Miracle-dz/UniFAR-FTUs huggingface dataset:
Expression of Podoplanin in Different Grades of Oral Squamous Cell Carcinoma

Background: The expression of podoplanin is up regulated in a number of different human cancers, including squamous cell carcinoma of the oral cavity and its relationship with tumor invasion raises the possibility that podoplanin expression could be used as a biomarker for diagnosis and prognosis. Aim: The aim of the present study is to evaluate the expression of podoplanin in different grades of squamous cell carcinoma (SCC) and to correlate the expression of podoplanin with relevant clinical features such as age, sex, site and associated habits. Materials and Methods: Retrospective study was carried on formalin fixed, paraffin embedded blocks of oral SCC (OSCC) from the archives of Department of Oral and Maxillofacial Pathology, Vishnu Dental College, Bhimavaram. Thirty diagnosed cases were included, of which 10 were well differentiated SCC (WDSCC) (n = 10), 10 moderately DSCC and 10 poorly DSCC. Demographics including age, sex, gender and associated habit history, were recorded. Immunohistochemical staining was done with podoplanin anti D2–40 antibody, for all the cases of OSCC and assessed qualitatively. The data obtained were tabulated and subjected to statistical analysis. Results: In the present study, 27 cases of SCC showed podoplanin expression and remaining three cases showed no expression. The scoring criterion suggested by Yuan et al. was followed for semi quantitative assessment. OSCC, seven cases presented weak expression (Immunoreactive score [IRS] 0–3), 15 presented moderate expression (IRS Score 4–7) and 5 presented high expression (IRS Score > 8). The assessment of podoplanin expression in the cytoplasm, the membrane and the cytoplasm and membrane (both) of tumor cells showed overall high positivity in the cytoplasmic followed by both and the membrane. Conclusion: Podoplanin could be a potent biomarker in assessing the cytoplasm/membrane staining of tumor cells. Furthermore, a high level of podoplanin expression is suggestive of high frequency of lymph node metastasis and immature status in the differentiation process of OSCC.
Dystroglycan Maintains Inner Limiting Membrane Integrity to Coordinate Retinal Development

Proper neural circuit formation requires the precise regulation of neuronal migration, axon guidance, and dendritic arborization. Mutations affecting the function of the transmembrane glycoprotein dystroglycan cause a form of congenital muscular dystrophy that is frequently associated with neurodevelopmental abnormalities. Despite its importance in brain development, the role of dystroglycan in regulating retinal development remains poorly understood. Using a mouse model of dystroglycanopathy (ISPDL79*) and conditional dystroglycan mutants of both sexes, we show that dystroglycan is critical for the proper migration, axon guidance, and dendritic stratification of neurons in the inner retina. Using genetic approaches, we show that dystroglycan functions in neuroepithelial cells as an extracellular scaffold to maintain the integrity of the retinal inner limiting membrane. Surprisingly, despite the profound disruptions in inner retinal circuit formation, spontaneous retinal activity is preserved. These results highlight the importance of dystroglycan in coordinating multiple aspects of retinal development. SIGNIFICANCE STATEMENT The extracellular environment plays a critical role in coordinating neuronal migration and neurite outgrowth during neural circuit development. The transmembrane glycoprotein dystroglycan functions as a receptor for multiple extracellular matrix proteins and its dysfunction leads to a form of muscular dystrophy frequently associated with neurodevelopmental defects. Our results demonstrate that dystroglycan is required for maintaining the structural integrity of the inner limiting membrane (ILM) in the developing retina. In the absence of functional dystroglycan, ILM degeneration leads to defective migration, axon guidance, and mosaic spacing of neurons and a loss of multiple neuron types during retinal development. These results demonstrate that disorganization of retinal circuit development is a likely contributor to visual dysfunction in patients with dystroglycanopathy.
cGMP production and analysis of BG505 SOSIP.664, an extensively glycosylated, trimeric HIV‐1 envelope glycoprotein vaccine candidate

