Miracle-dz/UniFAR-FTUs huggingface dataset:
Repurposing of the β-Lactam Antibiotic, Ceftriaxone for Neurological Disorders: A Review

To date, there is no cure or disease-modifying agents available for most well-known neurological disorders. Current therapy is typically focused on relieving symptoms and supportive care in improving the quality of life of affected patients. Furthermore, the traditional de novo drug discovery technique is more challenging, particularly for neurological disorders. Therefore, the repurposing of existing drugs for these conditions is believed to be an efficient and dynamic approach that can substantially reduce the investments spent on drug development. Currently, there is emerging evidence that suggests the potential effect of a beta-lactam antibiotic, ceftriaxone (CEF), to alleviate the symptoms of different experimentally-induced neurological disorders: Parkinson's disease, Alzheimer's disease, amyotrophic lateral sclerosis, epileptic-seizure, brain ischemia, traumatic brain injuries, and neuropathic pain. CEF also affects the markers of oxidative status and neuroinflammation, glutamatergic systems as well as various aggregated toxic proteins involved in the pathogenesis of different neurological disorders. Moreover, it was found that CEF administration to drug dependent animal models improved the withdrawal symptoms upon drug discontinuation. Thus, this review aimed to describe the effects of CEF against multiple models of neurological illnesses, drug dependency, and withdrawal. It also emphasizes the possible mechanisms of neuroprotective actions of CEF with respective neurological maladies.
Usefulness of Blood Pressure Variability Indices Derived From 24‐Hour Ambulatory Blood Pressure Monitoring in Detecting Autonomic Failure

Background Increased blood pressure (BP) variability and nondipping status seen on 24‐hour ambulatory BP monitoring are often observed in autonomic failure (ATF). Methods and Results We assessed BP variability and nocturnal BP dipping in 273 patients undergoing ambulatory BP monitoring at Southwestern Medical Center between 2010 and 2017. SD, average real variability, and variation independent of mean were calculated from ambulatory BP monitoring. Patients were divided into a discovery cohort (n=201) and a validation cohort (n=72). ATF was confirmed by formal autonomic function test. In the discovery cohort, 24‐hour and nighttime average real variability, SD, and variation independent of mean did not differ significantly between ATF (n=25) and controls (n=176, all P>0.05). However, daytime SD, daytime coefficient of variation, and daytime variation independent of mean of systolic BP (SBP) were all significantly higher in patients with ATF than in controls in both discovery and validation cohorts. Nocturnal BP dipping was more blunted in ATF patients than controls in both cohorts (both P<0.01). Using the threshold of 16 mm Hg, daytime SD SBP yielded a sensitivity of 77% and specificity of 82% in detecting ATF in the validation cohort, whereas nondipping status had a sensitivity of 80% and specificity of 44%. The area under the receiver operator characteristic of daytime SD SBP was greater than the area under the receiver operator characteristic of nocturnal SBP dipping (0.79 [0.66‐0.91] versus 0.73 [0.58‐0.87], respectively). Conclusions Daytime SD of SBP is a better screening tool than nondipping status in detecting autonomic dysfunction.
Constant-Delay Enumeration for Nondeterministic Document Spanners

We consider the information extraction framework known as document spanners and study the problem of efficiently computing the results of the extraction from an input document, where the extraction task is described as a sequential variable-set automaton (VA). We pose this problem in the setting of enumeration algorithms, where we can first run a preprocessing phase and must then produce the results with a small delay between any two consecutive results. Our goal is to have an algorithm that is tractable in combined complexity, i.e., in the sizes of the input document and the VA, while ensuring the best possible data complexity bounds in the input document size, i.e., constant delay in the document size. Several recent works at PODS’18 proposed such algorithms but with linear delay in the document size or with an exponential dependency in size of the (generally nondeterministic) input VA. In particular, Florenzano et al. suggest that our desired runtime guarantees cannot be met for general sequential VAs. We refute this and show that, given a nondeterministic sequential VA and an input document, we can enumerate the mappings of the VA on the document with the following bounds: the preprocessing is linear in the document size and polynomial in the size of the VA, and the delay is independent of the document and polynomial in the size of the VA. The resulting algorithm thus achieves tractability in combined complexity and the best possible data complexity bounds. Moreover, it is rather easy to describe, particularly for the restricted case of so-called extended VAs. Finally, we evaluate our algorithm empirically using a prototype implementation.
Predicting Rice Grain Yield Based on Dynamic Changes in Vegetation Indexes during Early to Mid-Growth Stages

