A novel zirconosilicate with the MWW topology is firstly hydrothermally synthesized in alkali media with secondary cycloamine and boric acid as a structure-directing agent (SDA) and a crystallization-supporting agent, respectively, and it is shown to be efficient for the Lewis acid-catalyzed reduction of cyclic ketone with secondary alcohol.

The modified LTCBDE with a T-shaped incision of the cystic duct and FREDDY laser lithotripsy is a safe and effective means of managing gallstones concomitant with large or impacted CBD stones.

Whenever the need to compile a new dynamically typed language arises, an appealing option is to repurpose an existing statically typed language Just-In-Time (JIT) compiler (repurposed JIT compiler). Existing repurposed JIT compilers (RJIT compilers), however, have not yet delivered the hoped-for performance boosts. The performance of JVM languages, for instance, often lags behind standard interpreter implementations. Even more customized solutions that extend the internals of a JIT compiler for the target language compete poorly with those designed specifically for dynamically typed languages. Our own Fiorano JIT compiler is an example of this problem. As a state-of-the-art, RJIT compiler for Python, the Fiorano JIT compiler outperforms two other RJIT compilers (Unladen Swallow and Jython), but still shows a noticeable performance gap compared to PyPy, today's best performing Python JIT compiler. In this paper, we discuss techniques that have proved effective in the Fiorano JIT compiler as well as limitations of our current implementation. More importantly, this work offers the first in-depth look at benefits and limitations of the repurposed JIT compiler approach. We believe the most common pitfall of existing RJIT compilers is not focusing sufficiently on specialization, an abundant optimization opportunity unique to dynamically typed languages. Unfortunately, the lack of specialization cannot be overcome by applying traditional optimizations.

MicroRNAs can function as tumor suppressor miRNAs. Bcl-2 is an antiapoptotic gene overexpressed in many tumors, including nasopharyngeal carcinoma (NPC). It is reported that microRNA-15a (miR-15a) and microRNA-16-1 (miR-16-1) could act as bcl-2 inhibitors. To investigate their effects on NPC, the authors used recombinant lentiviral vector to upregulate the expression of miR-15a/16-1 in NPC CNE-2Z cells. The authors divided cells into the control group, transfection group, radiotherapy group, and transfection-radiotherapy group. In this experiment, reverse transcription-quantitative polymerase chain reaction was used to detect the expression of miR-15a/16-1 and bcl-2 mRNA. Cell proliferation was analyzed by MTT assay. Flow cytometry was used to measure cell apoptosis. Radiosensitivity was measured using colony-forming experiment. The protein expression of bcl-2 was measured by western blot, the activation levels of caspase were detected by a spectrophotometric method. After transfection, cell proliferation was inhibited, while the apoptosis rate and radiosensitivity were increased. In addition, the activation of caspase-2 and caspase-3 was aggrandized correspondingly. Although the expression levels of bcl-2 mRNA in each group had no difference, the protein expression of bcl-2 was downregulated. These results suggested that miR-15a/16-1 could inhibit cell proliferation and increase the apoptosis and radiosensitivity of CNE-2 cells, by regulating the bcl-2 gene at post-transcriptional level and by increasing the activation of caspase-2 and caspase-3.

R is a popular dynamic language designed for statistical computing. Despite R's huge user base, the inefficiency in R's language implementation becomes a major pain-point in everyday use as well as an obstacle to apply R to solve large scale analytics problems. The two most common approaches to improve the performance of dynamic languages are: implementing more efficient interpretation strategies and extending the interpreter with Just-In-Time (JIT) compiler. However, both approaches require significant changes to the interpreter, and complicate the adoption by development teams as a result. This paper presents a new approach to improve execution efficiency of R programs by vectorizing the widely used Apply class of operations. Apply accepts two parameters: a function and a collection of input data elements. The standard implementation of Apply iteratively invokes the input function with each element in the data collection. Our approach combines data transformation and function vectorization to convert the looping-over-data execution of the standard Apply into a single invocation of a vectorized function that contains a sequence of vector operations over the input data. This conversion can significantly speed-up the execution of Apply operations in R by reducing the number of interpretation steps. We implemented the vectorization transformation as an R package. To enable the optimization, all that is needed is to invoke the package, and the user can use a normal R interpreter without any changes. The evaluation shows that the proposed method delivers significant performance improvements for a collection of data analysis algorithm benchmarks. This is achieved without any native code generation and using only a single-thread of execution.