Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis of the Most Common Three MEFV Mutations in 630 Patients with Familial Mediterranean Fever in Iranian Azeri Turkish Population.

Introduction The aim of the present study was to determine the frequency of M694V, M680I and V726A mutations of the MEFV gene in 630 Azeri Turkish patients with family Mediterranean fever. Material and Methods The MEFV gene mutations were detected using allele-specific oligonucleotide polymerase chain reaction. Outcomes 630 cases with a mean age ± SD of 28.54±16.54 ranging from 2.5 to 76 years old including 268 (42.54%) males and 362 (57.46%) females, were tested. Nineteen patients were homozygote for one mutation (3.02%), 127 were heterozygote for one mutation (20.2%) and 18 were compound heterozygote for two mutations (2.86%). Mutation analysis confirmed that the most common mutation was M694V 109 (8.65%). V726A and M680I mutations accounted for 4.44% of the alleles; V726A 32 (2.54%) and M680I 24 (1.9%). In this study, compound heterozygote for M694V and V726A, M694V and M680I, and V726A and M680I mutations were found in 1.43%, 0.79%, and 0.63% from West Azerbaijan province in exon 10. Mutation was found in 164 (26.03%) of cases regarding analysis of the three most common MEFV mutations, but in 466 (73.97%) of cases, no mutation was detected. Among our samples, the frequencies of mutant genotypes were 15 (2.38%), 1 (0.15%), 3 (0.47%), 9 (1.42%), 4 (0.63%) and 5 (0.79%), regarding M694V/M694V, M680I/M680I, V726A/V726A, M694V/ V726A, M680I/ V726A and M680I/ M694V, respectively. In our samples, 79 (12.53%), 26 (4.12%), and 22 (3.49%) cases had M694V/normal, V726A/normal, and M680I/normal genotypes regarding M694V, V726A, and M680I mutations, respectively. Conclusions The M694V mutation is the most common risk factor for family Mediterranean fever in our group.
Expression of osteopontin in the rat retina: effects of excitotoxic and ischemic injuries.

PURPOSE The cytokine osteopontin (OPN) has been localized to the retinal ganglion cell layer in the normal rodent retina, prompting the suggestion that it could serve as a useful marker for identifying and quantifying such neurons in models of retinal and optic nerve neurodegeneration. In the present study, we characterized the time course and cellular localization of OPN expression in the rat retina after excitotoxic and ischemic injuries. METHODS Excitotoxicity and ischemia-reperfusion experiments were performed by using standard techniques. Rats were killed at various time points, and the retinas were removed either for mRNA analysis or to be processed for immunohistochemistry. RESULTS In the normal retina, double-labeling immunofluorescence indicated that OPN is expressed by the majority of, if not all, RGCs, since OPN was associated with more cells than Brn-3, but was colocalized with Thy1.1. NMDA, kainic acid, and ischemia-reperfusion all caused decreases in the total retinal levels of Thy1 and Brn-3 mRNAs, reflecting injury to RGCs, but a dramatic, short-lived upregulation in OPN mRNA. The source of the increased OPN signal after excitotoxic-ischemic insults is unlikely to be injured RGCs, as no alteration in the intensity of OPN immunostaining in RGCs was apparent. Instead, additional cells, mostly contained within the IPL, were identified as positive for OPN. Double-labeling immunofluorescence showed that ED1 always colocalized with OPN in these cells, indicating their status as activated microglia. CONCLUSIONS OPN is exclusively expressed by RGCs in the physiological retina, but in response to retinal neurodegeneration is synthesized de novo by endogenous, activated microglia.
Joint Induction of Shape Features and Tree Classifiers

We introduce a very large family of binary features for two-dimensional shapes. The salient ones for separating particular shapes are determined by inductive learning during the construction of classification trees. There is a feature for every possible geometric arrangement of local topographic codes. The arrangements express coarse constraints on relative angles and distances among the code locations and are nearly invariant to substantial affine and nonlinear deformations. They are also partially ordered, which makes it possible to narrow the search for informative ones at each node of the tree. Different trees correspond to different aspects of shape. They are statistically and weakly dependent due to randomization and are aggregated in a simple way. Adapting the algorithm to a shape family is then fully automatic once training samples are provided. As an illustration, we classified handwritten digits from the NIST database; the error rate was 0.7 percent.
Interleukin 12p40 is required for dendritic cell migration and T cell priming after Mycobacterium tuberculosis infection

