Miracle-dz/UniFAR-FTUs huggingface dataset:
Prostate cancer association studies: Pitfalls and solutions to cancer misclassification in the PSA era

Widespread screening of American men for elevated PSA has changed the characteristics of prostate cancer cases in the U.S. The influence of the changed nature of prostate cancer cases in the PSA era and the need for careful consideration of who is a “case” and who is a “control” on the ability to detect associations of risk factors with prostate cancer in etiologic epidemiologic studies merits discussion. Issue 1: prostate cancer cases diagnosed in the PSA era are enriched with a pool of early lesions, which may differ in etiology, and are deficient in advanced lesions, which are the most likely to be the product of promotion and progression events. By admixing the two types of cases (i.e., imperfect specificity), the associations previously detected using epidemiologic designs when the majority of cases were clinically detected may no longer be apparent in the PSA era when the majority of cases are now detected in the pre‐clinical phase. Researchers must now tailor hypotheses such that they are testable using early stage cases or specifically augment the number of advanced cases when testing hypotheses related to extraprostatic growth and progression. Issue 2: even when controls are screened for elevated PSA to rule out the presence of prostate cancer, some proportion of those controls currently will have one or more foci of prostate cancer. The imperfect sensitivity of the PSA test coupled with diagnostic work‐up may in part result from (a) lack of PSA elevation in some men with prostate cancer or (b) failure of biopsy to sample the tumor focus in men with elevated PSA. Misclassification of men with undetected prostate cancer as controls usually produces a bias that tends to deflate associations. Given this type of disease misclassification, whether an association still can be statistically detected depends on the extent of misclassification, the magnitude of the true association, the prevalence of the exposure in the true controls, and the sample size, although in general moderate nondifferential misclassification does not lead to profound attenuation. However, under the same scenario attenuation does not occur in cohort or case‐cohort studies in which the rate or risk ratio (RR) is calculated. That prostate cancer cases diagnosed in the PSA era are enriched with early stage, minimally invasive disease in our opinion is likely to pose a far more serious obstacle to epidemiologic research on the etiology of clinically important prostate cancer than the issue of inclusion as controls some men who have undiagnosed prostate cancer because of imperfect sensitivity of PSA screening and biopsy sampling error. © 2003 Wiley‐Liss, Inc.
A method for determining significant structures in a cloud of earthquakes

The interpretation of a cloud of earthquake hypocenters in terms of causative structures is not a simple task. Locations are subject to uncertainties, which will not be the same for every earthquake. The data should therefore not be interpreted simply by inspection, which is difficult in the case of three-dimensional data anyway. Instead, we propose using the location uncertainties as a guide in processing the data. Earthquake locations are moved inside their uncertainty or confidence ellipsoids until a simplified picture of the earthquake cloud is obtained, which can then be interpreted in terms of some simplified structure such as faults. The aim of the approach is to give the simplest possible structure that is consistent with all the location and confidence ellipsoid data. The method is applied to three synthetic sets of data. These illustrate the potential and limitations of the method. Application to a real earthquake data set from Rabaul Caldera in Papua New Guinea gives an image of the caldera ring fault that suggests departures from the simple ring-fault structure previously assumed. Sensitivity analysis on the Rabaul data shows that the method is not unduly sensitive to the assumptions that have to be made in applying it.
Younger Dryas glacial advance in the southern Gulf of St. Lawrence, Canada: analogue for ice inception?

In the summer of 1999, the Maritimes and Northeast Pipeline Company excavated a 3‐m‐deep trench across northern Nova Scotia exposing a continuous transect of surficial deposits along a 237‐km corridor. A Lateglacial palaeosol with preserved A horizon (peat and wood) buried under 2–10 m of surface till consisted mainly of herbaceous plant material with few large wood fragments. Large pieces of wood from two sites yielded conventional radiocarbon ages of 10.9 14C kyr BP (GSC‐6435) and 10.8 14C kyr BP (GSC‐6419). Previous to these finds, only a few localities were known to reveal till overlying peat, so the extent of Younger Dryas (YD) glaciers could not be clearly established. Glacial flow lines indicated by fabric and fluting of the YD surface till sheet in northern Nova Scotia and ice‐marginal deposits imply an ice cap centred over eastern P.E. I. and the southern Gulf of St. Lawrence. This glacier also dammed a series of glacial lakes against the highland‐rimmed west coast of Cape Breton Island. Glaciers developed and advanced during the YD in the uplands and offshore shelf areas from small remnants of Late Wisconsinan ice. Renewed ice growth was enabled by increased precipitation and local cooling in the Gulf of St. Lawrence due to deflection of the jet stream and expanded sea‐ice cover in the North Atlantic. The YD may provide an analogy to glacier development in Maritime Canada during the interglacial/glacial transition.
Real-Time Correction of Closed-Captions

