Miracle-dz/UniFAR-FTUs huggingface dataset:
A Strategy for Selecting “Q-Markers” of Chinese Medical Preparation via Components Transfer Process Analysis with Application to the Quality Control of Shengmai Injection

Chinese medical preparation has complicated chemical constituents. Consequently, the proper quality control methods for these Chinese medical preparations have been great challenges to the traditional Chinese medicine modernization and internationalization. What components should be chosen for quality control is a big challenge in the development of traditional Chinese medicine. A new concept of “Quality Marker” was proposed by Liu et al. to solve this problem and established a new research paradigm for traditional Chinese medicine quality study. Several strategies were proposed by the researchers in traditional Chinese medicine, here, we used Shengmai injection as an example to discuss a strategy for selecting “Quality Markers” of Chinese medical preparation by the components transfer process analysis in the Shengmai injection manufacturing process. Firstly, a total of 87 compounds were identified or partially characterized in shengmai injection. Secondly, referenced to the quality control method in China pharmacopeia and considered the biomarkers in the original medicines and representative components in the manufacturing process, four ginsenosides in Panax ginseng (Hongshen), two compounds in Schisandra chinensis (Wuweizi), and a sugar from Ophiopogon japonicas (Maidong) were quantified. As a result, these seven representative compounds exhibited an acceptable transitivity throughout the Shengmai injection manufacturing process. Finally, combined with the active ingredients, components transfer process analysis, and comprehensive evaluation by “Spider-web” analysis, six compounds were selected as the quality markers for the quality control of Shengmai injection. Through this strategy of optimization for quality markers of Shengmai injection, we found that these six compounds could represent the main bioactive substances and be easily detected in the whole process of production. Furthermore, the quality control method was developed for quality assessment and control of these six quality markers in the Shengmai injection. The total content range of the selected quality markers in the 10 batches of the Shengmai injection is 13.844-22.557 mg/mL.
Microstructural Study of Two-Dimensional Organic-Inorganic Hybrid Perovskite Nanosheet Degradation under Illumination

Two-dimensional (2D) organic-inorganic hybrid perovskite materials have received substantial attention because of their exceptional optoelectronic properties. Although the applications of 2D perovskite nanosheets are promising in various optoelectronic devices, which all face harsh working conditions of light exposure, little is known about the photo-stability and degradation mechanisms of these 2D perovskite nanosheets. In this work, degradation of (C4H9NH3)2PbBr4 (BA2PbBr4) nanosheets when exposed to ultraviolet (UV) light and white light is explored. The morphology, optical properties, and microstructure of the nanosheets, under different conditions of light exposure, was studied in detail. UV light is more destructive compared to white light, which both led to a nanosheet breakdown. A combination of transmission electron microscopy (TEM) imaging and electron diffraction revealed that the organic moieties are most sensitive to light exposure and partial disorder toward complete disorder takes place during light exposure. Moreover, excessive light exposure further causes a [PbBr6]4− octahedron tilt and re-ordering within the perovskite structure. This study could enrich the understanding of 2D perovskite nanosheets and their photostability, offer a new perspective in interpreting the light–perovskite interaction, and further help the design of robust and light-tunable 2D perovskite-based optoelectronic devices.
Lightweight QoE Driven and Invulnerability Guarantee Opportunistic Control Scheme for Wireless Sensor Networks

This paper proposed the lightweight QoE driven adaptive invulnerability wireless communication control method, including the wireless communication terminal equipment with invulnerability antenna. According to the quality of wireless network channel quality, the lightweight QoE driven scheme was developed. According to user needs, the quality of the network, survivable ability of wireless communication terminal and wireless communication survivability requirements, we proposed three matching rules by considering the wireless hop number, data size and life cycle. The experiments demonstrated that the proposed scheme can optimize wireless communication terminal equipment and wireless opportunistic communication network construction, guarantee the user experience quality and improve wireless communication network survivability.
Current Understanding of Autophagy in Pregnancy

