Thermochromic Hydrogels with Adjustable Transition Behavior for Smart Windows.

With the fast economic development and accelerating urbanization, more and more skyscrapers made entirely of concrete and glass are being constructed. To keep a comfortable indoor environment, massive energy for air conditioning or heating appliances is consumed. A huge amount of heat (>30%) is gained or released through glass windows. Using smart windows with the capability to modulate light is an effective way to reduce building energy consumption. Thermochromic hydrogel is one of the potential smart window materials due to its excellent thermal response, high radiation-blocking efficiency, cost-effectiveness, biocompatibility, and good uniformity. In this work, polyhydroxypropyl acrylate (PHPA) hydrogels with controllable lower critical solution temperature (LCST) were prepared by photopolymerization. The transition temperature and transition rate under "static transition" conditions were investigated. Unlike "static" conditions in which the transition temperature was not affected by the initial and final temperature and heating/cooling ramp, the transition temperature varied with the rate of temperature change under dynamic conditions. The "dynamic" transition temperature of the PHPA hydrogel gradually increased with the increase of the heating rate. It was the result of the movement of the molecular chains lagging behind the temperature change when the temperature change was too fast. The results of the solar irradiation experiment by filling PHPA hydrogels into double glazing windows showed that the indoor temperature was about 15 °C lower than that of ordinary glass windows, indicating that it can significantly reduce the energy consumption of air conditioning. In addition, a wide range of adjustable transition temperatures and fast optical response make PHPA hydrogels potentially applicable to smart windows.

Development and Application of a Duplex RT-RPA Assay for the Simultaneous Detection of Cymbidium mosaic virus and Odontoglossum ringspot virus.

Cymbidium mosaic virus (CymMV) and Odontoglossum ringspot virus (ORSV) are among the world's most serious and widespread orchid viruses; they often infect orchids, causing devastating losses to the orchid industry. Therefore, it is critical to establish a method that can rapidly and accurately detect viruses in the field using simple instruments, which will largely reduce the further spread of viruses and improve the quality of the orchid industry and is suitable for mass promotion and application at grassroots agrotechnical service points. In this investigation, we established a rapid amplification method for virus detection at 39 °C for 35 min to detect the presence of CymMV and ORSV simultaneously, sensitively, and specifically in orchids. Primers for the capsid protein (CP)-encoding genes of both viruses were designed and screened, and the reaction conditions were optimized. The experimental amplification process was completed in just 35 min at 39 °C. There were no instances of nonspecific amplification observed when nine other viruses were present. The RPA approach had detection limits of 104 and 103 copies for pMD19T-CymMV and pMD19T-ORSV, respectively. Moreover, the duplex RT-RPA investigation confirmed sensitivity and accuracy via a comparison of detection results from 20 field samples with those of a gene chip. This study presents a precise and reliable detection method for CymMV and ORSV using RT-RPA. The results demonstrate the potential of this method for rapid virus detection. It is evident that this method could have practical applications in virus detection processes.

A hypoxia-derived gene signature to suggest cisplatin-based therapeutic responses in patients with cervical cancer.

Cervical cancer remains a significant global public health concern, often exhibits cisplatin resistance in clinical settings. Hypoxia, a characteristic of cervical cancer, substantially contributes to cisplatin resistance. To evaluate the therapeutic efficacy of cisplatin in patients with cervical cancer and to identify potential effective drugs against cisplatin resistance, we established a hypoxia-inducible factor-1 (HIF-1)-related risk score (HRRS) model using clinical data from patients treated with cisplatin. Cox and LASSO regression analyses were used to stratify patient risks and prognosis. Through qRT-PCR, we validated nine potential prognostic HIF-1 genes that successfully predict cisplatin responsiveness in patients and cell lines. Subsequently, we identified fostamatinib, an FDA-approved spleen tyrosine kinase inhibitor, as a promising drug for targeting the HRRS-high group. We observed a positive correlation between the IC50 values of fostamatinib and HRRS in cervical cancer cell lines. Moreover, fostamatinib exhibited potent anticancer effects on high HRRS groups in vitro and in vivo. In summary, we developed a hypoxia-related gene signature that suggests cisplatin response prediction in cervical cancer and identified fostamatinib as a potential novel treatment approach for resistant cases.

Automated and reusable deep learning (AutoRDL) framework for predicting response to neoadjuvant chemotherapy and axillary lymph node metastasis in breast cancer using ultrasound images: a retrospective, multicentre study.

