Miracle-dz/UniFAR-FTUs huggingface dataset:
The effects of breeding and selection on lactation in dairy cattle

https://doi.org/10.1093/af/vfad044 This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons. org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com © Cole, Makanjuola, Rochus, van Staaveren, Baes Feature Article The effects of breeding and selection on lactation in dairy cattle
Accuracy and precision of depth-resolved estimation of attenuation coefficients in optical coherence tomography

Abstract. Significance Parametric imaging of the attenuation coefficient μOCT using optical coherence tomography (OCT) is a promising approach for evaluating abnormalities in tissue. To date, a standardized measure of accuracy and precision of μOCT by the depth-resolved estimation (DRE) method, as an alternative to least squares fitting, is missing. Aim We present a robust theoretical framework to determine accuracy and precision of the DRE of μOCT. Approach We derive and validate analytical expressions for the accuracy and precision of μOCT determination by the DRE using simulated OCT signals in absence and presence of noise. We compare the theoretically achievable precisions of the DRE method and the least-squares fitting approach. Results Our analytical expressions agree with the numerical simulations for high signal-to-noise ratios and qualitatively describe the dependence on noise otherwise. A commonly used simplification of the DRE method results in a systematic overestimation of the attenuation coefficient in the order of μOCT2×Δ, where Δ is the pixel stepsize. When μOCT · | AFR | ≲ 1.8, μOCT is reconstructed with higher precision by the depth-resolved method compared to fitting over the length of an axial fitting range | AFR | . Conclusions We derived and validated expressions for the accuracy and precision of DRE of μOCT. A commonly used simplification of this method is not recommended as being used for OCT-attenuation reconstruction. We give a rule of thumb providing guidance in the choice of estimation method.
Scalable, modular continuous wave functional near-infrared spectroscopy system (Spotlight)

Abstract. Significance We present a fiberless, portable, and modular continuous wave-functional near-infrared spectroscopy system, Spotlight, consisting of multiple palm-sized modules—each containing high-density light-emitting diode and silicon photomultiplier detector arrays embedded in a flexible membrane that facilitates optode coupling to scalp curvature. Aim Spotlight’s goal is to be a more portable, accessible, and powerful functional near-infrared spectroscopy (fNIRS) device for neuroscience and brain–computer interface (BCI) applications. We hope that the Spotlight designs we share here can spur more advances in fNIRS technology and better enable future non-invasive neuroscience and BCI research. Approach We report sensor characteristics in system validation on phantoms and motor cortical hemodynamic responses in a human finger-tapping experiment, where subjects wore custom 3D-printed caps with two sensor modules. Results The task conditions can be decoded offline with a median accuracy of 69.6%, reaching 94.7% for the best subject, and at a comparable accuracy in real time for a subset of subjects. We quantified how well the custom caps fitted to each subject and observed that better fit leads to more observed task-dependent hemodynamic response and better decoding accuracy. Conclusions The advances presented here should serve to make fNIRS more accessible for BCI applications.
Coherence and collective oscillations of a two-component Bose-Einstein condensate

Ultracold atoms and Bose-Einstein condensates represent a burgeoning and ever exciting area of research which has produced numerous breakthroughs in the last 15 years. Ultracold atoms at nano-Kelvin temperatures, isolated in atom traps from the hot, room temperature environment, exhibit ultimate quantum properties and can serve as pure samples for testing quantum mechanics and performing precision measurements. The research described in this thesis investigates, both theoretically and experimentally, the coherence and dynamical evolution of a two-component Bose-Einstein condensate (BEC). The studies are performed on the |1〉 ≡ |F = 1,mF = −1〉 and |2〉 ≡ |F = 2,mF = +1〉 hyperfine ground states of a 87Rb BEC trapped on an atom chip. Conventional understanding has been that the fringe contrast in interferometric experiments on a BEC is limited by the mean-field dephasing due to strong interatomic interactions which lead to inhomogeneous collisional shifts. We have discovered using Ramsey interferometry a mean-field driven self-rephasing effect in a trapped twocomponent BEC. When combined with a spin-echo technique, we find that the selfrephasing leads to a coherence time of 2.8 s, the longest ever recorded for an interacting BEC. Secondly, we have developed a new technique based on periodic collective oscillations for precision measurements of the interspecies and intraspecies scattering lengths and derived an analytic mean-field theory for the phase and density dynamics in the twocomponent BEC. This technique has been applied to the measurements of the scattering lengths for the two components, |1〉 and |2〉, in 87Rb with a precision of 0.016%. Additionally, the two-body loss coefficients for these states have been measured.
Sharp feature extraction in point clouds

