Miracle-dz/UniFAR-FTUs huggingface dataset:
Bayesian Regression for Artifact correction in Electroencephalography

Many brain-computer interfaces (BCIs) measure brain activity using electroencephalography (EEG). Unfortunately, EEG is highly sensitive to artifacts originating from non-neural sources, requiring procedures to remove the artifactual contamination from the signal. This work presents a probabilistic interpretation for artifact correction that unifies session transfer of linear models and calibration to upcoming sessions. A linear artifact correction model is derived within a Bayesian multi-task learning (MTL) framework, which captures influences of artifact sources on EEG channels across different sessions to correct for artifacts in new sessions or calibrate with session-specific data. The new model was evaluated with a cross-correlation analysis on a real world EEG data set. We show that the new model matches stateof-the-art correlation reduction abilities, but ultimately converges to a simple group mean model for the experimental data set. This observation leaves the proposed MTL approach open for a more detailed investigations of artifact tasks.
Total flavonoids from Smilax glabra Roxb blocks epithelial‐mesenchymal transition and inhibits renal interstitial fibrosis by targeting miR‐21/PTEN signaling

Smilax glabra Roxb, a traditional Chinese herb, has been widely used in folk medicine. The current study was performed to investigate the protective effect of S. glabra Roxb extract, pure total flavonoids from Smilax glabra Roxb (PTFS), on renal interstitial fibrosis (RIF) and its underlying mechanism.
SIMD Assisted Fault Detection and Fault Attack Mitigation

Author(s): Chen, Zhi | Advisor(s): Nicolau, Alex | Abstract: Modern processors continue to aggressively scale down the feature size and reduce voltage levels to run faster and be more energy efficient. However, this trend also poses significant reliability concern as it makes transistors more susceptible to soft errors. Soft errors are transient. Although they don't impair the computing systems permanently, these errors can corrupt the output of a program or even crash the entire system. Hardware or software redundant techniques could be used to detect errors during the execution of a program. However, hardware redundancy, e.g. DMR (dual-modular redundancy) and TMR (triple-modular redundancy), leads to significant area overhead and very high energy cost. Software redundancy, e.g. instruction duplication, has lower performance and energy penalty and virtually no hardware cost by sacrificing a small degree of error coverage. Yet commodity processors generally don't require ``five-nines'' reliability as they are not mission-critical. Instead, performance and energy consumption have more priority. This dissertation proposes a novel approach to instruction duplication, which exploits the redundancy within SIMD instructions. The key idea is to pack the original data and its duplicate in the different lanes of the same vector register instead of executing two scalar instructions separately as these registers are underutilized on most applications. The proposed solution is implemented in the LLVM compiler as a stand-alone pass. Evaluation on a host of benchmarks reveal that proposed SIMD-based error detection technique causes much less performance, code size, and energy overheads.This dissertation further extends the proposed approach as a countermeasure to protect cryptographic algorithms. These algorithms are widely adopted in modern processors and embedded systems to protect information. A number of popular cryptographic algorithms in the Libgcrypt library are protected using the SIMD-based instruction duplication technique. A large amount of errors are injected to these algorithms. The results show that almost all injected faults can be detected with reasonable performance and code size cost.
Sensory Processing Across Conscious and Nonconscious Brain States: From Single Neurons to Distributed Networks for Inferential Representation

