Proteomic analysis of differential proteins in pancreatic carcinomas: Effects of MBD1 knock-down by stable RNA interference

<p>Abstract</p> <p>Background</p> <p>Methyl-CpG binding domain protein 1 (MBD1), a suppressor of gene transcription, may be involved in inactivation of tumor suppressor genes during tumorigenesis. Over-expression of MBD1 has been reported in human pancreatic carcinomas.</p> <p>Methods</p> <p>In this study, we established a MBD1-knock-down pancreatic cancer cell line (BxPC-3) using stable RNA interference, to compare the proteomic changes between control and MBD1-knock-down cells using two-dimensional gel electrophoresis and mass spectrometry.</p> <p>Results</p> <p>We identified five proteins that were up-regulated and nine proteins that were down-regulated. Most of the identified proteins are involved in tumorigenesis, some are prognostic biomarkers for human malignant tumors.</p> <p>Conclusion</p> <p>Our data suggest that these differential proteins may be associated with the function of MBD1, and provide some insight into the functional mechanism of MBD1 in the development of pancreatic cancer.</p

Introducing v0.5 of the AI Safety Benchmark from MLCommons

This paper introduces v0.5 of the AI Safety Benchmark, which has been created by the MLCommons AI Safety Working Group. The AI Safety Benchmark has been designed to assess the safety risks of AI systems that use chat-tuned language models. We introduce a principled approach to specifying and constructing the benchmark, which for v0.5 covers only a single use case (an adult chatting to a general-purpose assistant in English), and a limited set of personas (i.e., typical users, malicious users, and vulnerable users). We created a new taxonomy of 13 hazard categories, of which 7 have tests in the v0.5 benchmark. We plan to release version 1.0 of the AI Safety Benchmark by the end of 2024. The v1.0 benchmark will provide meaningful insights into the safety of AI systems. However, the v0.5 benchmark should not be used to assess the safety of AI systems. We have sought to fully document the limitations, flaws, and challenges of v0.5. This release of v0.5 of the AI Safety Benchmark includes (1) a principled approach to specifying and constructing the benchmark, which comprises use cases, types of systems under test (SUTs), language and context, personas, tests, and test items; (2) a taxonomy of 13 hazard categories with definitions and subcategories; (3) tests for seven of the hazard categories, each comprising a unique set of test items, i.e., prompts. There are 43,090 test items in total, which we created with templates; (4) a grading system for AI systems against the benchmark; (5) an openly available platform, and downloadable tool, called ModelBench that can be used to evaluate the safety of AI systems on the benchmark; (6) an example evaluation report which benchmarks the performance of over a dozen openly available chat-tuned language models; (7) a test specification for the benchmark

Introducing v0.5 of the AI Safety Benchmark from MLCommons

This paper introduces v0.5 of the AI Safety Benchmark, which has been created by the MLCommons AI Safety Working Group. The AI Safety Benchmark has been designed to assess the safety risks of AI systems that use chat-tuned language models. We introduce a principled approach to specifying and constructing the benchmark, which for v0.5 covers only a single use case (an adult chatting to a general-purpose assistant in English), and a limited set of personas (i.e., typical users, malicious users, and vulnerable users). We created a new taxonomy of 13 hazard categories, of which 7 have tests in the v0.5 benchmark. We plan to release version 1.0 of the AI Safety Benchmark by the end of 2024. The v1.0 benchmark will provide meaningful insights into the safety of AI systems. However, the v0.5 benchmark should not be used to assess the safety of AI systems. We have sought to fully document the limitations, flaws, and challenges of v0.5. This release of v0.5 of the AI Safety Benchmark includes (1) a principled approach to specifying and constructing the benchmark, which comprises use cases, types of systems under test (SUTs), language and context, personas, tests, and test items; (2) a taxonomy of 13 hazard categories with definitions and subcategories; (3) tests for seven of the hazard categories, each comprising a unique set of test items, i.e., prompts. There are 43,090 test items in total, which we created with templates; (4) a grading system for AI systems against the benchmark; (5) an openly available platform, and downloadable tool, called ModelBench that can be used to evaluate the safety of AI systems on the benchmark; (6) an example evaluation report which benchmarks the performance of over a dozen openly available chat-tuned language models; (7) a test specification for the benchmark

Spatiotemporal Trends and Variations of the Rainfall Amount, Intensity, and Frequency in TRMM Multi-satellite Precipitation Analysis (TMPA) Data

The spatiotemporal mean rain rate (MR) can be characterized by the rain frequency (RF) and the conditional rain rate (CR). We computed these parameters for each season using the TMPA 3-hourly, 0.25° gridded data for the 1998–2017 period at a quasi-global scale, 50°N~50°S. For the global long-term average, MR, RF, and CR are 2.83 mm/d, 10.55%, and 25.05 mm/d, respectively. The seasonal time series of global mean RF and CR show significant decreasing and increasing trends, respectively, while MR depicts only a small but significant trend. The seasonal anomaly of RF decreased by 5.29% and CR increased 13.07 mm/d over the study period, while MR only slightly decreased by −0.029 mm/day. The spatiotemporal patterns in MR, RF, and CR suggest that although there is no prominent trend in the total precipitation amount, the frequency of rainfall events becomes smaller and the average intensity of a single event becomes stronger. Based on the co-variability of RF and CR, the paper optimally classifies the precipitation over land and ocean into four categories using K-means clustering. The terrestrial clusters are consistent with the dry and wet climatology, while categories over the ocean indicate high RF and medium CR in the Inter Tropical Convergence Zone (ITCZ) region; low RF with low CR in oceanic dry zones; and low RF and high CR in storm track areas. Empirical Orthogonal Function (EOF) analysis was then performed, and these results indicated that the major pattern of MR is characterized by an El Niño-Southern Oscillation (ENSO) signal while RF and CR variations are dominated by their trends

