Miracle-dz/UniFAR-FTUs huggingface dataset:
Application of Data Science in Healthcare And The Implications That Arise

The era of technology has brought a variety of approaches that aim to improve the quality of service. This has changed from the traditional scopes of practice to a technically superior age where the machine plays a critical part in society. This has also raised competitiveness since every faction is obsessed with being the best, providing the best quality of service, and appealing to several intricate domains. These systems have a way to ensure data and information are safeguarded by firewalls that encrypt the data preventing any leakage or breaking the confidentiality between two individuals bound by an agreement of privilege and trust. However, these systems can be hacked or information sullied by cyber terrorists. These crimes have egregious implications and pose ethical dilemmas since crucial personal data is no longer covertly preserved. Some of these implications can prove to be detrimental for several actors. For instance, if critical personal records and history are leaked or shared between or among individuals who do not have the authority or jurisdiction to do so, this is considered a cyber-crime. As such, the law is allowed to prosecute and charge that individual. This paper aims to identify the underpinning factors that promote Data Science in Healthcare and provision. It highlights the study's relevance today and how we can relate to the changing scope of Medicine. It then outlines the significance of the research and use of Data Sciences in the United States. Through a variety of literature on the subject, this paper outlines how Artificial Intelligence functions in the 21st Century and how it has revolutionized the practice of practically every single relevant field and or sector. The main points discussed are the changing scope and practice of health, the utility of Artificial Intelligence and adoption of Data Science in Healthcare, and the significance to the U.S.
Acute tumor lysis syndrome secondary to hydroxycarbamide in chronic myelomonocytic leukemia

CHASTY RC, 1993, BRIT J HOSP MED, V49, P488; HANDE KR, 1993, AM J MED, V94, P133, DOI 10.1016-0002-9343(93)90174-N; Onida F, 2002, BLOOD, V99, P840, DOI 10.1182-blood.V99. 3.840; RAZIS E, 1994, ACTA HAEMATOL-BASEL, V91, P171; SULLAN S, 2001, SEMIN ONCOL S5, V28, P9
Dependence in macroeconomic variables: Assessing instantaneous and persistent relations between and within time series

The present thesis comprises two rather independent chapters. In general, the diagnosis and quantiﬁcation of dependence is a major aim of econometric studies. Along these lines, the concept of dependence serves as an encompassing framework to analyze time series with two very diﬀerent techniques. First, we consider a single macroeconomic time series. A series which incorporates only temporary deviations from deterministic terms provides a diﬀerent starting point for economic interpretations than an ‘unpredictable’ (random) series. In the context of dependency, we are interested if a time series is stationary or if it exhibits persistent dependence on larger horizons. We consider univariate tests for stationarity under distinct model settings. More recently, panel unit root tests have been developed to overcome power deﬁciencies and provide a more general economic statement. The standard panel unit root tests are not robust under time-varying variances and trending data. Against this background, we introduce a new test procedure which performs well in this setting. Departing from the framework of a single time series the diagnosis of dependencies between several variables provides evidence on relations in a macroeconomic system. As- suming stationarity of the series, we analyze instantaneous and persistent eﬀects between macroeconomic indices by means of vector autoregressive models. Dependencies can, be- yond the standard linear setting, be present in diverse forms. We ﬁrst refer to the variety of dependencies. Subsequently, we review and compare nonparametric measures which are developed to robustly diagnose various dependence types. Drawing on these dependence diagnostics helps to conduct a preliminary analysis of the data. In macroeconomics, the analyst might be further interested in causalities. We analyze causalities by means of struc- tural vector autoregressive models tracing the variables back to unanticipated independent shocks. Hereby, we are mainly interested in the identiﬁcation of instantaneous response ma- trices relying on non-Gaussianity of the structural shocks. We compare such independence based identiﬁcation procedures relying on beforehand selected nonparametric dependence measures. Furthermore, we highlight their performance by means of a simulation study. The assumption of at most one Gaussian (structural) shocks is essential for unique identi- ﬁcation of the instantaneous response to independent shocks. However, in a system with multiple Gaussian shocks the non-Gaussian ones can still be uniquely identiﬁed. We prove this result and, thereby, enable a more general deﬁnition of identiﬁability.
Opioid-induced hyperalgesia: abnormal or normal pain?

