Smart-building load forecasters are often trained offline on dense, multivariate, high-frequency data, but deployment may provide only hourly, feature-limited inputs. Missing features must then be reconstructed, and their errors can propagate through the model. If this input uncertainty is not reflected, prediction intervals may become miscalibrated, affecting demand-response scheduling. Our work examines where uncertainty should be placed once inference inputs are reconstructed. We develop a unified one-day-ahead probabilistic forecasting framework that aligns temporal resolution, reconstructs the unavailable inputs, and derives causal features, and we compare a modular post-hoc residual-quantile scheme with an integrated in-model quantile-learning scheme. The comparison uses three mid-scale Deep Learning (DL) backbones: recurrent, hybrid recurrent, and attention-based Temporal Fusion Transformer (TFT) models, under identical inputs, forecasting horizon, preprocessing rules, and training budgets. Results show that uncertainty placement is backbone-dependent. Integrated quantile learning is most reliable with the TFT, yielding 2.2–3.6% MAPE and 28–83 W RMSE on the labeled test window, while producing intervals about 5 × narrower than the modular intervals at the closest-to-nominal coverage level. Diebold-Mariano tests support the TFT ranking and the mixed behavior of the recurrent backbones. A reconstruction-sensitivity test shows that reconstructed inputs increase the Quantile Score (QS) by 106% while interval width remains nearly unchanged, indicating that the model does not automatically absorb reconstruction-induced uncertainty. Robustness checks against non-DL baselines and seasonal hold-out weeks support this ranking. Our results expose the limits of post-hoc residual quantiles when inference depends on reconstructed inputs.
Claudins (CLDNs) are a family of integral membrane proteins central to the formation of tight junctions, structures that are involved in paracellular transport and cellular growth and differentiation, and are critical for the maintenance of cellular polarity. Recent studies have provided evidence that CLDNs are aberrantly expressed in diverse types of human cancers, including hepatocellular carcinomas (HCCs). However, little is known about how CLDN expression is involved in cancer progression. In this study, we show that CLDN1 has a causal role in the epithelial–mesenchymal transition (EMT) in human liver cells, and that the c-Abl-Ras-Raf-1-ERK1/2 signaling axis is critical for the induction of malignant progression by CLDN1. Overexpression of CLDN1 induced expression of the EMT-regulating transcription factors Slug and Zeb1, and thereby led to repression of E-cadherin, β-catenin expression, enhanced expression of N-cadherin and Vimentin, a loss of cell adhesion, and increased cell motility in normal liver cells and HCC cells. In line with these findings, inhibition of either c-Abl or ERK clearly attenuated CLDN1-induced EMT, as evidenced by a reversal of N-cadherin and E-cadherin expression patterns, and restored normal motility. Collectively, these results indicate that CLDN1 is necessary for the induction of EMT in human liver cells, and that activation of the c-Abl-Ras-Raf-1-ERK1/2 signaling pathway is required for CLDN1-induced acquisition of the malignant phenotype. The present observations suggest that CLDN1 could be exploited as a biomarker for liver cancer metastasis and might provide a pivotal point for therapeutic intervention in HCC.
Impaired autophagy has been implicated in many neurodegenerative diseases, such as Parkinson's disease (PD), and might be responsible for deposition of aggregated proteins in neurons. However, little is known about how neuronal autophagy and clearance of aggregated proteins are regulated. Here, we show a role for Toll-like receptor 2 (TLR2), a pathogen-recognizing receptor in innate immunity, in regulation of neuronal autophagy and clearance of α-synuclein, a protein aggregated in synucleinopathies, including in PD. Activation of TLR2 resulted in the accumulation of α-synuclein aggregates in neurons as a result of inhibition of autophagic activity through regulation of the AKT/mTOR pathway. In contrast, inactivation of TLR2 resulted in autophagy activation and increased clearance of neuronal α-synuclein, and hence reduced neurodegeneration, in transgenic mice and in in vitro models. These results uncover roles of TLR2 in regulating neuronal autophagy and suggest that the TLR2 pathway may be targeted for autophagy activation strategies in treating neurodegenerative disorders.
This study investigated the green synthesis of zinc oxide nanoparticles (ZnO NPs) using banana peel extract (BPE), examining the impact of synthesis pH (8, 10, and 12) on nanoparticle characteristics and their photocatalytic inactivation efficacy. Optimal synthesis at pH 10 produced ZnO NPs with superior crystallinity, reduced structural defects, spherical morphology, and stable colloidal dispersion. Its photocatalytic effect was evaluated against foodborne pathogens, including Escherichia coli O157:H7, Salmonella Typhimurium, and Listeria monocytogenes under UVA irradiation. ZnO NPs synthesized at pH 10 demonstrated the most effective bacterial effect, generating the highest levels of reactive oxygen species (ROS) compared to ZnO NPs synthesized at other pH levels. The combination of ZnO NPs and UVA irradiation resulted in significant bacterial membrane damage and intracellular ROS production, leading to cell death. Additionally, the ZnO NPs retained stable photocatalytic activity over multiple reuse cycles, emphasizing their potential for long-term applications. These findings demonstrate the potential of upcycling food waste, such as banana peel, into valuable photocatalysts for efficient bacterial inactivation. This green synthesis approach provides a sustainable and effective strategy for developing photocatalysts that enhance food safety and environmental sustainability.
Phosphor materials doped with rare-earth ions have gained significant attention for their potential applications in solid-state lighting and display technologies. However, achieving high color purity and efficient luminescence is challenging. To address this limitation, we synthesized a series of Sm3+-doped Ca3La3(BO3)5 phosphors (Ca3La3(BO3)5:xSm3+; x = 0.005, 0.01, 0.03, 0.05, 0.07, 0.09, 0.11, or 0.13 mol) via a sol–gel method. The structural and luminescence properties of the Ca3La3(BO3)5:xSm3+ phosphors were analyzed by x-ray diffraction (XRD), Fourier transform infrared (FT-IR) spectroscopy, and photoluminescence (PL) measurements. XRD analyses confirmed that the phosphors possess a hexagonal crystal structure. FT-IR spectroscopy confirmed the presence of BO3 groups, with characteristic B–O stretching vibrations observed in the 1500–1000 cm−1 range and B–O–B bending modes at 520, 616, and 741 cm−1. These findings confirm the structural integrity of the borate network in Ca3La3(BO3)5:xSm3+ phosphors. The optical energy bandgap was estimated using diffuse reflectance spectroscopy. The PL excitation band was identified as the charge-transfer band of Sm3+. The emission spectra revealed three distinct peaks in the 500–750 nm range, corresponding to the 4G5/2 → 6HJ (J = 5/2, 7/2, or 9/2) transitions of Sm3+. The intensity of the peaks in the PL spectra of Ca3La3(BO3)5:xSm3+ increased as x increased up to 0.03 mol but decreased at higher x values. Analyses based on Judd–Ofelt theory and optical absorption data indicate that the synthesized phosphors exhibit significant emission cross-sections and effective line widths. Under 401 nm excitation, the Ca3La3(BO3)5:0.03Sm3+ phosphor exhibited high color purity (82–90
