A lightweight Yunnan Xiaomila detection and pose estimation based on improved YOLOv8.

Introduction: Yunnan Xiaomila is a pepper variety whose flowers and fruits become mature at the same time and multiple times a year. The distinction between the fruits and the background is low and the background is complex. The targets are small and difficult to identify. Methods: This paper aims at the problem of target detection of Yunnan Xiaomila under complex background environment, in order to reduce the impact caused by the small color gradient changes between xiaomila and background and the unclear feature information, an improved PAE-YOLO model is proposed, which combines the EMA attention mechanism and DCNv3 deformable convolution is integrated into the YOLOv8 model, which improves the model's feature extraction capability and inference speed for Xiaomila in complex environments, and achieves a lightweight model. First, the EMA attention mechanism is combined with the C2f module in the YOLOv8 network. The C2f module can well extract local features from the input image, and the EMA attention mechanism can control the global relationship. The two complement each other, thereby enhancing the model's expression ability; Meanwhile, in the backbone network and head network, the DCNv3 convolution module is introduced, which can adaptively adjust the sampling position according to the input feature map, contributing to stronger feature capture capabilities for targets of different scales and a lightweight network. It also uses a depth camera to estimate the posture of Xiaomila, while analyzing and optimizing different occlusion situations. The effectiveness of the proposed method was verified through ablation experiments, model comparison experiments and attitude estimation experiments. Results: The experimental results indicated that the model obtained an average mean accuracy (mAP) of 88.8%, which was 1.3% higher than that of the original model. Its F1 score reached 83.2, and the GFLOPs and model sizes were 7.6G and 5.7MB respectively. The F1 score ranked the best among several networks, with the model weight and gigabit floating-point operations per second (GFLOPs) being the smallest, which are 6.2% and 8.1% lower than the original model. The loss value was the lowest during training, and the convergence speed was the fastest. Meanwhile, the attitude estimation results of 102 targets showed that the orientation was correctly estimated exceed 85% of the cases, and the average error angle was 15.91°. In the occlusion condition, 86.3% of the attitude estimation error angles were less than 40°, and the average error angle was 23.19°. Discussion: The results show that the improved detection model can accurately identify Xiaomila targets fruits, has higher model accuracy, less computational complexity, and can better estimate the target posture.

A complex system health state assessment method with reference value optimization for interpretable BRB.

Health condition assessment is the basis for formulating and optimizing maintenance strategies of complex systems, which is crucial for ensuring the safe and stable operation of these systems. In complex system health condition assessment, it is not only necessary for the model to handle various uncertainties to ensure the accuracy of assessment results, but also to have a transparent and reasonable assessment process and interpretable, traceable assessment results. belief rule base (BRB) has been widely used as an interpretable modeling method in health condition assessment. However, BRB-based models currently face two issues: (1) inaccuracies in expert-provided parameters that can affect the model's accuracy, and (2) after model optimization, interpretability may be reduced. Therefore, this paper proposes a new method for complex system health condition assessment called interpretable BRB with reference value optimization (I-BRB). Firstly, to address the issue of inaccurate reference values, a reference value optimization algorithm with interpretability constraints is designed, which optimizes the reference values without compromising expert knowledge. Secondly, the remaining parameters are optimized using the projection covariance matrix adaptation evolution strategy (P-CMA-ES) with interpretability constraints to improve the model's accuracy. Finally, a case study evaluating the bearing components of a flywheel system is conducted to validate the proposed method. Experimental results demonstrate that I-BRB achieves higher accuracy in health condition assessment.

Modified Transannular Patching Palliation versus Modified Blalock-Taussig-Thomas Shunt in Infants with Severe Tetralogy of Fallot with Diminutive Pulmonary Arteries.

