Transcription factor signal transducer and activator of transcription 5 promotes growth of human prostate cancer cells in vivo

Purpose: Stat5a/b is the key mediator of prolactin (Prl) effects in prostate cancer cells via activation of Jak2. Prl is locally produced growth factor in human prostate cancer. Prl protein expression and constitutive activation of Stat5a/b are associated with high histological grade of clinical prostate cancer. Moreover, activation of Stat5a/b in primary prostate cancer predicts early disease recurrence. Here, we inhibited Stat5a/b by several different methodological approaches. Our goal was to establish a proof-of-principle that Stat5a/b is critical for prostate cancer cell viability in vitro and for prostate tumor growth in vivo. Experimental Design: We inhibited Stat5a/b protein expression by antisense oligonucleotides or RNA interference and transcriptional activity of Stat5a/b by adenoviral expression of a dominant-negative mutant of Stat5a/b in prostate cancer cells in culture. Moreover, Stat5a/b activity was suppressed in human prostate cancer xenograft tumors in nude mice. Stat5a/b regulation of BclXL and Cyclin-D1 protein levels was demonstrated by antisense suppression of Stat5a/b protein expression followed by Western blotting. Results and Conclusions: We show here that inhibition of Stat5a/b by antisense oligonuleotides, RNA interference, or adenoviral expression of DNStat5a/b all effectively kill prostate cancer cells. Moreover, we demonstrate that Stat5a/b is critical for human prostate cancer xenograft growth in nude mice. Stat5a/b effects on the viability of on prostate cancer cells involve Stat5a/b-regulation of BclXL and Cyclin-D1 protein levels, but not the expression or activation of Stat3. This work establishes Stat5a/b as a therapeutic target protein for prostate cancer. Pharmacological inhibition of Stat5a/b in prostate cancer can be achieved by small-molecule inhibitors of transactivation, dimerization or DNA-binding of Stat5a/b

Diffusion with Forward Models: Solving Stochastic Inverse Problems Without Direct Supervision

Denoising diffusion models are a powerful type of generative models used to capture complex distributions of real-world signals. However, their applicability is limited to scenarios where training samples are readily available, which is not always the case in real-world applications. For example, in inverse graphics, the goal is to generate samples from a distribution of 3D scenes that align with a given image, but ground-truth 3D scenes are unavailable and only 2D images are accessible. To address this limitation, we propose a novel class of denoising diffusion probabilistic models that learn to sample from distributions of signals that are never directly observed. Instead, these signals are measured indirectly through a known differentiable forward model, which produces partial observations of the unknown signal. Our approach involves integrating the forward model directly into the denoising process. This integration effectively connects the generative modeling of observations with the generative modeling of the underlying signals, allowing for end-to-end training of a conditional generative model over signals. During inference, our approach enables sampling from the distribution of underlying signals that are consistent with a given partial observation. We demonstrate the effectiveness of our method on three challenging computer vision tasks. For instance, in the context of inverse graphics, our model enables direct sampling from the distribution of 3D scenes that align with a single 2D input image.Comment: Project page: https://diffusion-with-forward-models.github.io

Seeing 3D Objects in a Single Image via Self-Supervised Static-Dynamic Disentanglement

Human perception reliably identifies movable and immovable parts of 3D scenes, and completes the 3D structure of objects and background from incomplete observations. We learn this skill not via labeled examples, but simply by observing objects move. In this work, we propose an approach that observes unlabeled multi-view videos at training time and learns to map a single image observation of a complex scene, such as a street with cars, to a 3D neural scene representation that is disentangled into movable and immovable parts while plausibly completing its 3D structure. We separately parameterize movable and immovable scene parts via 2D neural ground plans. These ground plans are 2D grids of features aligned with the ground plane that can be locally decoded into 3D neural radiance fields. Our model is trained self-supervised via neural rendering. We demonstrate that the structure inherent to our disentangled 3D representation enables a variety of downstream tasks in street-scale 3D scenes using simple heuristics, such as extraction of object-centric 3D representations, novel view synthesis, instance segmentation, and 3D bounding box prediction, highlighting its value as a backbone for data-efficient 3D scene understanding models. This disentanglement further enables scene editing via object manipulation such as deletion, insertion, and rigid-body motion.Comment: Project page: https://prafullsharma.net/see3d

