Gastric Cancer Assembloids Derived from Patient-Derived Xenografts: A Preclinical Model for Therapeutic Drug Screening.

The construction of reliable preclinical models is crucial for understanding the molecular mechanisms involved in gastric cancer and for advancing precision medicine. Currently, existing in vitro tumor models often do not accurately replicate the human gastric cancer environment and are unsuitable for high-throughput therapeutic drug screening. In this study, droplet microfluidic technology is employed to create novel gastric cancer assembloids by encapsulating patient-derived xenograft gastric cancer cells and patient stromal cells in Gelatin methacryloyl (GelMA)-Gelatin-Matrigel microgels. The usage of GelMA-Gelatin-Matrigel composite hydrogel effectively alleviated cell aggregation and sedimentation during the assembly process, allowing for the handling of large volumes of cell-laden hydrogel and the uniform generation of assembloids in a high-throughput manner. Notably, the patient-derived xenograft assembloids exhibited high consistency with primary tumors at both transcriptomic and histological levels, and can be efficiently scaled up for preclinical drug screening efforts. Furthermore, the drug screening results clearly demonstrated that the in vitro assembloid model closely mirrored in vivo drug responses. Thus, these findings suggest that gastric cancer assembloids, which effectively replicate the in vivo tumor microenvironment, show promise for enabling more precise high-throughput drug screening and predicting the clinical outcomes of various drugs.

Cation etching-induced deep self-reconstruction to form a polycrystalline structure for efficient electrochemical water oxidation.

In this work, we report a straightforward in situ leaching strategy to achieve rapid structure self-reconstruction of NiRu-OH/NF. The as-prepared electrode shows excellent OER performance with a low overpotential of 321 mV at 100 mA cm-2. It can deliver 10 mA cm-2 at 1.53 V in a water-alkali electrolyzer as the anode and operates steadily at 100 mA cm-2 with a small cell voltage for 50 h.

Effects of simulated multi-sensory stimulation integration on physiological and psychological restoration in virtual urban green space environment.

Virtual urban green environment images and audio stimuli had been proven to have restorative effects on subjects' physical and mental health. In this area, researchers predominantly focused on visual, auditory and olfactory aspects, while tactile and gustatory senses have been minimally explored. However, the optimal combination of sensory stimuli for promoting physical and mental recovery remains unclear. Therefore, a simulated sensory stimulation approach involving 240 participants was employed, with 30 individuals included in each of the eight experimental groups: the visual-auditory (VA), visual-auditory-olfactory (VAO), visual-auditory-tactile (VAT), visual-auditory-gustatory(VAG), visual-auditory-olfactory-tactile (VAOT), visual-auditory-olfactory-gustatory (VAOG), visual-auditory-tactile-gustatory (VATG), and visual-auditory-olfactory-tactile-gustatory (VAOTG) groups. This study aimed to explore the differences in participants' physiological and psychological health recovery after exposure to different combinations of simulated sensory stimuli in virtual UGSs. The results indicated that the following: (1) In terms of physiological recovery, the blood pressure of the 8 experimental groups decreased significantly after the experiment, indicating that the virtual urban green space environment has a certain recovery effect on physiological state. The combination of VAOTG stimuli in the multisensory group resulted in the best blood pressure recovery (p < 0.05). Tactile is an important sense to enhance the physiological recovery effect. Olfactory-tactile or tactile-gustatory stimuli interactions significantly enhance physiological recovery, emphasizing the importance of tactile stimulation in improving physiological recovery. (2) In terms of psychological recovery, the common trigger of olfactory-gustatory is the most key element to enhance psychological recovery through multi-sensory stimulation of virtual urban green space environment. VAOG stimulation had the best effect on psychological recovery (p < 0.05), followed by VAOTG stimulation (p < 0.05). Gustatory is an important sense to enhance the psychological recovery effect, and both the tactile-gustatory interaction and the olfactory-gustatory interaction significantly enhance the recovery effect. At the same time, the psychological recovery effect obtained by four or more sensory combinations was higher than that obtained by two or three sensory stimulation groups. This study confirms more possibilities for ways to restore physical and mental health through virtual natural environments. It expands the research on the benefits of virtual nature experience and provides theoretical support for the application of this method.

High Ductility and Excellent Thermoelectric Performance in Te-Stabilized Cubic Ag2TexS1-x Solid Solutions.

