Miracle-dz/UniFAR-FTUs huggingface dataset:
SARS-CoV-2 and the role of vertical transmission from infected pregnant women to their fetuses: systematic review.

Background Vertical transmission of SARS-CoV-2 has been reported but does not appear to be common. This study aims to systematically review the evidence for vertical transmission of SARS-CoV-2. Methods This review is part of an Open Evidence Review on the transmission dynamics of SARS-CoV-2 and the role of intrauterine mother to fetus transmission. Literature searches were performed in the WHO Covid-19 Database, LitCovid, medRxiv, and Google Scholar for SARS-CoV-2 using keywords and associated synonyms, search date up to 20 December 2020, no language restrictions. Results We included 106 studies assessing vertical transmission of SARS-CoV-2 from pregnant women to their neonates: these studies comprised 40 reviews (21 fulfilled systematic review methodology, including risk of bias assessment of included studies) and 66 primary studies including 32 case reports (of up to two cases) and 34 prospective and retrospective cohort studies, prospective and retrospective case series, observational studies (including asymptomatic screening), database studies and a quality improvement project. Almost all were conducted in a hospital setting. The 32 case reports were considered to be at high risk of bias, due to the study design; across the 34 remaining primary studies, risk of bias was low to moderate. Sixteen case reports examined vertical transmission, which was not related to maternal symptomatology. For the cohort and case series studies, the percentage of positive neonates ranged from 0% to 22% across the studies. Twenty studies reported no positive vertical transmission. Three studies that reported the highest positivity rates of 11%, 15% and 22% had specifically selected neonates with a positive test (within up to 35 days) within the study population and were therefore more selective populations. Across the cohort and case series studies there were 65/2391 (2.7%) neonates born to mothers with a diagnosis of COVID-19 tested positive for SARS-CoV-2 within 24 hours of birth. No evidence correlated maternal symptomatology to vertical transmission. Mode of delivery did not correlate with rates of vertical transmission. Of 25 studies, 7 identified SARS-CoV-2 in placental tissue; some of these did not demonstrate vertical transmission to the neonate. No study reported the results of viral culture to detect SARS-CoV-2. Conclusions The results of these studies indicate that vertical transmission is possible, but is not frequent, and factors that influence when vertical transmission occurs are unknown. Further studies using standardised methods to establish viral infection are needed to establish vertical transmission rates and to assess clinical and other conditions affecting transmission.
Teacher Professionalism in Light of Biometric Controls on Teacher Mobility and Autonomy

In this paper, I investigate the introduction of biometric technology, specifically fingerprint scanners, for the purposes of managing faculty members’ working hours at a higher education institution (HEI) located in the Middle Eastern Gulf States. Utilizing semi-structured interviews to collect qualitative data, three expatriate teachers of English discussed their experiences before and after management installed the fingerprint scanners, discussing the influence such a change has on their professional identities and the additional impacts on their teaching, their identification with the institution, and the overall culture of the HEI. The results show that the existence of the fingerprint scanners adversely affects the teachers’ professionalism, but the greater issue for the participants is the dynamic between faculty and management: in this case, characterized by one-directional communication from the administration and a lack of voice for teachers in decision-making.
Antiproliferative, Apoptotic Effects and Suppression of Oxidative Stress of Quercetin against Induced Toxicity in Lung Cancer Cells of Rats: In vitro and In vivo Study

In the present study, quercetin was examined against lung human cancer cells using A549 and H69 cancer cell lines in addition to normal non cancer cells (W138). Two genes Bax and Bcl-2 that play an important role in apoptosis pathways were investigated. Also Immunohistochemical study for caspase-3 which is considered as indicator for apoptosis was performed. Quercetin showed good anti proliferative activity against tested lung cancer cell lines, IC50 values on A549 are 8.65, 7.96 and 5.14 µg/ml at 24, 48 and 72h respectively. Also significant effects of quercetin on Bax, Bcl-2 and caspase-3 were observed, that can prove its ability to induce apoptosis. On the other hand quercetin showed good therapeutic effects against cyclophosphamide induced lung toxicity that were observed in the histopathology study. In vitro studies were also performed such as cell cycle analysis through flowcytometry. The obtained results from all these performed analysis proved that quercetin can induce apoptosis in human lung cancer cells, additionally quercetin showed ability to reduce MDA and increase SOD and GSHP levels which indicates its ability in suppressing oxidative stress, Quercetin has played a therapeutic role in cyclophosphamide induced lung toxicity as it has improved restoring of the damaged lung tissue as discussed in this research work.
Management effects on near-surface soil properties in a temperate corn-soybean cropping system

