Fermi-Dirac statistics and the number theory

We relate the Fermi-Dirac statistics of an ideal Fermi gas in a harmonic trap to partitions of given integers into distinct parts, studied in number theory. Using methods of quantum statistical physics we derive analytic expressions for cumulants of the probability distribution of the number of different partitions.Comment: 7pages, 2 figures, epl.cls, revised versio

Stereoselective Metal-Free Reduction of Chiral Imines in Batch and Flow Mode: A Convenient Strategy for the Synthesis of Chiral Active Pharmaceutical Ingredients

he convenient, metal-free reduction of imines that contain an inexpensive and removable chiral auxiliary allowed for the synthesis of the immediate precursors of chiral active pharmaceutical ingredients (APIs). This protocol was carried out under batch and flow conditions to give the correspoding prod- ucts in high yields with almost complete stereocontrol. In the presence of trichlorosilane, an inexpensive and nontoxic reduc- ing agent, and an achiral Lewis base such as N,N-dimethyl- Introduction The pharmaceutical industry is gradually progressing towards enantiopure formulations. Most newly introduced drugs are chiral, and it is expected that approximately 95 % of pharma- ceutical drugs will be chiral by 2020. [1] In this context, chiral amines are considered a class of paramount importance, be- cause they are found in a plethora of compounds such as those of pharmaceutical interest as well as those developed for agro- chemicals, fragrances, and fine chemicals. [2] The reduction of the C=N group is one of the most widely used approaches to synthesize chiral amines, and over the last ten years, successful catalytic enantioselective methods based on both metal-pro- moted [3] and organocatalyzed [4] strategies have been devel- oped. When an industrial synthesis of a chiral pharmaceutical prod- uct must be planned, however, issues such as the chemical effi- ciency and robustness of the procedure, its general applicabil- ity, and economic considerations become crucially important. For these reasons, the applications of many chiral catalytic sys- tems are often not feasible, and the use of inexpensive and readily available chiral auxiliaries becomes an attractive and economic alternative. This also holds true for the synthesis of [a] Dipartimento di Chimica, Università degli Studi di Milano, via Golgi 19, 20133 Milano, Italy E-mail: [email protected] http://users2.unimi.it/Benagliagroup [b] Istituto di Scienze e Tecnologie Molecolari ISTM-CNR, Via Golgi 19, 20133 Milano, Italy [c] Department of Chemistry and Chemistry Center of Évora, University of Évora, Rua Romão Ramalho, 59, 7000 Évora, Portugal Supporting information and ORCID(s) from the author(s) for this article are available on the WWW under http://dx.doi.org/10.1002/ejoc.201601268. Eur. J. Org. Chem. 2017, 39–44 © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim39 formamide, the formal syntheses of Rivastgmine, calcimimetic NPS R-568, and a Rho kinases inhibitor were successfully accom- plished. For the first time, both the diastereoselective imine re- duction and the auxiliary removal were efficiently performed in a micro- or mesoreactor under continuous-flow conditions, which paved the way towards the development of a practical process for the syntheses of industrially relevant, biologically active, enantiopure N-alkylamine

Adaptive Graph Construction for Isomap Manifold Learning

Isomap is a classical manifold learning approach that preserves geodesic distance of nonlinear data sets. One of the main drawbacks of this method is that it is susceptible to leaking, where a shortcut appears between normally separated portions of a manifold. We propose an adaptive graph construction approach that is based upon the sparsity property of the ℓ1 norm. The ℓ1 enhanced graph construction method replaces k-nearest neighbors in the classical approach. The proposed algorithm is first tested on the data sets from the UCI data base repository which showed that the proposed approach performs better than the classical approach. Next, the proposed approach is applied to two image data sets and achieved improved performances over standard Isomap

A Comparative Study of Two Prediction Models for Brain Tumor Progression

MR diffusion tensor imaging (DTI) technique together with traditional T1 or T2 weighted MRI scans supplies rich information sources for brain cancer diagnoses. These images form large-scale, high-dimensional data sets. Due to the fact that significant correlations exist among these images, we assume low-dimensional geometry data structures (manifolds) are embedded in the high-dimensional space. Those manifolds might be hidden from radiologists because it is challenging for human experts to interpret high-dimensional data. Identification of the manifold is a critical step for successfully analyzing multimodal MR images. We have developed various manifold learning algorithms (Tran et al. 2011; Tran et al. 2013) for medical image analysis. This paper presents a comparative study of an incremental manifold learning scheme (Tran. et al. 2013) versus the deep learning model (Hinton et al. 2006) in the application of brain tumor progression prediction. The incremental manifold learning is a variant of manifold learning algorithm to handle large-scale datasets in which a representative subset of original data is sampled first to construct a manifold skeleton and remaining data points are then inserted into the skeleton by following their local geometry. The incremental manifold learning algorithm aims at mitigating the computational burden associated with traditional manifold learning methods for large-scale datasets. Deep learning is a recently developed multilayer perceptron model that has achieved start-of-the-art performances in many applications. A recent technique named Dropout can further boost the deep model by preventing weight coadaptation to avoid over-fitting (Hinton et al. 2012). We applied the two models on multiple MRI scans from four brain tumor patients to predict tumor progression and compared the performances of the two models in terms of average prediction accuracy, sensitivity, specificity and precision. The quantitative performance metrics were calculated as average over the four patients. Experimental results show that both the manifold learning and deep neural network models produced better results compared to using raw data and principle component analysis (PCA), and the deep learning model is a better method than manifold learning on this data set. The averaged sensitivity and specificity by deep learning are comparable with these by the manifold learning approach while its precision is considerably higher. This means that the predicted abnormal points by deep learning are more likely to correspond to the actual progression region

