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This study aimed to investigate whether weekend admissions to psychiatric wards in Taiwan were associated with different patient characteristics or worse clinical outcomes.Patients with acute psychiatric admissions between 1996 and 2012 were included based on the National Health Insurance Research Database. The patients' baseline characteristics were recorded. The study outcomes included inpatient mortality, length of stay, and readmission within 30 days. Multivariable linear regression and multivariable logistic regression with adjustment for age, sex, diagnosis, and compulsory hospitalization status were performed.Among 661,709 acute psychiatric admissions, there were 82,450 weekend admissions. The patients with weekend admissions tended to be younger and the proportion of patients with schizophrenia, bipolar affective disorder, substance use disorder, and compulsory hospitalization were higher. Weekend admissions were associated with a shorter length of stay (30.3 days vs. 33.3 days, p 0.001), lower inpatient morality rate (0.07% vs. 0.11%, p = 0.007), but higher readmission rate (26.8% vs. 25.3%, p 0.001).The impact of weekend admission on clinical outcomes was relatively small compared to the differences in demographic and clinical characteristics. Despite the small influence of weekend admission, evaluation of the quality of care provided at weekends requires further attention and research to improve mental health care.

Low-temperature co-fired ceramics (LTCCs) have been attracting attention due to rapid advances in wireless telecommunications. Low-dielectric-constant (Dk) and low-dissipation-factor (Df) LTCCs enable a low propagation delay and high signal quality. However, the wide ranges of glass, ceramic filler compositions, and processing features in fabricating LTCC make property modulating difficult via experimental trial-and-error approaches. In this study, we explored Dk and Df values of LTCCs using a machine learning method with a Gaussian kernel ridge regression model. A principal component analysis and k-means methods were initially performed to visually analyze data clustering and to reduce the dimension complexity. Model assessments, by using a five-fold cross-validation, residual analysis, and randomized test, suggest that the proposed Dk and Df models had some predictive ability, that the model selection was appropriate, and that the fittings were not just numerical due to a rather small data set. A cross-plot analysis and property contour plot were performed for the purpose of exploring potential LTCCs for real applications with Dk and Df values less than 10 and 2 × 10−3, respectively, at an operating frequency of 1 GHz. The proposed machine learning models can potentially be utilized to accelerate the design of technology-related LTCC systems.

Purpose:To assess the predictive validity of the Bayley Scales of Infant and Toddler Development, Third Edition (Bayley-III) cognitive scores at 6 months of corrected age (CA) for cognitive outcomes at 24 months of CA in very-low-birth-weight (VLBW) infants and investigate the predictors of change in cognitive outcomes.Methods:We retrospectively evaluated VLBW children enrolled in the Taiwan Premature Infant Follow-up Network between 2010 and 2015 and completed the Bayley-III at CA of 6 and 24 months. The predictive validity of the cognitive performance at 6-month CA for the cognitive outcomes at 24-month CA was analyzed. The positive and negative predictive factors were also evaluated using logistic regression. Cut-off scores of Results:A total of 2,972 VLBW children, born with a mean weight of 1116.4 ± 257.5 g and mean gestational age of 29.0 ± 2.8 weeks, were evaluated. A cognitive score of Conclusion:Subnormal Bayley-III cognitive scores at 6-month CA were not predictive of subnormal cognitive function at 24-month CA. In children with normal cognition during early infancy, abnormal muscle tone and lower maternal socioeconomic status may influence the cognitive developing process; this highlighted the importance of early identification of high risk infants and complete preterm infant-associated public health policies to promote an improved neurodevelopmental outcome.

Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest cancers, with an overall five-year survival rate of 11%. Potential curative resection is possible if the tumor is detected at an early stage, with a five-year survival rate of 42%. The only current FDA-cleared biomarker for PDAC is the carbohydrate antigen 19-9 (CA19-9), which is intended for monitoring response to therapy but not for early detection. CA19-9 blood tests have varying sensitivity to detect PDAC and are prone to false positives in the presence of other underlying pancreatic conditions and to false negatives in subpopulations unable to express CA19-9. The goal of our pilot study was to determine if a multiomics approach using cell-free DNA and CA19-9 would be better than CA19-9 alone in detecting PDAC. In this retrospective study of 75 participants, we performed targeted methylation profiling of circulating cell-free DNA and quantitation of plasma CA19-9 abundance. Participants with PDAC (n=39) were 51% male with a mean age of 74.9 years, and consisted of stage II (n=9), stage III (n=11) and stage IV (n=19) pancreatic cancer. Controls (n=36) were 33% male with a mean age of 74.2 years, and included both healthy control/normal pancreas (n=17) and various benign abnormalities of the pancreas or biliary system (n=19). We developed a novel machine learning model that combines CA19-9 and methylation signals to build a joint multiomics prediction. We compared the joint predictions to those based on methylation or CA19-9 alone. Five resamplings of three-fold cross-validation were performed, and sensitivity was calculated for decision thresholds that achieved the desired test set specificity. Across all stages, the multiomics approach achieved a sensitivity of 93% at a specificity of 96%, which was greater than methylation or CA19-9 alone. At 96% specificity, methylation alone achieved a sensitivity of 74% while CA19-9 alone achieved a sensitivity of 87%. In stages II, III and IV, the multiomics approach achieved a sensitivity of 82%, 89%, and 100%, respectively and was also more sensitive than either methylation or CA19-9 alone. These proof-of-concept data demonstrate the promise of using a multiomics approach to develop a highly sensitive and specific test for the early detection of pancreatic cancer. Additional studies are underway, focusing on early-stage disease (stage I/II), to validate these results. Citation Format: Teng-Kuei Hsu, Tzu-Yu Liu, Billie Gould, Christine Decapite, Amer Zureikat, Alessandro Paniccia, Eric Ariazi, Marvin Bertin, Richard Bourgon, Kaitlyn Coil, Hayley Donnella, Adam Drake, Julie M. Granka, Preet Kaur, Maggie C. Louie, Shivani Mahajan, Amit Pasupathy, Ofer Shapira, Peter Ulz, Chun Yang, C. Jimmy Lin, Randall Brand. Plasma-based detection of pancreatic cancer: A multiomics approach [abstract]. In: Proceedings of the AACR Virtual Special Conference on Pancreatic Cancer; 2021 Sep 29-30. Philadelphia (PA): AACR; Cancer Res 2021;81(22 Suppl):Abstract nr PO-007.

Background The present study aimed to develop a new scale to evaluate the level of difficulty in treating major depressive disorder with antidepressants based on the lifetime treatment profile. Methods In addition to evaluating the difficulty of treatment with antidepressants (A subscale), the Treatment Resistance to Antidepressants Evaluation Scale (TRADES) is comprised of a subscale to account for the attributes that compromise the efficacy of treatment (B subscale). One hundred and six participants aged 18 to 65 years with remitted major depressive disorder were enrolled. Eligible cases were those with at least 2 years from disease onset until the scoring date of the TRADES (the index date), with a complete treatment record. Various psychosocial and clinical features, such as neuroticism, harm avoidance, and utilization of psychiatric services, were used to validate the TRADES. Results The mean duration of the course before and after the index date were 5.5 ± 3.5 and 3.1 ± 1.7 years, respectively. In a multiple regression analysis, the final total scores of the TRADES independently correlated with higher levels of neuroticism and harm avoidance. Total scores were also associated with a higher utilization of psychiatric outpatient and admission services before the index date. Furthermore, it is thought that total scores could predict a higher number of visits to psychiatric outpatient, emergency, and admission services following the index date. Conclusions The TRADES has acceptable validity and could help to quantify the level of treatment difficulty with antidepressants in major depressive disorder.

For this study the researchers designed learning activities to enhance students’ high level cognitive processes. Students learned new information in a classroom setting and then applied and analyzed their new knowledge in familiar authentic contexts by taking pictures of objects found there, describing them, and sharing their homework with peers. An experiment was carried out in which 58 junior high school students were divided into a control (n = 30) and an experimental (n = 28) group. The control group studied and completed learning activities with traditional textbooks while the experimental group used electronic textbooks and a learning system, Virtual Pen for Tablet PC (VPenTPC), in order to gauge the feasibility of the proposed approach. The post-test results show a significant difference between the control and experimental groups. In our analysis of the various approaches students took to complete the task, we were able to identify thirty cognitive and metacognitive strategies for using mobile technology, from which we selected the ten most frequently used ones. The results show that low ability students make better use of strategies than their high ability peers, resulting in significant learning gains. The results also show that most students perceive VPenTPC positively. Based on these results, we suggest some implications along with conclusions and directions for future research.