Live closed-captions for deaf and hard of hearing audiences are currently produced by stenographers, or by voice writers using speech recognition. Both techniques can produce captions with errors. We are currently developing a correction module that allows a user to intercept the real-time caption stream and correct it before it is broadcast. We report results of preliminary experiments on correction rate and actual user performance using a prototype correction module connected to the output of a speech recognition captioning system.
Do Me a Solid? Information Asymmetry, Liking, and Compliance Gaining Online

As the popularity of interactive social media grows, it is increasingly normal for individuals to reveal significant amounts of personal information online. Although this information is intended to support social networks, it can potentially be misused. We hypothesize that access to routine network site profile information can enable individuals to foster feelings of interpersonal attraction in their communication partner, which should increase the likelihood that their partner complies with requests for help. This study reports on an experiment conducted to assess these relationships. Results show participants who had access to personal information about their conversation partner in zero history dyads were more likely to gain their partner's compliance. Surprisingly, participants who benefited from the information asymmetry incurred a cost as well, as their partners reported liking them less compared to the control condition. Further, those who rated the information as valuable for getting their partner to like them were the least successful at gaining compliance.
A robust method of detecting hand gestures using depth sensors

Depth sensors, including Kinect and Xtion, open up a new possibility for future human-computer interaction (HCI). Even though there already are some mature methods of detecting human skeleton and poses using depth sensors, it is still an unsolved problem to detect hands and recognize delicate gestures effectively, because hands are too small a part in the images generated from depth sensor, so the details of hands are hard to extract. In this paper, we present a gesture detecting method that is able to: firstly segment hands through skin color segmentation and K-means clustering; secondly find the convex hull and the contour that form the hand shape; thirdly detect positions of each fingertip; and finally represent gestures using the sets of detected hand data. Having been tested with a series of applications, our method is proved to be robust and effective.
Attack Detection in Cloud Infrastructures Using Artificial Neural Network with Genetic Feature Selection

Detecting cyber-attacks in cloud infrastructures is essential for protecting cloud infrastructures from cyber-attacks. It is difficult to detect cyber-attacks in cloud infrastructures due to the complex and distributed natures of cloud infrastructures. In addition, various computing and storage devices, both mobile and stationary, are connected to cloud infrastructures to facilitate users access, which increases the difficulty and complexity of cyber-attack detection. In this paper, an effective approach is presented to detecting cyber-attacks in cloud infrastructures, including those through remote computing devices. This approach is to use an artificial neural network (ANN), which is trained using the network traffic data on the connecting links of the cloud infrastructures. Since ANN is computationally intensive, a technique using a genetic algorithm to reduce the number of features extracted from the network traffic data is developed and incorporated in our approach. This approach is illustrated by using two large data sets of network traffic, and shown that the results are better than those of existing methods for detecting cyber-attacks in cloud infrastructures.
An Integrated Approach to Identifying Cis-Regulatory Modules in the Human Genome

In eukaryotic genomes, it is challenging to accurately determine target sites of transcription factors (TFs) by only using sequence information. Previous efforts were made to tackle this task by considering the fact that TF binding sites tend to be more conserved than other functional sites and the binding sites of several TFs are often clustered. Recently, ChIP-chip and ChIP-sequencing experiments have been accumulated to identify TF binding sites as well as survey the chromatin modification patterns at the regulatory elements such as promoters and enhancers. We propose here a hidden Markov model (HMM) to incorporate sequence motif information, TF-DNA interaction data and chromatin modification patterns to precisely identify cis-regulatory modules (CRMs). We conducted ChIP-chip experiments on four TFs, CREB, E2F1, MAX, and YY1 in 1% of the human genome. We then trained a hidden Markov model (HMM) to identify the labels of the CRMs by incorporating the sequence motifs recognized by these TFs and the ChIP-chip ratio. Chromatin modification data was used to predict the functional sites and to further remove false positives. Cross-validation showed that our integrated HMM had a performance superior to other existing methods on predicting CRMs. Incorporating histone signature information successfully penalized false prediction and improved the whole performance. The dataset we used and the software are available at http://nash.ucsd. edu/CIS/.
The Role of American Sign Language in Improving Behavioral Functioning in Developmentally Delayed Children with Communication Disorders.

