Miracle-dz/UniFAR-FTUs huggingface dataset:
Role of cytokines in rheumatoid arthritis: an education in pathophysiology and therapeutics

Summary: Advances in cDNA and monoclonal antibody technology in the 1980s fuelled the discovery and characterization of the properties of cytokines. It became apparent that because cytokines were expressed in tissues derived from autoimmune diseases, they were likely to be of fundamental importance in disease pathogenesis and developing a new class of biological therapeutics. In this review, we describe the history of bench to bedside translation of work that led to the identification of tumor necrosis factor (TNF) as a key regulator of the loss of homeostatic immune‐inflammatory responses in rheumatoid arthritis (RA) and a good therapeutic target. First in human clinical trials in collaboration with a biotechnology company, the safety and efficacy of TNF blockade with a chimeric monoclonal antibody was substantiated in patients refractory to standard anti‐rheumatoid drugs. Abnormal immune‐inflammatory responses after therapy showed improvement and remain a focus of ongoing research in many laboratories. Longer term multi‐center studies that followed with several anti‐TNF biologicals have demonstrated the augmented efficacy, including inducing clinical remission, of low dose methotrexate and anti‐TNF therapy co‐therapy, but serious infections and lymphomas in a low frequency have been observed. In the course of the past decades, three ‘blockbuster’ anti‐TNF biologicals are in the clinic. Over a million patients with RA and other immune‐mediated diseases have been successfully treated, and a better perspective on the risk of harm and its management has become part of good clinical practice. This success has encouraged a burgeoning industry of biologicals for chronic diseases.
Molecular Evolution of the CYP2D Subfamily in Primates: Purifying Selection on Substrate Recognition Sites without the Frequent or Long-Tract Gene Conversion

The human cytochrome P450 (CYP) 2D6 gene is a member of the CYP2D gene subfamily, along with the CYP2D7P and CYP2D8P pseudogenes. Although the CYP2D6 enzyme has been studied extensively because of its clinical importance, the evolution of the CYP2D subfamily has not yet been fully understood. Therefore, the goal of this study was to reveal the evolutionary process of the human drug metabolic system. Here, we investigate molecular evolution of the CYP2D subfamily in primates by comparing 14 CYP2D sequences from humans to New World monkey genomes. Window analysis and statistical tests revealed that entire genomic sequences of paralogous genes were extensively homogenized by gene conversion during molecular evolution of CYP2D genes in primates. A neighbor-joining tree based on genomic sequences at the nonsubstrate recognition sites showed that CYP2D6 and CYP2D8 genes were clustered together due to gene conversion. In contrast, a phylogenetic tree using amino acid sequences at substrate recognition sites did not cluster the CYP2D6 and CYP2D8 genes, suggesting that the functional constraint on substrate specificity is one of the causes for purifying selection at the substrate recognition sites. Our results suggest that the CYP2D gene subfamily in primates has evolved to maintain the regioselectivity for a substrate hydroxylation activity between individual enzymes, even though extensive gene conversion has occurred across CYP2D coding sequences.
Stochastic Tunneling of Two Mutations in a Population of Cancer Cells

Cancer initiation, progression, and the emergence of drug resistance are driven by specific genetic and/or epigenetic alterations such as point mutations, structural alterations, DNA methylation and histone modification changes. These alterations may confer advantageous, deleterious or neutral effects to mutated cells. Previous studies showed that cells harboring two particular alterations may arise in a fixed-size population even in the absence of an intermediate state in which cells harboring only the first alteration take over the population; this phenomenon is called stochastic tunneling. Here, we investigated a stochastic Moran model in which two alterations emerge in a cell population of fixed size. We developed a novel approach to comprehensively describe the evolutionary dynamics of stochastic tunneling of two mutations. We considered the scenarios of large mutation rates and various fitness values and validated the accuracy of the mathematical predictions with exact stochastic computer simulations. Our theory is applicable to situations in which two alterations are accumulated in a fixed-size population of binary dividing cells.
What Are the New Implications of Chaos for Unpredictability?

