Miracle-dz/UniFAR-FTUs huggingface dataset:
A clinically feasible multiplex proteomic immunoassay as a novel functional diagnostic for pancreatic ductal adenocarcinoma

To date, targeted therapy for pancreatic ductal adenocarcinoma (PDAC) remains largely unsuccessful in the clinic. Current genomics-based technologies are unable to reflect the quantitative, dynamic signaling changes in the tumor, and require larger tumor samples that are difficult to obtain in PDAC patients. Therefore, a highly sensitive functional tool that can reliably and comprehensively inform intra-tumoral signaling events is direly needed to guide treatment decision. We tested the utility of a highly sensitive proteomics-based functional diagnostic platform, Collaborative Enzyme Enhanced Reactive-immunoassay (CEERTM), on fine-needle aspiration (FNA) samples obtained from 102 patients with radiographically-evident pancreatic tumors. Two FNA passes were collected from each patient, hybridized to customized chips coated with an array of capture antibodies, and detected using two enzyme-conjugated antibodies which emit quantifiable signals. We demonstrate that this technique is highly sensitive in detecting total and phosphorylated forms of multiple signaling molecules in FNA specimens, with reasonable correlation of marker intensities between two different FNA passes. Notably, signals of several markers were significantly higher in PDAC compared to non-cancerous samples. In PDAC samples, we found high total c-Met signal to be associated with poor survival, and confirmed this finding using an independent PDAC tissue microarray.
Modeling Activation Processes in Human Memory to Improve Tag Recommendations

Social tagging systems enable users to collaboratively assign freely chosen keywords (i.e., tags) to resources (e.g., Web links). In order to support users in finding descriptive tags, tag recommendation algorithms have been proposed. One issue of current state-of-the-art tag recommendation algorithms is that they are often designed in a purely data-driven way and thus, lack a thorough understanding of the cognitive processes that play a role when people assign tags to resources. A prominent example is the activation equation of the cognitive architecture ACT-R, which formalizes activation processes in human memory to determine if a specific memory unit (e.g., a word or tag) will be needed in a specific context. It is the aim of this thesis to investigate if a cognitive-inspired approach, which models activation processes in human memory, can improve tag recommendations. For this, the relation between activation processes in human memory and usage practices of tags is studied, which reveals that (i) past usage frequency, (ii) recency, and (iii) semantic context cues are important factors when people reuse tags. Based on this, a cognitive-inspired tag recommendation approach termed BLLAC+MPr is developed based on the activation equation of ACT-R. An extensive evaluation using six real-world folksonomy datasets shows that BLLAC+MPr outperforms current state-of-the-art tag recommendation algorithms with respect to various evaluation metrics. Finally, BLLAC+MPr is utilized for hashtag recommendations in Twitter to demonstrate its generalizability in related areas of tag-based recommender systems. The findings of this thesis demonstrate that activation processes in human memory can be utilized to improve not only social tag recommendations but also hashtag recommendations. This opens up a number of possible research strands for future work, such as the design of cognitive-inspired resource recommender systems.
Clinical implications of genomic profiles in metastatic breast cancer with a focus on TP53 and PIK3CA, the most frequently mutated genes

