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Many educational strategies have been proposed to improve students' learning motivation and outcomes. This paper reports the student learning outcome results of a three-year study centered on the Electronics course at the Department of Physics of National Taiwan University. In the first year, peer instruction (PI) with in-class lectures was implemented. In the second year, in-class lectures were replaced with online lectures in a flipped classroom (FC) approach, and PI in class was maintained. In the third year, PI-based conceptual questions (CQs) were scored as part of in-class homework to enhance motivation for online lecture preview. Learning performance was evaluated based on cumulative percentage of correct answers to CQs and summative assessment. The results revealed improved student performance on summative assessment with PI and FC approaches combined. Furthermore, when CQs were scored, overall learning outcomes were significantly enhanced. In addition, an advantage of using a PI plus FC approach over using PI alone is that more course materials can be covered in online videos, which prevents a loss of lecture content to the time-consuming, in-class discussions involved in PI. Our study indicates that when students' motivations to prepare before class are reinforced using graded CQs, the learning outcome enhancement of PI plus FC is even more significant.

Abstract The prevalence of osteoporosis has drastically increased recently. It is not only the most frequent but is also a major global public health problem due to its high morbidity. There are many risk factors associated with osteoporosis were identified. However, most studies have used the traditional multiple linear regression (MLR) to explore their relationships. Recently, machine learning (Mach-L) has become a new modality for data analysis because it enables machine to learn from past data or experiences without being explicitly programmed and could capture nonlinear relationships better. These methods have the potential to outperform conventional MLR in disease prediction. In the present study, we enrolled a Chinese post-menopause cohort followed up for 4 years. The difference of T-score (δ-T score) was the dependent variable. Information such as demographic, biochemistry and life styles were the independent variables. Our goals were: (1) Compare the prediction accuracy between Mach-L and traditional MLR for δ-T score. (2) Rank the importance of risk factors (independent variables) for prediction of δ T-score. Totally, there were 1698 postmenopausal women were enrolled from MJ Health Database. Four different Mach-L methods namely, Random forest (RF), eXtreme Gradient Boosting (XGBoost), Naïve Bayes (NB), and stochastic gradient boosting (SGB), to construct predictive models for predicting δ-BMD after four years follow-up. The dataset was then randomly divided into an 80% training dataset for model building and a 20% testing dataset for model testing. A 10-fold cross-validation technique for hyperparameter tuning was used. The model with the lowest root mean square error for the validation dataset was viewed as the best model for each ML method. The averaged metrics of the RF, SGB, NB, and XGBoost models were used to compare the model performance of the benchmark MLR model that used the same training and testing dataset as the Mach-L methods. We defined that the priority demonstrated in each model ranked 1 as the most critical risk factor and 22 as the last selected risk factor. For Pearson correlation, age, education, BMI, HDL-C, and TSH were positively and plasma calcium level, and baseline T-score were negatively correlated with δ-T score. All four Mach-L methods yielded lower prediction errors than the MLR method and were all convincing Mach-L models. From our results, it could be noted that education level is the most important factor for δ-T Score, followed by DBP, smoking, SBP, UA, age, and LDL-C. All four Mach-L outperformed traditional MLR. By using Mach-L, the most important six risk factors were selected which are, from the most important to the least: DBP, SBP, UA, education level, TG and sleeping hour. δ T score was positively related to SBP, education level, UA and TG and negatively related to DBP and sleeping hour in postmenopausal Chinese women.

Abstract Background Dengue virus (DENV) causes the most significant mosquito-borne viral disease with a wide spectrum of clinical manifestation, including neurological symptoms associated with lethal dengue diseases. Dopamine receptors are expressed in central nervous system, and dopamine antagonists have been reported to exhibit antiviral activity against DENV infection in vivo and in vitro. Although identification of host-cell receptor is critical to understand dengue neuropathogenesis and neurotropism, the involvement of dopamine receptors in DENV infection remains unclear. Results We exploited the sensitivity and precision of force spectroscopy to address whether dopamine type-2 receptors (D2R) directly interact with DENV particles at the first step of infection. Using optical tweezers, we quantified and characterized DENV binding to D2R expressed on Chinese hamster ovary (CHO) cells. Our finding suggested that the binding was D2R- and DENV-dependent, and that the binding force was in the range of 50–60 pN. We showed that dopamine antagonists prochlorperazine (PCZ) and trifluoperazine (TFP), previously reported to inhibit dengue infection, interrupt the DENV-D2R specific binding. Conclusions This study demonstrates that D2R could specifically recognize DENV particles and function as an attachment factor on cell surfaces for DENV. We propose D2R as a host receptor for DENV and as a potential therapeutic target for anti-DENV drugs. Graphical abstract

