Machine Learning for Polymer Design to Enhance Pervaporation-Based Organic Recovery.

Pervaporation (PV) is an effective membrane separation process for organic dehydration, recovery, and upgrading. However, it is crucial to improve membrane materials beyond the current permeability-selectivity trade-off. In this research, we introduce machine learning (ML) models to identify high-potential polymers, greatly improving the efficiency and reducing cost compared to conventional trial-and-error approach. We utilized the largest PV data set to date and incorporated polymer fingerprints and features, including membrane structure, operating conditions, and solute properties. Dimensionality reduction, missing data treatment, seed randomness, and data leakage management were employed to ensure model robustness. The optimized LightGBM models achieved RMSE of 0.447 and 0.360 for separation factor and total flux, respectively (logarithmic scale). Screening approximately 1 million hypothetical polymers with ML models resulted in identifying polymers with a predicted permeation separation index >30 and synthetic accessibility score <3.7 for acetic acid extraction. This study demonstrates the promise of ML to accelerate tailored membrane designs.

CBLC promotes the development of colorectal cancer by promoting ABI1 degradation to activate the ERK signaling pathway.

CBLC (CBL proto-oncogene C) is an E3 ubiquitin protein ligase that plays a key role in cancers. However, the function and mechanism of CBLC in colorectal cancer (CRC) has not been fully elucidated. The aim of this study was to investigate the function of CBLC in CRC and its underlying molecular mechanism. High CBLC levels were certified in tumor tissues of CRC patients, and its expression was positively associated with TNM stage. Next, we explored the role of CBLC in CRC using gain or loss of function. For biological function analysis, CCK-8 cell proliferation, colony formation, flow cytometry, scratch, and transwell assays collectively suggested that CBLC overexpression promoted cell proliferation, cell cycle progression, migration and invasion. As observed, CBLC knockdown exhibited exactly opposite effects, resulting in impaired tumorigenicity in vitro. Xenograft studies displayed that CBLC overexpression accelerated tumor growth and promoted tumor metastasis to the lung, while the inhibitory effects of CBLC knockdown on tumorigenicity and metastasis ability of CRC cells was also confirmed. Furthermore, the molecular mechanism of CBLC in CRC was explored. CBLC induced the activation of ERK signaling pathway, further leading to its pro-tumor role. Notably, CBLC decreased ABI1 (Abelson interactor protein-1, a candidate tumor suppressor) protein levels through its ubiquitin ligase activity, while ABI1 upregulation abolished the effects of CBLC on the tumorigenesis of CRC. Taken together, these results demonstrate that CBLC acts as a tumor promoter in CRC through triggering the ubiquitination and degradation of ABI1 and activating the ERK signaling pathway. CBLC may be a potential novel target for CRC.

DNA methylation marker identification and poly-methylation risk score in prediction of healthspan termination.

Aim: To elucidate the epigenetic consequences of DNA methylation in healthspan termination (HST), considering the current limited understanding. Materials & methods: Genetically predicted DNA methylation models were established (n = 2478). These models were applied to genome-wide association study data on HST. Then, a poly-methylation risk score (PMRS) was established in 241,008 individuals from the UK Biobank. Results: Of the 63,046 CpGs from the prediction models, 13 novel CpGs were associated with HST. Furthermore, people with high PMRSs showed higher HST risk (hazard ratio: 1.18; 95% CI: 1.13-1.25). Conclusion: The study indicates that DNA methylation may influence HST by regulating the expression of genes (e.g., PRMT6, CTSK). PMRSs have a promising application in discriminating subpopulations to facilitate early prevention.

MICDnet: Multimodal information processing networks for Crohn's disease diagnosis.

