Expression of HIV-1 genes in podocytes alone can lead to the full spectrum of HIV-1-associated nephropathy

Expression of HIV-1 genes in podocytes alone can lead to the full spectrum of HIV-1-associated nephropathy.BackgroundHuman immunodeficiency virus (HIV)-1-associated nephropathy (HIVAN) is characterized by collapsing focal and segmental glomerulosclerosis (FSGS) and microcystic tubular dilatation. HIV-1 infection is also associated with other forms of nephropathy, including mesangial hyperplasia. Since HIV-1 gene products are detected in podocytes and other renal cells, it remains uncertain whether podocyte-restricted HIV-1 gene expression can account for the full spectrum of renal lesions involving nonpodocytes.MethodsTo define the role of podocyte-restricted HIV-1 gene expression in the progression of HIVAN, we generated transgenic mice that express nonstructural HIV-1 genes selectively in podocytes.ResultsFour of the seven founder mice developed proteinuria and nephropathy. In a subsequently established transgenic line, reverse transcription-polymerase chain reaction (RT-PCR) analysis detected mRNAs for vif, vpr, nef, and spliced forms of tat and rev, but not vpu, in the kidney. In situ hybridization localized HIV-1 RNA to the podocyte. Transgenic mice on FVB/N genetic background exhibited cuboidal morphology of podocytes with reduced extension of primary and foot processes at 2 weeks of age. After 3 weeks of age, these mice developed massive and nonselective proteinuria with damage of podocytes and other glomerular cells and, after 4 weeks of age, collapsing FSGS and microcystic tubular dilatation. In marked contrast, transgenic mice with C57BL/6 genetic background showed either normal renal histology or only mild mesangial expansion without overt podocyte damage.ConclusionThe present study demonstrates that podocyte-restricted expression of HIV-1 gene products is sufficient for the development of collapsing glomerulosclerosis in the setting of susceptible genetic background

DualTeacher: Bridging Coexistence of Unlabelled Classes for Semi-supervised Incremental Object Detection

In real-world applications, an object detector often encounters object instances from new classes and needs to accommodate them effectively. Previous work formulated this critical problem as incremental object detection (IOD), which assumes the object instances of new classes to be fully annotated in incremental data. However, as supervisory signals are usually rare and expensive, the supervised IOD may not be practical for implementation. In this work, we consider a more realistic setting named semi-supervised IOD (SSIOD), where the object detector needs to learn new classes incrementally from a few labelled data and massive unlabelled data without catastrophic forgetting of old classes. A commonly-used strategy for supervised IOD is to encourage the current model (as a student) to mimic the behavior of the old model (as a teacher), but it generally fails in SSIOD because a dominant number of object instances from old and new classes are coexisting and unlabelled, with the teacher only recognizing a fraction of them. Observing that learning only the classes of interest tends to preclude detection of other classes, we propose to bridge the coexistence of unlabelled classes by constructing two teacher models respectively for old and new classes, and using the concatenation of their predictions to instruct the student. This approach is referred to as DualTeacher, which can serve as a strong baseline for SSIOD with limited resource overhead and no extra hyperparameters. We build various benchmarks for SSIOD and perform extensive experiments to demonstrate the superiority of our approach (e.g., the performance lead is up to 18.28 AP on MS-COCO). Our code is available at \url{https://github.com/chuxiuhong/DualTeacher}

Effects of grazing on C : N:P stoichiometry attenuate from soils to plants and insect herbivores in a semi-arid grassland

Understanding the processing of limiting nutrients among organisms is an important goal of community ecology. Less known is how human disturbances may alter the stoichiometric patterns among organisms from different trophic levels within communities. Here, we investigated how livestock grazing affects the C:N:P ecological stoichiometry of soils, plants (Leymus chinensis), and grasshoppers (Euchorthippus spp.) in a semi-arid grassland in northeastern China. We found that grazing significantly enhanced soil available N and leaf N content of the dominant L. chinensis grass by 15% and 20%, respectively. Grazing also reduced (soluble) C:N of L. chinensis leaves by 22%. However, grazing did not affect total C, N, or P contents nor their ratios in Euchorthippus grasshoppers. Our results reveal that the effects of grazing disturbances on elemental composition attenuated from lower to higher trophic levels. These findings support the theory that organisms from higher trophic levels have relatively stronger stoichiometric homeostasis compared to those from lower trophic levels. Moreover, grasshopper abundance dropped by 66% in the grazed areas, and they reduced the feeding time on their host L. chinensis grass by 43%, presumably to limit the intake of excess nitrogen from host plants. The energetic costs associated with the maintenance of elemental homeostasis likely reduced grasshopper individual performance and population abundance in the grazed areas. A comprehensive investigation of stoichiometric properties of organisms across trophic levels may enable a better understanding of the nature of species interactions, and facilitate predictions of the consequences of future environmental changes for a community organization.Peer reviewe

Spatial Pathomics Toolkit for Quantitative Analysis of Podocyte Nuclei with Histology and Spatial Transcriptomics Data in Renal Pathology

