A study of quantifying the influence of kitchen human activity on indoor air quality dynamics.

Indoor air quality (IAQ) is increasingly recognised as one of the critical factors influencing human health, particularly given the amount of time people spend indoors. This study investigated the impact of real-life kitchen human activity (KHA) on IAQ. We used low-cost sensors to measure real-time concentrations of smoke, carbon monoxide (CO), and particulate matter (PM10 and PM2.5) in the kitchen of a household with three adults, analysing KHAs by dividing them into five categories. The fixed effect model was employed to analyse the data, explaining the impact of different KHAs on IAQ. The results showed that compared to other KHAs, using the gas stove had the greatest impact on IAQ, with average increases of 13% in smoke, 24.4% in CO, 9.8% in PM10, and 5.34% in PM2.5. The study also found that without windows and with insufficient ventilation, only using the range hood cannot effectively and obviously reduce PM levels. These findings highlight the need for comprehensive IAQ management strategies and further research. Despite its limitations, this study also validated the potential of low-cost sensors in IAQ monitoring.

Influence of the Bias Voltage on Effective Electron Velocity in AlGaN/GaN High Electron Mobility Transistors.

The small-signal S parameters of the fabricated double-finger gate AlGaN/GaN high electron mobility transistors (HEMTs) were measured at various direct current quiescent operating points (DCQOPs). Under active bias conditions, small-signal equivalent circuit (SSEC) parameters such as Rs and Rd, and intrinsic parameters were extracted. Utilizing fT and the SSEC parameters, the effective electron velocity (νe-eff) and intrinsic electron velocity (νe-int) corresponding to each gate bias (VGS) were obtained. Under active bias conditions, the influence mechanism of VGS on νe-eff was systematically studied, and an expression was established that correlates νe-eff, νe-int, and bias-dependent parasitic resistances. Through the analysis of the main scattering mechanisms in AlGaN/GaN HEMTs, it has been discovered that the impact of VGS on νe-eff should be comprehensively analyzed from the aspects of νe-int and parasitic resistances. On the one hand, changes in VGS influence the intensity of polar optical phonon (POP) scattering and polarization Coulomb field (PCF) scattering, which lead to changes in νe-int dependent on VGS. The trend of νe-int with changes in VGS plays a dominant role in determining the trend of νe-eff with changes in VGS. On the other hand, both POP scattering and PCF scattering affect νe-eff through their impact on parasitic resistance. Since there is a difference in the additional scattering potential corresponding to the additional polarization charges (APC) between the gate-source/drain regions and the region under the gate, the mutual effects of PCF scattering on the under-gate electron system and the gate-source/drain electron system should be considered when adjusting the PCF scattering intensity through device structure optimization to improve linearity. This study contributes to a new understanding of the electron transport mechanisms in AlGaN/GaN HEMTs and provides a novel theoretical basis for improving device performance.

OD-MVSNet: Omni-dimensional dynamic multi-view stereo network.

Multi-view stereo based on learning is a critical task in three-dimensional reconstruction, enabling the effective inference of depth maps and the reconstruction of fine-grained scene geometry. However, the results obtained by current popular 3D reconstruction methods are not precise, and achieving high-accuracy scene reconstruction remains challenging due to the pervasive impact of feature extraction and the poor correlation between cost and volume. In addressing these issues, we propose a cascade deep residual inference network to enhance the efficiency and accuracy of multi-view stereo depth estimation. This approach builds a cost-volume pyramid from coarse to fine, generating a lightweight, compact network to improve reconstruction results. Specifically, we introduce the omni-dimensional dynamic atrous spatial pyramid pooling (OSPP), a multiscale feature extraction module capable of generating dense feature maps with multiscale contextual information. The feature maps encoded by the OSPP module can generate dense point clouds without consuming significant memory. Furthermore, to alleviate the issue of feature mismatch in cost volume regularization, we propose a normalization-based 3D attention module. The 3D attention module aggregates crucial information within the cost volume across the dimensions of channel, spatial, and depth. Through extensive experiments on benchmark datasets, notably DTU, we found that the OD-MVSNet model outperforms the baseline model by approximately 1.4% in accuracy loss, 0.9% in completeness loss, and 1.2% in overall loss, demonstrating the effectiveness of our module.

Enhanced multi view 3D reconstruction with improved MVSNet.

