Miracle-dz/UniFAR-FTUs huggingface dataset:
Initial Observations of elevated Alpha-n-Acetylgalactosaminidase Activity Associated with Autism and Observed Reductions from GC Protein—Macrophage Activating Factor Injections

Background: Autism spectrum disorders (ASD) are developmental disorders affecting 1:88 children, and which appear to be associated with a variety of complex immune dysregulations including autoimmunity. The enzyme, alpha-N-acetylgalactosaminidase (Nagalase) deglycosylates serum Gc protein (vitamin D 3 – binding protein) rendering it incapable of activating macrophage defenses. Increased Nagalase activity has been associated with a variety of malignancies, immune disorders and viral infections. Macrophage activating factor (GcMAF) has been repeatedly published as an intervention to lower serum Nagalase activity for a variety of cancer and HIV patients. GcMAF is a naturally occurring protein with well-established safety and therapeutic benefit(s) supported by numerous human studies. Methods: Initially, parents of 40 individuals with ASD sought testing for Nagalase serum activity as part of an evaluation of immune dysregulation. Nagalase enzyme activity measurement was performed by the European Laboratory of Nutrients (ELN), Bunnik, the Netherlands, using an end-point enzymatic assay of a chromogenic substrate. Some parents of patients with elevated Nagalase activity opted for weekly GcMAF injections provided by Immuno Biotech Ltd., Guernsey UK (www.gcmaf.eu). GcMAF is purified from human serum obtained from the American Red Cross using 25-hydroxyvitamin D3-Sepharose high affinity chromatography. The pro tein is then further diluted to obtain therapeutically appropriate levels for patients based on their clinical presentations. Results: Individuals with ASD (32 males and 8 females, n = 40, ages: 1 year 4 months - 21 years 2 months) had initial and post treatment assessment of Nagalase activity. Dosing of GcMAF was recommended based on previously reported response curves adjusted by the treating clinician for age, weight, and Nagalase levels. The average pre-treatment Nagalase activity of the autism group was 1.93 nmol/min/mg of substrate. This was well above the laboratory reported normal range of ,0.95 nmol/min/mg. For the ASD group the average level at the time of second testing was 1.03 nmol/min/mg, reflecting an average reduction of 0.90 nmol/min/mg ( P , 0.0001). Apart from the likely immunological benefits of lowering the Nagalase activity of these individuals, uncontrolled observations of GcMAF therapy indicated sub stantial improvements in language, socialization and cognition. No significant side-effects were reported during the course of injections. Conclusions: In this first report of Nagalase activity in patients with autism, it appears that most individuals have substantially higher levels than the expected healthy ranges. Although Nagalase is a nonspecific marker of immune dysregulation, its observed levels in autism may have both etiological and therapeutic significance. Importantly, this is also the first report of reduction of Nagalase activity in an autism population with GcMAF injections.
Cauchy Horizon Endpoints and Diierentiability Cauchy Horizon Endpoints and Diierentiability

Cauchy horizons are shown to be diierentiable at endpoints where only a single null generator leaves the horizon. A Cauchy horizon fails to have any null generator endpoints on a given open subset ii it is diierentiable on the open subset and also ii the horizon is (at least) of class C 1 on the open subset. Given the null convergence condition, a compact horizon which is of class C 2 almost everywhere has no endpoints and is (at least) of class C 1 at all points.
Best Subset Selection via a Modern Optimization Lens

In the last twenty-five years (1990-2014), algorithmic advances in integer optimization combined with hardware improvements have resulted in an astonishing 200 billion factor speedup in solving Mixed Integer Optimization (MIO) problems. We present a MIO approach for solving the classical best subset selection problem of choosing $k$ out of $p$ features in linear regression given $n$ observations. We develop a discrete extension of modern first order continuous optimization methods to find high quality feasible solutions that we use as warm starts to a MIO solver that finds provably optimal solutions. The resulting algorithm (a) provides a solution with a guarantee on its suboptimality even if we terminate the algorithm early, (b) can accommodate side constraints on the coefficients of the linear regression and (c) extends to finding best subset solutions for the least absolute deviation loss function. Using a wide variety of synthetic and real datasets, we demonstrate that our approach solves problems with $n$ in the 1000s and $p$ in the 100s in minutes to provable optimality, and finds near optimal solutions for $n$ in the 100s and $p$ in the 1000s in minutes. We also establish via numerical experiments that the MIO approach performs better than {\texttt {Lasso}} and other popularly used sparse learning procedures, in terms of achieving sparse solutions with good predictive power.
The Predictive Power of the Yield Spread: Further Evidence and a Structural Interpretation