Crohn's disease (CD) is a chronic inflammatory disease with increasing incidence worldwide and unclear etiology. Its clinical manifestations vary depending on location, extent, and severity of the lesions. In order to diagnose Crohn's disease, medical professionals need to comprehensively analyze patients' multimodal examination data, which includes medical imaging such as colonoscopy, pathological, and text information from clinical records. The processes of multimodal data analysis require collaboration among medical professionals from different departments, which wastes a lot of time and human resources. Therefore, a multimodal medical assisted diagnosis system for Crohn's disease is particularly significant. Existing network frameworks find it hard to effectively capture multimodal patient data for diagnosis, and multimodal data for Crohn's disease is currently lacking. In addition,a combination of data from patients with similar symptoms could serve as an effective reference for disease diagnosis. Thus, we propose a multimodal information diagnosis network (MICDnet) to learn CD feature representations by integrating colonoscopy, pathology images and clinical texts. Specifically, MICDnet first preprocesses each modality data, then uses encoders to extract image and text features separately. After that, multimodal feature fusion is performed. Finally, CD classification and diagnosis are conducted based on the fused features. Under the authorization, we build a dataset of 136 hospitalized inspectors, with colonoscopy images of seven areas, pathology images, and clinical record text for each individual. Training MICDnet on this dataset shows that multimodal diagnosis can improve the diagnostic accuracy of CD, and the diagnostic performance of MICDnet is superior to other models.

Effects of organic matter and dewaterability changes on sludge calorific value during acid treatment.

Acid treatment can increase the sludge calorific value to some extent by separating inorganic elements. In order to determine the mechanism by which acidification affects the sludge calorific value from an organic perspective, we investigated the changes in organic matter and dewaterability under different pH conditions. The results of this study showed that acidification conditioning retained organic matter while removing a greater amount of inorganic elements. Furthermore, acid treatment significantly increased the zeta potential and particle size of sludge particles and facilitated the precipitation of biological organic components from the supernatant to the surface of sludge particles. Acid-treated sludge exhibited a lower moisture content and a higher proportion of organic matter, and sludge treated with H2SO4, HCl, and HNO3 exhibited respective increases in calorific values of 12.14%, 7.92%, and 8.01% under pH 2. The calorific value of the acid-treated sludge was higher, making it more suitable for subsequent incineration. The findings of this study serve as a reference and foundation for efficient sludge incineration.

Identification of genetically predicted DNA methylation markers associated with non-small cell lung cancer risk among 34,964 cases and 448,579 controls.

BACKGROUND: Although the associations between genetic variations and lung cancer risk have been explored, the epigenetic consequences of DNA methylation in lung cancer development are largely unknown. Here, the genetically predicted DNA methylation markers associated with non-small cell lung cancer (NSCLC) risk by a two-stage case-control design were investigated. METHODS: The genetic prediction models for methylation levels based on genetic and methylation data of 1595 subjects from the Framingham Heart Study were established. The prediction models were applied to a fixed-effect meta-analysis of screening data sets with 27,120 NSCLC cases and 27,355 controls to identify the methylation markers, which were then replicated in independent data sets with 7844 lung cancer cases and 421,224 controls. Also performed was a multi-omics functional annotation for the identified CpGs by integrating genomics, epigenomics, and transcriptomics and investigation of the potential regulation pathways. RESULTS: Of the 29,894 CpG sites passing the quality control, 39 CpGs associated with NSCLC risk (Bonferroni-corrected p ≤ 1.67 × 10-6 ) were originally identified. Of these, 16 CpGs remained significant in the validation stage (Bonferroni-corrected p ≤ 1.28 × 10-3 ), including four novel CpGs. Multi-omics functional annotation showed nine of 16 CpGs were potentially functional biomarkers for NSCLC risk. Thirty-five genes within a 1-Mb window of 12 CpGs that might be involved in regulatory pathways of NSCLC risk were identified. CONCLUSIONS: Sixteen promising DNA methylation markers associated with NSCLC were identified. Changes of the methylation level at these CpGs might influence the development of NSCLC by regulating the expression of genes nearby. PLAIN LANGUAGE SUMMARY: The epigenetic consequences of DNA methylation in lung cancer development are still largely unknown. This study used summary data of large-scale genome-wide association studies to investigate the associations between genetically predicted levels of methylation biomarkers and non-small cell lung cancer risk at the first time. This study looked at how well larotrectinib worked in adult patients with sarcomas caused by TRK fusion proteins. These findings will provide a unique insight into the epigenetic susceptibility mechanisms of lung cancer.