Although 3D reconstruction has been widely used in many fields as a key component of environment perception, existing technologies still have the potential for further improvement in 3D scene reconstruction. We propose an improved reconstruction algorithm based on the MVSNet network architecture. To glean richer pixel details from images, we suggest deploying a DE module integrated with a residual framework, which supplants the prevailing feature extraction mechanism. The DE module uses ECA-Net and dilated convolution to expand the receptive field range, performing feature splicing and fusion through the residual structure to retain the global information of the original image. Moreover, harnessing attention mechanisms refines the 3D cost volume's regularization process, bolstering the integration of information across multi-scale feature volumes, consequently enhancing depth estimation precision. When assessed our model using the DTU dataset, findings highlight the network's 3D reconstruction scoring a completeness (comp) of 0.411 mm and an overall quality of 0.418 mm. This performance is higher than that of traditional methods and other deep learning-based methods. Additionally, the visual representation of the point cloud model exhibits marked advancements. Trials on the Blended MVS dataset signify that our network exhibits commendable generalization prowess.

Local Tumor Progression Predictive Model Based on MRI for Colorectal Cancer Liver Metastases after Radiofrequency Ablation.

PURPOSE: To investigate the post-radiofrequency ablation (RFA) magnetic resonance imaging (MRI) characteristics in patients with liver metastases from colorectal cancer and to build a predictive model for local tumor progression based on these imaging markers. MATERIALS AND METHODS: A cohort of 73 patients with 110 colorectal cancer liver metastases (CRCLM) who underwent RFA and MRI one month post-ablation was included in image signs analysis and predictive model training. Using a newly developed MRI appearance scoring criteria, MR Image Appearance Scoring at One Month after RFA (MRIAS 1MO), the semi-quantitative analysis of MRI findings within the ablation zone were conducted independently by two radiologists. The intraclass correlation coefficient (ICC) was calculated to evaluate measurement reliability. Differences in MRIAS 1MO scores were compared using Mann-Whitney U test, focusing on local tumor response outcomes. Using local tumor progression (LTP) as the primary end point, MRIAS 1MO scores and other lesion morphological and clinical characteristics were included to establish predictive model. Predication accuracy was subsequently evaluated using calibration curve, time-dependent concordance index (C index) curve, and LTP-free survival (LTPFS) curve. Another cohort comprising 60 patients with 76 CRCLMs provided additional MRIAS 1MO scores and clinical data associated with LTP. We evaluated the performance of the established predictive model using calibration curve, time-dependent C index curve, and LTPFS curve. RESULTS: The MRIAS 1MO criteria exhibited strong measurement reliability. The ICC values of T1S (scores from T1WI), T2S (scores form T2WI) and NCES (scores by adding T1S to T2S) MRIS (the overall scores) were 0.825, 0.779, 0.826 and 0.873, respectively. Lesions with LTP showed significantly higher median values for the overall MRIAS 1MO score (MRIS) compared to lesions without LTP (16 vs. 12, p < 0.001). MRIS and lesion diameter were independent prognostic factors of LTP and were included in predictive model (hazard ratio: MRIS over 13.5:4.275, lesion diameter larger than 30 mm: 2.056). The predictive model demonstrated an overall C index of 0.721 and risk stratification using the predictive model resulted in significantly different LPTFS times. In the validation cohort, the C index were 0.825, 0.794 and 0.764 at six, twelve and twenty-four months, respectively. Patients classified as high-risk in the validation cohort had a median LTPFS time of 10.0 months, while the median LTPFS time was not reached in the low-risk group. CONCLUSIONS: The semi-quantitative MRIAS 1MO criteria, used for post-RFA MRI appearance analysis, exhibited strong measurement reliability. Prediction models established based on overall MRIAS 1MO score (MRIS) and lesion diameter had good predictive performance for LTP in patients undergoing RFA for CRCLM treatment.

DRD4 alleviates acute kidney injury by suppressing ISG15/NOX4 axis-associated oxidative stress.

