Search

With the rapid developments in mass spectrometry (MS)-based proteomics methods, label-free semiquantitative proteomics has become an increasingly popular tool for profiling global protein abundances in an unbiased manner. However, the reproducibility of these data across time and LC-MS platforms is not well characterized. Here, we evaluate the performance of three LC-MS platforms (Orbitrap Elite, Q Exactive HF, and Orbitrap Fusion) in label-free semiquantitative analysis of cell surface proteins over a six-year period. Sucrose gradient ultracentrifugation was used for surfaceome enrichment, following gel separation for in-depth protein identification. With our established workflow, we consistently detected and reproducibly quantified >2300 putative cell surface proteins in a human acute myeloid leukemia (AML) cell line on all three platforms. To our knowledge this is the first study reporting highly reproducible semiquantitative proteomic data collection of biological replicates across multiple years and LC-MS platforms. These data provide experimental justification for semiquantitative proteomic study designs that are executed over multiyear time intervals and on different platforms. Multiyear and multiplatform experimental designs will likely enable larger scale proteomic studies and facilitate longitudinal proteomic studies by investigators lacking access to high throughput MS facilities. Data are available via ProteomeXchange with identifier PXD022721.

CoW using TSV technology has been suggested for process flexibility with high bandwidth in wafer level process (WLP). To connect I/O terminals, Thermo-compression process is generally used with using of non-conductive film (NCF). Although TC-NCF can suppress inner void and have a high production efficiency, solder sweep phenomenon, one of device fail reasons between signal bumps, was reported. This paper focus on design for three dimensional packaging technology and discuss optimal bonding process, NCF material and bump array design. TC-bonding simulation is also adapted for increasing experimental accuracy. The simulation is successfully estimated a sweep behavior and well matched with actual experimental results. It is expected that I/O counts per unit area is be expected to increase about 4 times and flexibility of I/O layout was also ensured.

Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is an unexplained chronic, debilitating illness characterized by fatigue, sleep disturbances, cognitive dysfunction, orthostatic intolerance and gastrointestinal problems. Using ultra performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS), we analyzed the plasma proteomes of 39 ME/CFS patients and 41 healthy controls. Logistic regression models, with both linear and quadratic terms of the protein levels as independent variables, revealed a significant association between ME/CFS and the immunoglobulin heavy variable (IGHV) region 3-23/30. Stratifying the ME/CFS group based on self-reported irritable bowel syndrome (sr-IBS) status revealed a significant quadratic effect of immunoglobulin lambda constant region 7 on its association with ME/CFS with sr-IBS whilst IGHV3-23/30 and immunoglobulin kappa variable region 3-11 were significantly associated with ME/CFS without sr-IBS. In addition, we were able to predict ME/CFS status with a high degree of accuracy (AUC = 0.774-0.838) using a panel of proteins selected by 3 different machine learning algorithms: Lasso, Random Forests, and XGBoost. These algorithms also identified proteomic profiles that predicted the status of ME/CFS patients with sr-IBS (AUC = 0.806-0.846) and ME/CFS without sr-IBS (AUC = 0.754-0.780). Our findings are consistent with a significant association of ME/CFS with immune dysregulation and highlight the potential use of the plasma proteome as a source of biomarkers for disease.