Association between sleep traits and biological aging risk: a Mendelian randomization study based on 157 227 cases and 179 332 controls.

STUDY OBJECTIVES: To investigate whether sleep traits are associated with the risk of biological aging using a case-control design with Mendelian randomization (MR) analyses. METHODS: We studied 336 559 participants in the UK Biobank cohort, including 157 227 cases of accelerated biological aging and 179 332 controls. PhenoAge, derived from clinical traits, estimated biological ages, and the discrepancies from chronological age were defined as age accelerations (PhenoAgeAccel). Sleep behaviors were assessed with a standardized questionnaire. propensity score matching matched control participants to age-accelerated participants, and a conditional multivariable logistic regression model estimated odds ratio (OR) and 95% confidence intervals (95% CI). Causal relationships between sleep traits and PhenoAgeAccel were explored using linear and nonlinear MR methods. RESULTS: A U-shaped association was found between sleep duration and PhenoAgeAccel risk. Short sleepers had a 7% higher risk (OR = 1.07; 95% CI: 1.03 to 1.11), while long sleepers had an 18% higher risk (OR = 1.18; 95% CI: 1.15 to 1.22), compared to normal sleepers (6-8 hours/day). Evening chronotype was linked to higher PhenoAgeAccel risk than morning chronotype (OR = 1.14; 95% CI: 1.10 to 1.18), while no significant associations were found for insomnia or snoring. Morning chronotype had a protective effect on PhenoAgeAccel risk (OR = 0.87, 95% CI: 0.79 to 0.95) per linear MR analysis. Genetically predicted sleep duration showed a U-shaped relationship with PhenoAgeAccel, suggesting a nonlinear association (pnonlinear < 0.001). CONCLUSIONS: The study suggests that improving sleep can slow biological aging, highlighting the importance of optimizing sleep as an intervention to mitigate aging's adverse effects.

Mucosal esophageal carcinoma following endoscopic submucosal dissection with giant gastric metastasis: A case report and review of literature.

BACKGROUND: Esophageal carcinoma is a highly aggressive digestive cancer responsible for a notable proportion of cancer-related deaths worldwide. Its elevated metastatic rate contributes to a poor prognosis in affected patients. In this case review, we aim to summarize the metastatic characteristics of intramural gastric metastasis (IGM) in mucosal esophageal squamous carcinoma. CASE SUMMARY: A 56-year-old man was admitted to our hospital because of a dry cough with an esophageal sensation for one year. Endoscopic examination revealed a 2.0 cm 1.0 cm, superficial esophageal squamous cell carcinoma, and the patient underwent endoscopic submucosal dissection (ESD). Fifteen months after ESD, positron emission tomography/computed tomography revealed that the metabolism of the stomach cardia wall had increased slightly. However, the mucosa of the gastric cardia was smooth under gastroendoscopy. Two years after ESD, endoscopic examination revealed a giant gastric cardia carcinoma, while the esophageal mucosa was smooth, and no advanced cancer was found. A biopsy of the gastric cardia indicated squamous-cell carcinoma. The patient received immunochemotherapy and radiotherapy for esophageal cancer for 8 mo and is currently under follow-up. CONCLUSION: Early-stage esophageal carcinoma with IGM is rare. Despite the ESD of the primary lesion, IGM may still occur and should be closely monitored after ESD.

Atomic Dispersed Hetero-Pairs for Enhanced Electrocatalytic CO2 Reduction.

