Miracle-dz/UniFAR-FTUs huggingface dataset:
Novel renal biomarker evaluation for early detection of acute kidney injury after transcatheter aortic valve implantation

ABSTRACT Acute kidney injury (AKI) following transcatheter aortic valve implantation (TAVI) is associated with increased morbidity and mortality. The biomarkers neutrophil gelatinase–associated lipocalin (NGAL), kidney injury molecule-1 (KIM-1), and interleukin-18 (IL-18) are predictive of AKI after cardiac surgery, but there is little data regarding these biomarkers after TAVI. We evaluated the associations between NGAL, KIM-1, and IL-18 levels and the incidence and severity of AKI and changes in serum creatinine after TAVI. This was a prospective pilot study of 66 TAVI cases. Urinary biomarkers were measured at baseline and at 2, 4, and 12 hours after TAVI. Demographics, procedural features, and renal function until discharge were compared between patients with and without subsequent AKI. Seventeen patients (25.8%) developed AKI postoperatively (stage 1, n = 14; stage 2, n = 1; stage 3, n = 2). There were no significant differences in unadjusted mean NGAL, KIM-1, and IL-18 levels between patients with and without AKI at 2, 4, and 12 hours following surgery. After adjusting for the Society of Thoracic Surgeons risk of mortality, this study of three urinary biomarkers showed no association with AKI or creatinine after TAVI. Ongoing efforts to predict and modify the risk of AKI after TAVI remain challenging.
Why are recessions good for your health?

A series of influential papers by Christopher J. Ruhm (2000, 2003, 2005, 2008) documents that recessions are “good for your health”—or, more specifically, that state-level mortality rates are strongly procyclical. The magnitude of the correlation is economically meaningful: a typical estimate from the literature suggests that a 1 percentage point increase in a state’s unemployment rate is associated with a 0.54 percent reduction in that state’s mortality rates. If this reflects a causal relationship that is also valid at the national level, then a 1 percentage point increase in the unemployment rate would translate (based on 2004 mortality rates) into about 12,000 fewer deaths per year. These findings are frequently interpreted as resulting from the rising opportunity cost of time that accompanies better labor market opportunities, and some empirical support exists for this interpretation. For example, Ruhm (2000) shows that obesity and smoking also exhibit a procyclical pattern, The economy and healTh †
MIMO evolving functional fuzzy models for interest rate forecasting

Forecasting the term structure of interest rates plays a crucial role in portfolio management, household finance decisions, business investment planning, and policy formulation. This paper proposes the use of evolving fuzzy inference systems for interest rate forecasting in the US and Brazilian markets. Evolving models provide a high level of system adaptation and learns the system dynamic continuously, which is essential for uncertain environments as fixed income markets. Besides the usefulness evaluation of evolving methods to forecast yields, this paper suggests the interest rate factors forecasting taking into account multi-input-multi-output (MIMO) evolving systems, which reduces computational time complexity and provides more accurate forecasts. Results based on mean squared forecast errors showed that MIMO evolving methods perform better than traditional benchmark for short and long-term maturities, for both fixed income markets evaluated.
Region-Based Convolutional Networks for Accurate Object Detection and Segmentation

Object detection performance, as measured on the canonical PASCAL VOC Challenge datasets, plateaued in the final years of the competition. The best-performing methods were complex ensemble systems that typically combined multiple low-level image features with high-level context. In this paper, we propose a simple and scalable detection algorithm that improves mean average precision (mAP) by more than 50 percent relative to the previous best result on VOC 2012-achieving a mAP of 62.4 percent. Our approach combines two ideas: (1) one can apply high-capacity convolutional networks (CNNs) to bottom-up region proposals in order to localize and segment objects and (2) when labeled training data are scarce, supervised pre-training for an auxiliary task, followed by domain-specific fine-tuning, boosts performance significantly. Since we combine region proposals with CNNs, we call the resulting model an R-CNN or Region-based Convolutional Network. Source code for the complete system is available at http://www.cs. berkeley.edu/~rbg/rcnn.
Pre-digestion to enhance volatile fatty acids (VFAs) concentration as a carbon source for denitrification in treatment of liquid swine manure

