Miracle-dz/UniFAR-FTUs huggingface dataset:
Intradiscal and intra-articular facet infiltrations with plasma rich in growth factors reduce pain in patients with chronic low back pain

Context: Low back pain (LBP) is a complex and disabling condition, and its treatment becomes a challenge. Aims: The aim of our study was to assess the clinical outcome of plasma rich in growth factors (PRGF Endoret) infiltrations (one intradiscal, one intra articular facet, and one transforaminal epidural injection) under fluoroscopic guidance control in patients with chronic LBP. PRGF Endoret which has been shown to be an efficient treatment to reduce joint pain. Settings and Design: The study was designed as an observational retrospective pilot study. Eighty six patients with a history of chronic LBP and degenerative disease of the lumbar spine who met inclusion and exclusion criteria were recruited between December 2010 and January 2012. Subjects and Methods: One intradiscal, one intra articular facet, and one transforaminal epidural injection of PRGF Endoret under fluoroscopic guidance control were carried out in 86 patients with chronic LBP in the operating theater setting. Statistical Analysis Used: Descriptive statistics were performed using absolute and relative frequency distributions for qualitative variables and mean values and standard deviations for quantitative variables. The nonparametric Friedman statistical test was used to determine the possible differences between baseline and different follow up time points on pain reduction after treatment. Results: Pain assessment was determined using a visual analog scale (VAS) at the first visit before (baseline) and after the procedure at 1, 3, and 6 months. The pain reduction after the PRGF Endoret injections showed a statistically significant drop from 8.4 ± 1.1 before the treatment to 4 ± 2.6, 1.7 ± 2.3, and 0.8 ± 1.7 at 1, 3, and 6 months after the treatment, respectively, with respect to all the time evaluations (P < 0.0001) except for the pain reduction between the 3rd and 6th month whose signification was lower (P < 0.05). The analysis of the VAS over time showed that at the end point of the study (6 months), 91% of patients showed an excellent score, 8.1% showed a moderate improvement, and 1.2% were in the inefficient score. Conclusions: Fluoroscopy guided infiltrations of intervertebral discs and facet joints with PRGF in patients with chronic LBP resulted in significant pain reduction assessed by VAS.
Application of machine learning algorithms to the study of noise artifacts in gravitational-wave data

The sensitivity of searches for astrophysical transients in data from the Laser Interferometer Gravitational-wave Observatory (LIGO) is generally limited by the presence of transient, non-Gaussian noise artifacts, which occur at a high enough rate such that accidental coincidence across multiple detectors is non-negligible. These ``glitches'' can easily be mistaken for transient gravitational-wave signals, and their robust identification and removal will help any search for astrophysical gravitational waves. We apply machine-learning algorithms (MLAs) to the problem, using data from auxiliary channels within the LIGO detectors that monitor degrees of freedom unaffected by astrophysical signals. Noise sources may produce artifacts in these auxiliary channels as well as the gravitational-wave channel. The number of auxiliary-channel parameters describing these disturbances may also be extremely large; high dimensionality is an area where MLAs are particularly well suited. We demonstrate the feasibility and applicability of three different MLAs: artificial neural networks, support vector machines, and random forests. These classifiers identify and remove a substantial fraction of the glitches present in two different data sets: four weeks of LIGO's fourth science run and one week of LIGO's sixth science run. We observe that all three algorithms agree on which events are glitches to within 10% for the sixth-science-run data, and support this by showing that the different optimization criteria used by each classifier generate the same decision surface, based on a likelihood-ratio statistic. Furthermore, we find that all classifiers obtain similar performance to the benchmark algorithm, the ordered veto list, which is optimized to detect pairwise correlations between transients in LIGO auxiliary channels and glitches in the gravitational-wave data. This suggests that most of the useful information currently extracted from the auxiliary channels is already described by this model. Future performance gains are thus likely to involve additional sources of information, rather than improvements in the classification algorithms themselves. We discuss several plausible sources of such new information as well as the ways of propagating it through the classifiers into gravitational-wave searches.
Decoupled Variational Gaussian Inference

Variational Gaussian (VG) inference methods that optimize a lower bound to the marginal likelihood are a popular approach for Bayesian inference. A difficulty remains in computation of the lower bound when the latent dimensionality L is large. Even though the lower bound is concave for many models, its computation requires optimization over O(L2) variational parameters. Efficient reparameterization schemes can reduce the number of parameters, but give inaccurate solutions or destroy concavity leading to slow convergence. We propose decoupled variational inference that brings the best of both worlds together. First, it maximizes a Lagrangian of the lower bound reducing the number of parameters to O(N), where N is the number of data examples. The reparameterization obtained is unique and recovers maxima of the lower-bound even when it is not concave. Second, our method maximizes the lower bound using a sequence of convex problems, each of which is parallellizable over data examples. Each gradient computation reduces to prediction in a pseudo linear regression model, thereby avoiding all direct computations of the covariance and only requiring its linear projections. Theoretically, our method converges at the same rate as existing methods in the case of concave lower bounds, while remaining convergent at a reasonable rate for the non-concave case.
A ribosomal DNA‐based framework for the detection and quantification of stress‐sensitive nematode families in terrestrial habitats