Predicting the grain yield during early to mid-growth stages is important for initial diagnosis of rice and quantitative regulation of topdressing. In this study, we conducted four experiments using different nitrogen (N) application rates (0–400 kg N∙ha−1) in three Japonica rice cultivars (Wuyunjing24, Ningjing4, and Lianjing7) grown in Jiangsu province, Eastern China, from 2015–2016. Spectral reflectance data were collected multiple times during early to mid-growth stages using an active mounted sensor (RapidScan CS-45, Holland Scientific Inc., Lincoln, NE, USA). Data were then used to calculate optimal vegetation indexes (normalized difference red edge, NDRE; normalized difference vegetation index, NDVI; ratio vegetation index, RVI; red-edge ratio vegetation index, RERVI), which were used to develop a dynamic change model and in-season grain yield prediction model. The NDRE index was more stable than other indexes (NDVI, RVI, RERVI), showing less standard deviation at the same N fertilizer rate. The R2 of the relationships between leaf area index (LAI), plant nitrogen accumulation (PNA), and NDRE also increased compared to other indexes. These findings suggest that NDRE is suitable for analysis of paddy rice N nutrition. According to real-time series changes in NDRE, the resulting dynamic model followed a sigmoid curve, with a coefficient of determination (R2) >0.9 and relative root-mean-square error <5%. Moreover, the feature platform value (saturation value, SV) of the NDRE-based model accurately predicted the differences between treatments and the final grain yield levels. R2 values of the relationship between SV and yield were >0.7. For every 0.1 increase in SV, grain yield increased by 3608.1 kg·ha−1. Overall, our new dynamic model effectively predicted grain yield at stem elongation and booting stages, providing real-time crop N nutrition data for management of N fertilizer topdressing in rice production.
Multi-Attribute Decision Making Method Based on Aggregated Neutrosophic Set

Multi-attribute decision-making refers to the decision-making problem of selecting the optimal alternative or sorting the scheme when considering multiple attributes, which is widely used in engineering design, economy, management and military, etc. But in real application, the attribute information of many objects is often inaccurate or uncertain, so it is very important for us to find a useful and efficient method to solve the problem. Neutrosophic set is proposed from philosophical point of view to handle inaccurate information efficiently, and a single-valued neutrosophic set (SVNS) is a special case of neutrosophic set, which is widely used in actual application fields. In this paper, a new method based on single-valued neutrosophic sets aggregation to solve multi-attribute decision making problem is proposed. Firstly, the neutrosophic decision matrix is obtained by expert assessment, a score function of single-valued neutrosophic sets (SVNSs) is defined to obtain the positive ideal solution (PIS) and the negative ideal solution (NIS). Then all alternatives are aggregated based on TOPSIS method to make decision. Finally numerical examples are given to verify the feasibility and rationality of the method.
Book Reviews

