Miracle-dz/UniFAR-FTUs huggingface dataset:
Variability of global net sea–air CO2 fluxes over the last three decades using empirical relationships

The interannual variability of net sea–air CO2 flux for the period 1982–2007 is obtained from a diagnostic model using empirical subannual relationships between climatological CO2 partial pressure in surface seawater (pCO2SW) and sea surface temperature (SST), along with interannual changes in SST and wind speed. These optimum subannual relationships show significantly better correlation between pCO2SW and SST than the previous relationships using fixed monthly boundaries.Our diagnostic model yields an interannual variability of±0.14 PgC yr−1 (1σ)with a 26-year mean of −1.48 PgC yr−1. The greatest interannual variability is found in the Equatorial Pacific, and significant variability is also found at northern and southern high-latitudes, depending in part, on which wind product is used. We provide an assessment of our approach by applying it to pCO2SW and SST output from a prognostic global biogeochemical ocean model. Our diagnostic approach applied to this model output shows reasonable agreement with the prognostic model net sea–air CO2 fluxes in terms of magnitude and phase of variability, suggesting that our diagnostic approach can capture much of the observed variability on regional to global scale. A notable exception is that our approach shows significantly less variability than the prognostic model in the Southern Ocean.
Supporting unbounded process parallelism in the SPC programming model

In automatic mapping of parallel programs to target parallel machines the efficiency of the compile-time cost estimation needed to steer the optimization process is highly dependent on the choice of programming model. Recently a new parallel programming model, called SPC, has been introduced that specifically aims at the efficient computation of reliable cost estimates, paving the way for automatic mapping. In SPC all algorithm level parallelism is explicitly specified, relying on compile-time transformation of the possibly unbounded algorithm level (data) parallelism to that of the actual target machine. In this paper we present SPC's process-algebraic framework in terms of which we demonstrate that the transformations needed to efficiently support unbounded process parallelism at program level are straightforward.
Developments in pulmonary arterial hypertension–targeted therapy for chronic thromboembolic pulmonary hypertension

Chronic thromboembolic pulmonary hypertension (CTEPH) is a rare disease characterised by the presence of organised chronic thromboembolic material occluding the proximal pulmonary arteries and a vasculopathy in the distal pulmonary arterial tree. Pulmonary endarterectomy (PEA) is a potential cure for many patients with CTEPH. However, PEA is not suitable for patients with a significant distal distribution of chronic thromboembolic material or with significant comorbidities. Also, a proportion of patients are left with residual CTEPH post PEA. Until recently, pulmonary arterial hypertension–targeted therapies have been used off licence to treat patients with inoperable or residual CTEPH. The CHEST1 study investigated the use of riociguat and was the first randomised controlled trial to show efficacy in inoperable or residual CTEPH. In this review, we explore the pathophysiology of CTEPH and review the current trial evidence for pulmonary arterial hypertension–targeted therapies. We also include a discussion of physiological considerations that require further investigation.
A single‐step, sensitive flow cytofluorometric assay for the simultaneous assessment of membrane‐bound and ingested Candida albicans in phagocytosing neutrophils

Distinguishing ingested particles from those attached to the cell surface is an essential requirement when performing quantitative studies of phagocytosis. In the present report, we describe a simple, sensitive and reliable flow cytofluorometric method that achieves this goal in a Candida albicans‐human neutrophils (PMN) system.
Population specific template construction and brain structure segmentation using deep learning methods