ObjectivesThe primary aim is to provide a summary of evidence for the diagnostic accuracies of multiplex PCR gastrointestinal (GI) panels—BioFire FilmArray and Luminex xTAG on the detection of gastroenteritis pathogens. The secondary aim is to compare the performance of these GI panels head to head.MethodsA comprehensive search up to 1 December 2019 was conducted on PubMed, Embase, Ovid Medline and Web of Science for studies that used FilmArray or Luminex xTAG Gastrointestinal Pathogen Panel (GPP) for diagnosis of acute gastroenteritis. A summary of diagnostic accuracies for the 16 pathogens were calculated by comparing the GI panels to the current gold standards (conventional standard microbiology techniques such as culture or PCR for bacteria, PCR or enzyme immunoassay (EIA) for viruses, microscopy or EIA for parasite). Hierarchical summary receiver operating characteristic (HSROC) curve analysis, pretest and post-test probabilities were used for estimating the pathogen detection performance.ResultsA total of 11 studies with 7085 stool samples were eligible for analysis. Multiplex PCRs demonstrated high diagnostic accuracy, with specificity ≧0.98 and area under the ROC curve (AUROC) ≧0.97 for all the pathogens except for Yersinia enterocolitica (AUROC 0.91). The FilmArray panel demonstrated a higher sensitivity than xTAG GPP for most of the pathogens with the exception of Rotavirus A (xTAG GPP and FilmArray were both 0.93).ConclusionsThis is the first meta-analysis that is a head-to-head comparison examining the performance of the novel multiplex PCR-based tests Luminex xTAG GPP and FilmArray GI panel in detecting each pathogen. Point estimates calculated from eligible studies showed that both GI panels are highly accurate and may provide important diagnostic information for early identification of gastroenteritis. In addition, although FilmArray has higher sensitivity and post-test probability than xTAG GPP for most of the pathogens, how this will translate to a clinical setting remains unclear.

43 Background: Blood-based screening tests for colorectal cancer (CRC) with high sensitivity and specificity are needed to improve adherence, facilitate early detection, and ultimately reduce mortality from CRC. Current stool-based tests have a sensitivity of 24-42% for colorectal advanced adenomas (AAs), while blood tests that rely on tumor-derived cell-free DNA (cfDNA) methylation signatures have shown limited sensitivity for AAs. Here we demonstrate the ability to detect AAs from blood using a multiomics test that incorporates both tumor- and immune-derived signatures, and compare it to the performance of a cfDNA methylation-only test. Methods: Participants enrolled in a prospective study (NCT03688906) were included in this analysis. The multiomics test includes signatures for cell-free nucleic acids based on next-generation sequencing, and for plasma proteins based on high-throughput multiplexed assays. Signatures are integrated computationally with a combination of convolutional neural networks and regularized logistic regression. We compared the multiomics test with one based on cfDNA methylation only. Results: This sub-study included 542 participants (AA: n = 122; colonoscopy-confirmed negative controls: n = 420). Participants with AA were 56% male with a mean age of 63 years, and colonoscopy-confirmed negative controls were 54% male with a mean age of 61 years. The multiomics test achieved a sensitivity of 41% (n = 50/122, 95% CI 34-48%) at 90% specificity (377/420). By contrast, the cfDNA methylation-only test achieved a sensitivity of 20% (24/122, 95% CI 15-25%) at 91% specificity (383/420). Performance was also analyzed by histological subtype and location, and superiority of the multiomics test to the cfDNA-methylation-only test was consistently observed. Conclusions: A novel multiomics blood test can detect colorectal AAs at a sensitivity and specificity comparable to existing stool-based tests. Combining signatures from both tumor- and immune-derived sources resulted in AA sensitivity greater than that of cfDNA-methylation alone.