Migration of dendritic cells (DCs) to the draining lymph node (DLN) is required for the activation of naive T cells. We show here that migration of DCs from the lung to the DLN after Mycobacterium tuberculosis (Mtb) exposure is defective in mice lacking interleukin (IL)-12p40. This defect compromises the ability of IL-12p40–deficient DCs to activate naive T cells in vivo; however, DCs that express IL-12p40 alone can activate naive T cells. Treatment of IL-12p40–deficient DCs with IL-12p40 homodimer (IL-12(p40)2) restores Mtb-induced DC migration and the ability of IL-12p40–deficient DCs to activate naive T cells. These data define a novel and fundamental role for IL-12p40 in the pathogen-induced activation of pulmonary DCs.
A novel transceiver architecture in the two-way relay channel with analog and digital beamforming

In this paper, we propose a novel transceiver architecture at relay for multiple input, multiple output two-way relay channel (TWRC). This transceiver architecture consists of the analog beamforming in RF domain and the MMSE based digital processing at baseband, which is simplified as RF-MDP-MIMO architecture. Under the RF-MDP-MIMO architecture, the objective of this paper is to find the optimal relay transmit and receive beamformings that can achieve the boundary of the optimal capacity region with limited relay power. Specifically, by formulating the original optimization problem into two semidefinite programing (SDP) problems with rank-one constraints which can be effectively solved by the penalty function method, we can optimize relay transmit and receive beamformings alternatively. Then, the maximum achievable sum rate of TWRC can be obtained with the bisection method. Further, the achievable capacity region is realized. Numerical experiments are conducted to show that our proposed RF-MDP-MIMO architecture outperforms the RF-MIMO architecture in terms of the optimal achievable capacity region.
Edinburgh Research Explorer Population genetic structure, antibiotic resistance, capsule switching and evolution of invasive pneumococci before conjugate vaccination in Malawi

of large collections of pneumococcal isolates sampled prior to vaccination is required. Here we present a population genomic study of the invasive pneumococcal isolates sampled before the implementation of PCV13 in Malawi. Materials and methods: We retrospectively sampled and whole genome sequenced 585 invasive isolates from 2004 to 2010. We determine the pneumococcal population genetic structure and assessed serotype prevalence, antibiotic resistance rates, and the occurrence of serotype switching. Results: Population structure analysis revealed 22 genetically distinct sequence clusters (SCs), which consisted of closely related isolates. Serotype 1 (ST217), a vaccine-associated serotype in clade SC2, showed highest prevalence (19.3%), and was associated with the highest MDR rate (81.9%) followed by serotype 12F, a non-vaccine serotype in clade SC10 with an MDR rate of 57.9%. Prevalence of serotypes was stable prior to vaccination although there was an increase in the PMEN19 clone, serotype 5 ST289, in clade SC1 in 2010 suggesting a potential undetected local outbreak. Coalescent analysis revealed recent emergence of the SCs and there was evidence of natural capsule switching in the absence of vaccine induced selection pressure. Furthermore, majority of the highly prevalent capsule-switched isolates were associated with acquisition of vaccine-targeted capsules. Conclusions: This study provides descriptions of capsule-switched serotypes and serotypes with potential to cause serotype replacement post-vaccination such as 12F. Continued surveillance is critical to monitor these serotypes and antibiotic resistance in order to design better infection prevention and control measures such as inclusion of emerging replacement serotypes in future conjugate vaccines. (cid:1)
A Possible Explanation of How High-Frequency Deep Brain Stimulation Suppresses Low-Frequency Tremors in Parkinson’s Disease