ABSTRACT Insufficient denitrification in biological treatment is often a result of the lack of a carbon source. In this study, use of the volatile fatty acids (VFAs) generated via pre-digestion as a carbon source to improve denitrification in sequencing batch reactor (SBR) treatment of liquid swine manure was investigated. The pre-digestion of swine manure was realized by storing the manure in a sealed container in room temperature and samples were taken periodically from the container to determine the VFA levels. The results showed that after 14 days of pre-digestion, the VFA level in the digested liquid was increased by 200%. A polynomial relationship for the VFA level in the digested manure with the digestion time was observed with a correlation coefficient being 0.9748. Two identical SBRs were built and operated on 8-h cycles in parallel, with one fed with pre-digested and the other raw swine manure. There were five phases included in each cycle, i.e., anaerobic (90 min), anoxic (150 min), anoxic/anaerobic (90 min), anoxic/aerobic (120 min), and settle/decant (30 min), and the feeding was split to 600 mL/200 mL and performed at the beginning of and 240 min into the cycle. The SBR fed on pre-digested swine manure achieved successful denitrification with only 0.35 mg/L nitrate left in the effluent, compared to 15.9 mg/L found in the effluent of the other SBR. Nitrite was not detected in the effluent from both SBRs. The results also indicated that there was no negative impact of feeding SBRs with the pre-digested liquid swine manure for treatment on the removal of other constituents such as total solids (TS), volatile solids (VS), suspended solids (SS), volatile suspended solids (VSS), and soluble chemical oxygen demand (COD). Therefore, anaerobic digestion as a pretreatment can be an effective way to condition liquid swine manure for SBR treatment to achieve sufficient nitrate removal.
Investigating Antibacterial Effects of Garlic (Allium sativum) Concentrate and Garlic-Derived Organosulfur Compounds on Campylobacter jejuni by Using Fourier Transform Infrared Spectroscopy, Raman Spectroscopy, and Electron Microscopy

ABSTRACT Fourier transform infrared (FT-IR) spectroscopy and Raman spectroscopy were used to study the cell injury and inactivation of Campylobacter jejuni from exposure to antioxidants from garlic. C. jejuni was treated with various concentrations of garlic concentrate and garlic-derived organosulfur compounds in growth media and saline at 4, 22, and 35°C. The antimicrobial activities of the diallyl sulfides increased with the number of sulfur atoms (diallyl sulfide < diallyl disulfide < diallyl trisulfide). FT-IR spectroscopy confirmed that organosulfur compounds are responsible for the substantial antimicrobial activity of garlic, much greater than those of garlic phenolic compounds, as indicated by changes in the spectral features of proteins, lipids, and polysaccharides in the bacterial cell membranes. Confocal Raman microscopy (532-nm-gold-particle substrate) and Raman mapping of a single bacterium confirmed the intracellular uptake of sulfur and phenolic components. Scanning electron microscopy (SEM) and transmission electron microscopy (TEM) were employed to verify cell damage. Principal-component analysis (PCA), discriminant function analysis (DFA), and soft independent modeling of class analogs (SIMCA) were performed, and results were cross validated to differentiate bacteria based upon the degree of cell injury. Partial least-squares regression (PLSR) was employed to quantify and predict actual numbers of healthy and injured bacterial cells remaining following treatment. PLSR-based loading plots were investigated to further verify the changes in the cell membrane of C. jejuni treated with organosulfur compounds. We demonstrated that bacterial injury and inactivation could be accurately investigated by complementary infrared and Raman spectroscopies using a chemical-based, “whole-organism fingerprint” with the aid of chemometrics and electron microscopy.
A Warping Window Approach to Real-time Vision-based Pedestrian Detection in a Truck's Blind Spot Zone