Acute kidney injury (AKI) is a life-threatening health condition associated with increasing morbidity and mortality. Despite extensive research on the mechanisms underlying AKI, effective clinical tools for prediction and treatment remain scarce. Oxidative stress and mitochondrial damage play a critical role in AKI and dopamine D4 receptor (DRD4) has been confirmed to be associated with oxidative stress. In this study, we hypothesized that DRD4 could attenuate AKI through its antioxidative and antiapoptotic effects. In vivo, DRD4 was remarkably decreased in the kidneys of mice subjected to ischemia/reperfusion injury (IRI) or cisplatin treatment. Notably, DRD4 significantly attenuated nephrotoxicity by suppressing oxidative stress and enhancing mitochondrial bioenergetics through the downregulation of reactive oxygen species (ROS) generation and NADPH oxidase 4 (NOX4) expression. In vitro, DRD4 demonstrated the ability to ameliorate oxidative stress-induced apoptosis in HK-2 cells subjected to hypoxia/reoxygenation- or cisplatin treatment. Transcriptome sequencing revealed that, mechanistically, DRD4 reduced the expression of its downstream target, interferon-stimulated gene 15 (ISG15), suppressing NOX4 ISGylation, enhancing the ubiquitination of NOX4, leading to its degradation, and ultimately counteracting oxidative stress-induced AKI. Altogether, these findings underscore the significance of DRD4 in AKI and elucidate DRD4 as a potential protectant against IRI or cisplatin-induced nephrotoxicity.

Tumor volume instead of recurrent T category predicts clinical outcome of patients with locally recurrent nasopharyngeal carcinoma salvaged by carbon ion radiation therapy.

BACKGROUND: Although carbon ion radiation therapy (CIRT) substantially improves the overall survival (OS) of patients with LR-NPC, approximately 40% of the patients may develop local recurrence. The purpose of study is to assess the value of tumor volume (TV) as a predictive tool to guide individualized CIRT. METHODS: Consecutive patients with LR-NPC treated using CIRT at Shanghai Proton and Heavy Ion Center between April 2015 and May 2019 were included. TV before CIRT was delineated and calculated. The generalized additive Cox model was used to examine the relationship between TV and OS and local progression-free survival (LPFS). A cutoff value of tumor volume was identified to best discriminate patients with different 2-year OS rates, using receiver operating characteristic (ROC) analysis. RESULTS: A total of 157 patients were enrolled. The median tumor volume was 22.49 (2.52-90.13) ml. In the univariable analyses, tumor volume was significantly associated with OS (p < 0.001) and LPFS (p = 0.01). The relationships with OS (p = 0.009) and LPFS (p = 0.020) remained significant in multivariable analyses. Using ROC analysis, a TV of 26.69 ml was identified to predict the 2-year OS rate. To facilitate potential clinical use, 25 ml was designated as the final cutoff value. The 2-year OS and LPFS rates were 88.6 % vs 62.3 %, and 54.7 % vs 35.5 %, for patients with a TV ≤ 25 ml and > 25 ml, respectively. CONCLUSION: Tumor volume could predict the OS and LPFS of patients. We propose that tumor volume should be considered in the risk stratification and CIRT-based treatment for patients with LR-NPC.

Prognostic Significance of Grade Discrepancy Between Primary Tumor and Venous Thrombus in Nonmetastatic Clear-cell Renal Cell Carcinoma: Analysis of the REMEMBER Registry and Implications for Adjuvant Therapy.

BACKGROUND: Further stratification of the risk of recurrence of clear-cell renal cell carcinoma (ccRCC) with venous tumor thrombus (VTT) will facilitate selection of candidates for adjuvant therapy. OBJECTIVE: To assess the impact of tumor grade discrepancy (GD) between the primary tumor (PT) and VTT in nonmetastatic ccRCC on disease-free survival (DFS), overall survival (OS), and cancer-specific survival (CSS). DESIGN, SETTING, AND PARTICIPANTS: This was a retrospective analysis of a multi-institutional nationwide data set for patients with pT3N0M0 ccRCC who underwent radical nephrectomy and thrombectomy. OUTCOMES MEASUREMENTS AND STATISTICAL ANALYSIS: Pathology slides were centrally reviewed. GD, a bidirectional variable (upgrading or downgrading), was numerically defined as the VTT grade minus the PT grade. Multivariable models were built to predict DFS, OS, and CSS. RESULTS AND LIMITATIONS: We analyzed data for 604 patients with median follow-up of 42 mo (excluding events). Tumor GD between VTT and PT was observed for 47% (285/604) of the patients and was an independent risk factor with incremental value in predicting the outcomes of interest (all p < 0.05). Incorporation of tumor GD significantly improved the performance of the ECOG-ACRIN 2805 (ASSURE) model. A GD-based model (PT grade, GD, pT stage, PT sarcomatoid features, fat invasion, and VTT consistency) had a c index of 0.72 for DFS. The hazard ratios were 8.0 for GD = +2 (p < 0.001), 1.9 for GD = +1 (p < 0.001), 0.57 for GD = -1 (p = 0.001), and 0.22 for GD = -2 (p = 0.003) versus GD = 0 as the reference. According to model-converted risk scores, DFS, OS, and CSS significantly differed between subgroups with low, intermediate, and high risk (all p < 0.001). CONCLUSIONS: Routine reporting of VTT upgrading or downgrading in relation to the PT and use of our GD-based nomograms can facilitate more informed treatment decisions by tailoring strategies to an individual patient's risk of progression. PATIENT SUMMARY: We developed a tool to improve patient counseling and guide decision-making on other therapies in addition to surgery for patients with the clear-cell type of kidney cancer and tumor invasion of a vein.

