Miracle-dz/UniFAR-FTUs huggingface dataset:
Early Childhood Intervention and Life-Cycle Skill Development Evidence from Head Start

This paper provides new evidence on the long-term benefits of Head Start for a recent birth cohort of children. I use the National Longitudinal Survey of Youth Mother-Child Supplement (CNLSY) to track children from before birth to early adulthood. The impact of Head Start is identified by comparing siblings in the same family who differ in their participation in the program. While this comparison is imperfect, I assess its validity directly by controlling for a wide variety of pretreatment covariates. I estimate that enrollment in Head Start leads to a long-term impact of about 0.23 standard deviations on a summary index of young adult outcomes. This gain is about one third of the size of the long-term outcome gap between children with family incomes in the bottom quartile and median permanent income in the sample, and is about 80 percent as large as the impact of “model” programs such as Perry Preschool and Abecedarian. For children whose mothers have low levels of cognitive skill, the long-term impact of the program is very large despite zero measured impact on test scores. This strongly suggests that Head Start generates skill gains that are not full captured by school-age test scores. JEL Codes: C21, I18, I28, I38, J13.
A generalization of moderated statistics to data adaptive semiparametric estimation in high-dimensional biology.

The widespread availability of high-dimensional biological sequencing data has made the simultaneous screening of numerous biological characteristics a central statistical problem in computational biology. While the dimensionality of such data sets continues to increase, the problem of teasing out the effects of biomarkers in studies measuring baseline confounders while avoiding model misspecification remains only partially addressed. Efficient estimators constructed from data adaptive estimates of nuisance functions provide an avenue for avoiding model misspecification; however, in the context of high-dimensional problems requiring simultaneous estimation of numerous parameters, standard variance estimators have proven unstable, resulting in unreliable Type-I error control under standard multiple testing corrections. We present the formulation of a general approach for applying variance moderation strategies to asymptotically linear estimators of parameters defined in the nonparametric model, for which a standard variance estimator is constructed from estimation of an influence function. A methodology for nonparametric variable importance analysis for use with high-dimensional biological data sets with modest sample sizes is introduced and the proposed technique is demonstrated to be robust in small samples even when relying on data adaptive estimators that eschew parametric forms. The general methodology is demonstrated in the context of targeted minimum loss estimation, with simulation studies comparing the merits of the proposed variance moderation approach to both the standard variance estimator and a linear modeling strategy with moderated inference. Application to an observational study of occupational exposure is presented to demonstrate the construction of stabilized variable importance measures of individual biomarkers.
The Using of ANN at Design of Window Function Having Useful Spectral Parameters

—The window functions are proposed structures at many application areas. The windows with helping two or more independent parameters show useful spectral characteristic. Kaiser window has been used at many areas because of the fact that it has good characteristic specification such as mainlobe width and the ripple ratio for various applications but Kaiser window having two parameters cannot control the sidelobe roll-off ratio. The ultraspherical window having three independent parameters has better sidelobe roll-off ratio than other windows in literature. At work, the coefficients of a new window function that has side-lobe roll-off ratio characteristic of ultraspherical window and Kaiser window’s main-lobe width and ripple ratio were obtained by helping of artificial neural networks (ANN). 3000 data are used for training and testing of ANN. Sixty-five percentage of these data was used for learning of ANN and thirty-five percentage of that was used for testing process. It was observed that between the desired values and the obtained values using ANN were good agreements. Hence, a new window having better mainlobe width ripple ratio and side lobe roll-off ratio than other windows in spectral parameters at literature was modeled with ANN.
PUBLISHED VERSION

