Miracle-dz/UniFAR-FTUs huggingface dataset:
Surveillance for congenital cytomegalovirus disease: a report from the National Congenital Cytomegalovirus Disease Registry.

A national surveillance program for congenital cytomegalovirus (CMV) disease was initiated in 1990. In 4 years 285 cases were reported without seasonal patterns. Mean birth statistics were as follows: gestational age, 36 weeks; weight, 2,224 g; length, 45 cm; and head circumference, 30 cm. Of the infants 68% had CNS involvement, which was significantly (P < .005) associated with a direct bilirubin level of > or = 3 mg/dL, petechiae, an alanine aminotransferase level of > 100 U/L, a platelet count of < or = 75,000/mm3, hepatomegaly, and splenomegaly (P < .05). Maternal demographics revealed that the mean age was 23 years (range, 13-38 years), 59% were white, 33% were black, 47% had low incomes (receiving Medicaid), and 45% were primiparous. Compared with 1990 birth statistics in the United States, mothers of infants with congenital CMV disease were younger, and a greater percentage of these mothers were black. Two distinct maternal groups were identified on the basis of age, socioeconomic status, and parity. This finding may reflect different modes of transmission and suggest target populations for future CMV vaccine initiatives.
Ca2+ store dynamics determines the pattern of activation of the store‐operated Ca2+ current ICRAC in response to InsP3 in rat basophilic leukaemia cells

1 ICRAC and intracellular inositol 1,4,5‐trisphosphate (InsP3) concentration is complex. In rat basophilic leukaemia (RBL‐1) cells dialysed with high intracellular Ca2+ buffer, the relationship is supra‐linear with a Hill coefficient of 12 and resembles an apparent ‘all‐or‐none’ phenomenon. The non‐linearity seems to arise from InsP3 metabolism. However, it is not clear which InsP3‐metabolising pathway engenders the non‐linear behaviour nor whether ICRAC is always activated to its maximal extent by InsP3. 2 Using the whole‐cell patch clamp technique, we dialysed RBL‐1 cells with different concentrations of the InsP3 analogue InsP3‐F. InsP3‐F is broken down by Ins(1,4,5)P3 5‐phosphatase but is not a substrate for Ins(1,4,5)P3 3‐kinase. The relationship between InsP3‐F and ICRAC amplitude was supra‐linear and very similar to that with InsP3 but was distinct from the graded relationship seen with the non‐metabolisable analogue Ins2,4,5P3. 3 In the presence of high intracellular Ca2+ buffer, InsP3‐F activated ICRAC to its maximal extent. With moderate Ca2+ buffer, however, sub‐maximal ICRAC could be obtained to a maximal InsP3‐F concentration. Nevertheless, the relationship between the amplitude of ICRAC and InsP3‐F concentration was still supra‐linear. 4 Submaximal ICRAC in response to InsP3‐F in the presence of moderate Ca2+ buffer was due to partial depletion of the stores, because the size of the current could be increased by thapsigargin. 5 The data suggest that first, Ins(1,4,5)P3 5‐phosphatase is an important factor which contributes to the non‐linear relationship between InsP3 concentration and the amplitude of ICRAC and second, InsP3 does not always activate ICRAC to its maximal extent. At moderate buffer strengths, submaximal ICRAC is evoked by maximal InsP3. However, the supra‐linear relationship between InsP3 concentration and amplitude of the current still holds.
Inequalities in Energy Transition: The Case of Network Charges in Germany

The German energy transition and the rising share of renewable energies in electricity generation have led to an increase in network costs and to higher network charges in recent years. We use socioeconomic data in order to investigate distributional effects within the period 2010-2016, and employ three different inequality metrics – the Gini coefficient, the Theil index and the Atkinson index – all of which unambiguously indicate regressive effects of network charges. Most recently, the three metrics show an increase of economic inequality of at least 0.67 % when accounting for network charges. This finding is due to 1. the relative inferiority of electricity, 2. the regressive impact of a fixed component of network charges, 3. considerable regional disparities, and 4. the higher prevalence of prosumers within high-income households.
Methotrexate reduces antibody responses to recombinant human α‐galactosidase A therapy in a mouse model of Fabry disease