OBJECTIVE: The purpose of this study was to compare pulmonary arterial (PA) growth and morbidity, mortality, reintervention and complete repair rates after modified transannular patching palliation (mTAP) versus modified Blalock-Taussig-Thomas shunt (mBTS) for palliation in infants with severe tetralogy of Fallot (TOF) with diminutive pulmonary arteries. METHODS: This was a retrospective case review study of 107 patients (64 males) with severe TOF who underwent staged repair with either mTAP (n = 55) or mBTS (n = 52) over an 8-year period. Procedure-related PA growth and morbidity, mortality, reintervention and complete repair rates were compared. RESULTS: Two deaths occurred in the mBTS group due to sudden cardiac arrest, and five patients needed reintervention after the mBTS procedure because of shunt thrombosis or stenosis. Postoperative complications of mBTS included sudden cardiac arrest, shunt thrombosis/stenosis, vocal cord palsy and diaphragmatic palsy. Unlike in the mBTS group, no death, severe complications or reintervention occurred in the mTAP group. Oxygen saturations post mTAP and mBTS were significantly higher, which improved from 67.73 ± 4.36% to 94.33 ± 2.19% in the mTAP group and from 68.24 ± 3.87% to 86.87 ± 3.38% in the mBTS group. The increase in oxygen saturation and pulmonary artery growth (from pre- to post palliation) was significantly better with mTAP than with mBTS palliation (p < 0.01). All 55 patients showed complete repair after mTAP, and the time from palliation to complete repair was significantly shorter in the mTAP group. CONCLUSIONS: In a severe form of TOF with the hypoplastic PA tree, mTAP seems to be a better strategy that is safe and better facilitates satisfactory pulmonary arterial growth until complete repair than the mBTS procedure.

Efficacy and safety comparison between axillary lymph node dissection with no axillary surgery in patients with sentinel node-positive breast cancer: a systematic review and meta-analysis.

BACKGROUND: This systematic review and meta-analysis aimed to study the evidence on the efficacy and safety of omitting axillary lymph node dissection (ALND) for patients with clinically node-negative but sentinel lymph node (SLN)-positive breast cancer using all the available evidence. METHODS: The Embase, Medline, and Cochrane Library databases were searched through February 25, 2023. Original trials that compared only the sentinel lymph node biopsy (SLNB) with ALND as the control group for patients with clinically node-negative but SLN-positive breast cancer were included. The primary outcomes were axillary recurrence rate, total recurrence rate, disease-free survival (DFS), and overall survival (OS). Meta-analyses were performed to compare the odds ratio (OR) in rates and the hazard ratios (HR) in time-to-event outcomes between both interventions. Based on different study designs, tools in the revised Cochrane risk of bias tool were used for randomized trials and the risk of bias in nonrandomized studies of interventions to assess the risk of bias for each included article. Funnel plots and Egger's test were used for the publication's bias assessment. RESULTS: In total, 30 reports from 26 studies were included in the systematic review (9 reports of RCTs, 21 reports of retrospective cohort studies). According to our analysis, omitting ALND in patients with clinically node-negative but SLN-positive breast cancer had a similar axillary recurrence rate (OR = 0.95, 95% confidence interval (CI): 0.76-1.20), DFS (HR = 1.02, 95% CI: 0.89-1.16), and OS (HR = 0.97, 95% CI: 0.92-1.03), but caused a significantly lower incidence of adverse events and benefited in locoregional recurrence rate (OR = 0.76, 95% CI: 0.59-0.97) compared with ALND. CONCLUSION: For patients with clinically node-negative but SLN-positive breast cancer (no matter the number of the positive SLN), this review showed that SLNB alone had a similar axillary recurrence rate, DFS, and OS, but caused a significantly lower incidence of adverse events and showed a benefit for the locoregional recurrence compared with ALND. An OS benefit was found in the Macro subset that used SLNB alone versus complete ALND. Therefore, omitting ALND is feasible in this setting. TRIAL REGISTRATION: CRD 42023397963.

Rapid detection of Yunnan Xiaomila based on lightweight YOLOv7 algorithm.

