Miracle-dz/UniFAR-FTUs huggingface dataset:
Local Mechanical Stimuli Regulate Bone Formation and Resorption in Mice at the Tissue Level

Bone is able to react to changing mechanical demands by adapting its internal microstructure through bone forming and resorbing cells. This process is called bone modeling and remodeling. It is evident that changes in mechanical demands at the organ level must be interpreted at the tissue level where bone (re)modeling takes place. Although assumed for a long time, the relationship between the locations of bone formation and resorption and the local mechanical environment is still under debate. The lack of suitable imaging modalities for measuring bone formation and resorption in vivo has made it difficult to assess the mechanoregulation of bone three-dimensionally by experiment. Using in vivo micro-computed tomography and high resolution finite element analysis in living mice, we show that bone formation most likely occurs at sites of high local mechanical strain (p<0.0001) and resorption at sites of low local mechanical strain (p<0.0001). Furthermore, the probability of bone resorption decreases exponentially with increasing mechanical stimulus (R2 = 0.99) whereas the probability of bone formation follows an exponential growth function to a maximum value (R2 = 0.99). Moreover, resorption is more strictly controlled than formation in loaded animals, and ovariectomy increases the amount of non-targeted resorption. Our experimental assessment of mechanoregulation at the tissue level does not show any evidence of a lazy zone and suggests that around 80% of all (re)modeling can be linked to the mechanical micro-environment. These findings disclose how mechanical stimuli at the tissue level contribute to the regulation of bone adaptation at the organ level.
Long-term Multi-wavelength Observations of GRS 1758-258 and the ADAF Model

We present a long-term multi-wavelength light curve of Galactic black hole candidate GRS 1758–258 by combining previously published and archival data from GRANAT , ROSAT , CGRO , RXTE , BeppoSAX , ASCA , EXOSAT , and the VLA. In addition we include ﬁrst spectral results from the balloon-borne Gamma-ray Arcminute Telescope Imaging System ( GRATIS ). In light of diver-gent analyses of the 1991–1993 ROSAT observations, we have re-analyzed these data; we ﬁnd the soft X-rays track the hard X-rays, and that the ﬁts require no black-body component— indicating that GRS 1758–258 did not go to the high state in 1993. We oﬀer an interpretation based on the ADAF model for a system with ˙ m < ∼ ˙ m crit . We ﬁnd the 1990–1993 coeval hard and soft X-ray observations support the ADAF predictions. We discuss a new way to constrain black-hole mass with spectral data and the ADAF theory, and apply this technique to GRS 1758–258 to ﬁnd M 1 > ∼ 8–9 M ⊙ at an assumed distance of 8.5 kpc. Further in-vestigations of the ADAF model allow us to evaluate the model critically against the data and ﬂux-ﬂux diagram of Barret, McClintock, & Grindlay (1996) and to understand the limits of the latter’s “X-ray burster box. ”
Novel Oxytocin Gene Expression in the Hindbrain Is Induced by Alcohol Exposure: Transgenic Zebrafish Enable Visualization of Sensitive Neurons

Background Fetal Alcohol Spectrum Disorders (FASD) are a collection of disorders resulting from fetal ethanol exposure, which causes a wide range of physical, neurological and behavioral deficits including heightened susceptibility for alcoholism and addictive disorders. While a number of mechanisms have been proposed for how ethanol exposure disrupts brain development, with selective groups of neurons undergoing reduced proliferation, dysfunction and death, the induction of a new neurotransmitter phenotype by ethanol exposure has not yet been reported. Principal Findings The effects of embryonic and larval ethanol exposure on brain development were visually monitored using transgenic zebrafish expressing cell-specific green fluorescent protein (GFP) marker genes. Specific subsets of GFP-expressing neurons were highly sensitive to ethanol exposure, but only during defined developmental windows. In the med12 mutant, which affects the Mediator co-activator complex component Med12, exposure to lower concentrations of ethanol was sufficient to reduce GFP expression in transgenic embryos. In transgenic embryos and larva containing GFP driven by an oxytocin-like (oxtl) promoter, ethanol exposure dramatically up-regulated GFP expression in a small group of hindbrain neurons, while having no effect on expression in the neuroendocrine preoptic area. Conclusions Alcohol exposure during limited embryonic periods impedes the development of specific, identifiable groups of neurons, and the med12 mutation sensitizes these neurons to the deleterious effects of ethanol. In contrast, ethanol exposure induces oxtl expression in the hindbrain, a finding with profound implications for understanding alcoholism and other addictive disorders.
Investigation of Forces Affecting Dust Particle Alignment in Cross Polarization