The stabilization at low temperatures of the Ag2S cubic phase could afford the design of high-performance thermoelectric materials with excellent mechanical behavior, enabling them to withstand prolonged vibrations and thermal stress. In this work, we show that the Ag2TexS1-x solid solutions, with Te content within the optimal range 0.20 ≤ x ≤ 0.30, maintain a stable cubic phase across a wide temperature range from 300 to 773 K, thus avoiding the detrimental phase transition from monoclinic to cubic phase observed in Ag2S. Notably, the Ag2TexS1-x (0.20 ≤ x ≤ 0.30) samples showed no fractures during bending tests and displayed superior ductility at room temperature compared to Ag2S, which fractured at a strain of 6.6%. Specifically, the Ag2Te0.20S0.80 sample demonstrated a bending average yield strength of 46.52 MPa at 673 K, significantly higher than that of Ag2S, whose bending average yield strength dropped from 80.15 MPa at 300 K to 12.66 MPa at 673 K. Furthermore, the thermoelectric performance of the Ag2TexS1-x (0.20 ≤ x ≤ 0.30) samples surpassed that of both InSe and pure Ag2S, with the Ag2Te0.30S0.70 sample achieving the highest ZT value of 0.59 at 723 K. These results indicate substantial potential for practical applications due to enhanced durability and thermoelectric performance.

Predicting Alzheimer's disease from cognitive footprints in mid and late life: How much can register data and machine learning help?

BACKGROUND: Real-world data with decades-long medical records are increasingly available alongside the growing adoption of machine learning in healthcare research. We evaluated the performance of machine learning models in predicting the risk of Alzheimer's disease (AD) using data from the Finnish national registers. METHODS: We conducted a case-control study using data from the Finnish MEDALZ (Medication use and Alzheimer's disease) study. Altogether 56,741 individuals with incident AD diagnosis (age ≥ 65 years at diagnosis and born after 1922) and their 1:1 age-, sex-, and region of residence-matched controls were included. The association of risk factors, evaluated at different age periods (45-54, 55-64, 65+), and AD were assessed with logistic regression. Predictive accuracies of logistic regressions were compared with seven machine learning models (L1-regularized logistic regression, Naive bayes, Decision tree, Random Forest, Multilayer perceptron, XGBoost, and LightGBM). FINDINGS: 63.5 % of cases and controls were females and the mean age was 79.1 (SD = 5.1). The strongest associations with AD were observed for head injuries at age 55-64 (OR, 95 % CI 1.33, 1.19-1.48) and 65+ (1.31, 1.23-1.40), followed by antidepressant use (1.30, 1.22-1.38) at 55-64 and antipsychotic use (1.27, 1.19-1.35) at 65+. The predictive accuracies of all models were low, with the best performance (AUC 0.603) observed in Random Forest for predicting AD onset at age 65-69. INTERPRETATION: Although significant associations were identified between many risk factors and AD, the low predictive accuracies suggest that specialised healthcare diagnosis data is not sufficient for predicting AD and linkage with other data sources is needed.

Smart Galactosidase-Responsive Antimicrobial Dendron: Towards More Biocompatible Membrane-Disruptive Agents.

Antimicrobial resistance is a global healthcare challenge that urgently needs the development of new therapeutic agents. Antimicrobial peptides and mimics thereof are promising candidates but mostly suffer from inherent toxicity issues due to the non-selective binding of cationic groups with mammalian cells. To overcome this toxicity issue, this work herein reports the synthesis of a smart antimicrobial dendron with masked cationic groups (Gal-Dendron) that could be uncaged in the presence of ß-galactosidase enzyme to form the activated Enz-Dendron and confer antimicrobial activity. Enz-Dendron show bacteriostatic activity toward Gram-negative (P. aeruginosa and E. coli) and Gram-positive (S. aureus) bacteria with minimum inhibitory concentration values of 96 µm and exerted its antimicrobial mechanism via a membrane disruption pathway, as indicated by inner and outer membrane permeabilization assays. Crucially, toxicity studies confirmed that the masked prodrug Gal-Dendron exhibited low hemolysis and is at least 2.4 times less toxic than the uncaged cationic Enz-Dendron, thus demonstrating the advantage of masking the cationic groups with responsive immolative linkers to overcome toxicity and selectivity issues. Overall, this study highlights the potential of designing new membrane-disruptive antimicrobial agents that are more biocompatible via the amine uncaging strategy.

