Responses of soil and rhizosphere microbial communities to Cd-hyperaccumulating willows and Cd contamination.

BACKGROUND: The pollution of soil by heavy metals, particularly Cd, is constitutes a critical international environmental concern. Willow species are renowned for their efficacy in the phytoremediation of heavy metals owing to their high Cd absorption rate and rapid growth. However, the mechanisms underlying microbial regulation for high- and low-accumulating willow species remain poorly understood. Therefore, we investigated the responses of soil and rhizosphere microbial communities to high- and low-Cd-accumulating willows and Cd contamination. We analyzed soil properties were analyzed in bulk soil (SM) and rhizosphere soil (RM) planted with high-accumulating (H) and low-accumulating (L) willow species. RESULTS: Rhizosphere soil for different willow species had more NH4+ than that of bulk soil, and RM-H soil had more than RM-L had. The available phosphorus content was greater in hyper-accumulated species than it was in lower-accumulated species, especially in RM-H. Genome sequencing of bacterial and fungal communities showed that RM-L exhibited the highest bacterial diversity, whereas RM-H displayed the greatest richness than the other groups. SM-L exhibited the highest diversity and richness of fungal communities. Ralstonia emerged as the predominant bacterium in RM-H, whereas Basidiomycota and Cercozoa were the most enriched fungi in SM-H. Annotation of the N and C metabolism pathways revealed differential patterns: expression levels of NRT2, NarB, nirA, nirD, nrfA, and nosZ were highest in RM-H, demonstrating the effects of NO3-and N on the high accumulation of Cd in RM-H. The annotated genes associated with C metabolism indicated a preference for the tricarboxylic pathway in RM-H, whereas the hydroxypropionate-hydroxybutyrate cycle was implicated in C sequestration in SM-L. CONCLUSIONS: These contribute to elucidation of the mechanism underlying high Cd accumulation in willows, particularly in respect of the roles of microbes and N and C utilization. This will provide valuable insights for repairing polluted soil using N and employing organic acids to improve heavy metal remediation efficiency.

Role of Na/K-ATPase α1 Caveolin-Binding Motif in Adipogenesis.

Deficiencies in mice and in humans have brought to the fore the importance of the caveolar network in key aspects of adipocyte biology. The conserved N-terminal caveolin-binding motif (CBM) of the ubiquitous Na/K-ATPase (NKA) α1 isoform, which allows NKA/caveolin-1 (Cav1) interaction, influences NKA signaling and caveolar distribution. It has been shown to be critical for animal development and ontogenesis, as well as lineage-specific differentiation of human induced pluripotent stem cells (hiPSC). However, its role in postnatal adipogenesis has not been fully examined. Using a genetic approach to alter CBM in hiPSC-derived adipocytes (iAdi-mCBM) and in mice (mCBM), we investigated the regulatory function of NKA CBM signaling in adipogenesis. Seahorse XF cell metabolism analyses revealed impaired glycolysis and decreased ATP synthesis-coupled respiration in iAdi-mCBM. These metabolic dysfunctions were accompanied by evidence of extensive remodeling of the extracellular matrix (ECM), including increased collagen staining, overexpression of ECM marker genes, and heightened TGF-ß signaling uncovered by RNAseq analysis. Rescue of mCBM by lentiviral delivery of WT NKA α1 or treatment of mCBM hiPSC with the TGF-ß inhibitor SB431542 normalized ECM, suggesting that NKA CBM signaling integrity is required for adequate control of TGF-ß signaling and ECM stiffness during adipogenesis. The physiological impact was revealed in mCBM male mice with reduced fat mass accompanied by histological and transcriptional evidence of elevated adipose fibrosis and decreased adipocyte size. Based on these findings, we propose that the genetic alteration of the NKA/Cav1 regulatory path uncovered in human iAdi leads to lipodystrophy in mice.

System-level time computation and representation in the suprachiasmatic nucleus revealed by large-scale calcium imaging and machine learning.

