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Currently, building energy consumption prediction typically relies on vast amounts of historical data. However, for newly constructed buildings, the scarcity of data leads to reduced prediction accuracy. To address this challenge, this paper proposes a novel approach that integrates transfer learning with a source domain reconstruction-based BiLSTM model for building energy consumption prediction. In the first stage, both source and target domains are clustered into profile types using k-means. For each profile type in the target domain, the most similar profiles in the source domain are identified using Maximum Mean Discrepancy and Dynamic Time Warping. The source domain is then reconstructed by combining these identified profiles based on their proportions in the target domain. Subsequently, a feature extraction method based on EMD-CWT-Conv is introduced. Empirical Mode Decomposition is first applied to decompose and filter the source domain data. Continuous Wavelet Transform is then employed to extract distinctive frequency-domain and time-domain features from both the reconstructed source domain and the target domain. Final predictions are made using transfer learning and fine-tuning. Experiments on a grocery shop and a school show that the proposed approach reduces Mean Absolute Percentage Error by at least 13.19% and 17.67%, respectively. [ABSTRACT FROM AUTHOR]

Introduction: Anxiety disorders are among the most common mental illnesses in the US. An estimated 31.1% of U.S. adults experience any anxiety disorder at some time in their lives. Understanding some of the molecular underpinnings of anxiety could lead to improved treatments over current strategies focusing on symptom relief rather than root causes. One significant neurotransmitter system exerting control over anxiety is the nicotinic receptor subdivision of the cholinergic system. The murine Lynx2 gene, encoding a protein modulator of nicotinic acetylcholine receptors, is expressed in anxiety-related neural circuitry in rodents and has been functionally associated with anxiety-like behavior. Methods: We examined variations in the human LYNX2 (LYPD1) gene and their potential effects on anxiety levels in a cohort of 624 participants. Participants completed validated anxiety questionnaires (e.g., STICSA and STAI), which assessed both their current anxiety and their general tendency to experience anxiety. Possible functional alterations due to one such mutation was assessed through atomic force microscopy (AFM) and computational modeling. Results: We identified a previously unreported single nucleotide polymorphism (SNP) in the mature protein-coding region of LYNX2 that was associated with significantly higher than normal anxiety scores. These elevated scores resembled those seen in patients clinically diagnosed with generalized anxiety disorder and panic disorder, although this genetically defined subpopulation did not typically report such diagnoses. Through computational modeling of the homopentameric α7 nicotinic receptor subtype and in vitro atomic force microscopy (AFM), we discovered that a specific LYNX2 SNP is linked to a reduced binding affinity between the LYNX2 protein and nAChRs, offering a potential functional explanation for the role that this mutation may play in anxiety. Discussion: A polymorphism in LYNX2, which codes for an inhibitory modulator of nicotinic acetylcholine receptors, has the potential to lead to sensitized nicotinic receptor activity in anxiety-related circuits. The LYNX2 protein has been shown to bind to multiple nicotinic acetylcholine receptor subtypes, including α4β2, α7, and α3β4 subtypes, each of which have been shown to be involved in affective behaviors. This work suggests that a subpopulation of individuals harboring a deleterious mutation in LYNX2 may predispose them to anxiety through abnormal nicotinic receptor control. In the future, this work may lead to the development of a biomarker for anxiety or a diagnostic tool for the early detection of individuals with susceptibility to anxiety. [ABSTRACT FROM AUTHOR]

Background: The heterogeneity of prognosis and treatment benefits among patients with gliomas is due to tumor microenvironment characteristics. However, biomarkers that reflect microenvironmental characteristics and predict the prognosis of gliomas are limited. Therefore, we aimed to develop a model that can effectively predict prognosis, differentiate microenvironment signatures, and optimize drug selection for patients with glioma. Materials and Methods: The CIBERSORT algorithm, bulk sequencing analysis, and single-cell RNA (scRNA) analysis were employed to identify significant cross-talk genes between M2 macrophages and cancer cells in glioma tissues. A predictive model was constructed based on cross-talk gene expression, and its effect on prognosis, recurrence prediction, and microenvironment characteristics was validated in multiple cohorts. The effect of the predictive model on drug selection was evaluated using the OncoPredict algorithm and relevant cellular biology experiments. Results: A high abundance of M2 macrophages in glioma tissues indicates poor prognosis, and cross-talk between macrophages and cancer cells plays a crucial role in shaping the tumor microenvironment. Eight genes involved in the cross-talk between macrophages and cancer cells were identified. Among them, periostin (POSTN), chitinase 3 like 1 (CHI3L1), serum amyloid A1 (SAA1), and matrix metallopeptidase 9 (MMP9) were selected to construct a predictive model. The developed model demonstrated significant efficacy in distinguishing patient prognosis, recurrent cases, and characteristics of high inflammation, hypoxia, and immunosuppression. Furthermore, this model can serve as a valuable tool for guiding the use of trametinib. Conclusions: In summary, this study provides a comprehensive understanding of the interplay between M2 macrophages and cancer cells in glioma; utilizes a cross-talk gene signature to develop a predictive model that can predict the differentiation of patient prognosis, recurrence instances, and microenvironment characteristics; and aids in optimizing the application of trametinib in glioma patients. [ABSTRACT FROM AUTHOR]