Autophagy is an evolutionarily conserved process in eukaryotes to maintain cellular homeostasis under environmental stress. Intracellular control is exerted to produce energy or maintain intracellular protein quality controls. Autophagy plays an important role in embryogenesis, implantation, and maintenance of pregnancy. This role includes supporting extravillous trophoblasts (EVTs) that invade the decidua (endometrium) until the first third of uterine myometrium and migrate along the lumina of spiral arterioles under hypoxic and low-nutrient conditions in early pregnancy. In addition, autophagy inhibition has been linked to poor placentation—a feature of preeclamptic placentas—in a placenta-specific autophagy knockout mouse model. Studies of autophagy in human placentas have revealed controversial results, especially with regard to preeclampsia and gestational diabetes mellitus (GDM). Without precise estimation of autophagy flux, wrong interpretation would lead to fixed tissues. This paper presents a review of the role of autophagy in pregnancy and elaborates on the interpretation of autophagy in human placental tissues.
Evolutionary Plasticity in Detoxification Gene Modules: The Preservation and Loss of the Pregnane X Receptor in Chondrichthyes Lineages

To appraise how evolutionary processes, such as gene duplication and loss, influence an organism’s xenobiotic sensitivity is a critical question in toxicology. Of particular importance are gene families involved in the mediation of detoxification responses, such as members of the nuclear receptor subfamily 1 group I (NR1I), the pregnane X receptor (PXR), and the constitutive androstane receptor (CAR). While documented in multiple vertebrate genomes, PXR and CAR display an intriguing gene distribution. PXR is absent in birds and reptiles, while CAR shows a tetrapod-specific occurrence. More elusive is the presence of PXR and CAR gene orthologs in early branching and ecologically-important Chondrichthyes (chimaeras, sharks and rays). Therefore, we investigated various genome projects and use them to provide the first identification and functional characterization of a Chondrichthyan PXR from the chimaera elephant shark (Callorhinchus milii, Holocephali). Additionally, we substantiate the targeted PXR gene loss in Elasmobranchii (sharks and rays). Compared to other vertebrate groups, the chimaera PXR ortholog displays a diverse expression pattern (skin and gills) and a unique activation profile by classical xenobiotic ligands. Our findings provide insights into the molecular landscape of detoxification mechanisms and suggest lineage-specific adaptations in response to xenobiotics in gnathostome evolution.
Morphometry of the trochlear nerve and superior oblique muscle volume in congenital superior oblique palsy.

PURPOSE To infer the pathogenic mechanism of congenital superior oblique palsy (SOP) by evaluating trochlear nerve diameter (CN4D) and superior oblique muscle (SO) volume in patients with congenital SOP. METHODS The medical records of 125 patients diagnosed with unilateral congenital SOP and 34 age-matched healthy controls were reviewed retrospectively. Using thin-section high-resolution magnetic resonance imaging, we evaluated the presence of the trochlear nerve, CN4D, SO volume, and their relationship. RESULTS Of the 125 patients with congenital SOP, 87 showed absence of the trochlear nerve (n = 87, 70%, absent group) and 38 showed bilateral presence of the trochlear nerve (n = 38, 30%, present group). The nonparetic side CN4D was smaller than controls in the absent group (P = 0.001), and larger than controls in the present group (P = 0.001). Trochlear nerve diameter positively correlated with SO volume in controls (P = 0.014, R(2) = 0.174) and in the nonparetic sides of congenital SOP (present group P = 0.023, R(2) = 0.135; absent group, P = 0.008, R(2) = 0.079). The paretic side SO volume did not show a linear correlation between CN4D and SO volume in the present group (P = 0.243). CONCLUSIONS In congenital SOP patients without a trochlear nerve, the nonparetic side CN4D was also reduced in contrast to those with a trochlear nerve. The relatively weaker relationship of CN4D and nonparetic side SO volume in the absent group than that of the present group suggests different pathogenic mechanisms underlying these two entities of congenital SOP.
Increased risk of appendectomy due to appendicitis after tonsillectomy in women

