Machine learning approaches for practical predicting outpatient near-future AECOPD based on nationwide electronic medical records.

In this research, we aimed to harness machine learning to predict the imminent risk of acute exacerbation in chronic obstructive pulmonary disease (AECOPD) patients. Utilizing retrospective data from electronic medical records of two Taiwanese hospitals, we identified 26 critical features. To predict 3- and 6-month AECOPD occurrences, we deployed five distinct machine learning algorithms alongside ensemble learning. The 3-month risk prediction was best realized by the XGBoost model, achieving an AUC of 0.795, whereas the XGBoost was superior for the 6-month prediction with an AUC of 0.813. We conducted an explainability analysis and found that the episode of AECOPD, mMRC score, CAT score, respiratory rate, and the use of inhaled corticosteroids were the most impactful features. Notably, our approach surpassed predictions that relied solely on CAT or mMRC scores. Accordingly, we designed an interactive prediction system that provides physicians with a practical tool to predict near-term AECOPD risk in outpatients.

Greenhouse gases emissions and carbon budget estimation in horizontal subsurface flow constructed wetlands with different plant species.

Constructed wetlands (CWs) are pivotal for wastewater treatment due to their high efficiency and numerous advantages. The impact of plant species and diversity on greenhouse gas (GHG) emissions from CWs requires a more comprehensive evaluation. Moreover, controversial perspectives persist about whether CWs function as carbon sinks or sources. In this study, horizontal subsurface flow (HSSF) CWs vegetated with Cyperus alternifolius, Typhae latifolia, Acorus calamus, and the mixture of these three species were constructed to evaluate pollutant removal efficiencies and GHG emissions, and estimate carbon budgets. Polyculture CWs can stably remove COD (86.79 %), NH4+-N (97.41 %), NO3--N (98.55 %), and TP (98.48 %). They also mitigated global warming potential (GWP) by suppressing N2O emissions compared with monoculture CWs. The highest abundance of the Pseudogulbenkiania genus, crucial for denitrification, was observed in polyculture CWs, indicating that denitrification dominated in nitrogen removal. While the highest nosZ copy numbers were observed in CWs vegetated with Cyperus alternifolius, suggesting its facilitation of denitrification-related microbes. Selecting Cyperus alternifolius to increase species diversity is proposed for simultaneously maintaining the water purification capacity and reducing GHG emissions. Carbon budget estimations revealed that all four types of HSSF CWs were carbon sinks after six months of operation, with carbon accumulation capacity of 4.90 ± 1.50 (Cyperus alternifolius), 3.31 ± 2.01 (Typhae latifola), 1.78 ± 1.30 (Acorus calamus), and 2.12 ± 0.88 (polyculture) kg C/m2/yr. This study implies that under these operation conditions, CWs function as carbon sinks rather than sources, aligning with carbon peak and neutrality objectives and presenting significant potential for carbon reduction efforts.

CD226 promotes renal fibrosis by regulating macrophage activation and migration.

It has been found that CD226 plays an important role in regulating macrophage function, but its expression and function in macrophages during renal fibrogenesis have not been studied. Our data demonstrated that CD226 expression in macrophages was obviously upregulated in the unilateral ureteral obstruction model, while CD226 deficiency attenuated collagen deposition in renal interstitium along with fewer M1 within renal cortex and renal medulla and a lower level of proinflammatory factors compared to that of control littermates. Further studies demonstrated that Cd226-/- bone marrow-derived macrophages transferring could significantly reduce the tubular injury, collagen deposition, and proinflammatory cytokine secretion compared with that of Cd226+/+ bone marrow-derived macrophages transferring in the unilateral ureteral obstruction model. Mechanistic investigations revealed that CD226 promoted proinflammatory M1 macrophage accumulation in the kidney via suppressing KLF4 expression in macrophages. Therefore, our results uncovered a pathogenic role of CD226 during the development of chronic kidney disease by promoting monocyte infiltration from peripheral blood into the kidney and enhancing macrophage activation toward the inflammatory phenotype by suppressing KLF4 expression.