Electrochemical carbon dioxide reduction reaction (CO2RR) involves a variety of intermediates with highly correlated reaction and ad-desorption energies, hindering optimization of the catalytic activity. For example, increasing the binding of the *COOH to the active site will generally increase the *CO desorption energy. Breaking this relationship may be expected to dramatically improve the intrinsic activity of CO2RR, but remains an unsolved challenge. Herein, we addressed this conundrum by constructing a unique atomic dispersed hetero-pair consisting of Mo-Fe di-atoms anchored on N-doped carbon carrier. This system shows an unprecedented CO2RR intrinsic activity with TOF of 3336 h-1, high selectivity toward CO production, Faradaic efficiency of 95.96% at - 0.60 V and excellent stability. Theoretical calculations show that the Mo-Fe diatomic sites increased the *COOH intermediate adsorption energy by bridging adsorption of *COOH intermediates. At the same time, d-d orbital coupling in the Mo-Fe di-atom results in electron delocalization and facilitates desorption of *CO intermediates. Thus, the undesirable correlation between these steps is broken. This work provides a promising approach, specifically the use of di-atoms, for breaking unfavorable relationships based on understanding of the catalytic mechanisms at the atomic scale.

Long noncoding RNA PSMA3-AS1 functions as a competing endogenous RNA to promote gastric cancer progression by regulating the miR-329-3p/ALDOA axis.

LncRNA PSMA3-AS1 functions as an oncogene in several cancers, including ovarian cancer, lung cancer, and colorectal cancer. However, its role in gastric cancer (GC) progression remains unclear. In this study, the levels of PSMA3-AS1, miR-329-3p, and aldolase A (ALDOA) in 20 paired human GC tissues and adjacent nontumorous tissues were measured by real-time PCR. GC cells were transfected with recombinant plasmid carrying full-length PSMA3-AS1 or shRNA targeting PSMA3-AS1. The stable transfectants were selected by G418. Then, the effects of PSMA3-AS1 knockdown or overexpression on GC progression in vitro and in vivo were evaluated. The results showed that PSMA3-AS1 was highly expressed in human GC tissues. Stable knockdown of PSMA3-AS1 significantly restrained proliferation/migration/invasion, enhanced cell apoptosis, and induced oxidative stress in vitro. Tumor growth and matrix metalloproteinase expression in tumor tissues were markedly inhibited, while oxidative stress was enhanced in nude mice after stable PSMA3-AS1 knockdown. Additionally, PSMA3-AS1 negatively regulated miR-329-3p while positively regulated ALDOA expression. MiR-329-3p directly targeted ALDOA-3'UTR. Interestingly, miR-329-3p knockdown or ALDOA overexpression partially attenuated the tumor-suppressive effects of PSMA3-AS1 knockdown. Conversely, PSMA3-AS1 overexpression exhibited the opposite effects. PSMA3-AS1 promoted GC progression by regulating the miR-329-3p/ALDOA axis. PSMA3-AS1 might serve as a promising and effective target for GC treatment.

Endoscopic and pathological characteristics of de novo colorectal cancer: Retrospective cohort study.

