Exploring a pathway to optimise the carbon tax policy in terms of the economy, the environment and health: A scenario-based system dynamics approach.

The carbon tax, a pivotal policy instrument in tackling climate change, holds the potential to significantly influence the development of society. To comprehensively analyse the effectiveness of the policy on carbon taxation and explore its possible optimisation path, this study utilizes system dynamics theory to establish a simulation model. A detailed analysis and evaluation of this policy is then conducted from the perspectives of the economy, the environment, and health. To guarantee the precision of the simulation model, a new, comprehensive evaluation method is proposed, which can test the degree of fit of the relative trend and absolute data of the simulation model with reality. The findings reveal that, despite its negative economic implications, a carbon tax policy has positive ramifications for the environment, energy, health, industrial structure, and carbon intensity targets. Furthermore, the synergistic reinforcement effect of R&D and new energy support policies on carbon taxation surpasses the impact of any individual policy alone. Notably, the influence of auxiliary policies has a temporal difference on this policy. Based on these insights, the study concludes with practical policy recommendations.

Clinical relevance, mechanisms, and evolution of polymyxin B heteroresistance carbapenem-resistant Klebsiella pneumoniae: A genomic, retrospective cohort study.

OBJECTIVES: To study the clinical relevance, mechanisms, and evolution of polymyxin B (POLB) heteroresistance (PHR) in carbapenem-resistant Klebsiella pneumoniae (CRKP), potentially leading to a significant rise in POLB full resistant (FR) CRKP. METHODS: Total of 544 CRKP isolates from 154 patients treated with POLB were categorized into PHR and POLB non-heteroresistance (NHR) groups. We performed statistical analysis to compare clinical implications and treatment responses. We employed whole-genome sequencing, bioinformatics, and PCR to study the molecular epidemiology, mechanisms behind PHR, and its evolution into FR. RESULTS: We observed a considerable proportion (118 of 154, 76.62%) of clinically undetected PHR strains before POLB exposure, with a significant subset of them (33 of 118, 27.97%) evolving into FR after POLB treatment. We investigated the clinical implications, epidemiological characteristics, mechanisms, and evolutionary patterns of PHR strains in the context of POLB treatment. About 92.86% (39 of 42) of patients had PHR isolates before FR, highlighting the clinical importance of PHR. the ST15 exhibited a notably lower PHR rate (1 of 8, 12.5% vs. 117 of 144, 81.25%; p < 0.01). The ST11 PHR strains showing significantly higher rate of mgrB mutations by endogenous insertion sequences in their resistant subpopulation (RS) compared with other STs (78 of 106, 73.58% vs. 4 of 12, 33.33%; p < 0.01). The mgrB insertional inactivation rate was lower in FR isolates than in the RS of PHR isolates (15 of 42, 35.71% vs. 84 of 112, 75%; p < 0.01), whereas the pmrAB mutation rate was higher in FR isolates than in the RS of PHR isolates (8 of 42, 19.05% vs. 2 of 112, 1.79%; p < 0.01). The evolution from PHR to FR was influenced by subpopulation dynamics and genetic adaptability because of hypermutability. DISCUSSION: We highlight significant genetic changes as the primary driver of PHR to FR in CRKP, underscoring polymyxin complexity.

A multicenter study on two-stage transfer learning model for duct-dependent CHDs screening in fetal echocardiography.

Duct-dependent congenital heart diseases (CHDs) are a serious form of CHD with a low detection rate, especially in underdeveloped countries and areas. Although existing studies have developed models for fetal heart structure identification, there is a lack of comprehensive evaluation of the long axis of the aorta. In this study, a total of 6698 images and 48 videos are collected to develop and test a two-stage deep transfer learning model named DDCHD-DenseNet for screening critical duct-dependent CHDs. The model achieves a sensitivity of 0.973, 0.843, 0.769, and 0.759, and a specificity of 0.985, 0.967, 0.956, and 0.759, respectively, on the four multicenter test sets. It is expected to be employed as a potential automatic screening tool for hierarchical care and computer-aided diagnosis. Our two-stage strategy effectively improves the robustness of the model and can be extended to screen for other fetal heart development defects.