Blood-based markers can be used to non-invasively predict cancer progression after treatment. Here, cell-free DNA (cfDNA) and plasma proteins were evaluated to explore biological signatures of progression in non-small cell lung cancer (NSCLC). Baseline plasma samples (n=24; 16 progressors, 8 non-progressors) were from patients diagnosed in 2004 with stage I-III NSCLC, collected prior to surgical resection, and retrospectively analyzed. Six patients were treated with neoadjuvant therapies, one with adjuvant therapy, and 17 with surgery alone. Progression was defined as a relapse event or death by any cause. Whole-genome sequencing was performed to characterize cfDNA fragments, which reflect nucleosome protection and chromatin state. Transcriptional activation for protein-coding genes was inferred by modeling fragment distribution around each transcription start site. Univariate comparisons of gene activation between progressors and non-progressors and Cox proportional hazard ratios (HRs) were calculated by grouping patients above or below the median of the marker of interest. This analysis revealed IL-1RN, the gene encoding for the IL-1RA antagonist to the IL-1 receptor complex, as the gene most negatively correlated with progression-free survival (PFS) (r = -0.76, p < 0.0001; Cox HR = 13.77, p < 0.001). This gene was also significantly more active in progressors than in non-progressors (p < 0.005). The binding activity of ~500 transcription factors was also inferred by measuring chromatin accessibility across the genome, revealing SOX-9 to be significantly associated with progression (p < 0.0001, FDR = 1.1%) and the factor most negatively correlated with PFS (r = -0.72, p < 0.001, FDR = 16.9%). Both IL-1RN and SOX-9 have been previously reported to affect survival in NSCLC patients.In addition, the abundances of ~450 proteins including cytokines, receptors, and enzymes were measured. Six proteins were identified as differentially abundant between progression groups. Among these, IL-1α was more abundant in progressors vs. non-progressors (effect size = 0.92, p < 0.05). Notably, IL-1RA abundance did not differ between these groups. Both IL-1α (r = -0.61, p < 0.01; Cox HR = 3.78, p < 0.05) and IL-1RA (r = -0.75, p < 0.001; Cox HR = 1.13, p = 0.78) were negatively correlated with PFS in progressors. Finally, all features and analytes were integrated to identify biological signatures that may be shared among proteins and cfDNA. These signatures differed significantly (p < 0.05) between progressors and non-progressors, suggesting differences in cytokine signaling. The multiomics platform described here integrates biological signals with computational featurization to reveal clinically relevant signatures. Specifically, findings from a small cohort of early-stage NSCLC patients demonstrated the potential of this platform to reveal signatures of progression in NSCLC. Citation Format: Francesco Vallania, Hayley Warsinske, Peter Ulz, Tzu-Yu Liu, Karen Assayag, Krishnan K. Palaniappan, Mitch Bailey, Irving Wang, David E. Weinberg, Riley Ennis, C Jimmy Lin, Anne-Marie Martin, Nancy Krunic. Multiomic plasma profiling identifies potential signatures of disease progression in early-stage NSCLC [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 2433.

BackgroundBlood-based methods using cell-free DNA (cfDNA) are under development as an alternative to existing screening tests. However, early-stage detection of cancer using tumor-derived cfDNA has proven challenging because of the small proportion of cfDNA derived from tumor tissue in early-stage disease. A machine learning approach to discover signatures in cfDNA, potentially reflective of both tumor and non-tumor contributions, may represent a promising direction for the early detection of cancer.MethodsWhole-genome sequencing was performed on cfDNA extracted from plasma samples (N=546 colorectal cancer and 271 non-cancer controls). Reads aligning to protein-coding gene bodies were extracted, and read counts were normalized. cfDNA tumor fraction was estimated using IchorCNA. Machine learning models were trained using k-fold cross-validation and confounder-based cross-validation to assess generalization performance.ResultsIn a colorectal cancer cohort heavily weighted towards early-stage cancer (80% stage I/II), we achieved a mean AUC of 0.92 (95% CI 0.91-0.93) with a mean sensitivity of 85% (95% CI 83-86%) at 85% specificity. Sensitivity generally increased with tumor stage and increasing tumor fraction. Stratification by age, sequencing batch, and institution demonstrated the impact of these confounders and provided a more accurate assessment of generalization performance.ConclusionsA machine learning approach using cfDNA achieved high sensitivity and specificity in a large, predominantly early-stage, colorectal cancer cohort. The possibility of systematic technical and institution-specific biases warrants similar confounder analyses in other studies. Prospective validation of this machine learning method and evaluation of a multi-analyte approach are underway.

International audience; Modern developments in light microscopy have allowed the observation of cell deformation with remarkable spatiotemporal resolution and reproducibility. Analyzing such phenomena is of particular interest for the signal processing and computer vision communities due to the numerous computational challenges involved, from image acquisition all the way to shape analysis and pattern recognition and interpretation. This article aims at providing an up-to-date overview of the problems, solutions, and remaining challenges in deciphering the morphology of living cells via computerized approaches, with a particular focus on shape description frameworks and their exploitation using machine-learning techniques. As a concrete illustration, we use our recently acquired data on amoeboid cell deformation, motivated by its direct implication in immune responses, bacterial invasion, and cancer metastasis.