Breast cancer (BC) has been genetically profiled through large-scale genome analyses. However, the role and clinical implications of genetic alterations in metastatic BC (MBC) have not been evaluated. Therefore, we conducted whole-exome sequencing (WES) and RNA-Seq of 37 MBC samples and targeted deep sequencing of another 29 MBCs. We evaluated somatic mutations from WES and targeted sequencing and assessed gene expression and performed pathway analysis from RNA-Seq. In this analysis, PIK3CA was the most commonly mutated gene in estrogen receptor (ER)-positive BC, while in ER-negative BC, TP53 was the most commonly mutated gene (p = 0.018 and p < 0.001, respectively). TP53 stopgain/loss and frameshift mutation was related to low expression of TP53 in contrast nonsynonymous mutation was related to high expression. The impact of TP53 mutation on clinical outcome varied with regard to ER status. In ER-positive BCs, wild type TP53 had a better prognosis than mutated TP53 (median overall survival (OS) (wild type vs. mutated): 88.5 ± 54.4 vs. 32.6 ± 10.7 (months), p = 0.002). In contrast, mutated TP53 had a protective effect in ER-negative BCs (median OS: 0.10 vs. 32.6 ± 8.2, p = 0.026). However, PIK3CA mutation did not affect patient survival. In gene expression analysis, CALM1, a potential regulator of AKT, was highly expressed in PIK3CA-mutated BCs. In conclusion, mutation of TP53 was associated with expression status and affect clinical outcome according to ER status in MBC. Although mutation of PIK3CA was not related to survival in this study, mutation of PIK3CA altered the expression of other genes and pathways including CALM1 and may be a potential predictive marker of PI3K inhibitor effectiveness.
Involvement of protein phosphatases in the destabilization of methamphetamine-associated contextual memory

Destabilization refers to a memory that becomes unstable when reactivated and is susceptible to disruption by amnestic agents. Here we delineated the cellular mechanism underlying the destabilization of drug memory. Mice were conditioned with methamphetamine (MeAM) for 3 d, and drug memory was assessed with a conditioned place preference (CPP) protocol. Anisomycin (ANI) was administered 60 min after the CPP retrieval to disrupt reconsolidation. We found that destabilization of MeAM CPP after the application of ANI was blocked by the N-methyl-d-aspartate receptor (NMDAR) antagonist MK-801 and the NR2B antagonist ifenprodil (IFN) but not by the NR2A antagonist NVP-AAM077 (NVP). In addition, decrease in the phosphorylation of GluR1 at Serine845 (p-GluR1-Ser845), decrease in spine density, and a reduction in the AMPAR/NMDAR ratio in the basolateral amygdala (BLA) were reversed after the MK-801 treatment. The effect of ANI on destabilization was prevented by the protein phosphatase 2B (calcineurin, CaN) inhibitors cyclosporine A (CsA) and FK-506 and the protein phosphatase 1 (PP1) inhibitors calyculin A (CA) and okadaic acid (OA). These results suggest that memory destabilization involves the activation of NR2B-containing NMDARs, which in turn allows the influx of Ca2+. Increased intracellular Ca2+ stimulates CaN, leading to the dephosphorylation and inactivation of inhibitor 1 and the activation of PP1. PP1 then dephosphorylates p-GluR1-Ser845 to elicit AMPA receptor (AMPAR) endocytosis and destabilization of the drug memory.
Deep Learning for Massive MIMO CSI Feedback

In frequency division duplex mode, the downlink channel state information (CSI) should be sent to the base station through feedback links so that the potential gains of a massive multiple-input multiple-output can be exhibited. However, such a transmission is hindered by excessive feedback overhead. In this letter, we use deep learning technology to develop CsiNet, a novel CSI sensing and recovery mechanism that learns to effectively use channel structure from training samples. CsiNet learns a transformation from CSI to a near-optimal number of representations (or codewords) and an inverse transformation from codewords to CSI. We perform experiments to demonstrate that CsiNet can recover CSI with significantly improved reconstruction quality compared with existing compressive sensing (CS)-based methods. Even at excessively low compression regions where CS-based methods cannot work, CsiNet retains effective beamforming gain.
Impact of Entrepreneurship Training on Entrepreneurial Efficacy and Alertness among Adolescent Youth

Our study focuses on the impact of systematic entrepreneurship training comprising both active and passive learning activities on entrepreneurial alertness and efficacy among adolescent youth. Reports from a two-wave online survey among 328 students from five secondary schools (aged 13-16 years; 34.8% male and 65.2% female) reveal that those who went through entrepreneurship training (treatment group, N=142) had significantly higher entrepreneurial alertness and efficacy levels compared to those who did not go through training (control group, N=186). We also find that even with gender effects accounted for, the higher entrepreneurial alertness and efficacy levels in the treatment group are due in part to both passive and active/hands-on elements of the program. Our study offers direct evidence that conducting entrepreneurship training programs among secondary school students could be an effective means to enhance entrepreneurial competencies among the youth. Specifically, our findings highlight the value of entrepreneurial training in improving age-appropriate competencies of entrepreneurial alertness and efficacy.
Physician-Assisted Suicide and Midwest Social Workers: Where Do They Stand?

