Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of Validated and Stability-Indicating LC-DAD and LC-MS/MS Methods for Determination of Avanafil in Pharmaceutical Preparations and Identification of a Novel Degradation Product by LCMS-IT-TOF

Avanafil (AVA), one of the most effective drugs prescribed for erectile dysfunction, is a pyrimidine-derivative PDE5 inhibitor. In the current work, new LC methods were developed and validated for quantitative determination of avanafil and qualitative determination of its degradation products. The quantitative determination of avanafil was carried out using liquid chromatography with photodiode array detection (LC-DAD) and liquid chromatography-tandem mass spectrometry LC-MS/MS methods, and fully validated according to the ICH Q2 (R1) guideline, while qualitative determination was performed using a liquid chromatography mass spectrometry-ion trap-time of flight (LCMS-IT-TOF) instrument. The separation of avanafil and its degradation products was carried out using the same reversed-phase chromatographic conditions, in which a second-generation C18-bonded monolithic silica column (Chromolith® High Resolution RP-18e, 100 × 4.6 mm, Merck KGaA) was used as stationary phase. Briefly, the methods enable quantitation of avanafil with high accuracy (recovery > 95%) and precision (RSD% < 2.0), within the ranges of 0.5–20 μg/mL for LC-DAD and 150–6000 ng/mL for LC-MS/MS. In the forced degradation studies, over and above currently existing data, a new oxidation-based degradation product, whose predicted m/z is 367.1168, was identified and its structure was confirmed by high-resolution mass spectrometric analysis. As the main advantage, either an LC-DAD or LC-MS/MS instrument can be chosen for interference-free quantitation of AVA, according to the facilities in quality-control laboratories.
Opinion-Unaware Blind Quality Assessment of Multiply and Singly Distorted Images via Distortion Parameter Estimation

Over the past couple of decades, numerous image quality assessment (IQA) algorithms have been developed to estimate the quality of images that contain a single type of distortion. Although in practice, images can be contaminated by multiple distortions, previous research on the quality assessment of multiply distorted images is very limited. In this paper, we propose an efficient algorithm to blindly assess the quality of both multiply and singly distorted images based on predicting the distortion parameters using a bag of natural scene statistics (NSS) features. Our method, called MUltiply and Singly distorted Image QUality Estimator (MUSIQUE), operates via three main stages. In the first stage, a two-layer classification model is employed to identify the distortion types (i.e., Gaussian blur, JPEG compression, and white noise) that may exist in an image. In the second stage, specific regression models are employed to predict the three distortion parameters (i.e., <inline-formula> <tex-math notation="LaTeX">$\sigma _{G}$ </tex-math></inline-formula> for Gaussian blur, <inline-formula> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> for JPEG compression, and <inline-formula> <tex-math notation="LaTeX">$\bar {\sigma }_{N}$ </tex-math></inline-formula> for white noise) by learning the different NSS features for different distortion types and combinations. In the final stage, the three estimated distortion parameter values are mapped and combined into an overall quality estimate based on quality-mapping curves and the most-apparent-distortion strategy. Experimental results tested on three multiply distorted and seven singly distorted image quality databases demonstrate that the proposed MUSIQUE algorithm can achieve better/competitive performance as compared with other state-of-the-art FR/NR IQA algorithms.
Functional Informed Fiber Tracking Using Combination of Diffusion and Functional MRI

Fiber tractography using diffusion weighted MRI (DWI) is a primary tool for mapping structural connectivity in the human brain in vivo. However, this method suffers from a number of inherent limitations that have a significant impact on its capability in faithfully constructing fiber bundles for specific function. In this paper, a novel tractography algorithm combining DWI and functional MRI (fMRI) was proposed. Specifically, a spatio-temporal correlation tensor that characterizes the anisotropy of fMRI signals in white matter was introduced to complement the estimation of fiber orientation density function from DWI. The proposed method has been demonstrated to identify functional pathways implicated in fMRI task. It can effectively follow tracts in the genu of the corpus callosum that connects to the frontal lobe cortex, obtain connections between the thalamus and the anterior insula under sensory simulation, and reconstruct optic radiations in the visual circuit under visual stimulation. Taken together, the method we proposed in this work may benefit our understanding of structure-function relations in the human brain.
Samuel Hartlib’s influence on the early history of The Royal Society