Podocytes, specialized epithelial cells that envelop the glomerular capillaries, play a pivotal role in maintaining renal health. The current description and quantification of features on pathology slides are limited, prompting the need for innovative solutions to comprehensively assess diverse phenotypic attributes within Whole Slide Images (WSIs). In particular, understanding the morphological characteristics of podocytes, terminally differentiated glomerular epithelial cells, is crucial for studying glomerular injury. This paper introduces the Spatial Pathomics Toolkit (SPT) and applies it to podocyte pathomics. The SPT consists of three main components: (1) instance object segmentation, enabling precise identification of podocyte nuclei; (2) pathomics feature generation, extracting a comprehensive array of quantitative features from the identified nuclei; and (3) robust statistical analyses, facilitating a comprehensive exploration of spatial relationships between morphological and spatial transcriptomics features.The SPT successfully extracted and analyzed morphological and textural features from podocyte nuclei, revealing a multitude of podocyte morphomic features through statistical analysis. Additionally, we demonstrated the SPT's ability to unravel spatial information inherent to podocyte distribution, shedding light on spatial patterns associated with glomerular injury. By disseminating the SPT, our goal is to provide the research community with a powerful and user-friendly resource that advances cellular spatial pathomics in renal pathology. The implementation and its complete source code of the toolkit are made openly accessible at https://github.com/hrlblab/spatial_pathomics

Vitamin C Enhances the Generation of Mouse and Human Induced Pluripotent Stem Cells

SummarySomatic cells can be reprogrammed into induced pluripotent stem cells (iPSCs) by defined factors. However, the low efficiency and slow kinetics of the reprogramming process have hampered progress with this technology. Here we report that a natural compound, vitamin C (Vc), enhances iPSC generation from both mouse and human somatic cells. Vc acts at least in part by alleviating cell senescence, a recently identified roadblock for reprogramming. In addition, Vc accelerates gene expression changes and promotes the transition of pre-iPSC colonies to a fully reprogrammed state. Our results therefore highlight a straightforward method for improving the speed and efficiency of iPSC generation and provide additional insights into the mechanistic basis of the reprogramming process

Antitumor activity and mechanisms of action of total glycosides from aerial part of Cimicifuga dahurica targeted against hepatoma

<p>Abstract</p> <p>Background</p> <p>Medicinal plant is a main source of cancer drug development. Some of the cycloartane triterpenoids isolated from the aerial part of <it>Cimicifuga dahurica </it>showed cytotoxicity in several cancer cell lines. It is of great interest to examine the antiproliferative activity and mechanisms of total triterpenoid glycosides of <it>C. dahurica </it>and therefore might eventually be useful in the prevention or treatment of Hepatoma.</p> <p>Methods</p> <p>The total glycosides from the aerial part (TGA) was extracted and its cytotoxicity was evaluated in HepG2 cells and primary cultured normal mouse hepatocytes by an MTT assay. Morphology observation, Annexin V-FITC/PI staining, cell cycle analysis and western blot were used to further elucidate the cytotoxic mechanism of TGA. Implanted mouse H₂₂hepatoma model was used to demonstrate the tumor growth inhibitory activity of TGA <it>in vivo</it>.</p> <p>Results</p> <p>The IC₅₀values of TGA in HepG2 and primary cultured normal mouse hepatocytes were 21 and 105 μg/ml, respectively. TGA induced G₀/G₁cell cycle arrest at lower concentration (25 μg/ml), and triggered G₂/M arrest and apoptosis at higher concentrations (50 and 100 μg/ml respectively). An increase in the ratio of Bax/Bcl-2 was implicated in TGA-induced apoptosis. In addition, TGA inhibited the growth of the implanted mouse H₂₂tumor in a dose-dependent manner.</p> <p>Conclusion</p> <p>TGA may potentially find use as a new therapy for the treatment of hepatoma.</p

Processing Centroids of Smearing Star Image of Star Sensor

A novel method was presented for increasing the accuracy of subpixel centroid estimation for smearing star image. Model of the smearing trajectory of smearing star was built. It helped to study the analytical form of the errors, caused by image smearing, for centroid estimation. In the algorithm, the errors were estimated with accuracy and used to revise the centroid processed by CoM (centre of mass). Simulations have been run to study the effect of angular rates, integration time, and actual position of star on the accuracy of centroid estimation. Results were presented which suggested that the proposed algorithm had a precision better than 1/10 of a pixel when the angular rate was up to 3.0 deg/s

Processing Centroids of Smearing Star Image of Star Sensor

A novel method was presented for increasing the accuracy of subpixel centroid estimation for smearing star image. Model of the smearing trajectory of smearing star was built. It helped to study the analytical form of the errors, caused by image smearing, for centroid estimation. In the algorithm, the errors were estimated with accuracy and used to revise the centroid processed by CoM (centre of mass). Simulations have been run to study the effect of angular rates, integration time, and actual position of star on the accuracy of centroid estimation. Results were presented which suggested that the proposed algorithm had a precision better than 1/10 of a pixel when the angular rate was up to 3.0 deg/s

Optimal structure of learning-type set-point in various set-point-related indirect ilc algorithms

According to the literature statistics, only less than 10% of reported iterative learning control (ILC) methods have been devoted to the indirect approach. Motivated by the full potential of research opportunities in this field, a number of studies on indirect ILC were proposed recently, where ILC-based P-type control and learning-type model predictive control (L-MPC) are two successful stories. All indirect ILC algorithms consist of two loops: an ILC in the outer loop and a local controller in the inner loop. The local controllers are, respectively, a P-type controller in the ILC-based P-type control and a model predictive control (MPC) in the L-MPC. Logically, this leads to the question of what type of ILC should be chosen respectively for the two above-mentioned indirect ILC methods. In this study, P-type ILC and anticipatory P-type (A-P-type) ILC are studied and compared, because they are typical and widely implemented. Based on mathematical analysis and simulation test, it has been proved that the A-P-type ILC should be used in the ILC-based P-type control and while the P-type ILC should be used in the L-MPC. Furthermore, an improved L-MPC with batch-varying learning gain was proposed to handle the trade-off between convergence rate and robustness performance. The simulation results on injection molding process and a nonlinear batch process validated the feasibility and effectiveness of the proposed algorithm. © 2011 American Chemical Society