Translational toxicology in setting occupational exposure limits for dusts and hazard classification – a critical evaluation of a recent approach to translate dust overload findings from rats to humans

Background We analyze the scientific basis and methodology used by the German MAK Commission in their recommendations for exposure limits and carcinogen classification of “granular biopersistent particles without known specific toxicity” (GBS). These recommendations are under review at the European Union level. We examine the scientific assumptions in an attempt to reproduce the results. MAK’s human equivalent concentrations (HECs) are based on a particle mass and on a volumetric model in which results from rat inhalation studies are translated to derive occupational exposure limits (OELs) and a carcinogen classification. Methods We followed the methods as proposed by the MAK Commission and Pauluhn 2011. We also examined key assumptions in the metrics, such as surface area of the human lung, deposition fractions of inhaled dusts, human clearance rates; and risk of lung cancer among workers, presumed to have some potential for lung overload, the physiological condition in rats associated with an increase in lung cancer risk. Results The MAK recommendations on exposure limits for GBS have numerous incorrect assumptions that adversely affect the final results. The procedures to derive the respirable occupational exposure limit (OEL) could not be reproduced, a finding raising considerable scientific uncertainty about the reliability of the recommendations. Moreover, the scientific basis of using the rat model is confounded by the fact that rats and humans show different cellular responses to inhaled particles as demonstrated by bronchoalveolar lavage (BAL) studies in both species. Conclusion Classifying all GBS as carcinogenic to humans based on rat inhalation studies in which lung overload leads to chronic inflammation and cancer is inappropriate. Studies of workers, who have been exposed to relevant levels of dust, have not indicated an increase in lung cancer risk. Using the methods proposed by the MAK, we were unable to reproduce the OEL for GBS recommended by the Commission, but identified substantial errors in the models. Considerable shortcomings in the use of lung surface area, clearance rates, deposition fractions; as well as using the mass and volumetric metrics as opposed to the particle surface area metric limit the scientific reliability of the proposed GBS OEL and carcinogen classification.International Carbon Black Associatio

Purification of Nuclear Poly(A)-binding Protein Nab2 Reveals Association with the Yeast Transcriptome and a Messenger Ribonucleoprotein Core Structure

Nascent mRNAs produced by transcription in the nucleus are subsequently processed and packaged into mRNA ribonucleoprotein particles (messenger ribonucleoproteins (mRNPs)) before export to the cytoplasm. Here, we have used the poly(A)-binding protein Nab2 to isolate mRNPs from yeast under conditions that preserve mRNA integrity. Upon Nab2-tandem affinity purification, several mRNA export factors were co-enriched (Yra1, Mex67, THO-TREX) that were present in mRNPs of different size and mRNA length. High-throughput sequencing of the co-precipitated RNAs indicated that Nab2 is associated with the bulk of yeast transcripts with no specificity for different mRNA classes. Electron microscopy revealed that many of the mRNPs have a characteristic elongated structure. Our data suggest that mRNPs, although associated with different mRNAs, have a unifying core structure

Prediction of Brain Tumor Progression Using Multiple Histogram Matched MRI Scans

In a recent study [1], we investigated the feasibility of predicting brain tumor progression based on multiple MRI series and we tested our methods on seven patients\u27 MRI images scanned at three consecutive visits A, B and C. Experimental results showed that it is feasible to predict tumor progression from visit A to visit C using a model trained by the information from visit A to visit B. However, the trained model failed when we tried to predict tumor progression from visit B to visit C, though it is clinically more important. Upon a closer look at the MRI scans revealed that histograms of MRI scans such as T1, T2, FLAIR etc taken at different times have slight shifts or different shapes. This is because those MRI scans are qualitative instead of quantitative so MRI scans taken at different times or by different scanners might have slightly different scales or have different homogeneities in the scanning region. In this paper, we proposed a method to overcome this difficulty. The overall goal of this study is to assess brain tumor progression by exploring seven patients\u27 complete MRI records scanned during their visits in the past two years. There are ten MRI series in each visit, including FLAIR, T1-weighted, post-contrast T1-weighted, T2-weighted and five DTI derived MRI volumes: ADC, FA, Max, Min and Middle Eigen Values. After registering all series to the corresponding DTI scan at the first visit, we applied a histogram matching algorithm to non-DTI MRI scans to match their histograms to those of the corresponding MRI scans at the first visit. DTI derived series are quantitative and do not require the histogram matching procedure. A machine learning algorithm was then trained using the data containing information from visit A to visit B, and the trained model was used to predict tumor progression from visit B to visit C. An average of 72% pixel-wise accuracy was achieved for tumor progression prediction from visit B to visit C

