Miracle-dz/UniFAR-FTUs huggingface dataset:
Vascular Responses to Leukocyte Products in Atherosclerotic Primates

Little is known about the possible role of leukocytes in the pathogenesis of vasospasm. We hypothesized that vasoactive products released by leukocytes might produce constriction of atherosclerotic arteries. To test this hypothesis, we infused fmet-leu-phe (fMLP), a peptide that activates leukocytes to release their vasoactive products, into the perfused hind limb of normal and atherosclerotic cynomolgus monkeys. Infusion of fMLP did not change resistance of large arteries in normal monkeys. In contrast, fMLP produced pronounced constriction of large arteries in atherosclerotic monkeys. To determine whether leukotrienes, platelet-activating factor, or prostaglandin E2 (PGEJ2 which are released by leukocytes, may contribute to leukocyte-induced vasoconstriction in atherosclerotic monkeys, we injected leukotriene D49 platelet-activating factor, and PGE2 intra-arterially into the perfused hind limb. Leukotriene D4 and platelet-activating factor had minimal effects on large arteries in both normal and atherosclerotic monkeys. PGE2 produced marked constriction of large arteries in atherosclerotic, but not normal, monkeys. Thus, pronounced constriction in atherosclerotic, but not normal, arteries during infusion of fMLP suggests that products released by leukocytes may mediate vasoconstriction in atherosclerotic vessels. Vasoconstrictor responses to PGE2 are profoundly potentiated by atherosclerosis, which suggests that PGE2 may contribute to leukocyte-induced vasoconstriction.
Passive and Low Energy Cooling of Buildings

Preface. 1. Overview: The Various Passive Cooling Systems and Their Applicability to Different Climates and Building Types. 2. Minimizing Cooling Needs by Building Design. 3. Ventilative Cooling. 4. Radiant Cooling. 5. Evaporative Cooling Systems. 6. The Earth as a Cooling Source for Buildings. 7. Cooling of Attached Outdoor Spaces. Index.
A Learning-Style Theory for Understanding Autistic Behaviors

Understanding autism's ever-expanding array of behaviors, from sensation to cognition, is a major challenge. We posit that autistic and typically developing brains implement different algorithms that are better suited to learn, represent, and process different tasks; consequently, they develop different interests and behaviors. Computationally, a continuum of algorithms exists, from lookup table (LUT) learning, which aims to store experiences precisely, to interpolation (INT) learning, which focuses on extracting underlying statistical structure (regularities) from experiences. We hypothesize that autistic and typical brains, respectively, are biased toward LUT and INT learning, in low- and high-dimensional feature spaces, possibly because of their narrow and broad tuning functions. The LUT style is good at learning relationships that are local, precise, rigid, and contain little regularity for generalization (e.g., the name–number association in a phonebook). However, it is poor at learning relationships that are context dependent, noisy, flexible, and do contain regularities for generalization (e.g., associations between gaze direction and intention, language and meaning, sensory input and interpretation, motor-control signal and movement, and social situation and proper response). The LUT style poorly compresses information, resulting in inefficiency, sensory overload (overwhelm), restricted interests, and resistance to change. It also leads to poor prediction and anticipation, frequent surprises and over-reaction (hyper-sensitivity), impaired attentional selection and switching, concreteness, strong local focus, weak adaptation, and superior and inferior performances on simple and complex tasks. The spectrum nature of autism can be explained by different degrees of LUT learning among different individuals, and in different systems of the same individual. Our theory suggests that therapy should focus on training autistic LUT algorithm to learn regularities.
The Affinity of Hemoglobin for Oxygen