From the beginning of chaos research until today, the unpredictability of chaos has been a central theme. It is widely believed and claimed by philosophers, mathematicians and physicists alike that chaos has a new implication for unpredictability, meaning that chaotic systems are unpredictable in a way that other deterministic systems are not. Hence, one might expect that the question ‘What are the new implications of chaos for unpredictability? ’ has already been answered in a satisfactory way. However, this is not the case. I will critically evaluate the existing answers and argue that they do not fit the bill. Then I will approach this question by showing that chaos can be defined via mixing, which has never before been explicitly argued for. Based on this insight, I will propose that the sought-after new implication of chaos for unpredictability is the following: for predicting any event, all sufficiently past events are approximately probabilistically irrelevant. 1. Introduction 2. Dynamical Systems and Unpredictability 2.1. Dynamical systems 2.2. Natural invariant measures 2.3. Unpredictability 3. Chaos 3.1. Defining chaos 3.2. Defining chaos via mixing 4. Criticism of Answers in the Literature 4.1. Asymptotic unpredictability? 4.2. Unpredictability due to rapid or exponential divergence? 4.3. Macro-predictability and Micro-unpredictability? 5. A General New Implication of Chaos for Unpredictability 5.1. Approximate probabilistic irrelevance 5.2. Sufficiently past events are approximately probabilistically irrelevant for predictions 6. Conclusion Introduction Dynamical Systems and Unpredictability 2.1. Dynamical systems 2.2. Natural invariant measures 2.3. Unpredictability Dynamical systems Natural invariant measures Unpredictability Chaos 3.1. Defining chaos 3.2. Defining chaos via mixing Defining chaos Defining chaos via mixing Criticism of Answers in the Literature 4.1. Asymptotic unpredictability? 4.2. Unpredictability due to rapid or exponential divergence? 4.3. Macro-predictability and Micro-unpredictability? Asymptotic unpredictability? Unpredictability due to rapid or exponential divergence? Macro-predictability and Micro-unpredictability? A General New Implication of Chaos for Unpredictability 5.1. Approximate probabilistic irrelevance 5.2. Sufficiently past events are approximately probabilistically irrelevant for predictions Approximate probabilistic irrelevance Sufficiently past events are approximately probabilistically irrelevant for predictions Conclusion
High telomerase activity is associated with cell cycle deregulation and rapid progression in endometrioid adenocarcinoma of the uterus.

Telomerase activity, a mechanism granting cellular immortality, has been detected in most cancer entities, but its association with clinical, histopathologic, and prognostic parameters is not fully understood. We investigated whether quantitative telomerase levels are correlated to established prognostic factors, telomere lengths, cell cycle kinetics, and the clinical course in endometrioid adenocarcinoma of the uterus (EC). A modified telomeric repeat amplification protocol (TRAP) was used to quantify the relative telomerase activity in a series of 53 primary tumors. Mean telomere length was determined by Southern blot analysis. Cell cycle kinetics were studied immunohistochemically on paraffin sections using monoclonal antibodies to 2 distinct proliferation-specific proteins: Ki-67, which is expressed throughout the cell cycle, and a novel cell cycle-associated protein, repp86, the expression of which is restricted to the cell cycle phases S, G2, and M. The ratio of the 2 immunolabeling indices defines the rate of transition through the restriction point. Telomerase activity was detected in 50 of 53 ECs (94%). Its levels correlated significantly with FIGO stage (P =.01) and FIGO grade (P =.003) but not with myometrial invasion. They were weakly associated with the overall proliferative activity (Ki-67, r =.48) but significantly with the repp86 index (r =.64) and even more strongly with the repp86:Ki-67 ratio (r =.77). There was no correlation with mean telomere length. In the group of tumors with high telomerase activity, 5 patients had relapses and 2 died of the disease within a median follow-up period of 29 months. Recurrence showed no relation to FIGO grade and stage. No events were observed in the group with low telomerase activity. In a multivariate model including tumor stage, histopathologic grade, depth of myometrial invasion, and Ki-67 indices, telomerase activity emerged as the only independent predictor of disease progression (P =.0002). It is concluded that beyond a link to proliferation, high telomerase activity reflects a deregulation of the cell cycle associated with an increased rate of cells entering S phase and a higher degree of malignancy. Therefore, quantitative analysis of telomerase activity may be useful for identifying EC patients at high risk for recurrence.
A Variational Analysis of Stochastic Gradient Algorithms