Seasonal influenza A virus subtype H3N2 (A/H3N2) circulates globally and has been linked to higher hospitalization rates and summer outbreaks in temperate regions. Here, A/H3N2 circulation in Shanghai, China was systematically studied using data and materials generated by the Shanghai influenza surveillance network from 2005 to 2023. Time-series analysis of incidence and subtyping data showed that A/H3N2 co-circulated with other (sub)types and dominated in multiple seasonal influenza peaks, preferentially in summer. Whole genomes of 528 representative strains were sequenced, and spatiotemporal phylodynamic analysis using these and GISAID-archived sequences demonstrated that in the years before the COVID-19 pandemic, phylogenetically similar strains were circulating locally and elsewhere. However, clade 1a.1 (within 3C.2a.1b.2a), circulated in and only in Shanghai and domestically in 2022, while the sibling clade 2 predominated in other regions. Interestingly, clade 1a.1 was swiftly and completely replaced by clade 2, mostly 2a.3a.1, at the start of 2023. In hemagglutination inhibition and neutralization assays, sera from healthy donors collected in 2022 displayed higher or similar reactivity against 2a.3a.1 compared to 1a.1. By contrast, transcription and replication competence of 2a.3a.1 in MDCK cells was higher than 1a.1. These results indicated that instead of antigenicity differences enabling evasion of pre-existing immunity, higher replicative capability more likely contributed to 2a.3a.1 viruses achieving dominance in China. In addition to summarizing patterns of A/H3N2 local circulation in Shanghai, this work revealed an unusual episode in A/H3N2 global circulation and evolution dynamics in connection to the COVID-19 pandemic and explored possible mechanistic explanations.

The primary objective of geophysical research is the exploration of underground structures and to serve as a valuable tool for geological interpretation. The formation structure and properties can be determined by analyzing the physical properties of the underground medium reflected by geophysical data, such as density, velocity, magnetic susceptibility, resistivity, and more. Given the numerous solutions of a single geophysical method, comprehensive geophysical interpretation is currently a feasible and effective approach. This study explores lithology prediction, providing a summary of the basic principles and steps of comprehensive geophysical interpretation methods for lithology prediction. Additionally, it outlines the main technical methods of comprehensive lithology prediction involving two kinds of technical routes: knowledge-driven and data-driven. The knowledge-driven method uses prior information. It is simple, direct, and easy to understand, but has weak adaptability to the complexity and high dimension data. The data-driven method employs a mathematical statistics strategy to explore the relationship between data and has a robust capacity to adapt to complex scenarios. In solving practical problems, the supervised machine learning method, based on sufficient rock physical properties research, not only incorporates prior knowledge but also maximizes its internal data exploration ability. It can enhance the accuracy of lithology prediction and interpretation, better establish the corresponding relationship between geophysical and geological information, and support the exploration needs of resources and energy.

A single specimen of a rare species of armored searobin, Paraheminodus kamoharai Kawai, Imamura et Nakaya, 2004, is recorded from Taiwan, South China Sea, for the first time. While this particular species has been recorded in the Philippines and Japan, it has never been found in Taiwan. Therefore, our specimen fills the distribution gap of this species in the western Pacific Ocean. A detailed description of the specimen is provided and compared to the data of type specimens. Additionally, a checklist of species recorded from Taiwan is provided. Currently, 20 peristediine species under six genera are recorded from Taiwan’s EEZ.

Abstract Two‐photon high‐speed fluorescence calcium imaging stands as a mainstream technique in neuroscience for capturing neural activities with high spatiotemporal resolution. However, challenges arise from the inherent tradeoff between acquisition speed and image quality, grappling with a low signal‐to‐noise ratio (SNR) due to limited signal photon flux. Here, a contrast‐enhanced video‐rate volumetric system, integrating a tunable acoustic gradient (TAG) lens‐based high‐speed microscopy with a TAG‐SPARK denoising algorithm is demonstrated. The former facilitates high‐speed dense z‐sampling at sub‐micrometer‐scale intervals, allowing the latter to exploit the spatial redundancy of z‐slices for self‐supervised model training. This spatial redundancy‐based approach, tailored for 4D (xyzt) dataset, not only achieves >700% SNR enhancement but also retains fast‐spiking functional profiles of neuronal activities. High‐speed plus high‐quality images are exemplified by in vivo Purkinje cells calcium observation, revealing intriguing dendritic‐to‐somatic signal convolution, i.e., similar dendritic signals lead to reverse somatic responses. This tailored technique allows for capturing neuronal activities with high SNR, thus advancing the fundamental comprehension of neuronal transduction pathways within 3D neuronal architecture.