v CHAPTER 1. GENERAL INTRODUCTION.1 The Case for Cover Crops.3 Challenges and Opportunities for Cover Crops and Soil Water Research.5 The Role of Soil Texture in Water Retention.8 The Role of Soil Organic Matter in Water Retention.9 Exploring Interactions between Texture, Organic Matter, Structure, and Water Retention.13 Advancing our Understanding of Near-Surface Soil Water Retention.16 References.18 CHAPTER 2. LONG-TERM EFFECTS OF A RYE WINTER COVER CROP ON NEAR-SURFACE SOIL WATER RETENTION IN A CORN SILAGE-SOYBEAN CROPPING SYSTEM.25 Abstract.25 Introduction.26 Methods.30 Results & Discussion.34 Conclusion.37 Tables & Figures.37 References.48 CHAPTER 3. FARM-SCALE EVALUATION OF EPIPEDON MAPS ON DES MOINES LOBE SOILS: A CASE STUDY OF THE BOYD RESEARCH FARM.53 Abstract.53 Introduction.54 Methodology.57 Results & Discussion.59 Conclusion.61 Maps, Tables, and Figures.62 References.71
Identification of novel toxin-producing cyanobacteria in Iowan lakes

Cyanobacteria is a diverse phylum of photosynthetic bacteria with many species harboring the ability to produce toxins. Toxin-producing cyanobacteria are a growing ecological concern in freshwaters that has garnered much attention from prominent health agencies due to their global distribution and potentially life-threatening health impacts. The cyanotoxin microcystin is routinely detected in Iowa lakes as part of the Iowa Department of Natural Resources (IDNR) Beach Monitoring program. This study focused analytical efforts on lesser studied toxins (anatoxin-a, cylindrospermopsin, and saxitoxin) and aimed to isolate and identify cyanobacteria that produce these cyanotoxins in Iowan lakes. While direct measurements of lake waters give insight into the presence and concentration of various toxins, determining which strains produce these toxins is complicated by several factors. Freshwater cyanobacteria grow in mixed communities, where many species are present. Different cyanobacterial strains are able to produce the same toxin types, and some strains produce multiple toxins. Many species have highly similar morphology, so microscopic identification cannot delineate toxic from non-toxic strains. Cyanobacteria were enriched through serial dilutions on a selective growth medium. Individual strains were further isolated using flow cytometry. Cyanobacterial cultures were screened for toxin functional genes (anaC, cyrJ, sxtI) via PCR, which revealed three cultures containing the sxtI gene involved in saxitoxin production. Cloning of the sxtI insert from one sample and a subsequent search of its amino acid residues using NCBI’s BLASTx showed the closest sequence match was a carbamoyltransferase enzyme belonging to an uncultured Nostoc species. Amplification, cloning, and Sanger sequencing of the 16S rRNA gene, followed by a nucleotide search in NCBI’s BLASTn indicated that the most abundant organisms in the culture
Imputation-Free Learning from Incomplete Observations

Although recent works have developed methods that can generate estimations (or imputations) of the missing entries in a dataset to facilitate downstream analysis, most depend on assumptions that may not align with real-world applications and could suffer from poor performance in subsequent tasks. This is particularly true if the data have large missingness rates or a small population. More importantly, the imputation error could be propagated into the prediction step that follows, causing the gradients used to train the prediction models to be biased. Consequently, in this work, we introduce the importance guided stochastic gradient descent (IGSGD) method to train multilayer perceptrons (MLPs) and long short-term memories (LSTMs) to directly perform inference from inputs containing missing values without imputation. Specifically, we employ reinforcement learning (RL) to adjust the gradients used to train the models via back-propagation. This not only reduces bias but allows the model to exploit the underlying information behind missingness patterns. We test the proposed approach on real-world time-series (i.e., MIMIC-III), tabular data obtained from an eye clinic, and a standard dataset (i.e., MNIST), where our imputation-free predictions outperform the traditional two-step imputation-based predictions using state-of-the-art imputation methods.
Duality-invariant superconformal higher-spin models