A Survey on Self-supervised Learning: Algorithms, Applications, and Future Trends.

Deep supervised learning algorithms typically require a large volume of labeled data to achieve satisfactory performance. However, the process of collecting and labeling such data can be expensive and time-consuming. Self-supervised learning (SSL), a subset of unsupervised learning, aims to learn discriminative features from unlabeled data without relying on human-annotated labels. SSL has garnered significant attention recently, leading to the development of numerous related algorithms. However, there is a dearth of comprehensive studies that elucidate the connections and evolution of different SSL variants. This paper presents a review of diverse SSL methods, encompassing algorithmic aspects, application domains, three key trends, and open research questions. Firstly, we provide a detailed introduction to the motivations behind most SSL algorithms and compare their commonalities and differences. Secondly, we explore representative applications of SSL in domains such as image processing, computer vision, and natural language processing. Lastly, we discuss the three primary trends observed in SSL research and highlight the open questions that remain. A curated collection of valuable resources can be accessed at https://github.com/guijiejie/SSL.

The influence of activity patterns and relative humidity on particle resuspension in classrooms.

This paper investigates the impact of children's recess activity patterns on particulate matter (PM) resuspension in indoor environments, highlighting the complex, multi-dimensional nature of these activities and their interaction with environmental parameters. Despite the recognized role of indoor human activity in PM resuspension, research specifically addressing the effects of children's movements has been sparse. Through experimental scenarios that account for the characteristics of student activities, such as movement speed, trajectory, the number of participants, aisle widths, and varying humidity levels, this study uncovers significant differences in PM resuspension rates. It reveals that not only do movement speed and trajectory have a profound impact, but also the interaction between humidity and these factors plays a critical role, especially under lower humidity conditions. Additionally, the study demonstrates how the combination of people density and spatial configurations can significantly influence resuspension rates. The findings offer valuable insights for designing strategies to mitigate particle pollution in classrooms and similar indoor environments.

TransfIGN: A Structure-Based Deep Learning Method for Modeling the Interaction between HLA-A*02:01 and Antigen Peptides.

The intricate interaction between major histocompatibility complexes (MHCs) and antigen peptides with diverse amino acid sequences plays a pivotal role in immune responses and T cell activity. In recent years, deep learning (DL)-based models have emerged as promising tools for accelerating antigen peptide screening. However, most of these models solely rely on one-dimensional amino acid sequences, overlooking crucial information required for the three-dimensional (3-D) space binding process. In this study, we propose TransfIGN, a structure-based DL model that is inspired by our previously developed framework, Interaction Graph Network (IGN), and incorporates sequence information from transformers to predict the interactions between HLA-A*02:01 and antigen peptides. Our model, trained on a comprehensive data set containing 61,816 sequences with 9051 binding affinity labels and 56,848 eluted ligand labels, achieves an area under the curve (AUC) of 0.893 on the binary data set, better than state-of-the-art sequence-based models trained on larger data sets such as NetMHCpan4.1, ANN, and TransPHLA. Furthermore, when evaluated on the IEDB weekly benchmark data sets, our predictions (AUC = 0.816) are better than those of the recommended methods like the IEDB consensus (AUC = 0.795). Notably, the interaction weight matrices generated by our method highlight the strong interactions at specific positions within peptides, emphasizing the model's ability to provide physical interpretability. This capability to unveil binding mechanisms through intricate structural features holds promise for new immunotherapeutic avenues.

Recent advances in the characterization of essential genes and development of a database of essential genes.

Over the past few decades, there has been a significant interest in the study of essential genes, which are crucial for the survival of an organism under specific environmental conditions and thus have practical applications in the fields of synthetic biology and medicine. An increasing amount of experimental data on essential genes has been obtained with the continuous development of technological methods. Meanwhile, various computational prediction methods, related databases and web servers have emerged accordingly. To facilitate the study of essential genes, we have established a database of essential genes (DEG), which has become popular with continuous updates to facilitate essential gene feature analysis and prediction, drug and vaccine development, as well as artificial genome design and construction. In this article, we summarized the studies of essential genes, overviewed the relevant databases, and discussed their practical applications. Furthermore, we provided an overview of the main applications of DEG and conducted comprehensive analyses based on its latest version. However, it should be noted that the essential gene is a dynamic concept instead of a binary one, which presents both opportunities and challenges for their future development.

