Production of cost-competitive bioethanol and value-added co-products from distillers' grains: Techno-economic evaluation and environmental impact analysis.

Here, Baijiu distillers' grains (BDGs) were employed in biorefinery development to generate value-added co-products and bioethanol. Through ethyl acetate extraction at a 1:6 solid-liquid ratio for 10 h, significant results were achieved, including 100 % lactic acid and 92 % phenolics recovery. The remaining BDGs also achieved 99 % glucan recovery and 81 % glucan-to-glucose conversion. Simultaneous saccharification and fermentation of remaining BDGs at 30 % loading resulted in 78.5 g bioethanol/L with a yield of 94 %. The minimum selling price of bioethanol varies from $0.149-$0.836/kg, contingent on the co-product market prices. The biorefinery processing of one ton of BDGs caused a 60 % reduction in greenhouse gas emissions compared to that of the traditional production of 88 kg corn-lactic acid, 70 kg antioxidant phenolics, 234 kg soybean protein, and 225 kg corn-bioethanol, along with emissions from BDG landfilling. The biorefinery demonstrated a synergistic model of cost-effective bioethanol production and low-carbon emission BDGs treatment.

Investigating the causal impact of gut microbiota on glioblastoma: a bidirectional Mendelian randomization study.

BACKGROUND: Currently, the influence of microbiota on the occurrence, progression, and treatment of cancer is a topic of considerable research interest. Therefore, based on the theory of the gut-brain axis proved by previous studies, our objective was to uncover the causal relationship between glioblastoma and the gut microbiome using Mendelian randomization analysis. METHODS: We conducted a bidirectional Mendelian randomization study using summary statistics of gut microbiota derived from the MiBioGen consortium, the largest database of gut microbiota. Summary statistics for glioblastoma were obtained from IEU OpenGWAS project, which included 91 cases and 218,701 controls. We assessed the presence of heterogeneity and horizontal pleiotropy in the analyzed data. We primarily employed the inverse variance weighting method to investigate the causal relationship between gut microbiota and glioblastoma after excluding cases of horizontal pleiotropy. Four other analysis methods were employed as supplementary. Excluding abnormal results based on leave-one-out sensitivity analysis. Finally, reverse Mendelian randomization analysis was performed. RESULTS: Four genus-level taxa and one family-level taxa exhibited causal associations with glioblastoma. And these results of reverse Mendelian randomization analysis shown glioblastoma exhibited causal associations with three genus-level taxa and one family-level taxa. However, the Prevotella7(Forward, P=0.006, OR=0.34, 95%CI:0.158-0.732; Reverse, P=0.004, OR=0.972, 95%CI:0.953-0.991) shown the causal associations with glioblastoma in the bidirectional Mendelian randomization. CONCLUSIONS: In this bidirectional Mendelian randomization study, we identified five gut microbiota species with causal associations to glioblastoma. However, additional randomized controlled trials are required to clarify the impact of gut microbiota on glioblastoma and to reveal its precise mechanisms.

Design of the footprints of uncertainty for a class of typical interval type-2 fuzzy PI and PD controllers.

The Footprint of Uncertainty (FOU) for an Interval type-2 (IT2) input fuzzy set is a key parameter of IT2 fuzzy PI and PD (FPI and FPD) controllers because it provides additional options to handle uncertainties. Because the effects of FOUs on IT2 FPI and FPD controller are still unclear, it is necessary to provide a guideline for designing the FOUs. In this study, from the input-output relationship of IT2 FPI and FPD controllers using typical and popular configuration, the variation trend of variable gains, in relation to the increase of FOUs, was analyzed. It is mathematically proven that variable gains of typical controllers decrease or not increase as the FOUs increase. These theoretical results can be extended to provide a guideline for designing FOUs of typical IT2 FPI and FPD controllers. Real-time response of linear voice coil motor is used to show the effectiveness of our results.

Transcriptome analysis of differential gene expression in the longissimus dorsi muscle from Debao and landrace pigs based on RNA-sequencing.

RNA-seq analysis was used to identify differentially expressed genes (DEGs) at the genetic level in the longissimus dorsi muscle from two pigs to investigate the genetic mechanisms underlying the difference in meat quality between Debao pigs and Landrace pigs. Then, these DEGs underwent functional annotation, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, and protein-protein interaction (PPI) analyses. Finally, the expression levels of specific DEGs were assessed using qRT-PCR. The reference genome showed gene dosage detection of all samples which showed that the total reference genome comprised 22342 coding genes, including 14743 known and 190 unknown genes. For detection of the Debao pig genome, we obtained 14168 genes, including 13994 known and 174 unknown genes. For detection of the Landrace pig genome, we obtained 14404 genes, including 14223 known and 181 unknown genes. GO analysis and KEGG signaling pathway analysis show that DEGs are significantly related to metabolic regulation, amino acid metabolism, muscular tissue, muscle structure development etc. We identified key genes in these processes, such as FOS, EGR2, and IL6, by PPI network analysis. qRT-PCR confirmed the differential expression of six selected DEGs in both pig breeds. In conclusion, the present study revealed key genes and related signaling pathways that influence the difference in pork quality between these breeds and could provide a theoretical basis for improving pork quality in future genetic thremmatology.