Sharp feature extraction has been playing an important role in point cloud processing. In this study, a novel method for extracting sharp features from point clouds is presented. It is proposed that in a given point cloud, the displacement between each of the points and the weighted average position in the given neighbourhood of that point is calculated, and the point is labelled as the candidate sharp feature point if the displacement is salient. The normal directions of the obtained candidate sharp feature points are estimated by means of local principal component analysis. Tensor voting is performed to refine the normal estimates. The displacement between a point and its locally weighted average position is projected along the estimated normal direction. The points with extreme projection values are defined as the final sharp feature points. The implementation of the proposed method on both synthesised and practical scanned point clouds show that the method is effective and robust for the purpose of sharp feature extraction.
A Statistical Model of Galaxies

Average optical properties of galaxies as available in recent literature are summarized. The dierential and integral galaxy number density, the eective radius and the surface brightnes profile of “typical” galaxies are given as function of the galaxy morphological type and total I magnitude, down to a limiting magnitude of I = 24. These typical values are believed to be useful when only galaxy statistical properties, i.e. properties averaged over large galaxy samples, are of interest, e.g. when planning future large-scale surveys and data reduction undertakings. A simple model giving the number and average photometric properties of galaxies as function of a limited set of parameters is useful for dierent
A systematic review to identify and collate factors influencing patient journeys through clinical trials

Summary Patient-centred trial design and delivery; improves recruitment and retention; increases participant satisfaction; encourages participation by a more representative cohort; and allows researchers to better meet participants’ needs. Research in this area mostly focusses on narrow facets of trial participation. We aimed to systematically identify the breadth of patient-centred factors influencing participation and engagement in trials, and collate them into a framework. Through this we hoped to assist researchers to identify factors that could improve patient-centred trial design and delivery. Robust qualitative and mixed methods systematic reviews are becoming increasingly common in health research. The protocol for this review was prospectively registered on PROSPERO, CRD42020184886. We used the SPIDER (Sample, Phenomenon of Interest, Design, Evaluation, Research Type) framework as a standardised systematic search strategy tool. 3 databases were searched as well as references checking, and thematic synthesis was conducted. Screening agreement was performed and code and theme checking were conducted by 2 independent researchers. Data were drawn from 285 peer-reviewed articles. 300 discrete factors were identified, and sorted into 13 themes and subthemes. The full catalogue of factors is included in the Supplementary Material. A summary framework is included in the body of the article. This paper focusses on outlining common ground that themes share, highlighting critical features, and exploring interesting points from the data. Through this, we hope researchers from multiple specialities may be better able to meet patients’ needs, protect patients’ psychosocial wellbeing, and optimise trial recruitment and retention, with direct positive impact on research time and cost efficiency.
Novel dense stereo algorithms for high-quality depth estimation from images

This dissertation addresses the problem of inferring scene depth information from a collection of calibrated images taken from different viewpoints via stereo matching. Although it has been heavily investigated for decades, depth from stereo remains a long-standing challenge and popular research topic for several reasons. First of all, in order to be of practical use for many real-time applications such as autonomous driving, accurate depth estimation in real-time is of great importance and one of the core challenges in stereo. Second, for applications such as 3D reconstruction and view synthesis, high-quality depth estimation is crucial to achieve photo realistic results. However, due to the matching ambiguities, accurate dense depth estimates are difficult to achieve. Last but not least, most stereo algorithms rely on identification of corresponding points among images and only work effectively when scenes are Lambertian. For non-Lambertian surfaces, the "brightness constancy" assumption is no longer valid. This dissertation contributes three novel stereo algorithms that are motivated by the specific requirements and limitations imposed by different applications. In addressing high speed depth estimation from images, we present a stereo algorithm that achieves high quality results while maintaining real-time performance. We introduce an adaptive aggregation step in a dynamic-programming framework. Matching costs are aggregated in the vertical direction using a computationally expensive weighting scheme based on color and distance proximity. We utilize the vector processing capability and parallelism in commodity graphics hardware to speed up this process over two orders of magnitude. In addressing high accuracy depth estimation, we present a stereo model that makes use of constraints from points with known depths - the Ground Control Points (GCPs) as referred to in stereo literature. Our formulation explicitly models the influences of GCPs in a Markov Random Field. A novel regularization prior is naturally integrated into a global inference framework in a principled way using the Bayes rule. Our probabilistic framework allows GCPs to be obtained from various modalities and provides a natural way to integrate information from various sensors. In addressing non-Lambertian reflectance, we introduce a new invariant for stereo correspondence which allows completely arbitrary scene reflectance (bidirectional reflectance distribution functions - BRDFs). This invariant can be used to formulate a rank constraint on stereo matching when the scene is observed by several lighting configurations in which only the lighting intensity varies. KEYWORDS: Stereo Matching, Bilateral Filtering, Dynamic Programming, Global Optimization, Light Transport Constancy Author's.
Zn2+ decoration of microtubules arrests axonal transport and displaces tau, doublecortin, and MAP2C