Abstract The purpose of this study was to evaluate the risk of appendectomy due to appendicitis after tonsillectomy in Koreans using national cohort data. Using the national cohort study from the Korean Health Insurance Review and Assessment Service, 1:4 matched tonsillectomy participants (9015) and control participants (36,060) were selected. The Cox-proportional hazard model was used. In this analysis, a crude and adjusted model for age, sex, income, region of residence, and the past medical histories of hypertension, diabetes mellitus, and dyslipidemia were used. For the subgroup analyses, the participants were divided as follows: children (⩽14 years old) vs adolescents and adults (≥15 years old) and men vs women. The adjusted hazard ratio (HR) of tonsillectomy for appendectomy was 1.06 (95% confidence interval, CI = 0.89–1.27, P = .517). In the subgroup analysis, the HR was 1.03 (95% CI = 0.82–1.30, P = .804) in children and 1.10 (95% CI = 0.84–1.47, P = .468) in adolescents and adults. In another subgroup analysis, the HR was 0.89 (95% CI = 0.70–1.12, P = .314) in men and 1.39 (95% CI = 1.06–1.83, P = .018) in women. The risk of appendectomy was higher in the tonsillectomy group but only in women.
Silicone ring implantation in an excisional murine wound model.

UNLABELLED A good animal model for wound healing is indispensable for researchers to study the basic mechanism of tissue repair, and to develop strategies for clinical treatment. Small mammalian wound healing models are the most popular animal models for wound healing research because they are inexpensive, readily obtainable, and easy to handle. One significant challenge of using mice to evaluate wound repair is that wound contraction originates outside of tissue, whereas in humans, re-epithelialization and granulation tissue formation occurs within the wound space. METHODS The present study describes a new excisional skin wound model utilizing an implanted silicone ring on the dorsal side of the mouse for 1 week prior to creating a fullthickness skin defect wound. RESULTS The results showed that the time required for complete epithelialization of the wound was extended, the re epithelialization ratio was increased, and more granulation tissue was formed. CONCLUSION Permitting the wound to heal mainly through re-epithelialization and granulation tissue formation, this new technique can result in a new excisional murine wound model that closer approximates human wound healing, allowing for more relevant evaluation of molecular signaling and cellular metabolism that occur during skin wound healing. ﻿.
Corank-1 projections and the randomised Horn problem

Let $\hat{\boldsymbol x}$ be a normalised standard complex Gaussian vector, and project an Hermitian matrix $A$ onto the hyperplane orthogonal to $\hat{\boldsymbol x}$. In a recent paper Faraut [Tunisian J. Math. \textbf{1} (2019), 585--606] has observed that the corresponding eigenvalue PDF has an almost identical structure to the eigenvalue PDF for the rank 1 perturbation $A + b \hat{\boldsymbol x} \hat{\boldsymbol x}^\dagger$, and asks for an explanation. We provide this by way of a common derivation involving the secular equations and associated Jacobians. This applies too in related setting, for example when $\hat{\boldsymbol x}$ is a real Gaussian and $A$ Hermitian, and also in a multiplicative setting $A U B U^\dagger$ where $A, B$ are fixed unitary matrices with $B$ a multiplicative rank 1 deviation from unity, and $U$ is a Haar distributed unitary matrix. Specifically, in each case there is a dual eigenvalue problem giving rise to a PDF of almost identical structure.
Variational approximations using Fisher divergence

Modern applications of Bayesian inference involve models that are sufficiently complex that the corresponding posterior distributions are intractable and must be approximated. The most common approximation is based on Markov chain Monte Carlo, but these can be expensive when the data set is large and/or the model is complex, so more efficient variational approximations have recently received considerable attention. The traditional variational methods, that seek to minimize the Kullback--Leibler divergence between the posterior and a relatively simple parametric family, provide accurate and efficient estimation of the posterior mean, but often does not capture other moments, and have limitations in terms of the models to which they can be applied. Here we propose the construction of variational approximations based on minimizing the Fisher divergence, and develop an efficient computational algorithm that can be applied to a wide range of models without conjugacy or potentially unrealistic mean-field assumptions. We demonstrate the superior performance of the proposed method for the benchmark case of logistic regression.
RF Chain Reduction for MIMO Systems: A Hardware Prototype