ObjectiveTo investigate the specific role of inflammation in the connection between obesity and the overall incidence of cancer.MethodsA total of 356,554 participants in MJ cohort study were included. Systemic inflammation markers from blood samples and anthropometric measurements were determined using professional instruments. The Cox model was adopted to evaluate the association.ResultsOver a median follow-up of 8.2 years, 9,048 cancer cases were identified. For individual systemic inflammation biomarkers, the overall cancer risk significantly escalated as blood C-reactive protein (CRP) (hazard ratio (HR)=1.036 (1.017-1.054)) and globulin (GLO) (HR=1.128 (1.105-1.152)) levels increased, and as hemoglobin (HEMO) (HR=0.863 (0.842-0.884)), albumin (ALB) (HR=0.846 (0.829-0.863)) and platelets (PLA) (HR=0.842 (0.827-0.858)) levels decreased. For composite indicators, most of them existed a significant relationship to the overall cancer risk. Most indicators were correlated with the overall cancer and obesity-related cancer risk, but there was a reduction of association with non-obesity related cancer risk. Most of indicators mediated the association between anthropometric measurements and overall cancer risk.ConclusionsSystemic inflammatory state was significantly associated with increased risks of cancer risk. Inflammation biomarkers were found to partly mediate the association between obesity and cancer risk.

Abstract Background The human leukocyte antigen (HLA) genes, exhibiting significant genetic diversity, are associated with susceptibility to various clinical diseases and diverse in drug responses. High costs of HLA sequencing and the population-specific architecture of this genetic region necessitate the establishment of a population-specific HLA imputation reference panel. Moreover, there is a lack of understanding about the genetic and phenotypic landscape of HLA variations within the Taiwanese population. Methods We created models for a Taiwanese-specific HLA imputation reference panel. These models were trained with the array genotype data and HLA sequencing data from 845 Taiwanese subjects. HLA imputation was applied for 59,448 Taiwanese subjects to characterize the HLA allele and haplotype frequencies. Additionally, a phenome-wide association study (PheWAS) was conducted to identify the phenotypes associated with HLA variations. The association of the biallelic HLA variants with the binary and quantitative traits were evaluated with additive logistic and linear regression models, respectively. Furthermore, an omnibus test with likelihood-ratio test was applied for each HLA amino acid position in the multiallelic HLA amino acid polymorphisms to compare the difference between a fitted model and a null model following a χ2 distribution of n-1 degree of freedom at a position with n residues. Finally, we estimated the prevalence of adverse drug reactions (ADR)-related HLA alleles in the Taiwanese population. Results In this study, the reference panel models displayed remarkable accuracy, with averages of 99.3%, 98.9%, and 99.1% for 2-, 4-, 6-digit alleles of the eight classical HLA genes, respectively. For PheWAS, a total of 18,136 significant associations with HLA variants across 26 phenotypes are identified (p < 5×10-8), highlighting the pleiotropy feature of the HLA region. Among the independent signals, 15 are novel, including the association of HLA-B pos 138 variation with ankylosing spondylitis (AS), and rs9266290 and rs9266292 with allergy. Through an analysis spanning the entire HLA region, we identified clusters of phenotype correlations. Finally, the carriers of pharmacogenomic related HLA alleles, including HLA-C*01:02 (35.86%), HLA-B*58:01 (20.9%), and HLA-B*15:02 (8.38%), were characterized in the Taiwanese general population. Conclusions We successfully delivered the HLA imputation for 59,448 Taiwanese subjects and characterized the genetic and phenotypic landscapes of the HLA variations. In addition, we quantified the estimated prevalence of the ADR-related HLA alleles in the Taiwanese population. The developed HLA imputation reference panel could be used for estimation of population HLA allele frequencies, which can facilitate further studies in the role of HLA variants in a wider range of phenotypes in the population.