We develop a general formalism of duality rotations for bosonic conformal spin-s gauge fields, with s ≥ 2, in a conformally flat four-dimensional spacetime. In the s = 1 case this formalism is equivalent to the theory of U(1) duality-invariant nonlinear electrodynamics developed by Gaillard and Zumino, Gibbons and Rasheed, and generalised by Ivanov and Zupnik. For each integer spin s ≥ 2 we demonstrate the existence of families of conformal U(1) duality-invariant models, including a generalisation of the so called ModMax Electrodynamics (s = 1). Our bosonic results are then extended to the N = 1 and N = 2 supersymmetric cases. We also sketch a formalism of duality rotations for conformal gauge fields of Lorentz type (m/2, n/2), for positive integers m and n.
Proportional mean model for panel count data with multiple modes of recurrence

Abstract : Panel count data is common when the study subjects are exposed to recurrent events, observed only at discrete time points. In this article, we consider the regression analysis of panel count data with multiple modes of recurrence. We propose a proportional mean model to estimate the effect of covariates on the underlying counting process due to different modes of recurrence. The simultaneous estimation of baseline cumulative mean functions and regression parameters of (k > 1) recurrence modes are studied in detail. Asymptotic properties of the proposed estimators are also established. A Monte Carlo simulation study is carried out to validate the finite sample behaviour of the proposed estimators. The methods are applied to a real data arising from skin cancer chemoprevention trial.
The Quantum Optics of Asymmetric Mirrors With Coherent Light Absorption

The local observables of the quantised electromagnetic field near a mirror-coated interface depend strongly on the properties of the media on both sides. In macroscopic quantum electrodynamics, this fact is taken into account with the help of optical Green’s functions which correlate the position of an observer with all other spatial positions and photon frequencies. Here we present an alternative, more intuitive approach and obtain the local field observables with the help of a quantum mirror image detector method. In order to correctly normalise electric field operators, we demand that spontaneous atomic decay rates simplify to their respective free space values far away from the reflecting surface. Our approach is interesting, since mirror-coated interfaces constitute a common basic building block for quantum photonic devices.
Constraining the magnetic vector in the quiet solar photosphere and the impact of instrumental degradation

With the advent of next generation high resolution telescopes, our understanding of how the magnetic field is organized in the internetwork (IN) photosphere is likely to advance.We aim to evaluate the extent to which we can retrieve information about the magnetic vector in the IN photosphere using inversions. We use snapshots produced from high resolution 3D magnetohydrodynamic (MHD) simulations and employ the Stokes Inversions based on Response functions (SIR) code to produce synthetic observables in the near infrared spectral window observed by the GREGOR Infrared Spectrograph (GRIS), which contains the highly magnetically sensitive photospheric Fe I line pair at 15648.52 A and 15652.87 A. We perform nearly 14 million inversions to test how well the true MHD atmospheric parameters can be constrained. Finally, we degrade the synthetic Stokes vectors spectrally and spatially to GREGOR resolutions and examine how this influences observations, considering the impact of stray light, spatial resolution and signal-to-noise (S-to-N). We find the depth-averaged parameters can be recovered by the inversions of the undegraded profiles, and by adding gradients to magnetic field strength, inclination and line of sight velocity we show an improvement in the chi squared value is achieved. We evaluate the extent to which we can constrain these parameters at various optical depths, with the kinematic and thermodynamic parameters sensitive deeper in the atmosphere than the magnetic parameters. We find the S-to-N and spatial resolution play a significant role in determining how the atmosphere appears and the magnetic and kinematic parameters are invariant upon inclusion of unpolarized stray light. We studied a linear polarization feature which resembles those recently observed by GRIS, appearing as loop-like structures with similar magnetic flux density.
A contextual analysis of multi-layer perceptron models in classifying hand-written digits and letters: limited resources