Acute myeloid leukemia (AML) therapy requires very intensive chemotherapy/stem cell transplant that cures only 60% of children and 25% of adults with AML. The development of chimeric antigen receptor (CAR) T cells for AML has been hampered by the lack of optimal surface antigens. We hypothesized that unbiased identification of AML cell surface proteins will enable creation of novel CAR T cell immunotherapies and that integration of AML surfaceome data with AML RNA and DNA sequencing data will facilitate identification of new therapeutic targets. We also hypothesized that pediatric and adult surfaceomes are overlapping but distinguishable. Sucrose gradient ultracentrifugation was used for surfaceome enrichment of patient leukemia and normal bone marrow samples without chemical labeling or affinity purification followed by GeLC-MS. The MOLM-14 human AML cell line served as internal control. Mass spectrometry data were processed with MaxQuant, and 9 GO terms were used to define putative cell surface proteins. Samples with at least 6 of 12 known AML surface markers detected were further analyzed. RNA and DNA were extracted for a subset of samples and sequenced. To date, we have surfaceome data for 25 pediatric AML, 12 pediatric normal bone marrow, 9 adult AML, and 9 MOLM-14 samples. A median of 2050 ± 319 surface proteins were detected in the MOLM-14 samples. The mean Pearson correlation coefficient for these replicates was 0.82, confirming the robustness of our workflow. In addition, we found a high representation of known AML cell surface proteins among the captured surface proteins, such as CD33, CD123 and FLT3. A median 1269 ± 474 and 1994 ± 480 surface proteins were found in the pediatric primary AML and normal control samples, respectively and 1790 ± 553 proteins were identified in adult primary AML samples. The average correlation coefficients for the pediatric AML, pediatric control, and adult AML samples were 0.63, 0.75, and 0.63, respectively. Comparison of the pediatric AML and control samples revealed 208 unique and 19 differentially expressed (>2-fold, p We demonstrate that the surfaceome of primary pediatric and adult AML patient samples can be surveyed at depth for the discovery of new potential immunotherapeutic targets. The presence of unique or differentially expressed surface proteins increases our optimism that targeting AML with potent immunotherapies may become clinically feasible. A proteogenomic integration of these AML surfaceome and RNA and DNA sequencing datasets may illuminate AML biology as well as present novel therapeutic targets. Citation Format: Tina Glisovic-Aplenc, Kevin Nestler, Congcong Lu, Lusha Cao, Joseph Cesare, Hyoungjoo Lee, Mitchelle Matesva, Terzah Horton, Sarah Tasian, John Shern, Saar Gill, Benjamin Garcia, Richard Aplenc. Surveying the AML surfaceome for novel immunotherapeutic targets [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 5120.

This study simulated the performance of Cu-cored solder joints in microelectronic components subjected to the extreme thermal cycling conditions often encountered in the automobile industry by comparing the thermal cycling behavior of Cu-cored solder joints containing two different coating layers of Sn–3.0Ag and Sn–1.0In with that of a baseline Sn–3.0Ag–0.5Cu solder joint under a severe temperature cycling range of −55 to +150 °C. Both Cu-cored solder joints can be considered a potential solution to interconnects in microelectronic semiconductor packages used under harsh thermal conditions on account of their greater resistance to thermal stress caused by the severe temperature cycling than the baseline Sn–3.0Ag–0.5Cu solder joint.

The ultrafine-grained Al 6061 alloy, which was fabricated by the combination of cryogenic rolling with warm rolling, achieved high ultimate tensile strength of 420 MPa. Compared with the results by other severe plastic deformation methods, the strengthening effect by the combination of cryogenic rolling with warm rolling was found significantly effective. This notable increase of tensile strength was achieved by the formation of finer precipitates during warm rolling. The presence fine precipitates of diameter below 100 nm, in ultrafine-grained matrix, were confirmed with TEM and STEM. The estimated precipitation strengthening by the fine precipitates was approximately 100 MPa. Based on the results, it was found that cryogenic rolling combined with warm rolling would be effective in increasing strength.

Copper-cored solder can be regarded as the next-generation solder for microelectronic semiconductors exposed to harsh operating conditions owing to its excellent sustainability under extreme thermal conditions, e.g., in microelectronic semiconductors used in transportation systems. Cu-cored solder joints with two different coating layers, Sn–3.0Ag and Sn–1.0In, were compared with the baseline Sn–3.0Ag–0.5Cu solder. The fracture strength and failure mode were examined using the high-speed ball-pull and normal-speed shear tests. The Cu-cored solder joint with the Sn–1.0In plating layer exhibited the highest ball-pull and shear strengths. In addition, it showed a much lower percentage of interface fracture between the Cu-core and plating layer than the interface fracture percentage in the Sn–3.0Ag plating layer due to the improved wettability between the Cu-core and Sn–1.0In plating layer.

In this paper, we propose a practical downlink multiuser multiple-input-multiple-output (MU-MIMO) system. The proposed MU-MIMO system focuses on improving two limiting factors for practical implementations of MU-MIMO: 1) feedback rate and 2) computational complexity. First, users efficiently feed their channel-state information back with low feedback rate based on the proposed channel-quantization method. Second, the beamforming matrix at the base station is easily computed using singular value decomposition (SVD). Numerical results show that the proposed MU-MIMO system achieves a higher sum rate than conventional MU-MIMO systems, particularly at low feedback rate, while the computational complexity is kept reasonable.