A brain template, such as MNI152 is a digital (magentic resonance image or MRI) representation of the brain in a reference coordinate system for the neuroscience research. Structural atlases, such as AAL and DKA, delineate the brain into cortical and subcortical structures which are used in Voxel Based Morphometry (VBM) and fMRI analysis. Many population specific templates, i.e. Chinese, Korean, etc., have been constructed recently. It was observed that there are morphological differences between the average brain of the eastern and the western population. In this thesis, we report on the development of a population specific brain template for the young Indian population. This is derived from a multi-centeric MRI dataset of 100 Indian adults (21 30 years old). Measurements made with this template indicated that the Indian brain, on average, is smaller in height and width compared to the Caucasian and the Chinese brain. A second problem this thesis examines is automated segmentation of cortical and non-cortical human brain structures, using multiple structural atlases. This has been hitherto approached using computationally expensive non-rigid registration followed by label fusion. We propose an alternative approach for this using a Convolutional Neural Network (CNN) which classifies a voxel into one of many structures. Evaluation of the proposed method on various datasets showed that the mean Dice coefficient varied from 0.844±0.031 to 0.743±0.019 for datasets with the least (32) and the most (134) number of labels, respectively. These figures are marginally better or on par with those obtained with the current state of the art methods on nearly all datasets, at a reduced computational time. We also propose an end-to-end trainable Fully Convolutional Neural Network (FCNN) architecture called the M-net, for segmenting deep (human) brain structures. A novel scheme is used to learn to combine and represent 3D context information of a given slice in a 2D slice. Consequently, the M-net utilizes only 2D convolution though it operates on 3D data. Experiment results show that the M-net outperforms other state-of-the-art model-based segmentation methods in terms of dice coefficient and is at least 3 times faster than them.
Early Diagnosis of Pancreatic Ductal Adenocarcinoma by Combining Relative Expression Orderings With Machine-Learning Method

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive and lethal cancer deeply affecting human health. Diagnosing early-stage PDAC is the key point to PDAC patients’ survival. However, the biomarkers for diagnosing early PDAC are inexact in most cases. Therefore, it is highly desirable to identify an effective PDAC diagnostic biomarker. In the current work, we designed a novel computational approach based on within-sample relative expression orderings (REOs). A feature selection technique called minimum redundancy maximum relevance was used to pick out optimal REOs. We then compared the performances of different classification algorithms for discriminating PDAC and its adjacent normal tissues from non−PDAC tissues. The support vector machine algorithm is the best one for identifying early PDAC diagnostic biomarker. At first, a signature composed of nine gene pairs was acquired from microarray gene expression data sets. These gene pairs could produce satisfactory classification accuracy up to 97.53% in fivefold cross-validation. Subsequently, two types of data from diverse platforms, namely, microarray and RNA-Seq, were used to validate this signature. For microarray data, all (100.00%) of 115 PDAC tissues and all (100.00%) of 31 PDAC adjacent normal tissues were correctly recognized as PDAC. In addition, 88.24% of 17 non-PDAC (normal or pancreatitis) tissues were correctly classified. For the RNA-Seq data, all (100.00%) of 177 PDAC tissues and all (100.00%) of 4 PDAC adjacent normal tissues were correctly recognized as PDAC. Validation results demonstrated that the signature had a good cross-platform effect for early detection of PDAC. This work developed a new robust signature that might be a promising biomarker for early PDAC diagnosis.
Dynamic Modeling of Twitter Users

Social Networks are popular platforms for users to express themselves, facilitate interactions, and share knowledge. Today, users in social networks have personalized profiles that contain their dynamic attributes representing their interest and behavior over time such as published content, and location check-ins. Several proposed models emerged that analyze those profiles with their dynamic content in order to measure the degree of similarity between users. This similarity value can be further used in friend suggesting and link prediction. The main drawback of the majority of these models is that they rely on a static snapshot of attributes which do not reflect the change in user interest and behavior over time. In this paper a novel framework for modeling the dynamic of userâs behavior and measuring the similarity between usersâ profiles in twitter is proposed. In this proposed framework, dynamic attributes such as topical interests and the associated locations in tweets are used to represent userâs interest and behavior respectively. Experiments on a real dataset from twitter showed that the proposed framework that utilizes those attributes outperformed multiple standard models that utilize a static snapshot of data.
Palmitoyl Protein Thioesterase 1 Is Essential for Myogenic Autophagy of C2C12 Skeletal Myoblast