The delivery of oxygen to the cell depends in good measure upon the affinity with which hemoglobin binds oxygen or releases it from erythrocytes for use by the other cells of the body. The release of oxygen by normal human hemoglobin A, over a range of partial pressures of oxygen, may be represented graphically by the familiar, sigmoid-shaped, oxygen dissociation curve. Numerically, the affinity with which hemoglobin binds oxygen may be expressed by the T50 (P50) value which is defined as the partial pressure of oxygen at which 50% of the hemoglobin is saturated with oxygen at a temperature of 37 C and pH of 7.40. If the affinity with which hemoglobin binds oxygen is increased, the oxygen dissociation curve is shifted to the left and the T50 value is decreased from the normal value of 26 to 27 mm Hg Po2. If the affinity with which hemoglobin binds oxygen is decreased, the T50 value is increased. In the past, little physiologic significance was attached to variations in the normal position of the oxygen dissociation curve. Within the last several years, however, renewed attention has been paid to mechanisms whereby alterations in the release of oxygen to the tissues from hemoglobin might enhance the organism's responses to hypoxia. This interest was heightened by the discovery that certain intracellular organic phosphate compounds, namely, adenosine triphosphate (ATP) and, particularly, 2,3 diphosphoglycerate (DPG) exert profound effects upon the release of oxygen from hemoglobin. On oxygenation and deoxygenation the hemoglobin molecule undergoes conformational changes which are associated with binding of certain ligands whereby the combination of one ligand at a particular binding site of hemoglobin further facilitates the binding of other ligands at distant binding sites.
The local protein-protein interactional feature can be caught by machine-learning method

Protein-protein interactions (PPIs) are central for most biological processes. Much effort has been put into the development of methodology for predicting PPIs and the construction of PPIs networks. Though a high accurate rate those methods have achieved, it was found that the accurate rate strongly depends on the balance of datasets. Compare to negative datasets, positive datasets contain some proteins which are called hub protein intact more proteins. And the unbalance between datasets leads to an excellent performance of PPIs prediction. But when one used balance datasets, the performance is disappointed. Different Biological functions are supported by different local PPIs network. Does it mean that local PPIs network has its own feature? In this paper, we managed to catch features of local networks in three species on the condition that there is no unbalance between positive dataset and negative dataset. Features of local PPIs network fades as the network extended. The associate rules method is used to analyze features of local PPI network. All the datasets used in this study are derived from public available database.
Tracking Edges, Corners and Vertices in an Image

Abstract. In a range of imaging problems, particularly those where the images are of man‐made objects, edges join at points which comprise three or more distinct boundaries between textures. In such cases the set of edges in the plane forms what a mathematician would call a planar graph. Smooth edges in the graph meet one another at junctions, called ‘vertices’, the ‘degrees’ of which denote the respective numbers of edges that join there. Conventional image reconstruction methods do not always draw clear distinctions among different degrees of junction, however. In such cases the algorithm is, in a sense, too locally adaptive; it inserts junctions without checking more globally to determine whether another configuration might be more suitable. In this paper we suggest an alternative approach to edge reconstruction, which combines a junction classification step with an edge‐tracking routine. The algorithm still makes its decisions locally, so that the method retains an adaptive character. However, the fact that it focuses specifically on estimating the degree of a junction means that it is relatively unlikely to insert multiple low‐degree junctions when evidence in the data supports the existence of a single high‐degree junction. Numerical and theoretical properties of the method are explored, and theoretical optimality is discussed. The technique is based on local least‐squares, or local likelihood in the case of Gaussian data. This feature, and the fact that the algorithm takes a tracking approach which does not require analysis of the full spatial data set, mean that it is relatively simple to implement.
A new random mapping model

We introduce a new random mapping model, $T_n^{\hat D}$, which maps the set $\{1,2,. ,n\}$ into itself.The random mapping $T_n^{\hat D}$ is constructed using a collection of exchangeable random variables $\hat{D}_1, . ,\hat{D}_n$ which satisfy $\sum_{i=1}^n\hat{D}_i=n$. In the random digraph,$G_n^{\hat D}$, which represents the mapping $T_n^{\hat D}$, the in-degree sequence for the vertices is given by the variables $\hat{D}_1, \hat{D}_2, . , \hat{D}_n$, and, in some sense,$G_n^{\hat D}$ can be viewed as an analogue of the general independent degree models from random graph theory. We show that the distribution of the number of cyclic points, the number of components,and the size of a typical component can be expressed in terms of expectations of various functions of $\hat{D}_1, \hat{D}_2, . , \hat{D}_n$. We also consider two special examples of $T_n^{\hat D}$ which correspond to random mappings with preferential and anti-preferential attachment, respectively, and determine, for these examples, exact and asymptotic distributions for the statistics mentioned above.
Intelligence and Development: A Cognitive Theory