Indigenous communities of soil‐resident nematodes have a high potential for soil health assessment as nematodes are diverse, abundant, trophically heterogeneous and easily extractable from soil. The conserved morphology of nematodes is the main operational reason for their under‐exploitation as soil health indicators, and a user‐friendly biosensor system should preferably be based on nonmorphological traits. More than 80% of the most environmental stress‐sensitive nematode families belong to the orders Mononchida and Dorylaimida. The phylogenetic resolution offered by full‐length small subunit ribosomal DNA (SSU rDNA) sequences within these two orders is highly different. Notwithstanding several discrepancies between morphology and SSU rDNA‐based systematics, Mononchida families (indicated here as M1–M5) are relatively well‐supported and, consequently, family‐specific DNA sequences signatures could be defined. Apart from Nygolaimidae and Longidoridae, the resolution among Dorylaimida families was poor. Therefore, a part of the more variable large subunit rDNA (≈ 1000 bp from the 5′‐end) was sequenced for 72 Dorylaimida species. Sequence analysis revealed a subclade division among Dorylaimida (here defined as D1–D9, PP1–PP3) that shows only distant similarity with ‘classical’ Dorylaimid systematics. Most subclades were trophically homogeneous, and — in most cases — specific morphological characteristics could be pinpointed that support the proposed division. To illustrate the practicability of the proposed molecular framework, we designed primers for the detection of individual subclades within the order Mononchida in a complex DNA background (viz. in terrestrial or freshwater nematode communities) and tested them in quantitative assays (real‐time polymerase chain reaction). Our results constitute proof‐of‐principle for the concept of DNA sequence signatures‐based monitoring of stress sensitive nematode families in environmental samples.
A RARE CASE OF BULL DOG FETAL MONSTER IN A NON DESCRIPTIVE SHE BUFFALOE

A Case of dystocia due to bull dog fetus in a six years old third parity non descriptive buffalo was detailed in the present report. The full term non descriptive buffaloe was presented with the complaint of straining since 10 hrs. After careful physical, clinical, per vaginal examination the case was diagnosed as dystocia due to incomplete dilatation of cervix and presence of abnormal fetus in the birth canal. Following epidural anesthesia dystocia was successfully managed by per vaginal delivery and its recovery is reported.
Catabolism in chronic heart failure.

Why can chronic heart failure be a catabolic state? The resting metabolic rate of some, but not all patients with heart failure is increased. Resting metabolic rate may account for up to 70% of daily energy expenditure in heart failure, and increases with NYHA functional class. Resting energy expenditure accounts for a greater proportion of daily energy expenditure in heart failure patients, although there may be no difference in absolute resting energy expenditure, when compared with healthy controls. Paradoxically, average daily energy expenditure in heart failure patients falls with increased severity of disease. These facts may be explained through greater energy conservation, as a result of a reduction in physical activity rather than energy loss through an increase in resting metabolic rate. Factors which contribute to an increased resting metabolic rate in heart failure may be increased cardiac and ventilatory work, and increased resting peripheral oxygen consumption. Oxidative lipid metabolism is activated in heart failure and free fatty acid levels are reported to be higher in heart failure than normal. In these patients, left ventricular ejection fraction is inversely correlated with rates of free fatty acid oxidation, whereas plasma norepinephrine and growth hormone are positively related to free fatty acid oxidation rates. Increased norepinephrine stimulates hormone-sensitive lipase which results in increased lipolysis and loss of body fat stores. Loss of muscle bulk happens early in the course of chronic heart failure, as a consequence of both the disease process and physical inactivity. Reduced bone mass is common in heart failure and is related to low levels of serum vitamin D and secondary hyperparathyroidism. Ultimately, cardiac cachexia
Causal mediation analysis for the Cox proportional hazards model with a smooth baseline hazard estimator