The suprachiasmatic nucleus (SCN) is the mammalian central circadian pacemaker with heterogeneous neurons acting in concert while each neuron harbors a self-sustained molecular clockwork. Nevertheless, how system-level SCN signals encode time of the day remains enigmatic. Here we show that population-level Ca2+ signals predict hourly time, via a group decision-making mechanism coupled with a spatially modular time feature representation in the SCN. Specifically, we developed a high-speed dual-view two-photon microscope for volumetric Ca2+ imaging of up to 9000 GABAergic neurons in adult SCN slices, and leveraged machine learning methods to capture emergent properties from multiscale Ca2+ signals as a whole. We achieved hourly time prediction by polling random cohorts of SCN neurons, reaching 99.0% accuracy at a cohort size of 900. Further, we revealed that functional neuron subtypes identified by contrastive learning tend to aggregate separately in the SCN space, giving rise to bilaterally symmetrical ripple-like modular patterns. Individual modules represent distinctive time features, such that a module-specifically learned time predictor can also accurately decode hourly time from random polling of the same module. These findings open a new paradigm in deciphering the design principle of the biological clock at the system level.

Low serum total cholesterol levels predict inferior prognosis of patients with POEMS syndrome.

Low serum cholesterol levels are associated with increased tumor morbidity and mortality. However, the relationship between serum lipid profile and POEMS syndrome (polyneuropathy, organomegaly, endocrinopathy, M-protein, skin changes) is still unclear. The aim of our study was to clarify the importance of the serum lipid profile in predicting the severity and prognosis of patients with POEMS syndrome. Forty-three patients with newly diagnosed POEMS syndrome admitted to the Department of Hematology of Jiangsu Provincial People's Hospital between August 2013 and February 2023 were selected. They had explicit serum lipid profiles. There were 27 males and 16 females with a median age of 54 years (range, 28-77 years). Survival curves were plotted using the Kaplan-Meier method, and comparisons between the two groups were performed using the log-rank test. The Cox proportional-hazards model examined risk factors associated with the prognosis of POEMS syndrome. Receiver-operator characteristic (ROC) curves assessed the predictive accuracy. 23 (53.5%) patients had low total cholesterol (TC) levels. Low levels of TC were concerned with unfavorable progression-free survival (PFS) (p = 0.007) and overall survival (OS) (p = 0.004), and at the same time, the low circulating TC concentration was an independent risk factor for PFS (p = 0.020) and OS (p = 0.011). Low TC values could improve the risk stratification, especially in high-risk patients. In conclusion, low serum TC levels may predict inferior prognosis in patients with POEMS syndrome; in future clinical application, low TC may be a reliable indicator of prognosis.

Targeting mitochondrial dynamics and redox regulation in cardiovascular diseases.

Accumulating evidence highlights the pivotal role of mitochondria in cardiovascular diseases (CVDs). Understanding the molecular mechanisms underlying mitochondrial dysfunction is crucial for developing targeted therapeutics. Recent years have seen substantial advancements in unraveling mitochondrial regulatory pathways in both normal and pathological states and the development of potent drugs. However, specific delivery of drugs into the mitochondria is still a challenge. We present recent findings on regulators of mitochondrial dynamics and reactive oxygen species (ROS), critical factors influencing mitochondrial function in CVDs. We also discuss advancements in drug delivery strategies aimed at overcoming the technical barrier in targeting mitochondria for CVD treatment.

Programmable ultrasound imaging guided theranostic nanodroplet destruction for precision therapy of breast cancer.

Ultrasound-stimulated contrast agents have gained significant attention in the field of tumor treatment as drug delivery systems. However, their limited drug-loading efficiency and the issue of bulky, imprecise release have resulted in inadequate drug concentrations at targeted tissues. Herein, we developed a highly efficient approach for doxorubicin (DOX) precise release at tumor site and real-time feedback via an integrated strategy of "programmable ultrasonic imaging guided accurate nanodroplet destruction for drug release" (PND). We synthesized DOX-loaded nanodroplets (DOX-NDs) with improved loading efficiency (15 %) and smaller size (mean particle size: 358 nm). These DOX-NDs exhibited lower ultrasound activation thresholds (2.46 MPa). By utilizing a single diagnostic transducer for both ultrasound stimulation and imaging guidance, we successfully vaporized the DOX-NDs and released the drug at the tumor site in 4 T1 tumor-bearing mice. Remarkably, the PND group achieved similar tumor remission effects with less than half the dose of DOX required in conventional treatment. Furthermore, the ultrasound-mediated vaporization of DOX-NDs induced tumor cell apoptosis with minimal damage to surrounding normal tissues. In summary, our PND strategy offers a precise and programmable approach for drug delivery and therapy, combining ultrasound imaging guidance. This approach shows great potential in enhancing tumor treatment efficacy while minimizing harm to healthy tissues.