Beyond deficit-based models of learners' cognition: Interpreting engineering students' difficulties with sense-making in terms of fine-grained epistemological and conceptual dynamics

Researchers have argued against deficit-based explanations of students' troubles with mathematical sense-making, pointing instead to factors such as epistemology: students' beliefs about knowledge and learning can hinder them from activating and integrating productive knowledge they have. In this case study of an engineering major solving problems (about content from his introductory physics course) during a clinical interview, we show that "Jim" has all the mathematical and conceptual knowledge he would need to solve a hydrostatic pressure problem that we posed to him. But he reaches and sticks with an incorrect answer that violates common sense. We argue that his lack of mathematical sense-making-specifically, translating and reconciling between mathematical and everyday/common-sense reasoning-stems in part from his epistemological views, i.e., his views about the nature of knowledge and learning. He regards mathematical equations as much more trustworthy than everyday reasoning, and he does not view mathematical equations as expressing meaning that tractably connects to common sense. For these reasons, he does not view reconciling between common sense and mathematical formalism as either necessary or plausible to accomplish. We, however, avoid a potential "deficit trap"-substituting an epistemological deficit for a concepts/skills deficit-by incorporating multiple, context-dependent epistemological stances into Jim's cognitive dynamics. We argue that Jim's epistemological stance contains productive seeds that instructors could build upon to support Jim's mathematical sense-making: He does see common-sense as connected to formalism (though not always tractably so) and in some circumstances this connection is both salient and valued.Comment: Submitted to the Journal of Engineering Educatio

Solaris photometric survey: Search for circumbinary companions using eclipse timing variations

Eclipse timing variations (ETV) have been a successful tool for detecting circumbinary companions to eclipsing binaries (EB). While TESS and Kepler have been prolific for ETV searches, they sometimes can be limited by time and sky coverage which can be addressed by specialised ground-based ETV surveys. We present the initial results from the Solaris photometric survey which uses four 0.5m robotic telescopes in the southern hemisphere to look for circumbinary companions. We present the method of light curve extraction, detrending, and EB modelling using the observations from the Solaris network. Using these light curves we extract precise eclipse timing for 7 EB and look for companions using a Lomb-Scargle periodogram search. We find two possible periodic signals for the target GSC 08814-01026. With the system having strong activity, we check for the feasibility of orbital solutions at these two periods. We find that the 245 +/- 1 d period is due to an M-dwarf mass companion. This makes GSC 08814-01026 a candidate compact hierarchical triple system. The other periodic signal at 146 +/- 1 d is an artefact of stellar activity.Comment: 14 pages, 9 figures, Accepted for publication in MNRAS. This is a pre-copyedited, author-produced PDF of an article accepted for publication in MNRAS following peer revie

Cyclic multiplex fluorescent immunohistochemistry and machine learning reveal distinct states of astrocytes and microglia in normal aging and Alzheimer’s disease

