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Living organisms receive necessary energy substances directly from cellular respiration. The completion of electron storage and transportation requires the process of cellular respiration with the aid of electron transport chains. Therefore, the work of deciphering electron transport proteins is inevitably needed. The identification of these proteins with high performance has a prompt dependence on the choice of methods for feature extraction and machine learning algorithm. In this study, protein sequences served as natural language sentences comprising words. The nominated word embedding-based feature sets, hinged on the word embedding modulation and protein motif frequencies, were useful for feature choosing. Five word embedding types and a variety of conjoint features were examined for such feature selection. The support vector machine algorithm consequentially was employed to perform classification. The performance statistics within the 5-fold cross-validation including average accuracy, specificity, sensitivity, as well as MCC rates surpass 0.95. Such metrics in the independent test are 96.82, 97.16, 95.76 percent, and 0.9, respectively. Compared to state-of-the-art predictors, the proposed method can generate more preferable performance above all metrics indicating the effectiveness of the proposed method in determining electron transport proteins. Furthermore, this study reveals insights about the applicability of various word embeddings for understanding surveyed sequences. [ABSTRACT FROM AUTHOR]

Blockage by the human hand/body is an important impairment in realizing practical millimeter-wave wireless systems. Prior works on blockage modeling are either based on theoretical studies of double knife-edge diffraction or its modifications, high-frequency simulations of electromagnetic effects, or measurements with experimental millimeter-wave prototypes. While such studies are useful, they do not capture the form-factor constraints of user equipments (UEs), such as moderate array sizes, gains, and beamwidths. In this work, we study the impact of hand/body blockage with a UE at 28 GHz built on commercial millimeter-wave components. We report five controlled studies with different types of hand holdings/grips, antenna types, and directional/narrow beams. For both hard and loose hand grips, we report considerably lower blockage loss estimates than prior works. Critical in estimating the loss is the definition of a “region of interest” (RoI) around the UE where the impact of the hand/body is seen. Toward this goal, we define an RoI that includes the spatial area where significant energy is seen in either the no blockage or blockage modes. Our studies show that significant spatial area coverage improvement can be seen with a loose hand grip due to hand reflections. [ABSTRACT FROM AUTHOR]

It is well-known that the major reason for the rapid proliferation of cancer cells are the hypomethylation of the whole cancer genome and the hypermethylation of the promoter of particular tumor suppressor genes. Locating 5-methylcytosine (5mC) sites in promoters is therefore a crucial step in further understanding of the relationship between promoter methylation and the regulation of mRNA gene expression. High throughput identification of DNA 5mC in wet lab is still time-consuming and labor-extensive. Thus, finding the 5mC site of genome-wide DNA promoters is still an important task. We compared the effectiveness of the most popular and strong machine learning techniques namely XGBoost, Random Forest, Deep Forest, and Deep Feedforward Neural Network in predicting the 5mC sites of genome-wide DNA promoters. A feature extraction method based on k-mers embeddings learned from a language model were also applied. Overall, the performance of all the surveyed models surpassed deep learning models of the latest studies on the same dataset employing other encoding scheme. Furthermore, the best model achieved AUC scores of 0.962 on both cross-validation and independent test data. We concluded that our approach was efficient for identifying 5mC sites of promoters with high performance. [ABSTRACT FROM AUTHOR]

Although the flow-mediated dilation (FMD) index is considered the most reliable indicator of vascular endothelial function, previous studies have proved that the dilatation index (DI) measured by the highly reproducible air pressure sensing system (APSS) is just as accurate in effectively determining endothelial function. Besides, not only is APSS cheaper than the instrument for FMD ratio measurement, but operation of the former is also simple to facilitate its domestic use. However, APSS had the pitfall of being time consuming because of the large amount of computation involved. This paper attempted to validate a new self-developed endothelial function screening (EFS) device that utilized the photoplethysmography (PPG) system for acquiring PPG waveform signals from the index finger through an infrared sensor and a pressure cuff for applying pressure on ipsilateral upper arm for eliciting reactive hyperemic response to assess vascular health. The mean peak amplitude of the signals during the hyperemic phase was divided by that of the baseline to produce an EFS ratio. About 52 volunteers of age 34.76 ± 15.23 years without history of cardiovascular diseases were recruited for vascular endothelial function evaluation using the EFS device and the APSS. Bland–Altman analysis showed good consistency between the EFS ratio and DI. Besides, linear regression analysis demonstrated highly significant correlation between the two sets of data (p < 0.001, r2 = 0.6261) In conclusion, this paper, which attempted to validate a self-developed, economical, and time-efficient device that can be operated in a domestic setting, demonstrated that the EFS device yielded consistent results on vascular endothelial function comparable to those acquired through APSS. [ABSTRACT FROM AUTHOR]

