Miracle-dz/UniFAR-FTUs huggingface dataset:
Tumor-infiltrating lymphocytes in breast cancer predict the response to chemotherapy and survival outcome: A meta-analysis

Tumor-infiltrating lymphocytes (TILs) influence tumor prognosis and the chemotherapeutic response. Here, we quantified the clinical relevance of TILs, including the effect of TILs on lymphocyte subpopulations and assessed their consistency in breast cancer. We searched published literature from January 2000 to January 2016. The main parameters analyzed were pathological complete response (pCR) and survival outcome following chemotherapy in patients with breast cancer. Pooled odds ratio (OR) or relative risk (RR) values with 95% confidence intervals (CIs) were computed using random and fixed-effects models. Subgroup and heterogeneity analyses were also conducted. Twenty-three studies, which included 13,100 patients, met the inclusion criteria. The pooled results showed that TILs were associated with clinicopathological parameters of biologically aggressive phenotypes, such as high tumor grade or estrogen/progesterone receptor negativity, but they were not correlated with human epidermal growth factor receptor-2 expression. Moreover, a high TIL level was associated with a significantly improved pCR rate compared with a low TIL level (OR, 2.81; P < 0.001), particularly in the triple-negative breast cancer subtype (OR, 4.67; P < 0.001). An analysis of lymphocyte subpopulations showed that infiltration by CD8 lymphocytes, but not by CD4 lymphocytes and Foxp3 cells, was associated with a high pCR rate. Furthermore, a high TIL level was associated with significantly longer disease-free survival and overall survival. Our present meta-analysis indicates that an increased number of TILs predicted pCR to chemotherapy and improved survival. A high TIL level, characterized mainly by the infiltration of CD8 lymphocytes, is a strong predictive and prognostic factor.
Predicting the Frequency of Water Quality Standard Violations Using Bayesian Calibration of Eutrophication Models

Abstract The water quality standard setting process usually relies on mathematical models with strong mechanistic basis, as this provides assurance that the model will more realistically project the effects of alternative management schemes. From an operational standpoint, the interpretation of model results should be coupled with rigorous error analysis and explicit consideration of the predictive uncertainty and natural variability. In this study, our main objective is to attain effective model calibration and rigorous uncertainty assessment by integrating environmental mathematical modeling with Bayesian analysis. We use a complex aquatic biogeochemical model that simulates multiple elemental cycles (org. C, N, P, Si, O), multiple functional phytoplankton (diatoms, green algae and cyanobacteria) and zooplankton (copepods and cladocerans) groups. The Bayesian calibration framework is illustrated using three synthetic datasets that represent oligo-, meso- and eutrophic lake conditions. Scientific knowledge, expert judgment, and observational data were used to formulate prior probability distributions and characterize the uncertainty pertaining to a subset of the model parameters, i.e., a vector comprising the 35 most influential parameters based on an earlier sensitivity analysis of the model. Our study also underscores the lack of perfect simulators of natural system dynamics using a statistical formulation that explicitly accounts for the discrepancy between mathematical models and environmental systems. The model reproduces the key epilimnetic temporal patterns and provides realistic estimates of predictive uncertainty for water quality variables of environmental management interest. Our analysis also demonstrates how the Bayesian parameter estimation can be used for assessing the exceedance frequency and confidence of compliance of different water quality criteria. The proposed methodological framework can be very useful in the policy-making process and can facilitate environmental management decisions in the Laurentian Great Lakes region.
CFD analysis of Diffuser Augmented Wind Turbine model for wind tunnel investigation

