Micromass co-culture of human articular chondrocytes and human bone marrow mesenchymal stem cells to investigate stable neocartilage tissue formation in vitro

Cell therapies for articular cartilage defects rely on expanded chondrocytes. Mesenchymal stem cells (MSC) represent an alternative cell source should their hypertrophic differentiation pathway be prevented. Possible cellular instruction between human articular chondrocytes (HAC) and human bone marrow MSC was investigated in micromass pellets. HAC and MSC were mixed in different percentages or incubated individually in pellets for 3 or 6 weeks with and without TGF-beta1 and dexamethasone (±T±D) as chondrogenic factors. Collagen II, collagen X and S100 protein expression were assessed using immunohistochemistry. Proteoglycan synthesis was evaluated applying the Bern score and quantified using dimethylmethylene blue dye binding assay. Alkaline phosphatase activity (ALP) was detected on cryosections and soluble ALP measured in pellet supernatants. HAC alone generated hyaline-like discs, while MSC formed spheroid pellets in ±T±D. Co-cultured pellets changed from disc to spheroid shape with decreasing number of HAC, and displayed random cell distribution. In -T-D, HAC expressed S100, produced GAG and collagen II, and formed lacunae, while MSC did not produce any cartilage-specific proteins. Based on GAG, collagen type II and S100 expression chondrogenic differentiation occurred in -T-D MSC co-cultures. However, quantitative experimental GAG and DNA values did not differ from predicted values, suggesting only HAC contribution to GAG production. MSC produced cartilage-specific matrix only in +T+D but underwent hypertrophy in all pellet cultures. In summary, influence of HAC on MSC was restricted to early signs of neochondrogenesis. However, MSC did not contribute to the proteoglycan deposition, and HAC could not prevent hypertrophy of MSC induced by chondrogenic stimuli.

Purification and characterization of a 34-kDa, heat stable glycoprotein from Synadenium grantii latex: action on human fibrinogen and fibrin clot

Latex glycoprotein (LGP) from Synadenium grantii latex was purified by the combination of heat precipitation and gel permeation chromatography. LGP is a heat stable protein even at 80 degrees C showed a sharp single band both in SDS-PAGE as well as in native (acidic) PAGE. LGP is a monomeric protein appears as single band under reducing condition. It is a less hydrophobic protein showed sharp single peak in RP-HPLC with retention time of 13.3 m. The relative molecular mass of LGP is 34.4 kDa. CD spectrum of LGP explains less content of alpha-helix (7%), and high content of beta-pleated sheets (48%) and random coils (46%). The N-terminal sequence of LGP is D-F-P-S-D-W-Y-A-Y-E-G-Y-V-I-D-R-P-F-S. Purified LGP is a fibrinogen degrading protease hydrolyses all the three subunits in the order of Aalpha, Bbeta and gamma. The hydrolytic pattern is totally different from plasmin as well as thrombin. LGP reduces recalcification time from 165 to 30 s with citrated human plasma but did not show thrombin like as well as factor Xa-like activity. Although LGP induces procoagulant activity, it hydrolyses partially cross-linked fibrin clot. It hydrolyses all the subunits of partially cross-linked fibrin clot (alpha- chains, beta-chain and gamma-gamma dimer). LGP is a serine protease, inhibited by PMSF. Other serine protease inhibitors, aprotinin and leupeptin did not inhibit the caseinolytic activity as well as fibrinogenolytic activity. We report purification and characterization of a glycoprotein from Synadenium grantii latex with human fibrino(geno)lytic activity.

Foundations of stochastic geometry and theory of random sets

The first section of this chapter starts with the Buffon problem, which is one of the oldest in stochastic geometry, and then continues with the definition of measures on the space of lines. The second section defines random closed sets and related measurability issues, explains how to characterize distributions of random closed sets by means of capacity functionals and introduces the concept of a selection. Based on this concept, the third section starts with the definition of the expectation and proves its convexifying effect that is related to the Lyapunov theorem for ranges of vector-valued measures. Finally, the strong law of large numbers for Minkowski sums of random sets is proved and the corresponding limit theorem is formulated. The chapter is concluded by a discussion of the union-scheme for random closed sets and a characterization of the corresponding stable laws.