Background Astrocytes and microglia react to Aβ plaques, neurofibrillary tangles, and neurodegeneration in the Alzheimer’s disease (AD) brain. Single-nuclei and single-cell RNA-seq have revealed multiple states or subpopulations of these glial cells but lack spatial information. We have developed a methodology of cyclic multiplex fluorescent immunohistochemistry on human postmortem brains and image analysis that enables a comprehensive morphological quantitative characterization of astrocytes and microglia in the context of their spatial relationships with plaques and tangles. Methods Single FFPE sections from the temporal association cortex of control and AD subjects were subjected to 8 cycles of multiplex fluorescent immunohistochemistry, including 7 astroglial, 6 microglial, 1 neuronal, Aβ, and phospho-tau markers. Our analysis pipeline consisted of: (1) image alignment across cycles; (2) background subtraction; (3) manual annotation of 5172 ALDH1L1+ astrocytic and 6226 IBA1+ microglial profiles; (4) local thresholding and segmentation of profiles; (5) machine learning on marker intensity data; and (6) deep learning on image features. Results Spectral clustering identified three phenotypes of astrocytes and microglia, which we termed “homeostatic,” “intermediate,” and “reactive.” Reactive and, to a lesser extent, intermediate astrocytes and microglia were closely associated with AD pathology (≤ 50 µm). Compared to homeostatic, reactive astrocytes contained substantially higher GFAP and YKL-40, modestly elevated vimentin and TSPO as well as EAAT1, and reduced GS. Intermediate astrocytes had markedly increased EAAT2, moderately increased GS, and intermediate GFAP and YKL-40 levels. Relative to homeostatic, reactive microglia showed increased expression of all markers (CD68, ferritin, MHC2, TMEM119, TSPO), whereas intermediate microglia exhibited increased ferritin and TMEM119 as well as intermediate CD68 levels. Machine learning models applied on either high-plex signal intensity data (gradient boosting machines) or directly on image features (convolutional neural networks) accurately discriminated control vs. AD diagnoses at the single-cell level. Conclusions Cyclic multiplex fluorescent immunohistochemistry combined with machine learning models holds promise to advance our understanding of the complexity and heterogeneity of glial responses as well as inform transcriptomics studies. Three distinct phenotypes emerged with our combination of markers, thus expanding the classic binary “homeostatic vs. reactive” classification to a third state, which could represent “transitional” or “resilient” glia.España Ministry of Science, Innovation, and Universities FPU fellowship to CM-CMassachusetts Alzheimer’s Disease Research Center grant P30AG062421 to BTH, and 1R56AG061196 to BTHAlzheimer’s Association (AACF17-524184 and AACF-17-524184-RAPID to AS-P

The Case for Dynamic Models of Learners' Ontologies in Physics

In a series of well-known papers, Chi and Slotta (Chi, 1992; Chi & Slotta, 1993; Chi, Slotta & de Leeuw, 1994; Slotta, Chi & Joram, 1995; Chi, 2005; Slotta & Chi, 2006) have contended that a reason for students' difficulties in learning physics is that they think about concepts as things rather than as processes, and that there is a significant barrier between these two ontological categories. We contest this view, arguing that expert and novice reasoning often and productively traverses ontological categories. We cite examples from everyday, classroom, and professional contexts to illustrate this. We agree with Chi and Slotta that instruction should attend to learners' ontologies; but we find these ontologies are better understood as dynamic and context-dependent, rather than as static constraints. To promote one ontological description in physics instruction, as suggested by Slotta and Chi, could undermine novices' access to productive cognitive resources they bring to their studies and inhibit their transition to the dynamic ontological flexibility required of experts.Comment: The Journal of the Learning Sciences (In Press

Research Productivity of DESIDOC Journal of Library and Information Technology: A Bibliometric Review

Examines the research productivity of the DESIDOC Journal of Library and Information Technology (DJLIT) during 2012-2020 based on the data extracted from the Scopus database. The bibliometric parameters; year-wise distribution of publications with citations, RCI, ACPP, CAI, Citation analysis, the collaboration of authors, institutions and countries etc were applied, to measure the research productivity. The study revealed that the number of publications over the years fluctuates and joint authors' contribution found high at the rate of 358(67.42%), followed by single authorship 173(32.58%). The author, B. M. Gupta, was the most productive and cited author and the University of Delhi contributing 42 publications was identified as top in ten highly effective institutions. The study found DJLIT to be publishing quality research covering the different aspects of library and information science