ABSTRACT Physician-assisted suicide (PAS) is explicitly legal in five states and by court decision in one. Legislative bills have been introduced in other states including Minnesota, Iowa, and Wisconsin. This quantitative study was designed to understand Midwest, hospice and palliative care at end-of-life social workers’ attitudes toward PAS, preferred terminology, perception of preparedness for the implementation, and awareness of PAS legislation in their state. Sixty-two social workers from Minnesota, Iowa, and Wisconsin completed an anonymous online survey. The results indicated that over one-half of the participants supported PAS legislation and is consistent with previous research on social workers across the country. While there was a range of perceived preparedness for implementation, a majority felt moderately to very prepared. Professional and personal values as well as professional experience influenced their perceived preparedness. Few social workers had accurate awareness of PAS legislation in their state or had attended workshops/events for further education or as a policy advocate. To practice competently and advocate at all levels of practice, hospice and palliative care at end-of-life social workers’ need to understand their own attitudes and values toward PAS and pursue additional education around this ethical issue.
Persuasiveness web 2.0 in behavioral intention: A conceptual model

The use of the web 2.0 technology as a work tool or communication tool in individual activities grow significant and has begun to be also used in the organization. The Web 2.0 technology is not just an information technology system but also a social technology, which is still a debate of factors that can influence it. Aspects of social interaction owned by this technology impact on the behavior and attitude of the users. This research proposes a conceptual model intervention of persuasive technology that explains what the factor affect the users? Behavioral intention to use technology in an organization. This study reviews of the web 2.0 technology by using Theory of Planned Behavior and Behavior Change Support Systems to analysis its persuasiveness. In this research will be tested persuasive system design model that has a significant impact on perceptions of persuasion and system usage. More specifically, the proposed model extends Theory of Planned Behavior with seven constructs namely, Perceived Ease of Use, Attitude Toward The Behavior, Perceived Usefulness, Social Influence, Subjective Norm, Self-Efficacy, Perceived Behavioral Control. This proposed model explain factors that affect the usage of the web 2.0 persuasive technology. It offers opportunities for future research in understanding the acceptance of persuasive technology.
A Combined Rule-Based & Machine Learning Audio-Visual Emotion Recognition Approach

This paper proposes an audio-visual emotion recognition system that uses a mixture of rule-based and machine learning techniques to improve the recognition efficacy in the audio and video paths. The visual path is designed using the Bi-directional Principal Component Analysis (BDPCA) and Least-Square Linear Discriminant Analysis (LSLDA) for dimensionality reduction and discrimination. The extracted visual features are passed into a newly designed Optimized Kernel-Laplacian Radial Basis Function (OKL-RBF) neural classifier. The audio path is designed using a combination of input prosodic features (pitch, log-energy, zero crossing rates and Teager energy operator) and spectral features (Mel-scale frequency cepstral coefficients). The extracted audio features are passed into an audio feature level fusion module that uses a set of rules to determine the most likely emotion contained in the audio signal. An audio visual fusion module fuses outputs from both paths. The performances of the proposed audio path, visual path, and the final system are evaluated on standard databases. Experiment results and comparisons reveal the good performance of the proposed system.
Conducting Credit Assignment by Aligning Local Representations