In a valuable article1 on ‘The origins of the Royal Society’ Miss R. H. Syfret considers various possible influences on the foundation of the Royal Society, among them that of John Amos Comenius and his group of friends in England, particularly Samuel Hartlib and Theodore Haak. She mentions also J. V. Andreae, to whose writings Comenius owed much, but comes to the conclusion that he was only one of many influences on the schemes of what she calls the Comenian group and that his connexion with the Royal Society, depending as it does on his relation to the Comenian group and then on their relation to the Royal Society, is at best remote and indirect.2 She then considers the question of the connexion between the Comenian group and the group that in 1645 began the meetings which led to the foundation of the Royal Society, and finds enough circumstantial evidence to show some connexion between the two groups.3 She holds that the publication of Comenius’s Via Lucis in 1668 and its dedication to the Royal Society supports, in a general way but by no means exclusively, the supposition of a connexion between the two groups.4 She believes that, if the Invisible College were indeed, as has always been assumed, Dr Wallis’s scientific group, this fact would point quite conclusively to the Royal Society’s origins in the schemes of Samuel Hartlib ; but, after considering various points about the Invisible College, she concludes that it seems that it was something quite different from that group.5
Typhoidal Salmonella: Distinctive virulence factors and pathogenesis

Although nontyphoidal Salmonella (NTS; including Salmonella Typhimurium) mainly cause gastroenteritis, typhoidal serovars (Salmonella Typhi and Salmonella Paratyphi A) cause typhoid fever, the treatment of which is threatened by increasing drug resistance. Our understanding of S. Typhi infection in human remains poorly understood, likely due to the host restriction of typhoidal strains and the subsequent popularity of the S. Typhimurium mouse typhoid model. However, translating findings with S. Typhimurium across to S. Typhi has some limitations. Notably, S. Typhi has specific virulence factors, including typhoid toxin and Vi antigen, involved in symptom development and immune evasion, respectively. In addition to unique virulence factors, both typhoidal and NTS rely on two pathogenicity‐island encoded type III secretion systems (T3SS), the SPI‐1 and SPI‐2 T3SS, for invasion and intracellular replication. Marked differences have been observed in terms of T3SS regulation in response to bile, oxygen, and fever‐like temperatures. Moreover, approximately half of effectors found in S. Typhimurium are either absent or pseudogenes in S. Typhi, with most of the remaining exhibiting sequence variation. Typhoidal‐specific T3SS effectors have also been described. This review discusses what is known about the pathogenesis of typhoidal Salmonella with emphasis on unique behaviours and key differences when compared with S. Typhimurium.
Sparse Weighted Naive Bayes Classifier for Efficient Classification of Categorical Data

Feature selection has become a key challenge in machine learning with the rapid growth of data size in real-world applications. However, existing feature selection methods mainly focus on numeric data, which will lead to quality loss when handling classification problems involving categorical variables. In this paper, we proposed an improvement of Bayesian Classifier with the sparse regression technology. To the best of our knowledge, this is the first attempt to extend sparse regression for directly process of categorical variables. We implemented the idea for the case of weighted naive Bayes classifier. The introduction of L1 regularized learning ensures the algorithm to retain only a minimal subset of variables for model building, while at the same time achieves a near-optimal decision hyper-plane, which leads to excellent performance in case of high dimensional or small sample size situations. We carried out benchmark test on five UCI benchmark categorical data sets, which proved that the proposed algorithm have competitive performances over the original weighted naive bayes classifier and several state-of-the-art feature selection methods including L1 logistic regression and SVM-RFE.
Transfer Pretrained Sentence Encoder to Sentiment Classification

