Miracle-dz/UniFAR-FTUs huggingface dataset:
Bacteraemia and antibiotic sensitivity in a tertiary neonatal intensive care unit

Background Neonatal sepsis is an important cause of mortality and morbidity in neonatal intensive care populations worldwide. Data on rates of bacteraemia and antibiotic resistance patterns are limited, particularly in the developing world. Methods We retrospectively reviewed positive blood cultures obtained in the neonatal intensive care unit between 01 January 2015 and 31 December 2015. All neonates, either born at the tertiary hospital or transferred from referral units, regardless of diagnosis, who had a positive blood culture were included. Results There were 702 admissions during the study period and 437 positive cultures. Male patients made up 55.1% (65/118), and the gender was unknown for 11.0% (13/118). Late onset sepsis accounted for 85.7% (102/119) and early onset sepsis, 14.3% (17/119). Of the 119 organisms cultured, 76 (63.8%) were Gram-negative, 35 (29.4%) were Gram-positive and 8 (6.7%) were Candida species. Klebsiella was the most common genus at 42% (50/119). Of the clinically relevant organisms recovered, 37.0% (44/119) were susceptible to the empiric first-line regimen of penicillin and gentamycin. Furthermore, 69.7% (53/76) of the Gram-negative organisms produced extended-spectrum beta-lactamases. Conclusion The majority of organisms cultured were considered contaminants and were not clinically relevant. Improvements in culture collection processes are needed. The majority of organisms considered clinically relevant were resistant to the first-line antibiotic regimen. To improve the likelihood of clinical success, empiric antibiotic regimens should be based on local data, if possible.
Induction of Rapid Metabolism of Metolachlor in Sorghum (Sorghum bicolor) Shoots by CGA-92194 and Other Antidotes

Sorghum [Sorghum bicolor (L.) Moench. ‘G-623 GBR’] bioassays indicated that shoot growth was more susceptible to metolachlor [2-chloro-N-(2-ethyl-6-methylphenyl)-N-(2-methoxy-1-methylethyl)acetamide] and more responsive to the antidote CGA-92194 {α-[(1,3-dioxolan-2-yl-methoxy)-imino] benzeneacetonitrile} than root growth. Seed treatment with CGA-92194 increased seedling shoot tolerance to metolachlor approximately tenfold. CGA-92194 seed treatment enhanced shoot absorption of 14C-metolachlor approximately twofold. Metolachlor was initially metabolized to the glutathione conjugate in untreated shoots and those treated with CGA-92194. However, CGA-92194 seed treatment caused accelerated metolachlor metabolism in the shoot, decreasing metolachlor content and increasing formation of the glutathione conjugate. Cyometrinil {(Z)-α[(cyanomethoxy)imino] benzeneacetonitrile}, flurazole [phenylmethyl 2-chloro-4-(trifluoromethyl)-5-thiazolecarboxylate], naphthalic anhydride (1H,3H-naphtho[1,8-cd]-pyran-1,3-dione), and dichlormid (2,2-dichloro-N,N-di-2-propenylacetamide) also protected sorghum from metolachlor injury and enhanced metolachlor absorption and metabolism. The degree of protection conferred by a particular antidote was correlated with its ability to enhance metabolism of metolachlor in shoot tissue. These results are consistent with the hypothesis that the above antidotes protect sorghum from metolachlor injury by inducing rapid detoxification of metolachlor through conjugation with glutathione.
Diachronic semantic change in language influences, and is constrained by, how people use and learn language

