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Considering the importance of combustion diagnosis in industrial manufacturing and many fields, efficient, quick, and real-time multidimensional reconstruction is necessary and indispensable. Hence, focusing on the combustion field dynamic and multi-dimensional reconstruction, a modified U-ConvLSTM model was proposed to combine with the TDLAS method to resolve the real-time reconstruction and short prediction. By dividing the combustion field into space and time slices, we used discretized spatiotemporal slices to complete the 2-D distribution reconstruction and then expanded them into higher dimensions. The simulation results demonstrate that our design can effectively reconstruct different 2-D distributions, achieving a reconstruction error of less than 5%. Three-step predictions also performed well, a PSNR no less than 30 dB, and an SSIM no less than 0.75. In general, our multidimensional combustion field reconstruction method, based on the spatiotemporal discretization U-ConvLSTM model, can enhance the accuracy of combustion field reconstruction and provide short-term predictions. This work will contribute to closed-loop control in industrial fields.

Tunable diode laser absorption spectroscopy (TDLAS) technology, combined with chromatographic imaging algorithms, is commonly used for two-dimensional temperature and concentration measurements in combustion fields. However, obtaining critical temperature information from limited detection data is a challenging task in practical engineering applications due to the difficulty of deploying sufficient detection equipment and the lack of sufficient data to invert temperature and other distributions in the combustion field. Therefore, we propose a sparse projection multi-view synthesis model based on U-Net that incorporates the sequence learning properties of gated recurrent unit (GRU) and the generalization ability of residual networks, called GMResUNet. The datasets used for training all contain projection data with different degrees of sparsity. This study shows that the synthesized full projection data had an average relative error of 0.35%, a PSNR of 40.726, and a SSIM of 0.997 at a projection angle of 4. At projection angles of 2, 8, and 16, the average relative errors of the synthesized full projection data were 0.96%, 0.19%, and 0.18%, respectively. The temperature field reconstruction was performed separately for sparse and synthetic projections, showing that the application of the model can significantly improve the reconstruction accuracy of the temperature field of high-energy combustion.

The etiological significance of neurological disorders in autism is debated, but it is clear that they complicate support provision and clinical management, and can have negative impact on outcomes. This systematic review and meta-analysis explored the full range of co-occurring neurological disorders in autism. We estimated the odds of having neurological complications compared to the general population and other neurodevelopmental conditions, as well as the overall prevalence of different neurological disorders. Seventy-nine articles were eligible for the systematic review, including 28 case-control studies, 43 prevalence studies, and 8 cohort studies. Findings were heterogeneous across studies. Overall, autistic individuals were significantly more likely than the general population to exhibit epilepsy, macrocephaly, hydrocephalus, cerebral palsy, migraine/headache, and congenital abnormalities of the nervous system, with prevalence estimates ranging from 1.1% (0%-3.3%; hydrocephalus) to 14.2% (11.3%-17.2%; epilepsy). Epilepsy was also more common in autism than in attention-deficit/hyperactivity disorder (odds ratio [95% confidence interval] = 4.06 [2.81-5.88]). Findings indicate that awareness of neurological disorders and neurological check-ups are indicated in autism to ensure adequate physical health care and support. Prospective studies of neurological disorders in children diagnosed with or at risk of autism might further enhance our understanding of causal pathways.

High-resolution flame temperature images are essential indicators for evaluating combustion conditions. Tunable diode laser absorption spectroscopy (TDLAS) is an effective combustion diagnostic method. In actual engineering, due to the limitation of line-of-sight (LOS) measurement, TDLAS technology has the problems of small data volume and low dimensionality in measuring combustion fields, which seriously limits the development of TDLAS in combustion diagnosis. This article demonstrates a TDLAS imaging method based on a 64-pixel area array sensor to reconstruct the two-dimensional temperature field of the flame. This paper verifies the robustness of the Algebraic Reconstruction Technique (ART) algorithm through numerical simulation and studies the effects of temperature, concentration, and pressure on the second harmonic intensity based on the HITRAN database. The two-dimensional temperature field of the flame was reconstructed, and reconstruction accuracy was verified using thermocouples. The maximum relative error was 3.71%. The TDLAS detection system based on a 64-pixel area array sensor provides a way to develop high-precision, high-complexity flame temperature measurement technology.

miR-30b, which is encoded by the gene located on chromosome 8q24.22, plays an important role in a variety of diseases. In most types of tumors, miR-30b significantly inhibits the proliferation, migration, and invasion of cancer cells through the regulation of target genes. Moreover, miR-30b can inhibit the PI3K/AKT signaling pathway through the regulation of EGFR, AKT, Derlin-1, GNA13, SIX1, and other target genes, thus inhibiting the EMT process of tumor cells and promoting apoptosis. In addition, miR-30 plays a significant role in alleviating drug resistance in tumor cells. Although the use of miR-30b as a clinical diagnostic indicator or anticancer drug is still facing great difficulties in the short term, with the deepening of research, the potential application of miR-30b is emerging.

The therapeutic efficacy of peptide‐based drugs is commonly hampered by the intrinsic propensity to aggregation. A notable example is human calcitonin (hCT), a peptide hormone comprising 32 amino acids, which is synthesized and secreted by thyroid gland parafollicular cells (C cells). This hormone plays a vital role in regulating blood calcium levels and upholding bone integrity. Despite its physiological importance, utilizing hCT as a drug is hampered by its inclination to form amyloid. To address this limitation, an alternative is provided by salmon calcitonin (sCT), which possesses a lower aggregation propensity. Although sharing the same disulfide bond at the N terminus as hCT, sCT differs from hCT at a total of 16 amino acid positions. However, due to the dissimilarity in sequences, using sCT as a clinical replacement occasionally results in adverse side effects in patients. Earlier investigations have highlighted the significant roles of Tyr‐12 and Asn‐17 in inducing the formation of amyloid fibrils. By introducing double mutations at these sites, the ability to hinder aggregation can be significantly augmented. This study delves into the oligomerization and helical structure formation of the hCT double mutant (Y12LN17H hCT, noted as DM hCT), as well as two single mutants (Y12L and N17H), aiming to elucidate the mechanism behind hCT fibrillization. In addition, computational prediction tools were employed again to identify potential substitutes. Although the results yielded were not entirely satisfactory, a comparison between the newly examined and previously found hCT double mutants provides insights into the reduced aggregation propensity of the latter. This research endeavor holds the promise of informing the design of more effective therapeutic peptide drugs in the future. [ABSTRACT FROM AUTHOR]

Polymeric assemblies have emerged as a kind of promising platform in biomedical fields. The strong potency of polyphenols to form versatile interactions with other small or polymeric molecules via covalent and noncovalent linkages enables polyphenols to serve as molecular glues, offering a magic driving force to assemble multiple functional molecules into a diversity of polyphenol‐based systems, such as nanoparticles and hydrogels. Due to the unique merits of anti‐tumor, anti‐bacterial, antioxidant, anti‐inflammatory, and cardioprotective properties of polyphenols, polyphenol‐based systems hold great promise to deal with a wide spectrum of diseases. This review comprehensively overviews the components, intermolecular interactions, and preparation methods implicated in the assembly of polyphenol‐based systems. In addition, their medical applications are introduced, particularly the treatments of various diseases, such as tumors, bacterial infections, inflammatory bowel disease (IBD), cardiovascular diseases (CVDs), chronic periodontitis, acute lung injury, osteoarthritis, and renal fibrosis. [ABSTRACT FROM AUTHOR]