Introduction: Real-time fruit detection is a prerequisite for using the Xiaomila pepper harvesting robot in the harvesting process. Methods: To reduce the computational cost of the model and improve its accuracy in detecting dense distributions and occluded Xiaomila objects, this paper adopts YOLOv7-tiny as the transfer learning model for the field detection of Xiaomila, collects images of immature and mature Xiaomila fruits under different lighting conditions, and proposes an effective model called YOLOv7-PD. Firstly, the main feature extraction network is fused with deformable convolution by replacing the traditional convolution module in the YOLOv7-tiny main network and the ELAN module with deformable convolution, which reduces network parameters while improving the detection accuracy of multi-scale Xiaomila targets. Secondly, the SE (Squeeze-and-Excitation) attention mechanism is introduced into the reconstructed main feature extraction network to improve its ability to extract key features of Xiaomila in complex environments, realizing multi-scale Xiaomila fruit detection. The effectiveness of the proposed method is verified through ablation experiments under different lighting conditions and model comparison experiments. Results: The experimental results indicate that YOLOv7-PD achieves higher detection performance than other single-stage detection models. Through these improvements, YOLOv7-PD achieves a mAP (mean Average Precision) of 90.3%, which is 2.2%, 3.6%, and 5.5% higher than that of the original YOLOv7-tiny, YOLOv5s, and Mobilenetv3 models, respectively, the model size is reduced from 12.7 MB to 12.1 MB, and the model's unit time computation is reduced from 13.1 GFlops to 10.3 GFlops. Discussion: The results shows that compared to existing models, this model is more effective in detecting Xiaomila fruits in images, and the computational complexity of the model is smaller.

A wireless sensor network node fault diagnosis model based on belief rule base with power set.

Wireless sensor network (WSN) is inevitably subject to node failures due to their harsh operating environments and extra-long working hours. In order to ensure reliable and correct data collection, WSN node fault diagnosis is necessary. Fault diagnosis of sensor nodes usually requires the extraction of data features from the original collected data. However, the data features of different types of faults sometimes have similarities, making it difficult to distinguish and represent the types of faults in the diagnosis results, these indistinguishable types of faults are called ambiguous information. Therefore, a belief rule base with power set (PBRB) fault diagnosis method is proposed. In this method, the power set identification framework is used to represent the fuzzy information, the evidential reasoning (ER) method is used as the reasoning process, and the projection covariance matrix adaptive evolution strategy (P-CMA-ES) is used as the parameter optimization algorithm. The results of the case study show that PBRB method has higher accuracy and better stability compared to other commonly used fault diagnosis methods. According to the research results, PBRB can not only represent the fault types that are difficult to distinguish, but also has the advantage of small sample training. This makes the model obtain high fault diagnosis accuracy and stability.

A new unmanned aerial vehicle intrusion detection method based on belief rule base with evidential reasoning.

With the growing security demands in the public, civil and military fields, unmanned aerial vehicle (UAV) intrusion detection has attracted increasing attention. In view of the shortcomings of the current UAV intrusion detection model using Wi-Fi data traffic in terms of detection accuracy, sample size reduction, and model interpretability, this paper proposes a new detection algorithm for UAV intrusion. This paper presents an interpretable intrusion detection model for UAVs based on the belief rule base (BRB). BRB can effectively use various types of information to establish any nonlinear relationship between the model input and output. It can model and simulate any nonlinear model and optimize the model parameters. However, the rule combination explosion problem is encountered in BRB if there are too many attributes. Therefore, an evidential reasoning (ER) algorithm is proposed for solving this problem. By combining the capabilities of the ER and the BRB methodologies, a new evaluation model, named the EBRB-based model, is proposed here for predicting UAV intrusion detection, even in the case of a massive number of attributes. The global optimization of the model is ensured. A new interpretable and globally optimized UAV intrusion detection model is proposed, which is the main contribution of this paper. An experimental case is used to demonstrate the implementation and application of the proposed UAV intrusion detection method.

ReDisX, a machine learning approach, rationalizes rheumatoid arthritis and coronary artery disease patients uniquely upon identifying subpopulation differentiation markers from their genomic data.