Cisplatin induces acute kidney injury by downregulating miR-30e-5p that targets Galnt3 to activate the AMPK signaling pathway.

Cisplatin nephrotoxicity is an etiological factor for acute kidney injury (AKI). MicroRNA (miRNA) expression is dysregulated in cisplatin-induced AKI (cAKI) although the underlying mechanisms are unclear. A cAKI model was established by intraperitoneally injecting cisplatin, and key miRNAs were screened using high-throughput miRNA sequencing. The functions of key miRNAs were determined using the cell viability, live/dead, reactive oxygen species (ROS), and 5-ethynyl-2'-deoxyuridine (EdU) proliferation assays. Additionally, the macrophage membrane was wrapped around a metal-organic framework (MOF) loaded with miRNA agomir to develop a novel composite material, macrophage/MOF/miRNA agomir nanoparticles (MMA NPs). High-throughput miRNA sequencing revealed that miR-30e-5p is a key miRNA that is downregulated in cAKI. The results of in vitro experiments demonstrated that miR-30e-5p overexpression partially suppressed the cisplatin-induced or lipopolysaccharide (LPS)-induced downregulation of cell viability, proliferation, upregulation of ROS production, and cell death. Meanwhile, the results of in vivo and in vitro experiments demonstrated that MMA NPs alleviated cAKI by exerting anti-inflammatory effects. Mechanistically, cisplatin downregulates the expression of miR-30e-5p, and the downregulated miR-30e-5p can target Galnt3 to activate the adenosine 5'-monophosphate activated protein kinase (AMPK) signaling pathway, which promotes the progression of AKI. Our study found that miR-30e-5p is a key downregulated miRNA in cAKI. The downregulated miR-30e-5p promotes AKI progression by targeting Galnt3 to activate the AMPK signaling pathway. The newly developed MMA NPs were found to have protective effects on cAKI, suggesting a potential novel strategy for preventing cAKI.

Prioritized experience replay in path planning via multi-dimensional transition priority fusion.

Introduction: Deep deterministic policy gradient (DDPG)-based path planning algorithms for intelligent robots struggle to discern the value of experience transitions during training due to their reliance on a random experience replay. This can lead to inappropriate sampling of experience transitions and overemphasis on edge experience transitions. As a result, the algorithm's convergence becomes slower, and the success rate of path planning diminishes. Methods: We comprehensively examines the impacts of immediate reward, temporal-difference error (TD-error), and Actor network loss function on the training process. It calculates experience transition priorities based on these three factors. Subsequently, using information entropy as a weight, the three calculated priorities are merged to determine the final priority of the experience transition. In addition, we introduce a method for adaptively adjusting the priority of positive experience transitions to focus on positive experience transitions and maintain a balanced distribution. Finally, the sampling probability of each experience transition is derived from its respective priority. Results: The experimental results showed that the test time of our method is shorter than that of PER algorithm, and the number of collisions with obstacles is less. It indicated that the determined experience transition priority accurately gauges the significance of distinct experience transitions for path planning algorithm training. Discussion: This method enhances the utilization rate of transition conversion and the convergence speed of the algorithm and also improves the success rate of path planning.

Evaluating the Performance of Different Large Language Models on Health Consultation and Patient Education in Urolithiasis.