SoxB1 sub-family of transcriptional regulators are expressed in progenitor (NP) cells throughout the neuroaxis and are generally downregulated during neuronal differentiation. Gainand loss-of-function studies indicate that Sox1, Sox2 and Sox3 are key regulators of NP differentiation and that their roles in CNS development are largely redundant. Nevertheless, mutation of each SoxB1 individually results in a different array of CNS defects, raising the possibility that SoxB1 proteins have subtly different functions in NP cells. To explore the mechanism of SOXB1 functional redundancy, and to identify genes that are most sensitive to loss of the Sox3 gene, we performed genome wide expression profiling of Sox3 null NP cells. Nineteen genes with abnormal expression were identified, including the homeobox gene Dbx1. Analysis of Sox3 null embryos revealed that Dbx1 was significantly reduced in the neural tube and developing brain and that SOX3 bound directly to conserved elements associated with this gene in cultured NP cells and in vivo. These data define Dbx1 as a direct SOX3 target gene whose expression, intriguingly, is not fully rescued by other SOXB1 transcription factors, suggesting that there are inherent differences in SOXB1 protein activity. Citation: Rogers N, McAninch D, Thomas P (2014) Dbx1 Is a Direct Target of SOX3 in the Spinal Cord. PLoS ONE 9(4): e95356. doi:10.1371/journal.pone. 0095356 Editor: Berta Alsina, Universitat Pompeu Fabra, Spain Received January 14, 2014; Accepted March 26, 2014; Published April 21, 2014 Copyright: 2014 Rogers et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: These authors have no support or funding to report. Competing Interests: The authors have declared that no competing interests exist. * E-mail: paul.thomas@adelaide.edu. au
Chronic intermittent hypoxia increases encoding pigment epithelium-derived factor gene expression, although not that of the protein itself, in the temporal cortex of rats*,**

Objective: Obstructive sleep apnea syndrome is mainly characterized by intermittent hypoxia (IH) during sleep, being associated with several complications. Exposure to IH is the most widely used animal model of sleep apnea, short-term IH exposure resulting in cognitive and neuronal impairment. Pigment epithelium-derived factor (PEDF) is a hypoxia-sensitive factor acting as a neurotrophic, neuroprotective, and antiangiogenic agent. Our study analyzed performance on learning and cognitive tasks, as well as PEDF gene expression and PEDF protein expression in specific brain structures, in rats exposed to long-term IH. Methods: Male Wistar rats were exposed to IH (oxygen concentrations of 21-5%) for 6 weeks-the chronic IH (CIH) group-or normoxia for 6 weeks-the control group. After CIH exposure, a group of rats were allowed to recover under normoxic conditions for 2 weeks (the CIH+N group). All rats underwent the Morris water maze test for learning and memory, PEDF gene expression and PEDF protein expression in the hippocampus, frontal cortex, and temporal cortex being subsequently assessed. Results: The CIH and CIH+N groups showed increased PEDF gene expression in the temporal cortex, PEDF protein expression remaining unaltered. PEDF gene expression and PEDF protein expression remained unaltered in the frontal cortex and hippocampus. Long-term exposure to IH did not affect cognitive function. Conclusions: Long-term exposure to IH selectively increases PEDF gene expression at the transcriptional level, although only in the temporal cortex. This increase is probably a protective mechanism against IH-induced injury.
A New Recurrence Formula for Efficient Computation of Spherical Harmonic Transform

A new recurrence formula to calculate the associated Legendre functions is proposed for efficient computation of the spherical harmonic transform. This new recurrence formula makes the best use of the fused multiply–add (FMA) operations implemented in modern computers. The computational speeds in calculating the spherical harmonic transform are compared between a numerical code in which the new recurrence formula is implemented and another code using the traditional recurrence formula. This comparison shows that implementation of the new recurrence formula contributes to a faster transform. Furthermore, a scheme to maintain the accuracy of the transform, even when the truncation wavenumber is huge, is also explained.
Rule-based classification framework for remote sensing data

