Monobutyl phthalate induces the expression change of G-Protein-Coupled Receptor 30 in rat testicular Sertoli cells

The aim of the study was to explore whether G-Protein-Coupled Receptor 30 (GPR30) was expressed in rat testicular Sertoli cells and to assess the impact of monobutyl phthalate (MBP) on the expression of GPR30 in Sertoli cells. By using RT-PCR, Western-Blot and immunofluorescent microscopy, the expression of GPR30 in rat Sertoli cells was found at both gene and protein level. Cultures of Sertoli cells were exposed to MBP (10– –1000 mM) or a vehicle. The results indicated that the expression of GPR30 increased at gene and protein levels in Sertoli cells following administration of MBP even at a relatively low concentration. We suggest that changes of GPR30 expression may play an important role in the effects of the xenoestrogen MBP on Sertoli cell function. (Folia Histochemica et Cytobiologica 2013, Vol. 51, No. 1, 18–24

Analysis on the vibration modes of the electric vehicle motor stator

The lightweight design of the electric vehicle motor brought about more serious vibration and noise problem of the motor. An accurate modal calculation was the basis for the study of the vibration and noise characteristics of the electric vehicle motor. The finite element method was used to perform the modal simulation of the PMSM. Through the reasonable simplification and equivalence of the motor stator model, the first 7 orders natural frequencies and corresponding modes of the motor stator under the free state were calculated. After that, the accuracy of the finite element model was verified by the hammering modal experiment of the prototype. Furthermore, the above results will provide the theoretical basis for the electric vehicle motor’s vibration control and NVH improvement

Split, Encode and Aggregate for Long Code Search

Code search with natural language plays a crucial role in reusing existing code snippets and accelerating software development. Thanks to the Transformer-based pretraining models, the performance of code search has been improved significantly compared to traditional information retrieval (IR) based models. However, due to the quadratic complexity of multi-head self-attention, there is a limit on the input token length. For efficient training on standard GPUs like V100, existing pretrained code models, including GraphCodeBERT, CodeBERT, RoBERTa (code), take the first 256 tokens by default, which makes them unable to represent the complete information of long code that is greater than 256 tokens. Unlike long text paragraph that can be regarded as a whole with complete semantics, the semantics of long code is discontinuous as a piece of long code may contain different code modules. Therefore, it is unreasonable to directly apply the long text processing methods to long code. To tackle the long code problem, we propose SEA (Split, Encode and Aggregate for Long Code Search), which splits long code into code blocks, encodes these blocks into embeddings, and aggregates them to obtain a comprehensive long code representation. With SEA, we could directly use Transformer-based pretraining models to model long code without changing their internal structure and repretraining. Leveraging abstract syntax tree (AST) based splitting and attention-based aggregation methods, SEA achieves significant improvements in long code search performance. We also compare SEA with two sparse Trasnformer methods. With GraphCodeBERT as the encoder, SEA achieves an overall mean reciprocal ranking score of 0.785, which is 10.1% higher than GraphCodeBERT on the CodeSearchNet benchmark.Comment: 9 page

A novel approach to sequence validating protein expression clones with automated decision making

<p>Abstract</p> <p>Background</p> <p>Whereas the molecular assembly of protein expression clones is readily automated and routinely accomplished in high throughput, sequence verification of these clones is still largely performed manually, an arduous and time consuming process. The ultimate goal of validation is to determine if a given plasmid clone matches its reference sequence sufficiently to be "acceptable" for use in protein expression experiments. Given the accelerating increase in availability of tens of thousands of unverified clones, there is a strong demand for rapid, efficient and accurate software that automates clone validation.</p> <p>Results</p> <p>We have developed an Automated Clone Evaluation (ACE) system – the first comprehensive, multi-platform, web-based plasmid sequence verification software package. ACE automates the clone verification process by defining each clone sequence as a list of multidimensional discrepancy objects, each describing a difference between the clone and its expected sequence including the resulting polypeptide consequences. To evaluate clones automatically, this list can be compared against user acceptance criteria that specify the allowable number of discrepancies of each type. This strategy allows users to re-evaluate the same set of clones against different acceptance criteria as needed for use in other experiments. ACE manages the entire sequence validation process including contig management, identifying and annotating discrepancies, determining if discrepancies correspond to polymorphisms and clone finishing. Designed to manage thousands of clones simultaneously, ACE maintains a relational database to store information about clones at various completion stages, project processing parameters and acceptance criteria. In a direct comparison, the automated analysis by ACE took less time and was more accurate than a manual analysis of a 93 gene clone set.</p> <p>Conclusion</p> <p>ACE was designed to facilitate high throughput clone sequence verification projects. The software has been used successfully to evaluate more than 55,000 clones at the Harvard Institute of Proteomics. The software dramatically reduced the amount of time and labor required to evaluate clone sequences and decreased the number of missed sequence discrepancies, which commonly occur during manual evaluation. In addition, ACE helped to reduce the number of sequencing reads needed to achieve adequate coverage for making decisions on clones.</p

All-trans retinoic acid regulates the expression of the extracellular matrix protein fibulin-1 in the guinea pig sclera and human scleral fibroblasts

Purpose: Fibulin-1 (FBLN1) mRNA is expressed in human sclera and is an important adhesion modulatory protein that can affect cell-matrix interactions and tissue remodeling. Scleral remodeling is influenced by all-trans retinoic acid (RA). Our purpose was to confirm the presence of fibulin-1 protein in guinea pig sclera and investigate the effect of RA on the expression of fibulin-1 in guinea pig sclera in vivo and in cultured human scleral fibroblasts (HSFs). Methods: Confocal fluorescence microscopy was used to study fibulin-1 and aggrecan expression and localization in sclera from control guinea pigs and in animals given RA by daily gavage from 4 to 8 days of age. The effects of RA (from 10⁻⁹ to 10⁻⁵ M) on fibulin-1 expression in HSFs were observed by immunohistochemistry and assayed by real-time PCR and western blot analysis. Results: Fibulin-1 protein expression was detected by confocal fluorescence microscopy in guinea pig sclera and in cultured HSFs. Upregulation of fibulin-1 in scleral tissue was observed after feeding with RA. In vitro, the level of Fbln1 mRNA was increased after treatment of HSFs with RA (at concentrations of 10⁻⁸ to 10⁻⁶ M; p<0.001), with a maximum effect at 10⁻⁷ M. Fibulin-1 protein levels were significantly increased after treatment of HSFs with 10⁻⁷ M of RA for 24 or 48 h (p<0.05). Conclusions: Fibulin-1 protein was expressed in guinea pig sclera and cultured HSFs. Expression was regulated by RA, a molecule known to be involved in the regulation of eye growth. Further studies on the role of fibulin-1 in the regulation of eye growth, including during the development of myopia, are therefore warranted