Miracle-dz/UniFAR-FTUs huggingface dataset:
Construction of a pathological risk model of occult lymph node metastases for prognostication by semi-automated image analysis of tumor budding in early-stage oral squamous cell carcinoma

It is challenging to identify at diagnosis those patients with early oral squamous cell carcinoma (OSCC), who have a poor prognosis and those that have a high risk of harboring occult lymph node metastases. The aim of this study was to develop a standardized and objective digital scoring method to evaluate the predictive value of tumor budding. We developed a semi-automated image-analysis algorithm, Digital Tumor Bud Count (DTBC), to evaluate tumor budding. The algorithm was tested in 222 consecutive patients with early-stage OSCC and major endpoints were overall (OS) and progression free survival (PFS). We subsequently constructed and cross-validated a binary logistic regression model and evaluated its clinical utility by decision curve analysis. A high DTBC was an independent predictor of both poor OS and PFS in a multivariate Cox regression model. The logistic regression model was able to identify patients with occult lymph node metastases with an area under the curve (AUC) of 0.83 (95% CI: 0.78–0.89, P <0.001) and a 10-fold cross-validated AUC of 0.79. Compared to other known histopathological risk factors, the DTBC had a higher diagnostic accuracy. The proposed, novel risk model could be used as a guide to identify patients who would benefit from an up-front neck dissection.
The fuzzy gene filter: A classifier performance assesment

The Fuzzy Gene Filter (FGF) is an optimised Fuzzy Inference System designed to rank genes in order of differential expression, based on expression data generated in a microarray experiment. This paper examines the effectiveness of the FGF for feature selection using various classification architectures. The FGF is compared to three of the most common gene ranking algorithms: t-test, Wilcoxon test and ROC curve analysis. Four classification schemes are used to compare the performance of the FGF vis-a-vis the standard approaches: K Nearest Neighbour (KNN), Support Vector Machine (SVM), Naive Bayesian Classifier (NBC) and Artificial Neural Network (ANN). A nested stratified Leave-One-Out Cross Validation scheme is used to identify the optimal number top ranking genes, as well as the optimal classifier parameters. Two microarray data sets are used for the comparison: a prostate cancer data set and a lymphoma data set.
Functions of the Nonverbal in CMC: Emoticons and Illocutionary Force

The term 'emoticons'—short for 'emotion icons'—refers to graphic signs, such as the smiley face, that often accompany computer-mediated textual communication. They are most often characterized as iconic indicators of emotion, conveyed through a communication channel that is parallel to the linguistic one. In this article, it is argued that this conception of emoticons fails to account for some of their important uses. We present a brief outline of speech act theory and use it to provide a complementary account of emoticons, according to which they also function as indicators of illocutionary force. More broadly, we identify and illustrate three ways in which emoticons function: 1) as emotion indicators, mapped directly onto facial expression; 2) as indicators of non-emotional meanings, mapped conventionally onto facial expressions, and 3) as illocutionary force indicators that do not map conventionally onto a facial expression. In concluding, we draw parallels between emoticons and utterance-final punctuation marks, and show how our discussion of emoticons bears upon the broader question of the bounds between linguistic and non-linguistic communication.
Localized growth and functionalization of silicon nanowires for MEMS sensor applications

We present a mechanism for the growth of silicon nanowires (SiNW) directly from a silicon substrate or polysilicon block, without the use of a metal catalyst, silicon vapor or CVD gasses. This allows for "in place" growth of nanostructures without further need for manipulation or alignment for subsequent applications. Wires in the 2040 nm diameter range with lengths over 85 /spl mu/m can be easily grown by this technique. The critical parameters in the growth of these nanowires are the surface treatment and the carrier gas used. The growth and functionalization of SiNW can enhance the sensitivity of sensor systems. Wire growth is stress driven and enhanced by surface Si atom diffusion due to the presence of hydrogen gas.
Branch-explore-merge: facilitating real-time revision control in collaborative visual exploration