Neuronal activity is markedly different across brain states: it varies from desynchronized activity during wakefulness to the synchronous alternation between active and silent states characteristic of deep sleep. Surprisingly, limited attention has been paid to investigating how brain states affect sensory processing. While it was long assumed that the brain was mostly disconnected from external stimuli during sleep, an increasing number of studies indicates that sensory stimuli continue to be processed across all brain states—albeit differently. In this review article, we first discuss what constitutes a brain state. We argue that—next to global, behavioral states such as wakefulness and sleep—there is a concomitant need to distinguish bouts of oscillatory dynamics with specific global/local activity patterns and lasting for a few hundreds of milliseconds, as these can lead to the same sensory stimulus being either perceived or not. We define these short-lasting bouts as micro-states. We proceed to characterize how sensory-evoked neural responses vary between conscious and nonconscious states. We focus on two complementary aspects: neuronal ensembles and inter-areal communication. First, we review which features of ensemble activity are conducive to perception, and how these features vary across brain states. Properties such as heterogeneity, sparsity and synchronicity in neuronal ensembles will especially be considered as essential correlates of conscious processing. Second, we discuss how inter-areal communication varies across brain states and how this may affect brain operations and sensory processing. Finally, we discuss predictive coding (PC) and the concept of multi-level representations as a key framework for understanding conscious sensory processing. In this framework the brain implements conscious representations as inferences about world states across multiple representational levels. In this representational hierarchy, low-level inference may be carried out nonconsciously, whereas high levels integrate across different sensory modalities and larger spatial scales, correlating with conscious processing. This inferential framework is used to interpret several cellular and population-level findings in the context of brain states, and we briefly compare its implications to two other theories of consciousness. In conclusion, this review article, provides foundations to guide future studies aiming to uncover the mechanisms of sensory processing and perception across brain states.
Response to Comment on "All-optical machine learning using diffractive deep neural networks"

In their Comment, Wei et al. (arXiv:1809.08360v1 [cs.LG]) claim that our original interpretation of Diffractive Deep Neural Networks (D2NN) represent a mischaracterization of the system due to linearity and passivity. In this Response, we detail how this mischaracterization claim is unwarranted and oblivious to several sections detailed in our original manuscript (Science, DOI: 10.1126/science.aat8084) that specifically introduced and discussed optical nonlinearities and reconfigurability of D2NNs, as part of our proposed framework to enhance its performance. To further refute the mischaracterization claim of Wei et al., we, once again, demonstrate the depth feature of optical D2NNs by showing that multiple diffractive layers operating collectively within a D2NN present additional degrees-of-freedom compared to a single diffractive layer to achieve better classification accuracy, as well as improved output signal contrast and diffraction efficiency as the number of diffractive layers increase, showing the deepness of a D2NN, and its inherent depth advantage for improved performance. In summary, the Comment by Wei et al. does not provide an amendment to the original teachings of our original manuscript, and all of our results, core conclusions and methodology of research reported in Science (DOI: 10.1126/science.aat8084) remain entirely valid.
Failure to Affect Decision Criteria During Recognition Memory With Continuous Theta Burst Stimulation

A decision criterion establishes the minimum amount of memory evidence required for recognition. When a liberal criterion is set, items are recognized based on weak evidence whereas a conservative criterion requires greater memory strength for recognition. The decision criterion is a fundamental aspect of recognition memory but little is known about the underlying neural mechanisms of maintaining a criterion. We used continuous theta burst stimulation (cTBS) with the goal of inhibiting prefrontal cortex excitability while participants performed recognition tests. We hypothesized that inhibiting the right inferior frontal gyrus (rIFG), right middle frontal gyrus (rMFG), and right dorsolateral prefrontal cortex (rDLPFC) would cause participants to establish less conservative decision criteria without affecting recognition memory performance. Participants initially performed recognition memory tests while maintaining conservative decision criteria during fMRI scanning. Peak activity in the successful retrieval effect contrast (Hits > Correct Rejections) provided subject-specific cTBS target sites. During three separate sessions, participants completed the same recognition memory paradigm while maintaining conservative and liberal decision criteria both before and after cTBS. Across two experiments we failed to significantly alter decision criteria placement by applying cTBS to the rIFG, rMFG, and rDLPFC despite efforts to precisely target individualized brain areas. However, we unexpectedly improved discriminability following cTBS to the rDLPFC specifically when participants maintained a liberal criterion. Although this finding may guide future studies investigating the neural mechanisms underlying discriminability in recognition memory, cTBS proved ineffective at altering decision criteria.
[Classification of surgical complications].