INTRODUCTION It is well recognized that opioids are the analgesics of choice for the treatment of moderate to severe pain. However, a large number of clinical studies have reported that opioids can unexpectedly elicit hyperalgesia (enhanced responses to noxious stimulation) and allodynia (pain elicited by normal innocuous stimuli) [1–8]. Opioid-induced hyperalgesia has also been reported in animal experimental models [9–17]. This pain hypersensitivity has been classified as abnormal pain [16,18] as opposed to physiological (or normal) pain associated with nociception. Physiological pain has an important function in the preservation of an organism since it is an essential warning device that alerts the organism of impending danger from actual or potentially injurious stimuli in the environment. Does this suggest that there are good and bad pains, i.e. normal or abnormal pain? From a clinical viewpoint, the response is probably yes, if pain is only considered as a simple sensory system. However, pain is also a motivational process inducing a coordinated set of behavioral changes that serves to remember danger, some behaviors being facilitated and others inhibited. In this respect, pain sensitivity may be the result of a balance between the activity of inhibitory (antinociceptive) and facilitatory (pronociceptive) circuitries within the CNS with regard to the environmental state. If the main function of antinociceptive systems is to reduce pain during threatening situations as pain may compromise defensive behavior, it has been argued [19] that the function of pronociceptive systems might be that of motivating recuperative behaviors that would be adaptive for maintaining preservation of the organism when signals for safety succeed to threats. From an adaptive viewpoint, one hypothesis is that a pain hypersensitivity state is a normal step following defensive analgesia associated with the recruitment of endogenous antinociceptive systems. Since it is unlikely that evolution has selected mechanisms that are only activated by endogenous antinociceptive systems such as opioid systems, it might be suggested that delayed hyperalgesia following the use of exogenous opioids is a normal rather than an abnormal pain. In this review, we will first examine experimental evidences which indicate that exogenous opioids simultaneously activate both inhibitory and facilitatory pain systems leading to a long-lasting enhancement in pain sensitivity after analgesia. Second, we will assess the possible role of such an enhancement in pain sensitivity in the development of both tolerance to the analgesic effect of opioids and pain chronicisation processes.
Exponential Stabilization of Switched Discrete‐Time Systems with All Unstable Modes

This paper studies the exponential stabilization of switched discrete‐time systems whose subsystems are unstable. A new sufficient condition for the exponential stability of the class of systems is proposed. The result obtained is based on the determination of a lower bound of the maximum dwell time by virtue of the multiple Lyapunov functions method. The key feature is that the given stability condition does not need the value of the Lyapunov function to uniformly decrease at every switching instant. An example is provided to illustrate the effectiveness of the proposed result.
Drotrecogin alfa (activated) (recombinant human activated protein C) for the treatment of severe sepsis

ObjectiveTo review the data supporting drotrecogin alfa (activated) for severe sepsis treatment. Data SourcesPublished research and data from the Protein C Worldwide Evaluation in Severe Sepsis (PROWESS) trial. Data Extraction and SynthesisThe coagulation cascade and intense inflammation play a central role in the development of organ failure due to severe sepsis. Drotrecogin alfa (activated) has anti-inflammatory, antithrombotic, profibrinolytic, and other properties that may explain the beneficial results seen in both animal models and humans with severe sepsis. Drotrecogin alfa (activated) produces a robust reduction in the mortality rate of patients with severe sepsis that is evident across nearly every subgroup examined in the phase III clinical trial and has an acceptable safety profile with bleeding during infusion as the only significant risk associated with therapy. The relative risk reductions for mortality seen in Gram-negative, Gram-positive, pneumonia, abdominal sources, shock, and nonshock are similar to the intent-to-treat population, 19.4%. Treatment also increases days alive and free from mechanical ventilation and shock. ConclusionsCoagulopathy and systemic inflammation are almost universal in patients with severe sepsis. Treatment of this disorder with drotrecogin alfa (activated) directly addresses these derangements and substantially reduces morbidity and mortality rates with potential for bleeding during infusion as the only known risk.
Response of Government and the Citrus Industry to the Discovery of Asian Citrus Psyllid in Arizona

Author(s): Wright, Glenn C. ; Caravetta, G. John | Abstract: In October, 2009, about three months after the first find of Asian Citrus Psyllid (ACP) in San Luis Rio Colorado Sonora, a colony of ACP was found just across the border in San Luis Arizona. Since then, twelve additional sites have been found in Arizona, all except two in Yuma County. Less than 50 individual ACP have been found since 2009 and all have been eradicated. No ACP found in Arizona has yet tested positive for HLB. As of now, much of southwest Arizona is under federal quarantine for ACP and trees and fruit that move out of the quarantine area require special treatment. The response of the Arizona Department of Agriculture to the discovery of ACP has been to increase trapping and eradication activities using funds received from the Federal and State Government. The University of Arizona and the citrus industry have responded by establishing screenhouses to produce trees that can provide disease-free budwood. The industry has also developed a plan to establish an area-wide spray program if eradication efforts are not successful. Extension and outreach efforts have been directed toward the industry and the homeowner. The location of ACP finds in Arizona suggests that both Mexican ACP populations near the border and the transport of citrus fruit from the interior of Mexico lead to the establishment of the insect in Arizona. The small numbers of ACP found in Arizona, in contrast with populations found in coastal California, suggest that ACP populations may be adversely affected by the arid climate of the region, and that timely detection and eradication efforts are the keys to controlling spread of the ACP in arid regions.
Overlapping neural representations for the position of visible and 1 invisible objects 2