Acknowledgements. 1. The Scope of the Theory. 2. The Psychometrics of Intelligence: Fact or Fantasy? 3. The Intelligent Synapse. 4. Multiple Minds and Multiple Confusions. 5. Knowledge, Specific Abilities, and General Intelligence. 6. The Development of Intelligence. 7. Processing Speed and Development: Some Experiments. 8. Patterns of Abilities: Regularities or Chaos? 9. Conclusion. Bibliography. Index of Names. Index of Subjects.
($\ell,0)$-Carter partitions, a generating function, and their crystal theoretic interpretation

In this paper we give an alternate combinatorial description of the "$(\ell,0)$-JM partitions" (see \cite{F}) that are also $\ell$-regular. Our main theorem is the equivalence of our combinatoric and the one introduced by James and Mathas (\cite{JM}). The condition of being an $(\ell,0)$-JM partition is fundamentally related to the hook lengths of the partition. The representation-theoretic significance of their combinatoric on an $\ell$-regular partition is that it indicates the irreducibility of the corresponding specialized Specht module over the finite Hecke algebra (see \cite{JM}). We use our result to find a generating series which counts the number of such partitions, with respect to the statistic of a partition's first part. We then apply our description of these partitions to the crystal graph $B(\Lambda_0)$ of the basic representation of $\hat{\mathfrak{sl}_{\ell}}$, whose nodes are labeled by $\ell$-regular partitions. Here we give a fairly simple crystal-theoretic rule which generates all $\ell$-regular $(\ell,0)$-JM partitions in the graph $B(\Lambda_0)$. Finally, we mention how our construction can be generalized to recent results of M. Fayers (see \cite{F}) and S. Lyle (see \cite{L}) to count the total number of (not necessarily $\ell$-regular) Specht modules which stay irreducible at a primitive $\ell$th root of unity (for $\ell >2$).
c-Kit as a Novel Potential Therapeutic Target in Colorectal Cancer.

Organ Sharing registry. Liver Transpl 2014; 20(9):1045–1056. 17. Heimbach JK, Hirose R, Stock PG, et al. Delayed hepatocellular carcinoma model for end-stage liver disease exception score improves disparity in access to liver transplant in the United States. Hepatology 2015; 61(5):1643–1650. 18. Marvin MR, Ferguson N, Cannon RM, et al. MELDEQ: an alternative Model for End-Stage Liver Disease score for patients with hepatocellular carcinoma. Liver Transpl 2015;21(5):612–622. 19. Vitale A, Volk ML, De Feo TM, et al. A method for establishing allocation equity among patients with and
An Empirical Analysis of NMT-Derived Interlingual Embeddings and Their Use in Parallel Sentence Identification

End-to-end neural machine translation has overtaken statistical machine translation in terms of translation quality for some language pairs, specially those with large amounts of parallel data. Besides this palpable improvement, neural networks provide several new properties. A single system can be trained to translate between many languages at almost no additional cost other than training time. Furthermore, internal representations learned by the network serve as a new semantic representation of words—or sentences—which, unlike standard word embeddings, are learned in an essentially bilingual or even multilingual context. In view of these properties, the contribution of the present paper is twofold. First, we systematically study the neural machine translation (NMT) context vectors, i.e., output of the encoder, and their power as an interlingua representation of a sentence. We assess their quality and effectiveness by measuring similarities across translations, as well as semantically related and semantically unrelated sentence pairs. Second, as extrinsic evaluation of the first point, we identify parallel sentences in comparable corpora, obtaining an <inline-formula><tex-math notation="LaTeX">$F_1=98.2\%$</tex-math></inline-formula> on data from a shared task when using only NMT context vectors. Using context vectors jointly with similarity measures <inline-formula> <tex-math notation="LaTeX">$F_1$</tex-math></inline-formula> reaches <inline-formula><tex-math notation="LaTeX"> $98.9\%$</tex-math></inline-formula>.
to Leeds Beckett Repository

