Miracle-dz/UniFAR-FTUs huggingface dataset:
Increased VIP levels in peripheral blood outside migraine attacks as a potential biomarker of cranial parasympathetic activation in chronic migraine

Aim The aim of this article is to determine vasoactive intestinal peptide (VIP) levels outside migraine attacks in peripheral blood as a potential biomarker for chronic migraine (CM). Methods Women older than 17 and diagnosed as CM were recruited. Matched healthy women with no headache history and women with episodic migraine (EM) served as control groups, together with a series of patients with episodic cluster headache in a pain-free period. VIP levels were determined in blood samples obtained from the right antecubital vein by ELISA outside a migraine attack, the patients having taken no symptomatic medication the day before. For ethical reasons, preventives were not stopped. Results We assessed plasma samples from 119 women with CM, 33 healthy women, 51 matched women with EM and 18 patients (16 males) with cluster headache matched for age. VIP levels were significantly increased in CM (165.1 pg/ml) as compared to control healthy women (88.5 pg/ml) and episodic cluster headache patients (101.1 pg/ml). VIP levels in EM (134.9 pg/ml) were significantly higher compared to controls and numerically lower than those of CM. Thresholds of 71.8 and 164.5 pg/ml optimized the sensitivity and specificity to differentiate CM from healthy controls and EM, respectively. Variables such as age, CM duration, the presence of aura, analgesic overuse, depression, fibromyalgia, vascular risk factors, history of triptan consumption or kind of preventive treatment did not significantly influence VIP levels. Conclusion Increased interictal VIP level measured in peripheral blood could be a biomarker helping in CM diagnosis, though it does not clearly differentiate between EM and CM.
The Microbial Ferrous Wheel in a Neutral pH Groundwater Seep

Evidence for microbial Fe redox cycling was documented in a circumneutral pH groundwater seep near Bloomington, Indiana. Geochemical and microbiological analyses were conducted at two sites, a semi-consolidated microbial mat and a floating puffball structure. In situ voltammetric microelectrode measurements revealed steep opposing gradients of O2 and Fe(II) at both sites, similar to other groundwater seep and sedimentary environments known to support microbial Fe redox cycling. The puffball structure showed an abrupt increase in dissolved Fe(II) just at its surface (∼5 cm depth), suggesting an internal Fe(II) source coupled to active Fe(III) reduction. Most probable number enumerations detected microaerophilic Fe(II)-oxidizing bacteria (FeOB) and dissimilatory Fe(III)-reducing bacteria (FeRB) at densities of 102 to 105 cells mL−1 in samples from both sites. In vitro Fe(III) reduction experiments revealed the potential for immediate reduction (no lag period) of native Fe(III) oxides. Conventional full-length 16S rRNA gene clone libraries were compared with high throughput barcode sequencing of the V1, V4, or V6 variable regions of 16S rRNA genes in order to evaluate the extent to which new sequencing approaches could provide enhanced insight into the composition of Fe redox cycling microbial community structure. The composition of the clone libraries suggested a lithotroph-dominated microbial community centered around taxa related to known FeOB (e.g., Gallionella, Sideroxydans, Aquabacterium). Sequences related to recognized FeRB (e.g., Rhodoferax, Aeromonas, Geobacter, Desulfovibrio) were also well-represented. Overall, sequences related to known FeOB and FeRB accounted for 88 and 59% of total clone sequences in the mat and puffball libraries, respectively. Taxa identified in the barcode libraries showed partial overlap with the clone libraries, but were not always consistent across different variable regions and sequencing platforms. However, the barcode libraries provided confirmation of key clone library results (e.g., the predominance of Betaproteobacteria) and an expanded view of lithotrophic microbial community composition.
Confidence-Weighted Dual-Teacher Networks With Biased Contrastive Learning for Semi-Supervised Semantic Segmentation in Remote Sensing Images