Books reviewed: A. Stewart Fotheringham and Michael Wegener (eds.), Spatial Models and GIS: New Potential and New Models Aura Reggiani (ed.), Spatial Economic Science: New Frontiers in Theory and Methodology Jean-Claude Thill (ed.), Geographic Information Systems in Transportation Research Barbara Dluhosch, Industrial Location and Economic Integration: Centrifugal and Centripetal Forces in the New Europe Jean-Marie Huriot and Jacques-Francois Thisse, Economics of Cities: Theoretical Perspectives Manfred M. Fischer and Yee Leung (eds.), GeoComputational Modelling: Techniques and Applications Eduardo A. Haddad, Regional Inequality and Structural Changes: Lessons from the Brazilian Experience Manfred M. Fischer, Luis Suarez-Villa, and Michael Steiner (eds.), Innovation, Networks and Localities Marjolein C.J. Caniels, Knowledge Spillovers and Economic Growth: Regional Growth Differentials across Europe Martin van der Velde and Henk van Houtum (eds.), Borders, Regions, and People Peter W. J. Batey and Peter Friedrich (eds.), Regional Competition John E. Anderson and Robert W. Wassmer, Bidding for Business: The Efficacy of Local Economic Development Incentives in a Metropolitan Area Philip Hanson and Michael Bradshaw (eds.), Regional Economic Change in Russia Gred Hise and William Deverell, Eden by Design: The 1930 Olmsted-Bartholomew Plan for the Los Angeles Region
Improved Automated Detection of Subpixel-Scale Inundation - Revised Dynamic Surface Water Extent (DSWE) Partial Surface Water Tests

In order to produce useful hydrologic and aquatic habitat data from the Landsat system, the U.S. Geological Survey has developed the “Dynamic Surface Water Extent” (DSWE) Landsat Science Product. DSWE will provide long-term, high-temporal resolution data on variations in inundation extent. The model used to generate DSWE is composed of five decision-rule based tests that do not require scene-based training. To allow its general application, required inputs are limited to the Landsat at-surface reflectance product and a digital elevation model. Unlike other Landsat-based water products, DSWE includes pixels that are only partially covered by water to increase inundation dynamics information content. Previously published DSWE model development included one wetland-focused test developed through visual inspection of field-collected Everglades spectra. A comparison of that test’s output against Everglades Depth Estimation Network (EDEN) in situ data confirmed the expectation that omission errors were a prime source of inaccuracy in vegetated environments. Further evaluation exposed a tendency toward commission error in coniferous forests. Improvements to the subpixel level “partial surface water” (PSW) component of DSWE was the focus of this research. Spectral mixture models were created from a variety of laboratory and image-derived endmembers. Based on the mixture modeling, a more “aggressive” PSW rule improved accuracy in herbaceous wetlands and reduced errors of commission elsewhere, while a second “conservative” test provides an alternative when commission errors must be minimized. Replication of the EDEN-based experiments using the revised PSW tests yielded a statistically significant increase in mean overall agreement (4%, p = 0.01, n = 50) and a statistically significant decrease (11%, p = 0.009, n = 50) in mean errors of omission. Because the developed spectral mixture models included image-derived vegetation endmembers and laboratory spectra for soil groups found across the US, simulations suggest where the revised DSWE PSW tests perform as they do in the Everglades and where they may prove problematic. Visual comparison of DSWE outputs with an unusual variety of coincidently collected images for locations spread throughout the US support conclusions drawn from Everglades quantitative analyses and highlight DSWE PSW component strengths and weaknesses.
Monte Carlo study on the detection of classical order by disorder in real antiferromagnetic Ising pyrochlores

We use Monte Carlo simulations to evaluate the feasibility of detecting thermal order by disorder in real antiferromagnetic Ising pyrochlores frustrated by a magnetic field applied in the $[110]$ direction. Building on an ideal system with only nearest-neighbour exchange interactions and a perfectly oriented field, we consider the effects of dipolar interactions and field misalignment. Our approach is special in that it relies more in the possibility to switch on and off the entropic drive towards order than in the absence of (or immunity to) a particular perturbation. It can then be applied, in principle, to other uncontrolled interactions expected to be naturally present in real materials. We establish the conditions under which entropic effects can be discerned from an interaction drive towards order, show how to use neutron scattering as a means to unveil this mechanism, and discuss possible materials where to test these ideas.
Machine Learning Methods That Economists Should Know About