It has long been understood that the mind has evolved to learn language. Recently this claim has been inverted to question how language evolves to become learnable. However, there is little evidence on how semantic evolution is shaped by language acquisition and processing in a naturally occurring language. In this research, we provide evidence to demonstrate how the human mind and language evolution of semantic constraint one and other. In study 1, we showed that words that people found more difficult to learn and process changed their semantics at a higher rate over the past 200 years, suggesting human cognition constrains semantic evolution. The alternative causality of study 1 is that semantic change since 1800 leads to difficulty in learning and processing. This alternative is unlikely because knowledge unknown to people living today, that is, historical meanings of words before their birth, could not influence how people learn and process language. However, semantic change, if occurred in recent decades, could in turn hamper semantic processing, but only for those people who were born early enough to experience it. We found supporting evidence in study 2 that semantic change between 1970 and 2000 hinders the processing speed in middle-aged adults (aged 45-55) but not in younger adults (aged <25). Taken together, our two studies highlight the importance of studying language evolution in relation to processing and acquisition patterns across generations of language users.
Modeling that predicts elementary particles and explains data about dark matter, early galaxies, and the cosmos

We try to solve three decades-old physics challenges. List all elementary particles. Describe dark matter. Describe mechanisms that govern the rate of expansion of the universe. We propose new modeling. The modeling uses extensions to harmonic oscillator mathematics. The modeling points to all known elementary particles. The modeling suggests new particles. Based on those results, we do the following. We explain observed ratios of dark matter amounts to ordinary matter amounts. We suggest details about galaxy formation. We suggest details about inflation. We suggest aspects regarding changes in the rate of expansion of the universe. We interrelate the masses of some elementary particles. We interrelate the strengths of electromagnetism and gravity. Our work seems to offer new insight regarding applications of harmonic oscillator mathematics. Our work seems to offer new insight regarding three branches of physics. The branches are elementary particles, astrophysics, and cosmology.
Ethical Leadership Anomaly On Organizational Commitment In The Indonesian FDA

This study aims to determine the influence of Ethical Leadership on Job Satisfaction, Ethical Climate and Organizational Commitment at staff at the Drug Registration Directorate of the Indonesian Food and Drug Authority (Indonesian FDA). Data collection techniques are done by distributing questionnaires using ordinal scales. This research was conducted on 70 respondents by using quantitative descriptive approach and hypothesis testing. The collected data is then analyzed using the technique analysis Structural Equation Modeling (SEM) through SmartPLS (Partial Least Square) software. The results of this study show that Ethical Leadership positively and significantly influence on Ethical Climate, Job Satisfaction and Organizational Commitment at the Drug Registration Directorate of the Indonesian Food and Drug Authority (Indonesian FDA).
An unexpected large continental source of reactive bromine and chlorine with significant impact on wintertime air quality

Abstract Halogen atoms affect the budget of ozone and the fate of pollutants such as hydrocarbons and mercury. Yet their sources and significances in polluted continental regions are poorly understood. Here we report the observation of unprecedented levels (averaging at 60 parts per trillion) of bromine chloride (BrCl) at a mid-latitude site in North China during winter. Widespread coal burning in rural households and a photo-assisted process were the primary source of BrCl and other bromine gases. BrCl contributed about 55% of both bromine and chlorine atoms. The halogen atoms increased the abundance of ‘conventional’ tropospheric oxidants (OH, HO2 and RO2) by 26%–73%, and enhanced oxidation of hydrocarbon by nearly a factor of two and the net ozone production by 55%. Our study reveals the significant role of reactive halogen in winter atmospheric chemistry and the deterioration of air quality in continental regions where uncontrolled coal combustion is prevalent.
Water acceleration in a supraglacial channel predicts locations of step-pool sequence formation