Radio frequency (RF) chain circuits play a major role in digital receiver architectures, allowing passband communication signals to be processed in baseband. When operating at high frequencies, these circuits tend to be costly. This increased cost imposes a major limitation on future multiple-input–multiple-output (MIMO) communication technologies. A common approach to mitigate the increased cost is to utilize hybrid architectures, in which the received signal is combined in analog into a lower dimension, thus reducing the number of RF chains. In this article we study the design and hardware implementation of hybrid architectures via minimizing channel estimation error. We first derive the optimal solution for complex-gain combiners and propose an alternating optimization algorithm for phase-shifter combiners. We then present a hardware prototype implementing analog combining for RF chain reduction. The prototype consists of a specially designed configurable combining board as well as a dedicated experimental setup. Our hardware prototype allows us evaluating the effect of analog combining in MIMO systems using actual communication signals. The experimental study, which focuses on channel estimation accuracy in MIMO channels, demonstrates that using the proposed prototype, the achievable channel estimation performance is within a small gap in a statistical sense from that obtained using a costly receiver in which each antenna is connected to a dedicated RF chain.
Fully Connected Neural Networks Ensemble with Signal Strength Clustering for Indoor Localization in Wireless Sensor Networks

The paper introduces a method which improves localization accuracy of the signal strength fingerprinting approach. According to the proposed method, entire localization area is divided into regions by clustering the fingerprint database. For each region a prototype of the received signal strength is determined and a dedicated artificial neural network (ANN) is trained by using only those fingerprints that belong to this region (cluster). Final estimation of the location is obtained by fusion of the coordinates delivered by selected ANNs. Sensor nodes have to store only the signal strength prototypes and synaptic weights of the ANNs in order to estimate their locations. This approach significantly reduces the amount of memory required to store a received signal strength map. Various ANN topologies were considered in this study. Improvement of the localization accuracy as well as speedup of learning process was achieved by employing fully connected neural networks. The proposed method was verified and compared against state-of-the-art localization approaches in real world indoor environment by using both stationary and mobile sensor nodes.
Using syntactical and logical forms to evaluate textual inference competence

In the light of recent breakthroughs in transfer learning for Natural Language Processing, much progress was achieved on Natural Language Inference. Different models are now presenting high accuracy on popular inference datasets such as SNLI, MNLI and SciTail. At the same time, there are different indicators that those datasets can be exploited by using some simple linguistic patterns. This fact poses difficulties to our understanding of the actual capacity of machine learning models to solve the complex task of textual inference. We propose a new set of tasks that require specific capacities over linguistic logical forms such as: i) Boolean coordination, ii) quantifiers, iii) definitive description, and iv) counting operators. By evaluating a model on our stratified dataset, we can better pinpoint the specific inferential difficulties of a model in each kind of textual structure. We evaluate two kinds of neural models that implicitly exploit language structure: recurrent models and the Transformer network BERT. We show that although BERT is clearly more efficient to generalize over most logical forms, there is space for improvement when dealing with counting operators.
A Novel WPT System Based on Dual Transmitters and Dual Receivers for High Power Applications: Analysis, Design and Implementation

Traditional Wireless Power Transfer (WPT) systems only have one energy transmission path, which can hardly meet the power demand for high power applications, e.g., railway applications (electric trains and trams, etc.) due to the capacity constraints of power electronic devices. A novel WPT system based on dual transmitters and dual receivers is proposed in this paper to upgrade the power capacity of the WPT system. The reliability and availability of the proposed WPT system can be dramatically improved due to the four energy transmission paths. A three-dimensional finite element analysis (FEA) tool ANSYS MAXWELL (ANSYS, Canonsburg, PA, USA) is adopted to investigate the proposed magnetic coupling structure. Besides, the effects of the crossing coupling mutual inductances among the transmitters and receivers are analyzed. It shows that the same-side cross couplings will decrease the efficiency and transmitted power. Decoupling transformers are employed to mitigate the effects of the same-side cross couplings. Meanwhile, the output voltage in the secondary side can be regulated at its designed value with a fast response performance, and the system can continue work even with a faulty inverter. Finally, a scale-down experimental setup is provided to verify the proposed approach. The experimental results indicate that the proposed method could improve the transmitted power capacity, overall efficiency and reliability, simultaneously. The proposed WPT structure is a potential alternative for high power applications.
Novel rectangular biphasic and monophasic waveforms delivered by a radiofrequency-powered defibrillator compared with conventional capacitor-based waveforms in transvenous cardioversion of atrial fibrillation.