Pattern recognition of microcirculation with super-resolution ultrasound imaging provides markers for early tumor response to anti-angiogenic therapy.

Rationale: Cancer treatment outcome is traditionally evaluated by tumor volume change in clinics, while tumor microvascular heterogeneity reflecting tumor response has not been fully explored due to technical limitations. Methods: We introduce a new paradigm in super-resolution ultrasound imaging, termed pattern recognition of microcirculation (PARM), which identifies both hemodynamic and morphological patterns of tumor microcirculation hidden in spatio-temporal space trajectories of microbubbles. Results: PARM demonstrates the ability to distinguish different local blood flow velocities separated by a distance of 24 µm. Compared with traditional vascular parameters, PARM-derived heterogeneity parameters prove to be more sensitive to microvascular changes following anti-angiogenic therapy. Particularly, PARM-identified "sentinel" microvasculature, exhibiting evident structural changes as early as 24 hours after treatment initiation, correlates significantly with subsequent tumor volume changes (|r| > 0.9, P < 0.05). This provides prognostic insight into tumor response much earlier than clinical criteria. Conclusions: The ability of PARM to noninvasively quantify tumor vascular heterogeneity at the microvascular level may shed new light on early-stage assessment of cancer therapy.

Weakly Supervised Solar Panel Mapping via Uncertainty Adjusted Label Transition in Aerial Images.

This paper proposes a novel uncertainty-adjusted label transition (UALT) method for weakly supervised solar panel mapping (WS-SPM) in aerial Images. In weakly supervised learning (WSL), the noisy nature of pseudo labels (PLs) often leads to poor model performance. To address this problem, we formulate the task as a label-noise learning problem and build a statistically consistent mapping model by estimating the instance-dependent transition matrix (IDTM). We propose to estimate the IDTM with a parameterized label transition network describing the relationship between the latent clean labels and noisy PLs. A trace regularizer is employed to impose constraints on the form of IDTM for its stability. To further reduce the estimation difficulty of IDTM, we incorporate uncertainty estimation to first improve the accuracy of noisy dataset distillation and then mitigate the negative impacts of falsely distilled examples with an uncertainty-adjusted re-weighting strategy. Extensive experiments and ablation studies on two challenging aerial data sets support the validity of the proposed UALT.

Resveratrol modulates the inflammatory response in hPDLSCs via the NRF2/HO-1 and NF-κB pathways and promotes osteogenic differentiation.

OBJECTIVE: The purpose of this study was to investigate resveratrol's specific role as an anti-inflammatory and osteogenic differentiation of hPDLSCs in periodontitis and to reveal the mechanisms involved. BACKGROUND: Numerous studies have shown that inhibiting the inflammatory response of periodontal tissues and promoting the regeneration of alveolar bone are crucial treatments for periodontitis. Resveratrol has been found to have certain anti-inflammatory property. However, the anti-inflammatory mechanism and osteogenic effect of resveratrol in periodontitis are poorly understood. MATERIALS AND METHODS: We constructed an in vitro periodontitis model by LPS stimulation of hPDLSCs and performed WB, RT-qPCR, and immunofluorescence to analyze inflammatory factors and related pathways. In addition, we explored the osteogenic ability of resveratrol in in vitro models. RESULTS: In vitro, resveratrol ameliorated the inflammatory response associated with activation of the NF-κB pathway through activation of the NRF2/HO-1 pathway, characterized by inhibition of p65/p50 nuclear translocation and the proinflammatory cytokines interleukin-1ß levels. Resveratrol also has a positive effect on osteogenic differentiation. CONCLUSIONS: Observations suggest that resveratrol modulates the inflammatory response in hPDLSCs via the NRF2/HO-1 and NF-κB pathways and promotes osteogenic differentiation.