BACKGROUND: Endoscopy has rapidly developed in recent years and has enabled further investigation into the origin and features of intestinal tumors. The small size and concealed position of these tumors make it difficult to distinguish them from nonneoplastic polyps and carcinoma in adenoma (CIA). The invasive depth and metastatic potential determine the operation regimen, which in turn affects the overall survival and distant prognosis. The previous studies have confirmed the malignant features and clinicopathological features of de novo colorectal cancer (CRC). AIM: To provide assistance for diagnosis and treatment, but the lack of a summary of endoscopic features and assessment of risk factors that differ from the CIA prompted us to conduct this retrospective study. METHODS: In total, 167 patients with small-sized CRCs diagnosed by endoscopy were reviewed. The patients diagnosed as advanced CRCs and other malignant cancers or chronic diseases that could affect distant outcomes were excluded. After screening, 63 cases were excluded, including 33 de novo and 30 CIA cases. Patient information, including their follow-up information, was obtained from an electronic His-system. The characteristics between two group and risk factors for invasion depth were analyzed with SPSS 25.0 software. RESULTS: Nearly half of the de novo CRCs were smaller than 1 cm (n = 16, 48.5%) and the majority were located in the distal colon (n = 26, 78.8%). The IIc type was the most common macroscopic type of de novo CRC. In a Pearson analysis, the differential degree, Sano, JNET, and Kudo types, surrounding mucosa, and chicken skin mucosa (CSM) were correlated with the invasion depth (P < 0.001). CSM was a significant risk factor for deep invasion and disturbed judgment of endoscopic ultrasound. A high degree of tumor budding and tumor-infiltrating lymphocytes are accompanied by malignancy. Finally, de novo CRCs have worse outcomes than CIA CRCs. CONCLUSION: This is the first comprehensive study to analyze the features of de novo CRCs to distinguish them from nonneoplastic polyps. It is also the first study paying attention to CSM invasive depth measurement. This study emphasizes the high metastatic potential of de novo CRCs and highlights the need for more research on this tumor type.

Machine Learning in Environmental Research: Common Pitfalls and Best Practices.

Machine learning (ML) is increasingly used in environmental research to process large data sets and decipher complex relationships between system variables. However, due to the lack of familiarity and methodological rigor, inadequate ML studies may lead to spurious conclusions. In this study, we synthesized literature analysis with our own experience and provided a tutorial-like compilation of common pitfalls along with best practice guidelines for environmental ML research. We identified more than 30 key items and provided evidence-based data analysis based on 148 highly cited research articles to exhibit the misconceptions of terminologies, proper sample size and feature size, data enrichment and feature selection, randomness assessment, data leakage management, data splitting, method selection and comparison, model optimization and evaluation, and model explainability and causality. By analyzing good examples on supervised learning and reference modeling paradigms, we hope to help researchers adopt more rigorous data preprocessing and model development standards for more accurate, robust, and practicable model uses in environmental research and applications.

Highly Selective Electrochemical Nitrate to Ammonia Conversion by Dispersed Ru in a Multielement Alloy Catalyst.

Electrochemical reduction of nitrate to ammonia (NH3) converts an environmental pollutant to a critical nutrient. However, current electrochemical nitrate reduction operations based on monometallic and bimetallic catalysts are limited in NH3 selectivity and catalyst stability, especially in acidic environments. Meanwhile, catalysts with dispersed active sites generally exhibit a higher atomic utilization and distinct activity. Herein, we report a multielement alloy nanoparticle catalyst with dispersed Ru (Ru-MEA) with other synergistic components (Cu, Pd, Pt). Density functional theory elucidated the synergy effect of Ru-MEA than Ru, where a better reactivity (NH3 partial current density of -50.8 mA cm-2) and high NH3 faradaic efficiency (93.5%) is achieved in industrially relevant acidic wastewater. In addition, the Ru-MEA catalyst showed good stability (e.g., 19.0% decay in FENH3 in three hours). This work provides a potential systematic and efficient catalyst discovery process that integrates a data-guided catalyst design and novel catalyst synthesis for a range of applications.

Polygenic risk for termination of the 'healthspan' and its interactions with lifestyle factors: A prospective cohort study based on 288,359 participants.

