Fluctuation Exchange Analysis of Superconductivity in the Standard Three-Band CuO2 Model

The fluctuation exchange, or FLEX, approximation for interacting electrons is applied to study instabilities in the standard three-band model for CuO2 layers in the high-temperature superconductors. Both intra-orbital and near-neigbor Coulomb interactions are retained. The filling dependence of the d(x2-y2) transition temperature is studied in both the "hole-doped" and "electron-doped" regimes using parameters derived from constrained-occupancy density-functional theory for La2CuO4. The agreement with experiment on the overdoped hole side of the phase diagram is remarkably good, i.e., transitions emerge in the 40 K range with no free parameters. In addition the importance of the "orbital antiferromagnetic," or flux phase, charge density channel is emphasized for an understanding of the underdoped regime.Comment: REVTex and PostScript, 31 pages, 26 figures; to appear in Phys. Rev. B (1998); only revised EPS figures 3, 4, 6a, 6b, 6c, 7 and 8 to correct disappearance of some labels due to technical problem

Uzaktan algılama görüntülerinin sınıflandırılması için sınır özniteliklerinin belirlenmesi ve adaptasyonu algoritması

Various types of sensors collect very large amounts of data from the earth surface. The characteristics of the data are related to sensor type with its own imaging geometry. Consequently, sensor types affect processing techniques used in remote sensing. In general, image processing techniques used in remote sensing are usually valid for multispectral data which is relatively in a low dimensional feature space. Therefore, advanced algorithms are needed for hyperspectral data which have at least 100-200 features (attributes/bands). Additionally, the training process is very important and affects the generalization capability of a classifier in supervised learning. Enough number of training samples is required to make proper classification. In remote sensing, collecting training samples is difficult and costly. Consequently, a limited number of training samples is often available in practice. Conventional statistical classifiers assume that the data have a specific distribution. For real world data, these kinds of assumptions may not be valid. Additionally, proper parameter estimation is difficult, especially for hyperspectral data. Normally, when the number of bands used in the classification process increases, precise detailed class determination is expected. For high dimensional feature space, when a new feature is added to the data, classification error decreases, but at the same time, the bias of the classification error increases. If the increment of the bias of the classification error is more than the reduction in classification error, then the use of the additional feature decreases the performance of the decision algorithm. This phenomenon is called the Hughes effect, and it may be much more harmful with hyperspectral data than with multispectral data. Our motivation in this study is to overcome some of these general classification problems by developing a classification algorithm which is directly based on the available training data rather than on the underlying statistical data distribution. Our proposed algorithm, Border Feature Detection and Adaptation (BFDA), uses border feature vectors near the decision boundaries which are adapted to make a precise partitioning in the feature space by using maximum margin principle. The BFDA algorithm well suited for classification of remote sensing images is developed with a new approach to choosing and adapting border feature vectors with the training data. This approach is especially effective when the information source has a limited amount of data samples, and the distribution of the data is not necessarily Gaussian. Training samples closer to class borders are more prone to generate misclassification, and therefore are significant feature vectors to be used to reduce classification errors. The proposed classification algorithm searches for such error-causing training samples in a special way, and adapts them to generate border feature vectors to be used as labeled feature vectors for classification. The BFDA algorithm can be considered in two parts. The first part of the algorithm consists of defining initial border feature vectors using class centers and misclassified training vectors. With this approach, a manageable number of border feature vectors is achieved. The second part of the algorithm is adaptation of border feature vectors by using a technique which has some similarity with the learning vector quantization (LVQ) algorithm. In this adaptation process, the border feature vectors are adaptively modified to support proper distances between them and the class centers, and to increase the margins between neighboring border features with different class labels. The class centers are also adapted during this process. Subsequent classification is based on labeled border feature vectors and class centers. With this approach, a proper number of feature vectors for each class is generated by the algorithm. In supervised learning, the training process should be unbiased to reach more accurate results in testing. In the BFDA, accuracy is related to the initialization of the border feature vectors and the input ordering of the training samples. These dependencies make the classifier a biased decision maker. Consensus strategy can be applied with cross validation to reduce these dependencies. In this study, major performance analysis and comparisons were made by using the AVIRIS data. Using the BFDA, we obtained satisfactory results with both multispectral and hyperspectal data sets. The BFDA is also a robust algorithm with the Hughes effect. Additionally, rare class members are more accurately classified by the BFDA as compared to conventional statistical methods.  Keywords: Remote sensing, hyperspectral data classification, consensual classification.Geleneksel görüntü işleme tekniklerinin direkt olarak uzaktan algılamaya uygulanması, sadece multispektral datalar için geçerli olabilir. Öznitelik vektörü boyutu 100-200 civarında olan hiperspektral dataların analizi için gelişmiş algoritmalara ihtiyaç vardır. Bununla birlikte, uzaktan algılamada, genellikle sınırlı sayıda eğitim örneğinin olması, özellikle öznitelik vektörünün boyutunun büyük olduğu hiperspektral datalarda, parametrik sınıflayıcıların kullanımını kısıtlar. Bu çalışmanın amacı, istatistiksel dağılıma bağlı olmayan, sadece eldeki eğitim örneklerine dayanan bir algoritma geliştirerek yukarıda özetlenen uzaktan algılama için genel sınıflandırma problemlerinin üstesinden gelmektir. Önerilen Sınır Özniteliklerinin Belirlenmesi ve Adaptasyonu (SÖBA) algoritması, karar yüzeylerine yakın sınır öznitelik vektörlerini kullanır ve bu sınır öznitelik vektörleri, maksimum marjin prensibini sağlayacak şekilde adapte edilerek, öznitelik uzayında doğru bölütlemenin yapılmasını sağlar. SÖBA algoritması iki bölümden oluşur. İlk aşamada sınır öznitelik vektörlerinin başlangıç değerleri uygun eğitim kümesi elemanlarından, yönetilebilir sayıda atanır. Daha sonra uygulanan adaptasyon işlemiyle, öğrenme süreci gerçekleştirilerek sınır özniteliklerinin, sonuç değerlerine ulaşması hedeflenir. Sınıflandırma sonuç sınır öznitelik vektörlerine olan  en yakın 1 komşuluk (1-EK) kuralı uyarınca yapılır. Ek olarak, SÖBA algoritmasının sınır öznitelik vektörlerinin başlangıç değerlerine ve eğitim kümesi elemanlarının eğitimde kullanılma sırasına bağlı olarak her çalışmasında kabul edilebilir derecede farklı sınır karar yüzeyleri oluşturması, konsensüs yapılarda kullanılması için elverişli bir özelliktir. Böylece birçok defa çalıştırılan SÖBA kararlarının uygun kurallarla birleştirilmesiyle tek bir sınıflayıcının aldığı karardan çok daha doğru kararlar elde edilebilir. Anahtar Kelimeler: Uzaktan algılama, hiperspektral data sınıflandırma, konsensüs