66 Background: Despite population screening efforts, screening rates for colorectal cancer (CRC) remain suboptimal. A non-invasive, blood-based screening test with high sensitivity and specificity in early-stage disease should improve adherence and ultimately reduce mortality; however, tests based only on tumor-derived biomarkers have limited sensitivity. Here we used a multiomic, machine learning platform to discover, refine, and combine tumor- and immune-derived signals to develop a blood test for the detection of early-stage CRC. Methods: Samples from 591 participants enrolled in a prospective study including average-risk screening and case-control cohorts (NCT03688906) were included in this analysis (CRC: n = 43; colonoscopy-confirmed CRC-negative controls: n = 548). Participants with CRC were 60% male with a mean age of 63, and controls were 55% male with a mean age of 60. Stage distribution was 54% early (I/II) and 34% late (III/IV) with 11% unknown. Plasma was analyzed by whole-genome sequencing, bisulfite sequencing, and protein quantification methods. Computational methods were used to assess and infer the performance of individual and combined assays. Results: For colorectal adenocarcinoma, which represents ~95% of all CRCs, our multiomic test achieved a mean sensitivity of 92% in early stage (n = 17) and 84% in late stage (n = 11) at a specificity of 90%. Across all CRC pathological subtypes, our test achieved a mean sensitivity of 80% in early stage (n = 19) and 83% in late stage (n = 12) at a specificity of 90%; the test detected the single squamous cell carcinoma but missed both neuroendocrine tumors. Individual assays achieved a mean sensitivity of 50% in early stage and 66% in late stage at a specificity of 90%. Conclusions: In a prospective cohort, we demonstrated high sensitivity and specificity for early-stage adenocarcinoma by combining tumor- and immune-derived signals from cfDNA, epigenetic, and protein biomarkers. While most CRCs are adenocarcinomas, detection of all pathological subtypes is required to maximize sensitivity in a screening population. Further analysis of molecular and pathological subtypes, as well as the entire ~3000 patient cohort, is underway. Clinical trial information: NCT03688906.

ObjectivesThe purpose of this study was to assess the value of implantable cardioverter-defibrillator (ICD) electrograms (EGMs) in identifying clinically documented ventricular tachycardias (VTs).BackgroundTwelve-lead electrocardiograms (ECG) of spontaneous VT often are not available in patients referred for catheter ablation of post-infarction VT. Many of these patients have ICDs, and the ability of ICD EGMs to identify a specific configuration of VT has not been described.MethodsIn 21 consecutive patients referred for catheter ablation of post-infarction VT, 124 VTs (mean cycle length: 393 ± 103 ms) were induced, and ICD EGMs were recorded during VT. Clinical VT had been documented with 12-lead ECGs in 15 of 21 patients. The 12-lead ECGs of the clinical VTs were compared with 64 different inducible VTs (mean cycle length: 390 ± 91 ms) to assess how well the ICD EGMs differentiated the clinical VTs from the other induced VTs. The exit site of 62 VTs (mean cycle length: 408 ± 112 ms) was identified by pace mapping (10 to 12 of 12 matching leads). The spatial resolution of pace mapping to identify a VT exit site was determined for both the 12-lead ECGs and the ICD EGMs using a customized MATLAB program (version 7.5, The MathWorks, Inc., Natick, Massachusetts).ResultsAnalysis of stored EGMs by comparison of receiver-operating characteristic curve cutoff values accurately distinguished the clinical VTs from 98% of the other inducible VTs. The mean spatial resolution of a 12-lead ECG pace map for the VT exit site was 2.9 ± 4.0 cm2(range 0 to 17.5 cm2) compared with 8.9 ± 9.0 cm2(range 0 to 35 cm2) for ICD EGM pace maps. The spatial resolution of pace mapping varied greatly between patients and between VTs. The spatial resolution of ICD EGMs was

Early, presymptomatic intervention with oseltamivir (corresponding to the onset of a published host-based genomic signature of influenza infection) resulted in decreased overall influenza symptoms (aggregate symptom scores of 23.5 vs 46.3), more rapid resolution of clinical disease (20 hours earlier), reduced viral shedding (total median tissue culture infectious dose [TCID50] 7.4 vs 9.7), and significantly reduced expression of several inflammatory cytokines (interferon-γ, tumor necrosis factor-α, interleukin-6, and others). The host genomic response to influenza infection is robust and may provide the means for early detection, more timely therapeutic interventions, a meaningful reduction in clinical disease, and an effective molecular means to track response to therapy.