We describe the properties of BG505 SOSIP.664 HIV‐1 envelope glycoprotein trimers produced under current Good Manufacturing Practice (cGMP) conditions. These proteins are the first of a new generation of native‐like trimers that are the basis for many structure‐guided immunogen development programs aimed at devising how to induce broadly neutralizing antibodies (bNAbs) to HIV‐1 by vaccination. The successful translation of this prototype demonstrates the feasibility of producing similar immunogens on an appropriate scale and of an acceptable quality for Phase I experimental medicine clinical trials. BG505 SOSIP.664 trimers are extensively glycosylated, contain numerous disulfide bonds and require proteolytic cleavage, all properties that pose a substantial challenge to cGMP production. Our strategy involved creating a stable CHO cell line that was adapted to serum‐free culture conditions to produce envelope glycoproteins. The trimers were then purified by chromatographic methods using a 2G12 bNAb affinity column and size‐exclusion chromatography. The chosen procedures allowed any adventitious viruses to be cleared from the final product to the required extent of >12 log10. The final cGMP production run yielded 3.52 g (peptidic mass) of fully purified trimers (Drug Substance) from a 200 L bioreactor, a notable yield for such a complex glycoprotein. The purified trimers were fully native‐like as judged by negative‐stain electron microscopy, and were stable over a multi‐month period at room temperature or below and for at least 1 week at 50°C. Their antigenicity, disulfide bond patterns, and glycan composition were consistent with trimers produced on a research laboratory scale. The methods reported here should pave the way for the cGMP production of other native‐like Env glycoprotein trimers of various designs and genotypes.
The Differential Response of Proteins to Macromolecular Crowding

The habitat in which proteins exert their function contains up to 400 g/L of macromolecules, most of which are proteins. The repercussions of this dense environment on protein behavior are often overlooked or addressed using synthetic agents such as poly(ethylene glycol), whose ability to mimic protein crowders has not been demonstrated. Here we performed a comprehensive atomistic molecular dynamic analysis of the effect of protein crowders on the structure and dynamics of three proteins, namely an intrinsically disordered protein (ACTR), a molten globule conformation (NCBD), and a one-fold structure (IRF-3) protein. We found that crowding does not stabilize the native compact structure, and, in fact, often prevents structural collapse. Poly(ethylene glycol) PEG500 failed to reproduce many aspects of the physiologically-relevant protein crowders, thus indicating its unsuitability to mimic the cell interior. Instead, the impact of protein crowding on the structure and dynamics of a protein depends on its degree of disorder and results from two competing effects: the excluded volume, which favors compact states, and quinary interactions, which favor extended conformers. Such a viscous environment slows down protein flexibility and restricts the conformational landscape, often biasing it towards bioactive conformations but hindering biologically relevant protein-protein contacts. Overall, the protein crowders used here act as unspecific chaperons that modulate the protein conformational space, thus having relevant consequences for disordered proteins.
Association of Risk Factors, Mortality, and Care Costs of Adults With Acute Myeloid Leukemia With Admission to the Intensive Care Unit