Mounting behaviour in finishing pigs: Stable individual differences are not due to dominance or stage of sexual development

Every year around 100 million male piglets are castrated in the EU, usually without anaesthesia or post-operative analgesia. This surgical intervention is painful and stressful. Several main players within the pig industry have voluntarily agreed to end the practice of surgical pig castration in the EU by 2018. One alternative to castration is entire male pig production. However, entire males behave differently than castrates, for example, by performing more mounting behaviour, which is suggested to be a welfare problem. The aim of our study was to develop a comprehensive ethogram of different types of mounting and to investigate properties, causes and consequences of mounting behaviour in finishing pigs. The study included 80 entire male and 80 female pigs from two farrowing batches born six weeks apart. Mixed sex and single-sex housing of pigs are both common in pig farming, so to ensure our study was representative, the 160 pigs were assigned to social groups of 20 in three treatments: entire male pigs only (MM, 2 groups, n = 40), entire females only (FF, 2 groups, n = 40) and entire males and females mixed together (MF, 4 groups, n = 80). Measurements took place during the final six weeks before slaughter (between 63.5 and 105.5 kg). Observations of mounting behaviour on 12 days per batch suggested that: (i) males mounted more than females, (ii) within sex, there was no effect of treatment on the amount of mounting (although the statistical power of the study to detect these effects was low), and (iii) there were individual differences in mounting that were stable over time (within sex). Classification of mounting into different categories revealed that sexual mounting was most common overall and in males but only rare in females. Compared to other types of mounting (e.g. caused by crowding or during a fight), sexual mounts lasted longer and provoked more screaming by the recipient. There were no relationships between mounting behaviour on the one hand and dominance rank in food competition tests, the circulating levels of sex hormones (oestradiol, testosterone and progesterone) at the end of the study, the health scores (lameness and scratches) or weight gain on the other hand. The stable individual differences of mounting over time suggest that mounting behaviour is a trait of the individual rather than the appearance of random outbreaks. However, these differences in mounting cannot be explained by dominance behaviour or by differences in sex hormone concentrations that could indicate the onset of puberty. Mounting behaviour and in particular sexual mounting provoked high pitched screaming of the recipients indicating that mounting is a welfare problem. For the welfare assessment of entire male pig production the performance of mounting behaviour should be considered. (C) 2013 Elsevier B.V. All rights reserved.

Matching Recall and Storage in Sequence Learning with Spiking Neural Networks

Storing and recalling spiking sequences is a general problem the brain needs to solve. It is, however, unclear what type of biologically plausible learning rule is suited to learn a wide class of spatiotemporal activity patterns in a robust way. Here we consider a recurrent network of stochastic spiking neurons composed of both visible and hidden neurons. We derive a generic learning rule that is matched to the neural dynamics by minimizing an upper bound on the Kullback–Leibler divergence from the target distribution to the model distribution. The derived learning rule is consistent with spike-timing dependent plasticity in that a presynaptic spike preceding a postsynaptic spike elicits potentiation while otherwise depression emerges. Furthermore, the learning rule for synapses that target visible neurons can be matched to the recently proposed voltage-triplet rule. The learning rule for synapses that target hidden neurons is modulated by a global factor, which shares properties with astrocytes and gives rise to testable predictions.

Differential healing response attributed to culprit lesions of patients with acute coronary syndromes and stable coronary artery after implantation of drug-eluting stents: An optical coherence tomography study

BACKGROUND Pathology studies have shown delayed arterial healing in culprit lesions of patients with acute coronary syndrome (ACS) compared with stable coronary artery disease (CAD) after placement of drug-eluting stents (DES). It is unknown whether similar differences exist in-vivo during long-term follow-up. Using optical coherence tomography (OCT), we assessed differences in arterial healing between patients with ACS and stable CAD five years after DES implantation. METHODS AND RESULTS A total of 88 patients comprised of 53 ACS lesions with 7864 struts and 35 stable lesions with 5298 struts were suitable for final OCT analysis five years after DES implantation. The analytical approach was based on a hierarchical Bayesian random-effects model. OCT endpoints were strut coverage, malapposition, protrusion, evaginations and cluster formation. Uncovered (1.7% vs. 0.7%, adjusted p=0.041) or protruding struts (0.50% vs. 0.13%, adjusted p=0.038) were more frequent among ACS compared with stable CAD lesions. A similar trend was observed for malapposed struts (1.33% vs. 0.45%, adj. p=0.072). Clusters of uncovered or malapposed/protruding struts were present in 34.0% of ACS and 14.1% of stable patients (adj. p=0.041). Coronary evaginations were more frequent in patients with ST-elevation myocardial infarction compared with stable CAD patients (0.16 vs. 0.13 per cross section, p=0.027). CONCLUSION Uncovered, malapposed, and protruding stent struts as well as clusters of delayed healing may be more frequent in culprit lesions of ACS compared with stable CAD patients late after DES implantation. Our observational findings suggest a differential healing response attributable to lesion characteristics of patients with ACS compared with stable CAD in-vivo.