Academics and policy makers have long been interested in understanding the interplay of factors that influence resident perceptions of local tourism development. This article reports the results of a study that is both methodologically and contextually novel. It uses the Chi-square Automatic Interaction Detection (CHAID) method, which is usually associated with other fields, most notably consumer marketing, to examine residents’ perceptions of tourism development in rural Poland. It contributes to the literature by revealing the need to segment residents appropriately and highlights which constituencies in rural communities are most likely to be positively (and negatively) disposed toward tourism. This creates opportunities for more nuanced policy interventions. This study examines residents’ attitudes toward tourism in the context of the contrasting types of community and their demographic features in rural Poland. The research focuses on the attitudes of residents from four different types of communities: “satellite communities” representing integrated rural areas; “star communities” representing intermediate rural areas where tourism already features; “aspiring star communities” which are intermediate rural areas with the potential for tourism development; and “peripheral communities” which are economically marginal but may have the potential to develop a tourism offer via niche chi-square tests show significant statistical differences in categorical variables between the two The tests illustrate that the group” and heterogeneity in connection with the following traits: (chi-square 21.573; p < level p the 32.221; p < self-assessment and type of two
A modified Corrado test for assessing abnormal security returns

Event studies typically use the methodology developed by Fama et al. [1969. The adjustment of stock prices to new information. International Economic Review 10, no. 1: 1–21] to segregate a stock's return into expected and unexpected components. Moreover, conventional practice assumes that abnormal returns evolve in terms of a normal distribution. There is, however, an increasing tendency for event studies to employ non-parametric testing procedures due to the mounting empirical evidence which shows that stock returns are incompatible with the normal distribution. This paper focuses on the widely used non-parametric ranking procedure developed by Corrado [1989. A nonparametric test for abnormal security price performance in event studies. Journal of Financial Economics 23, no. 2: 385–95] for assessing the significance of abnormal security returns. In particular, we develop a consistent estimator for the variance of the sum of ranks of the abnormal returns, and show how this leads to a more efficient test statistic (as well as to less cumbersome computational procedures) than the test originally proposed by Corrado (1989). We also use the theorem of Berry [1941. The accuracy of the Gaussian approximation to the sum of independent variates. Transactions of the American Mathematical Society 49, no. 1: 122–36] and Esseen [1945. Fourier analysis of distribution functions: A mathematical study of the Laplace–Gaussian law. Acta Mathematica 77, no. 1: 1–125] to demonstrate how the distribution of the modified Corrado test statistic developed here asymptotically converges towards the normal distribution. This shows that describing the distributional properties of the sum of the ranks in terms of the normal distribution is highly problematic for small sample sizes and small event windows. In these circumstances, we show that a second-order Edgeworth expansion provides a good approximation to the actual probability distribution of the modified Corrado test statistic. The application of the modified Corrado test developed here is illustrated using data for the purchase and sale by UK directors of shares in their own companies.
Data-Driven Discrimination at Work

A data revolution is transforming the workplace. Employers are increasingly relying on algorithms to decide who gets interviewed, hired or promoted. Proponents of the new data science claim that automated decision systems can make better decisions faster, and are also fairer, because they replace biased human decision-makers with “neutral” data. However, data are not neutral and algorithms can discriminate. The legal world has not yet grappled with these challenges to workplace equality. The risks posed by data analytics call for fundamentally rethinking anti-discrimination doctrine. When decision-making algorithms produce biased outcomes, they may seem to resemble familiar disparate impact cases, but that doctrine turns out to be a poor fit. Developed in a different context, disparate impact doctrine fails to address the ways in which algorithms can introduce bias and cause harm. This Article argues instead for a plausible, revisionist interpretation of Title VII, in which disparate treatment and disparate impact are not the only recognized forms of discrimination. A close reading of the text suggests that Title VII also prohibits classification bias — namely, the use of classification schemes that have the effect of exacerbating inequality or disadvantage along the lines of race or other protected category. This description matches well the concerns raised by workplace analytics. Framing the problem in terms of classification bias leads to some quite different conclusions about how the anti-discrimination norm should be applied to algorithms, suggesting both the possibilities and limits of Title VII’s liability focused model.
A likelihood framework for deterministic hydrological models and the importance of non-stationary autocorrelation