Human Airway Organoids and Multimodal Imaging-Based Toxicity Evaluation of 1-Nitropyrene.

Despite significant advances in understanding the general health impacts of air pollution, the toxic effects of air pollution on cells in the human respiratory tract are still elusive. A robust, biologically relevant in vitro model for recapitulating the physiological response of the human airway is needed to obtain a thorough understanding of the molecular mechanisms of air pollutants. In this study, by using 1-nitropyrene (1-NP) as a proof-of-concept, we demonstrate the effectiveness and reliability of evaluating environmental pollutants in physiologically active human airway organoids. Multimodal imaging tools, including live cell imaging, fluorescence microscopy, and MALDI-mass spectrometry imaging (MSI), were implemented to evaluate the cytotoxicity of 1-NP for airway organoids. In addition, lipidomic alterations upon 1-NP treatment were quantitatively analyzed by nontargeted lipidomics. 1-NP exposure was found to be associated with the overproduction of reactive oxygen species (ROS), and dysregulation of lipid pathways, including the SM-Cer conversion, as well as cardiolipin in our organoids. Compared with that of cell lines, a higher tolerance of 1-NP toxicity was observed in the human airway organoids, which might reflect a more physiologically relevant response in the native airway epithelium. Collectively, we have established a novel system for evaluating and investigating molecular mechanisms of environmental pollutants in the human airways via the combinatory use of human airway organoids, multimodal imaging analysis, and MS-based analyses.

Simultaneous SERS Sensing of Cysteine and Homocysteine in Blood Based on the CBT-Cys Click Reaction: Toward Precisive Diagnosis of Schizophrenia.

At present, there is a lack of sufficiently specific laboratory diagnostic indicators for schizophrenia. Serum homocysteine (Hcy) levels have been found to be related to schizophrenia. Cysteine (Cys) is a demethylation product in the metabolism of Hcy, and they always coexist with highly similar structures in vivo. There are few reports on the use of Cys as a diagnostic biomarker for schizophrenia in collaboration with Hcy, mainly because the rapid, economical, accurate, and high-throughput simultaneous detection of Cys and Hcy in serum is highly challenging. Herein, a click reaction-based surface-enhanced Raman spectroscopy (SERS) sensor was developed for simultaneous and selective detection of Cys and Hcy. Through the efficient and specific CBT-Cys click reaction between the probe containing cyan benzothiazole and Cys/Hcy, the tiny methylene difference between the molecular structures of Cys and Hcy was converted into the difference between the ring skeletons of the corresponding products that could be identified by plasmonic silver nanoparticle enhanced molecular fingerprint spectroscopy to realize discriminative detection. Furthermore, the SERS sensor was successfully applied to the detection in related patient serum samples, and it was found that the combined analysis of Cys and Hcy can improve the diagnostic accuracy of schizophrenia compared to a single indicator.

Exposure to real-ambient bedroom light at night delayed circadian rhythm in healthy Chinese young adults: A cross-sectional study.

BACKGROUND: Light at night (LAN) have attracted increased research attention on account of its widespread health hazards. However, the underlying mechanism remains unknown. The objective of this study was to investigate the effects of real-ambient bedroom LAN exposure on circadian rhythm among young adults and potential sex differences. METHODS: Bedroom LAN exposure was measured at 60-s intervals for 2 consecutive days using a portable illuminance meter. Circadian phase was determined by the dim light melatonin onset (DLMO) time in 7 time-series saliva samples. RESULTS: The mean age of the 142 participants was 20.7 ± 0.8 years, and 59.9% were women. The average DLMO time was 21:00 ± 1:11 h, with men (21:19 ± 1:12 h) later than women (20:48 ± 1:07 h). Higher level of LAN intensity (LANavg ≥ 3lx vs. LANavg < 3lx) was associated with an 81.0-min later in DLMO time (95% CI: 0.99, 1.72), and longer duration of nighttime light intensity ≥ 5lx (LAN5; LAN5 ≥ 45 min vs. LAN5 < 45 min) was associated with a 51.6-min later in DLMO time (95% CI: 0.46, 1.26). In addition, the delayed effect of LAN exposure on circadian phase was more pronounced in men than in women (all P-values <0.05). CONCLUSIONS: Overall, bedroom LAN exposure was significantly associated with delayed circadian rhythm. Additionally, the delayed effect is more significant in men. Keeping bedroom dark at night may be a practicable option to prevent circadian disruption and associated health implications. Future studies with more advanced light measurement instrument and consensus methodology for DLMO assessment are warranted.