OBJECTIVES: To investigate whether a polygenic risk score (PRS) and its interactions with lifestyle factors are associated with termination of the 'healthspan' (the number of years living without serious diseases or degeneration). DESIGN, EXPOSURES AND PARTICIPANTS: Death or the incidence of any of seven independent morbidities (cancer, congestive heart failure, myocardial infarction, chronic obstructive pulmonary disease, stroke, dementia, and diabetes) strongly associated with aging were considered to define the termination of the healthspan. A total of 288,359 healthy participants from the UK Biobank were included in this prospective cohort study to evaluate the associations between PRS, lifestyle, and healthspan. The PRS was generated by weighting 12 healthspan-related genetic loci, which and scores were then categorized into three groups in Cox regression models. A lifestyle index was developed that incorporated body mass index (BMI), alcohol consumption, diet, smoking, and physical activity, and these scores were also categorized into three groups. The risk of termination of the healthspan was calculated across the different PRS and lifestyle index groups using Cox regression models. Interactions were estimated with the marginal effect of lifestyle on the risk of termination of healthspan across values of the moderator PRS using kernel estimation. RESULTS: During an average follow-up of 9.83 years, 68,903 healthspan-termination events occurred. It was calculated that people with high polygenic risk could reduce their risk of healthspan termination by 40 % if they maintain a favorable lifestyle. The marginal effect of lifestyle on the risk of healthspan termination increased with growing genetic risk. Smoking and diet showed monotonic changes in opposite directions, while BMI, physical activity, and alcohol had a U-shaped interaction with genetic risk. CONCLUSIONS: Favorable lifestyle can attenuate the risk of termination of the healthspan, especially for people with high genetic risk. The improvement afforded by ideal lifestyle behaviors varies for each individual.

Oxygen-Vacancy-Rich Piezoelectric BiO2- x Nanosheets for Augmented Piezocatalytic, Sonothermal, and Enzymatic Therapies.

Piezocatalytic therapy is a new-emerging reactive oxygen species (ROS)-enabled therapeutic strategy that relies on built-in electric field and energy-band bending of piezoelectric materials activated by ultrasound (US) irradiation. Despite becoming a hot topic, material development and mechanism exploration are still underway. Herein, as-synthesized oxygen-vacancy-rich BiO2- x nanosheets (NSs) demonstrate outstanding piezoelectric properties. Under US, a piezo-potential of 0.25 V for BiO2- x NSs is sufficient to tilt the conduction band to be more negative than the redox potentials of O2 /• O2 - , • O2 - /H2 O2 , and H2 O2 /• OH, which initiates a cascade reaction for ROS generation. Moreover, the BiO2- x NSs exhibit peroxidase and oxidase-like activities to augment ROS production, especially in the H2 O2 -overexpressed tumor microenvironment. Density functional theory calculations show that the generated oxygen vacancies in BiO2- x NSs are favorable for H2 O2  adsorption and increasing the carrier density to produce ROS. Furthermore, the quick movement of electrons enables an excellent sonothermal effect, for example, rapid rise in temperature to nearly 65 °C upon US with low power (1.2 W cm-2 ) and short time (96 s). Therefore, this system realizes a multimode synergistic combination of piezocatalytic, enzymatic, and sonothermal therapies, providing a new direction for defect engineering-optimized piezoelectric materials for tumor therapy.

Predicting Extraction Selectivity of Acetic Acid in Pervaporation by Machine Learning Models with Data Leakage Management.

The extraction of acetic acid and other carboxylic acids from water is an emerging separation need as they are increasingly produced from waste organics and CO2 during carbon valorization. However, the traditional experimental approach can be slow and expensive, and machine learning (ML) may provide new insights and guidance in membrane development for organic acid extraction. In this study, we collected extensive literature data and developed the first ML models for predicting separation factors between acetic acid and water in pervaporation with polymers' properties, membrane morphology, fabrication parameters, and operating conditions. Importantly, we assessed seed randomness and data leakage problems during model development, which have been overlooked in ML studies but will result in over-optimistic results and misinterpreted variable importance. With proper data leakage management, we established a robust model and achieved a root-mean-square error of 0.515 using the CatBoost regression model. In addition, the prediction model was interpreted to elucidate the variables' importance, where the mass ratio was the topmost significant variable in predicting separation factors. In addition, polymers' concentration and membranes' effective area contributed to information leakage. These results demonstrate ML models' advances in membrane design and fabrication and the importance of vigorous model validation.