Supraglacial channels constitute an important part of the 12 glacial hydrological system, both by influencing surface energy exchange as 13 well as routing meltwater to eventually feed enand subglacial drainage 14 systems. Subglacial systems have received considerable scientific interest, but 15 specific studies of supraglacial channels are sparse. This limited scientific 16 understanding represents a missing link between meltwater generation and 17 en/subglacial hydraulics; among others it poses a problem for constraining and 18 validating glacier hydrological models. In this study, we utilize drifters carrying 19 a navigation system to obtain the spatial and temporal evolution of the water 20 velocity along a supraglacial channel on Austre Brøggerbreen (Svalbard). This 21 velocity is then used to compute melt rates of the channel walls and to 22 estimate the discharge in the channel. We further present, to our knowledge, 23 the first dataset of the spatio-temporal evolution of the water acceleration 24 in the channel. The highest accelerations occur at the location of step-pool 25 Journal of Glaciology, Vol. 00, No. 000, 0000 2 sequences and peaks in the acceleration signal can already be seen before the 26 step-pool sequences have developed in the channel. We therefore conclude that 27 water acceleration can be used to predict where step-pool sequences will form. 28
Amplification of Turbulence in Contracting Prestellar Cores in Primordial Minihalos

We investigate the amplification of turbulence through gravitational contraction of the primordial gas in minihalos. We perform numerical simulations to follow the cloud collapse, assuming polytropic equations of state for different initial turbulent Mach numbers and resolutions. We find that the turbulent velocity is amplified solely by gravitational contraction, and eventually becomes comparable to the sound speed, even for small initial turbulent Mach numbers ( 0≳0.05 ). We derive an analytic formula for the amplification of turbulent velocity in a collapsing cloud, and find that our numerical results are consistent with the formula. These results suggest that the turbulence can play an important role in collapsing clouds for general cases.
A Monotone Approximate Dynamic Programming Approach for the Stochastic Scheduling, Allocation, and Inventory Replenishment Problem: Applications to Drone and Electric Vehicle Battery Swap Stations

There is a growing interest in using electric vehicles (EVs) and drones for many applications. However, battery-oriented issues, including range anxiety and battery degradation, impede adoption. Battery swap stations are one alternative to reduce these concerns that allow the swap of depleted for full batteries in minutes. We consider the problem of deriving actions at a battery swap station when explicitly considering the uncertain arrival of swap demand, battery degradation, and replacement. We model the operations at a battery swap station using a finite horizon Markov decision process model for the stochastic scheduling, allocation, and inventory replenishment problem (SAIRP), which determines when and how many batteries are charged, discharged, and replaced over time. We present theoretical proofs for the monotonicity of the value function and monotone structure of an optimal policy for special SAIRP cases. Because of the curses of dimensionality, we develop a new monotone approximate dynamic programming (ADP) method, which intelligently initializes a value function approximation using regression. In computational tests, we demonstrate the superior performance of the new regression-based monotone ADP method compared with exact methods and other monotone ADP methods. Furthermore, with the tests, we deduce policy insights for drone swap stations.
Correlation functions for open XXZ spin 1/2 quantum chains with unparallel boundary magnetic fields

In this paper we continue our derivation of the correlation functions of open quantum spin 1/2 chains with unparallel magnetic fields on the edges; this time for the more involved case of the XXZ spin 1/2 chains. We develop our study in the framework of the quantum separation of variables, which gives us both the complete spectrum characterization and simple scalar product formulae for separate states, including transfer matrix eigenstates. Here, we leave the boundary magnetic field in the first site of the chain completely arbitrary, and we fix the boundary field in the last site N of the chain to be a specific value along the z-direction. This is a natural first choice for the unparallel boundary magnetic fields. We prove that under these special boundary conditions, on the one side, we have a simple enough complete spectrum description in terms of homogeneous Baxter like TQ-equation. On the other side, we prove a simple enough description of the action of a basis of local operators on transfer matrix eigenstates as linear combinations of separate states. Thanks to these results, we achieve our main goal to derive correlation functions for a set of local operators both for the finite and half-infinite chains, with multiple integral formulae in this last case.
Dermoscopic Image Classification with Neural Style Transfer