A multi-parameter intrahepatic cholangiocarcinoma scoring system based on modified contrast-enhanced ultrasound LI-RADS M criteria for differentiating intrahepatic cholangiocarcinoma from hepatocellular carcinoma.

PURPOSE: To develop a multi-parameter intrahepatic cholangiocarcinoma (ICC) scoring system and compare its diagnostic performance with contrast-enhanced ultrasound (CEUS) liver imaging reporting and data system M (LR-M) criteria for differentiating ICC from hepatocellular carcinoma (HCC). METHODS: This retrospective study enrolled 62 high-risk patients with ICCs and 62 high-risk patients with matched HCCs between January 2022 and December 2022 from two institutions. The CEUS LR-M criteria was modified by adjusting the early wash-out onset (within 45 s) and the marked wash-out (within 3 min). Then, a multi-parameter ICC scoring system was established based on clinical features, B-mode ultrasound features, and modified LR-M criteria. RESULT: We found that elevated CA 19-9 (OR=12.647), lesion boundary (OR=11.601), peripheral rim-like arterial phase hyperenhancement (OR=23.654), early wash-out onset (OR=7.211), and marked wash-out (OR=19.605) were positive predictors of ICC, whereas elevated alpha-fetoprotein (OR=0.078) was a negative predictor. Based on these findings, an ICC scoring system was established. Compared with the modified LR-M and LR-M criteria, the ICC scoring system showed the highest area under the curve (0.911 vs. 0.831 and 0.750, both p<0.05) and specificity (0.935 vs. 0.774 and 0.565, both p<0.05). Moreover, the numbers of HCCs categorized as LR-M decreased from 27 (43.5%) to 14 (22.6%) and 4 (6.5%) using the modified LR-M criteria and ICC scoring system, respectively. CONCLUSION: The modified LR-M criteria-based multi-parameter ICC scoring system had the highest specificity for diagnosing ICC and reduced the number of HCC cases diagnosed as LR-M category.

Progress and Implications from Genetic Studies of Bipolar Disorder.

With the advancements in gene sequencing technologies, including genome-wide association studies, polygenetic risk scores, and high-throughput sequencing, there has been a tremendous advantage in mapping a detailed blueprint for the genetic model of bipolar disorder (BD). To date, intriguing genetic clues have been identified to explain the development of BD, as well as the genetic association that might be applied for the development of susceptibility prediction and pharmacogenetic intervention. Risk genes of BD, such as CACNA1C, ANK3, TRANK1, and CLOCK, have been found to be involved in various pathophysiological processes correlated with BD. Although the specific roles of these genes have yet to be determined, genetic research on BD will help improve the prevention, therapeutics, and prognosis in clinical practice. The latest preclinical and clinical studies, and reviews of the genetics of BD, are analyzed in this review, aiming to summarize the progress in this intriguing field and to provide perspectives for individualized, precise, and effective clinical practice.

The impact of quetiapine monotherapy or in combination with lithium on the thyroid function in patients with bipolar depression: A retrospective study.