Skeletal muscle differentiation is an essential process for the maintenance of muscle development and homeostasis. Reactive oxygen species (ROS) are critical signaling molecules involved in muscle differentiation. Palmitoyl protein thioesterase 1 (PPT1), a lysosomal enzyme, is involved in removing thioester-linked fatty acid groups from modified cysteine residues in proteins. However, the role of PPT1 in muscle differentiation remains to be elucidated. Here, we found that PPT1 plays a critical role in the differentiation of C2C12 skeletal myoblasts. The expression of PPT1 gradually increased in response to mitochondrial ROS (mtROS) during muscle differentiation, which was attenuated by treatment with antioxidants. Moreover, we revealed that PPT1 transactivation occurs through nuclear factor erythroid 2-regulated factor 2 (Nrf2) binding the antioxidant response element (ARE) in its promoter region. Knockdown of PPT1 with specific small interference RNA (siRNA) disrupted lysosomal function by increasing its pH. Subsequently, it caused excessive accumulation of autophagy flux, thereby impairing muscle fiber formation. In conclusion, we suggest that PPT1 is factor a responsible for myogenic autophagy in differentiating C2C12 myoblasts.
Blind identification of FIR systems driven by Markov-like input signals

We propose a new algorithm for the blind identification and equalization of finite impulse response (FIR) systems using the second-order statistics of the received signal. The new algorithm is set in the same context as the algorithms of Tong et al. (1994) and Moulines et al. (1995), however, unlike those earlier approaches it is designed to allow correlated input signals. Specifically, the algorithm accommodates finite memory sources and sources whose autocorrelation function decays exponentially. Numerical simulations compare the equalization performance of the new algorithm to those of Tong and Moulines. It is shown that our algorithm yields consistently lower bit-error rates at a wide variety of signal-to-noise ratios and at various equalizer lengths. Moreover, the algorithm maintains this advantage even if it has no a priori information of source correlation or if source symbols are uncorrelated.
From normal fear to pathological anxiety.

In this article the authors address how pathological anxiety may develop from adaptive fear states. Fear responses (e.g., freezing, startle, heart rate and blood pressure changes, and increased vigilance) are functionally adaptive behavioral and perceptual responses elicited during danger to facilitate appropriate defensive responses that can reduce danger or injury (e.g., escape and avoidance). Fear is a central motive state of action tendencies subserved by fear circuits, with the amygdala playing a central role. Pathological anxiety is conceptualized as an exaggerated fear state in which hyperexcitability of fear circuits that include the amygdala and extended amygdala (i.e., bed nucleus of the stria terminalis) is expressed as hypervigilance and increased behavioral responsivity to fearful stimuli. Reduced thresholds for activation and hyperexcitability in fear circuits develop through sensitization- or kindling-like processes that involve neuropeptides, hormones, and other proteins. Hyperexcitability in fear circuits is expressed as pathological anxiety that is manifested in the various anxiety disorders.
Dimensionality Reduction technique using Neural Networks – A Survey

A self-organizing map (SOM) is a classical neural network method for dimensionality reduction. It comes under the unsupervised class. SOM is a neural network that is trained using unsupervised learning to produce a low-dimensional, discretized representation of the input space of the training samples, called a map. SOM uses a neighborhood function to preserve the topological properties of the input space. SOM operates in two modes: training and mapping. Using the input examples, training builds the map. It is also called as vector quantization. In this paper, we first survey related dimension reduction methods and then examine their capabilities for face recognition. In this work, different dimensionality reduction techniques such as Principal component analysis [PCA], independent component analysis [ICA] and self-organizing map [SOM] are selected and applied in order to reduce the loss of classification performance due to changes in facial expression. The experiments were conducted on ORL face database and the results show that SOM is a better technique.
A Dynamic LVQ Algorithm for Improving the Generalisation of Nearest Neighbour Classifiers

A new dynamic strategy for Kohonen’s LVQ algorithms using growing and pruning methods is introduced. Once the learning system converges to a solution using a fixed number of prototypes, the growing method incrementally adds new prototypes in those local regions where the misclassification error is greater. The initial locations of the new prototypes are viewed as estimates of the new equilibrium points of the learning algorithm with the augmented number of prototypes. This constructive process is repeated until the classification accuracy measured with a validation set stops decreasing. Then, a pruning algorithm is executed to remove all those prototypes that do not form class borders. Experimental results using NIST hand-written databases are promising since we show that dynamic LVQ algorithms outperform their static counterparts for the same codebook size.
Social background, adult body-height and health.