The paper discusses results of integration of experimental campaign with CFD studies, where a small wind turbine placed inside a wind tunnel test section has been examined. Investigation included determination of model size influence on flow blockage effects. The CFD model helps to reduce time spent at the experimental stand and, therefore, costs pointing to the model with acceptable dimensions. CFD has been also used to anticipate the loads that may be exerted on the model thus helping to understand necessary measures in the upcoming aeromechanics study of the construction. The study concerns a wind turbine placed inside a diffuser channel, a solution known as Diffuser Augmented Wind Turbine (DAWT). This type of wind energy converter has recently been an object of numerous studies at the Institute of Turbomachinery of Lodz University of Technology. The examined model sizes included: D_5, D_10, D_20, D_30, D_80 and D_real, where the number states the per cent of test object's cross section area related to the cross-section area of the wind tunnel outlet nozzle supplying air to the test location. The numerical study has been based on a commercial solver ANSYS CFX 15.0, the comparison of the obtained results with available experimental data for D_5, D_30 and D_80 models has been made, leading to the analysis of the results obtained and the conclusions.
Test Scores, Dropout Rates, and Transfer Rates as Alternative Indicators of High School Performance

This study investigated the relationships among several different indicators of high school performance: test scores, dropout rates, transfer rates, and attrition rates. Hierarchical linear models were used to analyze panel data from a sample of 14,199 students who took part in the National Education Longitudinal Survey of 1988. The results generally support the notion of an alternative as opposed to a common view of school effectiveness: Schools that are effective in promoting student learning (growth in achievement) are not necessarily effective in reducing dropout or transfer rates. In fact, after control for student inputs, high schools exhibit relatively little variability in dropout rates but considerable variation in transfer rates. In addition, characteristics of schools that contributed to performance in one area often did not contribute to performance in another. Given these findings, the authors suggest that, along with test scores, dropout and transfer rates should be used to judge school performance.
Capturing the Patient’s Perspective: a Review of Advances in Natural Language Processing of Health-Related Text

Summary Background: Natural Language Processing (NLP) methods are increasingly being utilized to mine knowledge from unstructured health-related texts. Recent advances in noisy text processing techniques are enabling researchers and medical domain experts to go beyond the information encapsulated in published texts (e.g., clinical trials and systematic reviews) and structured questionnaires, and obtain perspectives from other unstructured sources such as Electronic Health Records (EHRs) and social media posts. Objectives: To review the recently published literature discussing the application of NLP techniques for mining health-related information from EHRs and social media posts. Methods: Literature review included the research published over the last five years based on searches of PubMed, conference proceedings, and the ACM Digital Library, as well as on relevant publications referenced in papers. We particularly focused on the techniques employed on EHRs and social media data. Results: A set of 62 studies involving EHRs and 87 studies involving social media matched our criteria and were included in this paper. We present the purposes of these studies, outline the key NLP contributions, and discuss the general trends observed in the field, the current state of research, and important outstanding problems. Conclusions: Over the recent years, there has been a continuing transition from lexical and rule-based systems to learning-based approaches, because of the growth of annotated data sets and advances in data science. For EHRs, publicly available annotated data is still scarce and this acts as an obstacle to research progress. On the contrary, research on social media mining has seen a rapid growth, particularly because the large amount of unlabeled data available via this resource compensates for the uncertainty inherent to the data. Effective mechanisms to filter out noise and for mapping social media expressions to standard medical concepts are crucial and latent research problems. Shared tasks and other competitive challenges have been driving factors behind the implementation of open systems, and they are likely to play an imperative role in the development of future systems.
Divergent CSF τ alterations in two common tauopathies: Alzheimer's disease and progressive supranuclear palsy