Parkinson’s disease (PD) is a neurodegenerative disorder of the central nervous system and one of its key symptoms is rest tremor. Deep brain stimulation (DBS) effectively suppresses rest tremor in Parkinson’s disease. Despite being a successful treatment option, its underlying principle and the mechanism by which it attenuates tremors is not yet fully understood. Since existing methods for tuning DBS parameters are largely trial and error, understanding how DBS works can help to reduce time and costs, and could also ultimately lead to better treatment strategies for PD. In this paper, we set out to analyze how a high-frequency stimulation applied through DBS can help reduce the low-frequency rest tremors observed in PD patients. We identify key elements in the sensorimotor loop (the feedback loop consisting of sensory feedbacks and motor responses) that play a role in the interaction of high-frequency DBS signal and the low-frequency tremor. Based on the analysis of these elements, we draw insights about the working of DBS and the role of frequency and the nature of stimulation. We verify these observations with numerical examples and a bench top experimental example.
Observers for discrete-time systems with multiple delays

A systematic approach to design observers for discrete-time systems with multiple delays, under general conditions, is presented. The main feature of the proposed technique is that necessary and sufficient conditions for asymptotic stability are derived while the observer's order is independent from the number of delays and is equal to the state dimension. To illustrate the efficiency of the proposed technique, two numerical examples are provided.
Thinking with the Body

To explore the question of physical thinking – using the body as an instrument of cognition – we collected extensive video and interview data on the creative process of a noted choreographer and his company as they made a new dance. A striking case of physical thinking is found in the phenomenon of marking. Marking refers to dancing a phrase in a less than complete manner. Dancers mark to save energy. But they also mark to explore the tempo of a phrase, or its movement sequence, or the intention behind it. Because of its representational nature, marking can serve as a vehicle for thought. Importantly, this vehicle is less complex than the version of the same phrase danced ‘full-out’. After providing evidence for distinguishing different types of marking, three ways of understanding marking as a form of thought are considered: marking as a gestural language for encoding aspects of a target movement, marking as a method of priming neural systems involved in the target movement, and marking as a method for improving the precision of mentally projecting aspects of the target.
Examining the Impact of Blur on Recognition by Convolutional Networks

State-of-the-art algorithms for semantic visual tasks---such as image classification and semantic segmentation---are based on the use of convolutional neural networks. These networks are commonly trained, and evaluated, on large annotated datasets of high-quality images that are free of artifacts. In this paper, we investigate the effect of one such artifact that is quite common in natural capture settings---blur. We show that standard pre-trained network models suffer a significant degradation in performance when applied to blurred images. We investigate the extent to which this degradation is due to the mismatch between training and input image statistics. Specifically, we find that fine-tuning a pre-trained model with blurred images added to the training set allows it to regain much of the lost accuracy. By considering different combinations of sharp and blurred images in the training set, we characterize how much degradation is caused by loss of information, and how much by the uncertainty of not knowing the nature and magnitude of blur. We find that by fine-tuning on a diverse mix of blurred images, convolutional neural networks can in fact learn to generate a blur invariant representation in their hidden layers. Broadly, our results provide practitioners with useful insights for developing vision systems that perform reliably on real world images affected by blur.
Government Spending and Legislative Organization: Quasi-experimental Evidence from Germany

This paper presents empirical evidence of a positive effect of council size on government spending using a dataset of 2,056 municipalities in the German state of Bavaria over a period of 21 years. We apply a regression discontinuity design to avoid an endogeneity bias. In particular, we exploit discontinuities in the legal rule that relate population size of a municipality in order to council size to identify a causal relationship between council size and public spending, and find a robust positive impact of council size on spending. Moreover, we show that municipalities primarily adjust current expenditure in response to a rise in council size. (JEL D72, H72, R51)
A Dynamic Recurrent Model for Next Basket Recommendation