OBJECTIVES: To evaluate the effectiveness of four large language models (LLMs) (Claude, Bard, ChatGPT4, and New Bing) that have large user bases and significant social attention, in the context of medical consultation and patient education in urolithiasis. MATERIALS AND METHODS: In this study, we developed a questionnaire consisting of 21 questions and 2 clinical scenarios related to urolithiasis. Subsequently, clinical consultations were simulated for each of the four models to assess their responses to the questions. Urolithiasis experts then evaluated the model responses in terms of accuracy, comprehensiveness, ease of understanding, human care, and clinical case analysis ability based on a predesigned 5-point Likert scale. Visualization and statistical analyses were then employed to compare the four models and evaluate their performance. RESULTS: All models yielded satisfying performance, except for Bard, who failed to provide a valid response to Question 13. Claude consistently scored the highest in all dimensions compared with the other three models. ChatGPT4 ranked second in accuracy, with a relatively stable output across multiple tests, but shortcomings were observed in empathy and human caring. Bard exhibited the lowest accuracy and overall performance. Claude and ChatGPT4 both had a high capacity to analyze clinical cases of urolithiasis. Overall, Claude emerged as the best performer in urolithiasis consultations and education. CONCLUSION: Claude demonstrated superior performance compared with the other three in urolithiasis consultation and education. This study highlights the remarkable potential of LLMs in medical health consultations and patient education, although professional review, further evaluation, and modifications are still required.

Platelet-derived extracellular vesicles aggravate septic acute kidney injury via delivering ARF6.

Circulating plasma extracellular vesicles (EVs) mostly originate from platelets and may promote organ dysfunction in sepsis. However, the role of platelet-derived EVs in sepsis-induced acute kidney injury (AKI) remains poorly understood. The present study extracted EVs from the supernatant of human platelets treated with phosphate buffer saline (PBS) or lipopolysaccharide (LPS). Then, we subjected PBS-EVs or LPS-EVs to cecal ligation and puncture (CLP) mice in vivo or LPS-stimulated renal tubular epithelial cells (RTECs) in vitro. Our results indicated that LPS-EVs aggravate septic AKI via promoting apoptosis, inflammation and oxidative stress. Further, ADP-ribosylation factor 6 (ARF6) was identified as a differential protein between PBS-EVs and LPS-EVs by quantitative proteomics analysis. Mechanistically, ARF6 activated ERK/Smad3/p53 signaling to exacerbate sepsis-induced AKI. LPS upregulated ARF6 in RTECs was dependent on TLR4/MyD88 pathway. Both genetically and pharmacologically inhibition of ARF6 attenuated septic AKI. Moreover, platelets were activated by TLR4 and its downstream mediator IKK controlled platelet secretion during sepsis. Inhibition of platelet secretion alleviated septic AKI. Collectively, our study demonstrated that platelet-derived EVs may be a therapeutic target in septic AKI.

Predictive value of single-nucleotide polymorphism signature for nephrolithiasis recurrence: a 5-year prospective study.

Background: Genetic variations are linked to kidney stone formation. However, the association of single nucleotide polymorphism (SNPs) and stone recurrence has not been well studied. This study aims to identify genetic variants associated with kidney stone recurrences and to construct a predictive nomogram model using SNPs and clinical features to predict the recurrence risk of kidney stones. Methods: We genotyped 49 SNPs in 1001 patients who received surgical stone removal between Jan 1 and Dec 31 of 2012. All patients were confirmed stone-free by CT scan and then received follow-up at least 5 years. SNP associations with stone recurrence were analyzed by Cox proportion hazard model. A predictive nomogram model using SNPs and clinical features to predict the recurrence risk of kidney stones was developed by use of LASSO Cox regression. Results: The recurrence rate at 3, 5, 7 years were 46.8%, 71.2%, and 78.4%, respectively. 5 SNPs were identified that had association with kidney stone recurrence risk. We used computer-generated random numbers to assign 500 of these patients to the training cohort and 501 patients to the validation cohort. A nomogram that combined the 14-SNPs-based classifier with the clinical risk factors was constructed. The areas under the curve (AUCs) at 3, 5 and 7 years of this nomogram was 0.645, 0.723, and 0.75 in training cohort, and was 0.631, 0.708, and 0.727 in validation cohort, respectively. Results show that the nomogram presented a higher predictive accuracy than those of the SNP classifier or clinical factors alone. Conclusion: SNPs are significantly associated with kidney stone recurrence and should add prognostic value to the traditional clinical risk factors used to assess the kidney stone recurrence. A nomogram using clinical and genetic variables to predict kidney stone recurrence has revealed its potential in the future as an assessment tool during the follow-up of kidney stone patients.

