). Moreover, multivariable analyses still supported the above finding in the generic domain. Correlation analysis showed that most of the domains were positively correlated with each other. However, the pregnancy domain was negatively correlated with the metabolic domain. Reasons responsible for the unsatisfactory uptake may be the absence of specific definitions of core outcomes, as well as the lack of awareness among researchers regarding this COS. LIMITATIONS, REASONS FOR CAUTION: Due to the lack of standardized definition of outcomes, it was difficult to avoid some subjectivity in the process of consistency assessment. WIDER IMPLICATIONS OF THE FINDINGS: Two years after its publication, there was no substantial improvement in the uptake of the COS on PCOS. This suggests that this COS may require further revision, refinement, and promotion to improve the comparability of PCOS studies. STUDY FUNDING/COMPETING INTEREST(S): This work was funded by Beijing Municipal Health Science and Technology Achievements and Appropriate Technology Promotion Project (BHTPP2022069), and the special fund of Beijing Key Clinical Specialty Construction Project. The authors do not have conflicts of interest to declare. TRIAL REGISTRATION NUMBER: N/A.

Research on the recycling and disposal of Chinese pesticide packaging waste based on evolutionary game theory.

This study aims to determine the optimal strategy and driving factors of the critical nodes of pesticide packaging waste recycling by constructing the recycling process of "village collection-town transport-county management." Counties, towns, and villages are the central nodes of collection, coordination, and communication in the recycling process. Their strategy selection and influencing factor analysis are related to the development of recycling. The county processing center, township transit center, and village recycling center were selected to construct a game model, and strategy and parameter assumptions were made to obtain the optimal strategy combination. The results showed that strict supervision, professional transportation and strict implementation are the best strategies for counties, towns and villages, respectively. Simulation analysis confirmed that factors such as cost, reward restricted the strategy selection of each subject. The higher the supervision, transport, and input costs, the lower the enthusiasm of counties, towns, and villages to participate in recycling, respectively. Reasonable control of reward could help each participant choose a stable strategy. The study provided the idea of a pilot before promotion for the government and emphasized the importance of controlling incentive policies and relative costs to improve the recycling process of pesticide packaging waste.

Cross-modality image translation: CT image synthesis of MR brain images using multi generative network with perceptual supervision.

BACKGROUND: Computed tomography (CT) and magnetic resonance imaging (MRI) are the mainstream imaging technologies for clinical practice. CT imaging can reveal high-quality anatomical and physiopathological structures, especially bone tissue, for clinical diagnosis. MRI provides high resolution in soft tissue and is sensitive to lesions. CT combined with MRI diagnosis has become a regular image-guided radiation treatment plan. METHODS: In this paper, to reduce the dose of radiation exposure in CT examinations and ameliorate the limitations of traditional virtual imaging technologies, we propose a Generative MRI-to-CT transformation method with structural perceptual supervision. Even though structural reconstruction is structurally misaligned in the MRI-CT dataset registration, our proposed method can better align structural information of synthetic CT (sCT) images to input MRI images while simulating the modality of CT in the MRI-to-CT cross-modality transformation. RESULTS: We retrieved a total of 3416 brain MRI-CT paired images as the train/test dataset, including 1366 train images of 10 patients and 2050 test images of 15 patients. Several methods (the baseline methods and the proposed method) were evaluated by the HU difference map, HU distribution, and various similarity metrics, including the mean absolute error (MAE), structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), and normalized cross-correlation (NCC). In our quantitative experimental results, the proposed method achieves the lowest MAE mean of 0.147, highest PSNR mean of 19.27, and NCC mean of 0.431 in the overall CT test dataset. CONCLUSIONS: In conclusion, both qualitative and quantitative results of synthetic CT validate that the proposed method can preserve higher similarity of structural information of the bone tissue of target CT than the baseline methods. Furthermore, the proposed method provides better HU intensity reconstruction for simulating the distribution of the CT modality. The experimental estimation indicates that the proposed method is worth further investigation.