Abstract. The land cover classification is an important task in geoscience applications. Many methods and implementations are based on multispectral data processing. The presented work aims to benefit from the nonlinear source separation process to enhance land cover identification. The source separation technique aims to provide underlying images and to compensate the mixing process. Nonlinear separation is more realistic due to multiple distortions occurring on the radiance path from soil to sensors. The presented paper addresses pattern recognition for remote sensing and proposes a framework based on feature extraction and decisional fusion. The first stage performs a nonlinear separation model based on Bayesian inferences. Nonlinearity is approximated by a multilayer neuron network. The separation process updates knowledge about unknown sources and model parameters iteratively. The second stage performs feature extraction. Based on a decisional fusion, the third stage realizes a classification process. This fusion scheme assigns, first, a suitable feature to each source/pattern based on the learning data set. Second, a majority vote determines the final label. Experimentation results demonstrate that the proposed fusion method enhances the recognition accuracy and represents a powerful tool for land identification.
Utilizing similarity and commonsense knowledge bases

The rapidly increasing quantity of publicly available videos has driven research into developing automatic tools for indexing, rating, searching and retrieval. Textual semantic representations, such as tagging, labelling and annotation, are often important factors in the process of indexing any video, because of their user-friendly way of representing the semantics appropriate for search and retrieval. Ideally, this annotation should be inspired by the human cognitive way of perceiving and of describing videos. The difference between the low-level visual contents and the corresponding human perception is referred to as the ‘semantic gap’. Tackling this gap is even harder in the case of unconstrained videos, mainly due to the lack of any previous information about the analyzed video on the one hand, and the huge amount of generic knowledge required on the other. This paper introduces a framework for the Automatic Semantic Annotation of unconstrained videos. The proposed framework utilizes two non-domain-specific layers: low-level visual similarity matching, and an annotation analysis that employs commonsense knowledgebases. Commonsense ontology is created by incorporating multiple-structured semantic relationships. Experiments and black-box tests are carried out on standard video databases for action recognition and video information retrieval. White-box tests examine the performance of the individual intermediate layers of the framework, and the evaluation of the results and the statistical analysis show that integrating visual similarity matching with commonsense semantic relationships provides an effective approach to automated video annotation.
Monte Carlo study and design of system for implementation of Rotational Total Skin Electron Irradiation technique

Total skin electron irradiation technique is used in treatment of the mycosis fungoid. The implementation of this technique requires non-standard measurements and complex dosimetry methods. Depending on the linear accelerator (Linac) type, bunker size, room dimensions and dosimetry equipment, the design of instruments for appropriate set up and implementation of TSEI in different radiation therapy centers varies. The studies which have been done in this article provide an introduction to the implementing of this method for the first time in Iran and its results can be used for the centers with similar specifications in the world. This article determined the electron beam characteristic of TSEI for the only electron accelerator, located at the radiation center of the Seyed Alshohada Hospital of Isfahan (NEPTUN 10PC), by performing Monte Carlo simulations and using EGSnrc-based codes (BEAMnrc and DOSXYZnrc). For the best uniformity of the vertical profile, the optimal angle of gantry was defined at SSD=350 cm. The effect of the degrader plane that is located at a distance of 20 cm from the patient surface, was evaluated on the amount of energy reduction of the beam, the opening of the electron beam field and the homogeneity of the dose distribution. The transversal dose distribution from the whole treatment with Stanford technique (six dual fields) and Rotational technique was simulated in a CT-based anthropomorphic phantom. Also, the percentage depth dose in the head, neck, thorax, abdomen and legs was obtained for both methods. The simulation results show that the 20̂ angle between the horizontal and the beam central axis is optimal in order to provide the best vertical dose uniformity. The mean energy decreases from 6.1 MeV (the exit window) to 3 MeV (the treatment surface) by placing a degrader with 0.8 cm thickness in front of the treatment plane. FWHM of the angular distribution of the electron beam increased from 15̂ at SSD=100 cm to more than 30̂ on the treatment surface by traversing the PMMA degrader. The MC calculated percentage depth dose curves in different organs of anthropomorphic phantom for RTSEI indicates that the depth of maximum dose is on the surface of the phantom and Isodose curve of 80% is formed at a depth less than 4 mm. the results also show, with the degrader plane in front of the patient, the degree of homogeneity of the dose distribution for both Stanford and rotational techniques is the same.
Chronometric Evidence for Central Postponement in Temporally Overlapping Tasks

