Breast cancer is an all too common disease in women, making how to effectively predict it an active research problem. A number of statistical and machine learning techniques have been employed to ...develop various breast cancer prediction models. Among them, support vector machines (SVM) have been shown to outperform many related techniques. To construct the SVM classifier, it is first necessary to decide the kernel function, and different kernel functions can result in different prediction performance. However, there have been very few studies focused on examining the prediction performances of SVM based on different kernel functions. Moreover, it is unknown whether SVM classifier ensembles which have been proposed to improve the performance of single classifiers can outperform single SVM classifiers in terms of breast cancer prediction. Therefore, the aim of this paper is to fully assess the prediction performance of SVM and SVM ensembles over small and large scale breast cancer datasets. The classification accuracy, ROC, F-measure, and computational times of training SVM and SVM ensembles are compared. The experimental results show that linear kernel based SVM ensembles based on the bagging method and RBF kernel based SVM ensembles with the boosting method can be the better choices for a small scale dataset, where feature selection should be performed in the data pre-processing stage. For a large scale dataset, RBF kernel based SVM ensembles based on boosting perform better than the other classifiers.
Abstract
Electrochromic (EC) materials with a dark-to-transmissive switch have great applications in optical communications, infrared wavelength detectors for spacecraft, and infrared camouflage ...coatings. However, such electroactive materials with high stability and cyclability are rare. Considering the advantages of the donor-acceptor approach (wide-range tuneable band position) and porous two-dimensional (2D) covalent organic framework (COF, well-ordered crystalline framework with stable structure and high surface area), in this work we constructed an extended delocalised π-electron layered dark purple EC-COF-1 by reacting the donor N,N,N′,N′-tetrakis(
p
-aminophenyl)-
p
-benzenediamine (TPBD) with the acceptor 2,1,3-benzothiadiazole-4,7-dicarboxaldehyde (BTDD). A sandwiched device made of EC-COF-1 exhibits the two-band bleaching (370 nm and 574 nm) in the visible region and becomes transparent under the applied potential with an induced absorption centring at 1400 nm. This discovery of a stable dark-to-transmissive switch in COF might open another door for their application in many EC devices for various purposes.
Diverse computations in the neocortex are aided by specialized GABAergic interneurons (INs), which selectively target other INs. However, much less is known about how these canonical disinhibitory ...circuit motifs contribute to network operations supporting spatial navigation and learning in the hippocampus. Using chronic two-photon calcium imaging in mice performing random foraging or goal-oriented learning tasks, we found that vasoactive intestinal polypeptide-expressing (VIP+), disinhibitory INs in hippocampal area CA1 form functional subpopulations defined by their modulation by behavioral states and task demands. Optogenetic manipulations of VIP+ INs and computational modeling further showed that VIP+ disinhibition is necessary for goal-directed learning and related reorganization of hippocampal pyramidal cell population dynamics. Our results demonstrate that disinhibitory circuits in the hippocampus play an active role in supporting spatial learning.
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•Ca2+ imaging indicates bimodal activity dynamics of VIP interneurons in vivo•Activity of VIP interneurons is modulated by task and learning demands•VIP-mediated disinhibition supports spatially guided reward learning
Turi et al. imaged activity of VIP-expressing interneurons of hippocampal area CA1 in vivo. They show learning-related reorganization in VIP population dynamics. VIP interneurons provide behavioral state-dependent disinhibition for CA1 pyramidal cells that supports spatial reward learning.