Using back-propagation and its variants to train deep networks is often problematic for new users. Issues such as exploding gradients, vanishing gradients, and high sensitivity to weight initialization strategies often make networks difficult to train, especially when users are experimenting with new architectures. Here, we present Local Representation Alignment (LRA), a training procedure that is much less sensitive to bad initializations, does not require modifications to the network architecture, and can be adapted to networks with highly nonlinear and discrete-valued activation functions. Furthermore, we show that one variation of LRA can start with a null initialization of network weights and still successfully train networks with a wide variety of nonlinearities, including tanh, ReLU-6, softplus, signum and others that may draw their inspiration from biology. A comprehensive set of experiments on MNIST and the much harder Fashion MNIST data sets show that LRA can be used to train networks robustly and effectively, succeeding even when back-propagation fails and outperforming other alternative learning algorithms, such as target propagation and feedback alignment.
Analyzing Knowledge Gain of Users in Informational Search Sessions on the Web

Web search is frequently used by people to acquire new knowledge and to satisfy learning-related objectives, but little is known about how a user»s knowledge evolves through the course of a search session. We present a study addressing the knowledge gain of users in informational search sessions. Using crowdsourcing, we recruited 500 distinct users and orchestrated real-world search sessions spanning 10 different topics and information needs. By using scientifically formulated knowledge tests we calibrated the knowledge of users before and after their search sessions, quantifying their knowledge gain. We investigated the impact of information needs on the search behavior and knowledge gain of users, revealing a significant effect of information need on user queries and navigational patterns, but no direct effect on the knowledge gain. Users on average exhibited a higher knowledge gain through search sessions pertaining to topics they were less familiar with. Our findings in this paper contribute important ground work towards advancing current research in understanding user knowledge gain through web search sessions.
CURE MONITORING OF COMPOSITES USING FIBER BRAGG GRATING SENSORS

Residual strain and residual stress formed in cure process are two key factors that affect the quality of composite products, and both of them correlate closely with the process induced strain of pre-preg. In this paper,the fiber Bragg grating (FBG) sensors were calibrated and embedded in pre-preg to in-situ monitor the strain and temperature evolution of laminates during the cure process. Based on the strain measured with the FBG sensors, the macro residual strain of the cross-ply laminate was studied. The results show that both of unidirectional and cross-ply laminates will shrink in the course of cure. There is a negative residual strain in the longitudinal direction and a positive residual strain in the transverse direction of any ply of a cross-ply laminate. The FBG sensor can be used as a new tool to monitor the cure process and analyze the residual stress of composites, and makes it possible to monitor composite structures from manufacture stage to service stage continuously.
A novel convolutional neural networks for emotion recognition based on EEG signal

Emotion recognition based on electroencephalogram (EEG) signal is attracting more and more attention. Many feature engineering based models have been investigated. However, these models require a lot of effort for manually designing feature set. And these features can be hardly transformed among different problems. To reduce the manual effort on features used in EEG-based recognition and improve the performance, we propose an end-to-end model which is based on Convolutional Neural Networks (CNNs). In order to represent the EEG signals better, the original channels of EEG are firstly rearranged by Pearson Correlation Coefficient and the rearranged EEGs are fed into CNN. experiments were carried on DEAP dataset. The experimental results on the DEAP dataset show that the proposed method achieves 77.98% accuracy on the Valence recognition and 72.98% on the Arousal recognition.
Buckling of laminated composite cylindrical skew panels