Collaborative work is characterized by participants seamlessly transitioning from working together (coupled) to working alone (decoupled). Groupware should therefore facilitate smoothly varying coupling throughout the entire collaborative session. Towards achieving such transitions for collaborative exploration and search, we propose a protocol based on managing revisions for each collaborator exploring a dataset. The protocol allows participants to diverge from the shared analysis path (branch), study the data independently (explore), and then contribute back their findings onto the shared display (merge). We apply this concept to collaborative search in multidimensional data, and propose an implementation where the public view is a tabletop display and the private views are embedded in handheld tablets. We then use this implementation to perform a qualitative user study involving a real estate dataset. Results show that participants leverage the BEM protocol, spend \ significant time using their private views (40% to 80% of total task time), and apply public view changes for consultation with collaborators.
A Pilot Examination of the Adapted Protocol for Classroom Pivotal Response Teaching.

Pivotal Response Training (PRT) is a naturalistic, behavioral intervention with a strong evidence-base that is designed to increase generalization and maintenance of responding in children with ASD. Although special education teachers report using PRT, little research to date has examined PRT use in the context of community school programs. There is some research to support that teachers have challenges implementing PRT with fidelity in the classroom. To address this issue, a research community partnership was used to adapt PRT specifically for classroom environments. The pilot project used a multiple baseline design across training groups to examine 20 teachers’ use of Classroom Pivotal Response Teaching (CPRT) with students with ASD in special education settings. Results indicated that teachers learned the strategies after a relatively brief training period that included coaching, were satisfied with the training and adapted materials, and that use of CPRT was associated with improved student engagement. A Pilot Examination of the Adapted Protocol for Classroom Pivotal Response Teaching Recent reviews of the literature for children with autism spectrum disorders (ASD) have identified several evidence-based practices that may be efficacious with this population (National Research Council, 2001; Odom, Collet-Klingenberg, Rogers, & Hatton, 2010; National Standards Project, 2009; Rogers & Vismara, 2008; Wong et al., 2014). Pivotal Response Training (PRT) is one practice that has been acknowledged as evidence-based for teaching a variety of skills relevant to ASD including symbolic and sociodramatic play, self-initiations, and joint attention. Several independent reviews of the research for use of PRT recommend it as an efficacious intervention (Humphries, 2003; Odom et al., 2010; National Standards Project, 2009; Simpson, 2005; Verschuur, Didden, Lang, Sigafoos, & Huskens, 2014; Wong et al., 2014). PRT is a naturalistic, behavioral intervention designed to increase generalization and maintenance of responding in children with ASD. The “pivotal” responses trained in PRT are motivation, initiation and responsivity to multiple cues (i.e. increasing breadth of attention). Specific components include providing clear and appropriate instructions, sharing control with the child, interspersing maintenance (i.e., already mastered) tasks and acquisition (more difficult) tasks, requiring responding to multiple aspects of items in the environment (e.g., color and shape), providing contingent consequences, reinforcing goal directed attempts at correct responding, and providing reinforcement directly related to behavior. While research highlights
Forecast Modelling via Variations in Binary Image-Encoded Information Exploited by Deep Learning Neural Networks

Traditional forecasting models fit a function approximation from dependent invariables to independent variables. However, they usually get into trouble when date are presented in various formats, such as text, voice and image. This study proposes a novel image-encoded forecasting method that input and output binary digital two-dimensional (2D) images are transformed from decimal data. Omitting any data analysis or cleansing steps for simplicity, all raw variables were selected and converted to binary digital images as the input of a deep learning model, convolutional neural network (CNN). Using shared weights, pooling and multiple-layer back-propagation techniques, the CNN was adopted to locate the nexus among variations in local binary digital images. Due to the computing capability that was originally developed for binary digital bitmap manipulation, this model has significant potential for forecasting with vast volume of data. The model was validated by a power loads predicting dataset from the Global Energy Forecasting Competition 2012.
Why Certain Gifts Are Great to Give but Not to Get