Therapeutic enzymes are often recognized as foreign by the immune system of patients undergoing enzyme replacement therapy. The antibodies that develop may alter pharmacokinetics and biodistribution of the therapeutic protein, may be able to neutralize the activity of the enzyme, or may cause immune reactions in certain patients. We have explored treatment regimens to reduce the antibody response to human α‐galactosidase A (r‐hαGAL) in Fabry (αGAL knock‐out) and normal BALB/c mice. A wide variety of treatment modalities were tested, including high dose tolerance induction, increased frequency of therapeutic doses and immunosuppressive drugs in combination with administration of enzyme. The most substantial effects were observed in mice injected intravenously with r‐hαGAL in combination with methotrexate (MTX), which significantly lowered r‐hαGAL‐specific serum antibody levels. A short course of treatment with MTX was able to reduce antibody and spleen cell proliferative responses to long‐term r‐hαGAL treatment. MTX was able to suppress the development of r‐hαGAL‐specific IgG in antigen‐primed mice. However, MTX was not effective in dampening robust ongoing antibody responses. These experiments provide a framework for the design of clinical protocols to prevent the drug‐specific antibody responses of patients undergoing enzyme replacement therapy.
Cross-saturation and transferred cross-saturation experiments

Abstract Structural analyses of protein–protein interactions are required to reveal their functional mechanisms, and accurate protein–protein complex models, based on experimental results, are the starting points for drug development. In addition, structural information about proteins under physiologically relevant conditions is crucially important for understanding biological events. However, for proteins such as those embedded in lipid bilayers and transiently complexed with their effectors under physiological conditions, structural analyses by conventional methods are generally difficult, due to their large molecular weights and inhomogeneity. We have developed the cross-saturation (CS) method, which is an nuclear magnetic resonance measurement technique for the precise identification of the interfaces of protein–protein complexes. In addition, we have developed an extended version of the CS method, termed transferred cross-saturation (TCS), which enables the identification of the residues of protein ligands in close proximity to huge (>150 kDa) and heterogeneous complexes under fast exchange conditions (>0.1 s−1). Here, we discuss the outline, basic theory, and practical considerations of the CS and TCS methods. In addition, we will review the recent progress in the construction of models of protein–protein complexes, based on CS and TCS experiments, and applications of TCS to in situ analyses of biologically and medically important proteins in physiologically relevant states.
Identification of Mucosa-Invading and Intravascular Bacteria in Feline Small Intestinal Lymphoma

Persistent bacterial infections of the gastrointestinal mucosa are causally linked to gastric carcinoma and mucosa-associated lymphoid tissue (MALT) lymphoma in people and laboratory animals. We examined the relationship of mucosa-associated bacteria to alimentary lymphoma in cats. Intestinal biopsies from 50 cats with alimentary lymphoma (small cell, n = 33; large cell, n = 17) and 38 controls without lymphoma (normal to minimal change on histopathology, n = 18; lymphocytic-plasmacytic enteritis, n = 20) were evaluated. The number and spatial distribution of bacteria (ie, in luminal cellular debris, villus-associated mucus, adherent to epithelium, mucosal invasion, intravascular, or serosal) were determined by fluorescence in situ hybridization with the eubacterial probe EUB-338. Mucosa-invasive bacteria were more frequently observed in cats with large cell lymphoma (82%, P ≤ .001) than in cats with small cell lymphoma (18%), normal to minimal change on histopathology, and lymphocytic-plasmacytic enteritis (3%). Intravascular bacteria were observed solely in large cell lymphoma (29%), and serosal colonization was more common in cats with large cell lymphoma (57%) than with small cell lymphoma (11%, P ≤ .01), normal to minimal change (8%, P ≤ .01), and lymphocytic-plasmacytic enteritis (6%, P ≤ .001). The high frequency of invasive bacteria within blood vessels and serosa of cats with large cell lymphoma may account for the sepsis-related complications associated with large cell lymphoma and inform clinical management. Further studies are required to determine the role of intramucosal bacteria in the etiopathogenesis of feline alimentary lymphoma.
Cancer Regression in Patients After Transfer of Genetically Engineered Lymphocytes