Importance Adults with acute myeloid leukemia (AML) commonly require support in the intensive care unit (ICU), but risk factors for admission to the ICU and adverse outcomes remain poorly defined. Objective To examine risk factors, mortality, length of stay, and cost associated with admission to the ICU for patients with AML. Design, Setting, and Participants This study extracted information from the University HealthSystem Consortium database on patients 18 years or older with AML who were hospitalized for any cause between January 1, 2004, and December 31, 2012. The University HealthSystem Consortium database contains demographic, clinical, and cost variables prospectively abstracted by certified coders from discharge summaries. Outcomes were analyzed using univariate and multivariable statistical techniques. Data analysis was performed from November 15, 2013, to August 15, 2016. Main Outcomes and Measures Primary outcomes were admission to the ICU and inpatient mortality among patients requiring ICU care. Secondary outcomes included length of stay in the ICU, total hospitalization length of stay, and cost. Results Of the 43 249 patients with AML (mean [SD] age, 59.5 [16.6] years; 23 939 men and 19 310 women), 11 277 (26.1%) were admitted to the ICU. On multivariable analysis (with results reported as odds ratios [95% CIs]), independent risk factors for admission to the ICU included age younger than 80 years (1.56 [1.42-1.70]), hospitalization in the South (1.81 [1.71-1.92]), hospitalization at a low- or medium-volume hospital (1.25 [1.19-1.31]), number of comorbidities (10.64 [8.89-12.62] for 5 vs none), sepsis (4.61 [4.34-4.89]), invasive fungal infection (1.24 [1.11-1.39]), and pneumonia (1.73 [1.63-1.82]). In-hospital mortality was higher for patients requiring ICU care (4857 of 11 277 [43.1%] vs 2959 of 31 972 [9.3%]). On multivariable analysis, independent risk factors for death in patients requiring ICU care included age 60 years or older (1.16 [1.06-1.26]), nonwhite race/ethnicity (1.18 [1.07-1.30]), hospitalization on the West coast (1.19 [1.06-1.34]), number of comorbidities (18.76 [13.7-25.67] for 5 vs none), sepsis (2.94 [2.70-3.21]), invasive fungal infection (1.20 [1.02-1.42]), and pneumonia (1.13 [1.04-1.24]). Mean costs of hospitalization were higher for patients requiring ICU care ($83 354 vs $41 973) and increased with each comorbidity, from $50 543 for patients with no comorbidities to $124 820 for those with 5 or more comorbidities. Conclusions and Relevance Admission to the ICU is associated with high mortality and cost that increase proportionally with the comorbidity burden in adults with AML. Several demographic factors and medical characteristics identify patients at risk for admission to the ICU and mortality and provide an opportunity for testing primary prevention strategies.
Unsupervised learning of digit recognition using spike-timing-dependent plasticity

In order to understand how the mammalian neocortex is performing computations, two things are necessary; we need to have a good understanding of the available neuronal processing units and mechanisms, and we need to gain a better understanding of how those mechanisms are combined to build functioning systems. Therefore, in recent years there is an increasing interest in how spiking neural networks (SNN) can be used to perform complex computations or solve pattern recognition tasks. However, it remains a challenging task to design SNNs which use biologically plausible mechanisms (especially for learning new patterns), since most such SNN architectures rely on training in a rate-based network and subsequent conversion to a SNN. We present a SNN for digit recognition which is based on mechanisms with increased biological plausibility, i.e., conductance-based instead of current-based synapses, spike-timing-dependent plasticity with time-dependent weight change, lateral inhibition, and an adaptive spiking threshold. Unlike most other systems, we do not use a teaching signal and do not present any class labels to the network. Using this unsupervised learning scheme, our architecture achieves 95% accuracy on the MNIST benchmark, which is better than previous SNN implementations without supervision. The fact that we used no domain-specific knowledge points toward the general applicability of our network design. Also, the performance of our network scales well with the number of neurons used and shows similar performance for four different learning rules, indicating robustness of the full combination of mechanisms, which suggests applicability in heterogeneous biological neural networks.
Variation in natural exposure to anopheles mosquitoes and its effects on malaria transmission

Variation in biting frequency by Anopheles mosquitoes can explain some of the heterogeneity in malaria transmission in endemic areas. In this study in Burkina Faso, we assessed natural exposure to mosquitoes by matching the genotype of blood meals from 1066 mosquitoes with blood from residents of local households. We observed that the distribution of mosquito bites exceeded the Pareto rule (20/80) in two of the three surveys performed (20/85, 76, and 96) and, at its most pronounced, is estimated to have profound epidemiological consequences, inflating the basic reproduction number of malaria by 8-fold. The distribution of bites from sporozoite-positive mosquitoes followed a similar pattern, with a small number of individuals within households receiving multiple potentially infectious bites over the period of a few days. Together, our findings indicate that heterogeneity in mosquito exposure contributes considerably to heterogeneity in infection risk and suggest significant variation in malaria transmission potential.
Proportional Volume Sampling and Approximation Algorithms for A-Optimal Design