We propose that many giver-recipient discrepancies in the gift-giving literature can be explained, at least partially, by the notion that when evaluating the quality of a gift, givers primarily focus on the moment of exchange, whereas recipients primarily focus on how valuable a gift will be once owned. In this review, we summarize the variety of errors givers make and, more critically, position these errors within our newly developed framework. We hope this framework will provide a single point of reference for those interested in gift giving and spur novel predictions about the causes and consequences of miscalibrated gift choice.
A Robust Face Recognition Algorithm Using Markov Stationary Features and Adjacent Pixel Intensity Difference Quantization Histogram

In this paper, we present a robust face recognition algorithm using adjacent pixel intensity difference quantization (APIDQ) histogram combined with Markov Stationary Features (MSF). Previously, we have proposed a very simple yet highly reliable face recognition algorithm using Adjacent Pixel Intensity Difference Quantization (APIDQ) histogram. After the intensity variation vectors for all the pixels in an image are calculated, each vector is quantized directly in (dIx, dIy) plane instead of polar plane. By counting the number of elements in each quantized area in the (dIx, dIy) plane, a histogram can be created. This histogram, obtained by APIDQ for facial images, is utilized as a very effective personal feature. In this paper, we combine the APIDQ histogram with MSF so as to add spatial structure information to histogram. Experimental results show maximum average recognition rate of 97.16% is obtained for 400 images of 40 persons from the publicly available face database of AT&T Laboratories Cambridge. Furthermore, Top 1 recognition rate of 98.2% is achieved by using FB task of the publicly available face database of FERET.
A Mobile Care System With Alert Mechanism

Hypertension and arrhythmia are chronic diseases, which can be effectively prevented and controlled only if the physiological parameters of the patient are constantly monitored, along with the full support of the health education and professional medical care. In this paper, a role-based intelligent mobile care system with alert mechanism in chronic care environment is proposed and implemented. The roles in our system include patients, physicians, nurses, and healthcare providers. Each of the roles represents a person that uses a mobile device such as a mobile phone to communicate with the server setup in the care center such that he or she can go around without restrictions. For commercial mobile phones with Bluetooth communication capability attached to chronic patients, we have developed physiological signal recognition algorithms that were implemented and built-in in the mobile phone without affecting its original communication functions. It is thus possible to integrate several front-end mobile care devices with Bluetooth communication capability to extract patients' various physiological parameters [such as blood pressure, pulse, saturation of haemoglobin (SpO2), and electrocardiogram (ECG)], to monitor multiple physiological signals without space limit, and to upload important or abnormal physiological information to healthcare center for storage and analysis or transmit the information to physicians and healthcare providers for further processing. Thus, the physiological signal extraction devices only have to deal with signal extraction and wireless transmission. Since they do not have to do signal processing, their form factor can be further reduced to reach the goal of microminiaturization and power saving. An alert management mechanism has been included in back-end healthcare center to initiate various strategies for automatic emergency alerts after receiving emergency messages or after automatically recognizing emergency messages. Within the time intervals in system setting, according to the medical history of a specific patient, our prototype system can inform various healthcare providers in sequence to provide healthcare service with their reply to ensure the accuracy of alert information and the completeness of early warning notification to further improve the healthcare quality. In the end, with the testing results and performance evaluation of our implemented system prototype, we conclude that it is possible to set up a complete intelligent health care chain with mobile monitoring and healthcare service via the assistance of our system.
Mixtures of Sparse Autoregressive Networks

We consider high-dimensional distribution estimation through autoregressive networks. By combining the concepts of sparsity, mixtures and parameter sharing we obtain a simple model which is fast to train and which achieves state-of-the-art or better results on several standard benchmark datasets. Specifically, we use an L1-penalty to regularize the conditional distributions and introduce a procedure for automatic parameter sharing between mixture components. Moreover, we propose a simple distributed representation which permits exact likelihood evaluations since the latent variables are interleaved with the observable variables and can be easily integrated out. Our model achieves excellent generalization performance and scales well to extremely high dimensions.
The Crucial Role of the Pls1 Tetraspanin during Ascospore Germination in Podospora anserina Provides an Example of the Convergent Evolution of Morphogenetic Processes in Fungal Plant Pathogens and Saprobes