Through the adoptive transfer of lymphocytes after host immunodepletion, it is possible to mediate objective cancer regression in human patients with metastatic melanoma. However, the generation of tumor-specific T cells in this mode of immunotherapy is often limiting. Here we report the ability to specifically confer tumor recognition by autologous lymphocytes from peripheral blood by using a retrovirus that encodes a T cell receptor. Adoptive transfer of these transduced cells in 15 patients resulted in durable engraftment at levels exceeding 10% of peripheral blood lymphocytes for at least 2 months after the infusion. We observed high sustained levels of circulating, engineered cells at 1 year after infusion in two patients who both demonstrated objective regression of metastatic melanoma lesions. This study suggests the therapeutic potential of genetically engineered cells for the biologic therapy of cancer.
Absence of Bone Sialoprotein (BSP) Alters Profoundly Hematopoiesis and Upregulates Osteopontin

Matrix proteins of the SIBLING family interact with bone cells, extracellular matrix and mineral and are thus in a key position to regulate the microenvironment of the bone tissue, including its hematopoietic component. In this respect, osteopontin (OPN) has been implicated in the hematopoietic stem cell (HSC) niche as negative regulator of the HSC function. We investigated the impact on hematopoietic regulation of the absence of the cognate bone sialoprotein (BSP). BSP knockout (−/−) mice display increased bone marrow cellularity, and an altered commitment of hematopoietic precursors to myeloid lineages, leading in particular to an increased frequency of monocyte/macrophage cells. The B cell pool is increased in −/− bone marrow, and its composition is shifted toward more mature lymphocyte stages. BSP‐null mice display a decreased HSC fraction among LSK cells and a higher percentage of more committed progenitors as compared to +/+. The fraction of proliferating LSK progenitors is higher in −/− mice, and after PTH treatment the mutant HSC pool is lower than in +/+. Strikingly, circulating levels of OPN as well as its expression in the bone tissue are much higher in the −/−. Thus, a BSP‐null bone microenvironment affects the hematopoietic system both quantitatively and qualitatively, in a manner in part opposite to the OPN knockout, suggesting that the effects might in part reflect the higher OPN expression in the absence of BSP. J. Cell. Physiol. 230: 1342–1351, 2015. © 2014 Wiley Periodicals, Inc., A Wiley Company
Essays in Nonlinear Econometrics

In this dissertation, I study standard models, but investigate the necessity of (possibly large) deviations from basic assumptions. In Chapter 1, my co-author Ross Askanazi and I revisit the use of factor models in finance. Historical literature on the subject decomposes volatility into a factor component (systemic risk) and a remainder (idiosyncratic risk). Recent work has suggested that a market shock to volatility may increase both systemic risk and idiosyncratic risk — specifically, that idiosyncratic volatility of US equities data has a factor structure, with the factor highly correlated with, and possibly precisely the market volatility. In this paper we attempt to characterize the underlying factor and find that it can be decomposed into a statistical (PCA) and structural (market volatility) factor. We also show that this feature is more common than expected, appearing in diverse sets of financial data. Lastly, we find that this dual-factor approach is slightly dominated in forecasting environments by a single statistical factor. In Chapter 2 I revisit the classical Vector Autoregression (VAR) model, but allow parameters to time-vary. Time-Varying parameter models have become more popular in recent years, especially as they are adapted to accommodate larger datasets. However, all recent developments use standard priors, specifically the Inverse-Wishart class of priors over the parameter error covariance matrix. In this paper, I show that Inverse-Wishart priors have a number of negative properties, and that those properties are salient in a TVP context since there is little information from the likelihood. Fully aware of these deficiencies, the Bayesian Random Effects literature has developed a series of uninformative priors to correct these weaknesses. In this paper, I adapt one of those priors into an informative and easily understandable prior for covariances. I show that the new prior effects posterior inference and displays improved frequentist properties. I apply my prior to the canonical Primiceri (2005) dataset and find that their results were sensitive to the choice of prior. I further apply the prior to two forecasting exercises and find that while it improves forecasts for the Primiceri data, it does not for an alternative (larger) dataset. Degree Type Dissertation Degree Name Doctor of Philosophy (PhD) Graduate Group Economics First Advisor Francis X. Diebold Second Advisor Frank Schorfheide
Incommensurate antiferromagnetism in the two-dimensional Hubbard model.

