Search for serendipitous TNO occultation in X-rays

To study the population properties of small, remote objects beyond Neptune's orbit in the outer solar system, of kilometer size or smaller, serendipitous occultation search is so far the only way. For hectometer-sized Trans-Neptunian Objects (TNOs), optical shadows actually disappear because of diffraction. Observations at shorter wave lengths are needed. Here we report the effort of TNO occultation search in X-rays using RXTE/PCA data of Sco X-1 taken from June 2007 to October 2011. No definite TNO occultation events were found in the 334 ks data. We investigate the detection efficiency dependence on the TNO size to better define the sensible size range of our approach and suggest upper limits to the TNO size distribution in the size range from 30 m to 300 m. A list of X-ray sources suitable for future larger facilities to observe is proposed.Comment: Accepted to publish in MNRA

Developing An Optimal Multivariate Forecasts Model For Supply Chain Inventory Management—A Case Study Of A Taiwanese Electronic Components Distributor

By reducing the volume of inventory and the ratio of obsoleted stock, enterprises can not only lower their cost and risk in a great amount, but also increase their flexibility of capital management. Thus, inventory issues are always taken seriously in enterprises’ supply chains. In the last decades, both industries and academia have come up with multiple solutions to avoid the damage caused by market volatility and to diminish the bullwhip effect. Examples include Toyota Production System (TPS), vendor managed inventory (VMI), collaborative planning, forecasting, and replenishment (CPFR) and so forth. However, little research has addressed the issue regarding with the optimal order amount given the forecast of customers’ demand. The issue is important because order amount is directly related with stock shortage and the inventory cost. To answer the question, this research aims to develop an optimal multivariate forecast model to determine how much and when we should order so that the inventory cost and the rate of stock shortage can be minimized. We will develop a decision support system (DSS) to implement our model. The bullwhip effect shows that if a retailer periodically updates the mean and variance of demand based on observed customer’s demand data, the variance of the orders placed by the retailer will be greater than the variance of demand. Lee et al. (2007) suggested information sharing and coordinate orders among the supply chain are solutions to alleviate the adversity of supply chain uncertainty that mentioned above, including the whiplash effect and dead stock risk. This research will develop an optimal multivariate forecasts to solve the problem. Multivariate forecasts use more than one equations if the variables, such as lead time, backlog and stock, are jointly dependent. We will compare our proposed model with exponential-smoothing forecasting model and a moving-average model to see which model is more applicable. We will also compare a correlated demand with a demand with linear trend to determine which one will be used in our optimal forecasting model. Decision Support System (DSS) can integrate analytical models responsive to the view point of a business process such as demand management. Thus, we will implement our analytical model using DSS. Even though several researchers have already developed DSS regarding with inventory management, like Achabal’s research in 2000 and Cakir’s research in 2008, few of them emphasize environmental dynamics such as demand uncertainty, significant seasonality, short product life cycle or high competitive intensity. Our model will address this issue by developing a multivariate forecasting model which considers multiple uncertainty factors. We will collect data from an electronic components distributor (ABC company). The data collection will be started at the beginning of 2016 and completed before March 2016. The data will enable us to test and refine our analytical model and make the DSS more feasible. We expect the DSS can support the ABC company to decide how much they should order and when is the best time for ordering in terms of reducing inventory. Therefore, the contribution of this research can be two-folded: first, to design a DSS that can actually help the case company to manage their orders more effectively, and, second, to find out variables that are related to inventory optimization in a dynamic environment and to develop an analytical model that is more general to be applied in other industires

Exploring the landscape of ectodomain shedding by quantitative protein terminomics