ABSTRACT Pls1 tetraspanins were shown for some pathogenic fungi to be essential for appressorium-mediated penetration into their host plants. We show here that Podospora anserina, a saprobic fungus lacking appressorium, contains PaPls1, a gene orthologous to known PLS1 genes. Inactivation of PaPls1 demonstrates that this gene is specifically required for the germination of ascospores in P. anserina. These ascospores are heavily melanized cells that germinate under inducing conditions through a specific pore. On the contrary, MgPLS1, which fully complements a ΔPaPls1 ascospore germination defect, has no role in the germination of Magnaporthe grisea nonmelanized ascospores but is required for the formation of the penetration peg at the pore of its melanized appressorium. P. anserina mutants with mutation of PaNox2, which encodes the NADPH oxidase of the NOX2 family, display the same ascospore-specific germination defect as the ΔPaPls1 mutant. Both mutant phenotypes are suppressed by the inhibition of melanin biosynthesis, suggesting that they are involved in the same cellular process required for the germination of P. anserina melanized ascospores. The analysis of the distribution of PLS1 and NOX2 genes in fungal genomes shows that they are either both present or both absent. These results indicate that the germination of P. anserina ascospores and the formation of the M. grisea appressorium penetration peg use the same molecular machinery that includes Pls1 and Nox2. This machinery is specifically required for the emergence of polarized hyphae from reinforced structures such as appressoria and ascospores. Its recurrent recruitment during fungal evolution may account for some of the morphogenetic convergence observed in fungi.
Arm exoskeleton rehabilitation robot with assistive system for patient after stroke

Exercise dosage is proven to be an important factor in the physical treatment. Robot assistive approach can improve the rehabilitation quality and evaluate patient's recovery quantitatively. This paper presents kinematic structure, assistive control system, and integrated F/T sensor for an upper limb rehabilitation robot. By using the human arm dynamic, there are three rehabilitation modes presented in this paper: active mode, assistive mode, and passive mode. In assistive mode, we have two strategies to implement it. One is to amplify interactive torque, and the other is to apply assist-as-needed concept. The goal of this mode is to assist patients to finish motion tasks. The rehabilitation robot under investigation has 7 degree of freedom (DOF) actuated by DC motors, which are programmed to drive the robot arm in the 3D space. To validate our control design, some realistic experiments are conducted and its satisfactory performance is demonstrated. This work is approved clinical testing by the Department of Health, Executive Yuan, R.O. C. So far, we are demonstrating the effect of our controller.
Modelling distributed crewing with STAMP

Changes to crewing configurations in commercial airlines are likely to emerge as a means of reducing operating costs. To consider the safety implications for a distributed crewing configuration, System Theoretic Accident Model and Processes (STAMP) was applied to a rapid decompression hazard. High level control structures for current operations and distributed crewing are presented. The assumptions generated and additional steps to progress towards full System-Theoretic Process Analysis (STPA) analysis are discussed.
Local-field excitations in two-dimensional lattices of resonant atoms

We study excitations of the local field (locsitons) in nanoscale two-dimensional (2D) lattices of strongly interacting resonant atoms and various unusual effects associated with them. Locsitons in low-dimensional systems and the resulting spatial strata and more complex patterns on a scale of just a few atoms were predicted by us earlier [A. E. Kaplan and S. N. Volkov, Phys. Rev. Lett. 101, 133902 (2008)]. These effects present a radical departure from the classical Lorentz-Lorenz theory of the local field (LF), which assumes that the LF is virtually uniform on this scale. We demonstrate that the strata and patterns in the 2D lattices may be described as an interference of plane-wave locsitons, build an analytic model for such unbounded locsitons, and derive and analyze dispersion relations for the locsitons in an equilateral triangular lattice. We draw useful analogies between 1D and 2D locsitons but also show that the 2D case enables locsitons with the most diverse and unusual properties. Using the nearest-neighbor approximation, we find the locsiton frequency band for different mutual orientations of the lattice and the incident field. We demonstrate a formation of distinct vector locsiton patterns consisting of multiple vortices in the LF distribution and suggest a waymore » to design finite 2D lattices that exhibit such patterns at certain frequencies. We illustrate the role of lattice defects in supporting localized locsitons and also demonstrate the existence of 'magic shapes', for which the LF suppression at the exact atomic resonance is canceled. « less
Preoperative and intraoperative predictors of postoperative morbidity, poor graft function, and early rejection in 190 patients undergoing liver transplantation.