Spatiotemporal Trends and Variations of the Rainfall Amount, Intensity, and Frequency in TRMM Multi-satellite Precipitation Analysis (TMPA) Data

The spatiotemporal mean rain rate (MR) can be characterized by the rain frequency (RF) and the conditional rain rate (CR). We computed these parameters for each season using the TMPA 3-hourly, 0.25° gridded data for the 1998–2017 period at a quasi-global scale, 50°N~50°S. For the global long-term average, MR, RF, and CR are 2.83 mm/d, 10.55%, and 25.05 mm/d, respectively. The seasonal time series of global mean RF and CR show significant decreasing and increasing trends, respectively, while MR depicts only a small but significant trend. The seasonal anomaly of RF decreased by 5.29% and CR increased 13.07 mm/d over the study period, while MR only slightly decreased by −0.029 mm/day. The spatiotemporal patterns in MR, RF, and CR suggest that although there is no prominent trend in the total precipitation amount, the frequency of rainfall events becomes smaller and the average intensity of a single event becomes stronger. Based on the co-variability of RF and CR, the paper optimally classifies the precipitation over land and ocean into four categories using K-means clustering. The terrestrial clusters are consistent with the dry and wet climatology, while categories over the ocean indicate high RF and medium CR in the Inter Tropical Convergence Zone (ITCZ) region; low RF with low CR in oceanic dry zones; and low RF and high CR in storm track areas. Empirical Orthogonal Function (EOF) analysis was then performed, and these results indicated that the major pattern of MR is characterized by an El Niño-Southern Oscillation (ENSO) signal while RF and CR variations are dominated by their trends

Natural wood-based metamaterials for highly efficient microwave absorption

While wood substances that contain water have shown a dielectric effect, they have never been directly proposed as a microwave absorption material to reduce artificial electromagnetic pollution. In this study, the microwave absorption efficiency of wood that contains water was investigated based on its dielectric properties in the microwave frequency range to optimize the parameters of wood unit cells, including the orientation, moisture content (MC) and thickness. Subsequently, inspired by the design concept of electromagnetic metamaterials, single-layer wood unit cells containing moisture were directly integrated into double epoxy resin layers. This procedure formed a natural wood-based metamaterial (NWM) with a simple periodic Sandwich structure without the need for further chemical modification and/or high energy consuming processes. Such design strategies allowed the NWMs to overcome the limitation of inherent dielectric properties of natural wood and present significantly enhanced microwave absorption performance, as well as different absorption behavior. NWMs at a MC ≤ 70% displayed a selective absorption mode, while NWMs at MC ≥ 85% showed a broadband absorption mode. Both absorption modes could achieve the peak absorptivity \u3e 98%. Particularly, the NWM in the broadband absorption mode possessed an effective absorption (absorptivity \u3e 90%) bandwidth of 9.04 GHz, which was 7.6 times that of natural wood (1.19 GHz). The NWMs performed well when irradiated by microwaves with different angles and directions. Also, the thickness of NWMs was only 7 mm, allowing easy incorporation of the materials in engineering designs. The use of sustainable materials, impressive performance, high stability, practical thickness, and the facile and cost-effective production technique demonstrated NWMs with great potential in designing green buildings

Molecular Mechanism Underlying Lymphatic Metastasis in Pancreatic Cancer

As the most challenging human malignancies, pancreatic cancer is characterized by its insidious symptoms, low rate of surgical resection, high risk of local invasion, metastasis and recurrence, and overall dismal prognosis. Lymphatic metastasis, above all, is recognized as an early adverse event in progression of pancreatic cancer and has been described to be an independent poor prognostic factor. It should be noted that the occurrence of lymphatic metastasis is not a casual or stochastic but an ineluctable and designed event. Increasing evidences suggest that metastasis-initiating cells (MICs) and the microenvironments may act as a double-reed style in this crime. However, the exact mechanisms on how they function synergistically for this dismal clinical course remain largely elusive. Therefore, a better understanding of its molecular and cellular mechanisms involved in pancreatic lymphatic metastasis is urgently required. In this review, we will summarize the latest advances on lymphatic metastasis in pancreatic cancer

Rice stripe1-2 and stripe1-3 Mutants Encoding the Small Subunit of Ribonucleotide Reductase Are Temperature Sensitive and Are Required for Chlorophyll Biosynthesis.

We induced mutants, stripe1-2 (st1-2) and stripe1-3 (st1-3), from rice (Oryza sativa L.) Indica 9311 using Ethyl methanesulfonate (EMS). Both st1-2 and st1-3 mutants encoded the small subunit of ribonucleotide reductase 1 (RNRS1), differed in the location of the mutated base, and displayed white-stripe from the L2 stage through maturity. The mutants were sensitive to temperature, and their chlorophyll content increased with the increase in temperature; however, they did not revert to normal green leaf phenotype under field conditions. The mutant st1-2 showed loosely arranged thylakoid lamellar structure as compared with wild-type (WT) plants. Contrastingly, st1-3 displayed normal thylakoid lamellar structure, good agronomic traits, and higher yield than st1-2 but lower yield than WT. Three-dimensional structure prediction for RNRS1 indicated that the mutation in Val-171 residue in st1-2 influenced the connection of RNRS1 to iron, causing abnormal development of chloroplasts. Real-time PCR analysis showed that the expression levels associated with chlorophyll biosynthetic pathway and photosynthesis were affected in st1-2 and st1-3 at different temperatures and different developmental stages