Deep learning has been demonstrated to be very effective in many difficult tasks of computer vision(CV) and natural language processing(NLP). But this usually depends on a large number of available training samples to learn, and powerful computing resources (like GPU). In the tasks of computer vision, multiple deep layers of the model are initialized with weights pre-trained on the large-scale datasets like ImageNet, which improves the training speed of the network but does not reduce the performance of the network. Inspired by this, in this paper, in order to demonstrate that transfer is also effective in natural language processing(NLP), we transfer the encoder parts of two models trained under different tasks of NLP to sentiment classification. In the result, we can find that even if we use a simple classifier, we still get good accuracy. And we propose a persuasive explanation for the phenomenon that why the similarity of the representation vectors obtained by different encoders under the same sentence is not high.
Capn5 Expression in the Healthy and Regenerating Zebrafish Retina

Purpose Autosomal dominant neovascular inflammatory vitreoretinopathy (ADNIV) is a devastating inherited autoimmune disease of the eye that displays features commonly seen in other eye diseases, such as retinitis pigmentosa and diabetic retinopathy. ADNIV is caused by a gain of function mutation in Calpain-5 (CAPN5), a calcium dependent cysteine protease. Very little is known about the normal function of Capn5 in the adult retina, and there are conflicting results regarding its role during mammalian embryonic development. The zebrafish (Danio rerio) is an excellent animal model for studying vertebrate development and tissue regeneration, and represents a novel model to explore the function of Capn5 in the eye. Methods We characterized the expression of Capn5 in the developing zebrafish central nervous system (CNS) and retina, in the adult zebrafish retina, and in response to photoreceptor degeneration and regeneration using whole mount in situ hybridization, fluorescent in situ hybridization (FISH), and immunohistochemistry. Results In zebrafish, capn5 is strongly expressed in the developing embryonic brain, early optic vesicles, and in newly differentiated retinal photoreceptors. We found that expression of capn5 co-localized with cone specific markers in the adult zebrafish retina. We observed an increase in expression of Capn5 in a zebrafish model of chronic rod photoreceptor degeneration and regeneration. Acute light damage to the zebrafish retina, was accompanied by an increase in expression of Capn5 in the surviving cones and in a subset of Müller glia. Conclusions These studies suggest that Capn5 may play a role in CNS development, photoreceptor maintenance, and photoreceptor regeneration.
Visualization of the Intensity Field of a Focused Ultrasound Source In Situ

In an increasing number of applications of focused ultrasound (FUS) therapy, such as opening of the blood–brain barrier or collapsing microbubbles in a tumor, elevation of tissue temperature is not involved. In these cases, real-time visualization of the field distribution of the FUS source would allow localization of the FUS beam within the targeted tissue and allow repositioning of the FUS beam during tissue motion. In this paper, in order to visualize the FUS beam in situ, a 6-MHz single-element transducer ( $f$ /2) was used as the FUS source and aligned perpendicular to a linear array which passively received scattered ultrasound from the sample. An image of the reconstructed intensity field pattern of the FUS source using bistatic beamforming was then superimposed on a registered B-mode image of the sample acquired using the same linear array. The superimposed image is used to provide anatomical context of the FUS beam in the sample being treated. The intensity field pattern reconstructed from a homogeneous scattering phantom was compared with the field characteristics of the FUS source characterized by the wire technique. The beamwidth estimates at the FUS focus using the in situ reconstruction technique and the wire technique were 1.5 and 1.2 mm, respectively. The depth-of-field estimates for the in situ reconstruction technique and the wire technique were 11.8 and 16.8 mm, respectively. The FUS beams were also visualized in a two-layer phantom and a chicken breast. The novel reconstruction technique was able to accurately visualize the field of an FUS source in the context of the interrogated medium.
Predicting hospital admission at emergency department triage using machine learning