Semantic segmentation of remote sensing images is vital in remote sensing technology. High-quality models for this task require a vast amount of images, and manual annotation is a process that is time-consuming and labor-intensive. Consequently, this has catalyzed the emergence of semi-supervised semantic segmentation methods. However, the complexity of foreground categories in remote sensing images poses a challenge to maintaining prediction consistency. Moreover, inherent characteristics such as intraclass variations and interclass similarities result in a certain degree of confusion among features of different classes in the feature space. This impacts the final classification results. To improve the model’s consistency and optimize the classification of categories based on features, this article proposes a new semi-supervised semantic segmentation framework that combines consistency regularization and contrastive learning (CL). In terms of consistency regularization, the proposed method incorporates dual-teacher networks, introduces ClassMix for image augmentation, and uses confidence levels to integrate the predictions from these networks. By introducing perturbations at both the network and image levels, while simultaneously maintaining consistency, the predictive prowess and generalization ability of the model are enhanced. For CL, positive-unlabeled learning (PU-Learning) is used to improve the problem of mis-sampling when selecting features. At the same time, higher biased weights are allocated to more challenging negative samples, thereby elevating the complexity of feature learning and enhancing the discriminative capability of the final feature representation space. Our extensive experiments on the ISPRS Vaihingen dataset and the challenging iSAID dataset have served to underscore the superior performance of our approach.
Integration of polypyrrole microactuators and organic optoelectronic devices for lab-on-chip applications

Two type of devices suited for lab-on-a-chip applications were investigated. First, the integration of a polymeric microactuator deposited on top of carbon electrodes placed below a microfluidic channel. This actuator uses the out-of-plane strain of the polypyrrole/NaDBS (sodium dodecylbenzene sulfonate) system. The electrodeposition of polypyrrole on top of graphitic and carbon nanotube microelectrodes has been achieved. Secondly, the combination of an organic light emitting diode (OLED) with a microfuidic channel in order to build an integrated fluorescence detection system was also investigated.
Face tracking in video with hybrid of Lucas-Kanade and condensation algorithm

In this paper, we present a robust face tracking system for video indexing and retrieval. Our face tracker is designed based on the condensation algorithm. The strength of our face tracking is in the incorporation of Lucas-Kanade feature tracker in the measurement stage of condensation. Skin color and facial feature points are used for tracking. The pros and cons of using color and facial feature points complement each other and ensure the effectiveness of our system. We also adopt a bi-directional tracking approach to enhance the robustness. We demonstrate the efficacy of our technique in the challenging task of tracking faces in various video sources.
Estimating Power Spectrum of Sunyaev-Zeldovich Effect from the Cross-Correlation between WMAP and 2MASS

We estimate the power spectrum of SZ(Sunyaev-Zel’dovich)-eﬀect-induced temperature ﬂuctuations on sub-degree scales by using the cross correlation between the three-year WMAP maps and 2MASS galaxy distribution. We produced the SZ eﬀect maps by hydrodynamic simulation samples of the ΛCDM model, and show that the SZ eﬀect temperature ﬂuctuations are highly non-Gaussian. The PDF of the temperature ﬂuctuations has a long tail. More than 70% power of the SZ eﬀect temperature ﬂuctuations attributes to top ∼ 1% wavelet modes (long tail events). On the other hand, the CMB temperature ﬂuctuations basically are Gaussian. Although the mean power of CMB temperature ﬂuctuations on sub-degree scales is much higher than that of SZ eﬀect map, the SZ eﬀect temperature ﬂuctuations associated with top 2MASS clusters is comparable to the power of CMB temperature ﬂuctuations on the same scales. Thus, from noisy WMAP maps, one can have a proper estimation of the SZ eﬀect power at the positions of the top 2MASS clusters. The power spectrum given by these top wavelet modes is useful to constrain the parameter of density ﬂuctuations amplitude σ 8 . We ﬁnd that the power spectrum of these top wavelet modes of SZ eﬀect on sub-degree scales basically is consistent with the simulation maps produced with σ 8 = 0 . 84. The simulation samples of σ 8 = 0 . 74 show, however, signiﬁcant deviation from detected SZ power spectrum. It can be ruled out with conﬁdence level 99% if all other cosmological parameters are the same as that given by the three-year WMAP results.
Video based automatic fall detection in indoor environment