HYPOTHESIS Preoperative and intraoperative variables predict in part adverse outcome after liver transplantation. DESIGN Prospective, blinded, cohort study. SETTING Tertiary care hospital. SUBJECTS A total of 190 adult patients undergoing primary liver transplantation. MAIN OUTCOME MEASURE Adverse outcome was prospectively defined as either in-hospital death or prolonged postoperative hospitalization (>14 days) associated with morbidity. Potential preoperative and intraoperative risk factors were collected. Associations were tested by univariate analysis followed by multivariate analysis in which preoperative factors were entered before intraoperative factors. RESULTS Adverse outcome occurred in 44.7% of patients. Incidences of other complications were as follows: in-hospital mortality (8.4%), primary graft nonfunction (4.2%), poor early graft function (1.1%), and early rejection (31.2%). Univariate predictors of adverse outcome were United Network for Organ Sharing status (P =.003), Child-Turcotte-Pugh score (P =.02), POSSUM physiological score (P =.002), recipient age (P =.01), preoperative serum high-density lipoprotein cholesterol level (P =.03), preoperative serum creatinine level (P =.002), preoperative serum total IgG level (P =.004), duration in hospital preoperatively (P =.03), operative duration (P<.001), allogeneic erythrocyte transfusions (P<.001), total intraoperative fluids (P =.002), and use of inotropic agents (P =.01). In the final multivariate model, predictors of adverse outcome were United Network for Organ Sharing status (P =.03), recipient age (P =.002), and total intraoperative fluids (P =.04). Most patients who died or had a prolonged hospitalization exhibited dysfunction of more than 1 organ system, including pulmonary, renal, and infectious complications. CONCLUSIONS Adverse outcome occurs frequently after liver transplantation, usually involves multiple organ systems, and is predicted in part by several preoperative and intraoperative factors.
Prevalence of Keratoconus and Subclinical Keratoconus in Subjects with Astigmatism Using Pentacam Derived Parameters

Purpose To determine the prevalence of keratoconus (KCN) and subclinical KCN among subjects with two or more diopters (D) of astigmatism, and to compare Pentacam parameters among these subjects. Methods One hundred and twenty eight eyes of 64 subjects with astigmatism ≥2D were included in the study. All subjects underwent a complete ophthalmic examination which included refraction, visual acuity measurement, slit lamp biomicroscopy, retinoscopy, fundus examination, conventional corneal topography and elevation-based topography with Pentacam. The diagnosis of KCN and subclinical KCN was made by observing clinical findings and topographic features; and confirmed by corneal thickness and elevation maps of Pentacam. Several parameters acquired from Pentacam were analyzed employing the Mann-Whitney U Test. Results Mean age of the study population was 29.9±9.8 (range 15-45) years which included 39 (60.9%) female and 25 (39.1%) male subjects. Maximum corneal power, index of vertical asymmetry, keratoconus index and elevation values were significantly higher and pachymetry was significantly thinner in eyes with clinical or subclinical KCN than normal astigmatic eyes (P< 0.05). Conclusion The current study showed that subjects with 2D or more of astigmatism who present to outpatient clinics should undergo corneal topography screening for early diagnosis of KCN even if visual acuity is not affected. Pentacam may provide more accurate information about anterior and posterior corneal anatomy especially in suspect eyes.