We present a vision-based pedestrian tracking system targeting a very specific application: avoiding accidents in the blind spot zone of trucks. Existing blind spot safety systems do not offer a complete solution to this problem. Therefore we propose an active alarm system, which warns the truck driver if vulnerable road users occur in the blind spot zone. Our system is based solely on a vision sensor, and automatically detects vulnerable road users in the blind spot camera images. Due to the nature of this specific problem, this is a challenging task. Besides the demanding time constraint there is a need for a high accuracy, and we have to cope with the large distortion that a blind spot camera introduces. To achieve this we propose a warping windowmulti-pedestrian tracking algorithm. Our algorithm achieves real-time performance while maintaining high accuracy. To evaluate our algorithms we recorded several datasets with a real blind spot camera mounted on a real truck, consisting of realistic simulated dangerous blind spot situations.
Prognostic Value of Estimated Functional Capacity Incremental to Cardiac Biomarkers in Stable Cardiac Patients

Background Few studies have investigated functional capacity self‐assessment tools in either prediction of future major adverse cardiac outcomes beyond all‐cause mortality or direct comparisons with clinically available biomarkers. Methods and Results We estimated functional capacity using the Duke Activity Status Index (DASI) questionnaire in 8987 sequential stable patients without acute coronary syndrome who were undergoing elective diagnostic coronary angiography with 3‐year follow‐up of major adverse cardiac events (death, nonfatal myocardial infarction, or stroke). A low DASI score provided independent prediction of a 4.8‐fold increase in future risk of incident major adverse cardiac events at 3 years (quartiles 1 versus 4 hazard ratio [95% CI] 4.76 [4.03 to 5.61], P<0.001), and a 3.8‐fold increased risk after adjusting for traditional risk factors (3.77 [3.15 to 4.51], P<0.001). The prognostic value of the DASI score was evident in both primary and secondary prevention cohorts, with and without heart failure, as well as high and low C‐reactive protein and B‐type natriuretic peptide levels. The DASI score reclassified 15% of patients (P<0.001) beyond traditional risk factors in predicting future MACE. Conclusion A simple self‐assessment tool of functional capacity in stable patients undergoing elective diagnostic cardiac evaluation provides independent and incremental prognostic value for prediction of both significant coronary angiographic disease and long‐term adverse clinical events.
Lateral incisor agenesis, canine impaction and characteristics of supernumerary teeth in a South European male population

Objective: To assess the prevalence of lateral incisor agenesis impacted canines and supernumerary teeth in a young adult male population. Materials and Methods: The panoramic radiographs of 1745 military students (mean age: 18.6 ± 0.52 years) who attended the Center of Aviation Medicine of the Armed Forces of Greece during the period 1997-2011 were initially analyzed for lateral incisor agenesis by two observers. After exclusion of the known orthodontic cases, a subgroup of 1636 examinees (mean age: 18.6 ± 0.44 years) was evaluated for canine impaction and supernumerary teeth. Results: Twenty-eight missing lateral incisors were observed in 22 military students, indicating an incidence of 1.3% in the investigated population. No lateral incisor agenesis was detected in the mandibular arch. A prevalence rate of 0.8% was determined for canine impaction in the sample of young adults. The majority of impacted teeth (86.7%) were diagnosed in the maxillary arch. Thirty-five supernumerary teeth were observed in 24 examinees (prevalence rate: 1.5%). The ratio of supernumerary teeth located in the maxilla versus the mandible was 2.2:1. The most common type of supernumerary tooth was the upper distomolar. Conclusion: The prevalence of lateral incisor agenesis, canine impaction, and supernumerary teeth ranged from 0.8 to 1.5% in the sample of male Greek military students.
Matlab solvers benchmark for ABB's Model Predictive Control Optimization