Gypenoside L inhibits hepatocellular carcinoma by targeting the SREBP2-HMGCS1 axis and enhancing immune response.

Hepatocellular carcinoma (HCC) is a malignant tumor that occurs in the liver, with a high degree of malignancy and relatively poor prognosis. Gypenoside L has inhibitory effects on liver cancer cells. However, its mechanism of action is still unclear. This study aims to investigate the inhibitory effects of gypenoside L on HCC in vitro and in vivo, and explore its potential mechanisms. The results showed that gypenoside L reduced the cholesterol and triglyceride content in HepG2 and Huh-7 cells, inhibited cell proliferation, invasion and metastasis, arrested cell cycle at G0/G1 phase, promoted cell apoptosis. Mechanistically, it targeted the transcription factor SREPB2 to inhibit the expression of HMGCS1 protein and inhibited the downstream proteins HMGCR and MVK, thereby regulating the mevalonate (MVA) pathway. Overexpression HMGCS1 led to significant alterations in the cholesterol metabolism pathway of HCC, which mediated HCC cell proliferation and conferred resistance to the therapeutic effect of gypenoside L. In vivo, gypenoside L effectively suppressed HCC growth in tumor-bearing mice by reducing cholesterol production, exhibiting favorable safety profiles and minimal toxic side effects. Gypenoside L modulated cholesterol homeostasis, enhanced expression of inflammatory factors by regulating MHC I pathway-related proteins to augment anticancer immune responses. Clinical samples from HCC patients also exhibited high expression levels of MVA pathway-related genes in tumor tissues. These findings highlight gypenoside L as a promising agent for targeting cholesterol metabolism in HCC while emphasizing the effectiveness of regulating the SREBP2-HMGCS1 axis as a therapeutic strategy.

Transpiration-induced self-growth of texture hydrogel surfaces.

Trees grow by coupling the transpiration-induced nutrient absorption from external sources and photosynthesis-based nutrient integration. Inspired by this manner, we designed a class of polyion complex (PIC) hydrogels containing isolated liquid-filled voids for growing texture surfaces. The isolated liquid-filled voids were created via irreversible matrix reconfiguration in a deswelling-swelling process. During transpiration, these voids reversibly collapse to generate negative pressures within the matrices to extract polymerizable compounds from external sources and deliver them to the surface of the samples for photopolymerization. This growth process is spatial-controllable and can be applied to fabricate complex patterns consisting of different compositions, suggesting a new strategy for making texture surfaces.

Enhanced Tunability of Dual-Band Chiral Metasurface in the Mid-Infrared Range via Slotted Nanocircuit Design.

Multi-band circular dichroism (CD) response and tunability on the chiral metasurface are crucial for this device's applications in sensing and detection. This work proposes a dual-band CD Au-CaF2-Au dimer elliptical metasurface absorber, where chiroptical sensing is realized by breaking the geometric symmetry between two ellipses. The proposed metasurface can achieve high CD values of 0.8 and -0.74 for the dual-band within the 3-5 µm region, and the CD values can be manipulated by independently adjusting the geometric parameters of the metasurface. Furthermore, a slotted nanocircuit is introduced onto the metasurface to enhance its tunability by manipulating the geometry parameter in the design process, and the related mechanism is explained using an equivalent circuit model. The simulation of the sensing model revealed that the slotted nanocircuit enhances the sensor's tunability and significantly improves its bandwidth and sensitivity, achieving peak enhancements at approximately 753 nm and 1311 nm/RIU, respectively. Due to the strong dual-band positive (and negative) responses of the CD values, flexible wavelength tunability, and nonlinear sensitivity enhancement, this design provides a new approach for the development and application of mid-infrared chiroptical devices.

Identifying Frailty in Older Adults Receiving Home Care Assessment Using Machine Learning: Longitudinal Observational Study on the Role of Classifier, Feature Selection, and Sample Size.