Classifying hand-written digits and letters has taken a big leap with the introduction of ConvNets. However, on very constrained hardware the time necessary to train such models would be high. Our main contribution is twofold. First, we extensively test an end-to-end vanilla neural network (MLP) approach in pure numpy without any pre-processing or feature extraction done beforehand. Second, we show that basic data mining operations can significantly improve the performance of the models in terms of computational time, without sacrificing much accuracy. We illustrate our claims on a simpler variant of the Extended MNIST dataset, called Balanced EMNIST dataset. Our experiments show that, without any data mining, we get increased generalization performance when using more hidden layers and regularization techniques, the best model achieving 84.83% accuracy on a test dataset. Using dimensionality reduction done by PCA we were able to increase that figure to 85.08% with only 10% of the original feature space, reducing the memory size needed by 64%. Finally, adding methods to remove possibly harmful training samples like deviation from the mean helped us to still achieve over 84% test accuracy but with only 32.8% of the original memory size for the training set. This compares favorably to the majority of literature results obtained through similar architectures. Although this approach gets outshined by state-of-the-art models, it does scale to some (AlexNet, VGGNet) trained on 50% of the same dataset.
Negative thermal expansion and itinerant ferromagnetism in Mn1.4Fe3.6Si3

We report the thermal expansion, critical behavior, magnetocaloric effect (MCE), and magnetoresistance (MR) on the polycrystalline Mn1.4Fe3. 6Si3 compound around the ferromagnetic transition. A large negative volume thermal expansion (αV ∼ −20 × 10 −6 K) is observed across the transition temperature with a strong anisotropic variation of lattice parameters in the ab-plane. The anisotropic magnetoelasticity arises from the competition between magnetic ordering and structural deformation which could be responsible for the large MCE (∆Sm ≃ −6 J/Kg-K) across the magnetic transition in this compound. The large and negative MR (∼ −3% in 80 kOe) is also observed at the transition temperature which can be attributed to the suppression of spin disorder. Further, the Rhodes-Wolfarth ratio (RWR > 1) and identical field dependence of MR and MCE isotherms indicate the itinerant character of the 3d electrons. The critical exponents determined from the analysis of magnetization and MCE are consistent with the quasi-two-dimensional (2D) Ising model with long range exchange interactions which decays as J(r) ∼ r. This unconventional quasi-2D Ising character with long-range interactions can be ascribed to strong ab-plane anisotropy and the delocalized 3d electrons in the studied compound.
EditSpeech: A Text Based Speech Editing System Using Partial Inference and Bidirectional Fusion

This paper presents the design, implementation and evaluation of a speech editing system, named EditSpeech, which allows a user to perform deletion, insertion and replacement of words in a given speech utterance, without causing audible degradation in speech quality and naturalness. The EditSpeech system is developed upon a neural text-to-speech (NTTS) synthesis framework. Partial inference and bidirectional fusion are proposed to effectively incorporate the contextual information related to the edited region and achieve smooth transition at both left and right boundaries. Distortion introduced to the unmodified parts of the utterance is alleviated. The EditSpeech system is developed and evaluated on English and Chinese in multi-speaker scenarios. Objective and subjective evaluation demonstrate that EditSpeech outperforms a few baseline systems in terms of low spectral distortion and preferred speech quality. Audio samples are available online for demonstration11https://daxintan-cuhk.github. io/EditSpeech/.
Connes' integration and Weyl's laws

This paper deal with some questions regarding the notion of integral in the framework of Connes's noncommutative geometry. First, we present a purely spectral theoretic construction of Connes' integral. This answers a question of Alain Connes. We also deal with the compatibility of Dixmier traces with Lebesgue's integral. This answers another question of Alain Connes. We further clarify the relationship of Connes' integration with Weyl's laws for compact operators and Birman-Solomyak's perturbation theory. We also give a"soft proof"of Birman-Solomyak's Weyl's law for negative order pseudodifferential operators on closed manifold. This Weyl's law yields a stronger form of Connes' trace theorem. Finally, we explain the relationship between Connes' integral and semiclassical Weyl's law for Schroedinger operators. This is an easy consequence of the Birman-Schwinger principle. We thus get a neat link between noncommutative geometry and semiclassical analysis.
Isotonic Data Augmentation for Knowledge Distillation

