Ritterslater2075

tructural determinants of high BP levels are likely required, particularly those curtailing the obesogenic environment-targeting detection and treatment alone is unlikely to be sufficient.

Hybridization and polyploidization are powerful evolutionary factors that are associated with manifold developmental changes in plants such as irregular progression of meiosis and sporogenesis. The emergence of apomixis, which is asexual reproduction via seeds, is supposed to be connected to these factors and was often regarded as an escape from hybrid sterility. However, the functional trigger of apomixis is still unclear. Recently formed di- and polyploid Ranunculus hybrids, as well as their parental species were analysed for their modes of mega- and microsporogenesis by microscopy. Chromosomal configurations during male meiosis were screened for abnormalities. Meiotic and developmental abnormalities were documented qualitatively and collected quantitatively for statistical evaluations.

Allopolyploids showed significantly higher frequencies of erroneous microsporogenesis than homoploid hybrid plants. Among diploids, F

hybrids had significantly more disturbed meiosis than F

hybrids and parental plantale and male sporogenesis, with only minor effects of hybridity on microsporogenesis, but fatal effects on the course of megasporogenesis. read more Hence, pollen development continues without major alterations, while selection will favour apomixis as alternative to the female meiotic pathway. Relation of investigated errors of megasporogenesis with the observed occurrence of apospory in Ranunculus hybrids identifies disturbed female meiosis as potential elicitor of apomixis in order to rescue these plants from hybrid sterility. Male meiotic disturbance appears to be stronger in neopolyploids than in homoploid hybrids, while disturbances of megasporogenesis were not ploidy-dependent.

Electron tomography (ET) is an important technique for the study of complex biological structures and their functions. Electron tomography reconstructs the interior of a three-dimensional object from its projections at different orientations. However, due to the instrument limitation, the angular tilt range of the projections is limited within +70

to -70

. The missing angle range is known as the missing wedge and will cause artifacts.

In this paper, we proposed a novel algorithm, compressed sensing improved iterative reconstruction-reprojection (CSIIRR), which follows the schedule of improved iterative reconstruction-reprojection but further considers the sparsity of the biological ultra-structural content in specimen. The proposed algorithm keeps both the merits of the improved iterative reconstruction-reprojection (IIRR) and compressed sensing, resulting in an estimation of the electron tomography with faster execution speed and better reconstruction result. A comprehensive experiment has been carried out, in which CSIIRR was challenged on both simulated and real-world datasets as well as compared with a number of classical methods. The experimental results prove the effectiveness and efficiency of CSIIRR, and further show its advantages over the other methods.

The proposed algorithm has an obvious advance in the suppression of missing wedge effects and the restoration of missing information, which provides an option to the structural biologist for clear and accurate tomographic reconstruction.

The proposed algorithm has an obvious advance in the suppression of missing wedge effects and the restoration of missing information, which provides an option to the structural biologist for clear and accurate tomographic reconstruction.

FOLFOXIRI plus bevacizumab is used as a first-line therapy for patients with unresectable or metastatic colorectal cancer. However, there are no clear recommendations for second-line therapy after FOLFOXIRI plus bevacizumab combination. Here, we describe our planning for the EFFORT study to investigate whether FOLFIRI plus aflibercept has efficacy following FOLFOXIRI plus bevacizumab for mCRC.

EFFORT is an open-label, multicenter, single arm phase II study to evaluate whether a FOLFIRI plus aflibercept has efficacy following FOLFOXIRI plus bevacizumab for mCRC. Patients with unresectable or metastatic colorectal cancer who received FOLFOXIRI plus bevacizumab as a first-line therapy will receive aflibercept and FOLFIRI (aflibercept 4 mg/kg, irinotecan 150 mg/m

IV over 90 min, with levofolinate 200 mg/m

IV over 2 h, followed by fluorouracil 400 mg/m

bolus and fluorouracil 2400 mg/m

continuous infusion over 46 h) every 2 weeks on day 1 of each cycle. The primary endpoint is progression-free survival 0003 . Registered April 18, 2019.

Japan Registry of Clinical Trials jRCTs071190003 . Registered April 18, 2019.

One of the most essential problems in structural bioinformatics is protein fold recognition. In this paper, we design a novel deep learning architecture, so-called DeepFrag-k, which identifies fold discriminative features at fragment level to improve the accuracy of protein fold recognition. DeepFrag-k is composed of two stages the first stage employs a multi-modal Deep Belief Network (DBN) to predict the potential structural fragments given a sequence, represented as a fragment vector, and then the second stage uses a deep convolutional neural network (CNN) to classify the fragment vector into the corresponding fold.

Our results show that DeepFrag-k yields 92.98% accuracy in predicting the top-100 most popular fragments, which can be used to generate discriminative fragment feature vectors to improve protein fold recognition.

There is a set of fragments that can serve as structural "keywords" distinguishing between major protein folds. The deep learning architecture in DeepFrag-k is able to accurately identify these fragments as structure features to improve protein fold recognition.

There is a set of fragments that can serve as structural "keywords" distinguishing between major protein folds. The deep learning architecture in DeepFrag-k is able to accurately identify these fragments as structure features to improve protein fold recognition.