A readily available small molecular hole‐transporting material (HTM), OMe‐TATPyr, was synthesized and tested in perovskite solar cells (PSCs). OMe‐TATPyr is a two‐dimensional π‐conjugated molecule ...with a pyrene core and four phenyl‐thiophene bridged triarylamine groups. It can be readily synthesized in gram scale with a low lab cost of around US$ 50 g−1. The incorporation of the phenyl‐thiophene units in OMe‐TATPyr are beneficial for not only carrier transportation through improved charge delocalization and intermolecular stacking, but also potential trap passivation via Pb–S interaction as supported by depth‐profiling XPS, photoluminescence, and electrochemical impedance analysis. As a result, an impressive best power conversion efficiency (PCE) of up to 20.6 % and an average PCE of 20.0 % with good stability has been achieved for mixed‐cation PSCs with OMe‐TATPyr with an area of 0.09 cm2. A device with an area of 1.08 cm2 based on OMe‐TATPyr demonstrates a PCE of 17.3 %.
A 2D hole‐transporting material with a pyrene core and four phenyl‐thiophene bridged triarylamine groups, OMe‐TATPyr, was readily synthesized at low cost on a gram scale. A power conversion efficiency (PCE) of up to 20.6 % (average PCE 20.0 %) was achieved for mixed‐cation perovskite solar cells with OMe‐TATPyr, outperforming devices with Spiro‐OMeTAD.
Pd nanoparticles were successfully encapsulated inside mesoporous silicalite‐1 nanocrystals (Pd@mnc‐S1) by a one‐pot method. The as‐synthesized Pd@mnc‐S1 with excellent stability functioned as an ...active and reusable heterogeneous catalyst. The unique porosity and nanostructure of silicalite‐1 crystals endowed the Pd@mnc‐S1 material general shape‐selectivity for various catalytic reactions, including selective hydrogenation, oxidation, and carbon–carbon coupling reactions.
Shape‐selective reactions over a palladium nanoparticle (Pd NP) based catalyst were achieved by integrating Pd NPs inside mesoporous silicalite‐1 nanocrystals. The unique micro‐ and mesoporous structure of the zeolite nanocrystals endowed Pd NPs both high stability and excellent shape selectivity for organic synthesis.
The aim of an intrusion detection systems (IDS) is to detect various types of malicious network traffic and computer usage, which cannot be detected by a conventional firewall. Many IDS have been ...developed based on machine learning techniques. Specifically, advanced detection approaches created by combining or integrating multiple learning techniques have shown better detection performance than general single learning techniques. The feature representation method is an important pattern classifier that facilitates correct classifications, however, there have been very few related studies focusing how to extract more representative features for normal connections and effective detection of attacks. This paper proposes a novel feature representation approach, namely the cluster center and nearest neighbor (CANN) approach. In this approach, two distances are measured and summed, the first one based on the distance between each data sample and its cluster center, and the second distance is between the data and its nearest neighbor in the same cluster. Then, this new and one-dimensional distance based feature is used to represent each data sample for intrusion detection by a k-Nearest Neighbor (k-NN) classifier. The experimental results based on the KDD-Cup 99 dataset show that the CANN classifier not only performs better than or similar to k-NN and support vector machines trained and tested by the original feature representation in terms of classification accuracy, detection rates, and false alarms. I also provides high computational efficiency for the time of classifier training and testing (i.e., detection).
Pilot contamination attack (PCA) in a time division duplex wireless communication system is considered, where an eavesdropper (Eve) attacks the reverse pilot transmission phase in order to wiretap ...the data transmitted from a transmitter, Alice, to a receiver, Bob. We propose a new PCA scheme for Eve, wherein Eve does not emit any signal by itself but uses an intelligent reflecting surface (IRS) to reflect the pilot sent by Bob to Alice. The proposed new PCA scheme, referred to as IRS-PCA, increases the signal leakage from Alice to the IRS during the data transmission phase, which is then reflected by the IRS to Eve in order to improve the wiretapping capability of Eve. The proposed IRS-PCA scheme disables many existing countermeasures on PCA due to the fact that with IRS-PCA, Eve no longer needs to know the pilot sequence of Bob, and therefore, poses severe threat to the security of the legitimate wireless communication system. In view of this, the problems of 1) IRS-PCA detection and 2) secure transmission under IRS-PCA are considered in this paper. For IRS-PCA detection, a generalized cumulative sum (GCUSUM) detection procedure is proposed based on the framework of quickest detection , aiming at detecting the occurrence of IRS-PCA as soon as possible once it occurs. For secure transmission under IRS-PCA, a cooperative channel estimation scheme is proposed to estimate the channel of the IRS, based on which zero-forcing beamforming is designed to reduce signal leakage.