We discuss the relevance of the recent machine learning (ML) literature for economics and econometrics. First we discuss the differences in goals, methods, and settings between the ML literature and the traditional econometrics and statistics literatures. Then we discuss some specific methods from the ML literature that we view as important for empirical researchers in economics. These include supervised learning methods for regression and classification, unsupervised learning methods, and matrix completion methods. Finally, we highlight newly developed methods at the intersection of ML and econometrics that typically perform better than either off-the-shelf ML or more traditional econometric methods when applied to particular classes of problems, including causal inference for average treatment effects, optimal policy estimation, and estimation of the counterfactual effect of price changes in consumer choice models.
Research on the Construction of Grid Marketing Team

Referencing the idea of collaboration and share of Grid, aiming at limitations of organization structure in traditional marketing model, this paper defines the concept of Grid Marketing Team. After analyzing the characteristics of Grid Marketing Team of virtual, dynamic, collaborative and efficient, the paper proposes the organization structure and operation model between Grid Marketing Team and Marketing Grid, and discusses the standardization management issues of Grid Marketing Team during operation.
ADMM-IDNN: Iteratively Double-reweighted Nuclear Norm Algorithm for Group-prior based Nonconvex Compressed Sensing via ADMM

Group-prior based regularization method has led to great successes in various image processing tasks, which can usually be considered as a low-rank matrix minimization problem. As a widely used surrogate function of low-rank, the nuclear norm based convex surrogate usually lead to over-shrinking phenomena, since the nuclear norm shrinks the rank components (singular value) simultaneously. In this paper, we propose a novel Group-prior based nonconvex image compressive sensing (CS) reconstruction framework via a family of nonconvex nuclear norms functions which contain common concave and monotonically properties. To solve the resulting nonconvex nuclear norm minimization (NNM) problem, we develop a Group based iteratively double-reweighted nuclear norm algorithm (IDNN) via an alternating direction method of multipliers (ADMM) framework. Our proposed algorithm can convert the nonconvex nuclear norms optimization problem into a double-reweighted singular value thresholding (DSVT) problem. Extensive experiments demonstrate our proposed framework achieved favorable reconstruction performance compared with current state-of-the-art convex methods.
Treat to target approach for asthma

Abstract Recognition that about half of asthma deaths might be preventable if recommended guidelines are followed suggests that better implementation of established management strategies is needed. However, to achieve a further substantive reduction in asthma mortality, novel strategies will also be required. It is well established that asthma is a disease of chronic inflammation, with episodes of worsening inflammation associated with increased symptoms and/or exacerbations; however, current guidelines paradoxically recommend that initial treatment is only symptomatic, rather than directed at the underlying inflammatory mechanism. The “Treat to target” (TTT) approach has become a popular concept in the medical management of several common chronic conditions, including rheumatoid arthritis (RA), diabetes, hypertension and hyperlipidemia. For example, as part of a TTT approach, rheumatologists recommend methotrexate for RA with onset within 6 months. Applying the TTT approach to asthma, the primary target could be clinical remission and the primary goals as follows: eliminate symptoms and exacerbation risk; prevent airway remodeling; and normalize lung function. To construct a TTT algorithm for chronic asthma, the proposal is to eradicate short-acting β2-agonists (SABA) at all asthma severity levels and replace SABA with “Anti-Inflammatory Reliever Therapy” (AIR), using inhaled corticosteroids (ICS)/SABA or ICS/formoterol. For individuals with equal to or less than 12 months’ history of symptoms, fewer than two symptoms per month, no exacerbations in the last 12 months and normal lung function, the recommendation is early initiation of ICS/SABA or ICS/formoterol as AIR.
Near focus NBI endoscopy plus acetic acid for optical polyp characterization in the colorectum - A proof of principle study