Background Elevated CSF τ is considered a biomarker of neuronal injury in newly developed Alzheimer's disease (AD) and mild cognitive impairment (MCI) criteria. However, previous studies have failed to detect alterations of τ species in other primary tauopathies. We assessed CSF τ protein abnormalities in AD, a tauopathy with prominent Aβ pathology, and progressive supranuclear palsy (PSP), a primary tauopathy characterised by deposition of four microtubule-binding repeat (4R) τ with minimal Aβ pathology. Methods 26 normal control (NC), 37 AD, and 24 patients with PSP participated in the study. AD and PSP were matched for severity using the clinical dementia rating sum of boxes (CDR-sb) scores. The INNO BIA AlzBio3 multiplex immunoassay was used to measure CSF Aβ, total τ, and ptau181. Additional, novel ELISAs targeting different N-terminal and central τ epitopes were developed to examine CSF τ components and to investigate interactions between diagnostic group, demographics and genetic variables. Results PSP had lower CSF N-terminal and C-terminal τ concentrations than NC and AD measured with the novel τ ELISAs and the standard AlzBio3 τ and ptau assays. AD had higher total τ and ptau levels than NC and PSP. There was a gender by diagnosis interaction in AD and PSP for most τ species, with lower concentrations for male compared to female patients. Conclusions CSF τ fragment concentrations are different in PSP compared with AD despite the presence of severe τ pathology and neuronal injury in both disorders. CSF τ concentration likely reflects multiple factors in addition to the degree of neuronal injury.
Grass cell walls have a role in the inherent tolerance of grasses to the cellulose biosynthesis inhibitor isoxaben.

BACKGROUND Cellulose biosynthesis inhibitors (CBIs) are pre-emergence herbicides that inhibit anisotropic cell expansion resulting in a severely swollen and stunted growth phenotype. Resistance to group 21 CBIs, such as isoxaben, is conferred by missense mutations in CELLOSE SYNTHASE A (CesA) genes required for primary cell wall synthesis, concluding that this is their in vivo target. RESULTS Herein, we show that grasses exhibit tolerance to group 21 CBIs and explore the mechanism of tolerance to isoxaben in the grass Brachypodium distachyon (L.). Comparative genomics failed to identify synonymous point mutations that have been found to confer isoxaben resistance in the dicot Arabidopsis thaliana (L.). Brachypodium did not metabolize 14 C-isoxaben. We next explored the role of grass-specific non-cellulosic cell wall components, specifically the hemicellulose polysaccharide mix linkage glucans (MLG), as a potential tolerance mechanism by compensating for the loss of cellulose during cell elongation. A partial-transcriptional knockdown T-DNA insertion was found in a key MLG synthesis gene, Cellulose synthase-like F6 (CslF6) and this mutant was found to be 2.1 times more sensitive to isoxaben than wild-type plants. CONCLUSION These data suggest that the composition and compensatory response of grass cell walls may be a factor in conferring tolerance to group 21 CBIs. © 2017 Society of Chemical Industry.
Child Maltreatment Is Associated with a Reduction of the Oxytocin Receptor in Peripheral Blood Mononuclear Cells

Background: Child maltreatment (CM) and attachment experiences are closely linked to alterations in the human oxytocin (OXT) system. However, human data about oxytocin receptor (OXTR) protein levels are lacking. Therefore, we investigated oxytocin receptor (OXTR) protein levels in circulating immune cells and related them to circulating levels of OXT in peripheral blood. We hypothesized reduced OXTR protein levels, associated with both, experiences of CM and an insecure attachment representation. Methods: OXTR protein expressions were analyzed by western blot analyses in peripheral blood mononuclear cells (PBMC) and plasma OXT levels were determined by radioimmunoassay (RIA) in 49 mothers. We used the Childhood Trauma Questionnaire (CTQ) to assess adverse childhood experiences. Attachment representations (secure vs. insecure) were classified using the Adult Attachment Projective Picture System (AAP) and levels of anxiety and depression were assessed with the German version of the Hospital Depression and Anxiety scale (HADS-D). Results: CM-affected women showed significantly lower OXTR protein expression with significantly negative correlations between the OXTR protein expression and the CTQ sum score, whereas plasma OXT levels showed no significant differences in association with CM. Lower OXTR protein expression in PBMC were particularly pronounced in the group of insecurely attached mothers compared to the securely attached group. Anxiety levels were significantly higher in CM-affected women. Conclusion: This study demonstrated a significant association between CM and an alteration of OXTR protein expression in human blood cells as a sign for chronic, long-lasting alterations in this attachment-related neurobiological system.
MUCOADHESION: A NEW POLYMERIC CONTROLLED DRUG DELIVERY Review Article