AIMS To investigate the feasibility and efficacy of novel low-tilt biphasic waveforms in transvenous cardioversion of atrial fibrillation (AF), delivered by a radiofrequency-powered defibrillator. METHODS AND RESULTS The investigation was performed in three phases in an animal model of AF: a feasibility and efficacy study (in 10 adult Large White Landrace swine), comparison with low-tilt monophasic and standard capacitor-based waveforms, and an assessment of sequential shocks delivered over several pathways (in 15 adult Suffolk sheep). Defibrillation electrodes were positioned transvenously under fluoroscopic control in the high lateral right atrium and distal coronary sinus. When multiple defibrillation pathways were tested, a third electrode was also attached to the lower interatrial septum. The electrodes were then connected to a radiofrequency (RF)-powered defibrillator or a standard defibrillator. After confirmation of successful induction of sustained AF, defibrillation was attempted. Percentage success was calculated from the effects of all shocks delivered to all the animals within each set of experiments. Of the low-tilt (RF) biphasic waveforms delivered during internal atrial cardioversion, 100% success was achieved with a 6/6 ms 100/-50 V waveform (1.45+/-0.01 J). This waveform was similar in efficacy to low-tilt (RF) monophasic waveforms (88 vs. 92% success, 1.58+/-0.01 vs. 2.67+/-0.03 J; P=NS; delivered energy 41% lower) and superior to equivalent voltage standard monophasic (50% success, 0.67+/-0.00 J; P<0.001) and biphasic waveforms (72% success, 0.69+/-0.00 J; P=0.03). Sequential shocks delivered over dual pathways did not improve the efficacy of low-tilt biphasic waveforms. CONCLUSION A low-tilt biphasic waveform from a RF-powered defibrillator (6/6 ms 100/-50 V) is more efficacious than standard monophasic or biphasic waveforms (equivalent voltage) and is similar in efficacy to low-tilt monophasic waveforms.
Transition Subspace Learning based Least Squares Regression for Image Classification

Only learning one projection matrix from original samples to the corresponding binary labels is too strict and will consequentlly lose some intrinsic geometric structures of data. In this paper, we propose a novel transition subspace learning based least squares regression (TSL-LSR) model for multicategory image classification. The main idea of TSL-LSR is to learn a transition subspace between the original samples and binary labels to alleviate the problem of overfitting caused by strict projection learning. Moreover, in order to reflect the underlying low-rank structure of transition matrix and learn more discriminative projection matrix, a low-rank constraint is added to the transition subspace. Experimental results on several image datasets demonstrate the effectiveness of the proposed TSL-LSR model in comparison with state-of-the-art algorithms
Robustification of Deep Net Classifiers by Key Based Diversified Aggregation with Pre-Filtering

In this paper, we address a problem of machine learning system vulnerability to adversarial attacks. We propose and investigate a Key based Diversified Aggregation (KDA) mechanism as a defense strategy. The KDA assumes that the attacker (i) knows the architecture of classifier and the used de-fense strategy, (ii) has an access to the training data set but (iii) does not know the secret key. The robustness of the system is achieved by a specially designed key based randomization. The proposed randomization prevents the gradients’ back propagation or the creating of a "bypass" system. The randomization is performed simultaneously in several channels and a multi-channel aggregation stabilizes the results of randomization by aggregating soft outputs from each classifier in multi-channel system. The performed experimental evaluation demonstrates a high robustness and universality of the KDA against the most efficient gradient based attacks like those proposed by N. Carlini and D. Wagner [1] and the non-gradient based sparse adversarial perturbations like OnePixel attacks [2].