膜タンパク質が「はさみ分子」によって切断される部位を大規模に解明 --細胞間コミュニケーションの制御機構解明に向けて--. 京都大学プレスリリース. 2021-03-30.Ectodomain shedding is a proteolytic process that regulates the levels and functions of membrane proteins. Dysregulated shedding is linked to severe diseases, including cancer and Alzheimer's disease. However, the exact cleavage sites of shedding substrates remain largely unknown. Here, we explore the landscape of ectodomain shedding by generating large-scale, cell-type-specific maps of shedding cleavage sites. By means of N- and C-terminal peptide enrichment and quantitative mass spectrometry, we quantified protein termini in the culture media of 10 human cell lines and identified 489 cleavage sites on 163 membrane proteins whose proteolytic terminal fragments are downregulated in the presence of a broad-spectrum metalloprotease inhibitor. A major fraction of the presented cleavage sites was identified in a cell-type-specific manner and mapped onto receptors, cell adhesion molecules, and protein kinases and phosphatases. We confidently identified 86 cleavage sites as metalloprotease substrates by means of knowledge-based scoring

Mitochondrial lipoylation integrates age-associated decline in brown fat thermogenesis.

Thermogenesis in brown adipose tissue (BAT) declines with age; however, what regulates this process remains poorly understood. Here, we identify mitochondria lipoylation as a previously unappreciated molecular hallmark of aged BAT in mice. Using mitochondrial proteomics, we show that mitochondrial lipoylation is disproportionally reduced in aged BAT through a post-transcriptional decrease in the iron-sulfur (Fe-S) cluster formation pathway. A defect in the Fe-S cluster formation by the fat-specific deletion of Bola3 significantly reduces mitochondrial lipoylation and fuel oxidation in BAT, leading to glucose intolerance and obesity. In turn, enhanced mitochondrial lipoylation by α-lipoic acid supplementation effectively restores BAT function in old mice, thereby preventing age-associated obesity and glucose intolerance. The effect of α-lipoic acids requires mitochondrial lipoylation via the Bola3 pathway and does not depend on the anti-oxidant activity of α-lipoic acid. These results open up the possibility to alleviate the age-associated decline in energy expenditure by enhancing the mitochondrial lipoylation pathway

Cost-Sensitive Learning for Recurrence Prediction of Breast Cancer

Breast cancer is one of the top cancer-death causes and specifically accounts for 10.4% of all cancer incidences among women. The prediction of breast cancer recurrence has been a challenging research problem for many researchers. Data mining techniques have recently received considerable attention, especially when used for the construction of prognosis models from survival data. However, existing data mining techniques may not be effective to handle censored data. Censored instances are often discarded when applying classification techniques to prognosis. In this paper, we propose a cost-sensitive learning approach to involve the censored data in prognostic assessment with better recurrence prediction capability. The proposed approach employs an outcome inference mechanism to infer the possible probabilistic outcome of each censored instance and adopt the cost-proportionate rejection sampling and a committee machine strategy to take into account these instances with probabilistic outcomes during the classification model learning process. We empirically evaluate the effectiveness of our proposed approach for breast cancer recurrence prediction and include a censored-data-discarding method (i.e., building the recurrence prediction model by only using uncensored data) and the Kaplan-Meier method (a common prognosis method) as performance benchmarks. Overall, our evaluation results suggest that the proposed approach outperforms its benchmark techniques, measured by precision, recall and F1 score

An anomaly-based IDS framework using centroid-based classification

Botnet is an urgent problem that will reduce the security and availability of the network. When the bot master launches attacks to certain victims, the infected users are awakened, and attacks start according to the commands from the bot master. Via Botnet, DDoS is an attack whose purpose is to paralyze the victim’s service. In all kinds of DDoS, SYN flood is still a problem that reduces security and availability. To enhance the security of the Internet, IDS is proposed to detect attacks and protect the server. In this paper, the concept of centroid-based classification is used to enhance performance of the framework. An anomaly-based IDS framework which combines K-means and KNN is proposed to detect SYN flood. Dimension reduction is designed to achieve visualization, and weights can adjust the occupancy ratio of each sub-feature. Therefore, this framework is also suitable for use on the modern symmetry or asymmetry architecture of information systems. With the detection by the framework proposed in this paper, the detection rate is 96.8 percent, the accuracy rate is 97.3 percent, and the false alarm rate is 1.37 percent