BACKGROUND: Machine learning techniques are starting to be used in various health care data sets to identify frail persons who may benefit from interventions. However, evidence about the performance of machine learning techniques compared to conventional regression is mixed. It is also unclear what methodological and database factors are associated with performance. OBJECTIVE: This study aimed to compare the mortality prediction accuracy of various machine learning classifiers for identifying frail older adults in different scenarios. METHODS: We used deidentified data collected from older adults (65 years of age and older) assessed with interRAI-Home Care instrument in New Zealand between January 1, 2012, and December 31, 2016. A total of 138 interRAI assessment items were used to predict 6-month and 12-month mortality, using 3 machine learning classifiers (random forest [RF], extreme gradient boosting [XGBoost], and multilayer perceptron [MLP]) and regularized logistic regression. We conducted a simulation study comparing the performance of machine learning models with logistic regression and interRAI Home Care Frailty Scale and examined the effects of sample sizes, the number of features, and train-test split ratios. RESULTS: A total of 95,042 older adults (median age 82.66 years, IQR 77.92-88.76; n=37,462, 39.42% male) receiving home care were analyzed. The average area under the curve (AUC) and sensitivities of 6-month mortality prediction showed that machine learning classifiers did not outperform regularized logistic regressions. In terms of AUC, regularized logistic regression had better performance than XGBoost, MLP, and RF when the number of features was ≤80 and the sample size ≤16,000; MLP outperformed regularized logistic regression in terms of sensitivities when the number of features was ≥40 and the sample size ≥4000. Conversely, RF and XGBoost demonstrated higher specificities than regularized logistic regression in all scenarios. CONCLUSIONS: The study revealed that machine learning models exhibited significant variation in prediction performance when evaluated using different metrics. Regularized logistic regression was an effective model for identifying frail older adults receiving home care, as indicated by the AUC, particularly when the number of features and sample sizes were not excessively large. Conversely, MLP displayed superior sensitivity, while RF exhibited superior specificity when the number of features and sample sizes were large.

Molecular mechanism of RBM14-mediated promotion of proliferation, migration, and invasion in osteosarcoma.

Background: Osteosarcoma (OS) is an exceptionally aggressive bone neoplasm that predominantly impacts the paediatric and adolescent population, exhibiting unfavourable prognosis. The importance of RNA binding motif protein 14 (RBM14) in the aetiology of OS is not well understood, despite its established involvement in several other types of cancer. Methods: In this study, we conducted an analysis of the expression profiles of RBM14 in cancer tissues and cell lines. To achieve this, we will utilised data obtained from various databases including The Cancer Genome Atlas Program (TCGA) project, The Genotype-Tissue Expression (GTEx) Project, Gene Expression Omnibus (GEO) database, and cancer cell line encyclopedia (CCLE) data. Furthermore, this study also aims to examine the effects of RBM14 on the proliferation, migration, and invasive properties of OS cells using cell functional gain and loss studies. In this study, we carried out an in-depth investigation to explore possible molecular pathways that underlie the regulation of the malignant phenotype found in OS by RBM14. This investigation involved integrating data from RBM14 overexpression, RBM14 knockdown RNA-seq experiments, and an array comprising 6,096 perturbed genes obtained from the Genetic Perturbation Similarity Analysis Database (GPSAdb). This research offers an opportunity to build a robust conceptual framework for the potential advancement of novel therapeutic approaches that are especially aimed at attacking OS. Results: RBM14 plays an active role in OS by significantly contributing to the enhancement of cellular proliferation, migration, and invasion. At the molecular level, it is probable that RBM14 exerts control over the malignant characteristics of OS through its modulation of the Hippo signalling system. Conclusions: The above-mentioned findings underscore the significant importance of RBM14 as an intriguing target for therapy for the mitigation and management of OS. This particular protein holds an excellent opportunity for the development of novel and efficacious therapeutic approaches that possess the potential to yield favorable results for patients affected with OS.

Constructing a Built-In Electric Field To Accelerate Water Dissociation for Efficient Alkaline Hydrogen Evolution.

The alkaline hydrogen evolution reaction (HER) is intricately linked to the water dissociation kinetics. The quest for new strategies to accelerate this step is a pivotal aspect of enhancing the HER performance. Herein, we designed and synthesized a heterogeneous nickel phosphide/cobalt phosphide nanowire array grown on nickel foam (Ni2P/CoP/NF) to form a p-n junction structure. The built-in electric field (BEF) in the p-n junction optimizes the binding ability of hydrogen and hydroxyl intermediates, efficiently promoting water dissociation for the alkaline HER. Consequently, Ni2P/CoP/NF exhibits a lower overpotential of 58 and 118 mV at 30 and 100 mA cm-2, respectively, and high stability over 40 h at 300 mA cm-2 for the HER in 1 M KOH. Computational calculations combined with experiment results testify that the BEF presence in the p-n junction of Ni2P/CoP/NF effectively promotes water dissociation, regulates intermediate adsorption/desorption, and boosts electron transport. This study presents a rational design approach for high-performance heterogeneous electrocatalysts.