Stochastic Gradient Descent (SGD) is an important algorithm in machine learning. With constant learning rates, it is a stochastic process that, after an initial phase of convergence, generates samples from a stationary distribution. We show that SGD with constant rates can be effectively used as an approximate posterior inference algorithm for probabilistic modeling. Specifically, we show how to adjust the tuning parameters of SGD such as to match the resulting stationary distribution to the posterior. This analysis rests on interpreting SGD as a continuous-time stochastic process and then minimizing the Kullback-Leibler divergence between its stationary distribution and the target posterior. (This is in the spirit of variational inference.) In more detail, we model SGD as a multivariate Ornstein-Uhlenbeck process and then use properties of this process to derive the optimal parameters. This theoretical framework also connects SGD to modern scalable inference algorithms; we analyze the recently proposed stochastic gradient Fisher scoring under this perspective. We demonstrate that SGD with properly chosen constant rates gives a new way to optimize hyperparameters in probabilistic models.
Predicting duration of sleep from the three process model of regulation of alertness.

OBJECTIVES: Irregular working hours severely disturb sleep and wakefulness. This paper presents a modification of the quantitative (computerised) three process model of regulation of alertness to predict duration of sleep in connection with irregular sleep patterns. METHODS: The model uses a circadian "C" (sinusoidal) and homeostatic "S" (exponential) component (the duration of previous periods awake and asleep), which are summed to yield predicted alertness (on a scale of 1-16). It assumes that waking from sleep will occur at a given alertness level (S' + C') when recuperation is complete. Variables of electroencephalographic duration of sleep from two studies of irregular sleep were used to model the S and C variables in a regression approach to maximise prediction. The model performance was cross validated against published field and laboratory data. RESULTS: The model parameters were defined with a high degree of precision R2 = 0.99 and the validation yielded similar values R2 = 0.98-0.95, depending on the acrophase. The paper also describes a simplified graphical version of the computation model seen as a two dimensional duration of sleep nomogram. CONCLUSION: The model seems to predict group means for duration of sleep with high precision and may serve as a tool for evaluating work and rest schedules to reduce risks of sleep disturbances.
Direct and indirect measures of Level-2 perspective-taking in children and adults.

Studies with infants show divergence between performance on theory of mind tasks depending on whether direct or indirect measures are used. It has been suggested that direct measures assess a flexible but cognitively demanding ability to reason about the minds of others, whereas indirect measures assess distinct processes which afford more efficient but less flexible theory of mind abilities (Apperly & Butterfill, 2009). This leads to the prediction that performance on indirect measures should be subject to signature limits. The current study tested whether the Level-1/Level-2 distinction might constitute one such limit. The study adapted a task that has shown evidence of Level-1 perspective-taking on both direct and indirect measures (Samson, Apperly, Braithwaite, Andrews, & Bodley-Scott, 2010). The aim was to test Level-2 perspective-taking in a sample of 6- to 11-year-olds (N = 80) and adults (N = 20). Participants were able to make Level-2 judgements on the direct measure. In contrast with the findings from Level-1 perspective-taking, there was no evidence of automatic processing of Level-2 perspectives on the indirect measure. This finding is consistent with the view that theory of mind abilities assessed by indirect measures are subject to signature limits. The Level-1/Level-2 distinction, suitably refined, marks one way in which efficient but inflexible theory of mind abilities are limited.
Rac1b enhances cell survival through activation of the JNK2/c-JUN/Cyclin-D1 and AKT2/MCL1 pathways