Since last three decades delivery of the desired drug as muocoadhesive drug delivery systems has been subject of interest. Mucoadhesion involving a polymeric drug delivery system is a complex phenomenon that includes theory of diffusion, wetting, adsorption, fracture and interpenetration of polymer chains amongst various other processes. Various polymer-based properties such as the degree of cross-linking, chain length, hydration, pH and various functional groupings can influenced the mucoadhesion bonding with polymer. With the right dosage form design, local environment of the mucosa can be controlled and manipulated in order to optimize the rate of drug dissolution and permeation. In research various set up of in vitro and in vivo mucoadhesion testing carried out to select correct adhesive drug delivery technique. Mucoadhesive drug delivery system has wide application such as buccal, ophthalmic, nasal and vaginal drug delivery.
Sound Modular Verification of C Code Executing in an Unverified Context

Over the past decade, great progress has been made in the static modular verification of C code by means of separation logic-based program logics. However, the runtime guarantees offered by such verification are relatively limited when the verified modules are part of a whole program that also contains unverified modules. In particular, a memory safety error in an unverified module can corrupt the runtime state, leading to assertion failures or invalid memory accesses in the verified modules. This paper develops runtime checks to be inserted at the boundary between the verified and the unverified part of a program, to guarantee that no assertion failures or invalid memory accesses can occur at runtime in any verified module. One of the key challenges is enforcing the separation logic frame rule, which we achieve by checking the integrity of the footprint of the verified part of the program on each control flow transition from the unverified to the verified part. This in turn requires the presence of some support for module-private memory at runtime. We formalize our approach and prove soundness. We implement the necessary runtime checks by means of a program transformation that translates C code with separation logic annotations into plain C, and that relies on a protected module architecture for providing module-private memory and restricted module entry points. Benchmarks show the performance impact of this transformation depends on the choice of boundary between the verified and unverified parts of the program, but is below 4% for real-world applications.
Artificial Neural Network to Predict Vine Water Status Spatial Variability Using Multispectral Information Obtained from an Unmanned Aerial Vehicle (UAV)

Water stress, which affects yield and wine quality, is often evaluated using the midday stem water potential (Ψstem). However, this measurement is acquired on a per plant basis and does not account for the assessment of vine water status spatial variability. The use of multispectral cameras mounted on unmanned aerial vehicle (UAV) is capable to capture the variability of vine water stress in a whole field scenario. It has been reported that conventional multispectral indices (CMI) that use information between 500–800 nm, do not accurately predict plant water status since they are not sensitive to water content. The objective of this study was to develop artificial neural network (ANN) models derived from multispectral images to predict the Ψstem spatial variability of a drip-irrigated Carménère vineyard in Talca, Maule Region, Chile. The coefficient of determination (R2) obtained between ANN outputs and ground-truth measurements of Ψstem were between 0.56–0.87, with the best performance observed for the model that included the bands 550, 570, 670, 700 and 800 nm. Validation analysis indicated that the ANN model could estimate Ψstem with a mean absolute error (MAE) of 0.1 MPa, root mean square error (RMSE) of 0.12 MPa, and relative error (RE) of −9.1%. For the validation of the CMI, the MAE, RMSE and RE values were between 0.26–0.27 MPa, 0.32–0.34 MPa and −24.2–25.6%, respectively.
Molecular Pathogenesis of Infections Caused by Legionella pneumophila

SUMMARY The genus Legionella contains more than 50 species, of which at least 24 have been associated with human infection. The best-characterized member of the genus, Legionella pneumophila, is the major causative agent of Legionnaires' disease, a severe form of acute pneumonia. L. pneumophila is an intracellular pathogen, and as part of its pathogenesis, the bacteria avoid phagolysosome fusion and replicate within alveolar macrophages and epithelial cells in a vacuole that exhibits many characteristics of the endoplasmic reticulum (ER). The formation of the unusual L. pneumophila vacuole is a feature of its interaction with the host, yet the mechanisms by which the bacteria avoid classical endosome fusion and recruit markers of the ER are incompletely understood. Here we review the factors that contribute to the ability of L. pneumophila to infect and replicate in human cells and amoebae with an emphasis on proteins that are secreted by the bacteria into the Legionella vacuole and/or the host cell. Many of these factors undermine eukaryotic trafficking and signaling pathways by acting as functional and, in some cases, structural mimics of eukaryotic proteins. We discuss the consequences of this mimicry for the biology of the infected cell and also for immune responses to L. pneumophila infection.
A significance test for graph‐constrained estimation