Knowledge distillation uses both real hard labels and soft labels predicted by teacher model as supervision. Intuitively, we expect the soft label probabilities and hard label probabilities to be concordant. However, in the real knowledge distillations, we found critical rank violations between hard labels and soft labels for augmented samples. For example, for an augmented sample x = 0.7 * cat + 0.3 * panda, a meaningful soft label distribution should have the same rank: P(cat|x)>P(panda|x)>P(other|x). But real teacher models usually violate the rank: P(tiger|x)>P(panda|x)>P(cat|x). We attribute the rank violations to the increased difficulty of understanding augmented samples for the teacher model. Empirically, we found the violations injuries the knowledge transfer. In this paper, we denote eliminating rank violations in data augmentation for knowledge distillation as isotonic data augmentation (IDA). We use isotonic regression (IR) -- a classic statistical algorithm -- to eliminate the rank violations. We show that IDA can be modeled as a tree-structured IR problem and gives an O(c*log(c)) optimal algorithm, where c is the number of labels. In order to further reduce the time complexity of the optimal algorithm, we also proposed a GPU-friendly approximation algorithm with linear time complexity. We have verified on variant datasets and data augmentation baselines that (1) the rank violation is a general phenomenon for data augmentation in knowledge distillation. And (2) our proposed IDA algorithms effectively increases the accuracy of knowledge distillation by solving the ranking violations.
Investigation of Practical Aspects of Single Channel Speech Separation for ASR

Speech separation has been successfully applied as a frontend processing module of conversation transcription systems thanks to its ability to handle overlapped speech and its flexibility to combine with downstream tasks such as automatic speech recognition (ASR). However, a speech separation model often introduces target speech distortion, resulting in a sub-optimum word error rate (WER). In this paper, we describe our efforts to improve the performance of a single channel speech separation system. Specifically, we investigate a two-stage training scheme that firstly applies a feature level optimization criterion for pretraining, followed by an ASR-oriented optimization criterion using an end-to-end (E2E) speech recognition model. Meanwhile, to keep the model light-weight, we introduce a modified teacher-student learning technique for model compression. By combining those approaches, we achieve a absolute average WER improvement of 2.70% and 0.77% using models with less than 10M parameters compared with the previous state-of-the-art results on the LibriCSS dataset for utterance-wise evaluation and continuous evaluation, respectively
Quantitative bounds for Gowers uniformity of the Möbius and von Mangoldt functions

We establish quantitative bounds on the $U^k[N]$ Gowers norms of the M\"obius function $\mu$ and the von Mangoldt function $\Lambda$ for all $k$, with error terms of shape $O((\log\log N)^{-c})$. As a consequence, we obtain quantitative bounds for the number of solutions to any linear system of equations of finite complexity in the primes, with the same shape of error terms. We also obtain the first quantitative bounds on the size of sets containing no $k$-term arithmetic progressions with shifted prime difference.
Deep Gaussian Process Emulation using Stochastic Imputation

Abstract Deep Gaussian processes (DGPs) provide a rich class of models that can better represent functions with varying regimes or sharp changes, compared to conventional GPs. In this work, we propose a novel inference method for DGPs for computer model emulation. By stochastically imputing the latent layers, our approach transforms a DGP into a linked GP: a novel emulator developed for systems of linked computer models. This transformation permits an efficient DGP training procedure that only involves optimizations of conventional GPs. In addition, predictions from DGP emulators can be made in a fast and analytically tractable manner by naturally using the closed form predictive means and variances of linked GP emulators. We demonstrate the method in a series of synthetic examples and empirical applications, and show that it is a competitive candidate for DGP surrogate inference, combining efficiency that is comparable to doubly stochastic variational inference and uncertainty quantification that is comparable to the fully-Bayesian approach. A Python package dgpsi implementing the method is also produced and available at https://github.com/mingdeyu/DGP.