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Extended crop yield meta-analysis data do not support upward SCC revision
Abstract The Biden Administration raised its Social Cost of Carbon (SCC) estimate about fivefold based in part on global crop yield decline projections estimated on a meta-analysis data base first published in 2014. The data set contains 1722 records but half were missing at least one variable (usually the change in CO2) so only 862 were available for multivariate regression modeling. By re-examining the underlying sources I was able to recover 360 records and increase the sample size to 1222. Reanalysis on the larger data set yields very different results. While the original smaller data set implies yield declines of all crop types even at low levels of warming, on the full data set global average yield changes are zero or positive even out to 5 °C warming.
A first report of Biomphalaria pfeifferi in the Lower Shire Valley, Southern Malawi, a major intermediate snail host species for intestinal schistosomiasis
Abstract The distribution of certain permissive intermediate snail host species in freshwater is a crucial factor shaping transmission of intestinal schistosomiasis, a neglected tropical disease that causes much human suffering in Africa. To shed new light on southern Malawi, where cases of intestinal schistosomiasis have been found, repeated malacological surveys were conducted in Chikwawa and Nsanje Districts in the Lower Shire Valley, to detect and to characterize populations of Biomphalaria , the intermediate host for intestinal schistosomiasis. Sampling took place across a total of 45 freshwater sites, noting water conductivity, pH, temperature, total dissolved salts (TDS) and geographical elevation. The presence or absence of snails was predicted upon physiochemical and environmental conditions in Random Forest modelling. Water conductivity, TDS and geographical elevation were most important in predicting abundance of snails with water temperature and pH of slightly less important roles. This first report of B. pfeifferi in the Lower Shire Valley enhances understanding of the environmental factors that strongly associate and allow prediction of its local distribution. This represents a useful step towards developing appropriate intervention strategies to mitigate intestinal schistosomiasis transmission.
Gaussian decomposition method for full waveform data of LiDAR base on neural network
Reconfigurable security solution based on hopfield neural network for e-healthcare applications
Non-linear relationship between arteriosclerosis index and diabetes risk in non-obese east Asian adults
Dynamic behavior of the oropharynx airway during deep breath in patients with obstructive sleep apnoea hypopnoea syndrome observed by ultrasonography
Reassessing estrogen receptor expression thresholds for breast cancer prognosis in HER2-negative patients using shape restricted modeling
ANN-ANFIS model for optimising methylic composite biodiesel from neem and castor oil and predicting emissions of the biodiesel blend
Abstract Researchers and stakeholders have shown interest in heterogeneous composite biodiesel (HCB) due to its enhanced fuel properties and environmental friendliness (EF). The lack of high viscosity datasets for parent hybrid oils has hindered their commercialisation. Reliable models are lacking to optimise the transesterification parameters for developing HCB, and the scarcity of predictive models has affected climate researchers and environmental experts. In this study, basic fuel properties were analysed, and models were developed models for the yield of HCB and kinematic viscosity (KV) for composite biodiesel/neem castor seed oil methyl ester (NCSOME) using Artificial Neural Network (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS). Statistical indices such as computed coefficient of determination (R2), root-mean-square-error (RMSE), standard error of prediction (SEP), mean average error (MAE), and average absolute deviation (AAD) were used to evaluate the effectiveness of the techniques. Emission models for NCSOME-diesel blends were also established. The study investigated the impact of optimised fuel types/NCSOME-diesel (10–30 vol%), ZnO nanoparticle dosage (400–800 ppm), engine speed (1100–1700 rpm), and engine load (10–30%) on emission characteristics and environmental friendliness indices (EFI) such as carbon monoxide (CO), Oxides of Nitrogen (NOx), and Unburnt Hydrocarbon (UHC) using Response Surface Methodology (RSM). The ANFIS model demonstrated superior performance in terms of R2, RMSE, SEP, MAE, and AAD compared to the ANN model in predicting yield and KV of HCB. The optimal emission levels for CO (49.26 ppm), NOx (0.5171 ppm), and UHC (2.783) were achieved with a fuel type of 23.4%, nanoparticle dosage of 685.432 ppm, engine speed of 1329.2 rpm, and engine load of 10% to ensure cleaner EFI. The hybrid ANFIS and ANN models can effectively predict and model fuel-related characteristics and improve the HCB process, while the RSM model can be a valuable tool for climate and environmental stakeholders in accurate forecasting and promoting a cleaner environment. The valuable datasets can also provide reliable information for strategic planning in the biodiesel and automotive industries.
Sub-milliwatt threshold power and tunable-bias all-optical nonlinear activation function using vanadium dioxide for wavelength-division multiplexing photonic neural networks
Abstract The increasing demand for efficient hardware in neural computation highlights the limitations of electronic-based systems in terms of speed, energy efficiency, and scalability. Wavelength-division multiplexing (WDM) photonic neural networks offer a high-bandwidth, low-latency alternative but require effective photonic activation functions. Here, we propose a power-efficient and tunable-bias all-optical nonlinear activation function using vanadium dioxide (VO2) for WDM photonic neural networks. We engineered a SiN/BTO waveguide with a VO2 patch to exploit the phase-change material’s reversible insulator-to-metal transition (IMT) for nonlinear activation. We conducted numerical simulations to optimize the waveguide geometry and VO2 parameters, minimizing propagation and coupling losses while achieving a strong nonlinear response and low-threshold activation power. Our proposed device features a sub-milliwatt threshold power, a footprint of 5 μm, and an ELU-like activation function. Moreover, the bias of our device could be thermally tuned, improving the speed and power efficiency. On the other hand, performance evaluations using the CIFAR-10 dataset confirmed the device’s potential for convolutional neural networks (CNN). Our results show that a hybrid VO2/SiN/BTO platform could play a prominent role in the path toward the development of high-performance photonic neural networks.
