An intensive study of the geochemical characteristics (including the volatile elements Cl and S) of apatite associated with porphyry deposits was undertaken to address the debate about the crust- or ...mantle-derivation of their copper and gold and to better understand the controls on the transport of metals in magmatic fluids in post-subduction settings. New geochemical data on apatite reveal parameters to discriminate mineralized porphyry systems across Iran and western China (Tibet and Yunnan), from coeval barren localities across this post-subduction metallogenic belt. Apatites in fertile porphyries have higher Cl and S concentrations (reflecting water-rich crystallization conditions) than those from coeval barren ones. Our new isotopic data also indicate these volatiles are likely derived from pre-enriched sub-continental lithospheric mantle, metasomatized by previous oceanic subduction. This study demonstrates that refertilization of suprasubduction lithospheric mantle during previous collision events is a prerequisite for forming post-subduction fertile porphyries, providing an evidence-based alternative to current ore-enrichment models.
With the rapid development of big data, artificial intelligence teaching systems have gradually been developed extensively. The powerful artificial intelligence teaching systems have become a tool ...for teachers and students to learn independently in various universities. The characteristic of artificial intelligence teaching system is to get rid of the constraints of traditional teaching time and space and build a brand-new learning environment, which is the mainstream trend of future learning. As the carrier of students’ autonomous learning, the artificial intelligence teaching system provides a wealth of learning resources and learning tools on the one hand, and on the other hand, it gradually accumulates more and more learning behaviors, learning status, and other large amounts of data, which is an in-depth study of online learning and provides valuable and generative dynamic resources. Based on relevant researches on domestic and foreign related learning analysis and common big data analysis methods, combined with actual learning evaluation goals, this paper proposes an artificial intelligence teaching system using big data analysis methods and a modeling process framework for online learning evaluation and uses student data to carry out predictive evaluation modeling to evaluate student learning outcomes. The evaluation results can enable teachers to predict whether students can successfully complete the course of learning after a period of teaching. Through the final evaluation, students’ learning problems can be discovered in time based on the evaluation results, and targeted interventions can be made for students who are at risk. The scientific and objective learning evaluation obtained in this study through data analysis can not only provide teachers with relevant information and provide personalized guidance to students, but also improve the adaptive and personalized service functions of the learning platform of the artificial intelligence teaching system, greatly reducing teachers teaching burden. Artificial intelligence teaching evaluation can help educators understand the problems in teaching, adjust teaching strategies in time, and improve teaching results.
A strong doctor-patient relationship (DPR) is crucial to the effectiveness of treatment. It is imperative to maintain a good DPR during treatment. During 2019, Coronavirus Disease 2019 (COVID-19) ...brought new challenges to already difficult doctor-patient relationships. This paper summarized the current state of the DPR, compared the changes between China and other countries after the outbreak of COVID-19, and listed the solutions proposed by various countries. Finally, the author suggested some solutions in order to improve the DPR according to China's own circumstances.
Li‐CO2 batteries are regarded as next‐generation high‐energy‐density electrochemical devices. However, the greatest challenge arises from the formation of the discharge product, Li2CO3, which would ...accumulate and deactivate heterogenous catalysts to cause huge polarization. Herein, Ru(bpy)3Cl2 was employed as a solution‐phase catalyst for Li‐CO2 batteries and proved to be the most effective one screened so far. Spectroscopy and electrochemical analyses elucidate that the RuII center could interact with both CO2 and amorphous Li2C2O4 intermediate, thus promoting electroreduction process and delaying carbonate transformation. As a result, the charge potential is reduced to 3.86 V and over 60 discharge/charge cycles are achieved with a fixed capacity of 1000 mAh g−1 at a current density of 300 mA g−1. Our work provides a new avenue to improve the electrochemical performance of Li‐CO2 batteries with efficient mobile catalysts.
Ru(bpy)3Cl2 was applied as the first ruthenium‐based mobile catalyst to enhance the electrochemical performance of Li‐CO2 batteries. The RuII catalyst was discovered to interact with both CO2 molecules and amorphous Li2C2O4 intermediate, thus promoting CO2RR and delaying carbonate formation and consequently leading to lowered overpotential, enlarged capacity, and prolonged cyclability of the batteries.