Abstract Skin cancer, the most commonly found human malignancy, is primarily diagnosed visually via dermoscopic analysis, biopsy and histopathological examination. However, unlike other types of cancer, automated image classification of skin lesions is deemed to be more challenging due to the irregularity and variability in the lesions’ appearances. In this work, we propose an adaptation of the Neural Style Transfer (NST) as a novel image preprocessing step for skin lesion classification problems. We represent each dermoscopic image as a style image and transfer the style of the lesion onto a homogeneous content image. This transfers the main variability of each lesion onto the same localized region, which allows us to integrate the generated images together and extract low-rank latent embeddings via tensor decomposition. We evaluated the performance of our model on competition datasets collected and preprocessed from the International Skin Imaging Collaboration (ISIC) database. We show that the classification performance based on the extracted tensor features using the style-transferred images significantly outperforms that of the raw images by more than 10%, and is also competitive with well-studied, pretrained CNN models using transfer learning. Additionally, the tensor decomposition also affords clinical interpretations and insights by examining the images which correspond to the largest loadings in the top style embedding features as identified by the common supervised learning models. Supplementary materials for this article are available online.
Brain Inspired Object Recognition System

—This paper presents a new proposal of an efficient computational model of face and object recognition which uses cues from the distributed face and object recognition mechanism of the brain, and by gathering engineering equivalent of these cues from existing literature. Three distinct and widely used features – Histogram of Oriented Gradients (HOG), Local Binary Patterns (LBP), and Principal components (PCs) extracted from target images are used in a manner which is simple, and yet effective. The HOG and LBP features further undergo principal component analysis for dimensionality reduction. Our model uses multi-layer perceptrons (MLP) to classify these three features and fuse them at the decision level using sum rule. A computational theory is first developed by using concepts from the information processing mechanism of the brain. Extensive experiments are carried out using fifteen publicly available datasets to validate our proposed model’s performance in recognizing faces and objects with extreme variation of illumination, pose angle, expression, and background. Results obtained are extremely promising when compared with other face and object recognition algorithms including CNN and deep learning based methods. This highlights that simple computational processes, if clubbed properly, can produce competing performance with best algorithms.
Microwave-Assisted Synthesis, Molecular Docking Study and In Vitro Evaluation of Halogen Substituted Flavonols Against P388 Murine Leukemia Cells

Three halogen substituted flavonols ( 2a-2c ) have been synthesized via Algar-Flynn-Oyamada (AFO) under microwave irradiation. The reactions took place in 2 minutes with 72-85% yield. The structures of all compounds were characterized based on the interpretation of spectroscopic data include UV-Vis, FT-IR, 1 H NMR and HRMS . Molecular docking study was applied to ensure the biological activity of the compounds and observe the interactions between all compounds and P388 cell line. Based on the docking result, the compounds have a good potency as anticancer because they take up similar poses and similar binding orientation around the protein. Higher number of hydrogen bonds and van der Waals interactions between all three ligands with amino acid residues in the binding site which presumably cause all three compounds become active. In vitro evaluation of their cytotoxic activity was also performed. The compound 2a , 2b and 2c were proved to have a good cytotoxic activity against P388 murine leukemia cells with IC 50 values of 14.4, 1.23 and 1.32 µM, respectively.
O(9,9)
 symmetry of NS-NS couplings at order 
α′3

Recently, imposing the O(1, 1) symmetry on the circle reduction of the classical effective action of string theory, we have found all NS-NS couplings of type II superstring theories at order α′3. In this paper we use the cosmological reduction on the couplings and show that, up to one-dimensional field redefinitions and total derivative terms, they are invariant under the O(9, 9) transformations.
Structure and dynamics of the inner nebula around the symbiotic stellar system R Aquarii