Imaging microglia surveillance during sleep-wake cycles in freely behaving mice.

Microglia surveillance manifests itself as dynamic changes in cell morphology and functional remodeling. Whether and how microglia surveillance is coupled to brain state switches during natural sleep-wake cycles remains unclear. To address this question, we used miniature two-photon microscopy (mTPM) to acquire time-lapse high-resolution microglia images of the somatosensory cortex, along with EEG/EMG recordings and behavioral video, in freely-behaving mice. We uncovered fast and robust brain state-dependent changes in microglia surveillance, occurring in parallel with sleep dynamics and early-onset phagocytic microglial contraction during sleep deprivation stress. We also detected local norepinephrine fluctuation occurring in a sleep state-dependent manner. We showed that the locus coeruleus-norepinephrine system, which is crucial to sleep homeostasis, is required for both sleep state-dependent and stress-induced microglial responses and ß2-adrenergic receptor signaling plays a significant role in this process. These results provide direct evidence that microglial surveillance is exquisitely tuned to signals and stressors that regulate sleep dynamics and homeostasis so as to adjust its varied roles to complement those of neurons in the brain. In vivo imaging with mTPM in freely behaving animals, as demonstrated here, opens a new avenue for future investigation of microglia dynamics and sleep biology in freely behaving animals.

Efficient derivation of transgene-free porcine induced pluripotent stem cells enables in vitro modeling of species-specific developmental timing.

Sus scrofa domesticus (pig) has served as a superb large mammalian model for biomedical studies because of its comparable physiology and organ size to humans. The derivation of transgene-free porcine induced pluripotent stem cells (PiPSCs) will, therefore, benefit the development of porcine-specific models for regenerative biology and its medical applications. In the past, this effort has been hampered by a lack of understanding of the signaling milieu that stabilizes the porcine pluripotent state in vitro. Here, we report that transgene-free PiPSCs can be efficiently derived from porcine fibroblasts by episomal vectors along with microRNA-302/367 using optimized protocols tailored for this species. PiPSCs can be differentiated into derivatives representing the primary germ layers in vitro and can form teratomas in immunocompromised mice. Furthermore, the transgene-free PiPSCs preserve intrinsic species-specific developmental timing in culture, known as developmental allochrony. This is demonstrated by establishing a porcine in vitro segmentation clock model that, for the first time, displays a specific periodicity at ∼3.7 h, a timescale recapitulating in vivo porcine somitogenesis. We conclude that the transgene-free PiPSCs can serve as a powerful tool for modeling development and disease and developing transplantation strategies. We also anticipate that they will provide insights into conserved and unique features on the regulations of mammalian pluripotency and developmental timing mechanisms.

On-capillary alkylation micro-reactor: a facile strategy for proteo-metabolome profiling in the same single cells.

Single-cell multi-omics analysis can provide comprehensive insights to study cell-to-cell heterogeneity in normal and disease physiology. However, due to the lack of amplification technique, the measurement of proteome and metabolome in the same cell is challenging. Herein, a novel on-capillary alkylation micro-reactor (OCAM) was developed to achieve proteo-metabolome profiling in the same single cells, by which proteins were first covalently bound to an iodoacetic acid functionalized open-tubular capillary micro-reactor via sulfhydryl alkylation reaction, and metabolites were rapidly eluted, followed by on-column digestion of captured proteins. Compared with existing methods for low-input proteome sample preparation, OCAM exhibited improved efficiency, anti-interference ability and recovery, enabling the identification of an average of 1509 protein groups in single HeLa cells. This strategy was applied to single-cell proteo-metabolome analysis of mouse oocytes at different stages, 3457 protein groups and 171 metabolites were identified in single oocytes, which is the deepest coverage of proteome and metabolome from single mouse oocytes to date, achieving complementary characterization of metabolic patterns during oocyte maturation.

[Predictive value of pulse infusion index in the short-term prognosis of patients with sepsis-induced acute kidney injury].