Rac1b is a constitutively activated, alternatively spliced form of the small GTPase Rac1. Previous studies showed that Rac1b promotes cell proliferation and inhibits apoptosis. In the present study, we used microarray analysis to detect genes differentially expressed in HEK293T cells and SW480 human colon cancer cells stably overexpressing Rac1b. We found that the pro-proliferation genes JNK2, c-JUN and cyclin-D1 as well as anti-apoptotic AKT2 and MCL1 were all upregulated in both lines. Rac1b promoted cell proliferation and inhibited apoptosis by activating the JNK2/c-JUN/cyclin-D1 and AKT2/MCL1 pathways, respectively. Very low Rac1b levels were detected in the colonic epithelium of wild-type Sprague-Dawley rats. Knockout of the rat Rac1 gene exon-3b or knockdown of endogenous Rac1b in HT29 human colon cancer cells downregulated only the AKT2/MCL1 pathway. Our study revealed that very low levels of endogenous Rac1b inhibit apoptosis, while Rac1b upregulation both promotes cell proliferation and inhibits apoptosis. It is likely the AKT2/MCL1 pathway is more sensitive to Rac1b regulation.
Syndromic surveillance of influenza activity in Sweden: an evaluation of three tools

SUMMARY An evaluation was conducted to determine which syndromic surveillance tools complement traditional surveillance by serving as earlier indicators of influenza activity in Sweden. Web queries, medical hotline statistics, and school absenteeism data were evaluated against two traditional surveillance tools. Cross-correlation calculations utilized aggregated weekly data for all-age, nationwide activity for four influenza seasons, from 2009/2010 to 2012/2013. The surveillance tool indicative of earlier influenza activity, by way of statistical and visual evidence, was identified. The web query algorithm and medical hotline statistics performed equally well as each other and to the traditional surveillance tools. School absenteeism data were not reliable resources for influenza surveillance. Overall, the syndromic surveillance tools did not perform with enough consistency in season lead nor in earlier timing of the peak week to be considered as early indicators. They do, however, capture incident cases before they have formally entered the primary healthcare system.
Lixisenatide accelerates restoration of normoglycemia and improves human beta-cell function and survival in diabetic immunodeficient NOD–scid IL-2rgnull RIP-DTR mice engrafted with human islets

Objective Glucagon-like peptide-1 induces glucose-dependent insulin secretion and, in rodents, increases proliferation and survival of pancreatic beta cells. To investigate the effects on human beta cells, we used immunodeficient mice transplanted with human islets. The goal was to determine whether lixisenatide, a glucagon-like peptide-1 receptor agonist, improves human islet function and survival in vivo. Methods Five independent transplant studies were conducted with human islets from five individual donors. Diabetic human islet-engrafted immunodeficient mice were treated with lixisenatide (50, 150, and 500 µg/kg) or vehicle. Islet function was determined by blood glucose, plasma human insulin/C-peptide, and glucose tolerance tests. Grafts were analyzed for total beta- and alpha-cell number, percent proliferation, and levels of apoptosis. Results Diabetic mice transplanted with marginal human islet mass and treated with lixisenatide were restored to euglycemia more rapidly than vehicle-treated mice. Glucose tolerance tests, human plasma insulin, and glucose-stimulation indices of lixisenatide-treated mice were significantly improved compared to vehicle-treated mice. The percentages of proliferating or apoptotic beta cells at graft recovery were not different between lixisenatide-treated and vehicle-treated mice. Nevertheless, in one experiment we found a significant twofold to threefold increase in human beta-cell numbers in lixisenatide-treated compared to vehicle-treated mice. Conclusion Diabetic human islet-engrafted immunodeficient mice treated with lixisenatide show improved restoration of normoglycemia, human plasma insulin, and glucose tolerance compared to vehicle-treated mice engrafted with the same donor islets. Because the proliferative capacity of human beta cells is limited, improved beta-cell survival coupled with enhanced beta-cell function following lixisenatide treatment may provide the greatest benefit for diabetic patients with reduced functional islet mass.
Productive Parvovirus B19 Infection of Primary Human Erythroid Progenitor Cells at Hypoxia Is Regulated by STAT5A and MEK Signaling but not HIFα