10 Humans can covertly track the position of an object, even if the object is temporarily occluded. What are 11 the neural mechanisms underlying our capacity to track moving objects when there is no physical stimulus 12 for the brain to track? One possibility is that the brain “fills-in” information about invisible objects using 13 internally generated representations similar to those generated by feed-forward perceptual mechanisms. 14 Alternatively, the brain might deploy a higher order mechanism, for example using an object tracking 15 model that integrates visual signals and motion dynamics (Kwon et al., 2015). In the present study, we 16 used electroencephalography (EEG) and time-resolved multivariate pattern analyses to investigate the 17 spatial processing of visible and invisible objects. Participants tracked an object that moved in discrete 18 steps around fixation, occupying six consecutive locations. They were asked to imagine that the object 19 continued on the same trajectory after it disappeared and move their attention to the corresponding 20 positions. Time-resolved decoding of EEG data revealed that the location of the visible stimuli could be 21 decoded shortly after image onset, consistent with early retinotopic visual processes. For processing of 22 unseen/invisible positions, the patterns of neural activity resembled stimulus-driven mid-level visual 23 processes, but were detected earlier than perceptual mechanisms, implicating an anticipatory and more 24 variable tracking mechanism. Monitoring the position of invisible objects thus utilises similar perceptual 25
Fiscal Policy, Pricing Frictions and Monetary Accommodation

We investigate the theoretical conditions for effectiveness of government consumption expenditure expansions using US, Euro area and UK data. Fiscal expansions taking place when monetary policy is accommodative lead to large output multipliers in normal times. The 2009-2010 packages need not produce significant output multipliers, may have moderate debt effects, and only generate temporary inflation. Expenditure expansions accompanied by deficit/debt consolidations schemes may lead to short run output gains but their success depends on how monetary policy and expectations behave. Trade openness and the cyclicality of the labor wedge explain cross-country differences in the magnitude of the multipliers.
Parallel Database Systems : The Future of High Performance Database Processing

January 1992 Abstract: Parallel database machine architectures have evolved from the use of exotic hardware to a software parallel dataflow architecture based on conventional shared-nothing hardware. These new designs provide impressive speedup and scaleup when processing relational database queries. This paper reviews the techniques used by such systems, and surveys current commercial and research systems.
The progress in optic nerve regeneration , where are we ? Adult Central Nervous

Adult Central Nervous System (CNS) Regeneration The mammalian CNS is inferior to that of invertebrates in the sense that regeneration is extremely limited (Tanaka and Ferretti, 2009). In that sense, it is also inferior to the mammalian peripheral nervous system. The optic nerve has long served as a standard model to study the CNS. The optic nerve has the same organization and three types of glial cells as the CNS white matter. It also provides much easier access. Breakthrough in the field of optic nerve regeneration not only has enabled vision restoration for millions of patients with glaucoma or other secondary optic neuropathies, but also provides insight into the cure of neurodegenerative diseases like Alzheimer’s disease and motor neuron diseases.
Convergence Rate Estimates in the Global CLT for Compound Mixed Poisson Distributions

Using the estimates of the accuracy of the normal approximation to distributions of Poisson-binomial random sums from [I. G. Shevtsova, Theory Probab. Appl. , 62 (2018), pp. 278--294], we obtain moment-type estimates of the rate of convergence in the central limit theorem for Poisson and mixed Poisson random sums in the uniform and mean metrics. As corollaries, we provide estimates of the accuracy of the approximation to distributions of negative binomial random sums by the normal law (with the growth of the shape parameter) and by the variance-gamma mixture of the normal law (as the “success probability” tends to zero); in particular, we present estimates of the accuracy of the Laplace approximation to distributions of geometric random sums.
DEVELOPMENT OF A THIRD GENERATION SHALLOW-WATER WAVE MODEL WITH UNSTRUCTURED SPATIAL MESHING