An important problem within the social, behavioural and health sciences is how to partition an exposure effect (e.g. treatment or risk factor) among specific pathway effects and to quantify the importance of each pathway. Mediation analysis based on the potential outcomes framework is an important tool to address this problem and we consider the estimation of mediation effects for the proportional hazards model. We give precise definitions of the total effect, natural indirect effect and natural direct effect in terms of the survival probability, hazard function and restricted mean survival time within the standard two‐stage mediation framework. To estimate the mediation effects on different scales, we propose a mediation formula approach in which simple parametric models (fractional polynomials or restricted cubic splines) are utilized to approximate the baseline log‐cumulative‐hazard function. Simulation study results demonstrate low bias of the mediation effect estimators and close‐to‐nominal coverage probability of the confidence intervals for a wide range of complex hazard shapes. We apply this method to the Jackson heart study data and conduct a sensitivity analysis to assess the effect on the mediation effects inference when the no unmeasured mediator–outcome confounding assumption is violated.
Phosphorus Distribution between Liquid Iron and MgO Saturated Slags of the System CaO-MgO-FeOx-SiO2

Experiments have been made to study the phosphorus distribution between liquid iron and the CaO-SiO2-FeOx slag equilibrated in a magnesia crucible in the temperature range 1550° to 1650°C. The phosphorus distribution ratio (P2O5)/[P] increased with increasing CaO content and decreasing temperature. The maximum value of the distribution ratio was found to be present near the slag composition saturated with the tricalcium silicate and the magnesio wustite phases. The experimentally determined phosphorus content in metal disagrees with the phosphorus contents estimated from the treatments proposed by Balajiva et al., Turkdogan and Pearson, and Healy over the present range of slag compositions, except by Flood and Grjotheim.
Coarse Particulate Matter and Hospitalization for Respiratory Infections in Children Younger Than 15 Years in Toronto: A Case-Crossover Analysis

Objectives.The purpose of this study was to examine the association between ambient air pollution and hospitalization for respiratory infections among children who were younger than 15 years in Toronto during a 4-year period (1998–2001). Methods.Exposures averaged during periods that varied from 1 to 7 days were used to assess the effects of air pollutants, including thoracic particulate matter (PM10), fine (PM2.5) and coarse (PM10–2.5) particulate matter, carbon monoxide (CO), sulfur dioxide (SO2), nitrogen dioxide (NO2), and ozone (O3), on hospitalization for respiratory infections. A case-crossover design was used to calculate odds ratios for the hospitalization adjusted for daily weather conditions with an incremented exposure corresponding to the interquartile range in air pollution exposures. Results.When particulate matter and gaseous pollutants were mutually taken into account, the effect remained pronounced for PM10–2.5 in both boys and girls. The adjusted odds ratio for 6-day average exposure to PM10–2.5 with an increment of 6.5 μg/m3 was 1.15 (95% confidence interval: 1.02–1.30) for boys and 1.18 (95% confidence interval: 1.01–1.36) for girls. The effect also remained for PM10 in boys and for NO2 in girls. PM2.5, CO, SO2, and O3 showed no significant effects on hospitalization for respiratory infection in both genders when other pollutants were taken into consideration. Conclusion.Our study suggested a detrimental effect of relatively low levels of ambient particulate matter and gaseous pollutants, especially coarse particulate matter and NO2, on hospitalization for respiratory infections in children.
Induction of Inflammation by West Nile virus Capsid through the Caspase-9 Apoptotic Pathway

West Nile virus (WNV) is a member of the Flaviviridae family of vector-borne pathogens. Clinical signs of WNV infection include neurologic symptoms, limb weakness, and encephalitis, which can result in paralysis or death. We report that the WNV-capsid (Cp) by itself induces rapid nuclear condensation and cell death in tissue culture. Apoptosis is induced through the mitochondrial pathway resulting in caspase-9 activation and downstream caspase-3 activation. Capsid gene delivery into the striatum of mouse brain or interskeletal muscle resulted in cell death and inflammation, likely through capsid-induced apoptosis in vivo. These studies demonstrate that the capsid protein of WNV may be responsible for aspects of viral pathogenesis through induction of the apoptotic cascade.
Optimized Design of N-Phase Passive Mixer-First Receivers in Wideband Operation

Recent developments in CMOS passive mixers have demonstrated a number of new and useful capabilities, including dynamic RF port impedance control and filter up-conversion, while also allowing low noise figure and high out-of-band linearity, all of which track wide-ranging LO frequencies and tunable baseband bandwidth. However, these circuits also bring a unique set of challenges and requirements. Here we extend a previously derived LTI model for such mixers to the general N-phase case, and discuss basic limits in performance specifications including impedance matching, noise figure and linearity. We then extend this analysis to include high-frequency effects, especially as they relate to transistor properties. We show that essentially all of the key specification of such mixers can be described in terms of an impedance ratio, a characteristic cut-off frequency, the number of phases of the mixer and some process-related parameters. Finally, we discuss how these properties relate to power consumption of LO circuitry.
Atherectomy devices: technology update