Next basket recommendation becomes an increasing concern. Most conventional models explore either sequential transaction features or general interests of users. Further, some works treat users' general interests and sequential behaviors as two totally divided matters, and then combine them in some way for next basket recommendation. Moreover, the state-of-the-art models are based on the assumption of Markov Chains (MC), which only capture local sequential features between two adjacent baskets. In this work, we propose a novel model, Dynamic REcurrent bAsket Model (DREAM), based on Recurrent Neural Network (RNN). DREAM not only learns a dynamic representation of a user but also captures global sequential features among baskets. The dynamic representation of a specific user can reveal user's dynamic interests at different time, and the global sequential features reflect interactions of all baskets of the user over time. Experiment results on two public datasets indicate that DREAM is more effective than the state-of-the-art models for next basket recommendation.
‘The chicken and egg thing’: Cognitive representations and self-management of multimorbidity in people with diabetes and depression

Objective: Diabetes with depression is common and can lead to poorer outcomes in both conditions. The existing literature has demonstrated that patients’ single condition representations inform self-management, but less is known about the composition and impact of multimorbid representations. This study aimed to explore accounts of multimorbidity with a focus on the content of cognitive representations and reported management of diabetes and depression. Design: Semi-structured qualitative interviews were carried out with 17 people with diabetes and depression. Data were audio-taped, transcribed and analysed using an inductive thematic analysis and elements of grounded theory. Results: The nature of multimorbid representations varied and some participants, in particular those who prioritised other conditions, described diabetes and depression as unrelated and managed each separately. Others saw interactions between conditions, often in terms of causation, and described how diabetes and depression management could be either integrated or conflicting. Problems taking multiple-medications were frequently described, but participants differed in the confidence with which they described representations of multimorbidity. Conclusion: People hold multimorbid representations that appear to impact on their preferred self-management. An awareness of patients’ understanding of multimorbidity could have implications for the provision of care and intervention design in this population.
Analytical calculation of volumes-of-intersection for iterative, fully 3-D PET reconstruction

Use of iterative algorithms to reconstruct three-dimensional (3-D) positron emission tomography (PET) data requires the computation of the system probability matrix. The pure geometrical contribution can easily be approximated by the length-of-intersection (LOI) between lines-of-response (LOR) and individual voxels. However, more accurate geometrical projectors are desirable. Therefore, we have developed a fast method for the analytical calculation of the 3-D shape and volume of volumes-of-intersection (VOI). This method provides an alternative robust projector with a uniformly continuous sampling of the image space. The enhanced calculation effort is facilitated by using several speedup techniques. Exploiting intrinsic symmetry relations and the sparseness of the system matrix allows to create an efficiently compressed matrix which can be precomputed and completely stored in memory. In addition, a new voxel addressing scheme has been implemented. This scheme avoids time-consuming symmetry transformations of voxel addresses by using an octant-wise symmetrically ordered field of voxels. The above methods have been applied for a fully 3-D, iterative reconstruction of 3-D sinograms recorded with a Siemens/CTI ECAT HR+ PET scanner. A comparison of the performance of the reconstruction using LOI weighting and VOI weighting is presented
Oscillators and relaxation phenomena in Pleistocene climate theory

Ice sheets appeared in the northern hemisphere around 3 Ma (million years) ago and glacial–interglacial cycles have paced Earth's climate since then. Superimposed on these long glacial cycles comes an intricate pattern of millennial and sub-millennial variability, including Dansgaard–Oeschger and Heinrich events. There are numerous theories about these oscillations. Here, we review a number of them in order to draw a parallel between climatic concepts and dynamical system concepts, including, in particular, the relaxation oscillator, excitability, slow–fast dynamics and homoclinic orbits. Namely, almost all theories of ice ages reviewed here feature a phenomenon of synchronization between internal climate dynamics and astronomical forcing. However, these theories differ in their bifurcation structure and this has an effect on the way the ice age phenomenon could grow 3 Ma ago. All theories on rapid events reviewed here rely on the concept of a limit cycle excited by changes in the surface freshwater balance of the ocean. The article also reviews basic effects of stochastic fluctuations on these models, including the phenomenon of phase dispersion, shortening of the limit cycle and stochastic resonance. It concludes with a more personal statement about the potential for inference with simple stochastic dynamical systems in palaeoclimate science.
A Geometrical-Based Model for Cochannel Interference Analysis and Capacity Estimation of CDMA Cellular Systems