A Deep-Learning-Based Method Can Detect Both Common and Rare Genetic Disorders in Fetal Ultrasound.

A global survey indicates that genetic syndromes affect approximately 8% of the population, but most genetic diagnoses can only be performed after babies are born. Abnormal facial characteristics have been identified in various genetic diseases; however, current facial identification technologies cannot be applied to prenatal diagnosis. We developed Pgds-ResNet, a fully automated prenatal screening algorithm based on deep neural networks, to detect high-risk fetuses affected by a variety of genetic diseases. In screening for Trisomy 21, Trisomy 18, Trisomy 13, and rare genetic diseases, Pgds-ResNet achieved sensitivities of 0.83, 0.92, 0.75, and 0.96, and specificities of 0.94, 0.93, 0.95, and 0.92, respectively. As shown in heatmaps, the abnormalities detected by Pgds-ResNet are consistent with clinical reports. In a comparative experiment, the performance of Pgds-ResNet is comparable to that of experienced sonographers. This fetal genetic screening technology offers an opportunity for early risk assessment and presents a non-invasive, affordable, and complementary method to identify high-risk fetuses affected by genetic diseases. Additionally, it has the capability to screen for certain rare genetic conditions, thereby enhancing the clinic's detection rate.

Research on the articulated coupling effect of carbon tax policy under resource endowment in China.

The carbon tax is a policy tool that internalizes external costs through a tax mechanism, which helps to reduce the consumption of fossil energy and lower carbon dioxide emissions. China, as the largest carbon emitter, introducing a carbon tax can further enhance the effectiveness of emission reduction. However, the introduction of a carbon tax may exacerbate contradictions in other aspects of the social system. To this end, the paper establishes a dynamic model of the carbon tax system by combining grey system theory and the IPAT model and then explores the coupling effect of the carbon tax on the economy, energy, and environment under the premise of China's resource endowment. It is found that carbon tax will not only distort consumer behavior but also aggravate the degree of capital market distortion. In the time-series simulation, it is found that the emission reduction efficiency of the carbon tax will show an oscillation decline. The carbon tax undermines the carbon peak target by dampening demand for energy consumption. In addition, we also find that the change of energy structure is the root of driving the failure of the "Jevons Paradox" and the realization of the "environmental Kuznets curve," and the panel data of energy and economy are only the manifestation of these two phenomena. China needs to adjust its energy structure to achieve its carbon peaking target. These results are helpful for policymakers to rationally view the carbon peaking target and formulate reasonable emission reduction policies.

The Two-Stage Ensemble Learning Model Based on Aggregated Facial Features in Screening for Fetal Genetic Diseases.

With the advancement of medicine, more and more researchers have turned their attention to the study of fetal genetic diseases in recent years. However, it is still a challenge to detect genetic diseases in the fetus, especially in an area lacking access to healthcare. The existing research primarily focuses on using teenagers' or adults' face information to screen for genetic diseases, but there are no relevant directions on disease detection using fetal facial information. To fill the vacancy, we designed a two-stage ensemble learning model based on sonography, Fgds-EL, to identify genetic diseases with 932 images. Concretely speaking, we use aggregated information of facial regions to detect anomalies, such as the jaw, frontal bone, and nasal bone areas. Our experiments show that our model yields a sensitivity of 0.92 and a specificity of 0.97 in the test set, on par with the senior sonographer, and outperforming other popular deep learning algorithms. Moreover, our model has the potential to be an effective noninvasive screening tool for the early screening of genetic diseases in the fetus.

Influence factors and risk analysis of new energy vehicles from the perspective of system.

Compared to fuel vehicles, new energy vehicles have the advantages of energy-saving and emission reduction and, hence, are widely accepted. As the policy has been withdrawn gradually, the development of new energy vehicles has slowed down. Under the double effect of positive factors, such as policy support and public opinion support and malpractice factors, this study explored the development status of new energy vehicles and the related problems. Based on the qualitative and quantitative perspective, the improved Lotka-Volterra model and the health assessment method are adopted, and the system dynamics, behavioral economics, and other methods are combined to establish the driving flow diagram of new energy vehicles to explore the source of the problem and the governance measures. The analysis results showed that the support strength of policy and public opinion decreases gradually. However, the primary factor restricting the development of new energy vehicles is still cost. Cadmium and chromium in the soil exceed the national heavy metal content standards of China, while the other five heavy metals in the analysis period did not exceed the national standards.