An increase in population of elderly people living in isolated environments, leads to the need for an automated monitoring system. Fall is one of the major reasons for death of elderly people. So fall detection is an essential part of an automated indoor monitoring system. Video based automatic fall detection is robust and more reliable than other fall detection methods. Most of the available video based fall detection mechanisms are based on the extraction of motion dynamic features like velocity and intensity gradient of the person in video. These methods are computationally intensive and less accurate. This paper presents an accurate and computationally less intensive approach to detect fall, using only the static features of the person such as aspect ratio and inclination angle. Experimental results show that this method is robust in detecting all kinds of fall.
The thalamostriatal system in normal and diseased states

Because of our limited knowledge of the functional role of the thalamostriatal system, this massive network is often ignored in models of the pathophysiology of brain disorders of basal ganglia origin, such as Parkinson’s disease (PD). However, over the past decade, significant advances have led to a deeper understanding of the anatomical, electrophysiological, behavioral and pathological aspects of the thalamostriatal system. The cloning of the vesicular glutamate transporters 1 and 2 (vGluT1 and vGluT2) has provided powerful tools to differentiate thalamostriatal from corticostriatal glutamatergic terminals, allowing us to carry out comparative studies of the synaptology and plasticity of these two systems in normal and pathological conditions. Findings from these studies have led to the recognition of two thalamostriatal systems, based on their differential origin from the caudal intralaminar nuclear group, the center median/parafascicular (CM/Pf) complex, or other thalamic nuclei. The recent use of optogenetic methods supports this model of the organization of the thalamostriatal systems, showing differences in functionality and glutamate receptor localization at thalamostriatal synapses from Pf and other thalamic nuclei. At the functional level, evidence largely gathered from thalamic recordings in awake monkeys strongly suggests that the thalamostriatal system from the CM/Pf is involved in regulating alertness and switching behaviors. Importantly, there is evidence that the caudal intralaminar nuclei and their axonal projections to the striatum partly degenerate in PD and that CM/Pf deep brain stimulation (DBS) may be therapeutically useful in several movement disorders.
Finding Plagiarisms among a Set of Programs with JPlag

JPlag is a web service that finds pairs of similar programs among a given set of programs. It has successfully been used in practice for detecting plagiarisms among student Java program submissions. Support for the languages C, C++ and Scheme is also available. We describe JPlag's architecture and its comparsion algorithm, which is based on a known one called Greedy String Tiling. Then, the contribution of this paper is threefold: First, an evaluation of JPlag's performance on several rather different sets of Java programs shows that JPlag is very hard to deceive. More than 90 percent of the 77 plagiarisms within our various benchmark program sets are reliably detected and a majority of the others at least raise suspicion. The run time is just a few seconds for submissions of 100 programs of several hundred lines each. Second, a parameter study shows that the approach is fairly robust with respect to its configuration parameters. Third, we study the kinds of attempts used for disguising plagiarisms, their frequency, and their success.
Trajectory Clustering: A Non-Parametric Method for Grouping Gene Expression Time Courses with Applications to Mammary Development

Trajectory clustering is a novel and statistically well-founded method for clustering time series data from gene expression arrays. Trajectory clustering uses non-parametric statistics and is hence not sensitive to the particular distributions underlying gene expression data. Each cluster is clearly defined in terms of direction of change of expression for successive time points (its 'trajectory'), and therefore has easily appreciated biological meaning. Applying the method to a dataset from mouse mammary gland development, we demonstrate that it produces different clusters than Hierarchical, K-means, and Jackknife clustering methods, even when those methods are applied to differences between successive time points. Compared to all of the other methods, trajectory clustering was better able to match a manual clustering by a domain expert, and was better able to cluster groups of genes with known related functions.
A Framework to Clarify the Role of Knowledge Management Systems