STUDY OBJECTIVE To study the socio-demographic determinants of body-height and the bearing of these determinants on the association between body-height and health among Finnish adults. DATA AND METHOD Cross-sectional population survey including questions on social background, body-height and health, and retrospective questions on childhood living conditions. The data derive from a representative Survey on Living Conditions collected by Statistics Finland in 1994. The response rate was 73%. Male and female respondents > or =20 years were included in the analysis (N = 8212). Statistical methods include regression analysis and logistic regression analysis. RESULTS Body-height was strongly associated with year of birth, region, childhood living conditions and education among adult men and women. Body-height was also associated with limiting long-standing illness and perceived health as below good. Tall men had the best health and short men the poorest health. Among women the association of body-height with health differed from men, as tall women showed high levels of limiting long-standing illness, notably musculo-skeletal diseases. Adjusting for the background variables weakened but did not abolish the association between poor health and short stature among men and women. CONCLUSIONS Short stature is associated with poor health among Finnish men and women. A non-linear association among women was found for musculo-skeletal diseases. The studied social background factors explained only little of the association between body-height and health.
Criteria for effective fallow field eco schemes for farmland birds during non-breeding

Farmland eco schemes implemented under the current Common Agricultural Policy (CAP) of the European Union are often considered ineffective in halting farmland bird declines. Fallow fields, often seeded with dedicated seed mixtures, rate among the more beneficial eco scheme types. Yet, the CAP currently defines no minimum criteria for fallow fields to qualify as eco scheme, likely jeopardizing their potential biodiversity benefits. We investigated the attractiveness of four fallow field types established under CAP eco schemes and dedicated bird conservation programs in Southern Germany. Our 2-year surveys on > 100 fields focused on the non-breeding season, where food limitation can become particularly problematic. We modelled bird incidences also in response to vegetation structure and adjacent landscape features to derive minimum criteria for effective fallow field eco schemes. Fallow field types varied only mildly in overall species richness but showed striking differences in the attracted species. Finches in particular tended to preferentially visit 1-year fallow fields, while buntings tended towards 2-year and older field types. 1-year CAP fallows, however, are typically removed before mid-winter, and thus potentially act as a trap to farmland birds and other wildlife. The investigated species consistently preferred larger fallow fields with a more differentiated vegetation structure. Placement close to woods and hedgerows positively affected birds inhabiting woodland ecotones, while classic farmland species showed higher incidences on fallow fields embedded in open landscapes. ‘Policy implications’ Our findings call for the ongoing CAP revisions to specify minimum requirements that qualify fallow fields as eco schemes. These should include an at least biennial cycle, a diversification of seed mixtures, standards for fallow field size, and criteria for their placement in the landscape matrix.
An Improved Fuzzy C-means Method for Brain MR Images Segmentation

Due to the effect of noise in brain MR images, it is difficult for the traditional fuzzy c-means (FCM) clustering algorithm to obtain desirable segmentation results. Combining the information of patch to reduce the effect of noise has been a focus of current research. However, the traditional patch method is isotropic, so that it would lose the structure information easily. In this paper, a novel fuzzy C-means method based on the spatial similarity information is proposed. To be anisotropy and preserve more structure information, this method takes both the non-local information and spatial structural similarity (SSIM) between the image patches into consideration, and then a new distance function is established between every pixels and category centers for image segmentation. The efficiency of the proposed algorithm is demonstrated by experiments of synthetic brain MR images.
Cataractogenic lens injury prevents traumatic ganglion cell death and promotes axonal regeneration both in vivo and in culture.