This paper brings together two strands of the empirical macro literature: the reduced-form evidence that the yield spread helps in forecasting output and the structural evidence on the difficulties of estimating the effect of monetary policy on output in an intertemporal Euler equation. We show that including a short-term interest rate and inflation in the forecasting equation improves the forecasting performance of the spread for future output but the coefficients on the short rate and inflation are difficult to interpret using a standard macroeconomic framework. A decomposition of the yield spread into an expectations-related component and a term premium allows a better understanding of the forecasting model. In fact, the best forecasting model for output is obtained by considering the term premium, the short-term interest rate and inflation as predictors. We provide a possible structural interpretation of these results by allowing for time-varying risk aversion, linearly related to our estimate of the term premium, in an intertemporal Euler equation for output.
A Uniform Clustering Mechanism for Wireless Sensor Networks

In wireless sensor networks with dynamic clustering, the cluster heads are usually not selected on the basis of their locations. This causes irregular distribution of cluster heads and highly variable number of nodes in the clusters. Also, some of the clusters are spread over large areas within the network, causing limited spatial correlation between associated sensor nodes. These irregularities in cluster placements and dimensions negatively impact the efficiency of a wireless sensor network. For example, for a cooperative data aggregation, it necessitates variable or large sized packets while the aggregations, based on spatial correlation of sensor nodes, cannot be exploited easily. In this paper, we have developed a Distributed Uniform Clustering Algorithm (DUCA) for cluster based WSN. In DUCA, cluster formation mechanism is based on a virtual-grid system and sensing ranges of nodes that provide even distribution of clusters, homogenized cluster sizes, and reduced energy consumption. Simulation results show that DUCA improves the distribution of cluster heads by more than 2 times and reduces the energy consumption within a range of 15% to 50% as compared to the existing protocols.
Occupational Representativeness in Twitter

This paper describes an approach to detect one particular demographic characteristic, occupation (or profession) in Twitter user profiles. In this paper, we show how effective the approach is for estimating occupational population statistics in Australian Twitter by correlating them with real-world population obtained from 2011 Australian census data. We also demonstrate that we can gain more reliable social media insights in the context of occupational representativeness in Twitter if a non-standard occupation name is mapped into a standard occupation name. To our knowledge, this is the first attempt to build a machine learning model that automatically identifies linguistically noisy or open-ended occupations in Twitter, resulting in more reliable occupational population.
What Do Rich Countries Trade with Each Other? R&D and the Composition of U.S. And Swedish Trade

A long tradition in international economics explains comparative advantage by differences between countries in their stage of development, or their endowments of land, labor, and capital, and suggests that universal development will reduce the importance of trade. Sweden and the United States possess similar factor endowments and have converged in overall productivity, but their bilateral trade has grown. The example of these two countries suggests that mutual technological progress may promote trade, with the new basis for specialization being the different technology levels or R&D intensities of the goods being traded, rather than the initial endowments.
The Dynamics of Part-Time Work

This paper uses 14 years of data from the PSID to explore dynamic labor supply choices among adult women between full-time, part-time, or no labor market work. A variety of models indicate that past choices should be important in predicting current labor supply choices. This paper compares the effectiveness of several estimation strategies which require more or less historical information. The results indicate that past history in labor supply choices among adult women is very important in predicting current labor supply; given the lack of such data in many cases, the paper explores how much is lost when limited or no longitudinal information is available. In addition, the paper explores the substantive question of the role of part-time work in the labor market. Part-time workers are a very heterogeneous group; different part-time workers are in the midst of very different labor supply patterns. Most women use part-time work as a temporary alternative to full-time work or to being out of the labor market; few women use it as a transitional step into full-time employment. Simulations suggest the potential impact on future labor supply of mandating that low-skilled women who are out of the labor market enter part-time work.
An accelerometer based gestural capture system for performer based music composition

Previous studies by research groups in the field of music technology have resulted in a variety of gestural capture systems that enable various levels of interaction and control in the generation of sound. The majority of approaches developed to date have focused on direct control of synthesis parameters. More recently there has been a move toward a higher level of abstraction in which performers can interact in the music composition process. This seems to be motivated by the desire to realise the full creative expression achievable through fusion of physical performance and music. Such a goal poses a number of technical challenges several of which have been explored in the current project. The development of a small, low cost, low power accelerometer based gestural capture system is described. Using this device it is shown that significant subject to subject variation occurred (30% for a study group of n=36) when different test subjects executed the same movement. To compensate for this measurement uncertainty the development of a neural network based pattern recognition system is described which reduces variation to <5%. Refinement of the design of the gestural capture system is described to enable it to be implemented as a wireless network of body-worn sensors to capture movement data simultaneously from all four limbs. Theoretical consideration is then given to the scaling of the system to enable multiple performers to cooperate physically in the music composition process. This leads to the concept of a ‘performance orchestra’ where traditional musicians are replaced by instrumented dancers with direct control of accompanying sound.
Real-Time or Near Real-Time? - Towards a Real-Time Assessment Model