AIM To develop classification of surgical complications. MATERIAL AND METHODS The most common and recently proposed classifications of surgical complications (Clavien-Dindo, Occordion, R. Satava, et al., A. Kazaryan, et al.) were analyzed. Postoperative complications in 25 556 patients were retrospectively assessed. Incidence and severity of complications, methods of their correction, effect of complications on length of hospital-stay were analyzed. RESULTS As a result of comprehensive analysis, new classification of surgical complications was proposed. 5 grades of complications were identified. Classification is based on anatomical features, type of complications (within surgical access, organ or cavity), correction depending on this type, severity of complications. Causal relationship of complication with type of repair and increased length of hospital-stay was considered. CONCLUSION Classification proposed is anatomically justified, considers causal relationship of complications and their repair, as well as length of hospital-stay.
Kernel Methods and Measures for Classification with Transparency, Interpretability and Accuracy in Health Care

Support vector machines are a popular method in machine learning. They learn from data about a subject, for example, lung tumors in a set of patients, to classify new data, such as, a new patient’s tumor. The new tumor is classified as either cancerous or benign, depending on how similar it is to the tumors of other patients in those two classes—where similarity is judged by a kernel. The adoption and use of support vector machines in health care, however, is inhibited by a perceived and actual lack of rationale, understanding and transparency for how they work and how to interpret information and results from them. For example, a user must select the kernel, or similarity function, to be used, and there are many kernels to choose from but little to no useful guidance on choosing one. The primary goal of this thesis is to create accurate, transparent and interpretable kernels with rationale to select them for classification in health care using SVM—and to do so within a theoretical framework that advances rationale, understanding and transparency for kernel/model selection with atomic data types. The kernels and framework necessarily co-exist. The secondary goal of this thesis is to quantitatively measure model interpretability for kernel/model selection and identify the types of interpretable information which are available from different models for interpretation. Testing my framework and transparent kernels with empirical data I achieve classification accuracy that is better than or equivalent to the Gaussian RBF kernels. I also validate some of the model interpretability measures I propose.
Building a Reproducible Machine Learning Pipeline

Reproducibility of modeling is a problem that exists for any machine learning practitioner, whether in industry or academia. The consequences of an irreproducible model can include significant financial costs, lost time, and even loss of personal reputation (if results prove unable to be replicated). This paper will first discuss the problems we have encountered while building a variety of machine learning models, and subsequently describe the framework we built to tackle the problem of model reproducibility. The framework is comprised of four main components (data, feature, scoring, and evaluation layers), which are themselves comprised of well defined transformations. This enables us to not only exactly replicate a model, but also to reuse the transformations across different models. As a result, the platform has dramatically increased the speed of both offline and online experimentation while also ensuring model reproducibility.
Cancer of the corpus uteri

Endometrial cancer is the most common gynecological malignancy in high‐income countries. Although the overall prognosis is relatively good, high‐grade endometrial cancers have a tendency to recur. Recurrence needs to be prevented since the prognosis for recurrent endometrial cancer is dismal. Treatment tailored to tumor biology is the optimal strategy to balance treatment efficacy against toxicity. Standard treatment consists of hysterectomy and bilateral salpingo‐oophorectomy. Lymphadenectomy (with ongoing studies of sentinel node biopsy) enables identification of lymph node positive patients who need adjuvant treatment, including radiotherapy and chemotherapy. Adjuvant radiotherapy is used for Stage I–II patients with high‐risk factors and Stage III lymph node negative patients. In advanced disease, a combination of surgery to no residual disease and chemotherapy results in the best outcome. Surgery for recurrent disease is only advocated in patients with a good performance status with a relatively long disease‐free interval.
Living with Stroke: a Wales context

