A self-adaptive synthetic over-sampling technique for imbalanced classification

Traditionally, in supervised machine learning, (a significant) part of the available data (usually 50%-80%) is used for training and the rest—for validation. In many problems, however, the data are highly imbalanced in regard to different classes or does not have good coverage of the feasible data space which, in turn, creates problems in validation and usage phase. In this paper, we propose a technique for synthesizing feasible and likely data to help balance the classes as well as to boost the performance in terms of confusion matrix as well as overall. The idea, in a nutshell, is to synthesize data samples in close vicinity to the actual data samples specifically for the less represented (minority) classes. This has also implications to the so-called fairness of machine learning. In this paper, we propose a specific method for synthesizing data in a way to balance the classes and boost the performance, especially of the minority classes. It is generic and can be applied to different base algorithms, for example, support vector machines, k-nearest neighbour classifiers deep neural, rule-based classifiers, decision trees, and so forth. The results demonstrated that (a) a significantly more balanced (and fair) classification results can be achieved and (b) that the overall performance as well as the performance per class measured by confusion matrix can be boosted. In addition, this approach can be very valuable for the cases when the number of actual available labelled data is small which itself is one of the problems of the contemporary machine learning.     

Recent Biophysical Issues About the Preparation of Solute-Filled Lipid Vesicles

Here we report some recent biophysical issues on the preparation of solute-filled lipid vesicles and their relevance to the construction of “synthetic cells.” First, we introduce the “semi-synthetic minimal cells” as the liposome-based cell-like systems, which contain a minimal number of biomolecules required to display simple and complex biological functions. Next, we focus on recent aspects related to the construction of synthetic cells. Emphasis is given to the interplay between the methods of synthetic cell preparation and the physics of solute encapsulation. We briefly introduce the notion of structural and compositional “diversity” in synthetic cell populations.     

High-resolution ISAR imaging of fast rotating targets based on pattern-coupled Bayesian strategy for multiple measurement vectors

Very high resolution inverse synthetic aperture radar (ISAR) imaging of fast rotating targets is a complicated task. There may be insufficient pulses or may introduce migration through range cells (MTRC) during the coherent processing interval (CPI) when we use the conventional range Doppler (RD) ISAR technique. With compressed sensing (CS) technique, we can achieve the high-resolution ISAR imaging of a target with limited number of pulses. Sparse representation based method can achieve the super resolution ISAR imaging of a target with a short CPI, during which the target rotates only a small angle and the range migration of the scatterers is small. However, traditional CS-based ISAR imaging method generally faced with the problem of basis mismatch, which may degrade the ISAR image. To achieve the high resolution ISAR imaging of fast rotating targets, this paper proposed a pattern-coupled sparse Bayesian learning method for multiple measurement vectors, i.e. the PC-MSBL algorithm. A multi-channel pattern-coupled hierarchical Gaussian prior is proposed to model the pattern dependencies among neighboring range cells and correct the MTRC problem. The expectation-maximization (EM) algorithm is used to infer the maximum a posterior (MAP) estimate of the hyperparameters. Simulation results validate the effectiveness and superiority of the proposed algorithm.     

Alteration of the Route to Menaquinone towards Isochorismate-Derived Metabolites

Chorismate and isochorismate constitute branch-point intermediates in the biosynthesis of many aromatic metabolites in microorganisms and plants. To obtain unnatural compounds, we modified the route to menaquinone in Escherichia coli. We propose a model for the binding of isochorismate to the active site of MenD ((1R,2S, 5S,6S)-2-succinyl-5-enolpyruvyl-6-hydroxycyclohex-3-ene-1-carboxylate (SEPHCHC) synthase) that explains the outcome of the native reaction with α-ketoglutarate. We have rationally designed variants of MenD for the conversion of several isochorismate analogues. The double-variant Asn117Arg–Leu478Thr preferentially converts (5S,6S)-5,6-dihydroxycyclohexa-1,3-diene-1-carboxylate (2,3-trans-CHD), the hydrolysis product of isochorismate, with a >70-fold higher ratio than that for the wild type. The single-variant Arg107Ile uses (5S,6S)-6-amino-5-hydroxycyclohexa-1,3-diene-1-carboxylate (2,3-trans-CHA) as substrate with >6-fold conversion compared to wild-type MenD. The novel compounds have been made accessible in vivo (up to 5.3 g L⁻¹). Unexpectedly, as the identified residues such as Arg107 are highly conserved (>94 %), some of the designed variations can be found in wild-type SEPHCHC synthases from other bacteria (Arg107Lys, 0.3 %). This raises the question for the possible natural occurrence of as yet unexplored branches of the shikimate pathway.     