OBJECTIVE: To investigate the predictive value of pulse infusion index (PPI) in the short-term prognosis of patients with sepsis-induced acute kidney injury (AKI). METHODS: A retrospective cohort study was conducted. The clinical data of patients with sepsis-induced AKI admitted to intensive care unit (ICU) of the First Affiliated Hospital of Soochow University from July 2021 to December 2022 were enrolled. The basic information of the patients were collect, including age, gender, site of infection, underlying disease, mean arterial pressure (MAP) and heart rate (HR) at admission, as well as the use of mechanical ventilation and vasoactive drugs, and norepinephrine (NE) dosage. Laboratory indicators, sequential organ failure assessment (SOFA) score and PPI within 24 hours of admission were also recorded, and the patient's prognosis during ICU hospitalization was also recorded. The differences in clinical data between the patients of two groups with different prognosis were compared. Spearman correlation method was used to analyze the correlation between PPI and SOFA score. Binary multivariate Logistic regression analysis was used to screen independent risk factors for death during ICU hospitalization in sepsis patients with AKI. Receiver operator characteristic curve (ROC curve) was plotted to evaluate the predictive value of PPI for the short-term prognosis of patients with sepsis-induced AKI. RESULTS: A total of 102 patients with sepsis-induced AKI were enrolled, of which 70 patients in the survival group and 32 patients in the death group, with ICU mortality of 31.4. Compared with the survival group, SOFA score, HR, procalcitonin (PCT), serum creatinine (SCr), and NE dosage in the death group were significantly increased [SOFA score: 11.22±2.48 vs. 8.56±2.01, HR (bpm): 103.80±12.47 vs. 97.41±9.73, PCT (µg/L): 9.22 (5.24, 17.84) vs. 6.19 (3.86, 7.71), SCr (µmol/L): 163.2 (104.7, 307.9) vs. 125.5 (89.3, 221.0), Lac (mmol/L): 2.81 (1.95, 4.22) vs. 2.13 (1.74, 2.89), NE usage (µg×kg-1×min-1): 0.7 (0.4, 1.1) vs. 0.5 (0.2, 0.6), all P < 0.05], while PPI was significantly lower than that in survival group [0.83 (0.42, 1.55) vs. 1.70 (1.14, 2.20), P < 0.01]. Spearman correlation analysis showed that based on SOFA score, PPI was closely related to the severity of patients with sepsis-induced AKI (r = -0.328, P < 0.05). Binary multivariate Logistic regression analysis showed that PPI [odds ratio (OR) = 0.590, 95% confidence interval (95%CI) was 0.361-0.966, P = 0.002], SOFA score (OR = 1.406, 95%CI was 1.280-1.545, P < 0.001), PCT (OR = 2.061, 95%CI was 1.267-3.350, P = 0.006) were independent risk factors of the short-term prognosis of patients with sepsis-induced AKI. ROC curve analysis showed that the area under the ROC curve (AUC) of PPI for death during ICU hospitalization in patients with sepsis-induced AKI was 0.779 (95%CI was 0.686-0.855, P < 0.001), which superior to PCT (AUC = 0.677, 95%CI was 0.577-0.766, P = 0.004), and similar to SOFA score (AUC = 0.794, 95%CI was 0.703-0.868, P < 0.001). When the cut-off value of PPI was 0.72, the sensitivity was 50.0%, and the specificity was 97.1%. CONCLUSIONS: PPI has a good predictive value for the short-term prognosis of patients with sepsis-induced AKI during ICU hospitalization.

Subfemtowatt Laser Phase Tracking.

Low power optical phase tracking is an enabling capability for intersatellite laser interferometry, as minimum trackable power places significant constraints on mission design. Through the combination of laser stabilization and control-loop parameter optimization, we have demonstrated continuous tracking of a subfemtowatt optical field with a mean time between slips of more than 1000 s. Comparison with analytical models and numerical simulations verified that the observed experimental performance was limited by photon shot noise and unsuppressed laser frequency fluctuations. Furthermore, with two stabilized lasers, we have demonstrated 100 min of continuous phase tracking of Gravity Recovery and Climate Experiment (GRACE)-like signal dynamics with an optical carrier ranging in power between 1-7 fW with zero cycle slips. These results indicate the feasibility of future interspacecraft laser links operating with significantly reduced received optical power.