MOREMATA: Stata module (Mata) to provide various functions

This package includes various Mata functions. kern(): various kernel functions; kint(): kernel integral functions; kdel0(): canonical bandwidth of kernel; quantile(): quantile function; median(): median; iqrange(): inter-quartile range; ecdf(): cumulative distribution function; relrank(): grade transformation; ranks(): ranks/cumulative frequencies; freq(): compute frequency counts; histogram(): produce histogram data; mgof(): multinomial goodness-of-fit tests; collapse(): summary statistics by subgroups; _collapse(): summary statistics by subgroups; gini(): Gini coefficient; sample(): draw random sample; srswr(): SRS with replacement; srswor(): SRS without replacement; upswr(): UPS with replacement; upswor(): UPS without replacement; bs(): bootstrap estimation; bs2(): bootstrap estimation; bs_report(): report bootstrap results; jk(): jackknife estimation; jk_report(): report jackknife results; subset(): obtain subsets, one at a time; composition(): obtain compositions, one by one; ncompositions(): determine number of compositions; partition(): obtain partitions, one at a time; npartitionss(): determine number of partitions; rsubset(): draw random subset; rcomposition(): draw random composition; colvar(): variance, by column; meancolvar(): mean and variance, by column; variance0(): population variance; meanvariance0(): mean and population variance; mse(): mean squared error; colmse(): mean squared error, by column; sse(): sum of squared errors; colsse(): sum of squared errors, by column; benford(): Benford distribution; cauchy(): cumulative Cauchy-Lorentz dist.; cauchyden(): Cauchy-Lorentz density; cauchytail(): reverse cumulative Cauchy-Lorentz; invcauchy(): inverse cumulative Cauchy-Lorentz; rbinomial(): generate binomial random numbers; cebinomial(): cond. expect. of binomial r.v.; root(): Brent's univariate zero finder; nrroot(): Newton-Raphson zero finder; finvert(): univariate function inverter; integrate_sr(): univariate function integration (Simpson's rule); integrate_38(): univariate function integration (Simpson's 3/8 rule); ipolate(): linear interpolation; polint(): polynomial inter-/extrapolation; plot(): Draw twoway plot; _plot(): Draw twoway plot; panels(): identify nested panel structure; _panels(): identify panel sizes; npanels(): identify number of panels; nunique(): count number of distinct values; nuniqrows(): count number of unique rows; isconstant(): whether matrix is constant; nobs(): number of observations; colrunsum(): running sum of each column; linbin(): linear binning; fastlinbin(): fast linear binning; exactbin(): exact binning; makegrid(): equally spaced grid points; cut(): categorize data vector; posof(): find element in vector; which(): positions of nonzero elements; locate(): search an ordered vector; hunt(): consecutive search; cond(): matrix conditional operator; expand(): duplicate single rows/columns; _expand(): duplicate rows/columns in place; repeat(): duplicate contents as a whole; _repeat(): duplicate contents in place; unorder2(): stable version of unorder(); jumble2(): stable version of jumble(); _jumble2(): stable version of _jumble(); pieces(): break string into pieces; npieces(): count number of pieces; _npieces(): count number of pieces; invtokens(): reverse of tokens(); realofstr(): convert string into real; strexpand(): expand string argument; matlist(): display a (real) matrix; insheet(): read spreadsheet file; infile(): read free-format file; outsheet(): write spreadsheet file; callf(): pass optional args to function; callf_setup(): setup for mm_callf().