Abstract Background Influenza A virus (IAV) infection is a significant risk factor for respiratory diseases, but the host defense mechanisms against IAV remain to be defined. Immune regulators such as surfactant protein A (SP-A) and Toll-interacting protein (Tollip) have been shown to be involved in IAV infection, but whether SP-A and Tollip cooperate in more effective host defense against IAV infection has not been investigated. Methods Wild-type (WT), Tollip knockout (KO), SP-A KO, and Tollip/SP-A double KO (dKO) mice were infected with IAV for four days. Lung macrophages were isolated for bulk RNA sequencing. Precision-cut lung slices (PCLS) from WT and dKO mice were pre-treated with SP-A and then infected with IAV for 48 h. Results Viral load was significantly increased in bronchoalveolar lavage (BAL) fluid of dKO mice compared to all other strains of mice. dKO mice had significantly less recruitment of neutrophils into the lung compared to Tollip KO mice. SP-A treatment of PCLS enhanced expression of TNF and reduced viral load in dKO mouse lung tissue. Pathway analysis of bulk RNA sequencing data suggests that macrophages from IAV-infected dKO mice reduced expression of genes involved in neutrophil recruitment, IL-17 signaling, and Toll-like receptor signaling. Conclusions Our data suggests that both Tollip and SP-A are essential for the lung to exert more effective innate defense against IAV infection.

Abstract By incompletely understood mechanisms, type 2 (T2) inflammation present in the airways of severe asthmatics drives the formation of pathologic mucus which leads to airway mucus plugging. Here we investigate the molecular role and clinical significance of intelectin-1 (ITLN-1) in the development of pathologic airway mucus in asthma. Through analyses of human airway epithelial cells we find that ITLN1 gene expression is highly induced by interleukin-13 (IL-13) in a subset of metaplastic MUC5AC+ mucus secretory cells, and that ITLN-1 protein is a secreted component of IL-13-induced mucus. Additionally, we find ITLN-1 protein binds the C-terminus of the MUC5AC mucin and that its deletion in airway epithelial cells partially reverses IL-13-induced mucostasis. Through analysis of nasal airway epithelial brushings, we find that ITLN1 is highly expressed in T2-high asthmatics, when compared to T2-low children. Furthermore, we demonstrate that both ITLN-1 gene expression and protein levels are significantly reduced by a common genetic variant that is associated with protection from the formation of mucus plugs in T2-high asthma. This work identifies an important biomarker and targetable pathways for the treatment of mucus obstruction in asthma.

Abstract Background Canine mammary tumors (CMTs) in intact female dogs provide a natural model for investigating metastatic human cancers. Our prior research identified elevated expression of Anterior Gradient 2 (AGR2), a protein disulfide isomerase (PDI) primarily found in the endoplasmic reticulum (ER), in CMT tissues, highly associated with CMT progression. We further demonstrated that increased AGR2 expression actively influences the extracellular microenvironment, promoting chemotaxis in CMT cells. Unraveling the underlying mechanisms is crucial for assessing the potential of therapeutically targeting AGR2 as a strategy to inhibit a pro-metastatic microenvironment and impede tumor metastasis. Methods To identify the AGR2-modulated secretome, we employed proteomics analysis of the conditioned media (CM) from two CMT cell lines ectopically expressing AGR2, compared with corresponding vector-expressing controls. AGR2-regulated release of 14-3-3ε (gene: YWHAE) and α-actinin 4 (gene: ACTN4) was validated through ectopic expression, knockdown, and knockout of the AGR2 gene in CMT cells. Extracellular vesicles derived from CMT cells were isolated using either differential ultracentrifugation or size exclusion chromatography. The roles of 14-3-3ε and α-actinin 4 in the chemotaxis driven by the AGR2-modulated CM were investigated through gene knockdown, antibody-mediated interference, and recombinant protein supplement. Furthermore, the clinical relevance of the release of 14-3-3ε and α-actinin 4 was assessed using CMT tissue-immersed saline and sera from CMT-afflicted dogs. Results Proteomics analysis of the AGR2-modulated secretome revealed increased abundance in 14-3-3ε and α-actinin 4. Ectopic expression of AGR2 significantly increased the release of 14-3-3ε and α-actinin 4 in the CM. Conversely, knockdown or knockout of AGR2 expression remarkably reduced their release. Silencing 14-3-3ε or α-actinin 4 expression diminished the chemotaxis driven by AGR2-modulated CM. Furthermore, AGR2 controls the release of 14-3-3ε and α-actinin 4 primarily via non-vesicular routes, responding to the endoplasmic reticulum (ER) stress and autophagy activation. Knockout of AGR2 resulted in increased α-actinin 4 accumulation and impaired 14-3-3ε translocation in autophagosomes. Depletion of extracellular 14-3-3ε or α-actinin 4 reduced the chemotaxis driven by AGR2-modulated CM, whereas supplement with recombinant 14-3-3ε in the CM enhanced the CM-driven chemotaxis. Notably, elevated levels of 14-3-3ε or α-actinin 4 were observed in CMT tissue-immersed saline compared with paired non-tumor samples and in the sera of CMT dogs compared with healthy dogs. Conclusion This study elucidates AGR2’s pivotal role in orchestrating unconventional secretion of 14-3-3ε and α-actinin 4 from CMT cells, thereby contributing to paracrine-mediated chemotaxis. The insight into the intricate interplay between AGR2-involved ER stress, autophagy, and unconventional secretion provides a foundation for refining strategies aimed at impeding metastasis in both canine mammary tumors and potentially human cancers.