This paper implicitly discusses method of using dual orthogonal polarizations to optimally conserve frequency spectrum. This has, in the recent time, received considerable interest in the field of electromagnetic wave propagation in sand and dust storms. The realization of a dual-polarized system is thus limited by degree of cross polar discrimination that can be achieved between the two orthogonal channels. Cross polarization discrimination is a parameter widely used to quantify the effects of polarization interference. Apart from non-sphericity of falling dust particles, dust induced microwave cross polarization has been attributed to tendency of the particles to align in a particular direction. This paper investigates and identifies important forces acting on the alignment which are inputs to the cross polarization discrimination evaluation. The method adopted involves the use of reliable measure of turbulence shear, inertial torque and Brownian motion effects. The results showed the influence of the relevant forces on the alignment of the dust particles. Inertial torque becomes a domineering force for systematic alignment at some particle size range.
On architectural choices in deep learning: From network structure to gradient convergence and parameter estimation

We study mechanisms to characterize how the asymptotic convergence of backpropagation in deep architectures, in general, is related to the network structure, and how it may be influenced by other design choices including activation type, denoising and dropout rate. We seek to analyze whether network architecture and input data statistics may guide the choices of learning parameters and vice versa. Given the broad applicability of deep architectures, this issue is interesting both from theoretical and a practical standpoint. Using properties of general nonconvex objectives (with first-order information), we first build the association between structural, distributional and learnability aspects of the network vis-\`a-vis their interaction with parameter convergence rates. We identify a nice relationship between feature denoising and dropout, and construct families of networks that achieve the same level of convergence. We then derive a workflow that provides systematic guidance regarding the choice of network sizes and learning parameters often mediated4 by input statistics. Our technical results are corroborated by an extensive set of evaluations, presented in this paper as well as independent empirical observations reported by other groups. We also perform experiments showing the practical implications of our framework for choosing the best fully-connected design for a given problem.
Characterization of Structural Determinants of Granzyme B Reveals Potent Mediators of Extended Substrate Specificity*

Granzymes are trypsin-like serine proteases mediating apoptotic cell death that are composed of two genetically distinct subfamilies: granzyme A-like proteases resemble trypsin in their active site architecture, while granzyme B-like proteases are quite distinct. Granzyme B prefers substrates containing P4 to P1 amino acids Ile/Val, Glu/Met/Gln, Pro/Xaa, and aspartic acid N-terminal to the proteolytic cleavage. By investigating the narrow extended specificity of the granzyme B-like proteases the mediators of their unique specificity are being defined. The foci of this study were the structural determinants Ile99, Tyr174, Arg192, and Asn218. Even modest mutations of these residues resulted in unique extended specificity profiles as determined using combinatorial substrate libraries and individual fluorogenic substrates. As with other serine proteases, Ile99 completely defines and predicts P2 specificity, primarily through the binding constant Km. Asn218 variants have minor effects alone but in combination with mutations at Arg192 and Ile99 alter P2 through P4 extended specificity. For each variant, the activity on its cognate substrate was equal to that of granzyme B for the same substrate. Thus, mutations at these determinants change extended selectivity preferentially over catalytic power. Additionally Asn218 variants result in increased activity on the wild type substrate, while the N218A/I99A variant disrupts the additivity between P2 and P4 specificity. This defines Asn218 not only as a determinant of specificity but also as a structural component required for P2 and P4 independence. This study confirms four determinants of granzyme B extended substrate specificity that constitute a canon applicable to the study of the remaining family members.
Learning convolutional neural networks from few samples

Learning Convolutional Neural Networks (CNN) is commonly carried out by plain supervised gradient descent. With sufficient training data, this leads to very competitive results for visual recognition tasks when starting from a random initialization. When the amount of labeled data is limited, CNNs reveal their strong dependence on large amounts of training data. However, recent results have shown that a well chosen optimization starting point can be beneficial for convergence to a good generalizing minimum. This starting point was mostly found using unsupervised feature learning techniques such as sparse coding or transfer learning from related recognition tasks. In this work, we compare these two approaches against a simple patch based initialization scheme and a random initialization of the weights. We show that pre-training helps to train CNNs from few samples and that the correct choice of the initialization scheme can push the network's performance by up to 41% compared to random initialization.
Simulating the Effect of Dopamine Imbalance on Cognition : From Positive Affect to Parkinson ' s Disease

Cools (2006) suggested that prefrontal dopamine levels are related to cognitive stability whereas striatal dopamine levels are related to cognitive plasticity. With such a wide ranging role, almost all cognitive activities should be affected by dopamine levels in the brain. Not surprisingly, factors influencing brain dopamine levels have been shown to improve/worsen performance in many behavioral experiments. On the one hand, Nadler and his colleagues (2010) showed that positive affect (which is thought to increase cortical dopamine levels) improves a type of categorization that depends on explicit reasoning (rule-based) but not a type that depends on procedural learning (informationintegration). On the other hand, Parkinson’s disease (which is known to decrease dopamine levels in both the striatum and cortex) produces proactive interference in the odd-man-out task (Flowers & Robertson, 1985) and renders subjects insensitive to negative feedback during reversal learning (Cools et al., 2006). This article uses the COVIS model of categorization to simulate the effects of different dopamine levels in categorization, reversal learning, and the odd-man-out task. The results show a good match between the simulated and human data, which suggests that the role of dopamine in COVIS can account for several cognitive enhancements and deficits related to dopamine levels in healthy and patient populations.
Cell-cycle kinetics and ultraviolet light survival in UV-1, a Chinese hamster ovary cell mutant defective in post-replication recovery.