A numerical third-generation wave model dedicated both to deep water and nearshore applications is presented and applied to several test-cases to highlight its capabilities. Among its main features, this model uses a finite-elements technique for the discretization of the modelled area, which makes it suitable to represent complex bottom topographies and irregular shorelines. Furthermore, the piece-wise ray method used for wave propagation allows to use rather large time-steps, which in turn allows to keep the computational time at a very moderate level. The implementation of shallow-water physics in the model is also described, in particular with respect to depth-induced breaking. Several applications of the model are presented and compared to field or laboratory data for their validation. Finally, the main research and development items are mentioned and discussed. l. INTRODUCTION Although originally developed for deep water applications and meteorological purpose (e.g. WAMDI Group, 1988), spectral "phase-averaged" wave models are presently being extended towards the near-shore and coastal domains. This extension implies first to improve the physics of the numerical models so that they become able to reproduce shallow-water effects. Several efforts have already been attempted in this way by developing numerical formulations for bottom friction, depth-induced breaking and non-linear interactions between triplets of waves (the so-called "triad-interactions") (e.g. Ris et al., 1994 ; Van Vledder etal., 1994). A second point to consider is the ability of the model to deal with the complex bottom topography of coastal and near-shore areas. Compared to oceanic applications, these shallow-water domains usually need a finer spatial discretization to properly represent bathymetric gradients and irregular shorelines. This requires a number of properties from the numerics of the wave model (discretization techniques, integration schemes) which are usually not encountered in present thirdgeneration wave models. For instance, if one wishes to apply the WAM model to local areas, the size of the finite-difference spatial grid has to be decreased over the whole domain, which highly increases the number of computational points and 1 Research Engineer — Maritime Group 2 PhD Student— Maritime Group EDF Laboratoire National d'Hydraulique, 6, quai Watier 78400 CHATOU, FRANCE
Deep Against Net Image Super-Resolution Reconstruction Algorithm Based on W Distance

This paper proposes an image super-resolution algorithm based on the condition of Wasserstein distance generation against network, aiming at improving the quality of reconstructed images and improving the condition against the stability of neural network in image super-resolution research. Wasserstein distance algorithm is used to solve the instability problem of the traditional GAN network generator and make the model more stable. In the test sets such as Set5 and Set14, the three evaluation indexes PSNR, SSIM, and IFC of the SRWCGAN algorithm are superior to the VDSR algorithm, and the convergence speed and stability are better than those without W-distance.
Euclidean Distance Analysis to Detect Eye Movements for Accident Prevention using Face Mapping and Computer Vision

— Increase in road accidents lately is a predicament to irregular sleep patterns. Sleepy drivers pose a risk not only to themselves but also to others around them. Hence anti-sleep mechanisms for vehicles are the need of the hour. These mechanisms are one of the essential features to prevent micro sleep and should be accommodated in all vehicles to ensure road safety. The proposed paper thus highlights one such system that uses face mapping and Computer Vision to identify the facial features of the driver and hence generate an alert whenever fatigue is detected. In the scope of this research, Euclidean distance is calculated for both eyes between the eyelids to identify if the eyes are closed or not. Once closed eyes are detected, audio and textual alerts are generated to draw the driver’s attention. According to this research, once sleepiness is detected audio alerts initiated at regular time intervals show the best results to ensure road safety.
Network Model Diversity for Wireless Studies

Wireless network is one of interesting research domain. Many studies have been conducted related to security and network performance issue. There are two methods to represent a conducting study either based on test bed or simulation in order to access the real network environment. With a suitable network model selection, any research might achieve its objective. Therefore, this paper provides a collection of network models that has been used particularly in wireless network to assist researchers on selecting an appropriate network model for their conducting study. The result gathered from various technical and theoretical research papers. In future, the intention on more generic justification of selective parameters to be used for wireless study will be shared later on.
Error estimates for the simplified iteratively regularized Gauss–Newton method in Banach spaces under a Morozov-type stopping rule

Abstract Jin Qinian and Min Zhong [10] considered an iteratively regularized Gauss–Newton method in Banach spaces to find a stable approximate solution of the nonlinear ill-posed operator equation. They have considered a Morozov-type stopping rule (Rule 1) as one of the criterion to stop the iterations and studied the convergence analysis of the method. However, no error estimates have been obtained for this case. In this paper, we consider a modified variant of the method, namely, the simplified Gauss–Newton method under both an a priori as well as a Morozov-type stopping rule. In both cases, we obtain order optimal error estimates under Hölder-type approximate source conditions. An example of a parameter identification problem for which the method can be implemented is discussed in the paper.
Effective Governance of Transboundary Aquifers Through Institutions – Lessons Learned from River Basin Organizations

While international institutions governing transboundary surface waters have been established in a large number of international river and lake basins, transboundary aquifers remain widely neglected. Only recently, the international community has acknowledged the need for establishing institutionalized governance mechanisms and is moving ahead in some regions. Experiences from transboundary surface waters, however, show that the mere existence of such institutions is insufficient for effective management of water resources. Instead, the effectiveness of these institutions matters. The paper therefore draws lessons from RBO effectiveness and applies findings on their institutional design to groundwater institutions – aiming at ensuring the sustainable management of shared aquifers through institutions from the very beginning.