Objective To develop and validate machine-learning models that predict the risk of pan-cancer incidence using demographic, questionnaire and routine health check-up data in a large Asian population.Methods and analysis This study is a prospective cohort study including 433 549 participants from the prospective MJ cohort including a male cohort (n=208 599) and a female cohort (n=224 950).Results During an 8-year median follow-up, 5143 cancers occurred in males and 4764 in females. Compared with Lasso-Cox and Random Survival Forests, XGBoost showed superior performance for both cohorts. The XGBoost model with all 155 features in males and 160 features in females achieved an area under the curve (AUC) of 0.877 and 0.750, respectively. Light models with 31 variables for males and 11 variables for females showed comparable performance: an AUC of 0.876 (95% CI 0.858 to 0.894) in the overall population and 0.818 (95% CI 0.795 to 0.841) in those aged ≥40 years in the male cohort and an AUC of 0.746 (95% CI 0.721 to 0.771) in the overall population and 0.641 (95% CI 0.605 to 0.677) in those aged ≥40 years in the female cohort. High-risk individuals have at least ninefold higher risk of pan-cancer incidence compared with low-risk groups.Conclusion We developed and internally validated the first machine-learning models based on routine health check-up data to predict pan-cancer risk in the general population and achieved generally good discriminatory ability with a small set of predictors. External validation is warranted before the implementation of our risk model in clinical practice.

Abstract The development of highly efficient sodium‐ion batteries depends critically on the successful exploitation of advanced anode hosts that is capable of overcoming sluggish reaction kinetics while also withstanding severe structural deformation triggered by the large radius of Na+‐insertion. Herein, a hierarchically hybrid material with hetero‐Co3S4/NiS hollow nanosphere packaged into a densified N‐doped carbon matrix (Co3S4/NiS@N‐C) was designed and fabricated utilizing CoNi‐glycerate as the self‐sacrifice template, making the utmost of the synergistic effect of hetero‐Co3S4/NiS with strong electric field and rich reaction active‐sites together with the densified outer‐carbon scaffolds with remarkable electronic conductivity and robust mechanical toughness. As anticipated, as‐fabricated Co3S4/NiS@N‐C anode affords remarkable specific capacity, prolonged cycle lifespan up to 2 400 cycles with an only 0.05% fading each cycle at 20.0 A g−1, and excellent rate feature (354.9 mAh g−1 at 30.0 A g−1), one of the best performances for most existing Co3S4/NiS‐based anodes. Ex situ structural characterizations in tandem with theoretical analysis demonstrate the reversible insertion‐conversion mechanism of initially proceeding with Na+ de‐/intercalation and superior heterogeneous interfacial reaction behavior with strong Na+‐adsorption ability. Further, sodium‐ion full cell and hybrid capacitor based on Co3S4/NiS@N‐C anode exhibit impressive electrochemical characteristics on cycling performance and rate capability, showcasing its outstanding feasibility toward practical use.

Periodic mammography and/or sonography examinations are conducted across numerous hospitals nationalwidely, especially for antedees with a positive mammography screening. Despite the regular practice, clinical efficacy of hospital-based breast cancer surveillance remains unclear. Specifically, the impact of surveillance interval upon survival and prognostic surrogates stratified by menopausal status, as well as malignant transition rate should be deciphered. We retrieved cancer registry to ascertain 841 breast cancers with surveillance history through administration data. Healthy controls underwent breast surveillance and were concurrently free of cancer. More benign diseases rather than cancers were identified from premenopausal women (age ≤50 years) with sonography alone within one year, as well as older women (age >50) with both mammography and sonography one to two years before a cancer or benign diagnosis. Among breast cancers, mammography alone during the antecedent one to two years had a protective effect for diagnosing carcinoma in situ rather than invasive cancer (age-adjusted odds ratio: 0.048, P = 0.016). Three-state time homogeneous Markov model showed that hospital-based breast surveillance within 2 years of disease onset reduced the malignant transition rate by 65.16% (59.79–76.74%). The clinical efficacy of breast cancer surveillance was evidenced.

