Deficiency of FLCN in Mouse Kidney Led to Development of Polycystic Kidneys and Renal Neoplasia

The Birt–Hogg–Dubé (BHD) disease is a genetic cancer syndrome. The responsible gene, BHD, has been identified by positional cloning and thought to be a novel tumor suppressor gene. BHD mutations cause many types of diseases including renal cell carcinomas, fibrofolliculomas, spontaneous pneumothorax, lung cysts, and colonic polyps/cancers. By combining Gateway Technology with the Ksp-Cre gene knockout system, we have developed a kidney-specific BHD knockout mouse model. BHDflox/flox/Ksp-Cre mice developed enlarged kidneys characterized by polycystic kidneys, hyperplasia, and cystic renal cell carcinoma. The affected BHDflox/flox/Ksp-Cre mice died of renal failure at approximate three weeks of age, having blood urea nitrogen levels over tenfold higher than those of BHD flox/+/Ksp-Cre and wild-type littermate controls. We further demonstrated that these phenotypes were caused by inactivation of BHD and subsequent activation of the mTOR pathway. Application of rapamycin, which inhibits mTOR activity, to the affected mice led to extended survival and inhibited further progression of cystogenesis. These results provide a correlation of kidney-targeted gene inactivation with renal carcinoma, and they suggest that the BHD product FLCN, functioning as a cyst and tumor suppressor, like other hamartoma syndrome–related proteins such as PTEN, LKB1, and TSC1/2, is a component of the mTOR pathway, constituting a novel FLCN-mTOR signaling branch that regulates cell growth/proliferation

Multi-robot formation control based on distributed model of mobile sensor network

People are paying more and more attention to mobile sensor network now. Establishment of its model is conducive to in-depth analysis of the system. Based on the located information and graph theory, a distributed sensor network model is introduced. Delaunay triangulation used to describe nodes and the relationship between them and their transmission and fusion of information between nodes. And the description of coverage region of nodes uses Voronoi diagrams. With the model, disadvantages of existing located system based on a fixed infrastructure could be overcame. The flexibility of the sensor network would be greatly enhanced. And multi-robot formation control algorithm is also discussed with this model. It makes the network intelligent and more energy-efficient. The result shows it could complete complicated missions

Hyperspectral Image Mixed Noise Removal Using a Subspace Projection Attention and Residual Channel Attention Network

Although the existing deep-learning-based hyperspectral image (HSI) denoising methods have achieved tremendous success, recovering high-quality HSIs in complex scenes that contain mixed noise is still challenging. Besides, these methods have not fully explored the local and global spatial–spectral information of HSIs. To address the above issues, a novel HSI mixed noise removal network called subspace projection attention and residual channel attention network (SPARCA-Net) is proposed. Specifically, we propose an orthogonal subspace projection attention (OSPA) module to adaptively learn to generate bases of the signal subspace and project the input into such space to remove noise. By leveraging the local and global spatial relations, OSPA is able to reconstruct the local structure of the feature maps more precisely. We further propose a residual channel attention (RCA) module to emphasize the interdependence between feature maps and exploit the global channel correlation of them, which could enhance the channel-wise adaptive learning. In addition, multiscale joint spatial–spectral input and residual learning strategies are employed to capture multiscale spatial–spectral features and reduce the degradation problem, respectively. Synthetic and real HSI data experiments demonstrated that the proposed HSI denoising network outperforms many of the advanced methods in both quantitative and qualitative assessments

Hyperspectral Image Mixed Noise Removal Using a Subspace Projection Attention and Residual Channel Attention Network

Although the existing deep-learning-based hyperspectral image (HSI) denoising methods have achieved tremendous success, recovering high-quality HSIs in complex scenes that contain mixed noise is still challenging. Besides, these methods have not fully explored the local and global spatial–spectral information of HSIs. To address the above issues, a novel HSI mixed noise removal network called subspace projection attention and residual channel attention network (SPARCA-Net) is proposed. Specifically, we propose an orthogonal subspace projection attention (OSPA) module to adaptively learn to generate bases of the signal subspace and project the input into such space to remove noise. By leveraging the local and global spatial relations, OSPA is able to reconstruct the local structure of the feature maps more precisely. We further propose a residual channel attention (RCA) module to emphasize the interdependence between feature maps and exploit the global channel correlation of them, which could enhance the channel-wise adaptive learning. In addition, multiscale joint spatial–spectral input and residual learning strategies are employed to capture multiscale spatial–spectral features and reduce the degradation problem, respectively. Synthetic and real HSI data experiments demonstrated that the proposed HSI denoising network outperforms many of the advanced methods in both quantitative and qualitative assessments

Cholesterol alone or in combination is associated with frailty among community-dwelling older adults: A cross-sectional study

