Phytane and neoclerodane diterpenes from the aerial parts of Inula nervosa Wall.

Three new aliphatic diterpenes (1–3), together with three known neoclerodane-type diterpenes (4–6) were isolated from the aerial parts of Inula nervosa Wall. The structures of 1-3 were elucidated on the basis of 1D and 2D spectroscopic analysis. Additionally, phytane-type and neoclerodane-type diterpenes have not been reported in any species of the genus Inula yet. The phytane-type and neoclerodane-type diterpenes obtained from I. nervosa Wall. suggest this plant maybe have remote genetic relations with other Inula species.     

Genetic diversity of durum wheat as determined by AFLP in fluorescence

The DNA of fifteen Italian cultivars of durum wheat (Triticum turgidum L. ssp. durum) were analyzed by in fluorescence amplified fragment length polymorphism (fAFLP) in order to obtain the characteristic fingerprintings of genotypes and assess their genetic relatedness. Among 64 combinations of fluorescence labelled primers, three different combinations were chosen as producing a total of 6630 AFLP fragments, 2277 (34.3 %) of them being polymorphic. By using this fAFLP methodology a DNA fingerprinting of each durum wheat cultivar was generated for genotype identification. Analysis of the genetic relationships show the low variability among durum wheat cultivars.     

Erratum to: Exogenous Adipokine Peptide Resistin Protects Against Focal Cerebral Ischemia/Reperfusion Injury in Mice

Neurochemical Research -     

Efficient Reconstruction of Heterogeneous Networks from Time Series via Compressed Sensing

Recent years have witnessed a rapid development of network reconstruction approaches, especially for a series of methods based on compressed sensing. Although compressed-sensing based methods require much less data than conventional approaches, the compressed sensing for reconstructing heterogeneous networks has not been fully exploited because of hubs. Hub neighbors require much more data to be inferred than small-degree nodes, inducing a cask effect for the reconstruction of heterogeneous networks. Here, a conflict-based method is proposed to overcome the cast effect to considerably reduce data amounts for achieving accurate reconstruction. Moreover, an element elimination method is presented to use the partially available structural information to reduce data requirements. The integration of both methods can further improve the reconstruction performance than separately using each technique. These methods are validated by exploring two evolutionary games taking place in scale-free networks, where individual information is accessible and an attempt to decode the network structure from measurable data is made. The results demonstrate that for all of the cases, much data are saved compared to that in the absence of these two methods. Due to the prevalence of heterogeneous networks in nature and society and the high cost of data acquisition in large-scale networks, these approaches have wide applications in many fields and are valuable for understanding and controlling the collective dynamics of a variety of heterogeneous networked systems.     

Short stature in a patient with cystic fibrosis caused by a 6.7-kb human growth hormone gene deletion.

A recent longitudinal study in children with cystic fibrosis (CF) challenged the common idea that CF is causing short stature. The data, however, showed clearly that short stature cannot be explained by CF alone after the first year of life. We report on a girl suffering from CF and short stature in whom DNA analysis using polymerase chain reaction and Southern blot techniques of the human growth hormone (hGH) gene cluster revealed a 6.7-kb gene deletion encompassing the hGH-1 gene. Anti-hGH antibodies of polyclonal origin developed, leading to a growth arrest after only 2 months of hGH replacement. In addition, a family study was performed, and the haplotypes of the CF gene and hGH gene cluster were analyzed.     

A study on giant panda recognition based on images of a large proportion of captive pandas

1. As a highly endangered species, the giant panda (panda) has attracted significant attention in the past decades. Considerable efforts have been put on panda conservation and reproduction, offering the promising outcome of maintaining the population size of pandas. To evaluate the effectiveness of conservation and management strategies, recognizing individual pandas is critical. However, it remains a challenging task because the existing methods, such as traditional tracking method, discrimination method based on footprint identification, and molecular biology method, are invasive, inaccurate, expensive, or challenging to perform. The advances of imaging technologies have led to the wide applications of digital images and videos in panda conservation and management, which makes it possible for individual panda recognition in a noninvasive manner by using image‐based panda face recognition method.
2. In recent years, deep learning has achieved great success in the field of computer vision and pattern recognition. For panda face recognition, a fully automatic deep learning algorithm which consists of a sequence of deep neural networks (DNNs) used for panda face detection, segmentation, alignment, and identity prediction is developed in this study. To develop and evaluate the algorithm, the largest panda image dataset containing 6,441 images from 218 different pandas, which is 39.78% of captive pandas in the world, is established.
3. The algorithm achieved 96.27% accuracy in panda recognition and 100% accuracy in detection.
4. This study shows that panda faces can be used for panda recognition. It enables the use of the cameras installed in their habitat for monitoring their population and behavior. This noninvasive approach is much more cost‐effective than the approaches used in the previous panda surveys.

     

Differential response to hepatic differentiation stimuli of amniotic epithelial cells isolated from four regions of the amniotic membrane

Human Amniotic Epithelial Cells (hAEC) isolated from term placenta are a promising source for regenerative medicine. However, it has long been debated whether the hAEC population consists of heterogeneous or homogeneous cells. In a previous study, we investigated the characteristics of hAEC isolated from four different regions of the amniotic membrane finding significant heterogeneity. The aim of this study was to evaluate the hepatic differentiation capability of hAEC isolated from these four regions. Human term placentae were collected after caesarean section and hAEC were isolated from four regions of the amniotic membrane (R1-R4, according to their relative distance from the umbilical cord) and treated in hepatic differentiation conditions for 14 days. hAEC-derived hepatocyte-like cells showed marked differences in the expression of hepatic markers: R4 showed higher levels of Albumin and Hepatocyte Nuclear Factor (HNF) 4α whereas R1 expressed higher Cytochrome P450 enzymes, both at the gene and protein level. These preliminary results suggest that hAEC isolated from R1 and R4 of the amniotic membrane are more prone to hepatic differentiation. Therefore, the use of hAEC from a specific region of the amniotic membrane should be taken into consideration as it could have an impact on the outcome of therapeutic applications.