This thesis investigated the effects of adding the learning of American Sign Language (ASL) signs (as part of the Applied Behavior Analysis) on the negative behaviors of seven developmentally disabled children (aged 8-13 years). The children were measured on the following: overall disruptive behavior; aggressive behavior; tantrums; and the use of ASL signs over the course of 20 consecutive school days (10 days for the establishment of the baseline, 10 days for the study of the intervention). Results on these measures failed to provide support for the efficacy of ASL signs. Qualitative observations, however, did lend support for the use of the intervention. The lack of quantitative findings is explained by the fact that signs were being implemented before the baseline period and that most of the dramatic treatment gains had been obtained before the baseline measurements were completed. Although the study did not provide hard evidence for the efficacy of using ASL with the developmentally disabled population, there was enough anecdotal evidence to consider applying this intervention. The advantage of ASL is that even if it is not effective, the iatrogenic effects of using it are minimal at most. The findings from this study suggest that caretakers who take the time to implement ASL signs into the behavioral repertoire of developmentally disabled children will see dramatic improvements in the child's behavior. Two appendices containing behavior rating scales and 78 references are included. (KFT) Reproductions supplied by EDRS are the best that can be made from the original document.
Human-robot communication by means of hand-clapping (preliminary experiment with hand-clapping language)

Conventionally, human-robot communication and robot-robot communication are dealt with in different manners in general. From the viewpoint of cooperation between human staff and robots as a heterogeneous group, however, it is natural and desirable that all of the communication within the group is performed based on a uniform communication manner. In this study, we deal with a communication method that can be adopted for both human-robot and robot-robot communications. We discuss a communication style by means of simple sound such as the hand-clapping. A communication language is introduced; the configuration of syllables, the words and their parts of speech and the syntax are designed from a general point of view. Perceptibility assessment is performed for the syllables. A preliminary experimental study is performed and the feasibility of the proposed human-robot communication style is examined. The obtained result indicates the possibility and importance of usability viewpoint of the proposed communication approach.
Notch Signaling in Vascular Development

Abstract—Notch signaling is an extremely conserved and widely used mechanism regulating cell fate in metazoans. Interaction of Notch receptors (Notch) with their ligands (Delta-like or Jagged) leads to cleavage of the Notch intracellular domain (NICD) that migrates into the nucleus. In the nucleus, NICD associates with a transcription factor, RBP-Jk. The NICD-RBP-Jk complex, in turn, upregulates expression of primary target genes of Notch signaling, such as hairy and enhancer of split (HES) and HES-related repressor protein (HERP) transcriptional repressors. Recent evidence has demonstrated that the Notch pathway is involved in multiple aspects of vascular development, including proliferation, migration, smooth muscle differentiation, angiogenic processes, and arterial-venous differentiation. In this brief review, we focus on ligands, receptors, and target genes of Notch signaling in the vascular system and discuss (1) tissue distribution; (2) gain- and loss-of-function studies; and (3) the role of Notch components in human diseases involving the vascular system.
Rules for Comprehensible Technical Prose: A Survey of the Psycholinguistic Literature.

Abstract : Computerized systems that provide feedback on the comprehensibility of technical prose should be based on what is known scientifically about what makes prose difficult to understand. The experimental psycholinguistics literature was surveyed, and a set of rules for comprehensible technical prose was derived. The basic criteria for selection of the literature and the rules was that the rules should be applicable by an automatic system without needing deep knowledge of the domain. Rather, the rules should address the surface structure of sentences and text, and the semantic content at a 'shallow' level, defined in terms of the propositional representation for the text content. Many rules for good sentence syntax, coherence, text organization, and amount of content are proposed and justified by the experimental literature. THe major gaps in the empirical literature are described in a concluding section. Keywords: Readability; Documentation.
The effect of metamizole on ischemia/reperfusion injury in the rat ovary: An analysis of biochemistry, molecular gene expression, and histopathology

Objectives: In this study, we investigated the effect of metamizole on ischemia/reperfusion (I/R) injury an analysis of biochemistry, molecular gene expression, and histopathology in the rat ovary of female albino Wistar rats. Materials and Methods: Animals were divided into four groups; control group with induced ischemia-reperfusion (IRC), ischemia-reperfusion +100 mg/kg metamizole sodium (MS) (IRM-100), ischemia-reperfusion +200 mg/kg MS (IRM-200), and healthy group applied sham operation (SG). Results: Myeloperoxidase (MPO) activity and gene expression increased significantly in IRC and IRM-100 group rat ovarian tissue compared with the SG group (P < 0.0001). However, MPO activity and gene expression in IRM-200 group ovarian tissue decreased significantly compared with the IRC and IRM-100 groups (P < 0.0001). Histopathologically, pronounced congestion, dilated vessels, hemorrhage, edema, degenerative cells, and neutrophil migration and adhesion to the endothelium were observed in the IRC and IRM-100 group ovarian tissues. A small number of congested dilated vessels, mild congestion, and edema were observed in the IRM-200 group, but no neutrophil migration and adhesion to the endothelium or degenerative cells. Conclusions: At 200 mg/kg dose metamizole prevented ovarian injury induced with I/R. This data show that metamizole can be used in the ovarian I/R injury treatment.
Closer to 90–90–90. The cascade of care after 10 years of ART scale-up in rural Malawi: a population study