Graph‐constrained estimation methods encourage similarities among neighboring covariates presented as nodes of a graph, and can result in more accurate estimates, especially in high‐dimensional settings. Variable selection approaches can then be utilized to select a subset of variables that are associated with the response. However, existing procedures do not provide measures of uncertainty of estimates. Further, the vast majority of existing approaches assume that available graph accurately captures the association among covariates; violations to this assumption could severely hurt the reliability of the resulting estimates. In this article, we present a new inference framework, called the Grace test, which produces coefficient estimates and corresponding p‐values by incorporating the external graph information. We show, both theoretically and via numerical studies, that the proposed method asymptotically controls the type‐I error rate regardless of the choice of the graph. We also show that when the underlying graph is informative, the Grace test is asymptotically more powerful than similar tests that ignore the external information. We study the power properties of the proposed test when the graph is not fully informative and develop a more powerful Grace‐ridge test for such settings. Our numerical studies show that as long as the graph is reasonably informative, the proposed inference procedures deliver improved statistical power over existing methods that ignore external information.
Lesions caused by virulent Aeromonas hydrophila in farmed catfish (Ictalurus punctatus and I. punctatus × I. furcatus) in Mississippi

In 2009, a virulent strain of Aeromonas hydrophila caused severe disease and high mortalities (motile aeromonad septicemia variant [MASv]) in farmed channel catfish (Ictalurus punctatus) and hybrid catfish (I. punctatus × I. furcatus) in eastern Mississippi and Alabama. As is common in MAS, there is severe hemorrhagic dermatitis with ulceration, as well as abdominal hyperemia, petechiation, and mild ascites. Additional findings in MASv cases include panophthalmitis and orbital cellulitis, leading to ocular rupture, and brains are often hyperemic with mild random acute hemorrhage. In MASv infections, there is consistently also marked hemorrhage and edema in the submucosa and muscularis of the stomach, with lymphangitis and a few bacteria, plus splenomegaly with infarcts. Microscopically, spleens have necrosis of ellipsoids with macrophage infiltration and small numbers of bacteria; however, large infarcts are filled with bacteria. Other organs, such as liver, kidneys, and intestine, which are typically associated with MAS caused by various aeromonad species, are less affected. The findings in the stomach have not been reported in MAS in farmed catfish, to our knowledge, and the splenic changes are highly characteristic of MAS compared to infection with other gram-negative bacteria, including Edwardsiella ictaluri and other aeromonad species and strains.
Re$^3$: Re al-Time Recurrent Regression Networks for Visual Tracking of Generic Objects

Robust object tracking requires knowledge and understanding of the object being tracked: its appearance, its motion, and how it changes over time. A tracker must be able to modify its underlying model and adapt to new observations. We present Re<inline-formula><tex-math notation="LaTeX">$^3,$</tex-math></inline-formula> a real-time deep object tracker capable of incorporating temporal information into its model. Rather than focusing on a limited set of objects or training a model at test-time to track a specific instance, we pretrain our generic tracker on a large variety of objects and efficiently update on the fly; Re<inline-formula><tex-math notation="LaTeX">$^3$</tex-math> </inline-formula> simultaneously tracks and updates the appearance model with a single forward pass. This lightweight model is capable of tracking objects at 150 FPS while attaining competitive results on challenging benchmarks. We also show that our method handles temporary occlusion better than other comparable trackers using experiments that directly measure performance on sequences with occlusion.