Nitrate‐containing industrial wastewater poses a serious threat to the global food security and public health safety. As compared to the traditional microbial denitrification, electrocatalytic ...nitrate reduction shows better sustainability with ultrahigh energy efficiency and the production of high‐value ammonia (NH3). However, nitrate‐containing wastewater from most industrial processes, such as mining, metallurgy, and petrochemical engineering, is generally acidic, which contradicts the typical neutral/alkaline working conditions for both denitrifying bacteria and the state‐of‐the‐art inorganic electrocatalysts, leading to the demand for pre‐neutralization and the problematic hydrogen evaluation reaction (HER) competition and catalyst dissolution. Here, we report a series of Fe2M (M=Fe, Co, Ni, Zn) trinuclear cluster metal–organic frameworks (MOFs) that enable the highly efficient electrocatalytic nitrate reduction to ammonium under strong acidic conditions with excellent stability. In pH=1 electrolyte, the Fe2Co‐MOF demonstrates the NH3 yield rate of 20653.5 μg h−1 mg−1site with 90.55 % NH3‐Faradaic efficiency (FE), 98.5 % NH3‐selectivity and up to 75 hr of electrocatalytic stability. Additionally, successful nitrate reduction in high‐acidic conditions directly produce the ammonium sulfate as nitrogen fertilizer, avoiding the subsequent aqueous ammonia extraction and preventing the ammonia spillage loss. This series of cluster‐based MOF structures provide new insights into the design principles of high‐performance nitrate reduction catalysts under environmentally‐relevant wastewater conditions.
Efficient, direct, electrocatalytic ammonia production from nitrate in a strong acid environment is possible by using the efficient electron proton conduction capability of the dinitrogen ligand and the metal active site in an Fe‐based trinuclear cluster metal–organic framework (MOF).
Limited evidence is available about the association between serum uric acid and sub-stages of the spectrum from normoglycaemia to type 2 diabetes mellitus. We aimed to investigate the association ...between serum uric acid and risk of prediabetes and type 2 diabetes mellitus.
Eligible participants of the Rotterdam Study (n = 8,367) were classified into mutually exclusive subgroups of normoglycaemia (n = 7,030) and prediabetes (n = 1,337) at baseline. These subgroups were followed up for incident prediabetes (n = 1,071) and incident type 2 diabetes mellitus (n = 407), respectively. We used Cox proportional hazard models to determine hazard ratios (HRs) for incident prediabetes among individuals with normoglycaemia and incident type 2 diabetes mellitus among individuals with prediabetes.
The mean duration of follow-up was 7.5 years for incident prediabetes and 7.2 years for incident type 2 diabetes mellitus. A standard deviation increment in serum uric acid was significantly associated with incident prediabetes among individuals with normoglycaemia (HR 1.10, 95% confidence interval (CI) 1.01; 1.18), but not with incident type 2 diabetes mellitus among individuals with prediabetes (HR 1.07, 95% CI 0.94; 1.21). Exclusion of individuals who used diuretics or individuals with hypertension did not change our results. Serum uric acid was significantly associated with incident prediabetes among normoglycaemic women (HR 1.13, 95% CI 1.02; 1.25) but not among normoglycaemic men (HR 1.08, 95% CI 0.96; 1.21). In contrast, serum uric acid was significantly associated with incident type 2 diabetes mellitus among prediabetic men (HR 1.23, 95% CI 1.01; 1.48) but not among prediabetic women (HR 1.00, 95% CI 0.84; 1.19).
Our findings agree with the notion that serum uric acid is more closely related to early-phase mechanisms in the development of type 2 diabetes mellitus than late-phase mechanisms.
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