UV-I, an ultraviolet-sensitive mutant of CHO-KI, is abnormally slow to recover from the inhibition of DNA synthesis caused by u.v. irradiation. When synchronized UV-I cells are irradiated in G1, their movement into S phase is unaltered, but thymidine incorporation is depressed (compared with that in the parent cell similarly treated). When irradiated in S phase, again incorporation is more depressed, and S phase suffers a greater delay in UV-I than in the parent cell. UV-I and its parent have similar capacities for excision repair of u.v. -induced damage inflicted in G1, and so enter S phase with similar amounts of unrepaired damage. The single-cell survival was measured after irradiation at different points in the cell cycle. The mutant and parent cells have similar values of D0 (mean lethal dose) except in mitosis, when the parent cell shows markedly greater resistance to u.v. irradiation. Dq (quasi-threshold dose) is fairly constant for the parent cell, but in UV-I it falls to a minimum in S phase. The responses of UV-I to u.v. irradiation are generally consistent with its known defect in the process of post-replication recovery, i.e. the ability to join up the abnormally small DNA fragments synthesized on a u.v. -damaged template.
Left back : a century of battles over school reform

For the past one hundred years, Americans have argued and worried about the quality of their schools. Some charged that students were not learning enough, while others complained that the schools were not furthering social progress. In Left Back, education historian Diane Ravitch describes this ongoing battle of ideas and explains why school reform has so often disappointed. She recounts grandiose efforts to use the schools for social engineering, even while those efforts diminished the schools' ability to provide a high-quality education for all children. By illuminating the history of education in the twentieth century, Left Back points the way to reviving American schools today. Source: Simon & Schuster Last Updated: 05/07/2001 Last Sent to NetRead: 11/18/2007 Author Bio Diane Ravitch is one of the nation's foremost historians of education and a leading education policy analyst. Her landmark books deeply influenced the national discussion of education standards in the 1980s and 1990s. She has been a professor at Teachers College, Columbia University, and at New York University. She served in the U.S. Department of Education as assistant secretary in charge of education research. She currently holds the Brown Chair in Education Studies at the Brookings Institution, edits Brookings Papers on Education Policy, and is a member of the National Assessment Governing Board. She lives in Brooklyn, New York.
Volatility, Correlation, and the Market Trend

The influence of past stock price movements on volatilities and correlations is essential for understanding diversification and contagion in financial markets. We develop a model that makes the influence of past returns on volatilities and correlations explicit. Employing information about recent market movements leads to a more realistic model for the behavior of stock returns in a downturn than conventional models. Our approach offers a fresh perspective on the behavior of stock markets, and provides an alternative to the concept of exceedance correlation. We provide strong evidence for the existence of contagion in financial markets that cannot be absorbed by diversification.
Impact of Short Social Training on Prosocial Behaviors: An fMRI Study

Efficient brain–computer interfaces (BCIs) are in need of knowledge about the human brain and how it interacts, plays games, and socializes with other brains. A breakthrough can be achieved by revealing the microfoundations of sociality, an additional component of the utility function reflecting the value of contributing to group success derived from social identity. Building upon our previous behavioral work, we conduct a series of functional magnetic resonance imaging (fMRI) experiments (N = 10 in the Pilot Study and N = 15 in the Main Study) to measure whether and how sociality alters the functional activation of and connectivity between specific systems in the brain. The overarching hypothesis of this study is that sociality, even in a minimal form, serves as a natural mechanism of sustainable cooperation by fostering interaction between brain regions associated with social cognition and those related to value calculation. We use group-based manipulations to induce varying levels of sociality and compare behavior in two social dilemmas: Prisoner’s Dilemma and variations of Ultimatum Game. We find that activation of the right inferior frontal gyrus, a region previously associated with cognitive control and modulation of the valuation system, is correlated with activity in the medial prefrontal cortex (mPFC) to a greater degree when participants make economic decisions in a game with an acquaintance, high sociality condition, compared to a game with a random individual, low sociality condition. These initial results suggest a specific biological mechanism through which sociality facilitates cooperation, fairness and provision of public goods at the cost of individual gain. Future research should examine neural dynamics in the brain during the computation of utility in the context of strategic games that involve social interaction for a larger sample of subjects.
“No Free Lunch” Theorems Applied to the Calibration of Traffic Simulation Models