The magnetic states occurring in the two-dimensional Hubbard model in the vicinity of half-filling of the conduction band are studied in the case of weak correlations, using Hartree-Fock theory. It is shown that at zero temperature the commensurate antiferromagnetic state is unstable against domain-wall formation for arbitrarily small deviations from half-filling. The resulting incommensurate antiferromagnet remains initially insulating. The predictions of the model for the polarization and modulation wave vector of the incommensurate antiferromagnet are discussed.
Time series prediction of stock price using deep belief networks with intrinsic plasticity

In recent years, the stock market plays an important role, which has attracted more and more attentions. The key problem of the stock market prediction is how to design a method to improve the prediction performance. As we know, the biggest challenge is that the stock time series is essentially dynamic, nonlinear, complicated, nonparametric and chaotic. In this paper, we propose a novel method to predict the stock closing price based on the deep belief networks (DBNs) with intrinsic plasticity. In the experiments, the stock in S&P 500 is used to examine the performance. The back propagation algorithm is used for output training to make minor adjustments of structure parameters. The intrinsic plasticity (IP) is also applied into the network to make it have adaptive ability. It is believed that IP learning for adaptive adjustment of neuronal response to external inputs is beneficial for maximizing the input-output mutual information. Our results show that the application of IP learning can remarkably improve the prediction performance. Moreover, the effects of two kinds of IP rules on the performance of prediction are examined. Compared with Triesch's IP and without IP, DBN with Li's IP learning has much better prediction performance than the others. These results may have important implications on the modeling of neural network for complex time series prediction.
Structural changes of central white matter tracts in Kennedy's disease – a diffusion tensor imaging and voxel‐based morphometry study

Spinobulbar muscular atrophy [Kennedy's disease (KD)] is a rare X‐linked neurodegenerative disorder of mainly spinal and bulbar motoneurons. Recent studies suggest a multisystem character of this disease. The aim of this study was to identify and characterize structural changes of gray (GM) and white matter (WM) in the central nervous system.
Post-stay email marketing implications for the hotel industry: Role of email features, attitude, revisit intention and leisure involvement level

Despite their widespread use in the hotel industry, limited research has been conducted on post-stay emails. Therefore, this study was designed to examine the effects of post-stay email features on customers’ attitudes towards a brand and their revisit intention. The email features analysed in the study consisted of personalization, message copy, interactivity, financial and illustration. Further, the effectiveness of each email feature was measured based on respondents’ leisure involvement levels. Results indicated that personalization, interactivity and financial features were significant predictors of revisit intention depending on customers’ involvement levels, while attitude was a significant mediator. Overall, the findings from the study add to the knowledge of how post-stay emails can be used when formulating customer retention strategies.
The Survival and Breakdown of Victorious War Coalitions

States often take on coalition partners to secure victory in war, but afterwards their members may fall into conflict over the terms of the peace just secured, often at tremendous cost. What distinguishes those victorious coalitions that manage to sustain cooperation from those that disintegrate into hostility? To answer this question, I gather data on the characteristics of victorious wartime coalitions from 1859 to the present and estimate the duration of cooperation between their members in an event history framework. I explore the effects of coalition size, the diversity of preferences within the coalition, wartime alliance commitments, military strength, and several features of the war itself on the sustainability of peace amongst the members of victorious coalitions. While large coalitions are uniquely likely to break down, so are those with (a) diverse preferences and, surprisingly, (b) those comprised of a larger proportion of allies.
Machine Learning for Automatic Defence Against Distributed Denial of Service Attacks