Effect of ultrasonic standing waves on flotation bubbles.

Ultrasonic flotation was an effective method to float fine coal. In this study, the effects of the standing waves with different frequencies on ultrasonic flotation were investigated. The dynamic processes of bubble and coal-bubble were revealed by a high-speed camera. The results showed that under the action of Bjerknes force, bubble aggregates were formed within 450 ms and coal bubble aggregates were formed within 20 ms. The bubble aggregates were statistically analyzed by image processing method. The number of aggregates and small bubbles in the ultrasonic field at 100 kHz was greater than those at 80 and 120 kHz. Besides, 100 kHz ultrasonic flotation achieved the highest yields of clean coal (35.89%) and combustible recovery (45.77%). The cavitation bubbles acted as either a "medium" or an "inclusion", entrapping and entraining the coal particles in the flotation pulp. It promoted the aggregation of bubbles with coal particles, so the flotation efficiency was effectively improved in the presence of ultrasonic standing waves.

Study on the mechanism of high-frequency stimulation inhibiting low-Mg2+-induced epileptiform discharges in juvenile rat hippocampal slices.

Study on the mechanism of high-frequency stimulation inhibiting low-Mg2+-induced epileptiform discharges in juvenile rat hippocampal slices High-frequency stimulation (HFS) has been demonstrated to be an effective treatment for inhibiting epilepsy in some clinical and laboratory studies. However, the mechanisms underlying the therapeutic effects of HFS are not yet fully understood. In our present study, epileptiform discharges (EDs) in acutely isolated hippocampal slices of male Sprague-Dawley (SD) juvenile rats induced by low-Mg2+ artificial cerebrospinal fluid (ACSF), and electrical stimulation (square wave, 900 pulses, 50 % duty-cycle, 130 Hz) was performed on the CA3 using concentric bipolar electrodes. EDs of neurons in hippocampal were recorded by multi-electrode arrays (MEA). After stable EDs events had been recorded for at least 20 min, HFS was added, followed by 10 µmol/L gamma-aminobutyric acid type A (GABAA) receptors blocker bicuculline (BIC). The results show that the HFS can increase the discharges frequency of inter-ictal discharges (IIDs) and decrease the duration of ictal discharges (IDs). However, the HFS had no effect on the slices with 10 µmol/L BIC. These results indicated that the GABAA receptors are activated when HFS inhibited EDs, thereby achieving the inhibition of low-Mg2+-induced EDs in slices.

Effect of priming low-frequency magnetic fields on zero-Mg2+ -induced epileptiform discharges in rat hippocampal slices.

During the process of seizures, the addition of low-frequency magnetic fields has been proved to be an effective method to suppress epileptic discharges. However, whether adding magnetic fields before the appearance of epileptic discharges can produce this inhibition has not been studied. In the present study, we first constructed epilepsy models on brain slices by perfusing them with Mg2+-free artificial cerebrospinal fluid (aCSF). The events of seizures evolved from inter-ictal epileptiform discharges (IIDs) to inter-epileptiform discharges (IDs). Combined with the multi-electrode array platform, we designed a flexible moving coil to generate a 0.5 Hz magnetic field on the brain slices. Using this method, we added the magnetic fields to brain slices for 30 min before epileptiform discharges were induced. The experimental results demonstrated that although the priming magnetic fields could not completely inhibit epileptiform discharges, they can significantly reduce the frequency of IDs and increase the frequency of IIDs in the CA3 region of the hippocampal slices. In the control group, the rates of IDs and IIDs were 0.0024 ± 0.0006 Hz and 0.0138 ± 0.0043 Hz, respectively, while in the magnetic stimulation group, the rates were 0.0012 ± 0.0004 Hz and 0.0251 ± 0.0067 Hz. Moreover, the results indicated that changing the frequency of interictal discharges did not affect ictogenesis. The results demonstrated that the priming magnetic fields had a certain weakening effect on the frequency of IDs, which was achieved by reducing the signal propagation speed and increasing the excitability threshold of hippocampal neurons.