This paper presents a W-band wafer-scale phased- array transmitter with high-efficiency on-chip antennas. The 4\,\times\,4 array is based on an RF beamforming architecture with an equiphase distribution network and phased shifters placed on every element. The differential on-chip antennas are implemented using a 100 \mu\ m thick quartz superstrate and with a simulated efficiency of \sim \ 45\% at 110 GHz. The phased array is designed with low mutual coupling between the elements and results in a stable active antenna impedance versus scan angle. The phased array is built in the Jazz SBC18H3 SiGe BiCMOS process, and is 6.5\,\times \,6.0 \ mm^2. Measurements show two-dimensional pattern scanning capabilities with a directivity of 17.0 dB, an array gain of \sim \ 26.5~\ dB at 110 GHz, and an EIRP of 23–25 dBm at 108–114 GHz. The power consumption is 3.4 W from a 1.9 V supply. To our knowledge, this work represents the first W-band wafer-scale phased array to-date. The application areas are in point-to-point communication systems in the 100–120 GHz range. [ABSTRACT FROM PUBLISHER]

Differential on-chip microstrip and slot-ring antennas with a quartz superstrate are presented for wafer-scale silicon systems. The antennas are fed at the nonradiating edge, which is compatible with differential coupled-lines, and are built on a 0.13-\mum CMOS process with a layout that meets all the metal density rules. A high radiation efficiency is achieved using a 100-\mum quartz superstrate placed on top of the silicon chip. Both antennas have a measured gain varies from about 2 to 3 dBi at 91–94 GHz, with a -10-dB S11 bandwidth of 7–8 GHz and a simulated radiation efficiency of >50%. The designs are compatible with single- and multielement transceivers, and with wafer-scale imaging systems and power-combining arrays. [ABSTRACT FROM PUBLISHER]

This paper presents tunable lumped-element bandstop filters for the UHF-band cognitive radio systems. The two-pole filters are implemented using lumped elements with both single- and back-to-back silicon varactor diodes. The single diode filter tunes from 470 to 730 MHz with a 16-dB rejection bandwidth of 5 MHz and a filter quality factor of 52–65. The back-to-back diode filter tunes from 511 to 745 MHz, also with a 16-dB rejection bandwidth of 5 MHz and a quality factor of 68–75. Both filters show a low insertion loss of 0.3–0.4 dB. Nonlinear measurements at the filter null with \Delta f = 2 MHz show that the back-to-back diode filter results in 12-dBm higher third-order intermodulation intercept point than the single diode filter. A scaling series capacitor is used in the resonator arm of the back-to-back diode filter and allows a power handling of 25 dBm at the 16-dB rejection null. The cascaded response of two tunable filters is also presented for multiband rejection applications, or for a deeper rejection null (>36 dB with 0.6-dB loss at 600 MHz). The topology can be easily extended to higher order filters and design equations are presented. [ABSTRACT FROM AUTHOR]

This paper presents on-chip slot-ring and horn antennas for wafer-scale silicon systems. A high efficiency is achieved using a 100-\mu\ m quartz superstrate on top of the silicon chip, and a low-loss microstrip transformer using the silicon back-end metallization. A finite ground plane is also used to reduce the power coupled to the TEM mode. The slot-ring and 1-\lambda0^{2} horn achieve a measured gain of 0–2 and 6–8 dBi at 90–96 GHz, respectively, and a radiation efficiency of \sim \50%. The horns achieve a high antenna gain without occupying a large area on the silicon wafer, thus resulting in a low-cost system. The designs are compatible with either single- or two-antenna transceivers, or with wafer-scale imaging systems and power-combining arrays. [ABSTRACT FROM AUTHOR]

This paper presents a novel learning algorithm for efficient construction of the radial basis function (RBF) networks that can deliver the same level of accuracy as the support vector machines (SVMs) in data classification applications. The proposed learning algorithm works by constructing one RBF subnetwork to approximate the probability density function of each class of objects in the training data set. With respect to algorithm design, the main distinction of the proposed learning algorithm is the novel kernel density estimation algorithm that features an average time complexity of O(n log n), where vi is the number of samples in the training data set. One important advantage of the proposed learning algorithm, in comparison with the SVM, is that the proposed learning algorithm generally takes far less time to construct a data classifier with an optimized parameter setting. This feature is of significance for many contemporary applications, in particular, for those applications in which new objects are continuously added into an already large database. Another desirable feature of the proposed learning algorithm is that the RBF networks constructed are capable of carrying out data classification with more than two classes of objects in one single run. In other words, unlike with the SVM, there is no need to resort to mechanisms such as one-against-one or one-against-all for handling datasets with more than two classes of objects. The comparison with SVM is of particular interest, because it has been shown in a number of recent studies that SVM generally are able to deliver higher classification accuracy than the other existing data classification algorithms. As the proposed learning algorithm is instance-based, the data reduction issue is also addressed in this paper. One interesting observation in this regard is that, for all three data sets used in data reduction experiments, the number of training samples remaining after a naïve data reduction mechanism is applied is quite close to the number of support vectors identified by the SVM software. This paper also compares the performance of the RBF networks constructed with the proposed learning algorithm and those constructed with a conventional cluster-based learning algorithm. The most interesting observation learned is that, with respect to data classification, the distributions of training samples near the bound- aries between different classes of objects carry more crucial information than the distributions of samples in the inner parts of the clusters. [ABSTRACT FROM AUTHOR]