积极降压对脑小血管病防治策略的影响

2017年,美国心脏病学会和美国心脏协会提出将高血压诊断标准降为130/80 mm Hg (1 mm Hg=0. 133 kPa),这与其他指南有所不同。降压治疗的核心目标在于靶器官的保护,近年来降压治疗与脑小血管病预防的相关问题日益受到临床重视。目前研究结果表明,降压治疗对腔隙性卒中患者的二级预防以及白质病变进展的预防可能有一定积极意义,但确切的降压目标值并未确定。血压与临床结局可能呈现J型关系,血压过低或过高可能均有害,而取得最大获益的降压目标有待进一步探索。     

Intersecting Xenobiology and Neometabolism To Bring Novel Chemistries to Life

The diversity of life relies on a handful of chemical elements (carbon, oxygen, hydrogen, nitrogen, sulfur and phosphorus) as part of essential building blocks; some other atoms are needed to a lesser extent, but most of the remaining elements are excluded from biology. This circumstance limits the scope of biochemical reactions in extant metabolism – yet it offers a phenomenal playground for synthetic biology. Xenobiology aims to bring novel bricks to life that could be exploited for (xeno)metabolite synthesis. In particular, the assembly of novel pathways engineered to handle nonbiological elements (neometabolism) will broaden chemical space beyond the reach of natural evolution. In this review, xeno-elements that could be blended into nature's biosynthetic portfolio are discussed together with their physicochemical properties and tools and strategies to incorporate them into biochemistry. We argue that current bioproduction methods can be revolutionized by bridging xenobiology and neometabolism for the synthesis of new-to-nature molecules, such as organohalides.     

Results from the ORPHEUS dark matter detector

The ORPHEUS dark matter detector consists of 450 g superconducting tin granules with diameters of 28 and 36 μm. The detector has been operating in the shallow site of the underground laboratory in Bern (70 m.w.e). First preliminary results on WIMP detection sensitivity will be presented.     

CHAOTIC HYDRODYNAMICS IN HORIZONTAL GAS-LIQUID BUBBLY FLOW

ＮＯＭＥＮＣＬＡＴＵＲＥｄ———ｅｍｂｅｄｄｉｎｇｄｉｍｅｎｓｉｏｎ ,ｄｉｍｅｎｓｉｏｎｌｅｓｓＣｄ(ｒ)———ｃｏｒｒｅｌａｔｉｏｎｓｕｍ ,ｄｉｍｅｎｓｉｏｎｌｅｓｓＮ———ｎｕｍｂｅｒｏｆｐａｉｒｓｏｆｐｏｉｎｔｓｏｎｔｈｅｒｅｃｏｎ ｓｔｒｕｃｔｅｄａｔｔｒａｃｔｏｒ,ｄｉｍｅｎｓｉｏｎｌｅｓｓｒ———ｓｃａｌｉｎｇｄｉｓｔａｎｃｅ ,ｄｉｍｅｎｓｉｏｎｌｅｓｓＤｃ ———ｃｏｒｒｅｌａｔｉｏｎｄｉｍｅｎｓｉｏｎ ,ｄｉｍｅｎｓｉｏｎｌｅｓｓＫ———Ｋｏｌｍｏｇｏｒｏｖｅｎｔｒｏｐｙ ,ｂｉｔｓ/ｓＲ…     

基于扩展分形和CFAR特征融合的SAR图像目标识别

研究了多信息融合技术在SAR图像目标识别中的应用。将扩展分形特征(Extended Fractal)与双参数恒虚警特征(Double Parameter CFAR)形成的多信息进行融合处理。运用Dempster-Shafer证据理论，在决策层对SAR图像中的像素进行识别分类。实验结果表明通过融合对像素分类的准确性明显好于单特征的检测结果，减少了虚警概率，提高了系统的识别能力。