Biological and clinical significance of radiomics features obtained from magnetic resonance imaging preceding pre-carbon ion radiotherapy in prostate cancer based on radiometabolomics.

Introduction: We aimed to investigate the feasibility of metabolomics to explain the underlying biological implications of radiomics features obtained from magnetic resonance imaging (MRI) preceding carbon ion radiotherapy (CIRT) in patients with prostate cancer and to further explore the clinical significance of radiomics features on the prognosis of patients, based on their biochemical recurrence (BCR) status. Methods: Metabolomic results obtained using high-performance liquid chromatography coupled with tandem mass spectrometry of urine samples, combined with pre-RT radiomic features extracted from MRI images, were evaluated to investigate their biological significance. Receiver operating characteristic (ROC) curve analysis was subsequently conducted to examine the correlation between these biological implications and clinical BCR status. Statistical and metabolic pathway analyses were performed using MetaboAnalyst and R software. Results: Correlation analysis revealed that methionine alteration extent was significantly related to four radiomic features (Contrast, Difference Variance, Small Dependence High Gray Level Emphasis, and Mean Absolute Deviation), which were significantly correlated with BCR status. The area under the curve (AUC) for BCR prediction of these four radiomic features ranged from 0.704 to 0.769, suggesting that the higher the value of these four radiomic features, the greater the decrease in methionine levels after CIRT and the lower the probability of BCR. Pre-CIRT MRI radiomic features were associated with CIRT-suppressed metabolites. Discussion: These radiomic features can be used to predict the alteration in the amplitude of methionine after CIRT and the BCR status, which may contribute to the optimization of the CIRT strategy and deepen the understanding of PCa.

Desmethoxycurcumin aids IFNα's anti-HBV activity by antagonising CRYAB reduction and stabilising IFNAR1 protein.

The eradication of chronic hepatitis B (CHB) caused by hepatitis B virus (HBV) infection is a crucial goal in clinical practice. Enhancing the anti-HBV activity of interferon type I (IFNI) is a key strategy for achieving a functional cure for CHB. In this study, we investigated the effect of combined treatment with IFNα and Desmethoxycurcumin (DMC) on HBV replication in HepG2 cells and explored the underlying mechanism. Our results indicated IFNα alone was ineffective in completely inhibiting HBV replication, which was attributed to the virus-induced down-regulation of IFNI receptor 1 (IFNAR1) protein. However, the addition of a low dose of DMC significantly synergized with IFNα, leading to notable enhancement of IFNα anti-HBV activity. This effect was achieved by stabilising the IFNAR1 protein. Further investigation revealed that low dose DMC effectively blocked the ubiquitination-mediated degradation of IFNAR1, which was accomplished by rescuing the protein levels of alphaB-crystallin (CRYAB) and orchestrating the interaction between CRYAB and the E3 ubiquitin ligase, ß-Trcp. Importantly, over-expression of CRYAB was found to favour the antiviral activity of IFNα against HBV replication. In conclusion, our study demonstrates that low-dose DMC enhanced the anti-HBV activity of IFNα by counteracting the reduction of CRYAB and stabilising the IFNAR1 protein.

Modifying Escherichia coli to mimic Shigella for medical microbiology laboratory teaching: a new strategy to improve biosafety in class.

Introduction: Laboratory teaching of medical microbiology involves highly pathogenic microorganisms, thus posing potential biosafety risks to the students and the teacher. To address these risks, non/low-pathogenic microorganisms were modified to mimic highly pathogenic ones or highly pathogenic microorganisms were attenuated directly using the CRISPR/Cas9 technology. This study describes the modification of Escherichia coli DH5α to mimic Shigella and its evaluation as a safe alternative for medical laboratory teaching. Methods: To generate E. coli DH5α△FliC△tnaA2a, the tnaA and FliC genes in E. coli DH5α were knocked out using CRISPR/Cas9 technology; a plasmid bearing the O-antigen determinant of S. flexneri 2a was then constructed and transformed. Acid tolerance assays and guinea pig eye tests were used to assess the viability and pathogenicity, respectively. Questionnaires were used to analyze teaching effectiveness and the opinions of teachers and students. Results: The survey revealed that most teachers and students were inclined towards real-time laboratory classes than virtual classes or observation of plastic specimens. However, many students did not abide by the safety regulations, and most encountered potential biosafety hazards in the laboratory. E. coli DH5α△FliC△tnaA2a was biochemically and antigenically analogous to S. flexneri 2a and had lower resistance to acid than E. coli. There was no toxicity observed in guinea pigs. Most of teachers and students were unable to distinguish E. coli DH5α△FliC△tnaA2a from pure S. flexneri 2a in class. Students who used E. coli DH5α△FliC△tnaA2a in their practice had similar performance in simulated examinations compared to students who used real S. flexneri 2a, but significantly higher than the virtual experimental group. Discussion: This approach can be applied to other high-risk pathogenic microorganisms to reduce the potential biosafety risks in medical laboratory-based teaching and provide a new strategy for the development of experimental materials.