De novo mutations in SMCHD1 cause Bosma arhinia microphthalmia syndrome and abrogate nasal development

Bosma arhinia microphthalmia syndrome (BAMS) is an extremely rare and striking condition characterized by complete absence of the nose with or without ocular defects. We report here that missense mutations in the epigenetic regulator SMCHD1 mapping to the extended ATPase domain of the encoded protein cause BAMS in all 14 cases studied. All mutations were de novo where parental DNA was available. Biochemical tests and in vivo assays in Xenopus laevis embryos suggest that these mutations may behave as gain-of-function alleles. This finding is in contrast to the loss-of-function mutations in SMCHD1 that have been associated with facioscapulohumeral muscular dystrophy (FSHD) type 2. Our results establish SMCHD1 as a key player in nasal development and provide biochemical insight into its enzymatic function that may be exploited for development of therapeutics for FSHD

Head CT is of limited diagnostic value in critically ill patients who remain unresponsive after discontinuation of sedation

<p>Abstract</p> <p>Background</p> <p>Prolonged sedation is common in mechanically ventilated patients and is associated with increased morbidity and mortality. We sought to determine the diagnostic value of head computed tomography (CT) in mechanically ventilated patients who remain unresponsive after discontinuation of sedation.</p> <p>Methods</p> <p>A retrospective review of adult (age >18 years of age) patients consecutively admitted to the medical intensive care unit of a tertiary care medical center. Patients requiring mechanical ventilation for management of respiratory failure for longer than 72 hours were included in the study group. A group that did not have difficulty with awakening was included as a control.</p> <p>Results</p> <p>The median time after sedation was discontinued until a head CT was performed was 2 days (interquartile range 1.375–2 days). Majority (80%) of patients underwent head CT evaluation within the first 48 hours after discontinuation of sedation. Head CT was non-diagnostic in all but one patient who had a small subarachnoid hemorrhage. Twenty-five patients (60%) had a normal head CT. Head CT findings did not alter the management of any of the patients. The control group was similar to the experimental group with respect to demographics, etiology of respiratory failure and type of sedation used. However, while 37% of subjects in the control group had daily interruption of sedation, only 19% in the patient group had daily interruption of sedation (p < 0.05).</p> <p>Conclusion</p> <p>In patients on mechanical ventilation for at least 72 hours and who remain unresponsive after sedative discontinuation and with a non-focal neurologic examination, head CT is performed early and is of very limited diagnostic utility. Routine use of daily interruption of sedation is used in a minority of patients outside of a clinical trial setting though it may decrease the frequency of unresponsiveness from prolonged sedation and the need for head CT in patients mechanically ventilated for a prolonged period.</p