Minckley et al. identify a Zn2+-dependent mechanism that regulates microtubule-based processes, in which microtubule decoration by Zn2+ disturbs axonal transport via directly inhibiting movement of motor proteins and regulating microtubule binding of doublecortin (DCX), tau, and MAP2C, without disrupting other MAPs.
Turbulence Interaction with Driven Transport Barriers in the Scrape‐Off Layer of Tokamaks

Two mechanisms able to generate a transport barrier by the stabilization of turbulence are analysed: the radial shear of the E × B velocity and the radial gradient of this shear. These two mechanisms are artificially forced in dedicated 2D non‐linear fluid simulations to study their impact on turbulence in the Scrape‐Off Layer. Generated barriers are characterized by their turbulence stopping capability and a key parameter, the barrier efficiency, is pointed out. The first two moments of this criterion can be studied to quantify the global efficiency, shape and width of the barrier as well as its intermittency. It is found that both mechanisms can drive intermittent or stable barriers but the radial shear mechanism generates stronger relaxation events. Small scale structures are damped by the E × B velocity shear while the radial shear gradient acts mainly on large scales. (© 2016 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)
Particle swarm optimization: theoretical analysis, modifications, and applications to constrained optimization problems

ii Statement of originality . iv Acknowledgement. v Statements of authorships . vi 1 Contextual statement . 1 2 Literature review . 7 3 Theoretical analysis and modifications of PSO . 58 3.1 Influence of rotation matrices on the performance of PSO variants . 58 3.2 Rotation invariance and local convergence. 95 3.3 Analysis of the standard particle swarm 2011 . 136 4 Application of PSO to continuous space COPs . 161 4.1 PSO for COPs . 161 4.2 Disjoint feasible ragions in a COP . 170 4.2. 1 Locating Disjoint feasible ragions in a COP . 170 4.2. 2 An adptive topology for PSO to address COPs . 196 4.3 PSO on the edge of feasiblity . 213 4.4 Application of PSO to discrete space COPs . 222 5 Conclusions . 231 Appendix . 232
Inverse problems with Poisson data: statistical regularization theory, applications and algorithms

Inverse problems with Poisson data arise in many photonic imaging modalities in medicine, engineering and astronomy. The design of regularization methods and estimators for such problems has been studied intensively over the last two decades. In this review we give an overview of statistical regularization theory for such problems, the most important applications, and the most widely used algorithms. The focus is on variational regularization methods in the form of penalized maximum likelihood estimators, which can be analyzed in a general setup. Complementing a number of recent convergence rate results we will establish consistency results. Moreover, we discuss estimators based on a wavelet-vaguelette decomposition of the (necessarily linear) forward operator. As most prominent applications we briefly introduce Positron emission tomography, inverse problems in fluorescence microscopy, and phase retrieval problems. The computation of a penalized maximum likelihood estimator involves the solution of a (typically convex) minimization problem. We also review several efficient algorithms which have been proposed for such problems over the last five years.
C I Isoelectronic Sequence: Observations of 2sm2pn-2sm-1 2pn+1 Intersystem Transitions and Improved Measurements for CI XII, K XIV, Ca XV, Sc XVI, Ti XVII, and V XVIII

Spectra of C1 through V (excluding Ar) were produced with a 1 GW (15 ns) pulse from a Nd-glass laser impinging on solid targets and observed with a 10.7-m grazing incidence spectrograph. Strong 2sm2pn-2sm-1 2pn+1 transition arrays in the C I isoelectronic sequence were recorded, from which intersystem lines were identified and improved wavelength measurements of allowed lines were made. Hartree-Fock calculations of the radial integrals were compared with those obtained from least squares fits of the energy levels of this sequence.
On Bronshtein's Theorem

We study hyperbolic first order systems and propose a new method proving Gevrey well posedness, constructing a symmetrizer, motivated by a special Lyapunov function for linear ODE. The proof not only gives a priori estimates straightforward so simply but also clarifies some effects coming from the spectral structures other than the multiplicities of the eigenvalues.
LHPP Inhibits the Viability, Migration, and Proliferation of PDAC Cells and Significantly Affects the Expression of SDC1 and S100p