Abstract Background and study aim: Optical polyp characterization (OPC) in the colorectum is an upcoming challenge for endoscopists. Narrow band imanging (NBI) has been proposed to be helpful for OPC. However, data from clinical studies have shown that quality of OPC differs markedly between endoscopists. The aim of this study was to test the value of a combined NBI plus acetic acid (NBI + AA) approach for OPC in the colorectum. Patients and methods: This was a prospective, single-arm study at a tertiary referral center in Germany. The study was designed as a proof of principle study. Initially polyps were characterized using High-definition white light (HDWL) only. Additionally, the same polyps were investigated using NBI + AA (1.5% solution) in order to predict polyp pathology in a real time setting. The near focus function was used for both HDWL and NBI + AA assessment. The primary endpoint was accuracy of colorectal polyp prediction when using NBI + AA. Results: A total of 63 polyps were detected in 55 patients. NBI + AA based accuracy of real-time predictions was 85.5% compared to 80.6% using HDWL (p = .450). Accuracy was 90.2% in the high confidence setting for both NBI + AA and HDWL predictions. A higher share of polyps were assessed with high confidence when using NBI + AA compared to HDWL (p = .006). The use of NBI + AA led to a better identification of polyp margins (p < .001) compared to HDWL. Conclusions: The use of acetic acid led to a high level of accuracy and confidence in the prediction of polyp histology. These data justify further investigation in a randomized controlled study.
Access, accountability, and the proliferation of psychological therapy: On the introduction of the IAPT initiative and the transformation of mental healthcare

Psychological therapy today plays a key role in UK public mental health. In large part, this has been through the development of the (specifically English) Improving Access to Psychological Therapies (IAPT) programme. Through IAPT, millions of citizens have encountered interventions such as cognitive behaviour therapy, largely for the treatment of depression and anxiety. This article interrogates how this national response to problems of mental ill-health – and the problematization itself – was developed, accounted for, and sustained. By imbricating economic expertise with accounts of mental ill-health and mechanisms of treatment, IAPT has revivified psychological framings of pathology and therapy. However, it has done so in ways that are more familiar within biomedical contexts (e.g. through recourse to randomized controlled trial studies). Today, the initiative is a principal player in relation to which other services are increasingly developed. Indeed, in many respects IAPT has transformed from content to context within UK public mental health (in a process of what I term ‘contextification’). By documenting these developments, this paper contributes to re-centring questions about the place and role of psychology in contemporary healthcare. Doing so helps to complicate assumptions about the dominance of linear forms of (de)biomedicalization in health-systems.
Colonic crypts are natural gatekeepers of microbial metabolites to protect stem cells.

Gut microbiota is co-evolving with their mammalian hosts for over 15 million years in a mutual symbiotic relationship. While the cellular and molecular mechanisms are currently unknown, they rise to the occasion in adapting to each other’s requirements in a fascinating way. For example, during the third trimester of pregnancy, gut microbiota undergoes a complete makeover to produce more fats by increasing Proteobacteria and Actinobacteria to support the growing baby and pregnant mother (1). The critical nature of gut microbiota is now highly appreciated in development and maintenance of immune, neural, vascular as well as in metabolic systems of the host. Microbiota is considered as a special evolving organ within humans. Several genetic and external factors such as mutations, inflammation, infections, diet, antibiotics are known to affect the dynamics of microbial communities in the intestine and play a critical role in modulating both innate and adaptive immune systems. Several efforts have been made and others are currently underway to define complex microbiota-host interactions (good vs . bad; cause vs . consequence) using animal models. More importantly, it is becoming clear that excessive usage of antibiotics at every stage of human life might have tremendously contributed to the increased metabolic disorders in human populations.
LogoPhish: A New Two-Dimensional Code Phishing Attack Detection Method

Traditional phishing detection methods are mostly based on computer platforms and cannot be directly applied to mobile devices. This paper proposes a new two-dimensional code phishing detection method called LogoPhish. We use the logo to determine the identity between a two-dimensional code's actual identity and the described identity. The method includes two processes: logo extraction and identity detection. The first process uses a mobile phone to scan the two-dimensional code to extract the logo and perform image processing. The second process uses the Google image search engine to determine the identity of the logo. Since the relationship between the logo and the domain name is exclusive, it is reasonable to use the domain name as an identifier. The experimental results show that LogoPhish has a good effect and is superior to the traditional detection method in the detection of two-dimensional code phishing attacks.