A Predominant Role for Parenchymal c-Jun Amino Terminal Kinase (JNK) in the Regulation of Systemic Insulin Sensitivity

It has been established that c-Jun N-terminal kinase 1 (JNK1) is essential to the pathogenesis of insulin resistance and type 2 diabetes. Although JNK influences inflammatory signaling pathways, it remains unclear whether its activity in macrophages contributes to adipose tissue inflammation and ultimately to the regulation of systemic metabolism. To address whether the action of this critical inflammatory kinase in bone marrow-derived elements regulates inflammatory responses in obesity and is sufficient and necessary for the deterioration of insulin sensitivity, we performed bone marrow transplantation studies with wild type and JNK1-deficient mice. These studies illustrated that JNK1-deficiency in the bone marrow-derived elements (BMDE) was insufficient to impact macrophage infiltration or insulin sensitivity despite modest changes in the inflammatory profile of adipose tissue. Only when the parenchymal elements lacked JNK1 could we demonstrate a significant increase in systemic insulin sensitivity. These data indicate that while the JNK1 activity in BMDE is involved in metabolic regulation and adipose milieu, it is epistatic to JNK1 activity in the parenchymal tissue for regulation of metabolic homeostasis

A genomic snapshot of demographic and cultural dynamism in Upper Mesopotamia during the Neolithic Transition

Upper Mesopotamia played a key role in the Neolithic Transition in Southwest Asia through marked innovations in symbolism, technology, and foodways. We present thirteen ancient genomes (c.8500-7500 calBCE) from Pre-Pottery Neolithic Çayönü in the Tigris basin together with bioarchaeological and material culture data. Our findings reveal that Çayönü was a genetically diverse population, carrying a mixed ancestry from western and eastern Fertile Crescent, and that the community received immigrants. Our results further suggest that the community was organised along biological family lines. We document bodily interventions such as head-shaping and cauterization among the individuals examined, reflecting Çayönü's cultural ingenuity. Finally, we identify Upper Mesopotamia as the likely source of eastern gene flow into Neolithic Anatolia, in line with material culture evidence. We hypothesise that Upper Mesopotamia's cultural dynamism during the Neolithic Transition was the product not only of its fertile lands but also of its interregional demographic connections

The effect of local corticosteroid injection on F-wave conduction velocity and sympathetic skin response in carpal tunnel syndrome

The aim of this study was to evaluate the efficacy of steroid injection for the treatment of the carpal tunnel syndrome (CTS), with F-wave parameters and sympathetic skin response (SSR). Seventeen hands of 10 women patients were treated with local steroid injection with 2-month follow-up. All patients underwent single injection into the carpal tunnel. Response to injection was measured nerve conduction studies (NCSs), median nerve F waves, and SSR before and after treatment. To determine the normal values, 42 hands of 21 healthy women were also studied. There was a significant improvement of sensory and motor nerve conduction values when compared to baseline values (P < 0.01). At the end of follow-up period, the median sensory distal latency and the sensory latency differences between the median and the ulnar nerve were improved 35 and 65%, respectively. The maximum, mean F-wave amplitudes and chronodispersion showed a slight improvement with respect to baseline values and controls, but statistical significance was not achieved after treatment. Although no statistically significant improvements were observed in SSR parameters, slightly decreased amplitudes and increased habituation of SSR were noted at the end of the treatment. The present study shows that the local steroid injection results in improvement in NCSs values, but the F-wave parameters were not effectual in short-term outcome of CTS treatment. These findings suggest that the sensory latency differences between the median and the ulnar wrist-to-digit 4 are better parameters in the median nerve recovery after treatment than the median sensory distal latency. Furthermore, the SSR does not seem to be a sensitive method in follow-up of CTS treatment