Keystroke dynamics-based authentication (KDA) is to verify a user's identity using not only the password but also keystroke dynamics. With a small number of patterns available, data quality is of great importance in KDA applications. Recently, the authors have proposed to employ artificial rhythms and tempo cues to improve data quality: consistency and uniqueness of typing patterns. This paper examines whether improvement in uniqueness and consistency translates into improvement in authentication performance in real-world applications. In particular, we build various novelty detectors using typing patterns based on various strategies in which artificial rhythms and/or tempo cues are implemented. We show that artificial rhythms and tempo cues improve authentication accuracies and that they can be applicable in practical authentication systems.

Objective: Computerized continuous performance test (CPT) is a widely used diagnostic tool for attention-deficit hyperactivity disorder (ADHD). It measures the number of correctly detected stimuli as well as response times. Typically, when calculating a cut-off score for discriminating between normal and abnormal, only the normal children's data are collected. Then the average and standard deviation of each measure or variable is computed. If any of variables is larger than 2 sigma above the average, that child is diagnosed as abnormal. We will call this approach as ''T-score 70'' classifier. However, its performance has a lot to be desired due to a high false negative error. In order to improve the classification accuracy we propose to use novelty detection approaches for supporting ADHD diagnosis. Methods and material: Novelty detection is a model building framework where a classifier is constructed using only one class of training data and a new input pattern is classified according to its similarity to the training data. A total of eight novelty detectors are introduced and applied to our ADHD datasets collected from two modes of tests, visual and auditory. They are evaluated and compared with the T-score model on validation datasets in terms of false positive and negative error rates, and area under receiver operating characteristics curve (AuROC). Results: Experimental results show that the cut-off score of 70 is suboptimal which leads to a low false positive error but a very high false negative error. A few novelty detectors such as Parzen density estimators yield much more balanced classification performances. Moreover, most novelty detectors outperform the T-score method for most age groups statistically with a significance level of 1% in terms of AuROC. In particular, we recommend the Parzen and Gaussian density estimators, kernel principal component analysis, one-class support vector machine, and K-means clustering novelty detector which can improve upon the T-score method on average by at least 30% for the visual test and 40% for the auditory test. In addition, their performances are relatively stable over various parameter values as long as they are within reasonable ranges. Conclusion: The proposed novelty detection approaches can replace the T-score method which has been considered the ''gold standard'' for supporting ADHD diagnosis. Furthermore, they can be applied to other psychological tests where only normal data are available.

Response modeling has become a key factor to direct marketing. In general, there are two stages in response modeling. The first stage is to identify respondents from a customer database while the second stage is to estimate purchase amounts of the respondents. This paper focuses on the second stage where a regression, not a classification, problem is solved. Recently, several non-linear models based on machine learning such as support vector machines (SVM) have been applied to response modeling. However, there is a major difficulty. A typical training dataset for response modeling is so large that modeling takes very long, or, even worse, modeling may be impossible. Therefore, sampling methods have been usually employed in practice. However a sampled dataset usually leads to lower accuracy. In this paper, we employed an @e-tube based sampling for support vector regression (SVR) which leads to better accuracy than the random sampling method.

This paper proposes to use novelty detection approaches to alleviate the class imbalance in response modeling. Two novelty detectors, one-class support vector machine (1-SVM) and learning vector quantization for novelty detection (LVQ-ND), are compared with binary classifiers for a catalogue mailing task with DMEF4 dataset. The novelty detectors are more accurate and more profitable when the response rate is low. When the response rate is relatively high, however, a support vector machine model with modified misclassification costs performs the best. In addition, the novelty detectors turn in higher profits with a low mailing cost, while the SVM model is the most profitable with a high mailing cost.

In keystroke dynamics-based authentication, novelty detection methods are used since only the valid user's patterns are available when a classifier is first constructed. After a while, however, impostors' keystroke patterns become available from failed login attempts. We propose to employ the retraining framework where a novelty detector is retrained with the impostor patterns to enhance authentication accuracy. In this paper, learning vector quantization for novelty detection and support vector data description are retrained with the impostor patterns. Experimental results show that retraining improves the authentication performance and that learning vector quantization for novelty detection outperforms other widely used novelty detectors.