Diseases originate at the molecular-genetic layer, manifest through altered biochemical homeostasis, and develop symptoms later. Hence, symptomatic diagnosis is inadequate to explain the underlying molecular-genetic abnormality and individual genomic disparities. The current trends include molecular-genetic information relying on algorithms to recognize the disease subtypes through gene expressions. Despite their disposition toward disease-specific heterogeneity and cross-disease homogeneity, a gap still exists in describing the extent of homogeneity within the heterogeneous subpopulation of different diseases. They are limited to obtaining the holistic sense of the whole genome-based diagnosis resulting in inaccurate diagnosis and subsequent management. Addressing those ambiguities, our proposed framework, ReDisX, introduces a unique classification system for the patients based on their genomic signatures. In this study, it is a scalable machine learning algorithm deployed to re-categorize the patients with rheumatoid arthritis and coronary artery disease. It reveals heterogeneous subpopulations within a disease and homogenous subpopulations across different diseases. Besides, it identifies granzyme B (GZMB) as a subpopulation-differentiation marker that plausibly serves as a prominent indicator for GZMB-targeted drug repurposing. The ReDisX framework offers a novel strategy to redefine disease diagnosis through characterizing personalized genomic signatures. It may rejuvenate the landscape of precision and personalized diagnosis and a clue to drug repurposing.

Circulating Carnitine Levels and Breast Cancer: A Matched Retrospective Case-Control Study.

Introduction: Epidemiological studies investigating the association between carnitine and breast cancer are scarce. Materials and Methods: This 1:1 age-matched retrospective case-control study identified 991 female breast cancer cases and 991 female controls without breast cancer using pathological testing. We used targeted metabolomics technology to measure 16 types of whole blood carnitine compounds, such as free carnitine (C0) and octadecanoylcarnitine (C18). Results: The average age for cases and controls was approximately 50 ± 8.7 years. After adjusting for covariates, each standard deviation (SD) increase in malonylcarnitine (C3DC; OR 0.91; 95% CI 0.83-1.00), decenoylcarnitine (C10:1; OR 0.87; 95% CI 0.79-0.96), and decadienoylcarnitine (C10:2; OR 0.90; 95% CI 0.82-0.99) level was associated with decreased odds of breast cancer. However, higher butyrylcarnitine (C4) levels were associated with increased odds of breast cancer (OR 1.12; 95% CI 1.02-1.23). No statistically significant relationship was noted between other carnitine compounds and breast cancer. The false discovery rates for C3DC, C4, C10:1 and C10:2 were 0.172, 0.120, 0.064 and 0.139, respectively. Conclusions: Higher levels of C3DC, C10:1, and C10:2 were protective factors for breast cancer, whereas increased C4 levels were a risk factor for the disease.

A novel belief rule base expert system with interval-valued references.

As an essential parameter in the belief rule base (BRB), referential values refer to evaluation criteria for describing attributes using quantitative data or linguistic terms, the rationality and preciseness of which are important to the modeling accuracy. At present, the studies on referential values of BRB are mainly related to single-valued data. However, due to the inherent uncertainty, ambiguity, and vagueness of expert knowledge, the single-valued references provided by experts cannot represent qualitative information adequately. In this paper, a novel BRB with interval-valued references (BRB-IR) is proposed, in which qualitative knowledge and quantitative data can be integrated to construct models. First, the interval-valued referential values provided by experts are optimized by a nonlinear optimization algorithm to obtain the optimal referential values. Furthermore, other model parameters are optimized by the projection covariance matrix adaptation evolutionary strategy (P-CMA-ES) algorithm. Finally, a case study for pipeline leak detection is constructed to verify the model's effectiveness, and the results show that the proposed BRB-IR is more effective and characterizes expert knowledge better than the classical BRB using single-valued references.

TCF-1+ PD-1+ CD8+T cells are associated with the response to PD-1 blockade in non-small cell lung cancer patients.