Adherence to the Mediterranean diet regulates the association between osteopenia and the risk of all-cause mortality in general population.

BACKGROUND: This study aimed to explore the association of adherence to the Mediterranean diet (MD), osteopenia and the risk of all-cause mortality in general population. METHODS: This retrospective cohort study included 5452 participants ≥ 50 years from the National Health and Nutrition Examination Survey (NHANES). The associations of osteopenia and adherence to the MD with all-cause mortality, as well as the interaction and moderating effects between the osteopenia and adherence to the MD on the all-cause mortality, were explored via univariate and multivariable Cox proportional hazards models. RESULTS: The follow-up was from October 1, 2006, to December 31, 2019. The median survival time of patients was 81 months. In total, 4724 people were survived and 728 were dead. Osteopenia was associated with increased risk of all-cause mortality in people [hazards ratio (HR) = 1.57, 95% confidence interval (CI) 1.23-1.99]. No significant risk of all-cause mortality was found in people with high adherence to the MD compared with those with low adherence to the MD (P > 0.05). Compared to subjects with no osteopenia who had high adherence to the MD, osteopenia people who had high adherence to the MD (HR = 1.52, 95% CI 1.17-1.98) or low adherence to the MD (HR = 1.81, 95% CI 1.23-2.66) were at increased risk of all-cause mortality after adjusting for confounding factors. The relationship between osteopenia and the risk of all-cause mortality was decreased in those with high adherence to the MD (HR = 1.57, 95% CI 1.17-2.11) compared with those with low adherence to the MD (HR = 1.62, 95% CI 1.08-2.41) after adjusting for confounding factors. CONCLUSION: The adherence to the MD regulated the association between osteopenia and the risk of all-cause mortality, which suggested the importance of adherence to the MD in those with osteopenia, and the MD could be advocated in general people.

Anti-thrombotic poly(AAm-co-NaAMPS)-xanthan hydrogel-expanded polytetrafluoroethylene (ePTFE) vascular grafts with enhanced endothelialization and hemocompatibility properties.

Cardiovascular diseases (CVDs) are the leading cause of death among all non-communicable diseases globally. Although expanded polytetrafluoroethylene (ePTFE) has been widely used for larger-diameter vascular graft transplantation, the persistent thrombus formation and intimal hyperplasia of small-diameter vascular grafts (SDVGs) made of ePTFE to treat severe CVDs remain the biggest challenges due to lack of biocompatibility and endothelium. In this study, bi-layered poly(acrylamide-co-2-Acrylamido-2-methyl-1-propanesulfonic acid sodium)-xanthan hydrogel-ePTFE (poly(AAm-co-NaAMPS)-xanthan hydrogel-ePTFE) vascular grafts capable of promoting endothelialization and prohibiting thrombosis were synthesized and fabricated. While the external ePTFE layer of the vascular grafts provided the mechanical stability, the inner hydrogel layer offered much-needed cytocompatibility, hemocompatibility, and endothelialization functions. The interface morphology between the inner hydrogel layer and the outer ePTFE layer was observed by scanning electron microscope (SEM), which revealed that the hydrogel was well attached to the porous ePTFE through mechanical interlocking. Among all the hydrogel compositions tested with cell culture using human umbilical vein endothelial cells (HUVECs), the hydrogel with the molar ratio of 40:60 (NaAMPS/AAm) composition (i.e., Hydrogel 40:60) exhibited the best endothelialization function, as it produced the largest endothelialization area that was three times more than of that of plain ePTFE on day 14, maintained the highest average cell viability, and had the best cell morphology. Hydrogel 40:60 also showed excellent hemocompatibility, prolonged activated partial thromboplastin time (aPTT), and good mechanical properties. Overall, bi-layered poly(AAm-co-NaAMPS)-xanthan hydrogel-ePTFE vascular grafts with the Hydrogel 40:60 composition could potentially solve the critical challenge of thrombus formation in vascular graft transplantation applications.

Enhancing perovskite solar cells efficiency through cesium fluoride mediated surface lead iodide modulation.