This thesis reports the findings of a doctoral study which sought to explore the everyday lives of survivors of stroke and their caregivers, who live in Wales, UK. Through an exploratory multi-method approach to research, data collected from survivors of stroke, their caregivers, and Stroke Club observations provided in-depth insight into the participants' lives, and the contribution of Stroke Clubs to the personal support networks of the study participants. The findings of this study provide an insight into post-stroke life in Wales. The findings also highlight the value of social enterprise (Stroke Clubs) in the context of supporting survivors of stroke and their caregivers, in the months and years after stroke. The key conclusions of this study are that survivors and caregivers benefit from life-long support. Furthermore, the social enterprise sector provides an avenue of informal support and has the potential to meet the aims of policy makers, by filling the gaps in existing service provision. This thesis contributes to the fields of sociology, social policy, social enterprise, and research methods for research involving vulnerable adults. Recommendations for practitioners, policy makers, and social entrepreneurs are underpinned by the study findings and include suggestions for effective collaboration between the sectors. The limitations of this study are that the sample consisted of eight (of nine) participants who engaged with Stroke Clubs. Therefore, a contrast between those who attended Stroke Club and those who did not could not be established. However, this thesis does form the basis of such study in the future as it highlights what the participants perceive to be benefits of Stroke Club.
Bio-Cooperative Approach for the Human-in-the-Loop Control of an End-Effector Rehabilitation Robot

The design of patient-tailored rehabilitative protocols represents one of the crucial factors that influence motor recovery mechanisms, such as neuroplasticity. This approach, including the patient in the control loop and characterized by a control strategy adaptable to the user's requirements, is expected to significantly improve functional recovery in robot-aided rehabilitation. In this paper, a novel 3D bio-cooperative robotic platform is developed. A new arm-weight support system is included into an operational robotic platform for 3D upper limb robot-aided rehabilitation. The robotic platform is capable of adapting therapy characteristics to specific patient needs, thanks to biomechanical and physiological measurements, and thus closing the subject in the control loop. The level of arm-weight support and the level of the assistance provided by the end-effector robot are varied on the basis of muscular fatigue and biomechanical indicators. An assistance-as-needed approach is applied to provide the appropriate amount of assistance. The proposed platform has been experimentally validated on 10 healthy subjects; they performed 3D point-to-point tasks in two different conditions, i.e., with and without assistance-as-needed. The results have demonstrated the capability of the proposed system to properly adapt to real needs of the patients. Moreover, the provided assistance was shown to reduce the muscular fatigue without negatively influencing motion execution.
Checkpoint inhibitors in patients with metastatic renal cell carcinoma: Results from the International Metastatic Renal Cell Carcinoma Database Consortium

To the authors' knowledge, outcomes and prognostic tools have yet to be clearly defined in patients with metastatic renal cell carcinoma (mRCC) who are treated with immuno‐oncology (IO) checkpoint inhibitors (programmed death‐ligand 1 [PD‐L1] inhibitors). In the current study, the authors aimed to establish IO efficacy benchmarks in patients with mRCC and update patient outcomes in each International Metastatic Renal Cell Carcinoma Database Consortium (IMDC) prognostic class.
Prospective Validation of a Standardized Ultrasonography-Based Ovarian Cancer Risk Assessment System.