FasterMLP efficient vision networks combining attention mechanisms and wavelet downsampling
Artificial intelligence can extract important features for diagnosing axillary lymph node metastasis in early breast cancer using contrast-enhanced ultrasonography
Abstract Contrast-enhanced ultrasound (CEUS) plays a pivotal role in the diagnosis of primary breast cancer and in axillary lymph node (ALN) metastasis. However, the imaging features that are clinically crucial for lymph node metastasis have not been fully elucidated. Hence, we developed a bimodal model to predict ALN metastasis in patients with early breast cancer by integrating CEUS images with the annotated imaging features. The model adopted a light-gradient boosting machine to produce feature importance, enabling the extraction of clinically crucial imaging features. In this retrospective study, the diagnostic performance of the model was investigated using 788 CEUS images of ALNs obtained from 788 patients who underwent breast surgery between 2013 and 2021, with the ground truth defined by the pathological diagnosis. The results indicated that the test cohort had an area under the receiver operating characteristic curve (AUC) value of 0.93 (95% confidence interval: 0.88, 0.98). The model had an accuracy of 0.93, which was higher than the radiologist’s diagnosis (accuracy of 0.85). The most important imaging features were heterogeneous enhancement, diffuse cortical thickening, and eccentric cortical thickening. Our model has an excellent diagnostic performance, and the extracted imaging features could be crucial for confirming ALN metastasis in clinical settings.
Identifying robust biomarkers for the diagnosis and subtype distinction of inflammatory bowel disease through comprehensive serum metabolomic profiling
Research on the ventilation and control method of double-hole complementary construction of long tunnel
Prelimbic cortex is involved in the regulation of morphine-induced conditioned place preference in both resistant and sensitive mice
Abstract A primary behavioral pathology in drug addiction is the overpowering motivational strength and decreased ability to control the desire to obtain drugs, which shows some variation between different individuals. Here, using a morphine-induced conditioned place preference (CPP) model with footshock, we found that mice exhibited significant individual differences in morphine-induced addiction. Despite the consequences of footshock, a small percentage of mice (24%) still showed stable morphine preference, demonstrating resistant to punishment. The majority of mice (76%) were relatively sensitive to punishment and showed termination of morphine preference. As a region of advanced cognitive function in the mammalian brain, the medial prefrontal cortex (mPFC) is involved in regulating drug-induced addictive behaviors. We found that activating the pyramidal neurons in the prelimbic cortex (PrL) could effectively reverse morphine-induced CPP in resistant mice, and inhibiting pyramidal neurons in the PrL could promote morphine-induced CPP in sensitive mice. To further explore the differences between resistant and sensitive mice, we analyzed the differences in gene expression in their PrL regions through RNA-seq analysis. The results showed that compared to sensitive mice, the significantly downregulated differentially expressed genes (DEGs), such as Panx2, Tcf7l2, Htr2c, Htr5a, Orai3, Slc24a4 and Cacnb2, in resistant mice were mainly involved in synaptic formation and neurodevelopment. We speculated that there may be defects in the neuronal system of resistant mice, and caused they are more prone to morphine-induced CPP. These findings are likely to contribute to research in gene therapy, and they may also serve as potential therapeutic targets for drug addiction.
Dexmedetomidine ameliorates hepatic ischemia reperfusion injury via modulating SIRT3 mediated mitochondrial quality control
Abstract Ischaemia-reperfusion (IR) damage is an inevitable adverse effect of liver surgery. Recent research has found that IR damage is involved in severe mitochondrial dysfunction. Mitochondrial biosynthesis and dynamics control mitochondrial mass, distribution, and function. Sirtuin 3 (SIRT3) is widely known for preserving health and functionality of mitochondria. DEX has been proven to alleviate liver damage through antioxidant and anti-apoptotic pathways. But it’s unclear how DEX protects mitochondria at this time. In this research, the mechanism behind the protective benefits of DEX was examined using the rat liver IR model and the rat liver cells (BRL-3 A) hypoxia reoxygenation (HR) model. We discovered that DEX treatment restored mitochondrial membrane potential, promoted ATP production, prevented oxidative stress, and decreased apoptosis in BRL-3 A cells. Furthermore, HR damage increased mitochondrial fission while decreasing mitochondrial fusion and biogenesis in BRL-3 A cells, which DEX partially corrected. The benefits of DEX on mitochondrial protection were reversed after addition of SR-18,292. Additionally, DEX showed the ability to enhance SIRT3 expression, and after cells were transfected with SIRT3 siRNA, DEX’s effects on mitochondria were partially prevented. Similarly, in the rat model, DEX alleviating liver histopathological injury and oxidative stress. DEX inhibited IR-induced mitochondrial damage through improving ETC complex I- IV activities and ATP content, reducing apoptosis, controlling mitochondrial quality, and upregulating the expression of SIRT3. Additionally, our research shows that DEX’s ability to protect the liver against IR damage is mediated by the modulation of mitochondrial quality control. Overall, the modification of SIRT3 activity could be responsible for this outcome.