The presence of an excessive amount of lead iodide on the surface of perovskite solar cells (PSCs) is a significant contributing factor that adversely affects the stability of these devices when exposed to continuous light. To address this issue, we developed an effective strategy involving polishing PbI2 on a perovskite surface using CsF. In this study, we investigated the effects of CsF post-treatment on perovskite films and their photovoltaic properties. The results of the time-resolved photoluminescence and ultraviolet photoelectron spectroscopy tests reveal the significant positive impact of our passivation method based on CsF, which reduces the valence band offset between the perovskite and hole transport layers while simultaneously enhancing the carrier interface transport. PSCs treated with CsF exhibited a photoelectric conversion efficiency (PCE) of 24.25% and an increased fill factor (FF) of 81.72%, which surpassed those of the original PSCs (PCE = 22.12% and FF = 77.40%). Furthermore, after aging for over 2500 h at room temperature and in 30 ± 10% humidity, the PCE of the unpacked PSCs reduced to only 42% of the initial value. Furthermore, the devices treated with CsF maintained their impressive performance, with the PCE maintaining optimal levels at 91% of the initial efficiency.

Data mining from process monitoring of typical polluting enterprise.

With the increasing volume of environmental monitoring data, extracting valuable insights from multivariate time series sensor data can facilitate comprehensive information utilization and support informed decision-making in environmental management. However, there is a dearth of comprehensive research on multivariate data analysis for process monitoring in typical polluting enterprises. In this study, an artificial neural network model based on back-propagation algorithm (BP-ANN) was developed to predict the wastewater and exhaust gas emissions using IoT data obtained from process monitoring of a typical polluting enterprise located in Taizhou, Zhejiang Province, China. The results indicate that the model constructed has a high predictive coefficient of determination (R2) with values of 0.8510, 0.9565, 0.9561, 0.9677, and 0.9061 for chemical oxygen demand (COD), potential of hydrogen (pH), electrical conductivity (EC), flue gas emission (FGE), and non-methane hydrocarbon concentration (NMHC) respectively. For the first time, the variable importance measure (VIM)-assisted BP-ANN was employed to investigate the internal and external correlations between wastewater and exhaust gas treatment, thereby enhancing the interpretability of mapping features in the BP-ANN model. The predicted errors for pH and FGE have been demonstrated to fall within the range of - 0.62 ~ 0.30 and - 0.21 ~ 0.15 m3/s, respectively, with average relative errors of 1.05% and 9.60%, which is advantageous in detecting anomalous data and forecasting pollution indicator values. Our approach successfully addresses the challenge of segregating data analysis for wastewater disposal and exhaust gas disposal in the process monitoring of polluting enterprises, while also unearthing potential variables that significantly contribute to the BP-ANN model, thereby facilitating the selection and extraction of characteristic variables.

A live-cell image-based machine learning strategy for reducing variability in PSC differentiation systems.

The differentiation of pluripotent stem cells (PSCs) into diverse functional cell types provides a promising solution to support drug discovery, disease modeling, and regenerative medicine. However, functional cell differentiation is currently limited by the substantial line-to-line and batch-to-batch variabilities, which severely impede the progress of scientific research and the manufacturing of cell products. For instance, PSC-to-cardiomyocyte (CM) differentiation is vulnerable to inappropriate doses of CHIR99021 (CHIR) that are applied in the initial stage of mesoderm differentiation. Here, by harnessing live-cell bright-field imaging and machine learning (ML), we realize real-time cell recognition in the entire differentiation process, e.g., CMs, cardiac progenitor cells (CPCs), PSC clones, and even misdifferentiated cells. This enables non-invasive prediction of differentiation efficiency, purification of ML-recognized CMs and CPCs for reducing cell contamination, early assessment of the CHIR dose for correcting the misdifferentiation trajectory, and evaluation of initial PSC colonies for controlling the start point of differentiation, all of which provide a more invulnerable differentiation method with resistance to variability. Moreover, with the established ML models as a readout for the chemical screen, we identify a CDK8 inhibitor that can further improve the cell resistance to the overdose of CHIR. Together, this study indicates that artificial intelligence is able to guide and iteratively optimize PSC differentiation to achieve consistently high efficiency across cell lines and batches, providing a better understanding and rational modulation of the differentiation process for functional cell manufacturing in biomedical applications.