Background: Biological markers contribute to the precise intervention across the continuum of frailty severity. Few studies have explored the advantages of biological markers collected as part of primary care data among community-dwelling older adult population and controversy remains regarding the classic biological markers for frailty. Methods: We recruited a total of 8791 adults with a mean age of 71.95 years who met the inclusion and exclusion criteria in Guancheng District and Dalang Town, Dongguan, China. Frailty was assessed by a Chinese frailty evaluation scale. Frailty status was classified with 33-item modified frailty index and latent class analysis was applied to explore the latent classes (subtypes) of frailty. We measured biological markers on blood samples collected. We identify association between specific biological markers or patterns and frailty by logistic regression and association rule mining (ARM) based on the Apriori algorithm. Results: Multivariable analysis of our data showed that an elevated white blood cell (WBC) count and high cholesterol (CHOL) level were associated with pre-frailty (adjusted odds ratio [aOR] = 1.231, 95 % confidence interval [CI] = 1.009–1.501; aOR = 0.703, 95 % CI = 0.623–0.793) and frailty (aOR = 1.500, 95 % CI = 1.130–1.993; aOR = 0.561, 95 % CI = 0.461–0.684) compared with the normal groups. Importantly, significantly high level of CHOL was associated with a lower risk of four frailty subtypes compared with relatively healthy participants with the most power of association in the multi-frail group (aOR = 0.182, 95 % CI = 0.086–0.386). Based on ARM technique to develop correlation analysis to identify important high-risk clusters among older adult transitions from non-frail to frailty, patterns for normal level of CHOL co-occurred with an elevated creatinine (CREA) level have a significant association with the risk of frailty (aOR = 7.787, 95 % CI = 1.978–30.648) after adjusting for targeted confounders. Conclusions: Our study highlights the correlation between classic biological markers, especially CHOL and frailty status and subtypes among community-dwelling older adult, in the primary care setting. Further large-scale prospective studies are still needed to confirm the role of classic biological markers in frailty

Ion-Boosting the Charge Density and Piezoelectric Response of Ferroelectrets to Significantly High Levels

In contrast to molecular-dipole polymers, such as PVDF, ferroelectrets are a new class of flexible spatially heterogeneous piezoelectric polymers with closed or open voids that act as deformable macro-dipoles after charging. With a spectrum of manufacturing processes being developed to engineer the heterogeneous structures, ferroelectrets are made with attractive piezoelectric properties well-suited for applications, such as pressure sensors, acoustic transducers, etc. However, the sources of the macro-dipole charges have usually been the same, microscopic dielectric barrier discharges within the voids, induced when the ferroelectrets are poled under a large electric field typically via a so-called corona poling, resulting in the separation and trapping of opposite charges into the interior walls of the voids. Such a process is inherently self-limiting, as the reverse internal field from the macro-dipoles eventually extinguishes the microdischarges, resulting in limited density of ions and not too high overall piezoelectric performance. Here, a new method to form ferroelectrets with gigantic electroactivity is proposed and demonstrated with the aid of an external ion booster. A laminate consisting of expanded polytetrafluoroethylene (ePTFE) and fluorinated-ethylene-propylene (FEP) was prefilled with bipolar ions produced externally by an ionizer and sequentially poled to force the separation of positive and negative ions into the open fibrous structure, rendering an impressive piezoelectric d33 coefficient of 1600 pC/N an improvement by a factor of 4 in comparison with the d33 of a similar sandwich poled with nonenhanced corona poling. The (pre)filling clearly increases the ion density in the open voids significantly. The charges stored in the open-cell structure stays at a high level for at least 4 months. In addition, an all-organic nanogenerator was made from an ePTFE-based ferroelectret, with conducting poly(3,4-ethylene dioxythiophene): poly(styrenesulfonate) (PEDOT: PSS) coated fabric electrodes. When poled with this ion-boosting process, it yielded an output power twice that of a similar sample poled in a conventional corona-only process. The doubling in output power is mainly brought about by the significantly higher charge density achieved with the aid of external booster. Furthermore, aside from the bipolar ions, extra monopolar ions can during the corona poling be blown into the open pores by using for instance a negative ionic hair dryer to produce a unipolar ePTFE-based ferroelectret with its d33 coefficient enhanced by a factor of 3. Ion-boosting poling thus unleashes a new route to produce bipolar or unipolar open-cell ferroelectrets with highly enhanced piezoelectric response.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Team Jilt Sietsm

Associations Among Multimorbid Conditions in Hospitalized Middle-aged and Older Adults in China: Statistical Analysis of Medical Records

BackgroundMultimorbidity has become a new challenge for medical systems and public health policy. Understanding the patterns of and associations among multimorbid conditions should be given priority. It may assist with the early detection of multimorbidity and thus improve quality of life in older adults. ObjectiveThis study aims to comprehensively analyze and compare associations among multimorbid conditions by age and sex in a large number of middle-aged and older Chinese adults. MethodsData from the home pages of inpatient medical records in the Shenzhen National Health Information Platform were evaluated. From January 1, 2017, to December 31, 2018, inpatients aged 50 years and older who had been diagnosed with at least one of 40 conditions were included in this study. Their demographic characteristics (age and sex) and inpatient diagnoses were extracted. Association rule mining, Chi-square tests, and decision tree analyses were combined to identify associations between multiple chronic conditions. ResultsIn total, 306,264 hospitalized cases with available information on related chronic conditions were included in this study. The prevalence of multimorbidity in the overall population was 76.46%. The combined results of the 3 analyses showed that, in patients aged 50 years to 64 years, lipoprotein metabolism disorder tended to be comorbid with multiple chronic conditions. Gout and lipoprotein metabolism disorder had the strongest association. Among patients aged 65 years or older, there were strong associations between cerebrovascular disease, heart disease, lipoprotein metabolism disorder, and peripheral vascular disease. The strongest associations were observed between senile cataract and glaucoma in men and women. In particular, the association between osteoporosis and malignant tumor was only observed in middle-aged and older men, while the association between anemia and chronic kidney disease was only observed in older women. ConclusionsMultimorbidity was prevalent among middle-aged and older Chinese individuals. The results of this comprehensive analysis of 4 age-sex subgroups suggested that associations between particular conditions within the sex and age groups occurred more frequently than expected by random chance. This provides evidence for further research on disease clusters and for health care providers to develop different strategies based on age and sex to improve the early identification and treatment of multimorbidity