Since the advent of information technology, the availability of up-to-date information has been a key goal. Recently the term “real-time” has been discussed in combination with technologies such as in-memory-computing. However, the promoted acceleration of data availability and response times are accompanied by high investment costs to implement such infrastructures. Businesses are faced with the question of whether the increased real-time capability actually translates into benefits beyond pure up-to-date availability. This paper argues that the additional value for the business is essential and proposes a model for assessing and classifying business processes' real-time level. It aims to assess the information requirement of specific processes and determines the suitable level of data availability. It shows that real-time information is not always required and investments in real-time infrastructures may represent an overinvestment. A case study from the automotive industry illustrates the model and presents some early implications for the usability in practice.
Least Squares Magnetic-Field Optimization for Portable Nuclear Magnetic Resonance Magnet Design

Single-sided and mobile nuclear magnetic resonance (NMR) sensors have the advantages of portability, low cost, and low power consumption compared to conventional high-field NMR and magnetic resonance imaging (MRI) systems. We present fast, flexible, and easy-to-implement target field algorithms for mobile NMR and MRI magnet design. The optimization finds a global optimum in a cost function that minimizes the error in the target magnetic field in the sense of least squares. When the technique is tested on a ring array of permanent-magnet elements, the solution matches the classical dipole Halbach solution. For a single-sided handheld NMR sensor, the algorithm yields a 640 G field homogeneous to 16 100 ppm across a 1.9 cm3 volume located 1.5 cm above the top of the magnets and homogeneous to 32 200 ppm over a 7.6 cm3 volume. This regime is adequate for MRI applications. We demonstrate that the homogeneous region can be continuously moved away from the sensor by rotating magnet rod elements, opening the way for NMR sensors with adjustable ldquosensitive volumes.rdquo
CDN-on-Demand: An affordable DDoS Defense via Untrusted Clouds

We present CDN-on-Demand, a software-based defense that administrators of small to medium websites install to resist powerful DDoS attacks, with a fraction of the cost of comparable commercial CDN services. Upon excessive load, CDNon-Demand serves clients from a scalable set of proxies that it automatically deploys on multiple IaaS cloud providers. CDN-onDemand can use less expensive and less trusted clouds to minimize costs. This is facilitated by the clientless secure-objects, which is a new mechanism we present. This mechanism avoids trusting the hosts with private keys or user-data, yet does not require installing new client programs. CDN-on-Demand also introduces the origin-connectivity mechanism, which ensures that essential communication with the content-origin is possible, even in case of severe DoS attacks. A critical feature of CDN-on-Demand is in facilitating easy deployment. We introduce the origin-gateway module, which deploys CDN-on-Demand automatically and transparently, i.e., without introducing changes to web-server configuration or website content. We implement CDN-on-Demand and evaluate each component separately as well as the complete system.
Molecular Mechanisms Controlling the Migration of Striatal Interneurons

In the developing telencephalon, the medial ganglionic eminence (MGE) generates many cortical and virtually all striatal interneurons. While the molecular mechanisms controlling the migration of interneurons to the cortex have been extensively studied, very little is known about the nature of the signals that guide interneurons to the striatum. Here we report that the allocation of MGE-derived interneurons in the developing striatum of the mouse relies on a combination of chemoattractive and chemorepulsive activities. Specifically, interneurons migrate toward the striatum in response to Nrg1/ErbB4 chemoattraction, and avoid migrating into the adjacent cortical territories by a repulsive activity mediated by EphB/ephrinB signaling. Our results also suggest that the responsiveness of MGE-derived striatal interneurons to these cues is at least in part controlled by the postmitotic activity of the transcription factor Nkx2-1. This study therefore reveals parallel mechanisms for the migration of MGE-derived interneurons to the striatum and the cerebral cortex.
Amyloidogenic Propensity of a Natural Variant of Human Apolipoprotein A-I: Stability and Interaction with Ligands

A number of naturally occurring mutations of human apolipoprotein A-I (apoA-I) have been associated with hereditary amyloidoses. The molecular mechanisms involved in amyloid-associated pathology remain largely unknown. Here we examined the effects of the Arg173Pro point mutation in apoA-I on the structure, stability, and aggregation propensity, as well as on the ability to bind to putative ligands. Our results indicate that the mutation induces a drastic loss of stability, and a lower efficiency to bind to phospholipid vesicles at physiological pH, which could determine the observed higher tendency to aggregate as pro-amyloidogenic complexes. Incubation under acidic conditions does not seem to induce significant desestabilization or aggregation tendency, neither does it contribute to the binding of the mutant to sodium dodecyl sulfate. While the binding to this detergent is higher for the mutant as compared to wt apoA-I, the interaction of the Arg173Pro variant with heparin depends on pH, being lower at pH 5.0 and higher than wt under physiological pH conditions. We suggest that binding to ligands as heparin or other glycosaminoglycans could be key events tuning the fine details of the interaction of apoA-I variants with the micro-environment, and probably eliciting the toxicity of these variants in hereditary amyloidoses.
Predator Mimicry: Metalmark Moths Mimic Their Jumping Spider Predators