A common assumption in cellular communications is the circular-cell approximation. In this paper, an alternative analysis based on the hexagonal shape of the cells is presented. A geometrical-based stochastic model is proposed to describe the angle of arrival of the interfering signals in the reverse link of a cellular system. Explicit closed form expressions are derived, and simulations performed exhibit the characteristics and validate the accuracy of the proposed model. Applications in the capacity estimation of WCDMA cellular networks are presented. Dependence of system capacity of the sectorization of the cells and the base station antenna radiation pattern is explored. Comparisons with data in literature validate the accuracy of the proposed model. The degree of error of the hexagonal and the circular-cell approaches has been investigated indicating the validity of the proposed model. Results have also shown that, in many cases, the two approaches give similar results when the radius of the circle equals to the hexagon inradius. A brief discussion on how the proposed technique may be applied to broadband access networks is finally made.
Aqueous Levels of Pigment Epithelium-Derived Factor and Macular Choroidal Thickness in High Myopia

Purpose. To investigate the correlation between aqueous and serum levels of pigment epithelium-derived factor (PEDF) and macular choroidal thickness in high myopia patients, both with and without choroidal neovascularization (CNV). Methods. Serum and aqueous levels of PEDF were measured by enzyme-linked immunosorbent assay in 36 high myopia patients (36 eyes) with no CNV (non-CNV group), 14 high myopia patients (14 eyes) with CNV (CNV group), and 42 nonmyopia patients (42 eyes) (control group). Macular choroidal thickness was measured by enhanced-depth imaging optical coherence tomography. Results. Aqueous levels of PEDF were significantly higher in CNV group compared with non-CNV (P < 0.001) and control (P < 0.001) groups. Macular choroidal thicknesses were significantly decreased in the non-CNV and CNV groups compared with the control (P < 0.001) group. A statistically significant difference (P = 0.012) was found between the CNV and non-CNV groups. There was a positive correlation between aqueous PEDF and macular choroidal thickness in the non-CNV group (P = 0.005), but no correlation with the CNV group. No correlation between serum PEDF and macular choroidal thickness was detected in the three groups. Conclusion. Variations in aqueous PEDF levels coincide with changes in macular choroidal thickness in high myopia patients with no CNV, while no such relationship exists in high myopia patients with CNV.
Distinguishing signs of opioid overdose and indication for naloxone: an evaluation of six overdose training and naloxone distribution programs in the United States.

AIMS This study assessed overdose and naloxone administration knowledge among current or former opioid abusers trained and untrained in overdose-response in the United States. DESIGN AND PARTICIPANTS Ten individuals, divided equally between those trained or not trained in overdose recognition and response, were recruited from each of six sites (n = 62). SETTING US-based overdose training and naloxone distribution programs in Baltimore, San Francisco, Chicago, New York and New Mexico. MEASUREMENTS Participants completed a brief questionnaire on overdose knowledge that included the task of rating 16 putative overdose scenarios for: (i) whether an overdose was occurring and (ii) if naloxone was indicated. Bivariate and multivariable analyses compared results for those trained to untrained. Responses were also compared to those of 11 medical experts using weighted and unweighted kappa statistics. FINDINGS Respondents were primarily male (72.6%); 45.8% had experienced an overdose and 72% had ever witnessed an overdose. Trained participants recognized more opioid overdose scenarios accurately (t(60) = 3.76, P < 0.001) and instances where naloxone was indicated (t(59) = 2.2, P < 0.05) than did untrained participants. Receipt of training and higher perceived competency in recognizing signs of an opioid overdose were associated independently with higher overdose recognition scores. Trained respondents were as skilled as medical experts in recognizing opioid overdose situations (weighted kappa = 0.85) and when naloxone was indicated (kappa = 1.0). CONCLUSIONS Results suggest that naloxone training programs in the United States improve participants' ability to recognize and respond to opioid overdoses in the community. Drug users with overdose training and confidence in their abilities to respond may effectively prevent overdose mortality.