This article has been withdrawn by the authors. We discovered an error after this manuscript was published as a Paper in Press. Specifically, we learned that the structures of glycans presented for the PD-L1 peptide were drawn and labeled incorrectly. We wish to withdraw this article and submit a corrected version for review.

In this paper, we propose a generalized minimum mean square error (MMSE) beamforming for downlink multicell multiple-input multiple-output (MIMO) systems with local BS cooperations. Unlike the previous beamforming strategies which have been designed for an idealized multicell MIMO system model, we consider a complicated but realistic multicell MIMO system model. Our realistic multicell MIMO system model captures (i) different average SNRs of users due to random geometrical distribution of users within cells, (ii) channel feedback latency due to air propagation time and signal processing time, and (iii) different channel aging effects for intracell channel information and intercell channel information due to an additional backhaul latency during the exchange of information between pairs of neighboring cells. The key novelty of this paper is that we derive the closed-form beamforming expressions of the generalized MMSE beamforming using convex optimization technique. The closed-form beamforming expression gives insights on how the geometry of users and channel feedback latency affect the construction of optimal beamforming vectors. Numerical results verify that the proposed generalized MMSE beamforming outperforms previous beamforming strategies in both BER and average throughput performances.

In this paper, a practical downlink multiuser multiple-input multiple-output (MU-MIMO) scheme with low-rate feedback channel is considered. Firstly, we propose a preferred-beam feedback method, which effectively conveys information of user channel and inter-user interference. Secondly, we propose a new low-complexity method to compute unitary preprocessing matrix based on the feedback method. The proposed method reduces considerably computational complexity by utilizing SVD operation compared to the conventional unitary preprocessing scheme. Finally, for the case that the number of users exceeds the number of transmit antennas, we provide two simple user selection algorithms to exploit multiuser diversity. Numerical results show that the proposed low-complexity unitary preprocessing scheme achieves higher sum-rate than previous linear preprocessing schemes, especially at low SNR or with small number of active users.

Crystal growth and haze formation on photomasks become serious problems in UV lithography. As the wavelength becomes shorter, photons carry more energy, so the chances of having a photochemical reaction become much higher. Pellicle, adhesive, residue from cleaning or resist strip process, and any contaminant in air can react with UV to form unwanted crystals and a haze layer on reticles. These will reduce the light transmission during exposure process. Thus, frequent mask inspection and periodic mask cleaning are needed to overcome these problems. However, these will in turn increase manufacturing cost and reduce mask life. Thus, a proper mask inspection tool is required to provide early warning of haze formation. In this work, we devised a new ellipsometric technique to investigate the inner surface of mask without removing pellicle. Ellipsometry is known to have mono-layer sensitivity and it can be used to measure any film or partial film formed on non-patterned spot in early stage of growth. However, when a pellicle covers the surface of mask, the ellipsometric data reflected from surface are extremely distorted due to the non-normal transmission through the pellicle. Thus, data analysis becomes extremely difficult without knowing the optical properties of pellicles. In order to solve this problem we developed compensation technique in which two blank pellicles are situated in the optical path in a way to compensate the polarization changes caused by the pellicle on mask. With this method, the conventional ellipsometry spectra of {Δ, Ψ} are deduced.

Keystroke dynamics-based authentication (KDA) is to verify a user's identification using not only the password but also keystroke patterns. The authors have shown in previous research that uniqueness and consistency of keystroke patterns are important factors to authentication accuracy and that they can be improved by employing artificial rhythms and tempo cues. In this paper, we implement the pause strategy and/or auditory cues for KDA and assess their effectiveness using various novelty detectors. Experimental results show that improved uniqueness and consistency lead to enhanced authentication performance, in particular for those users with poor typing ability.

In keystroke dynamics based user authentication, an access system utilizes not only a valid user's password, but also his/her typing patterns. Although high performances in terms of FAR(False Acceptance Rate) and FRR(False Rejection Rate) have been reported, most researches used a large number of valid users' typing patterns in order to implement complex algorithms in building a classifier[1]. However, collecting sufficient typing patterns to construct a complex classifier is practically impossible. When only a handful of typing patterns are available, the only way to compensate the lack of quantity is to improve quality. To improve the quality of typing patterns, using artificial rhythms and cues were proposed[2]. In this paper, we aim at verifying the effectiveness of artificial rhythms and cues by testing hypotheses.