When the stimuli from two tasks arrive in rapid succession (the overlapping tasks paradigm), response delays are typically observed. Two general types of models have been proposed to account for these delays. Postponement models suppose that processing stages in the second task are delayed due to a single-channel bottleneck. Capacity-sharing models suppose that processing on both tasks occurs at reduced rates because of sharing of common resources. Postponement models make strong and distinctive predictions for the behaviour of variables slowing particular second-task stages, when assessed in singleand dual-task conditions. In Experiment 1, subjects were required to make manual classification responses to a tone (S1) and a letter (S2), presented at stimulus onset asynchronies of 50, 100, and 400msec, making R1 responses to S1 as promptly as possible. The second response, R2, but not R1, was delayed in the dual task condition, and the effects of two S2 variables (degradation and repetition) on R2 response times in dualand single-task conditions closely matched the predictions of a postponement model with a processing bottleneck at the decision/responseselection stage. In Experiment 2, subjects were encouraged to emit both responses close together in time. Use of this response grouping procedure had little effect on the magnitude of R2 response times, or on the pattern of stimulus factor effects on R2, supporting the hypothesis that the same underlying postponement process was operating. R1 response times were, however, dramatically delayed, and were now affected by S2 difficulty variables. The results provide strong support for postponement models of dual-task interference in the overlapping tasks paradigm, even when response times are delayed on both tasks.
Application of Fractal Analysis to Determination of Ultimate Flexural Loads on Reinforced Concrete Slabs

Based on the fractal geometry theory and dimensionless treatment to the geometric parameters, the cracks of reinforced concrete slabs subject to bending are patterned with a uniform Cantor set. Each segment of the yield lines presumed for analyzing the ultimate flexural loads is linked to a fractal dimension. The fractional calculus techniques based on virtual work principle are employed to determine the loads. In respect of the common design cases, the calculations from the traditional yield line method may then be modified by a factor γf. The reported tests on reinforced concrete slab in bending are compared with the results from the presented techniques, which is accompanied by discussions.
Critical Behavior at Surfaces of Strong Coupling Paramagnetic Systems Exhibiting a Paramagnetic-Ferrimagnetic Transition

The purpose of this work is the investigation of the critical surface effects of two strongly coupled paramagnetic sublattices exhibiting a para-ferrimagnetic transition. The model is of Landau-Ginzburg type, whose bulk free energy is a functional of two kind of order parameters (local magnetizations) and This free energy involves, beside quadratic and quartic terms in both and , a lowest-order coupling, where Co<0 is the coupling constant measuring the interaction between the two sublattices. Two terms H and are also introduced, to describe the interaction within an external magnetic field H. We introduce a surface free energy expanded in terms of the local order parameters. The magnetization at the surface are s and We show, in particular, that the model can be reduced to an effective theory written in terms of the overall magnetization and the associated fraction of magnetization . This formulation leads us to define an effective extrapolation length s. We then derive all the critical properties of the system close to the critical temperature Tc. In particular, we determine the critical behavior of the overall surface magnetization , in terms of b above and below Tc. The variations of s with the magnetic field H, and when a surface field Hs is applied actually at Tc, are derived. We determine, also, the associated susceptibilities at the surface s and s.s. The determination of the full profile of the magnetization close to the surface will be the subject of a future communication.
Amplitude analysis of the Λ + c → pK − π + decay and Λ + c baryon polarization measurement in semileptonic beauty hadron decays