Objective To predict hospital admission at the time of ED triage using patient history in addition to information collected at triage. Methods This retrospective study included all adult ED visits between March 2014 and July 2017 from one academic and two community emergency rooms that resulted in either admission or discharge. A total of 972 variables were extracted per patient visit. Samples were randomly partitioned into training (80%), validation (10%), and test (10%) sets. We trained a series of nine binary classifiers using logistic regression (LR), gradient boosting (XGBoost), and deep neural networks (DNN) on three dataset types: one using only triage information, one using only patient history, and one using the full set of variables. Next, we tested the potential benefit of additional training samples by training models on increasing fractions of our data. Lastly, variables of importance were identified using information gain as a metric to create a low-dimensional model. Results A total of 560,486 patient visits were included in the study, with an overall admission risk of 29.7%. Models trained on triage information yielded a test AUC of 0.87 for LR (95% CI 0.86–0.87), 0.87 for XGBoost (95% CI 0.87–0.88) and 0.87 for DNN (95% CI 0.87–0.88). Models trained on patient history yielded an AUC of 0.86 for LR (95% CI 0.86–0.87), 0.87 for XGBoost (95% CI 0.87–0.87) and 0.87 for DNN (95% CI 0.87–0.88). Models trained on the full set of variables yielded an AUC of 0.91 for LR (95% CI 0.91–0.91), 0.92 for XGBoost (95% CI 0.92–0.93) and 0.92 for DNN (95% CI 0.92–0.92). All algorithms reached maximum performance at 50% of the training set or less. A low-dimensional XGBoost model built on ESI level, outpatient medication counts, demographics, and hospital usage statistics yielded an AUC of 0.91 (95% CI 0.91–0.91). Conclusion Machine learning can robustly predict hospital admission using triage information and patient history. The addition of historical information improves predictive performance significantly compared to using triage information alone, highlighting the need to incorporate these variables into prediction models.
Malaysian mental health law

The Malaysian Mental Health Act 2001 did not come into effect until the Mental Health Regulations 2010 came into force. The Act provides a framework for the delivery of comprehensive care, treatment, control, protection and rehabilitation of those with mental disorders. The Act governs the establishment of private and government psychiatric hospitals, psychiatric nursing homes and community mental health centres. This paper outlines the provisions of the Act and the Regulations.
Mitochondrial transcription and translation: overview

Mitochondria are the major source of ATP in the cell. Five multi-subunit complexes in the inner membrane of the organelle are involved in the oxidative phosphorylation required for ATP production. Thirteen subunits of these complexes are encoded by the mitochondrial genome often referred to as mtDNA. For this reason, the expression of mtDNA is vital for the assembly and functioning of the oxidative phosphorylation complexes. Defects of the mechanisms regulating mtDNA gene expression have been associated with deficiencies in assembly of these complexes, resulting in mitochondrial diseases. Recently, numerous factors involved in these processes have been identified and characterized leading to a deeper understanding of the mechanisms that underlie mitochondrial diseases.
Uncovering Discrete Synaptic Proteomes to Understand Neurological Disorders

The mammalian nervous system is an immensely heterogeneous organ composed of a diverse collection of neuronal types that interconnect in complex patterns. Synapses are highly specialized neuronal cell-cell junctions with common and distinct functional characteristics that are governed by their protein composition or synaptic proteomes. Even a single neuron can possess a wide-range of different synapse types and each synapse contains hundreds or even thousands of proteins. Many neurological disorders and diseases are caused by synaptic dysfunction within discrete neuronal populations. Mass spectrometry (MS)-based proteomic analysis has emerged as a powerful strategy to characterize synaptic proteomes and potentially identify disease driving synaptic alterations. However, most traditional synaptic proteomic analyses have been limited by molecular averaging of proteins from multiple types of neurons and synapses. Recently, several new strategies have emerged to tackle the ‘averaging problem’. In this review, we summarize recent advancements in our ability to characterize neuron-type specific and synapse-type specific proteomes and discuss strengths and limitations of these emerging analysis strategies.
Comparison of the Effect of Melatonin Treatment before and after Brain Ischemic Injury in the Inflammatory and Apoptotic Response in Aged Rats