Abstract The canonical studies on Mie scattering unravel strong electric/magnetic optical responses in nanostructures, laying foundation for emerging meta-photonic applications. Conventionally, the morphology-sensitive resonances hinge on the normalized frequency, i.e. particle size over wavelength, but non-paraxial incidence symmetry is overlooked. Here, through confocal reflection microscopy with a tight focus scanning over silicon nanostructures, the scattering point spread functions unveil distinctive spatial patterns featuring that linear scattering efficiency is maximal when the focus is misaligned. The underlying physical mechanism is the excitation of higher-order multipolar modes, not accessible by plane wave irradiation, via displacement resonance, which showcases a significant reduction of nonlinear response threshold, sign flip in all-optical switching, and spatial resolution enhancement. Our result fundamentally extends the century-old light scattering theory, and suggests new dimensions to tailor Mie resonances.

Abstract In this paper, a fuzzy hierarchical optimization framework is proposed for identifying potential antiviral targets for treating severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in the heart. The proposed framework comprises four objectives for evaluating the elimination of viral biomass growth and the minimization of side effects during treatment. In the application of the framework, Dulbecco’s modified eagle medium (DMEM) and Ham’s medium were used as uptake nutrients on an antiviral target discovery platform. The prediction results from the framework reveal that most of the antiviral enzymes in the aforementioned media are involved in fatty acid metabolism and amino acid metabolism. However, six enzymes involved in cholesterol biosynthesis in Ham’s medium and three enzymes involved in glycolysis in DMEM are unable to eliminate the growth of the SARS-CoV-2 biomass. Three enzymes involved in glycolysis, namely BPGM, GAPDH, and ENO1, in DMEM combine with the supplemental uptake of L-cysteine to increase the cell viability grade and metabolic deviation grade. Moreover, six enzymes involved in cholesterol biosynthesis reduce and fail to reduce viral biomass growth in a culture medium if a cholesterol uptake reaction does not occur and occurs in this medium, respectively.

[Objective] Rural biomass energy is vital for implementing the rural revitalization and carbon neutrality strategies. However, the absence of a biomass energy statistical system hindered data-based academic research and targeted decision making. [Methods] To fill this research gap, this study integrated various micro household survey data and established a large-sample multi-source heterogeneous dataset—the Chinese Rural Household Energy Consumption dataset. We then applied the Bayesian spatiotemporal model to estimate the historical data of China’s rural biomass energy consumption from 1992 to 2016. [Results] The results show that: (1) During 1992-2016, biomass consumption in rural areas of China experienced a decline, from 195 million tons of standard coal in 1992 to 125 million tons in 2016, with an annual average decline of 1.7%. Since the “energy consumption revolution” initiative was put forward, the decline of rural biomass consumption has obviously accelerated, with an annual average decline of 3.8%, indicating that the “energy consumption revolution” initiative has been very effective and greatly promoted the transformation of rural energy consumption. (2) During 1992-2016, the number of rural households using biomass also continued to decline, with the number of households using firewood and straw reduced by nearly 50%. (3) Biomass consumption is mainly concentrated in the western and northeastern regions, where firewood consumption accounts for about 80% of national consumption, and straw consumption accounts for about 65%-70%. [Conclusion] The framework and methods of this study are helpful for solving the problem of missing data in academic research, and the research conclusions can provide a theoretical support for improving the level of high-quality energy use in remote rural areas of China.

Tissue-clearing and labeling techniques have revolutionized brain-wide imaging and analysis, yet their application to clinical formalin-fixed paraffin-embedded (FFPE) blocks remains challenging. We introduce HIF-Clear, a novel method for efficiently clearing and labeling centimeter-thick FFPE specimens using elevated temperature and concentrated detergents. HIF-Clear with multi-round immunolabeling reveals neuron circuitry regulating multiple neurotransmitter systems in a whole FFPE mouse brain and is able to be used as the evaluation of disease treatment efficiency. HIF-Clear also supports expansion microscopy and can be performed on a non-sectioned 15-year-old FFPE specimen, as well as a 3-month formalin-fixed mouse brain. Thus, HIF-Clear represents a feasible approach for researching archived FFPE specimens for future neuroscientific and 3D neuropathological analyses.