We study optimal design problems in which the goal is to choose a set of linear measurements to obtain the most accurate estimate of an unknown vector. We study the [Formula: see text]-optimal design variant where the objective is to minimize the average variance of the error in the maximum likelihood estimate of the vector being measured. We introduce the proportional volume sampling algorithm to obtain nearly optimal bounds in the asymptotic regime when the number [Formula: see text] of measurements made is significantly larger than the dimension [Formula: see text] and obtain the first approximation algorithms whose approximation factor does not degrade with the number of possible measurements when [Formula: see text] is small. The algorithm also gives approximation guarantees for other optimal design objectives such as [Formula: see text]-optimality and the generalized ratio objective, matching or improving the previously best-known results. We further show that bounds similar to ours cannot be obtained for [Formula: see text]-optimal design and that [Formula: see text]-optimal design is NP-hard to approximate within a fixed constant when [Formula: see text].
Matching Node Embeddings for Graph Similarity

Graph kernels have emerged as a powerful tool for graph comparison. Most existing graph kernels focus on local properties of graphs and ignore global structure. In this paper, we compare graphs based on their global properties as these are captured by the eigenvectors of their adjacency matrices. We present two algorithms for both labeled and unlabeled graph comparison. These algorithms represent each graph as a set of vectors corresponding to the embeddings of its vertices. The similarity between two graphs is then determined using the Earth Mover's Distance metric. These similarities do not yield a positive semidefinite matrix. To address for this, we employ an algorithm for SVM classification using indefinite kernels. We also present a graph kernel based on the Pyramid Match kernel that finds an approximate correspondence between the sets of vectors of the two graphs. We further improve the proposed kernel using the Weisfeiler-Lehman framework. We evaluate the proposed methods on several benchmark datasets for graph classification and compare their performance to state-of-the-art graph kernels. In most cases, the proposed algorithms outperform the competing methods, while their time complexity remains very attractive.
Towards Runtime Monitoring of Node.js and Its Application to the Internet of Things

In the last years Node.js has emerged as a framework particularly suitable for implementing lightweight IoT applications, thanks to its underlying asynchronous event-driven, non blocking I/O model. However, verifying the correctness of programs with asynchronous nested callbacks is quite difficult, and, hence, runtime monitoring can be a valuable support to tackle such a complex task. Runtime monitoring is a useful software verification technique that complements static analysis and testing, but has not been yet fully explored in the context of Internet of Things (IoT) systems. Trace expressions have been successfully employed for runtime monitoring in widespread multiagent system platforms. Recently, their expressive power has been extended to allow parametric specifications on data that can be captured and monitored only at runtime. Furthermore, they can be language and system agnostic, through the notion of event domain and type. This paper investigates the use of parametric trace expressions as a first step towards runtime monitoring of programs developed in Node.js and Node-RED, a flow-based IoT programming tool built on top of Node.js. Runtime verification of such systems is a task that mostly seems to have been overlooked so far in the literature. A prototype implementing the proposed system for Node.js, in order to dynamically check with trace expressions the correct usage of API functions, is presented. The tool exploits the dynamic analysis framework Jalangi for monitoring Node.js programs and allows detection of errors that would be difficult to catch with other techniques. Furthermore, it offers a simple REST interface which can be exploited for runtime verification of Node-RED components, and, more generally, IoT devices.
An alternative policy evaluation of the British Columbia carbon tax: broadening the application of Elinor Ostrom's design principles for managing common-pool resources