Atherectomy is a procedure which is performed to remove atherosclerotic plaque from diseased arteries. Atherosclerotic plaques are localized in either coronary or peripheral arterial vasculature and may have different characteristics depending on the texture of the plaque. Atherectomy has been used effectively in treatment of both coronary and peripheral arterial disease. Atherectomy devices are designed differently to either cut, shave, sand, or vaporize these plaques and have different indications. In this article, current atherectomy devices are reviewed.
Oral Administration of the Probiotic Strain Escherichia coli Nissle 1917 Reduces Susceptibility to Neuroinflammation and Repairs Experimental Autoimmune Encephalomyelitis-Induced Intestinal Barrier Dysfunction

Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system (CNS) with an increasing incidence in developed countries. Recent reports suggest that modulation of the gut microbiota might be one promising therapy for MS. Here, we investigated whether the probiotic Escherichia coli strain Nissle 1917 (ECN) could modulate the outcome of experimental autoimmune encephalomyelitis (EAE), a murine model of MS. We evidenced that daily oral treatment with ECN, but not with the archetypal K12 E. coli strain MG1655, reduced the severity of EAE induced by immunization with the MOG35–55 peptide. This beneficial effect was associated with a decreased secretion of inflammatory cytokines and an increased production of the anti-inflammatory cytokine IL-10 by autoreactive CD4 T cells, both in peripheral lymph nodes and CNS. Interestingly, ECN-treated mice exhibited increased numbers of MOG-specific CD4+ T cells in the periphery contrasting with severely reduced numbers in the CNS, suggesting that ECN might affect T cell migration from the periphery to the CNS through a modulation of their activation and/or differentiation. In addition, we demonstrated that EAE is associated with a profound defect in the intestinal barrier function and that treatment with ECN, but not with MG1655, repaired intestinal permeability dysfunction. Collectively, our data reveal that EAE induces a disruption of the intestinal homeostasis and that ECN protects from disease and restores the intestinal barrier function.
Can Agent Development Affect Developer's Strategy?

Peer Designed Agents (PDAs), computer agents developed by non-experts, is an emerging technology, widely advocated in recent literature for the purpose of replacing people in simulations and investigating human behavior. Its main premise is that strategies programmed into these agents reliably reflect, to some extent, the behavior used by their programmers in real life. In this paper we show that PDA development has an important side effect that has not been addressed to date -- the process that merely attempts to capture one's strategy is also likely to affect the developer's strategy. The phenomenon is demonstrated experimentally, using several performance measures. This result has many implications concerning the appropriate design of PDA-based simulations, and the validity of using PDAs for studying individual decision making. Furthermore, we obtain that PDA development actually improved the developer's strategy according to all performance measures. Therefore, PDA development can be suggested as a means for improving people's problem solving skills.
The Relationship of Technology and Design Maturity to DoD Weapon System Cost Change and Schedule Change During Engineering and Manufacturing Development,

Every year, the Department of Defense (DoD) invests billions of dollars on the development of major defense acquisition programs. In return, many of these weapon systems have been plagued with cost overruns and schedule delays during the Engineering and Manufacturing Development (EMD) phase. Under conflicting guidance, DoD and the Government Accountability Office (GAO) suggest that weapon systems should contain subsystems with relatively mature technologies (based on the Technology Readiness Level of critical technologies) and design mature (based on the percentage of releasable design drawings) during system development. However, there has been no published research to date on the relationship between technology and design maturity guidance and the resulting cost change and schedule change of DoD weapon systems. The research presented in this paper confirms that the GAO guidance for design maturity can lead to a reduction of cost growth and schedule slippage of DoD weapon systems. In contrast, the research indicates that technology maturity guidance only impacts the schedule change of weapon systems, and not the potential cost change. With the quantitative results from the research, DoD and GAO officials can provide additional guidance to the planning and execution of the EMD phase, such as allocation of research and development funds to maturity achievement efforts.
ML-FOREST: A Multi-Label Tree Ensemble Method for Multi-Label Classification

Multi-label classification deals with the problem where each example is associated with multiple class labels. Since the labels are often dependent to other labels, exploiting label dependencies can significantly improve the multi-label classification performance. The label dependency in existing studies is often given as prior knowledge or learned from the labels only. However, in many real applications, such prior knowledge may not be available, or labeled information might be very limited. In this paper, we propose a new algorithm, called Ml-Forest , to learn an ensemble of hierarchical multi-label classifier trees to reveal the intrinsic label dependencies. In Ml-Forest, we construct a set of hierarchical trees, and develop a label transfer mechanism to identify the multiple relevant labels in a hierarchical way. In general, the relevant labels at higher levels of the trees capture more discriminable label concepts, and they will be transferred into lower level children nodes that are harder to discriminate. The relevant labels in the hierarchy are then aggregated to compute label dependency and make the final prediction. Our empirical study shows encouraging results of the proposed algorithm in comparison with the state-of-the-art multi-label classification algorithms under Friedman test and post-hoc Nemenyi test.