An amplitude analysis of Λ + c → pK − π + decays together with a measurement of the Λ + c polarization vector in semileptonic beauty hadron decays is presented. A sample of 400 000 candidates is selected from proton-proton collisions recorded by the LHCb detector at a center-of-mass energy of 13 TeV. An amplitude model is developed and the resonance fractions as well as two-and three-body decay parameters are reported. The mass and width of the Λ (2000) state are also determined. A signiﬁcant Λ + c polarization is found. A large sensitivity of the Λ + c → pK − π + decay to the polarization is seen, making the amplitude model suitable for Λ + c polarization measurements in other systems.
RhoB controls endothelial barrier recovery by inhibiting Rac1 trafficking the cell border.

signaling pathway, relative subfamily barrier plasma membrane localization GTPase, central cell–cell junction stabilization Interestingly, Rac1 endosomal internalization and recycling are necessary for the polarized targeting and function of this GTPase to plasma membrane domains, such as circular Endothelial barrier dysfunction underlies chronic inflammatory diseases. In searching for new proteins essential to the human endothelial inflammatory response, we have found that the endosomal GTPase RhoB is up-regulated in response to inflammatory cytokines and expressed in the endothelium of some chronically inflamed tissues. We show that although RhoB and the related RhoA and RhoC play additive and redundant roles in various aspects of endothelial barrier function, RhoB specifically inhibits barrier restoration after acute cell contraction by preventing plasma membrane extension. During barrier restoration, RhoB trafficking is induced between vesicles containing RhoB nanoclusters and plasma membrane protrusions. The Rho GTPase Rac1 controls membrane spreading and stabilizes endothelial barriers. We show that RhoB colocalizes with Rac1 in endosomes and inhibits Rac1 activity and trafficking to the cell border during barrier recovery. Inhibition of endosomal trafficking impairs barrier reformation, whereas induction of Rac1 translocation to the plasma membrane accelerates it. Therefore, RhoB-specific regulation of Rac1 trafficking controls endothelial barrier integrity during inflammation.
Sound-Dr: Reliable Sound Dataset and Baseline Artificial Intelligence System for Respiratory Illnesses

As the burden of respiratory diseases continues to fall on society worldwide, this paper proposes a high-quality and reliable dataset of human sounds for studying respiratory illnesses, including pneumonia and COVID-19. It consists of coughing, mouth breathing, and nose breathing sounds together with metadata on related clinical characteristics. We also develop a proof-of-concept system for establishing baselines and benchmarking against multiple datasets, such as Coswara and COUGHVID. Our comprehensive experimentsshow that the Sound-Dr dataset has richer features, better performance, and is more robust to dataset shifts in various machine learning tasks. It is promising for a wide range of real-time applications on mobile devices. The proposed dataset and system will serve as practical tools to support healthcare professionals in diagnosing respiratory disorders. The dataset and code are publicly available here: https://github.com/ReML-AI/Sound-Dr/.
Intravenous administration of adipose tissue-derived stem cells enhances nerve healing and promotes BDNF expression via the TrkB signaling in a rat stroke model

Previous studies have shown the beneficial effects of adipose-derived stem cells (ADSCs) transplantation in stroke. However, the molecular mechanism by which transplanted ADSCs promote nerve healing is not yet elucidated. In order to make clear the molecular mechanism for the neuroprotective effects of ADSCs and investigate roles of the BDNF–TrkB signaling in neuroprotection of ADSCs, we, therefore, examined the neurological function, brain water content, and the protein expression in middle cerebral artery occlusion (MCAO) rats with or without ADSCs transplantation. ADSCs were transplanted intravenously into rats at 30 minutes after MCAO. K252a, an inhibitor of TrkB, was administered into rats by intraventricular and brain stereotaxic injection. Modified neurological severity score tests were performed to measure behavioral outcomes. The results showed that ADSCs significantly alleviated neurological deficits and reduced brain water content in MCAO rats. The protein expression levels of BDNF and TrkB significantly increased in the cortex of MCAO rats with ADSCs treatment. However, K252a administration reversed the ADSCs-induced elevation of BDNF, TrkB, and Bcl-2 and reduction of Bax protein in MCAO rats. ADSCs promote BDNF expression via the TrkB signaling and improve functional neurological recovery in stroke rats.