Climate change is putting infrastructure, food supply, water resources, ecosystems, and human health at risk. These risks will be exacerbated depending on the degree of additional greenhouse gas emissions. Urgent action is needed to limit the severity of impacts associated with further warming. British Columbia (BC) has taken action to reduce greenhouse gas emissions from carbonbased fuels by introducing a carbon tax in 2008. As an innovative approach to climate change mitigation, especially in North America, studies evaluating its effectiveness are valuable. We assessed the long-term viability potential of the BC carbon tax using common pool resource design principles, a novel application of the design principles to environmental policy. We found that the design principles can be applied productively to environmental policy and larger scale air pollution problems. With regard to the BC carbon tax, our findings suggest that closer monitoring of user behavior, further increases of the tax over time, and pursuing efforts for a more elaborate system of nested enterprises and interjurisdictional cooperation could increase the long-term success of the BC carbon tax. We also found that the design principles allowed us to more comprehensively reach conclusions regarding the broader effectiveness of the tax when compared to existing policy analysis. Traditionally, climate policy evaluation has focused on the end goal without considering broader constraints and issues of resource allocation. We suggest that common pool resource theory, which is based on strong theoretical principles and encourages reflexivity, will be able to address those limitations.
Detailed Garment Recovery from a Single-View Image

Most recent garment capturing techniques rely on acquiring multiple views of clothing, which may not always be readily available, especially in the case of pre-existing photographs from the web. As an alternative, we pro- pose a method that is able to compute a rich and realistic 3D model of a human body and its outfits from a single photograph with little human in- teraction. Our algorithm is not only able to capture the global shape and geometry of the clothing, it can also extract small but important details of cloth, such as occluded wrinkles and folds. Unlike previous methods using full 3D information (i.e. depth, multi-view images, or sampled 3D geom- etry), our approach achieves detailed garment recovery from a single-view image by using statistical, geometric, and physical priors and a combina- tion of parameter estimation, semantic parsing, shape recovery, and physics- based cloth simulation. We demonstrate the effectiveness of our algorithm by re-purposing the reconstructed garments for virtual try-on and garment transfer applications, as well as cloth animation for digital characters.
Abstraction, Composition and Contracts: A Sheaf Theoretic Approach

Complex systems of systems (SoS) are characterized by multiple interconnected subsystems. Typically, each subsystem is designed and analyzed using methodologies and formalisms that are specific to the particular subsystem model of computation considered --- Petri nets, continuous time ODEs, nondeterministic automata, to name a few. When interconnecting subsystems, a designer needs to choose, based on the specific subsystems models, a common abstraction framework to analyze the composition. In this paper we introduce a new framework for abstraction, composition and analysis of SoS that builds on results and methods developed in sheaf theory, category theory and topos theory. In particular, we will be modeling behaviors of systems using sheaves, leverage category theoretic methods to define wiring diagrams and formalize composition and, by establishing a connection with topos theory, define a formal (intuitionistic/constructive) logic with a sound sheaf semantics
Acid‐base assessment of patients receiving hemodialysis. What are our management goals?

Acid‐base assessment of patients receiving conventional hemodialysis (HD) has been based solely on predialysis serum [total CO2], and treatment is currently driven by the KDOQI guideline from 2000. This guideline was directed solely at minimizing metabolic acidosis and thereby improving bone and muscle metabolism. In 2000, no data were available to assess the effects of acid‐base status on morbidity and mortality. Since then, new data have emerged from several large cohort studies about the association between variations in predialysis serum [total CO2], as well as blood pH, and morbidity and mortality risk. These studies have shown increased risk not only with very low predialysis [total CO2] values, but also with predialysis alkaline pH and very high predialysis serum [total CO2] values. At present, our major concern is not for patients with metabolic acidosis, but rather for the growing numbers of patients with metabolic alkalosis. This review discusses the controversies around assessing and treating acid‐base status in HD patients, and recommends a practical approach based on the results of these recent studies. The new approach provides recommendations for patients both with very low and very high predialysis serum [total CO2] values.