Experimental studies were made on isotropic cylindrical skew panels made of Aluminum 7075-T6 and laminated composite cylindrical skew panels under uniaxial compression. The experimental values of the critical buckling load (Pcr) were determined using five different methods. The values of Pcr were also determined using MSC/Nastran and CQUAD8 finite element. The experimental values of the Pcr obtained by different methods were compared with the finite element solution. The effects of the skew angle and aspect ratio on the critical buckling load of isotropic cylindrical skew panels made of Aluminum 7075-T6 were studied. The effects of the skew angle, aspect ratio, and the laminate stacking sequence on the critical buckling load of laminated composite cylindrical skew panels were also studied. It is found that the method IV (based on a plot of applied load (P) vs. average axial strain) yields the highest value for Pcr and method III (based on a plot of P vs. square of out-of-plane-deflection) the lowest value for Pcr. The experimental values given by method IV are seen to be closest to the finite element solution, the discrepancy being in the range of 5–23% for laminated composite cylindrical skew panels. For isotropic panels, it is found that the value Pcr initially increases with an increase in the skew angle and later decreases as the skew angle increases beyond 15°. For laminated composite panels, the Pcr value decreases as the aspect ratio increases for all laminate stacking sequences.
Efficacy of Mechanical Weeding Tools: A Study Into Alternative Weed Management Strategies Enabled by Robotics

The rise of herbicide resistant weed species has re-invigorated research in nonchemical methods for weed management. Robots, such as AgBot II, that can detect and classify weeds as they traverse a field are a key enabling factor for individualised treatment of weed species. Integral to the invidualized treatment of weed species are the the nonherbicide methods through which the weeds are managed. This letter explores mechanical methods as an alternative to weed management. Three implements are considered: below-surface tilling (arrow hoe), above-surface tilling (tines), and a cutting mechanism. These mechanisms were evaluated in a controlled field with varying rates of application to herbicide-resistant weeds of interest for Queensland, Australia. Statistical analysis demonstrated the efficacy of these implements and highlighted the importance of early intervention. It was found that a tine, deployed automatically on AgBot II, was effective for all of the weeds considered in this study, leading to an overall survival probability of <inline-formula><tex-math notation="LaTeX">$\text{0.28} \pm \text{0.15}$</tex-math> </inline-formula>. Further analysis demonstrated the significance of treatment time with late intervention commencing at week 6 resulting in a survival probability of <inline-formula><tex-math notation="LaTeX">$\text{0.54}\pm \text{0.08}$</tex-math></inline-formula> vs <inline-formula><tex-math notation="LaTeX">$\text{0.24}\pm \text{0.18}$ </tex-math></inline-formula> for earlier intervention at week 4.
Integrating neurobiological markers of depression.

CONTEXT Although psychiatric disorders are, to date, diagnosed on the basis of behavioral symptoms and course of illness, the interest in neurobiological markers of psychiatric disorders has grown substantially in recent years. However, current classification approaches are mainly based on data from a single biomarker, making it difficult to predict disorders characterized by complex patterns of symptoms. OBJECTIVE To integrate neuroimaging data associated with multiple symptom-related neural processes and demonstrate their utility in the context of depression by deriving a predictive model of brain activation. DESIGN Two groups of participants underwent functional magnetic resonance imaging during 3 tasks probing neural processes relevant to depression. SETTING Participants were recruited from the local population by use of advertisements; participants with depression were inpatients from the Department of Psychiatry, Psychosomatics, and Psychotherapy at the University of Wuerzburg, Wuerzburg, Germany. PARTICIPANTS We matched a sample of 30 medicated, unselected patients with depression by age, sex, smoking status, and handedness with 30 healthy volunteers. MAIN OUTCOME MEASURE Accuracy of single-subject classification based on whole-brain patterns of neural responses from all 3 tasks. RESULTS Integrating data associated with emotional and affective processing substantially increases classification accuracy compared with single classifiers. The predictive model identifies a combination of neural responses to neutral faces, large rewards, and safety cues as nonredundant predictors of depression. Regions of the brain associated with overall classification comprise a complex pattern of areas involved in emotional processing and the analysis of stimulus features. CONCLUSIONS Our method of integrating neuroimaging data associated with multiple, symptom-related neural processes can provide a highly accurate algorithm for classification. The integrated biomarker model shows that data associated with both emotional and reward processing are essential for a highly accurate classification of depression. In the future, large-scale studies will need to be conducted to determine the practical applicability of our algorithm as a biomarker-based diagnostic aid.