Background: Previous deep learning models have been proposed to predict the pathological complete response (pCR) and axillary lymph node metastasis (ALNM) in breast cancer. Yet, the models often leveraged multiple frameworks, required manual annotation, and discarded low-quality images. We aimed to develop an automated and reusable deep learning (AutoRDL) framework for tumor detection and prediction of pCR and ALNM using ultrasound images with diverse qualities. Methods: The AutoRDL framework includes a You Only Look Once version 5 (YOLOv5) network for tumor detection and a progressive multi-granularity (PMG) network for pCR and ALNM prediction. The training cohort and the internal validation cohort were recruited from Guangdong Provincial People's Hospital (GPPH) between November 2012 and May 2021. The two external validation cohorts were recruited from the First Affiliated Hospital of Kunming Medical University (KMUH), between January 2016 and December 2019, and Shunde Hospital of Southern Medical University (SHSMU) between January 2014 and July 2015. Prior to model training, super-resolution via iterative refinement (SR3) was employed to improve the spatial resolution of low-quality images from the KMUH. We developed three models for predicting pCR and ALNM: a clinical model using multivariable logistic regression analysis, an image model utilizing the PMG network, and a combined model that integrates both clinical and image data using the PMG network. Findings: The YOLOv5 network demonstrated excellent accuracy in tumor detection, achieving average precisions of 0.880-0.921 during validation. In terms of pCR prediction, the combined modelpost-SR3 outperformed the combined modelpre-SR3, image modelpost-SR3, image modelpre-SR3, and clinical model (AUC: 0.833 vs 0.822 vs 0.806 vs 0.790 vs 0.712, all p < 0.05) in the external validation cohort (KMUH). Consistently, the combined modelpost-SR3 exhibited the highest accuracy in ALNM prediction, surpassing the combined modelpre-SR3, image modelpost-SR3, image modelpre-SR3, and clinical model (AUC: 0.825 vs 0.806 vs 0.802 vs 0.787 vs 0.703, all p < 0.05) in the external validation cohort 1 (KMUH). In the external validation cohort 2 (SHSMU), the combined model also showed superiority over the clinical and image models (0.819 vs 0.712 vs 0.806, both p < 0.05). Interpretation: Our proposed AutoRDL framework is feasible in automatically predicting pCR and ALNM in real-world settings, which has the potential to assist clinicians in optimizing individualized treatment options for patients. Funding: National Key Research and Development Program of China (2023YFF1204600); National Natural Science Foundation of China (82227802, 82302306); Clinical Frontier Technology Program of the First Affiliated Hospital of Jinan University, China (JNU1AF-CFTP-2022-a01201); Science and Technology Projects in Guangzhou (202201020022, 2023A03J1036, 2023A03J1038); Science and Technology Youth Talent Nurturing Program of Jinan University (21623209); and Postdoctoral Science Foundation of China (2022M721349).

Relating the carbon sources to denitrifying community in full-scale wastewater treatment plants.

Carbon source is a key factor determining the denitrifying effectiveness and efficiency in wastewater treatment plants (WWTPs). Whereas, the relationships between diverse and distinct denitrifying communities and their favorable carbon sources in full-scale WWTPs were not well-understood. This study performed a systematic analysis of the relationships between the denitrifying community and carbon sources by using 15 organic compounds from four categories and activated sludge from 8 full-scale WWTPs. Results showed that, diverse denitrifying bacteria were detected with distinct relative abundances in 8 WWTPs, such as Haliangium (1.98-4.08%), Dechloromonas (2.00-3.01%), Thauera (0.16-1.06%), Zoogloea (0.09-0.43%), and Rhodoferax (0.002-0.104%). Overall, acetate resulted in the highest denitrifying activities (1.21-4.62 mg/L/h/gMLSS), followed by other organic acids (propionate, butyrate and lactate, etc.). Detectable dissimilatory nitrate reduction to ammonium (DNRA) was observed for all 15 carbon sources. Methanol and glycerol resulted in the highest DRNA. Acetate, butyrate, and lactate resulted in the lowest DNRA. Redundancy analysis and 16S cDNA amplicon sequencing suggested that carbon sources within the same category tended to correlate to similar denitrifiers. Methanol and ethanol were primarily correlated to Haliangium. Glycerol and amino acids (glutamate and aspartate) were correlated to Inhella and Sphaerotilus. Acetate, propionate, and butyrate were positively correlated to a wide range of denitrifiers, explaining the high efficiency of these carbon sources. Additionally, even within the same genus, different amplicon sequence variants (ASVs) performed distinctly in terms of carbon source preference and denitrifying capabilities. These findings are expected to benefit carbon source formulation and selection in WWTPs.

Biologically interpretable multi-task deep learning pipeline predicts molecular alterations, grade, and prognosis in glioma patients.

Deep learning models have been developed for various predictions in glioma; yet, they were constrained by manual segmentation, task-specific design, or a lack of biological interpretation. Herein, we aimed to develop an end-to-end multi-task deep learning (MDL) pipeline that can simultaneously predict molecular alterations and histological grade (auxiliary tasks), as well as prognosis (primary task) in gliomas. Further, we aimed to provide the biological mechanisms underlying the model's predictions. We collected multiscale data including baseline MRI images from 2776 glioma patients across two private (FAHZU and HPPH, n = 1931) and three public datasets (TCGA, n = 213; UCSF, n = 410; and EGD, n = 222). We trained and internally validated the MDL model using our private datasets, and externally validated it using the three public datasets. We used the model-predicted deep prognosis score (DPS) to stratify patients into low-DPS and high-DPS subtypes. Additionally, a radio-multiomics analysis was conducted to elucidate the biological basis of the DPS. In the external validation cohorts, the MDL model achieved average areas under the curve of 0.892-0.903, 0.710-0.894, and 0.850-0.879 for predicting IDH mutation status, 1p/19q co-deletion status, and tumor grade, respectively. Moreover, the MDL model yielded a C-index of 0.723 in the TCGA and 0.671 in the UCSF for the prediction of overall survival. The DPS exhibits significant correlations with activated oncogenic pathways, immune infiltration patterns, specific protein expression, DNA methylation, tumor mutation burden, and tumor-stroma ratio. Accordingly, our work presents an accurate and biologically meaningful tool for predicting molecular subtypes, tumor grade, and survival outcomes in gliomas, which provides personalized clinical decision-making in a global and non-invasive manner.