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Developing an intellectualist account of skill, Stanley and Williamson define skill as a kind of disposition to action-guiding knowledge. The present paper challenges their definition of skill. While we don’t dispute that skill may consist of a cognitive, a dispositional, and an action-guiding component, we argue that Stanley and Williamson’s account of each component is problematic. In the first section, we argue, against Stanley and Williamson, that the cognitive component of skill is not a case of propositional knowledge-wh, which is typically indexical. In the second section, we seek to show that Stanley and Williamson face difficulties in arguing for a generic claim about skill as a kind of disposition, and they fail to defend intellectualism about skill based on the dispositional account. In the third section we argue that Stanley and Williamson need a more detailed account of the action-guiding aspect of skill to avoid several difficulties, including a threat of a regress. We close with some lessons for the debate over intellectualism and anti-intellectualism about skill.

Ryle, the most prominent proponent of anti-intellectualism, and Stanley and Williamson, the most influential intellectualists, both invoke dispositions to explain the ascription of knowledge-how. It is now well known that conditional analyses of disposition suffer from two types of counterexamples: finkish and masked dispositions. If it is the case that dispositions play a role in the analysis of ascription of knowledge-how, and dispositions can be masked and finkish, then an important question arises: Can knowing-how be masked or finkish too? In response to this question, Hawley claims that (1) unlike dispositions, knowledge-how does not seem liable to finkishness, and (2) knowledge-how can be accounted for in terms of counterfactual success. In this paper both claims will be challenged. I seek to show that Hawley’s account faces two problems: an unjustified asymmetry and a puzzle. I also argue that knowledge-how can, intuitively, be finkish and masked, and therefore the counterfactual analysis of knowing-how suggested by Ryle, Stanley and Hawley fails. What is more, I show that the debate on finkish and masked dispositions can shed a new light on the practical component which is necessary for knowing-how.

Emotion is an intense mental experience often manifested by rapid heartbeat, breathing, sweating, and facial expressions. Emotion recognition from these physiological signals is a challenging problem with interesting applications such as developing wearable assistive devices and smart human-computer interfaces. This paper presents an automated method for emotion classification in children using electrodermal activity (EDA) signals. The time-frequency analysis of the acquired raw EDAs provides a feature space based on which different emotions can be recognized. To this end, the complex Morlet (C-Morlet) wavelet function is applied on the recorded EDA signals. The dataset used in this paper includes a set of multimodal recordings of social and communicative behavior as well as EDA recordings of 100 children younger than 30 months old. The dataset is annotated by two experts to extract the time sequence corresponding to three main emotions including “Joy”, “Boredom”, and “Acceptance”. The annotation process is performed considering the synchronicity between the children's facial expressions and the EDA time sequences. Various experiments are conducted on the annotated EDA signals to classify emotions using a support vector machine (SVM) classifier. The quantitative results show that the emotion classification performance remarkably improves compared to other methods when the proposed wavelet-based features are used.

Background: Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignancy associated with a poor prognosis. Only 10% of the patients survive longer than five years. So far, factors underlining long-term survivorship in PDAC are not well understood. Therefore, we aimed to identify the key players in the tumor immune microenvironment (TIME) associated with long-term survivorship in PDAC patients. Methods: The immune-related gene expression profiles of surgically resected PDAC tumors of patients who survived and remained recurrence-free of disease for > 3 years (long-term survivors, n=10) were compared to PDAC tumors of patients who had survived ≤ 6 months (short-term survivors, n=10) due to tumor recurrence. Samples were profiled using the nCounter® PanCancer Immune Profiling Panel of NanoString Technology. Validation was performed by spatial analysis of immune cells using the GeoMx™ Digital Spatial Profiler. Results: Tumor-infiltrating B cells were found to be significantly increased in the TIME of long-term survivors by gene expression profiling (p=0.018). The high tumor infiltration of B cells was confirmed by spatial protein profiling (p=0.049). This increase was accompanied by more T cells and antigen-presenting cell infiltration. Moreover, the activated immune cells were found to infiltrate in between tumor cells as well as in stromal areas in long-term survivors. In contrast, the TIME of short-term survivors was characterized by a high density of immunosuppressive cells like CD25 and regulatory T cells infiltrating in a highly fibrotic vicinity. Conclusion: This is the first comprehensive study that connects the immune landscape of gene expression profiles and protein spatial infiltration with the survivorship of PDAC patients. We found higher infiltration of B cells in TIME of long-term survivors which highlights the importance of targeting B cells and B cell-based therapy for future personalized immunotherapy in PDAC patients. Citation Format: Hosein M. Aziz, Lawlaw Saida, Willem de Koning, Andrew Stubbs, Yunlei Li, Casper H. J. van Eijck, Dana A. M. Mustafa. The tumor immune microenvironment is decisive in the survival of pancreatic ductal adenocarcinoma [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-118.