Knowledge management Systems (KMS) are IT applications that manage representations of organizational knowledge. This paper presents a conceptual model of KMS which adapts an artificial intelligence (AI) based view of knowledge. According to this view, knowledge can be defined in terms of agent, action, state, and goal. The conceptual model is intended to help differentiate the role of KMS from that of Information Systems (IS). The knowledge managed by the KMS is intended to enable an agent to choose actions that can be taken to accomplish a goal in the given state. The role of the IS is to make the agent aware of a situation described in terms of states, actions, and goal. The model suggests that whether we classify an IT application as KMS or IS depends on the contents it manages and to increase the effectiveness of KMS, it is often necessary to use IS that complement the KMS. Considering the importance and popularity of KMS in organizations, we believe the clarification of the role of KMS is useful.
Relationships between lifestyle factors and neutrophil functions in the elderly

We investigated the relationships between neutrophil functions and lifestyle factors in the elderly. The subjects (84 males, 73.9±5.8 years old; and 63 females, 70.0±4.6 years old) belonged to a recreational seniors club in Japan. Investigations of the subjects' stress, exercise habits, smoking habits, and alcohol‐drinking habits were performed. The phagocytosis and superoxide productivity of the neutrophils were measured with a nitroblue tetrazolium (NBT) reduction test. In addition, leukocyte counts and serum total protein (TP) levels were determined. The results revealed that aging, high serum levels, and stress‐coping factors (e.g., having hobbies, keeping pets, and close links with friends or family) significantly correlated with preferable neutrophil functions. In addition, significant effects of lifestyle factors on the balance between phagocytosis and subsequent superoxide production were observed. Thus, the results of the present study suggest that there are correlations between neutrophil functions and lifestyle factors in the elderly. J. Clin. Lab. Anal. 16:266–272, 2002. © 2002 Wiley‐Liss, Inc.
Process-induced skew variation for scaled 2-D and 3-D ICs

Technology scaling and three-dimensional integration are two design paradigms that offer high device density. Process variations affect these design paradigms in different ways. The effect of process variations on clock skew for a 2-D circuit implemented at scaled technology nodes and for a 3-D circuit with an increasing number of planes is investigated in this paper. An accurate model used to describe the effect of the proper sources of variations on each of these design approaches is proposed. The distribution of the pair-wise skew variation is obtained for single scaled or multi-plane (not scaled) clock distribution networks. The accuracy of the presented statistical skew model is verified through Monte-Carlo simulations. As shown in this paper, the clock skew variation due to technology scaling and/or die stacking exhibits a considerably different behavior. A comparison between these two design paradigms is offered such that the appropriate technology node and number of planes are selected to produce a low clock skew variation and high operating frequency. A popular global clock tree topology is employed in a planar (2-D) circuit where technology scaling is applied and in a 3-D circuit with an increasing number of planes. For this clock tree topology, the maximum supported clock frequency increases from 2.75 GHz to 3.74 GHz by proper die-stacking at a 90 nm technology node. 3-D integration is shown to be an alternative to reduce skew variation without the need of aggressive technology scaling.
Maximizing Survival Benefit With Primary Prevention Implantable Cardioverter-Defibrillator Therapy in a Heart Failure Population

Background— Although implantable cardioverter-defibrillator (ICD) therapy reduces mortality in moderately symptomatic heart failure patients with an ejection fraction ≤35%, many such patients do not require ICD shocks over long-term follow-up. Methods and Results— Using a modification of a previously validated risk prediction model based on routine clinical variables, we examined the relationship between baseline predicted mortality risk and the relative and absolute survival benefits of ICD treatment in the primary prevention Sudden Cardiac Death in Heart Failure Trial (SCD-HeFT). In the placebo arm, predicted 4-year mortality grouped into 5 equal-sized risk groups varied from 12% to 50% (c statistic=0.71), whereas the proportion of SCD in those same risk groups decreased from 52% to 24% of all deaths. ICD treatment decreased relative risk of SCD by 88% in the lowest-risk group versus 24% in the highest-risk group (P=0.009 for interaction) and decreased relative risk of total mortality by 54% in the lowest-risk group versus no benefit (2%) in the highest-risk group (P=0.014 for interaction). Absolute 4-year mortality reductions were 6.6%, 8.8%, 10.6%, 14.0%, and −4.9% across risk quintiles. In highest-risk patients (predicted annual mortality >20%), no benefit of ICD treatment was seen. Projected over each patient's predicted lifespan, ICD treatment added 6.3, 4.1, 3.0, 1.9, and 0.2 additional years of life in the lowest- to highest-risk groups, respectively. Conclusions— A clinical risk prediction model identified subsets of moderately symptomatic heart failure patients in SCD-HeFT in whom single-lead ICD therapy was of no benefit and other subsets in which benefit was substantial.
A Simple Method for Detecting Interactions between a Treatment and a Large Number of Covariates