PURPOSE: To determine whether TCF-1+PD-1+CD8+T cells are associated with the response to PD-1 blockade in non-small cell lung cancer (NSCLC) patients. METHODS: We investigated the expression of TCF-1+PD-1+CD8+T cells and elucidated their predictive role in NSCLC patients. Pretreatment specimens from 20 advanced NSCLC patients who underwent PD-1 immunotherapy or combined with chemotherapy were analyzed. The frequencies of TCF-1+ cells in PD-1+CD8+T cells were determined in these biospecimens using multi-label immunofluorescence staining and multi-spectral acquisition technology. The clinical roles of TCF-1+PD-1+CD8+T cells were assessed via analyzing our cases and human NSCLC data collected from public databases. RESULTS: A high frequency of TCF-1+PD-1+CD8+T cells was identified in responders compared with non-responders (p = 0.0024), and the patients with high expression of this cell subset had durable clinical benefit of anti-PD-1 therapy. There were no significant association between the expression of TCF-1+PD-1+CD8+T cells and patients' age, smoking history, pathologic type, and genetic status. In univariate analysis by the Cox hazard model, high frequency of TCF-1+ PD-1+ CD8+T cells was significantly correlated with patients' benefit of PD-1 blockade (p = 0.024). CONCLUSION: Our study indicated that TCF-1+PD-1+CD8+T cells are associated with the response to PD-1 blockade, and may be a predictor of anti-PD-1 therapy.

Cis-Regulatory Logic Produces Gene-Expression Noise Describing Phenotypic Heterogeneity in Bacteria.

Gene transcriptional process is random. It occurs in bursts and follows single-molecular kinetics. Intermittent bursts are measured based on their frequency and size. They influence temporal fluctuations in the abundance of total mRNA and proteins by generating distinct transcriptional variations referred to as "noise". Noisy expression induces uncertainty because the association between transcriptional variation and the extent of gene expression fluctuation is ambiguous. The promoter architecture and remote interference of different cis-regulatory elements are the crucial determinants of noise, which is reflected in phenotypic heterogeneity. An alternative perspective considers that cellular parameters dictating genome-wide transcriptional kinetics follow a universal pattern. Research on noise and systematic perturbations of promoter sequences reinforces that both gene-specific and genome-wide regulation occur across species ranging from bacteria and yeast to animal cells. Thus, deciphering gene-expression noise is essential across different genomics applications. Amidst the mounting conflict, it is imperative to reconsider the scope, progression, and rational construction of diversified viewpoints underlying the origin of the noise. Here, we have established an indication connecting noise, gene expression variations, and bacterial phenotypic variability. This review will enhance the understanding of gene-expression noise in various scientific contexts and applications.

Expression of miR-181a in Circulating Tumor Cells of Ovarian Cancer and Its Clinical Application.

Objective: To determine the possibility of early diagnosis and prognosis of ovarian cancer (OC) via detecting miR-181a in circulating tumor cells (CTCs) of OC and to solve clinical difficulties in OC tissue sample collection. Methods: EpCAM liposome magnetic beads (Ep-LMBs) were prepared by the reverse-phase evaporation method, and the performance of EpCAM was characterized. The cytotoxicity assay was detected by the MTT assay, and CTC capture efficiency was determined using OC cell lines. Blood and tissue samples were collected from 30 patients with OC and 30 normal ovarian tissue samples were selected. Expression of miR-181a in CTCs and tissue samples was measured by real-time fluorescence quantitative PCR (RT-qPCR) with U6 as an internal reference. Expression of miR-181a was interfered in OC cells and its relative expression was measured. Results: Ep-LMBs were successfully prepared with high stability. Cellular assays showed that these Ep-LMBs could capture up to 80% of OC cells. RT-qPCR showed that the expression of miR-181a was increased in OC tissues compared with that in normal ovarian tissues, and the relative expressions of miR-181a in cancerous tissues and CTCs were comparable. Correlation analysis with clinical characteristics revealed that miR-181a expression was correlated with the stage and metastasis of OC and the difference was statistically significant. Conclusion: MiR-181a may be involved in the development and progression of OC as an oncogene. Detection of miR-181a in Ep-LMB-captured CTCs is an effective and feasible alternative method for early diagnosis and prognostic evaluation of OC other than tissue tests.