Hydrosalpinx is a chronic inflammatory condition with high recurrence rate, and it is reported among female population having fallopian tubal factor infertility. Previously, we have reported that ...interventional ultrasound sclerotherapy improves endometrial receptivity and pregnancy rate with negligible adverse effects in patients suffering from hydrosalpinx. During present investigation, we have used next generation sequencing (NGS) to characterize the isomiR profiles from the endometrium of patients suffering from hydrosalpinx before and after interventional ultrasound sclerotherapy. Our results indicated that miRNA arm shift and switch remained unaffected when compared in patients before and after interventional ultrasound sclerotherapy. We observed that isomiRs with trimming at 3’ and isomiRs with canonical sequences were lower in post-treatment than in pre-treatment group. Gene ontology (GO) annotation and KEGG pathway analysis revealed that the expression of mature mir-30 was significantly lower in the pre-treatment as compared to post treatment group while the expression of mir-30 isomiR was 4.26-fold higher in pre-treatment when compared with the post-treatment group. These different expression patterns of mir-30 mature miRNA and mir-30 isomiRs in two groups are affecting the physiological function of the endometrium. Our results suggested that differential isomiR distribution in hydrosalpinx patients before and after treatment plays an important role in hydrosalpinx incidence and can help in designing novel strategy for the treatment of hydrosalpinx in female population.
The disposal of ladle furnace slag (ladle slag, LS) containing traces of heavy metals produced during steelmaking has become an environmental issue. The use of LS as a binding material in civil ...engineering is a potential solution. In this context, this study firstly attempted to activate LS with sodium hydroxide (NaOH), sodium sulfate (Na2SO4), and sodium metasilicate (Na2SiO3), and then blended it with ground granulated blast-furnace slag (GGBS) with different LS:GGBS ratios. The chemical-activated LS pastes and LS-GGBS pastes were cured for different ages, and then subjected to a compressive strength test. The results indicated NaOH, Na2SO4, and Na2SiO3 could not effectively activate this LS, with 28-day strength <2 MPa, whilst the LS-GGBS yielded much higher strength, up to 15.6 MPa at 28 days. Only a very low concentration of Pb leached out from the LS-GGBS at 14 days, and none of the possible heavy metals were detected at 56 days. This indicates that LS-GGBS can be potentially used as a binding material in civil engineering. The X-ray diffraction (XRD) revealed that the Ca(OH)2 in LS acted as the main activator for GGBS hydration; the MgO and CaCO3 in LS seemed to play similar roles as that of the Ca(OH)2. The XRD, thermogravimetric analysis (TGA), fourier transform infrared spectroscopy (FTIR), field emission scanning electron microscopy (FESEM), and energy dispersive X-ray spectroscopy (EDX) indicated that the main hydration product of LS-GGBS was calcium silicate hydrates (CSH).
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•NaOH, Na2SO4, and Na2SiO3 are not effective to activate ladle slag.•LS activates ground granulated blast-furnace slag (GGBS) effectively.•Ca(OH)2 in LS accelerates strength development of GGBS.•Hydration products of LS-GGBS immobilize heavy metals in LS.
Type 2 diabetes mellitus (T2DM) is a chronic, complex metabolic disease characterized by chronic hyperglycemia causing from insufficient insulin signaling because of insulin resistance or defective ...insulin secretion, and may induce severe complications and premature death. Sodium-glucose cotransporter-2 (SGLT2) inhibitors are oral drugs used to reduce hyperglycemia in patients with T2DM, including empagliflozin, ertugliflozin, dapagliflozin and canagliflozin. The primary objective of this article is to examine the clinical benefit, safety, and tolerability of the four SGLT2 inhibitors approved by the US FDA. SGLT2 inhibitors increase urinary glucose excretion via inhibiting SGLT2 to decrease renal reabsorption of filtered glucose and reduce the renal threshold for glucose. Rather than stimulating insulin release, SGLT2 inhibitors improve β-cell function by improving glucotoxicity, as well as reduce insulin resistance and increase insulin sensitivity. Early clinical trials have confirmed the beneficial effects of SGLT2 in T2DM with acceptable safety and excellent tolerability. In recent years, SGLT2 inhibitors has been successively approved by the FDA to decrease cardiovascular death and decrease the risk of stroke and cardiac attack in T2DM adults who have been diagnosed with cardiovascular disease, treating heart failure (HF) with reduced ejection fraction and HF with preserved ejection fraction, and treat diabetic kidney disease (DKD), decrease the risk of hospitalization for HF in T2DM and DKD patients. SGLT2 inhibitors are expected to be an effective treatment for T2DM patients with non alcoholic fatty liver disease. SGLT2 inhibitors have a similar safety profile to placebo or other active control groups, with major adverse events such as Ketoacidosis or hypotension and genital or urinary tract infections.