We investigate the structure, dynamics, and chemistry of the molecule-rich nebula around the stellar symbiotic system R Aqr, which is significantly affected by the presence of a white dwarf (WD) companion. We study the effects of the strong dynamical interaction between the AGB wind and the WD and of photodissociation by the WD UV radiation on the circumstellar shells. We obtained high-quality ALMA maps of the 12CO J=2-1, J=3-2, and J=6-5 lines and of 13CO J=3-2. The maps were analyzed by means of a heuristic 3D model that is able to reproduce the observations. In order to interpret this description of the molecule-rich nebula, we performed sophisticated calculations of hydrodynamical interaction and photoinduced chemistry. We find that the CO-emitting gas is distributed within a relatively small region<~ 1.5". Its structure consists of a central dense component plus strongly disrupted outer regions, which seem to be parts of spiral arms that are highly focused on the orbital plane. The structure and dynamics of these spiral arms are compatible with our hydrodynamical calculations. We argue that the observed nebula is the result of the dynamical interaction between the wind and the gravitational attraction of the WD. We also find that UV emission from the hot companion efficiently photodissociates molecules except in the densest and best-shielded regions, that is, in the close surroundings of the AGB star and some shreds of the spiral arms from which the detected lines come. We can offer a faithful description of the distribution of nebular gas in this prototypical source, which will be a useful template for studying material around other tight binary systems.
A Cognitive Regularizer for Language Modeling

The uniform information density (UID) hypothesis, which posits that speakers behaving optimally tend to distribute information uniformly across a linguistic signal, has gained traction in psycholinguistics as an explanation for certain syntactic, morphological, and prosodic choices. In this work, we explore whether the UID hypothesis can be operationalized as an inductive bias for statistical language modeling. Specifically, we augment the canonical MLE objective for training language models with a regularizer that encodes UID. In experiments on ten languages spanning five language families, we find that using UID regularization consistently improves perplexity in language models, having a larger effect when training data is limited. Moreover, via an analysis of generated sequences, we find that UID-regularized language models have other desirable properties, e.g., they generate text that is more lexically diverse. Our results not only suggest that UID is a reasonable inductive bias for language modeling, but also provide an alternative validation of the UID hypothesis using modern-day NLP tools.
Improving Graph Neural Networks with Simple Architecture Design

Graph Neural Networks have emerged as a useful tool to learn on the data by applying additional constraints based on the graph structure. These graphs are often created with assumed intrinsic relations between the entities. In recent years, there have been tremendous improvements in the architecture design, pushing the performance up in various prediction tasks. In general, these neural architectures combine layer depth and node feature aggregation steps. This makes it challenging to analyze the importance of features at various hops and the expressiveness of the neural network layers. As different graph datasets show varying levels of homophily and heterophily in features and class label distribution, it becomes essential to understand which features are important for the prediction tasks without any prior information. In this work, we decouple the node feature aggregation step and depth of graph neural network and introduce several key design strategies for graph neural networks. More specifically, we propose to use softmax as a regularizer and"Soft-Selector"of features aggregated from neighbors at different hop distances; and"Hop-Normalization"over GNN layers. Combining these techniques, we present a simple and shallow model, Feature Selection Graph Neural Network (FSGNN), and show empirically that the proposed model outperforms other state of the art GNN models and achieves up to 64% improvements in accuracy on node classification tasks. Moreover, analyzing the learned soft-selection parameters of the model provides a simple way to study the importance of features in the prediction tasks. Finally, we demonstrate with experiments that the model is scalable for large graphs with millions of nodes and billions of edges.
"Hey Alexa, What do You Know About the COVID-19 Vaccine?" - (Mis)perceptions of Mass Immunization Among Voice Assistant Users

In this paper, we analyzed the perceived accuracy of COVID-19 vaccine information spoken back by Amazon Alexa. Unlike social media, Amazon Alexa doesn't apply soft moderation to unverified content, allowing for use of third-party malicious skills to arbitrarily phrase COVID-19 vaccine information. The results from a 210-participant study suggest that a third-party malicious skill could successful reduce the perceived accuracy among the users of information as to who gets the vaccine first, vaccine testing, and the side effects of the vaccine. We also found that the vaccine-hesitant participants are drawn to pessimistically rephrased Alexa responses focused on the downsides of the mass immunization. We discuss solutions for soft moderation against misperception-inducing or altogether COVID-19 misinformation malicious third-party skills.