Distributed denial of service attacks pose a serious threat to many businesses which rely on constant availability of their network services. Companies like Google, Yahoo and Amazon are completely reliant on the Internet for their business. It is very hard to defend against these attacks because of the many different ways in which hackers may strike. Distinguishing between legitimate and malicious traffic is a complex task. Setting up filtering by hand is often impossible due to the large number of hosts involved in the attack. The goal of this paper is to explore the effectiveness of machine learning techniques in developing automatic defences against DDoS attacks. As a first step, a data collection and traffic filtering framework is developed. This foundation is then used to explore the potential of artificial neural networks in the defence against DDoS attacks.
Large, climate-sensitive soil carbon stocks mapped with pedology-informed machine learning in the North Pacific coastal temperate rainforest

Accurate soil organic carbon (SOC) maps are needed to predict the terrestrial SOC feedback to climate change, one of the largest remaining uncertainties in Earth system modeling. Over the last decade, global scale models have produced varied predictions of the size and distribution of SOC stocks, ranging from 1000 to >3000 Pg of C within the top 1 m. Regional assessments may help validate or improve global maps because they can examine landscape controls on SOC stocks and offer a tractable means to retain regionally-specific information, such as soil taxonomy, during database creation and modeling. We compile a new transboundary SOC stock database for coastal watersheds of the North Pacific coastal temperate rainforest, using soil classification data to guide gap-filling and machine learning approaches to explore spatial controls on SOC and predict regional stocks. Precipitation and topographic attributes controlling soil wetness were found to be the dominant controls of SOC, underscoring the dependence of C accumulation on high soil moisture. The random forest model predicted stocks of 4.5 Pg C (to 1 m) for the study region, 22% of which was stored in organic soil layers. Calculated stocks of 228 ± 111 Mg C ha−1 fell within ranges of several past regional studies and indicate 11–33 Pg C may be stored across temperate rainforest soils globally. Predictions compared very favorably to regionalized estimates from two spatially-explicit global products (Pearson’s correlation: ρ = 0.73 versus 0.34). Notably, SoilGrids 250 m was an outlier for estimates of total SOC, predicting 4-fold higher stocks (18 Pg C) and indicating bias in this global product for the soils of the temperate rainforest. In sum our study demonstrates that CTR ecosystems represent a moisture-dependent hotspot for SOC storage at mid-latitudes.
Therapeutic effect of mesenchymal stem cells on experimentally induced hypertensive cardiomyopathy in adult albino rats

ABSTRACT Hypertensive heart diseases affect millions of people worldwide. We aimed to investigate the hypertensive left ventricular histological changes and assess the effectiveness of bone marrow derived mesenchymal stem cells (MSCs) therapy in the treatment of hypertensive cardiomyopathy. Adult male albino rats were assigned into two groups: group I (control), group II (Experimental) subdivided into subgroup IIa (hypertensive) and subgroup IIb (stem cell therapy). Left ventricles (LVs) were processed for light and electron microscope. Mallory’s trichrome and immunostaining for caspase-3 and desmin were carried out. Hypertension caused left ventricular histological and immunohistochemical changes that had been effectively improved by MSCs therapy.
A Spatial-Temporal Approach to Evaluate the Dynamic Evolution of Green Growth in China

Green growth, a new growth mode to tackle resource and environment crises, is imperative in light of current environmental crises and resource depletion. Most evaluation strategies of green growth, while emphasizing the time dimension, ignore the spatial association and diffusion. This study provides a measurement framework of green growth with which to select a set of 18 indicators and evaluates the efficiency of green growth in different regions. The coefficient of variation and the regional Gini coefficient were applied to analyze the spatial variation of green growth. The Exploratory Spatial Data Analysis (ESDA) was used to identify the evolution of the geographical agglomeration in 30 administrative regions in mainland China. The results show that China’s green growth capacity is constantly improving, and the gap between regions in this respect is shrinking. The spatial evolution trend of green growth is expanding horizontally from the East to the West and vertically from the central to the southwest and the northwest regions. Green growth in the eastern and central regions is active but poor in the northeast region. Compared with the continuous stability of the 22 provinces, eight provinces exhibited spatial activity and growth spillover, which affected the adjacent regions. Promoting the outflow of capital and technology is key to increasing green growth in the eastern and central region, while increasing investment and introducing technology through policy advantages to promote industrial transformation is an urgent task for the northeast region.