The Ebola virus is a deadly pathogen that has been threatening public health for decades. Recent studies have revealed alternative viral invasion routes where Ebola virus approaches cells via interactions among phosphatidylserine (PS), PS binding ligands such as Gas6, and TAM family receptors such as Axl. In this study, we investigate the interactions among phosphatidylserine on the Ebola viral-like particle (VLP) membrane, human Gas6, and human Axl using atomic force microscope-based single molecule force spectroscopy to compare their binding strength and affinity from a biomechanical perspective. The impact of calcium ions on their interactions is also studied and quantified to provide more details on the calcium-dependent phosphatidylserine-Gas6 binding mechanism. Our results indicate that, in the presence of calcium ions, the binding strengths of VLP-Gas6 and VLP-Gas6-Axl increase but are still weaker than that of Gas6-Axl, and the binding affinity of VLP-Gas6 and VLP-Gas6-Axl is largely improved. The binding strength and affinity of Gas6-Axl basically remain the same, indicating no impact in the presence of calcium ions. Together, our study suggests that, under physiological conditions with calcium present, the Ebola virus can utilize its membrane phosphatidylserine to dock on cell surface via Gas6-Axl bound complex. [ABSTRACT FROM AUTHOR]

Simple Summary: Numerous genetic mutations have been documented in HCC, and associated with clinical characters. Herein, we demonstrated that XIRP2 mutation is one of the high-frequency mutations in HCC, and is associated with poor prognosis and drug resistance. The presence of the XIRP2 mutation leads to enhanced stability of the XIRP2 protein, whereas its depletion significantly heightens sensitivity to oxaliplatin by inducing an excessive influx of zinc and calcium ions. XIRP2 mutation holds potential as a prognostic biomarker and that targeting the XIRP2 protein may offer therapeutic benefits in the management of HCC. Hepatocellular carcinoma (HCC) is a prevalent malignant digestive tumor. Numerous genetic mutations have been documented in HCC, yet the clinical significance of these mutations remains largely unexplored. The objective of this study is to ascertain the clinical value and biological effects of xin actin binding repeat containing 2 (XIRP2) mutation in HCC. The gene mutation landscape of HCC was examined using data from the Cancer Genome Atlas and the International Cancer Genome Consortium databases. The prognostic significance of the XIRP2 mutation was assessed through KM plot analysis. The association between drug sensitivity and the XIRP2 mutation was investigated using the TIDE algorithm and CCK-8 experiments. The biological effects of the XIRP2 mutation were evaluated through qRT-PCR, protein stability experiments, and relevant biological experiments. The XIRP2 mutation is one of the high-frequency mutations in HCC, and is associated with poor prognosis. A total of 72 differentially expressed genes (DEGs) were observed in HCC tissues with the XIRP2 mutation as compared to those with the XIRP2 wildtype, and these DEGs were closely related to ion metabolic processes. The XIRP2 mutation was linked to alterations in the sensitivity of fludarabine, oxaliplatin, WEHI-539, and LCL-161. CCK-8 assays demonstrated that HCC cells carrying the XIRP2 mutation exhibited increased resistance to fludarabine and oxaliplatin, but enhanced sensitivity to WEHI-539 and LCL-161 as compared with those HCC cells with the XIRP2 wildtype. The XIRP2 mutation was found to have no impact on the mRNA levels of XIRP2 in tissues and cells, but it did enhance the stability of the XIRP2 protein. Mechanically, the inhibition of XIRP2 resulted in a significant increase in sensitivity to oxaliplatin through an elevation in zinc ions and a calcium ion overload. In conclusion, the XIRP2 mutation holds potential as a biomarker for predicting the prognosis and drug sensitivity of HCC and serves as a therapeutic target to enhance the efficacy of oxaliplatin. [ABSTRACT FROM AUTHOR]

Li-rich layered oxide is considered as one of the most prospective cathode materials for the future high-energy density Li-ion batteries (LIBs). However, the severe voltage and capacity degradation hinders its commercialization. To address the issues, Ce-doped Li-rich Li1.2Mn0.56Ni0.11Co0.13O2 (LMNCO) layered cathode was prepared here via a sol–gel method. The Ce-doped (1 wt.%) Li-rich LMNCO cathode exhibits a layered structure with slight of Li+/Ni2+ mixture. The first-cycle specific discharge capacity at a high rate of 5.0 C reaches up to 151.4 mAh·g−1, and still retains 126.6 mAh·g−1 after 200 cycles with a retention rate of 83.6%. It is higher than that of the undoped LMNCO and the Ce-doped (3 wt.%) Li-rich cathodes. The doping of Ce effectively stabilizes the lattice structure of the LMNCO, increases the crystal plane spacing, and generates a unique layered-spinel phase with three-dimensional Li diffusion channel structure and enhanced Li-ion diffusion rate. This provides an efficient approach to improve the performance of the Li-rich layered structure cathode in LIBs. [ABSTRACT FROM AUTHOR]