Background Recognition of human behavioral activities using local field potential (LFP) signals recorded from the Subthalamic Nuclei (STN) has applications in developing the next generation of deep brain stimulation (DBS) systems. DBS therapy is often used for patients with Parkinson’s disease (PD) when medication cannot effectively tackle patients’ motor symptoms. A DBS system capable of adaptively adjusting its parameters based on patients’ activities may optimize therapy while reducing the stimulation side effects and improving the battery life. Method STN-LFP reveals motor and language behavior, making it a reliable source for behavior classification. This paper presents LFP-Net, an automated machine learning framework based on deep convolutional neural networks (CNN) for classification of human behavior using the time-frequency representation of STN-LFPs within the beta frequency range. CNNs learn different features based on the beta power patterns associated with different behaviors. The features extracted by the CNNs are passed through fully connected layers and then to the softmax layer for classification. Results Our experiments on ten PD patients performing three behavioral tasks including “button press”, “target reaching”, and “speech” show that the proposed approach obtains an average classification accuracy of ∼88 %. Comparison with existing methods: The proposed method outperforms other state-of-the-art classification methods based on STN-LFP signals. Compared to well-known deep neural networks such as AlexNet, our approach gives a higher accuracy using significantly fewer parameters. Conclusions CNNs show a high performance in decoding the brain neural response, which is crucial in designing the automatic brain-computer interfaces and closed-loop systems.

This paper presents the results of our recent work on studying the effects of deep brain stimulation (DBS) and medication on the dynamics of brain local field potential (LFP) signals used for behavior analysis of patients with Parkinson s disease (PD). DBS is a technique used to alleviate the severe symptoms of PD when pharmacotherapy is not very effective. Behavior recognition from the LFP signals recorded from the subthalamic nucleus (STN) has application in developing closed-loop DBS systems, where the stimulation pulse is adaptively generated according to subjects performing behavior. Most of the existing studies on behavior recognition that use STN-LFPs are based on the DBS being off. This paper discovers how the performance and accuracy of automated behavior recognition from the LFP signals are affected under different paradigms of stimulation on/off. We first study the notion of beta power suppression in LFP signals under different scenarios (stimulation on/off and medication on/off). Afterward, we explore the accuracy of support vector machines in predicting human actions (button press and reach) using the spectrogram of STN-LFP signals. Our experiments on the recorded LFP signals of three subjects confirm that the beta power is suppressed significantly when the patients take medication (p-value, 40th IEEE International Conference on Engineering in Medicine and Biology (IEEE EMBC), Honolulu, Hawaii, July 17-21, 2018

Background Classification of human behavior from brain signals has potential application in developing closed-loop deep brain stimulation (DBS) systems. This paper presents a human behavior classification using local field potential (LFP) signals recorded from subthalamic nuclei (STN). Method A hierarchical classification structure is developed to perform the behavior classification from LFP signals through a multi-level framework (coarse to fine). At each level, the time-frequency representations of all six signals from the DBS leads are combined through an MKL-based SVM classifier to classify five tasks (speech, finger movement, mouth movement, arm movement, and random segments). To lower the computational cost, we alternatively use the inter-hemispheric synchronization of the LFPs to make three pairs out of six bipolar signals. Three classifiers are separately trained at each level of the hierarchical approach, which lead to three labels. A fusion function is then developed to combine these three labels and determine the label of the corresponding trial. Results Using all six LFPs with the proposed hierarchical approach improves the classification performance. Moreover, the synchronization-based method reduces the computational burden considerably while the classification performance remains relatively unchanged. Comparison with existing methods Our experiments on two different datasets recorded from nine subjects undergoing DBS surgery show that the proposed approaches remarkably outperform other methods for behavior classification based on LFP signals. Conclusion The LFP signals acquired from STNs contain useful information for recognizing human behavior. This can be a precursor for designing the next generation of closed-loop DBS systems.

Classification of human behavior is key to developing closed-loop Deep Brain Stimulation (DBS) systems, which may be able to decrease the power consumption and side effects of the existing systems. Recent studies have shown that the Local Field Potential (LFP) signals from both Subthalamic Nuclei (STN) of the brain can be used to recognize human behavior. Since the DBS leads implanted in each STN can collect three bipolar signals, the selection of a suitable pair of LFPs that achieves optimal recognition performance is still an open problem to address. Considering the presence of synchronized aggregate activity in the basal ganglia, this paper presents an FFT-based synchronization approach to automatically select a relevant pair of LFPs and use the pair together with an SVM-based MKL classifier for behavior recognition purposes. Our experiments on five subjects show the superiority of the proposed approach compared to other methods used for behavior classification., IEEE Conf on ICASSP 2017

In this article two modified versions of a simple and recently proposed method which is known as linear minimum mean square error (LMMSE) estimator for denoising of magnetic resonance (MR) images are presented. In the introduced approaches the self-similarity and natural redundancy of the acquired MR image are considered to achieve the higher performance of unknown signal estimation. Since, the MR data are in a large majority 3D, the proposed methods are developed to deal with 3D volumes. The introduced methods are compared with related state-of-the-art schemes over both clinical and simulated MR data. The quantitative and qualitative results show their superior denoising ability.