OBJECTIVE: This study aims to investigate whether quetiapine monotherapy or in combination with lithium significantly disturbs thyroid function in depressed patients with bipolar disorder (BD), and whether difference exists in the post-treatment thyroid function between the two therapies. METHODS: Based on the electric medical records, outpatients and inpatients with a current depressive episode of BD from January 2016 to December 2022 were screened. All patients were treated with quetiapine monotherapy or in combination with lithium. In addition to the demographic data and depression scale, thyroid profiles including total thyroxine (TT4), total triiodothyronine (TT3), free thyroxine (FT4), free triiodothyronine (FT3), thyroid-stimulating hormone (TSH), thyroid peroxidase antibody (TPOAb), and antithyroglobulin antibody (TGAb) were recorded, analyzed, and compared before and after the treatment. RESULTS: Totally, 73 eligible patients were enrolled, including 53 in the monotherapy group (MG) and 20 in the combined therapy group (CG). No significant differences in thyroid profiles were detected between the two groups at the baseline (p > 0.05). After one-month treatment, in the MG, serum levels of TT4, TT3, FT4, and FT3 reduced significantly (p < 0.05), while TSH, TPOAb, and TGAb increased significantly (p < 0.05). In the CG, serum levels of TT4, TT3, and FT4 reduced and TSH increased following one-month treatment (p < 0.05), with no significant change in FT3, TPOAb, or TGAb (p > 0.05). After one-month treatment, no difference of TT4, TT3, FT4, FT3, and TSH was found between the two groups (p > 0.05). CONCLUSION: Both quetiapine monotherapy and a combined therapy with lithium significantly disturbed thyroid function in patients with bipolar depression, while quetiapine monotherapy seems to be associated with immune dysregulation in the thyroid.

Real-time artificial intelligence predicts adverse outcomes in acute pancreatitis in the emergency department: Comparison with clinical decision rule.

OBJECTIVE: Artificial intelligence (AI) prediction is increasingly used for decision making in health care, but its application for adverse outcomes in emergency department (ED) patients with acute pancreatitis (AP) is not well understood. This study aimed to clarify this aspect. METHODS: Data from 8274 ED patients with AP in three hospitals from 2009 to 2018 were analyzed. Demographic data, comorbidities, laboratory results, and adverse outcomes were included. Six algorithms were evaluated, and the one with the highest area under the curve (AUC) was implemented into the hospital information system (HIS) for real-time prediction. Predictive accuracy was compared between the AI model and Bedside Index for Severity in Acute Pancreatitis (BISAP). RESULTS: The mean ± SD age was 56.1 ± 16.7 years, with 67.7% being male. The AI model was successfully implemented in the HIS, with Light Gradient Boosting Machine (LightGBM) showing the highest AUC for sepsis (AUC 0.961) and intensive care unit (ICU) admission (AUC 0.973), and eXtreme Gradient Boosting (XGBoost) showing the highest AUC for mortality (AUC 0.975). Compared to BISAP, the AI model had superior AUC for sepsis (BISAP 0.785), ICU admission (BISAP 0.778), and mortality (BISAP 0.817). CONCLUSIONS: The first real-time AI prediction model implemented in the HIS for predicting adverse outcomes in ED patients with AP shows favorable initial results. However, further external validation is needed to ensure its reliability and accuracy.

Gradient-based adaptive wavelet de-noising method for photoacoustic imaging in vivo.

Photoacoustic imaging (PAI) has been applied to many biomedical applications over the past decades. However, the received PA signal usually suffers from poor SNR. Conventional solution of employing higher-power laser, or doing long-time signal averaging, may raise the system cost, time consumption, and tissue damage. Another strategy is de-noising algorithm design. In this paper, we propose a gradient-based adaptive wavelet de-noising method, which sets the energy gradient mutation point of low-frequency wavelet components as the threshold. We conducted simulation, ex-vivo and in-vivo experiments using acoustic-resolution PAM. The quality of de-noised PA image/signal by our proposed algorithm has improved by at least 30%, in comparison to the traditional signal denoising algorithms, which produces better contrast and clearer details. Moreover, it produces good results when dealing with multi-layer structures. The proposed de-noising method provides potential to improve the SNR of PA signal under single-shot low-power laser illumination for biomedical applications in vivo.

S-Wave Accelerates Optimization-based Photoacoustic Image Reconstruction in vivo.