Physiological and biochemical changes during fruit maturation and ripening in highbush blueberry (Vaccinium corymbosum L.).

The sweetness of blueberry fruit increases over time, as acids are converted to sugars, and full flavor development is formed by harvest. We comprehensively analyzed the changes and correlation in physiological and biochemical characteristics of blueberries at different maturity stages, including texture, quality, taste and energy change. Our analysis revealed that total anthocyanin content increased and firmness decreased as fruit ripened. Percent moisture, titratable acid (TA), chlorophyll and carotenoid content also decreased, while total soluble solids (TSS), pH, TSS/TA ratio, vitamin C, soluble proteins, and ethylene production all increased. Antioxidant enzyme activity gradually increased during ripening but energy-related metabolites decreased. The flavor attributes of sweetness, bitterness, and sourness were readily perceived using an electronic tongue and a total of 76 volatile compounds were detected by GC-MS. In summary, the maturation of blueberries was correlated with increases of anthocyanins, nutrients, antioxidant activity, taste and aroma, but negatively correlated with energy metabolism.

The Interaction Between Epigenetic Changes, EMT, and Exosomes in Predicting Metastasis of Colorectal Cancers (CRC).

Worldwide, colorectal cancer (CRC) ranks as the third most common malignancy, and the second most deadly with nearly one million attributable deaths in 2020. Metastatic disease is present in nearly 25% of newly diagnosed CRC, and despite advances in chemotherapy, less than 20% will remain alive at 5 years. Epigenetic change plays a key role in the epithelial-to-mesenchymal transition (EMT), which is a crucial phenotype for metastasis and mainly includes DNA methylation, non-coding RNAs (ncRNAs), and N 6-methyladenosine (m6A) RNA, seemingly valuable biomarkers in CRCs. For ncRNAs, there exists a "molecular sponge effect" between long non-coding RNAs (lncRNAs), circular RNAs (circRNAs), and microRNAs (miRNAs). The detection of exosomes is a novel method in CRC monitoring, especially for predicting metastasis. There is a close relationship between exosomes and EMT in CRCs. This review summarizes the close relationship between epigenetic changes and EMT in CRCs and emphasizes the crucial function of exosomes in regulating the EMT process.

Upscaling 3D Engineered Trees for Off-Grid Desalination.

More than 70% of the population without access to safe drinking water lives in remote and off-grid areas. Inspired by natural plant transpiration, we designed and tested in this study an array of scalable three-dimensional (3D) engineered trees made of natural wood for continuous water desalination to provide affordable and clean drinking water. The trees took advantage of capillary action in the wood xylems and lifted water more than 1 foot off the ground with or without solar irradiation. This process overcame some major challenges of popular solar-driven water evaporation and water harvesting, such as intermittent operation, low water production rate, and system scaling. The trade-off between energy transfer and system footprint was tackled by optimizing the interspacing between the trees. The scaled system has a ratio of surface area (vapor generation) to project area (water transport) up to 118, significantly higher than the prevailing flat-sheet design. The extensive surface area evaporated water at a temperature cooler than the surrounding air, drawing on multiple environmental energy sources including solar, wind, or ambient heat in the air and realized continuous operation. The total energy for evaporation reached over 300% of the one-sun irradiance, enabling a freshwater production rate of 4.8 L m-2 h-1 from an array of 16 trees in an enclosed room and 14 L m-2 h-1 under a 3 m/s airflow. Furthermore, we found that the ambient heat in the air contributed 60%-70% of the total latent heat of vaporization when energy sources were decoupled. During long-term desalination tests, the engineered trees demonstrated a self-cleaning mechanism with daily cycles of salt accumulation and dissolution. Combining the quantification from an evaporation model and meteorology data covering the globe, we also demonstrated that the 3D engineered trees can be of particular interest for sustainable desalination in the Middle East and North Africa (MENA) regions.