OBJECTIVE To evaluate the performance of a system that standardizes ovarian cancer risk assessment and reporting on ultrasonography. METHODS We conducted a prospective community-based cohort study of average-risk women undergoing ultrasonography in 2016 using a reporting system that requires adnexal masses to be categorized as 1, 2, 3, or X based on standardized ultrasound criteria including size, presence of solid components, and vascularity assessed by Doppler. With a median follow-up of 18 months, the risk of ovarian cancer or borderline tumor diagnosis for each category was determined. RESULTS Among 43,606 women undergoing ultrasonography, 6,838 (16%) had an abnormal adnexal mass reported: 70% were category 1, 21% category 2, 3.7% category 3, and 5.4% category X. Among these women, 89 (1.3%) were subsequently diagnosed with ovarian cancer and 59 (0.9%) with borderline tumors. The risks of ovarian cancer diagnosis associated with masses reported as categories 1, 2, 3, and X were 0.2% (95% CI 0.05-0.3%), 1.3% (95% CI 0.7-1.9%), 6.0% (95% CI 3.0-8.9%), and 13.0% (95% CI 9.5-16.4%), respectively; risks of either ovarian cancer or borderline tumor were 0.4% (95% CI 0.2-0.6%), 2.3% (95% CI 1.6-3.1%), 10.4% (95% CI 6.6-14.1%), and 18.9% (95% CI 14.9-23.0%) respectively. Among 36,768 (84%) women with normal or benign adnexal findings reported, 38 women were diagnosed with ovarian cancer, for a risk of 0.1% (95% CI 0.07-0.14%). CONCLUSION In a community-based setting with low ovarian cancer prevalence, our standardized reporting system differentiated adnexal masses into four categories with distinct levels of risk with 9-10% of women having higher risk masses and 70% of women having masses associated with a risk of cancer similar to that of normal ultrasound findings. The system supports risk-based management by providing clinicians a more consistent assessment of risk based on ultrasound characteristics.
Dimensions of human-work domain interaction: A preliminary analysis for the design of a corporate digital library

The author applies the cognitive system engineering approach to investigate human-work interaction at a corporate setting. This study reports the preliminary analysis of data collected from diary analysis and interview of 20 subjects. The results present the identification of three dimensions for each of four interactive activities involved in human-work interaction and their relationships. An enhanced model and its implications for the development of a corporate digital library are further discussed.
Stationary Exercise in Overweight and Normal Weight Children.

PURPOSE This study examined differences in lower extremity kinematics and muscle activation patterns between normal weight (NW) and overweight (OW) children during stationary exercises (running in place, frontal kick, and butt kick) at submaximal intensity. METHODS Healthy children (aged 10-13 y) were stratified into OW (n = 10; body fat percentage: 34.97 [8.60]) and NW (n = 15; body fat percentage: 18.33 [4.87]). Electromyography was recorded for rectus femoris, vastus lateralis, biceps femoris, gastrocnemius, and tibialis anterior. In addition, the ratings of perceived exertion and range of motion of hip, knee, and ankle joints were collected during stationary exercises. Repeated-measures analysis of variance compared muscle activation, range of motion, and ratings of perceived exertion between groups and exercises. Friedman test examined sequencing of muscles recruitment. RESULTS Compared with NW, OW experienced significantly greater ratings of perceived exertion (13.7 [0.8] vs 11.7 [0.7]; P < .001) and electromyography amplitude in all muscles apart from vastus lateralis during stationary exercises. In addition, NW children used more consistent muscles' recruitment pattern in comparison with OW children. The range of motion was similar between groups at all joints. CONCLUSION OW children may adopt a more active neuromuscular strategy to provide greater stability and propulsion during stationary exercises. Stationary exercise can be prescribed to strengthen lower extremity muscles in OW children, but mode and intensity must be considered.
Antizyme Inhibitors in Polyamine Metabolism and Beyond: Physiopathological Implications

The intracellular levels of polyamines, cationic molecules involved in a myriad of cellular functions ranging from cellular growth, differentiation and apoptosis, is precisely regulated by antizymes and antizyme inhibitors via the modulation of the polyamine biosynthetic and transport systems. Antizymes, which are mainly activated upon high polyamine levels, inhibit ornithine decarboxylase (ODC), the key enzyme of the polyamine biosynthetic route, and exert a negative control of polyamine intake. Antizyme inhibitors (AZINs), which are proteins highly homologous to ODC, selectively interact with antizymes, preventing their action on ODC and the polyamine transport system. In this review, we will update the recent advances on the structural, cellular and physiological functions of AZINs, with particular emphasis on the action of these proteins in the regulation of polyamine metabolism. In addition, we will describe emerging evidence that suggests that AZINs may also have polyamine-independent effects on cells. Finally, we will discuss how the dysregulation of AZIN activity has been implicated in certain human pathologies such as cancer, fibrosis or neurodegenerative diseases.