OBJECTIVE: Photoacoustic imaging has undergone rapid development in recent years. To simulate photoacoustic imaging on a computer, the most popular MATLAB toolbox currently used for the forward projection process is k-Wave. However, k-Wave suffers from significant computation time. Here we propose a straightforward simulation approach based on superposed Wave (s-Wave) to accelerate photoacoustic simulation. METHODS: In this study, we consider the initial pressure distribution as a collection of individual pixels. By obtaining standard sensor data from a single pixel beforehand, we can easily manipulate the phase and amplitude of the sensor data for specific pixels using loop and multiplication operators. The effectiveness of this approach is validated through an optimization-based reconstruction algorithm. RESULTS: The results reveal significantly reduced computation time compared with k-Wave. Particularly in a sparse 3-D configuration, s-Wave exhibits a speed improvement >2000 times compared with k-Wave. In terms of optimization-based image reconstruction, in vivo imaging results reveal that using the s-Wave method yields images highly similar to those obtained using k-Wave, while reducing the reconstruction time by approximately 50 times. CONCLUSION: Proposed here is an accelerated optimization-based algorithm for photoacoustic image reconstruction, using the fast s-Wave forward projection simulation. Our method achieves substantial time savings, particularly in sparse system configurations. Future work will focus on further optimizing the algorithm and expanding its applicability to a broader range of photoacoustic imaging scenarios.

Photoacoustic imaging plus X: a review.

Significance: Photoacoustic (PA) imaging (PAI) represents an emerging modality within the realm of biomedical imaging technology. It seamlessly blends the wealth of optical contrast with the remarkable depth of penetration offered by ultrasound. These distinctive features of PAI hold tremendous potential for various applications, including early cancer detection, functional imaging, hybrid imaging, monitoring ablation therapy, and providing guidance during surgical procedures. The synergy between PAI and other cutting-edge technologies not only enhances its capabilities but also propels it toward broader clinical applicability. Aim: The integration of PAI with advanced technology for PA signal detection, signal processing, image reconstruction, hybrid imaging, and clinical applications has significantly bolstered the capabilities of PAI. This review endeavor contributes to a deeper comprehension of how the synergy between PAI and other advanced technologies can lead to improved applications. Approach: An examination of the evolving research frontiers in PAI, integrated with other advanced technologies, reveals six key categories named "PAI plus X." These categories encompass a range of topics, including but not limited to PAI plus treatment, PAI plus circuits design, PAI plus accurate positioning system, PAI plus fast scanning systems, PAI plus ultrasound sensors, PAI plus advanced laser sources, PAI plus deep learning, and PAI plus other imaging modalities. Results: After conducting a comprehensive review of the existing literature and research on PAI integrated with other technologies, various proposals have emerged to advance the development of PAI plus X. These proposals aim to enhance system hardware, improve imaging quality, and address clinical challenges effectively. Conclusions: The progression of innovative and sophisticated approaches within each category of PAI plus X is positioned to drive significant advancements in both the development of PAI technology and its clinical applications. Furthermore, PAI not only has the potential to integrate with the above-mentioned technologies but also to broaden its applications even further.

Sarcoidosis or Tuberculosis: Should Corticosteroids Be Used?

Sarcoidosis shows high similarity with tuberculosis in clinical manifestations and imaging features. It is rarely reported whether sarcoidosis patients with suspected latent tuberculosis can be treated safely with immunosuppressive therapy. We reported on a 54-year-old man who presented with enlarged lymph nodes persisting for decades, accompanied by renal impairment and refractory hypercalcemia. The patient was diagnosed with sarcoidosis and suspected latent tuberculosis (as suggested by a positive tuberculin test and tuberculosis interferon-gamma release assays) and received prednisone under follow-up. The patient showed significant amelioration in hypercalcemia and shrinkage of lymph nodes, without evidence of developing active tuberculosis. For sarcoidosis patients with suspected latent tuberculosis, immunosuppressive agents can be utilized safely based on close monitoring. Further efforts are required to reveal whether sarcoidosis and tuberculosis can trigger similar immune responses and what the clinical implications are.