We consider a setting in which we have a treatment and a large number of covariates for a set of observations, and wish to model their relationship with an outcome of interest. We propose a simple method for modeling interactions between the treatment and covariates. The idea is to modify the covariate in a simple way, and then fit a standard model using the modified covariates and no main effects. We show that coupled with an efficiency augmentation procedure, this method produces valid inferences in a variety of settings. It can be useful for personalized medicine: determining from a large set of biomarkers the subset of patients that can potentially benefit from a treatment. We apply the method to both simulated datasets and gene expression studies of cancer. The modified data can be used for other purposes, for example large scale hypothesis testing for determining which of a set of covariates interact with a treatment variable.
aadA Confers Streptomycin Resistance in Borrelia burgdorferi

ABSTRACT To enhance genetic manipulation of the Lyme disease spirochete Borrelia burgdorferi, we assayed the aadA gene for the ability to confer resistance to the antibiotics spectinomycin and streptomycin. Using the previously described pBSV2 as a backbone, a shuttle vector, termed pKFSS1, which carries the aadA open reading frame fused to the B. burgdorferi flgB promoter was constructed. The hybrid flgB promoter-aadA cassette confers resistance to spectinomycin and streptomycin in both B. burgdorferi and Escherichia coli. pKFSS1 has a replication origin derived from the 9-kb circular plasmid and can be comaintained in B. burgdorferi with extant shuttle vector pCE320, which has a replication origin derived from a 32-kb circular plasmid, or pBSV2, despite the fact that pKFSS1 and pBSV2 have the same replication origin. Our results demonstrate the availability of a new selectable marker and shuttle vector for genetically dissecting B. burgdorferi at the molecular level.
A graph-based approach to detecting tourist movement patterns using social media data

ABSTRACT Understanding the characteristics of tourist movement is essential for tourist behavior studies since the characteristics underpin how the tourist industry management selects strategies for attraction planning to commercial product development. However, conventional tourism research methods are not either scalable or cost-efficient to discover underlying movement patterns due to the massive datasets. With advances in information and communication technology, social media platforms provide big data sets generated by millions of people from different countries, all of which can be harvested cost efficiently. This paper introduces a graph-based method to detect tourist movement patterns from Twitter data. First, collected tweets with geo-tags are cleaned to filter those not published by tourists. Second, a DBSCAN-based clustering method is adapted to construct tourist graphs consisting of the tourist attraction vertices and edges. Third, network analytical methods (e.g. betweenness centrality, Markov clustering algorithm) are applied to detect tourist movement patterns, including popular attractions, centric attractions, and popular tour routes. New York City in the United States is selected to demonstrate the utility of the proposed methodology. The detected tourist movement patterns assist business and government activities whose mission is tour product planning, transportation, and development of both shopping and accommodation centers.
The Moving Query Windows for Shot Boundary Detection at TREC-12

Digital video is widely used in multimedia databases and requires e(cid:11)ective retrieval techniques. Shot boundary detection is a common (cid:12)rst step in analysing video content. The e(cid:11)ective detection of gradual transitions is an especially di(cid:14)cult task. Building upon our past research work, we have designed a novel decision stage for detection of gradual transitions. Its strength lies particularly in the accurate detection of gradual transition boundaries. In this paper, we describe our moving query window method and discuss its performance in the context of the trec-12 shot boundary detection task. We believe this approach is a valuable contribution to video retrieval and worth persueing in the future.