Depressive symptoms were associated with an elevated prevalence of gallstones among adults in the United States: A cross-sectional analysis of NHANES 2017-2020.

Background:  Gallstone disease is one of the most common gastrointestinal disorders. Despite extensive research exploring the risk factors associated with gallstones, the association between depressive symptoms and gallstones remains inadequately understood. This study aimed to assess the association between depressive symptoms and the prevalence of gallstones among adults in the United States. Methods: In this study, a cross-sectional design utilized data from the National Health and Nutrition Examination Survey (NHANES) spanning the years 2017 to 2020. The assessment of depressive symptoms was conducted through the utilization of the Patient Health Questionnaire-9 (PHQ-9), which assigns total scores ranging from 0 to 27. Participants with PHQ-9 scores equal to or exceeding 10 were categorized as having clinically relevant depressive symptoms. Multivariable adjusted logistic regression and subgroup analysis were used to assess the association between depressive symptoms and gallstone prevalence. Results: A total of 7,797 participants aged 20 years or older were enrolled in this study, of whom 835 had a self-reported history of gallstones. After multiple adjustments, each one-point increase in PHQ-9 scores was associated with a 5 % increase in the risk of gallstones (odds ratio [OR], 1.05; 95 % confidence interval [CI], 1.03, 1.07, P < 0.001). Compared to individuals with PHQ-9 scores < 10, participants with PHQ-9 total scores ≥ 10 exhibited a 79 % higher risk of gallstones (OR = 1.79, 95 % CI: 1.43, 2.23, P < 0.001). Conclusion: Depressive symptoms were associated with an elevated prevalence of gallstones. However, it is important to note that further validation through prospective cohort studies is warranted to confirm this finding.

Exercise may improve lung immunity after surgical stress: Evidence from a nephrectomy model via a bioinformatic analysis.

Exercise offers numerous benefits to cancer patients and plays an essential role in postsurgical cancer rehabilitation. However, there is a lack of research examining the effects of exercise after the surgical stress of nephrectomy. To address this gap, we created an animal model that simulated patients who had undergone nephrectomy with or without an exercise intervention. Next, we performed a bioinformatic analysis based on the data generated by the RNA sequencing of the lung tissue sample. An overrepresentation analysis was conducted using two genome databases (Gene Ontology and Kyoto Encyclopedia of Genes and Genomes [KEGG]). A KEGG analysis of the exercise-treated nephrectomy mice revealed enrichment in immune-related pathways, particularly in the NF-κB and B cell-related pathways. The expression of CD79A and IGHD, which are responsible for B cell differentiation and proliferation, was upregulated in the nephrectomy mice. Differential gene expression was categorized as significantly upregulated or downregulated according to nephrectomy and exercise groups. Notably, we identified several gene expression reversals in the nephrectomy groups with exercise that were not found in the nephrectomy without exercise or control groups. Our preliminary results potentially reveal a genetic landscape for the underlying mechanisms of the effects of exercise on our nephrectomy model.

Diurnally asymmetric cloud cover trends amplify greenhouse warming.

Surface air temperature (SAT) is a key indicator of climate change. Variations in cloud cover affect SAT by interacting with radiation. During daytime, clouds tend to cool the surface by blocking sunlight, while nighttime clouds warm the surface by trapping longwave radiation. Here, we show that, on the global scale, cloud cover, particularly low-level cloudiness, exhibits diurnally asymmetric trends in a warming climate. Cloud fraction on average decreases more during the day than at night. Climate models indicate that the diurnally asymmetric cloud cover variation is mainly driven by trends in the lower tropospheric stability and is largely attributed to the increasing greenhouse gases rather than natural variability. This asymmetry, therefore, turns out to be an amplifier of surface warming, by both decreasing the daytime cloud shortwave albedo effect and increasing the nighttime cloud longwave greenhouse effect.