A new fault diagnosis approach for analog circuits based on spectrum image and feature weighted kernel Fisher discriminant analysis.

Analog circuits are one of the most commonly used components in industrial equipment and, therefore, circuit failure may lead to significant causalities and even huge financial losses. To address this problem, this work presents a fault diagnosis method based on spectrum images for analog circuits. Unlike traditional analysis methods in a one-dimensional space, this study employs a computing method in the field of image processing to realize automatic feature extraction and fault diagnosis in a two-dimensional space. The proposed approach mainly involves the following steps. First, the sampling signals are converted into spectrum images by utilizing cross-wavelet transform, which can be further processed by the following image-based feature extraction method. Then, Krawtchouk moment is applied to extract both the global and local features of the spectrum images and finally form the feature vector. Feature weighted kernel Fisher discriminant analysis is then introduced for locating faults. Two typical analog circuits, video amplifier circuit and opamp high-pass filter circuit, are chosen to demonstrate the effectiveness of the proposed method. Simulation results show that the proposed approach based on spectrum images achieves a high accuracy, thus providing a highly effective means to fault diagnosis for analog circuits.

Analog Circuit Fault Diagnosis via Joint Cross-Wavelet Singular Entropy and Parametric t-SNE.

In this paper, a novel method with cross-wavelet singular entropy (XWSE)-based feature extractor and support vector machine (SVM) is proposed for analog circuit fault diagnosis. Primarily, cross-wavelet transform (XWT), which possesses a good capability to restrain the environment noise, is applied to transform the fault signal into time-frequency spectra (TFS). Then, a simple segmentation method is utilized to decompose the TFS into several blocks. We employ the singular value decomposition (SVD) to analysis the blocks, then Tsallis entropy of each block is obtained to construct the original features. Subsequently, the features are imported into parametric t-distributed stochastic neighbor embedding (t-SNE) for dimension reduction to yield the discriminative and concise fault characteristics. Finally, the fault characteristics are entered into SVM classifier to locate circuits' defects that the free parameters of SVM are determined by quantum-behaved particle swarm optimization (QPSO). Simulation results show the proposed approach is with superior diagnostic performance than other existing methods.

Transforming growth factor-ß1 gene polymorphisms with liver cirrhosis risk: A meta-analysis.

Although several epidemiological studies have investigated the association of transforming growth factor-ß1 (TGF-ß1) gene polymorphisms with the susceptibility to liver cirrhosis (LC), controversial results exist. Consequently, we performed a meta-analysis to accurately evaluate the relationship of TGF-ß1-509C/T and codon 10T/C polymorphisms with the risk of LC introduced by chronic hepatitis B/V virus (HBV/HCV) infection. A total of 9 case-control studies, involving 985 LC patients and 909 controls, were recruited for meta-analysis. The results suggested a significant association between the -509C/T polymorphism and LC risk in the total population. Stratification by ethnicity revealed similar associations in Egyptian and Caucasian populations, but not in Asian populations. Subgroup analyses by different etiologies also showed similar associations in HCV-induced LC, but not in HBV-induced LC. However, the overall data failed to show a significant association between codon 10T/C polymorphism and the risk of LC in the study. We concluded that TGF-ß1-509C/T polymorphism was significantly associated with LC susceptibility, while the codon 10T/C polymorphism seemed to have a limited role in predicting the occurrence of LC induced by HBV/HCV infection.

Prognostics of Lithium-Ion Batteries Based on Wavelet Denoising and DE-RVM.

Lithium-ion batteries are widely used in many electronic systems. Therefore, it is significantly important to estimate the lithium-ion battery's remaining useful life (RUL), yet very difficult. One important reason is that the measured battery capacity data are often subject to the different levels of noise pollution. In this paper, a novel battery capacity prognostics approach is presented to estimate the RUL of lithium-ion batteries. Wavelet denoising is performed with different thresholds in order to weaken the strong noise and remove the weak noise. Relevance vector machine (RVM) improved by differential evolution (DE) algorithm is utilized to estimate the battery RUL based on the denoised data. An experiment including battery 5 capacity prognostics case and battery 18 capacity prognostics case is conducted and validated that the proposed approach can predict the trend of battery capacity trajectory closely and estimate the battery RUL accurately.

A CMOS humidity sensor for passive RFID sensing applications.

This paper presents a low-cost low-power CMOS humidity sensor for passive RFID sensing applications. The humidity sensing element is implemented in standard CMOS technology without any further post-processing, which results in low fabrication costs. The interface of this humidity sensor employs a PLL-based architecture transferring sensor signal processing from the voltage domain to the frequency domain. Therefore this architecture allows the use of a fully digital circuit, which can operate on ultra-low supply voltage and thus achieves low-power consumption. The proposed humidity sensor has been fabricated in the TSMC 0.18 µm CMOS process. The measurements show this humidity sensor exhibits excellent linearity and stability within the relative humidity range. The sensor interface circuit consumes only 1.05 µW at 0.5 V supply voltage and reduces it at least by an order of magnitude compared to previous designs.