Aging is associated with an increase in stroke risk. Melatonin, a potent free radical scavenger and broad spectrum antioxidant, has been shown to counteract inflammation and apoptosis in brain injury. However, little is known on the possible protective effects of melatonin in aged individuals affected by brain ischemia. Also, using melatonin before or after an ischemic stroke may result in significantly different molecular outcomes. The objective of the present study was to compare the effects of pre-ischemia vs. post-ischemia melatonin administration in an ischemic lesion in the cortex and hippocampus of senescent Wistar rats. An obstruction of the middle cerebral artery (MCA) to 18-month-old animals was performed. In general, animals treated with melatonin from 24 h prior to surgery until 7 days after the surgical procedure (PrevT) experienced a significant decrease in the levels of tumor necrosis factor-α (TNF-α), interleukin-1β (IL-1β), glial fibrillary acidic protein (GFAP), Bcl-2-associated death promoter (BAD), and Bcl-2-associated X protein (BAX) in both cortex and hippocampus, while hippocampal levels of sirtuin 1 (SIRT1) and B-cell lymphoma 2 (Bcl-2) increased. Treatment of animals with melatonin only after surgery (AT) resulted in similar effects, but to a lesser extent than in the PrevT group. In any case, melatonin acted as a valuable therapeutic agent protecting aged animals from the harmful effects of cerebral infarction.
Switching Stabilization for Type-2 Fuzzy Systems With Network-Induced Packet Losses

This paper is concerned with the stabilization problem of type-2 fuzzy systems with network-induced packet losses. By regarding the packet lost process as an unstable mode of a switched system, the stability of the system is then guaranteed with the aid of the mode-dependent average dwell time approach in the sense of the slow and fast switching. The discrete-time multiple discontinuous Lyapunov function is also utilized for the analysis. Two sufficient conditions regarding the stability and the stabilization of the system are proposed. The state-feedback matrices can be then calculated from the conditions to ensure the criterion that the packet-loss rate is no larger than a specific constant. Two practical examples are given to illustrate the feasibility and effectiveness of the proposed method.
Objective assessment of clinical, oncological and cosmetic outcomes following volume replacement in patients undergoing oncoplastic breast-conserving surgery: protocol for a systematic review

Introduction Oncoplastic breast surgery allows the excision of larger tumours without compromising cosmetic outcome and can be broadly divided into volume displacement and volume replacement techniques. Although oncoplastic surgery has rapidly gained acceptance and is now widely practised, evidence is still lacking especially in patients who underwent volume replacement techniques. As it is a relatively new technique that has been described in the literature in the recent years, a summary of evidence from this literature can help clinicians to understand the clinical, oncologicalandcosmetic outcomes of such procedures. Methods and analysis All original studies including randomised controlled trials, cohort studies, case–control studies and case series involving more than 10 women undergoing partial breast reconstruction using a volume replacement technique will be included. The primary objective is to evaluate the clinical, oncological and cosmetic outcomes following volume replacement in patients undergoing oncoplastic breast-conserving surgery. The secondary objective is to review the patient-reported outcomes (PROMs) associated with oncoplastic breast surgery to help identify any unmet needs and to consider refining the existing PROMs to suit women undergoing volume replacement surgery. A comprehensive literature search, eligibility assessment and extraction of data will be conducted by two trained teams acting independently. Data will be extracted and stored in a database with standardised extraction fields to facilitate easy and consistent data entry. Heterogeneity will be assessed using the Cochrane tests. Ethics and dissemination This systematic review requires no ethical approval. It will be published in a peer-reviewed journal, and it will also be presented at nationalandinternational conferences. PROSPERO registration number CRD42017075700; Pre-results.