PURPOSE To examine and quantify neuroprotective and neurite-promoting activity on retinal ganglion cells (RGCs) after injury of the lens. METHODS In adult albino rats, penetrating lens injury was performed by intraocular injection. To test for injury-induced neuroprotective effects in vivo, fluorescence-prelabeled RGCs were axotomized by subsequent crush of the optic nerve (ON) with concomitant lens injury to cause cataract. The numbers of surviving RGCs were determined in retinal wholemounts and compared between the different experimental and control groups. To examine axonal regeneration in vivo, the ON was cut and replaced with an autologous piece of sciatic nerve (SN). Retinal ganglion cells with axons that had regenerated within the SN under lens injury or control conditions were retrogradely labeled with a fluorescent dye and counted on retinal wholemounts. Neurite regeneration was also studied in adult retinal explants obtained either after lens injury or without injury. The numbers of axons were determined after 1 and 2 days in culture. Putative neurotrophins (NTs) were studied within immunohistochemistry and Western blot analysis. RESULTS Cataractogenic lens injury performed at the same time as ON crush resulted in highly significant rescue of 746 +/- 126 RGCs/mm(2) (mean +/- SD; approximately 39% of total RGCs) 14 days after injury compared with controls without injury or with injection of buffer into the vitreous body (30 +/- 18 RGCs/mm(2)). When lens injury was performed with a delay of 3 days after ON crush, 49% of RGCs survived, whereas delay of 5 days still rescued 45% of all RGCs. In the grafting paradigm virtually all surviving RGCs after lens injury appeared to have regenerated an axon within the SN graft (763 +/- 114 RGCs/mm(2) versus 79 +/- 17 RGCs/mm(2) in controls). This rate of regeneration corresponds to approximately 40% of all RGCs. In the regeneration paradigm in vitro preceding lens injury and ON crush 5 days previous resulted in a maximum of regeneration of 273 +/- 39 fibers/explant after 1 day and 574 +/- 38 fibers/explant after 2 days in vitro. In comparison, in control retinal pieces without lens injury 28 +/- 13 fibers/explant grew out at 1 day, and 97 +/- 37 fibers/explant grew out at 2 days in culture. Immunohistochemical and Western blot analysis of potential NTs in the injured lens revealed no expression of ciliary neurotrophic factor (CNTF), brain-derived neurotrophic factor (BDNF), NT-4, nerve growth factor (NGF), and basic fibroblast growth factor (bFGF). CONCLUSIONS The findings indicate that the lens contains high neuroprotective and neuritogenic activity, which is not caused by NT. Compared with the data available in the literature, this neuroprotection is quantitatively among the highest ever reported within the adult rat visual system.
Mapping a social network of heterosexuals at high risk for HIV infection

Objective:To determine how heterosexuals at risk for HIV infection interconnect in social networks and how such relationships affect HIV transmission. Design:Cross-sectional study with face-to-face interviews to ascertain sociosexual connections; serologic testing. Participants:Prostitute women (n=133), their paying (n=129) and non-paying (n=47) male partners; injecting drug users (n= 200) and their sex partners (n=41). Participants were recruited in sexually transmitted disease and methadone clinics, an HIV-testing site, and through street outreach in Colorado Springs, Colorado, USA. Main outcome measures:Reported behaviors, risk perceptions, sociosexual linkages, and HIV prevalence. Results:Respondents were well informed, but reported engaging in high-risk behaviors frequently. Nevertheless, over 70% of respondents perceived themselves to be at low risk for HIV infection. The 595 respondents identified a social network of 5162 people to which they belonged. Network analytic methods indicated 147 separate connected components of this network; eight of the 19 HIV-positive individuals in the network were located in smaller components remote from the largest connected component. Conclusion:The isolated position of HIV-positive individuals may serve as a barrier to HIV transmission and may account for the lack of diffusion of HIV in heterosexual populations in this region. Network analysis appears useful for understanding the dynamics of disease transmission and warrants further development as a tool for intervention and control.