Pushing the Frontiers: Optogenetics for Illuminating the Neural Pathophysiology of Bipolar Disorder.

Bipolar disorder (BD), a disabling mental disorder, is featured by the oscillation between episodes of depression and mania, along with disturbance in the biological rhythms. It is on an urgent demand to identify the intricate mechanisms of BD pathophysiology. Based on the continuous progression of neural science techniques, the dysfunction of circuits in the central nervous system was currently thought to be tightly associated with BD development. Yet, challenge exists since it depends on techniques that can manipulate spatiotemporal dynamics of neuron activity. Notably, the emergence of optogenetics has empowered researchers with precise timing and local manipulation, providing a possible approach for deciphering the pathological underpinnings of mental disorders. Although the application of optogenetics in BD research remains preliminary due to the scarcity of valid animal models, this technique will advance the psychiatric research at neural circuit level. In this review, we summarized the crucial aberrant brain activity and function pertaining to emotion and rhythm abnormities, thereby elucidating the underlying neural substrates of BD, and highlighted the importance of optogenetics in the pursuit of BD research.

In Situ Transmission Electron Microscopy Investigation on Oriented Attachment of Nanodiamonds.

Oriented attachment (OA) plays an important role in the assembly of nanoparticles and the regulation of their size and morphology, which is expected to be an effective means to modulate the properties of nanodiamonds (NDs). However, there remains a dearth of comprehensive investigation into the OA mechanism of NDs. Using in situ transmission electron microscopy, we conducted atomic-resolution investigation on the OA events of ND pairs under electron beam irradiation. The occurrence of an OA event is contingent upon the alignment between two ND surfaces, and the coalesced particles undergo recrystallization to form spherical shapes. Both experimental observations and molecular dynamics (MD) simulations reveal that ND pairs exhibit a preference for coalescing along the {111} surfaces. Additionally, MD simulations indicate that kinetic factors, such as contact surface area and contact angle, also influence the coalescence process.

Early prediction of acute kidney injury in patients with gastrointestinal bleeding admitted to the intensive care unit based on extreme gradient boosting.

Background: Acute kidney injury (AKI) is a common and important complication in patients with gastrointestinal bleeding who are admitted to the intensive care unit. The present study proposes an artificial intelligence solution for acute kidney injury prediction in patients with gastrointestinal bleeding admitted to the intensive care unit. Methods: Data were collected from the eICU Collaborative Research Database (eICU-CRD) and Medical Information Mart for Intensive Care-IV (MIMIC-IV) database. The prediction model was developed using the extreme gradient boosting (XGBoost) model. The area under the receiver operating characteristic curve, accuracy, precision, area under the precision-recall curve (AUC-PR), and F1 score were used to evaluate the predictive performance of each model. Results: Logistic regression, XGBoost, and XGBoost with severity scores were used to predict acute kidney injury risk using all features. The XGBoost-based acute kidney injury predictive models including XGBoost and XGBoost+severity scores model showed greater accuracy, recall, precision AUC, AUC-PR, and F1 score compared to logistic regression. Conclusion: The XGBoost model obtained better risk prediction for acute kidney injury in patients with gastrointestinal bleeding admitted to the intensive care unit than the traditional logistic regression model, suggesting that machine learning (ML) techniques have the potential to improve the development and validation of predictive models in patients with gastrointestinal bleeding admitted to the intensive care unit.

Development of real-time individualized risk prediction models for contrast associated acute kidney injury and 30-day dialysis after contrast enhanced computed tomography.