In 1997, Wolpert and Macready derived “No free lunch theorems for optimization. ” They basically state that “the expected performance of any pair of optimization algorithms across all possible problems is identical. ” This is to say that there is no algorithm that outperforms the others over the entire domain of problems. In other words, the choice of the most appropriate algorithm depends upon the specific problem under investigation, and a certain algorithm, while providing good performance (both in terms of solution quality and convergence speed) on certain problems, may reveal weak on certain others. This apparently straightforward concept is not always acknowledged by optimization practitioners. A typical example, in the field of traffic simulation, concerns the calibration of traffic models. In this paper, a general method for verifying the robustness of a calibration procedure (suitable, in general, for any simulation optimization) is proposed based on a test with synthetic data. The main obstacle to this methodology is the significant computation time required by all the necessary simulations. For this reason, a Kriging approximation of the simulation model is proposed instead. The methodology is tested on a specific case study, where the effect on the optimization problem of different combinations of parameters, optimization algorithms, measures of goodness of fit, and levels of noise in the data is also investigated. Results show the clear dependence between the performance of a calibration procedure and the case study under analysis and ascertain the need for global solutions in simulation optimization with traffic models.
A continental-scale tool for acoustic identification of European bats

Summary 1. Acoustic methods are used increasingly to survey and monitor bat populations. However, the use of acoustic methods at continental scales can be hampered by the lack of standardized and objective methods to identify all species recorded. This makes comparable continent-wide monitoring difficult, impeding progress towards developing biodiversity indicators, transboundary conservation programmes and monitoring species distribution changes. 2. Here we developed a continental-scale classifier for acoustic identification of bats, which can be used throughout Europe to ensure objective, consistent and comparable species identifications. We selected 1350 full-spectrum reference calls from a set of 15 858 calls of 34 European species, from EchoBank, a global echolocation call library. We assessed 24 call parameters to evaluate how well they distinguish between species and used the 12 most useful to train a hierarchy of ensembles of artificial neural networks to distinguish the echolocation calls of these bat species. 3. Calls are first classified to one of five call-type groups, with a median accuracy of 97·6%. The median species-level classification accuracy is 83·7%, providing robust classification for most European species, and an estimate of classification error for each species. 4. These classifiers were packaged into an online tool, iBatsID, which is freely available, enabling anyone to classify European calls in an objective and consistent way, allowing standardized acoustic identification across the continent. 5. Synthesis and applications. iBatsID is the first freely available and easily accessible continental- scale bat call classifier, providing the basis for standardized, continental acoustic bat monitoring in Europe. This method can provide key information to managers and conservation planners on distribution changes and changes in bat species activity through time.
The Politics of Prostitution: Voluntary and forced prostitution: the ‘realistic approach’ of the Netherlands

Introduction Prostitution has existed for a long time and will continue to do so. This requires a realistic approach on the part of government … prohibition is not the way to proceed … but one should allow for voluntary prostitution. The authorities can then regulate prostitution and the prostitution sector. It can then become healthy, safe, transparent and cleansed from criminal side-effects. This quote is taken from a speech by the Minister of Justice, the Liberal Korthals, in October 1999, when he defended the repeal of the brothel ban in parliament (Handelingen Eerste Kamer (HEK) 1999–2000, 25437, 5-10-1999, 11). He added that all forms of forced prostitution would be combated vigorously. The quote presents in a nutshell the basic philosophy of current prostitution policy in the Netherlands, with its distinction between voluntary prostitution, to be regulated as sex work, and forced prostitution, to be prosecuted as illegal. The repeal was supported by the secular parties in parliament and opposed by the religious parties. Feminists in the Netherlands and the national women's policy agency supported the repeal of the ban, arguing that it is an important step forward in improving prostitutes' position. The new legislation marks a profound shift from the abolitionist legislation in the Morality Acts of 1911, which outlawed brothels and made pimping a criminal offence. Prostitutes or clients were never liable to prosecution.
Evaluating the benefits of home-based management of atrial fibrillation: current perspectives

Atrial fibrillation (AF) is the most common arrhythmia worldwide, leading to an extensive public health and economic burden. The increasing incidence and prevalence of AF is due to the advancing age of the population, structural heart disease, hypertension, diabetes, and thyroid disease. The majority of costs associated with AF have been attributed to the cost of hospitalization. In order to minimize costs and decrease hospitalizations, counseling on modifiable risk factors contributing to AF has been strongly emphasized. With the release of novel oral anticoagulants bypassing the need for anticoagulant bridging or laboratory monitoring, post-discharge nurse-led home intervention, and novel methods of heart rate monitoring, home-based AF management has reached a new level of ease and sophistication. In this review, we aimed to review modifiable risk factors for AF and various methods of home-based management of AF, along with their benefits.