Prospective pathway signaling and prognostic values of MicroRNA-9 in ovarian cancer based on gene expression omnibus (GEO): a bioinformatics analysis.

OBJECTIVE: MicroRNAs (miRNAs) play a vital role in the development of ovarian cancer (OC). The aim of this study to investigate the prognostic value and potential signaling pathways of hsa-miR-9-5p (miR-9) in OC through literature review and bioinformatics methods. METHODS: The expression of miR-9 in OC was assessed using the public datasets from the Gene Expression Omnibus (GEO) database. And a literature review was also performed to investigate the correlation between miR-9 expression and the OC prognosis. Two mRNA datasets (GSE18520 and GSE36668) of OC tissues and normal ovarian tissues (NOTs) were downloaded from GEO to identify the differentially expressed genes (DEGs). The target genes of hsa-miR-9-5p (TG-miR-9-5p) were predicted using miRWALK3.0 and TargetScan. Then the gene overlaps between DEGs in OC and the predicted TG-miR-9-5p were confirmed using a Venn diagram. After that, overlapping genes were subjected to Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Finally, a protein-protein interaction (PPI) network was constructed using STRING and Cytoscape, and the impact of hub genes on OC prognosis was analyzed. RESULTS: It was found that OC patients with miR-9 low expression had poor prognosis. A total of 107 DEGs related to both OC and miR-9 were identified. Dozens of DEGs were enriched in developmental process, extracellular matrix structural constituent, cell junction, axon guidance. In the PPI network analysis, 5 of the top 10 hub genes was significantly associated with decreased overall survival of OC patients, namely FBN1 (HR = 1.64, P < 0.05), PRRX1 (HR = 1.76, P < 0.05), SMC2 (HR = 1.22, P < 0.05), SMC4 (HR = 1.31, P < 0.05), and VCAN (HR = 1.48, P < 0.05). CONCLUSION: Low expression of miR-9 indicates poor prognosis of OC patients. MiR-9 plays a crucial role in the biological process of OC by binding to target genes, thus affecting the prognosis of patients.

Noninvasive ventilation with a helmet in patients with acute respiratory failure caused by chest trauma: a randomized controlled trial.

Noninvasive ventilation (NIV) is beneficial in acute respiratory failure (ARF) caused by chest trauma; however, NIV-related complications affect the efficacy. We evaluated whether NIV with helmet decreases the incidence of complications and improves its effects in a single center. Patients with ARF after chest trauma were randomized to receive NIV with helmet or face mask. The primary outcome was the rate of NIV-related complications. Secondary outcomes were PaO2/FiO2, patient's tolerance, intubation rate, length of intensive care unit (ICU) stay, and ICU mortality. The trial was terminated early after an interim analysis with 59 patients. The incidence of complications was lower in the helmet group [10% (3/29) vs 43% (13/30), P = 0.004], and PaO2/FiO2s were higher at 1 h and at the end of NIV (253.14 ± 64.74 mmHg vs 216.06 ± 43.86 mmHg, 277.07 ± 84.89 mmHg vs 225.81 ± 63.64 mmHg, P = 0.013 and 0.012) compared with them in face mask group. More patients reported excellent tolerance of the helmet vs face mask after 4 h of NIV [83% (24/29) vs 47% (14/30), P = 0.004] and at the end of NIV [69% (20/29) vs 30% (9/30), P = 0.03]. Differences in intubation rate, ICU stay, and mortality were non-significant (P = 0.612, 0.100, 1.000, respectively). NIV with helmet decreased NIV-related complications, increased PaO2/FiO2, and improved tolerance compared with NIV with face mask in patients with chest trauma.Trial registration: Registered in the Chinese Clinical Trial Registry (ChiCTR1900025915), a WHO International Clinical Trials Registry Platform ( http://www.chictr.org.cn/searchprojen.aspx ).