PURPOSE: This study aimed to develop preprocedural real-time artificial intelligence (AI)-based systems for predicting individualized risks of contrast-associated acute kidney injury (CA-AKI) and dialysis requirement within 30 days following contrast-enhanced computed tomography (CECT). METHOD: This single-center, retrospective study analyzed adult patients from emergency or in-patient departments who underwent CECT; 18,895 patients were included after excluding those who were already on dialysis, had stage V chronic kidney disease, or had missing data regarding serum creatinine levels within 7 days before and after CECT. Clinical parameters, laboratory data, medication exposure, and comorbid diseases were selected as predictive features. The patients were randomly divided into model training and testing groups at a 7:3 ratio. Logistic regression (LR) and random forest (RF) were employed to create prediction models, which were evaluated using receiver operating characteristic curves. RESULTS: The incidence rates of CA-AKI and dialysis within 30 days post-CECT were 6.69% and 0.98%, respectively. For CA-AKI prediction, LR and RF exhibited similar performance, with areas under curve (AUCs) of 0.769 and 0.757, respectively. For 30-day dialysis prediction, LR (AUC, 0.863) and RF (AUC, 0.872) also exhibited similar performance. Relative to eGFR-alone, the LR and RF models produced significantly higher AUCs for CA-AKI prediction (LR vs. eGFR alone, 0.769 vs. 0.626, p < 0.001) and 30-day dialysis prediction (RF vs. eGFR alone, 0.872 vs. 0.738, p < 0.001). CONCLUSIONS: The proposed AI prediction models significantly outperformed eGFR-alone for predicting the CA-AKI and 30-day dialysis risks of emergency department and hospitalized patients who underwent CECT.

Photoacoustic digital brain and deep-learning-assisted image reconstruction.

Photoacoustic tomography (PAT) is a newly developed medical imaging modality, which combines the advantages of pure optical imaging and ultrasound imaging, owning both high optical contrast and deep penetration depth. Very recently, PAT is studied in human brain imaging. Nevertheless, while ultrasound waves are passing through the human skull tissues, the strong acoustic attenuation and aberration will happen, which causes photoacoustic signals' distortion. In this work, we use 180 T1 weighted magnetic resonance imaging (MRI) human brain volumes along with the corresponding magnetic resonance angiography (MRA) brain volumes, and segment them to generate the 2D human brain numerical phantoms for PAT. The numerical phantoms contain six kinds of tissues, which are scalp, skull, white matter, gray matter, blood vessel and cerebrospinal fluid. For every numerical phantom, Monte-Carlo based optical simulation is deployed to obtain the photoacoustic initial pressure based on optical properties of human brain. Then, two different k-wave models are used for the skull-involved acoustic simulation, which are fluid media model and viscoelastic media model. The former one only considers longitudinal wave propagation, and the latter model takes shear wave into consideration. Then, the PA sinograms with skull-induced aberration is taken as the input of U-net, and the skull-stripped ones are regarded as the supervision of U-net to train the network. Experimental result shows that the skull's acoustic aberration can be effectively alleviated after U-net correction, achieving conspicuous improvement in quality of PAT human brain images reconstructed from the corrected PA signals, which can clearly show the cerebral artery distribution inside the human skull.

A Natural Moisture Gradient Affects Soil Fungal Communities on the South Shore of Hulun Lake, Inner Mongolia, China.

Soil moisture content (SWC) can change the diversity and composition of soil fungal communities by affecting soil texture and soil nutrients. To explore the response of soil fungal communities to moisture in the grassland ecosystem on the south shore of Hulun Lake, we set up a natural moisture gradient that was subdivided into high (HW), medium (MW), and low (LW) water contents. Vegetation was investigated by quadrat method, and aboveground biomass was collected by the mowing method. Soil physicochemical properties were obtained by internal experiments. The composition of the soil fungal community was determined using high-throughput sequencing technology. The results showed significant differences in soil texture, nutrients, and fungal species diversity under the moisture gradients. Although there was significant clustering of fungal communities in different treatments, the fungal community composition was not significantly different. According to the phylogenetic tree, the Ascomycota and Basidiomycota were the most important branches. The fungal species diversity was smaller when SWC was higher, and in this environment (HW), the fungal-dominant species were significantly related to SWC and soil nutrients. At this time, soil clay formed a protective barrier for the survival of the dominant classes Sordariomycetes and Dothideomycetes and increased their relative abundance. In summary, the fungal community responded significantly to SWC on the southern shore of the Hulun Lake ecosystem in Inner Mongolia, China, and the fungal community composition of the HW group was stable and easier to survive.