Abstract. The widespread application of deterministic hydrological models in research and practice calls for suitable methods to describe their uncertainty. The errors of those models are often heteroscedastic, non-Gaussian and correlated due to the memory effect of errors in state variables. Still, residual error models are usually highly simplified, often neglecting some of the mentioned characteristics. This is partly because general approaches to account for all of those characteristics are lacking, and partly because the benefits of more complex error models in terms of achieving better predictions are unclear. For example, the joint inference of autocorrelation of errors and hydrological model parameters has been shown to lead to poor predictions. This study presents a framework for likelihood functions for deterministic hydrological models that considers correlated errors and allows for an arbitrary probability distribution of observed streamflow. The choice of this distribution reflects prior knowledge about non-normality of the errors. The framework was used to evaluate increasingly complex error models with data of varying temporal resolution (daily to hourly) in two catchments. We found that (1) the joint inference of hydrological and error model parameters leads to poor predictions when conventional error models with stationary correlation are used, which confirms previous studies; (2) the quality of these predictions worsens with higher temporal resolution of the data; (3) accounting for a non-stationary autocorrelation of the errors, i.e. allowing it to vary between wet and dry periods, largely alleviates the observed problems; and (4) accounting for autocorrelation leads to more realistic model output, as shown by signatures such as the flashiness index. Overall, this study contributes to a better description of residual errors of deterministic hydrological models.
Effects of amphotericin B, nystatin, and other polyene antibiotics on chitin synthase.

The effects of amphotericin B (AmB), nystatin, filipin, and pimaricin were tested chitin synthase (EC 2.4.1.16) (chitosomes from yeast cells of Mucor rouxii). AmB and nystatin inhibited the enzyme at concentrations greater than or equal to 10 micrograms/ml, filipin was weakly inhibitory, and pimaricin had no effect. The inhibition of chitin synthase by AmB appears to be noncompetitive, with a Ki value of about 0.13 mM. the effect of nystatin was more complex and included a sharp stimulation of chitin synthase activity at approximately 50 micrograms/ml. Our findings suggest the existence of binding sites (sterols? ) on the chitosome that are selective for certain polyenes and that play a role in the operation of chitin synthase. Because the minimal growth inhibitory concentrations of AmB or nystatin are lower than the concentrations that inhibit chitin synthase in vitro, the possibility of chitosomal chitin synthase being a primary target for the antifungal action of these polyenes seems unlikely.
Calibration of a two-state pitch-wise HMM method for note segmentation in Automatic Music Transcription systems

Many methods for automatic music transcription involves a multi-pitch estimation method that estimates an activity score for each pitch. A second processing step, called note segmentation, has to be performed for each pitch in order to identify the time intervals when the notes are played. In this study, a pitch-wise two-state on/off firstorder Hidden Markov Model (HMM) is developed for note segmentation. A complete parametrization of the HMM sigmoid function is proposed, based on its original regression formulation, including a parameter alpha of slope smoothing and beta? of thresholding contrast. A comparative evaluation of different note segmentation strategies was performed, differentiated according to whether they use a fixed threshold, called "Hard Thresholding" (HT), or a HMM-based thresholding method, called "Soft Thresholding" (ST). This evaluation was done following MIREX standards and using the MAPS dataset. Also, different transcription scenarios and recording natures were tested using three units of the Degradation toolbox. Results show that note segmentation through a HMM soft thresholding with a data-based optimization of the {alpha,beta} parameter couple significantly enhances transcription performance.