We introduce an approach to sparsity penalized multi-class classifier design that accounts for multi-block structure of the data. The unified multi-class classifier is parameterized by a set of weights defined over the classes and over the blocks. The proposed sparse multi-block multi-class classifier imposes structured sparsity on the weights so that the same variables are selected for all classes and all blocks. The classifier is trained to minimize an objective function that captures the unified miss-classification probabilities of error over the classes in addition to the sparsity of the weights. The optimization of the objective function is implemented by a convex augmented Lagrangian and variable splitting method. This results in a classifier that automatically selects biomarkers for inclusion or exclusion in the classifier and results in significantly improved classifier performance. The approach is illustrated on publicly available longitudinal gene microarray data.

Cervical cancer is the second leading cause of cancer in women worldwide. Human papillomavirus (HPV) is responsible for all cases of cervical cancer. Commercial prophylactic HPV vaccines are now available, but unfortunately these vaccines have no therapeutic effect against established HPV infections. In order to accelerate the control of cervical cancer and treat established HPV infections, it is necessary to develop therapeutic vaccines to eradicate HPV by generating cell-mediated immunity against HPV infected cells. Two HPV-encoded early proteins, the E6 and E7 oncoproteins, are the preferred targets because they are consistently expressed in virtually all cervical cancer cells and are necessary for the induction and maintenance of HPV-associated disease. A variety of vaccine strategies have been employed targeting immune responses to these proteins. Peptide-based vaccines are a promising strategy for the development of therapeutic HPV vaccines because of their safety, stability, and ease of production. This review summarizes the prospects of peptide-based vaccines for the treatment of established HPV infections. We address the challenges that scientists currently face for developing peptide-based vaccines and explore feasible strategies for improving the potency of the induced immune response with the aim of treating established HPV infections.

Background The value of the 12-lead electrocardiogram (ECG) to identify the exit site of postinfarction ventricular tachycardia (VT) has been questioned. The purpose of this study was to assess the accuracy of a computerized algorithm for identifying a VT exit site on the basis of the 12-lead ECG. Methods and Results In 34 postinfarction patients, pace mapping was performed from within scar tissue. A computerized algorithm that used a supervised learning method (support vector machine) received the digitized pace-map morphologies combined with the pacing sites as training data. No other information (ie, infarct localization, bundle branch block morphology, axis, or R-wave pattern) was used in the algorithm. The training data were validated in 58 VTs in 33 patients. The sizes of 10 different anatomic sections within the heart were determined by using the pace maps as the determining factor. Accuracy was found to be 69% for pace maps, and when 2 adjacent regions were combined, accuracy improved to 88%. Validation of the data in 33 patients showed an accuracy of 71% for localizing a VT exit site to 1 of the 10 regions within the left ventricle. If combined with the best adjacent region, accuracy improved to 88%. The median anatomic size of each section was 21 cm 2 . The median spatial resolution of the 12-lead ECG pattern of the pace maps for a particular region was 15 cm 2 . Conclusion The 12-lead ECG of postinfarction VT contains localizing information that enables determination of a region of interest in the 10–20 cm 2 range for more than 70% of VT exit sites in a given sector.

Nanoscale rectennae (rectifying antennae) have been fabricated by combining rectifying organic self-assembled monolayers (SAM) with plasmonic materials because of their surface plasmon resonance (SPR) properties of capturing light. Gold antenna arrays are assembled by coating on CdSe tetrapod templates; the rectifying barrier is formed by self-assembled monolayer (SAM) of gold and electrolyte which contains alkylthiolate. Photocurrent measurements showed that electric currents can be induced at different wavelengths within visible range that strongly depend on the aspect ratio of the tetrapod. Adding gold layer can increase the generated photocurrents due to the rectification. Multiple mechanisms among the semiconductors, metals and electrolytes in response of photocurrent are indicated and analyzed.