Ornithine and breast cancer: a matched case-control study.

In vivo and vitro evidence indicates that ornithine and its related metabolic products play a role in tumor development. Whether ornithine is associated with breast cancer in humans is still unclear. We examined the association between circulating ornithine levels and breast cancer in females. This 1:1 age-matched case-control study identified 735 female breast cancer cases and 735 female controls without breast cancer. All cases had a pathological test to ascertain a breast cancer diagnosis. The controls were ascertained using pathologic testing, clinical examinations, and/or other tests. Fasting blood samples were used to measure ornithine levels. The average age for cases and controls were 49.6 years (standard deviation [SD] 8.7 years) and 48.9 years (SD 8.7 years), respectively. Each SD increase in ornithine levels was associated with a 12% reduction of breast cancer risk (adjusted odds ratio [OR] 0.88; 95% confidence interval [CI] 0.79-0.97). The association between ornithine and breast cancer did not differ by pathological stages of diagnosis or tumor grades (all P for trend > 0.1). We observed no effect measure modification by molecular subtypes (P for interaction = 0.889). In conclusion, higher ornithine levels were associated with lower breast cancer risk in females.

Identifying Transcription Factor Combinations to Modulate Circadian Rhythms by Leveraging Virtual Knockouts on Transcription Networks.

The mammalian circadian systems consist of indigenous, self-sustained 24-h rhythm generators. They comprise many genes, molecules, and regulators. To decode their systematic controls, a robust computational approach was employed. It integrates transcription-factor-occupancy and time-series gene-expression data as input. The model equations were constructed and solved to determine the transcriptional regulatory logics in the mouse transcriptome network. This hypothesizes to explore the underlying mechanisms of combinatorial transcriptional regulations for circadian rhythms in mouse. We reconstructed the quantitative transcriptional-regulatory networks for circadian gene regulation at a dynamic scale. Transcriptional-simulations with virtually knocked-out mutants were performed to estimate their influence on networks. The potential transcriptional-regulators-combinations modulating the circadian rhythms were identified. Of them, CLOCK/CRY1 double knockout preserves the highest modulating capacity. Our quantitative framework offers a quick, robust, and physiologically relevant way to characterize the druggable targets to modulate the circadian rhythms at a dynamic scale effectively.

Older Clients' Pathway through the Adaptation System for Independent Living in the UK.

Housing adaptation is recognized as an effective intervention for successful independent living and has been given a greater political priority. However, the current adaptation implementation is fragmented and sometimes confusing. This study is aimed at examining blockages in the adaptation system in the United Kingdom (UK) and identifying practical ways to tackle them. It adopted a mixed-method sequential explanatory research strategy. A questionnaire survey was first conducted in all local authorities in England, Scotland and Wales. This was followed by individual interviews and a focus group with professionals and older clients. The study found that multiple organizations are involved during the delivery of housing adaptations; poor cooperation between them is a major barrier to a seamless service. The adaptation process involves five key stages; there are many inconsistencies and inequities in the process across local authorities. Significant delays are found at all stages, the average length of time taken to complete an adaptation is unacceptably long. There are also many inconsistencies and inequities across different local authorities. This study identified some common deficiencies, which cause inefficiencies and ineffectiveness in housing adaptation practices and makes some recommendations on specific actions that need to be taken at both national and local levels to address them.

Aortic regurgitation after closure of ventricular septal defect by transcatheter device: the long-term complication.

Ventricular septal defect is the most common type of CHD, and transcatheter ventricular septal defect closure has been shown to be an alternative to surgical closure with acceptable mortality and morbidity as well as encouraging results. Short-term and mid-term follow-ups have indicated the safety and efficacy of transcatheter closure, but long-term follow-up results were rare. In this report, we first found that aortic regurgitation occurred in patients 9-12 years following transcatheter closure and regurgitation were gradually increased. The findings indicate that the long-term outcome of transcatheter closure of ventricular septal defect may not be as satisfied as expected.