Single‐atom catalysts (SACs) have attracted extensive attention in the catalysis field because of their remarkable catalytic activity, gratifying stability, excellent selectivity, and 100% atom ...utilization. With atomically dispersed metal active sites, Fe‐N‐C SACs can mimic oxidase by activating O2 into reactive oxygen species, O2−• radicals. Taking advantages of this property, single‐atom nanozymes (SAzymes) can become a great impetus to develop novel biosensors. Herein, the performance of Fe‐N‐C SACs as oxidase‐like nanozymes is explored. Besides, the Fe‐N‐C SAzymes are applied in biosensor areas to evaluate the activity of acetylcholinesterase based on the inhibition toward nanozyme activity by thiols. Moreover, this SAzymes‐based biosensor is further used for monitoring the amounts of organophosphorus compounds.
Fe‐N‐C single‐atom nanozymes with distributed FeN2 active sites possessing oxidase‐like activity are reported. Based on the inhibition mode by thiols, the Fe‐N‐C single‐atom nanozymes show promising application for evaluating the activity of acetylcholinesterase and constructing sensitive biosensors to detect mercapto molecules and organophosphorus compounds.
Spiking neural networks (SNNs) possess energy-efficient potential due to event-based computation. However, supervised training of SNNs remains a challenge as spike activities are non-differentiable. ...Previous SNNs training methods can be generally categorized into two basic classes, i.e., backpropagation-like training methods and plasticity-based learning methods. The former methods are dependent on energy-inefficient real-valued computation and non-local transmission, as also required in artificial neural networks (ANNs), whereas the latter are either considered to be biologically implausible or exhibit poor performance. Hence, biologically plausible (bio-plausible) high-performance supervised learning (SL) methods for SNNs remain deficient. In this paper, we proposed a novel bio-plausible SNN model for SL based on the symmetric spike-timing dependent plasticity (sym-STDP) rule found in neuroscience. By combining the sym-STDP rule with bio-plausible synaptic scaling and intrinsic plasticity of the dynamic threshold, our SNN model implemented SL well and achieved good performance in the benchmark recognition task (MNIST dataset). To reveal the underlying mechanism of our SL model, we visualized both layer-based activities and synaptic weights using the t-distributed stochastic neighbor embedding (t-SNE) method after training and found that they were well clustered, thereby demonstrating excellent classification ability. Furthermore, to verify the robustness of our model, we trained it on another more realistic dataset (Fashion-MNIST), which also showed good performance. As the learning rules were bio-plausible and based purely on local spike events, our model could be easily applied to neuromorphic hardware for online training and may be helpful for understanding SL information processing at the synaptic level in biological neural systems.
Objective
The outbreak of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2) has caused more than 26 million cases of Corona virus disease (COVID-19) in the world so far. To control the ...spread of the disease, screening large numbers of suspected cases for appropriate quarantine and treatment are a priority. Pathogenic laboratory testing is typically the gold standard, but it bears the burden of significant false negativity, adding to the urgent need of alternative diagnostic methods to combat the disease. Based on COVID-19 radiographic changes in CT images, this study hypothesized that artificial intelligence methods might be able to extract specific graphical features of COVID-19 and provide a clinical diagnosis ahead of the pathogenic test, thus saving critical time for disease control.
Methods
We collected 1065 CT images of pathogen-confirmed COVID-19 cases along with those previously diagnosed with typical viral pneumonia. We modified the inception transfer-learning model to establish the algorithm, followed by internal and external validation.
Results
The internal validation achieved a total accuracy of 89.5% with a specificity of 0.88 and sensitivity of 0.87. The external testing dataset showed a total accuracy of 79.3% with a specificity of 0.83 and sensitivity of 0.67. In addition, in 54 COVID-19 images, the first two nucleic acid test results were negative, and 46 were predicted as COVID-19 positive by the algorithm, with an accuracy of 85.2%.
Conclusion
These results demonstrate the proof-of-principle for using artificial intelligence to extract radiological features for timely and accurate COVID-19 diagnosis.
Key Points
• The study evaluated the diagnostic performance of a deep learning algorithm using CT images to screen for COVID-19 during the influenza season.
• As a screening method, our model achieved a relatively high sensitivity on internal and external CT image datasets.
• The model was used to distinguish between COVID-19 and other typical viral pneumonia, both of which have quite similar radiologic characteristics.
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