Compartmentalized Culture of Perivascular Stroma and Endothelial Cells in a Microfluidic Model of the Human Endometrium

The endometrium is the inner lining of the uterus. Following specific cyclic hormonal stimulation, endometrial stromal fibroblasts (stroma) and vascular endothelial cells exhibit morphological and biochemical changes to support embryo implantation and regulate vascular function, respectively. Herein, we integrated a resin-based porous membrane in a dual chamber microfluidic device in polydimethylsiloxane that allows long term in vitro co-culture of human endometrial stromal and endothelial cells. This transparent, 2-m porous membrane separates the two chambers, allows for the diffusion of small molecules and enables high resolution bright field and fluorescent imaging. Within our primary human co-culture model of stromal and endothelial cells, we simulated the temporal hormone changes occurring during an idealized 28-day menstrual cycle. We observed the successful differentiation of stroma into functional decidual cells, determined by morphology as well as biochemically as measured by increased production of prolactin. By controlling the microfluidic properties of the device, we additionally found that shear stress forces promoted cytoskeleton alignment and tight junction formation in the endothelial layer. Finally, we demonstrated that the endometrial perivascular stroma model was sustainable for up to 4 weeks, remained sensitive to steroids and is suitable for quantitative biochemical analysis. Future utilization of this device will allow the direct evaluation of paracrine and endocrine crosstalk between these two cell types as well as studies of immunological events associated with normal versus disease-related endometrial microenvironments

Reduced microvascular density in omental biopsies of children with chronic kidney disease

Endothelial dysfunction is an early manifestation of cardiovascular disease (CVD) and consistently observed in patients with chronic kidney disease (CKD). We hypothesized that CKD is associated with systemic damage to the microcirculation, preceding macrovascular pathology. To assess the degree of "uremic microangiopathy", we have measured microvascular density in biopsies of the omentum of children with CKD.Omental tissue was collected from 32 healthy children (0-18 years) undergoing elective abdominal surgery and from 23 age-matched cases with stage 5 CKD at the time of catheter insertion for initiation of peritoneal dialysis. Biopsies were analyzed by independent observers using either a manual or an automated imaging system for the assessment of microvascular density. Quantitative immunohistochemistry was performed for markers of autophagy and apoptosis, and for the abundance of the angiogenesis-regulating proteins VEGF-A, VEGF-R2, Angpt1 and Angpt2.Microvascular density was significantly reduced in uremic children compared to healthy controls, both by manual imaging with a digital microscope (median surface area 0.61% vs. 0.95%, p<0.0021 and by automated quantification (total microvascular surface area 0.89% vs. 1.17% p = 0.01). Density measured by manual imaging was significantly associated with age, height, weight and body surface area in CKD patients and healthy controls. In multivariate analysis, age and serum creatinine level were the only independent, significant predictors of microvascular density (r2 = 0.73). There was no immunohistochemical evidence for apoptosis or autophagy. Quantitative staining showed similar expression levels of the angiogenesis regulators VEGF-A, VEGF-receptor 2 and Angpt1 (p = 0.11), but Angpt2 was significantly lower in CKD children (p = 0.01).Microvascular density is profoundly reduced in omental biopsies of children with stage 5 CKD and associated with diminished Angpt2 signaling. Microvascular rarefaction could be an early systemic manifestation of CKD-induced cardiovascular disease

Heterarchy of Transcription Factors Driving Basal and Luminal Cell Phenotypes in Human Urothelium

Cell differentiation is effected by complex networks of transcription factors that co-ordinate re-organisation of the chromatin landscape. The hierarchies of these relationships can be difficult to dissect. During in vitro differentiation of normal human uro-epithelial cells, formaldehyde-assisted isolation of regulatory elements (FAIRE-seq) and RNA-seq were used to identify alterations in chromatin accessibility and gene expression changes following activation of the nuclear receptor PPARG as a differentiation-initiating event. Regions of chromatin identified by FAIRE-seq as having altered accessibility during differentiation were found to be enriched with sequence-specific binding motifs for transcription factors predicted to be involved in driving basal and differentiated urothelial cell phenotypes, including FOXA1, P63, GRHL2, CTCF and GATA3. In addition, co-occurrence of GATA3 motifs was observed within sub-sets of differentiation-specific peaks containing P63 or FOXA1 after induction of differentiation. Changes in abundance of GRHL2, GATA3, and P63 were observed in immunoblots of chromatin-enriched extracts. Transient siRNA knockdown of P63 revealed that P63 favoured a basal-like phenotype by inhibiting differentiation and promoting expression of basal marker genes. GATA3 siRNA prevented differentiation-associated downregulation of P63 protein and transcript, and demonstrated positive feedback of GATA3 on PPARG transcript, but showed no effect on FOXA1 transcript or protein expression. This approach indicates that as a transcriptionally-regulated programme, urothelial differentiation operates as a heterarchy wherein GATA3 is able to co-operate with FOXA1 to drive expression of luminal marker genes, but that P63 has potential to transrepress expression of the same genes