Cases of mimicry provide many of the nature's most convincing examples of natural selection. Here we report evidence for a case of predator mimicry in which metalmark moths in the genus Brenthia mimic jumping spiders, one of their predators. In controlled trials, Brenthia had higher survival rates than other similarly sized moths in the presence of jumping spiders and jumping spiders responded to Brenthia with territorial displays, indicating that Brenthia were sometimes mistaken for jumping spiders, and not recognized as prey. Our experimental results and a review of wing patterns of other insects indicate that jumping spider mimicry is more widespread than heretofore appreciated, and that jumping spiders are probably an important selective pressure shaping the evolution of diurnal insects that perch on vegetation.
Temporal reasoning in Timegraph I–II

We describe two temporal reasoning systems called Timegraph I and II, which can efficiently manage large sets of relations in the Point Algebra as well as metric information. Our representation is based on timegraphs, graphs partitioned into a set of chains on which the search is supported by a metagraph data structure. Timegraph I was originally developed by Taugher, Schubert and Miller in the context of story comprehension. Timegraph II provides useful extensions which makes the system more expressive in the representation of qualitative information and suitable for a larger class of applications. Experimental results show that our approach is very efficient, especially when the given relations admit representation as a collection of chains connected by relatively few cross-chain links.
A Unified Approach to Reliability Assessment of Multiphase DC–DC Converters in Photovoltaic Energy Conversion Systems

A systematic framework for reliability assessment and fault-tolerant design of multiphase dc-dc converters deployed in photovoltaic applications is presented. System-level steady-state models allow a detailed specification of component failure rates, and in turn establish the effects of ambient conditions and converter design on reliability. Markov reliability models are derived to estimate the mean time to system failure. Case studies applied to two- and three-phase, 250-W converters demonstrate that topological redundancy does not necessarily translate to improved reliability for all choices of switching frequency and capacitance. Capacitor voltage rating is found to be the dominant factor that affects system reliability.
Challenges of Nordic labour markets: A polarization of working life?

Labour-market polarization is characterized by increased employment in occupations at the top but also at the bottom of the skills and wage distributions, followed by a relative decline in middling occupations. This paper documents a polarization trend also in the Nordic labour markets and contrasts it to comparative findings for the USA. Employment growth in the top-paying occupations is found to have been dominated by a large increase in the category of Engineering professionals and other professionals, whereas the growth at the bottom end stems mainly from increased employment in Personal and protective services. The drop in the middle has been driven by a marked relative decline in the category Office clerks. Analysis of the extent to which differences in wage development across skill groups have enhanced or attenuated this process of polarization in employment patterns suggests that the U-shaped pattern of employment change prevails also after controlling for concomitant changes in relative occupational wages. Hence, it seems that also the Nordic countries have experienced a shift from skill-biased technological change to non-routine-biased technological change or, more likely, a combination of the two and that this process has not been particularly dampened by compressed wage structures or relatively more rigid wages.
Myocardial function in rat genetic models of low and high aerobic running capacity.

We recently evaluated treadmill aerobic running capacity in 11 inbred strains of rats and found that isolated working left ventricular function correlated (r = 0.86) with aerobic running capacity. Among these 11 strains the Buffalo (BUF) hearts produced the lowest and the DA hearts the highest isolated cardiac output. The goal of this study was to investigate the components of cardiac function (i.e., coronary flow, heart rates, stroke volume, contractile dynamics, and cross-bridge cycling) to characterize further the BUF and DA inbred strains as potential models of contrasting myocardial performance. Cardiac performance was assessed using the Langendorff-Neely working heart preparation. Isolated DA hearts were superior (P < 0.05) to the BUF hearts for cardiac output (63%), stroke volume (60%), aortic +dP/dt (47%), and aortic -dP/dt (46%). The mean alpha/beta-myosin heavy chain (MHC) isoform ratio for DA hearts was 21-fold higher relative to BUF hearts. At the steady-state mRNA level, DA hearts had a fivefold higher alpha/beta-ratio than the BUF hearts. The mean rate of ATP hydrolysis by MHCs was 64% greater in DA compared with BUF ventricles. These data demonstrate that the BUF and DA strains can serve as genetic models of contrasting low and high cardiac function.