Post-acquisition denoising of magnetic resonance (MR) images is an important step to improve any quantitative measurement of the acquired data. In this paper, assuming a Rician noise model, a new filtering method based on the linear minimum mean square error (LMMSE) estimation is introduced, which employs the self-similarity property of the MR data to restore the noise-less signal. This method takes into account the structural characteristics of images and the Bayesian mean square error (Bmse) of the estimator to address the denoising problem. In general, a twofold data processing approach is developed; first, the noisy MR data is processed using a patch-based L(2)-norm similarity measure to provide the primary set of samples required for the estimation process. Afterwards, the Bmse of the estimator is derived as the optimization function to analyze the pre-selected samples and minimize the error between the estimated and the underlying signal. Compared to the LMMSE method and also its recently proposed SNR-adapted realization (SNLMMSE), the optimized way of choosing the samples together with the automatic adjustment of the filtering parameters lead to a more robust estimation performance with our approach. Experimental results show the competitive performance of the proposed method in comparison with related state-of-the-art methods.

The balance between production and activation of MMPs and their inhibition by TIMPs is a crucial aspect of cancer invasion and metastasis. On the basis of the concept that MMPs synthesized in tissues seep into the bloodstream, we have examined MMP levels in the plasma of patients with cancer. In colorectal, breast, prostate, and bladder cancer, most patients with aggressive disease have increased plasma levels of gelatinase B. In patients with advanced colorectal cancer, high levels of either gelatinase B or TIMP complex were associated with shortened survival. We propose that these assays may be clinically useful in characterizing metastatic potential in selected kinds of cancer. In rheumatoid arthritis and systemic lupus erythematosus (SLE), serum and plasma levels of stromelysin-1 were approximately 3-5-fold increased. Fluctuating serum stromelysin-1 levels in SLE did not correspond with change in disease activity. In SLE, stromelysin-1 may be a component of the chronic tissue repair process rather than being responsible for inciting tissue damage. On the basis of these observations, we conclude that measurement of plasma/serum MMP and TIMP levels may provide important data for selecting and following patients considered for treatment with drugs that interfere with MMP activity.

This paper presents an LMMSE-based method for the three-dimensional (3D) denoising of MR images assuming a Rician noise model. Conventionally, the LMMSE method estimates the noise-less signal values using the observed MR data samples within local neighborhoods. This is not an efficient procedure to deal with this issue while the 3D MR data intrinsically includes many similar samples that can be used to improve the estimation results. To overcome this problem, we model MR data as random fields and establish a principled way which is capable of choosing the samples not only from a local neighborhood but also from a large portion of the given data. To follow the similar samples within the MR data, an effective similarity measure based on the local statistical moments of images is presented. The parameters of the proposed filter are automatically chosen from the estimated local signal-to-noise ratio. To further enhance the denoising performance, a recursive version of the introduced approach is also addressed. The proposed filter is compared with related state-of-the-art filters using both synthetic and real MR datasets. The experimental results demonstrate the superior performance of our proposal in removing the noise and preserving the anatomical structures of MR images.

Long-term chlorpromazine therapy has been associated with the asymptomatic development of a high incidence of antinuclear antibodies, coagulation inhibitors, and increased serum levels of IgM. The purpose of this study has been to characterize the natural history of this chlorpromazine-induced (CPZ) immunopathy. To this end we carried out a prospective study of schizophrenic patients with the immunopathy to compare the effect of continuing CPZ versus switching to haloperidol therapy. Although no marked differences were noted between the 2 groups at the end of 5 years, 6 of 29 patients who continued to receive CPZ, as compared to none of 14 patients on haloperidol, had progressive elevations of serum IgM. In spite of a high incidence of antinuclear antibodies, none of the patients developed a lupus-like syndrome. One patient, however, who had been maintained on CPZ for more than 15 years, developed Waldenstrom macroglobulinemia, as characterized by an IgM monoclonal gammopathy and a lymphocyte immunoglobulin heavy and kappa light chain gene rearrangement. Another CPZ-treated patient developed immune thrombocytopenia. Based on the potential serious sequelae of prolonged stimulation of the immune system by CPZ, we recommend that patients who develop an increase in serum IgM while on CPZ be switched to other types of anti-psychotic medications.

Kaposi's sarcoma (KS), or multiple idiopathic hemorrhagic sarcoma, is an uncommon vascular tumor of endothelial origin that most commonly arises in the skin. Pharyngeal involvement occurs in only 10% of cases of classic KS. Almost all cases have skin, lymph node, and/ or gastrointestinal involvement.1 In fact, any organ except the brain can be affected.? Abramson and Simonsreported that 84% of their cases of KS of the head and