The antiretroviral therapy (ART) programme supported by Médecins Sans Frontières in the rural Malawian district of Chiradzulu was one of the first in sub‐Saharan Africa to scale up ART delivery in 2002. After more than a decade of continuous involvement, we conducted a population survey to evaluate the cascade of care, including population viral load, in the district.
A Gender Mixture Detection Approach to Unsupervised Single-Channel Speech Separation Based on Deep Neural Networks

We propose an unsupervised speech separation framework for mixtures of two unseen speakers in a single-channel setting based on deep neural networks (DNNs). We rely on a key assumption that two speakers could be well segregated if they are not too similar to each other. A dissimilarity measure between two speakers is first proposed to characterize the separation ability between competing speakers. We then show that speakers with the same or different genders can often be separated if two speaker clusters, with large enough distances between them, for each gender group could be established, resulting in four speaker clusters. Next, a DNN-based gender mixture detection algorithm is proposed to determine whether the two speakers in the mixture are females, males, or from different genders. This detector is based on a newly proposed DNN architecture with four outputs, two of them representing the female speaker clusters and the other two characterizing the male groups. Finally, we propose to construct three independent speech separation DNN systems, one for each of the female–female, male–male, and female–male mixture situations. Each DNN gives dual outputs, one representing the target speaker group and the other characterizing the interfering speaker cluster. Trained and tested on the speech separation challenge corpus, our experimental results indicate that the proposed DNN-based approach achieves large performance gains over the state-of-the-art unsupervised techniques without using any specific knowledge about the mixed target and interfering speakers being segregated.
Fruits and Vegetables Calorie Counter Using Convolutional Neural Networks

People care about what types of fruit they're eating and the nutrients it contains because eating fruit is an essential part of leading a healthy life. This paper introduces an automatic way for detecting and recognizing the fruits in an image. The proposed method uses state of the art deep learning techniques for feature extraction and classification. Deep learning methods, especially convolutional neural networks, have been widely used for a variety of classification problems and have achieved promising results. Our trained model has achieved an accuracy of 75% in the task of classification of 43 different types of fruit. The similar methods have achieved up to 70% with fewer classes.
SALSA: scalable and low synchronization NUMA-aware algorithm for producer-consumer pools

We present a highly-scalable non-blocking producer-consumer task pool, designed with a special emphasis on lightweight synchronization and data locality. The core building block of our pool is SALSA, Scalable And Low Synchronization Algorithm for a single-consumer container with task stealing support. Each consumer operates on its own SALSA container, stealing tasks from other containers if necessary. We implement an elegant self-tuning policy for task insertion, which does not push tasks to overloaded SALSA containers, thus decreasing the likelihood of stealing. SALSA manages large chunks of tasks, which improves locality and facilitates stealing. SALSA uses a novel approach for coordination among consumers, without strong atomic operations or memory barriers in the fast path. It invokes only two CAS operations during a chunk steal. Our evaluation demonstrates that a pool built using SALSA containers scales linearly with the number of threads and significantly outperforms other FIFO and non-FIFO alternatives.
Adaptation to Climate Change: Land Use and Livestock Management Change in the U.S

This paper examines possible expected climate adaptations in a U.S. land use and livestock context. By using a Fractional Multinomial Logit model, we find that climate variables are affecting the allocation of land use by reducing crop land and increasing pasture land. Our projections indicate that more cropping land would be altered to livestock land under climate change. In addition, cattle stocking rate could increase by the end of this century along with more pasture land or less cattle inventory because of higher temperature.
A double‐blind, placebo‐controlled assessment of nortriptyline's side‐effects during 3‐year maintenance treatment in elderly patients with recurrent major depression

The authors assessed the severity of nortriptyline's side‐effects in older patients with recurrent major depression during placebo‐controlled, double‐blind maintenance therapy. Data were from 37 patients completing 2–3 years of maintenance therapy; 29 were on nortriptyline and eight were on placebo. The authors detected a time‐by‐treatment interaction for dry mouth (greater in nortriptyline‐treated patients), but no increased association of nortriptyline with constipation, weight change or orthostatic symptoms. Heart rate was consistently higher in nortriptyline‐maintained patients as compared with placebo. The total ‘side‐effect’ score on the Asberg Rating Scale, as well as complaints of physical tiredness, daytime sleepiness and nocturnal sleep disturbance, were related primarily to residual depression rather than treatment with nortriptyline. Copyright © 1999 John Wiley & Sons, Ltd.