This study focuses on the development of a vision- based treadmill system that is actuated by a permanent magnet synchronous motor (PMSM). The PMSM employed in our design is free of commutation spikes and maintenance, so that it can replace the DC motor that is often adopted in the conventional treadmill system. Moreover, to achieve a smooth low speed operation, the sinusoidal Back EMF and the SPWM are exploited to achieve sinusoidal commutation for the PMSM. In addition, two kinds of running modes - active and passive, are developed in this paper. In the active running mode, the running speed of the treadmill is set up by the user through a human-machine interface, while in the passive running mode the user can determine the magnitude of the retard force and running speed by himself/herself. To build a user friendly environment, a virtual touch panel is projected onto a screen/wall through a projector, and a vision-based motion detection algorithm is used to determine which button of the virtual touch panel is pressed by the user. In this way, without actually touching the panel, the user can operate the treadmill while running/jogging, so that greatly enhancing the convenience.

Metal clearing in chemical mechanical polishing was conducted using a fixed polishing recipe, with polish pressures tuned based upon blanket metal film’s bulk removal uniformity. A fixed recipe cannot count for incoming film’s non-uniformity, pattern lay out impact, and consumable life induced process drift. Consequently, a fixed recipe used in metal clearing step can result in poor within wafer Rs range on patterned wafers. This paper described Within Wafer (WIW) clearing uniformity and topography uniformity was improved via a novel metal clearing process control, and was adaptive to consumable and hardware’s variations.

Background Consider the problem of designing a panel of complex biomarkers to predict a patient’s health or disease state when one can pair his or her current test sample, called a target sample, with the patient’s previously acquired healthy sample, called a reference sample. As contrasted to a population averaged reference this reference sample is individualized. Automated predictor algorithms that compare and contrast the paired samples to each other could result in a new generation of test panels that compare to a person’s healthy reference to enhance predictive accuracy. This paper develops such an individualized predictor and illustrates the added value of including the healthy reference for design of predictive gene expression panels. Results The objective is to predict each subject’s state of infection, e.g., neither exposed nor infected, exposed but not infected, pre-acute phase of infection, acute phase of infection, post-acute phase of infection. Using gene microarray data collected in a large scale serially sampled respiratory virus challenge study we quantify the diagnostic advantage of pairing a person’s baseline reference with his or her target sample. The full study consists of 2886 microarray chips assaying 12,023 genes of 151 human volunteer subjects under 4 different inoculation regimes (HRV, RSV, H1N1, H3N2). We train (with cross-validation) reference-aided sparse multi-class classifier algorithms on this data to show that inclusion of a subject’s reference sample can improve prediction accuracy by as much as 14 %, for the H3N2 cohort, and by at least 6 %, for the H1N1 cohort. Remarkably, these gains in accuracy are achieved by using smaller panels of genes, e.g., 39 % fewer for H3N2 and 31 % fewer for H1N1. The biomarkers selected by the predictors fall into two categories: 1) contrasting genes that tend to differentially express between target and reference samples over the population; 2) reinforcement genes that remain constant over the two samples, which function as housekeeping normalization genes. Many of these genes are common to all 4 viruses and their roles in the predictor elucidate the function that they play in differentiating the different states of host immune response. Conclusions If one uses a suitable mathematical prediction algorithm, inclusion of a healthy reference in biomarker diagnostic testing can potentially improve accuracy of disease prediction with fewer biomarkers. Electronic supplementary material The online version of this article (doi:10.1186/s12859-016-0889-9) contains supplementary material, which is available to authorized users.

Peptide-based vaccines have been proposed as a therapeutic strategy for many infectious diseases, including human papilloma virus (HPV)-related cervical cancer. Peptide-based vaccines are a better treatment option than traditional chemotherapeutic agents and surgery, as they rely on the use of the body’s immune system to fight cancer cells, resulting in minimal risk of side effects. However, to increase the efficacy of peptide-based vaccines, the application of potent adjuvant and a suitable delivery system is essential. In this study, we developed a self-adjuvanting delivery system based on a combination of polymer and liposomes, for a therapeutic vaccine against cervical cancer. Peptide epitope (8Qm) derived from HPV-16 E7 protein was conjugated to dendritic poly(tert-butyl acrylate) as a primary delivery system and incorporated into cationic liposomes, which served as a secondary delivery system. Our vaccine candidate was able to kill established HPV-16 E7-positive tumor (TC-1) cells in mice following a single immunization. The immunized mice had 80% survival rate after two months. In contrast, both polymer-8Qm conjugate and liposomes bearing 8Qm failed to eradicate TC-1 tumors. The survival rate of mice was only 20% when immunized with 8Qm formulated with standard incomplete Freund’s adjuvant.