Human parvovirus B19 (B19V) causes a variety of human diseases. Disease outcomes of bone marrow failure in patients with high turnover of red blood cells and immunocompromised conditions, and fetal hydrops in pregnant women are resulted from the targeting and destruction of specifically erythroid progenitors of the human bone marrow by B19V. Although the ex vivo expanded erythroid progenitor cells recently used for studies of B19V infection are highly permissive, they produce progeny viruses inefficiently. In the current study, we aimed to identify the mechanism that underlies productive B19V infection of erythroid progenitor cells cultured in a physiologically relevant environment. Here, we demonstrate an effective reverse genetic system of B19V, and that B19V infection of ex vivo expanded erythroid progenitor cells at 1% O2 (hypoxia) produces progeny viruses continuously and efficiently at a level of approximately 10 times higher than that seen in the context of normoxia. With regard to mechanism, we show that hypoxia promotes replication of the B19V genome within the nucleus, and that this is independent of the canonical PHD/HIFα pathway, but dependent on STAT5A and MEK/ERK signaling. We further show that simultaneous upregulation of STAT5A signaling and down-regulation of MEK/ERK signaling boosts the level of B19V infection in erythroid progenitor cells under normoxia to that in cells under hypoxia. We conclude that B19V infection of ex vivo expanded erythroid progenitor cells at hypoxia closely mimics native infection of erythroid progenitors in human bone marrow, maintains erythroid progenitors at a stage conducive to efficient production of progeny viruses, and is regulated by the STAT5A and MEK/ERK pathways.
Adherent Raindrop Detection and Removal in Video

Raindrops adhered to a windscreen or window glass can significantly degrade the visibility of a scene. Detecting and removing raindrops will, therefore, benefit many computer vision applications, particularly outdoor surveillance systems and intelligent vehicle systems. In this paper, a method that automatically detects and removes adherent raindrops is introduced. The core idea is to exploit the local spatio-temporal derivatives of raindrops. First, it detects raindrops based on the motion and the intensity temporal derivatives of the input video. Second, relying on an analysis that some areas of a raindrop completely occludes the scene, yet the remaining areas occludes only partially, the method removes the two types of areas separately. For partially occluding areas, it restores them by retrieving as much as possible information of the scene, namely, by solving a blending function on the detected partially occluding areas using the temporal intensity change. For completely occluding areas, it recovers them by using a video completion technique. Experimental results using various real videos show the effectiveness of the proposed method.
Organic Matter Decomposition following Harvesting and Site Preparation of a Forested Wetland

Organic matter accumulation is an important process that affects ecosystem function in many northern wetlands. The cotton strip assay (CSA) was used to measure the effect of harvesting and two different site preparation treatments, bedding and trenching, on organic matter decomposition in a forested wetland. A Latin square experimental design was used to determine the effect of harvesting, site preparation, and relative position within the wetland on organic matter decomposition at soil depths of 5, 10, and 20 cm. Repeated measure analysis of variance was used to test for treatment effects on organic matter decomposition, soil temperature, and soil oxidation depth. Cellulose decomposition increased at each soil depth as site disturbance increased, with bedding > trenching > whole-tree harvest > reference. The cellulose decomposition response was correlated With changes in soil temperature; the temperature coefficient Q 10 equaled 6.0, which is greater than previously reported values. Position within the wetland relative to an adjoining river affected the decomposition and soil oxidation depth. Because the rate of decomposition is strongly controlled by temperature, higher rates of organic matter decay are expected to continue on harvested and regenerated sites until canopy closure reduces soil temperature.