Underwater Source Counting with Local-Confidence-Level-Enhanced Density Clustering.

Source counting is the key procedure of autonomous detection for underwater unmanned platforms. A source counting method with local-confidence-level-enhanced density clustering using a single acoustic vector sensor (AVS) is proposed in this paper. The short-time Fourier transforms (STFT) of the sound pressure and vibration velocity measured by the AVS are first calculated, and a data set is established with the direction of arrivals (DOAs) estimated from all of the time-frequency points. Then, the density clustering algorithm is used to classify the DOAs in the data set, with which the number of the clusters and the cluster centers are obtained as the source number and the DOA estimations, respectively. In particular, the local confidence level is adopted to weigh the density of each DOA data point to highlight samples with the dominant sources and downplay those without, so that the differences in densities for the cluster centers and sidelobes are increased. Therefore, the performance of the density clustering algorithm is improved, leading to an improved source counting accuracy. Experimental results reveal that the enhanced source counting method achieves a better source counting performance than that of basic density clustering.

Definitive carbon ion re-irradiation with pencil beam scanning in the treatment of unresectable locally recurrent rectal cancer.

This study aimed to evaluate the oncological outcomes and safety of carbon ion re-irradiation with pencil beam scanning (PBS) delivery technique for previously irradiated and unresectable locally recurrent rectal cancer (LRRC). Between June 2017 and September 2021, 24 patients of unresectable LRRC with prior pelvic photon radiotherapy who underwent carbon ion re-irradiation at our institute were retrospectively analyzed. Carbon ion radiotherapy was delivered by raster scanning with a median relative biological effectiveness-weighted dose of 72 Gy in 20 fractions. Weekly CT reviews were carried out, and offline adaptive replanning was performed whenever required. The median follow-up duration was 23.8 months (range, 6.2-47.1 months). At the last follow-up, two patients had a local disease progression, and 11 patients developed distant metastases. The 1- and 2-year local control, progression-free survival and overall survival rates were 100 and 93.3%, 70.8 and 45.0% and 86.7 and 81.3%, respectively. There were no Grade 3 or higher acute toxicities observed. Three patients developed Grade 3 late toxicities, one each with gastrointestinal toxicity, skin reaction and pelvic infection. In conclusion, definitive carbon ion re-irradiation with PBS provided superior oncologic results with tolerable toxicities and may be served as a curative treatment strategy in unresectable LRRC.

Dual CNN cross-teaching semi-supervised segmentation network with multi-kernels and global contrastive loss in ACDC.

The cross-teaching based on Convolutional Neural Network (CNN) and Transformer has been successful in semi-supervised learning; however, the information interaction between local and global relations ignores the semantic features of the medium scale, and at the same time, the information in the process of feature coding is not fully utilized. To solve these problems, we proposed a new semi-supervised segmentation network. Based on the principle of complementary modeling information of different kernel convolutions, we design a dual CNN cross-supervised network with different kernel sizes under cross-teaching. We introduce global feature contrastive learning and generate contrast samples with the help of dual CNN architecture to make efficient use of coding features. We conducted plenty of experiments on the Automated Cardiac Diagnosis Challenge (ACDC) dataset to evaluate our approach. Our method achieves an average Dice Similarity Coefficient (DSC) of 87.2% and Hausdorff distance ([Formula: see text]) of 6.1 mm on 10% labeled data, which is significantly improved compared with many current popular models. Supervised learning is performed on the labeled data, and dual CNN cross-teaching supervised learning is performed on the unlabeled data. All data would be mapped by the two CNNs to generate features, which are used for contrastive learning to optimize the parameters.