7 Acknowledgement 43 A Appendix 45 2 1 Introduction This project was assigned by ABB Ltd, a global company in power and automation technologies, with Model Predictive Control (MPC) optimization as the main topic. MPC uses many parameters and some of them could be tuned automatically using numerical optimization. These parameters are the one that interests us since finely tuned parameters result to a robust and stable MPC. There are many kind of methods and algorithms which could be used for numerical optimization and this project's goal is to provide a benchmark for some methods and algorithms in term of speed and result. MPC, also known as Receding Horizon Control, has been widely used to solve many kinds of problems in industry [7], but what is a Model Predictive Control ? It is a control method that explicitly uses a mathematical model of the process to get a prediction of control input for some period in the future to make the system's output aligns with its reference. Figure 1: MPC flow chart, copyright of Wikipedia.org As seen from Figure 1, the number of periods in the future is called prediction horizon. After creating a prediction of the future inputs for a number of prediction horizons, the MPC only executes the first predicted input control, and repeat the same prediction process again, with the same number of prediction horizon, this time from k + 1 until k + p + 1, where p is the number of prediction horizon, and executes the first predicted input control, and so on. By doing this, the MPC will always incorporate the noise or disturbances or anomalies that happened during each execution time when predicting the next inputs, resulting a robust control. 3 Figure 2: Basic structure of MPC [2] We depict MPC's basic structure in Figure 2. One of MPC's advantages is the ability to incorporate constraints and cost function, referred to as the objective function, and optimize them with relevance to input signals. By optimizing, we would like to have a set of parameters that produces the lowest or highest cost function value for minimization or maximization problem respectively and still satisfy our constraints.
Ultra-Processed Food Products and Obesity in Brazilian Households (2008–2009)

Background Production and consumption of industrially processed food and drink products have risen in parallel with the global increase in overweight and obesity and related chronic non-communicable diseases. The objective of this study was to analyze the relationship between household availability of processed and ultra-processed products and the prevalence of excess weight (overweight plus obesity) and obesity in Brazil. Methods The study was based on data from the 2008–2009 Household Budget Survey involving a probabilistic sample of 55,970 Brazilian households. The units of study were household aggregates (strata), geographically and socioeconomically homogeneous. Multiple linear regression models were used to assess the relationship between the availability of processed and ultra-processed products and the average of Body Mass Index (BMI) and the percentage of individuals with excess weight and obesity in the strata, controlling for potential confounders (socio-demographic characteristics, percentage of expenditure on eating out of home, and dietary energy other than that provided by processed and ultra-processed products). Predictive values for prevalence of excess weight and obesity were estimated according to quartiles of the household availability of dietary energy from processed and ultra-processed products. Results The mean contribution of processed and ultra-processed products to total dietary energy availability ranged from 15.4% (lower quartile) to 39.4% (upper quartile). Adjusted linear regression coefficients indicated that household availability of ultra-processed products was positively associated with both the average BMI and the prevalence of excess weight and obesity, whereas processed products were not associated with these outcomes. In addition, people in the upper quartile of household consumption of ultra-processed products, compared with those in the lower quartile, were 37% more likely to be obese. Conclusion Greater household availability of ultra-processed food products in Brazil is positively and independently associated with higher prevalence of excess weight and obesity in all age groups in this cross-sectional study.
Unified Approach for Energy Detection of Unknown Deterministic Signal in Cognitive Radio Over Fading Channels