Introduction Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignancy with a poor response to chemotherapy and an extremely poor prognosis. Recent studies have revealed that phospholysine phosphohistidine inorganic pyrophosphate phosphatase (LHPP) can inhibit the growth of various cancers. Therefore, the current study was conducted to investigate the antitumor effects of LHPP in PDAC and to explore its mechanism using proteomics analysis. Methods and Results Immunohistochemical analysis of clinical samples demonstrated that LHPP expression levels were lower in tumor tissues compared to adjacent nontumor tissues. Moreover, multivariate COX regression analysis showed that LHPP expression level was an independent prognostic factor for the patients with PDAC. Patients with high LHPP expression had a better prognosis. The lentiviral vectors for normal control (NC), LHPP knockdown (KD), and LHPP overexpression (OE) were infected with BxPC-3 and PANC-1 cell lines. Cell counting kit-8 assay, Transwell assay, and flow cytometry analyses showed that LHPP overexpression significantly inhibited the cell viability, migration, and proliferation of BxPC-3 and PANC-1 cells. Moreover, xenograft tumor model demonstrated that LHPP overexpression inhibited xenograft tumor growth in vivo. Subsequently, proteins with significantly altered expression in BxPC-3 cells after lentivirus infection were detected using proteomics analyses. Interestingly, compared to the NC group, the expression of Syndecan 1 (SDC1) was significantly upregulated in the KD group, while that of S100P was significantly downregulated in the OE group. Conclusion LHPP might emerge as an important target for delaying the advancement of PDAC, thereby providing a novel therapeutic approach for the treatment of PDAC.
An analysis of policies supporting the roles of family physicians in four regions in Canada during the COVID-19 pandemic

Policy supports are needed to ensure that Family Physicians (FPs) can carry out pandemic-related roles. We conducted a document analysis in four regions in Canada to identify regulation, expenditure, and public ownership policies during the COVID-19 pandemic to support FP pandemic roles. Policies supported FP roles in five areas: FP leadership, Infection Prevention and Control (IPAC), provision of primary care services, COVID-19 vaccination, and redeployment. Public ownership polices were used to operate assessment, testing and vaccination, and influenza-like illness clinics and facilitate access to personal protective equipment. Expenditure policies were used to remunerate FPs for virtual care and carrying out COVID-19-related tasks. Regulatory policies were region-specific and used to enact and facilitate virtual care, build surge capacity, and enforce IPAC requirements. By matching FP roles to policy supports, the findings highlight different policy approaches for FPs in carrying out pandemic roles and will help to inform future pandemic preparedness.
Quantitative phase imaging of living red blood cells combining digital holographic microscopy and deep learning

Digital holographic microscopy as a non‐contacting, non‐invasive, and highly accurate measurement technology, is becoming a valuable method for quantitatively investigating cells and tissues. Reconstruction of phases from a digital hologram is a key step in quantitative phase imaging for biological and biomedical research. This study proposes a two‐stage deep convolutional neural network named VY‐Net, to realize the effective and robust phase reconstruction of living red blood cells. The VY‐Net can obtain the phase information of an object directly from a single‐shot off‐axis digital hologram. We also propose two new indices to evaluate the reconstructed phases. In experiments, the mean of the structural similarity index of reconstructed phases can reach 0.9309, and the mean of the accuracy of reconstructions of reconstructed phases is as high as 91.54%. An unseen phase map of a living human white blood cell is successfully reconstructed by the trained VY‐Net, demonstrating its strong generality.
Confi gural Frequency Analysis (CFA), Multiple Correspondence Analysis (MCA) and Latent Class Analysis (LCA): An empirical comparison

Summary CFA, MCA and LCA are well known methods in categorical data analyses. In the present study CFA is applied as an instrument in terms of type exploration and confi rmation. MCA and LCA are used by means of a more detailed description and explanation of statistically signifi cant types. To demonstrate the combined use of the mentioned techniques the data-set of the 14th Shell Youth Study 2002 is used (herein focusing on attitudes of young adults towards social organizations and political institutions). CFA identifi es three types; MCA and LCA enable us to (complementary) represent and interpret the relational structure of the identifi ed types on manifest as well as latent measurement levels. On the manifest level types can be understood as categories of an ordinal confi dence-scale. Latent level perspective provides evidence for a specifi c locus of all three found types in latent space. All analyses (CFA, MCA and LCA) are performed with software LEM (Vermunt, 1997).