Detection of an unknown deterministic signal by using an energy detector is of promising for cognitive radio networks. In this paper, a new approach is proposed to analyze the performance of the energy detector. It is based on the contour integral representation of the Marcum-Q function and the use of the moment generating function (MGF) of the signal-to-noise ratio (SNR). A new decision variable is constructed for the case of maximal ratio combining (MRC) reception. With its help and the MGF based approach, the performance of the MRC energy detector over i.i. d. Rician fading channels is analyzed. This case is intractable with the conventional probability density function (PDF) based approach. Further the detection probability of MRC combined energy detector over Nakagami-m fading branches is derived. The simulation results are presented to support the developed MGF based method, decision variable formulation and derivations. The detector performance is evaluated over different fading and diversity parameters with the help of numerical and simulation examples. I. INTRODUCTION Cognitive radio technology allows unlicensed users (sec- ondary users) to dynamically use unoccupied free spectrum bands of primary users (licensed users), while avoiding in- terference to primary users. Energy detection can be used to explore the presence of primary user transmissions and hence to identify the available spectrum holes. When using this technique, the energy detector of the secondary user treats the received primary user transmission as an unknown deterministic signal. Hence, secondary users do not requires unauthorized, irrelevant details of the primary transmissions. Due to this application, the performance of the energy detector using diversity reception techniques and over various wireless fading environments is of interest. The technique of detecting an unknown deterministic signal by using an energy detector is introduced in (1). It is shown that the detection problem is a test of binary hypotheses and statistics of the decision variable is chi-square distributed, irre- spective of whether the process model is lowpass or bandpass. Kostylev (2) extends the formulation to fading channels. In his work, average detection (Pd) and false alarm (Pf ) probabilities over Rayleigh, Rician and Nakagami-m fading channels are presented. But the Nakagami-m channel result is limited to an integral form expression. In (3) Nakagami-m and Rician fading channels are considered. But the derivation of Nakagami-m is restricted to integer values of the shape parameter (m) while the result of Rician fading channel is limited to unity time bandwidth product (u) in the decision variable. Maximal ratio, selection and switch and stay diversity detectors are analyzed over i.i. d. Rayleigh fading channels in (3). Expressions of Pd over i.i. d. and correlated Rayleigh fading channels with square-law combining is derived in
Current state and trends in production of sheep meat in EU and Croatia

Summary Production of meat as a segment of sheep production developed at the beginning of the 19th century in intensive agriculture of western Europe and in the eastern part of the USA because of the increase in population and development of industry. In sheep breeding, more than in other branches of livestock-breeding, there are significant differences in production technology within the EU, which brings to local differences in meat characteristics. Production systems may be divided to extensive and intensive ones.Extensive prevails in Mediterranean countries, where small suckling lambs of small body weight are slaughtered. Intensive system is spread in northern European countries where lambs of larger body weight are appreciated. In recent years the production and consumption of sheep meat has been in decline, mostly because of foot root disease, and because of CAP (Common Agriculutral Policy) reform and large import of sheep and sheep meat from New Zealand and Australia. Production and consumption of sheep meat in Croatia have also been in decline mostly because of the Homeland war in which the number of sheep was cut by half. According to statistical data from 2009, the number of sheep from 1991 has still not been reached. As well as in other Mediterranean countries, in Croatia it is also popular to slaughter suckling lambs which are consumed in one peace or chopped in 2-4 pieces. Two best known traditional cured sheep meat are kastradina andstelja, none of which is protected.
The symbiotic star CH Cygni — I. Non-thermal bipolar jets

ABSTRA C T Very Large Array surface brightness and spectral index maps of the evolving extended emission of the triple symbiotic star CH Cygni are presented. These are derived from observations at 4.8, 8.4 and 14 GHz between 1985 and 1999. The maps are dominated by thermal emission around the central bright peak of the nebula, but we also find unambiguous non-thermal emission associated with the extended regions. Our observations confirm that this is a jet. The central region has been associated with the stellar components through Hubble Space Telescope imaging. If the jets are the result of ejection events at outburst, expansion velocities are consistent with those from other measurement methods. We propose that the non-thermal emission is caused by material ejected in the bipolar jets interacting with the circumstellar wind envelope. The resulting shocks lead to local enhancements in the magnetic field from the compact component of the order of 3 mG.
R&D Investments with Competitive Interactions

In this article we develop a model to analyze patent-protected R&D investment projects when there is (imperfect) competition in the development and marketing of the resulting product. The competitive interactions that occur substantially complicate the solution of the problem since the decision maker has to take into account not only the factors that affect her/his own decisions, but also the factors that affect the decisions of the other investors. The real options framework utilized to deal with investments under uncertainty is extended to incorporate the game theoretic concepts required to deal with these interactions. Implementation of the model shows that competition in R&D, in general, not only increases production and reduces prices, but also shortens the time of developing the product and increases the probability of a successful development. These benefits to society are countered by increased total investment costs in R&D and lower aggregate value of the R&D investment projects.