Miracle-dz/UniFAR-FTUs huggingface dataset:
A novel model of dermatomyositis induced by membrane antigen and calciphylaxis in rats

Dermatomyositis (DM) is a severe autoimmune disease of the connective tissue characterized by inflammatory and degenerative changes in the skin and muscle. However, the lack of experimental models of DM represents a challenge for the development of effective drugs. The aim of the present study was to establish a pharmacodynamic rat model of DM that would recapitulate the clinical manifestation seen in patients. The DM model was established using membrane antigen-induced autoimmune injury, followed by toxin-induced subcutaneous calciphylaxis. The rats were divided into five groups and were subcutaneously injected with membrane antigen. Of these, four antigen-immunized groups then received dihydrotestosterone (DHT), iron-dextrin (Fe-Dex), polymyxin (PMX) either individually or in combination to induce cutaneous calciphylaxis. The clinical manifestation score, ratio of infiltrated lymphocytes, ratio of arteriole calcified nodules in skeletal muscles, serum antibody levels [anti-histidyl tRNA synthetase (Jo-1) and anti-melanoma differentiation-associated protein 5 (MDA5)] and serum cytokine levels [tumor necrosis factor-α (TNF-α) and interferon-γ (IFN-γ)] were then detected. The results demonstrated that all five autoimmune groups displayed local cutaneous swelling and weakness, increased serum antibody and cytokine levels, and T lymphocyte infiltration in perimysial and perivascular sites. Moreover, pathological changes indicative of calciphylaxis were observed in the PMX and DHT + Fe-Dex + PMX. Among all groups, the rats in the PMX and DHT + Fe-Dex + PMX displayed characteristics most closely resembling those of DM pathogenesis in patients. In conclusion, membrane antigen immunization combined with toxin-induced calciphylaxis can be used as a DM model in rats. This model may be used for the development of effective drugs for DM treatment.
Skeleton-Based Positioning and Reference Point Deployment for Hybrid Wireless Localization

Nowadays, Wi-Fi or Bluetooth based indoor localization algorithms are designed to improve the effects from variation of receive signal strength (RSS) at different locations. However, the positioning accuracy of targeting device is highly related to the indoor topological layout, e.g., the blocking effects from partitioning walls. In this paper we propose a novel skeleton-based positioning and reference point deployment for hybrid wireless localization (S-PDH) that utilizes the spatial skeleton of indoor layout to improve location estimation accuracy. Firstly, we adopt an automatic map analysis algorithm to generate appropriate reference points (RPs) from the spatial skeleton of indoor layout. Secondly, we propose a novel clustering algorithm to consider both spatial layout and RSS information in order to enhance the accuracy of RP matching. Those outlier RPs resulting poor performance will be replaced by appropriate deployment of BLE APs. Finally, the cluster-based positioning algorithm is presented to estimate the target’s location. Compared to existing methods, both the simulation and experimental results show that the proposed S-PDH system can achieve better positioning accuracy along with the benefits of automatically generating the spatial layout information for wireless indoor localization.
Framework for a Predictive Progress Model – case of infrastructure projects

ABSTRACT Project progress is an apprehension for every project, as it indicates how the project is likely to meet the associated milestones. Utilizing historical data from archived projects can assist managers in predicting project progress. By leveraging the power of data analytics, this research attempts to highlight data trends based on data collected from 279 infrastructure projects in the UAE. Specifically, this research rigorously analyses the relationships between project budget, duration, and progress using K-means clustering techniques and hypothesis testing. We then provide predictive models using Autoregressive Integrated Moving Average – ARIMùA and Multivariate regression models that allow managers to predict with a 99.15% accuracy the monthly progress of an infrastructure project over the next three months. This research provides project managers with a comprehensive framework that combines data analytics techniques with agility practices to predict short-term project progress to take proactive measures on different influencing factors.
General Seismic Architecture of the Southern San Andreas Fault Zone around the Thousand Palms Oasis from a Large-N Nodal Array

We discuss general structural features of the Banning and Mission Creek strands (BF and MCF) of the southern San Andreas fault (SSAF) in the Coachella Valley, based on ambient noise and earthquake wavefields recorded by a seismic array with >300 nodes. Earthquake P arrivals show rapid changes in waveform characteristics over 20–40 m zones that coincide with the surface BF and MCF. These variations indicate that the BF and MCF are high-impedance contrast interfaces—an observation supported by the presence of seismic reflections. Another prominent but more diffuse change in SSAF structure is found ∼1 km northeast of the BF. This feature has average-to-low arrival times (P and S) and ambient noise levels (at <30 Hz), and likely represents a relatively fast velocity block sandwiched between broader MCF and BF zones. The maximal arrival delays (P ∼0.1 s and S ∼0.25 s) and the highest ambient noise levels (>2 times median) are consistently observed southwest of the BF—a combined effect of Coachella Valley sediments and rock damage on that side. Immediately northeast of the MCF, large S minus P delays suggest a broad high VP/VS zone associated with asymmetric rock damage across the SSAF. This general overview shows the BF and MCF as mature but distinctly different fault zones. Future analyses will further clarify these and other SSAF features in greater detail.
The Impact of the COVID-19 Pandemic on Greek Tourism-Updates and Comparisons

This paper updates and compares the results obtained in Papanikos’ (2020a) paper published in the Athens Journal of Tourism, which estimated the impact of the current pandemic on international Greek tourism receipts and tourist arrivals. The COVID-19 pandemic continues to have negative effects on Greek tourism and the Greek economy. In this paper, newly released data by the Bank of Greece for the entire 2021 period are used to assess the impact on Greek tourism during the two pandemic years of 2020 and 2021. In addition, the actual data from 2020 are compared with the estimates of the three scenarios made in the 2020 paper. One conclusion that emerges from the analysis of the data on international tourism is that in 2021 Greek tourism did much better than in 2020, but still is far away from the record year of 2019. In 2021, international tourist arrivals and international tourism receipts accounted for 48% and 58% of the 2019 values respectively. Keywords: pandemic, COVID-19, crisis, tourism, Greece, economic impact
The Study of Security Issues for the Industrial Control Systems Communication Protocols

With the depth integration of informatization and industrialization and the rapid development of the Internet, the industrial control systems (ICS) communication protocols' risks have become increasingly prominent. This paper firstly analyzes the security issues of the current mainstream industrial control systems communication protocols, such as Modbus, DNP3 and OPC. Then the security recommendations are raised against such issues. The safety of the whole industrial control network can be improved by improving the security of communication protocols. K eywords: ICS protocols; Modbus; DNP3; OPC; security
Training Method for Accurate Off‐Chip Training of One‐Selector‐One‐Resistor Crossbar Array with Nonlinearity and Wire Resistance

This work provides an off‐chip training method for a one‐selector‐one‐resistor (1S1R) crossbar array (CBA) device with wire resistance (rcc) and nonlinear conductance (g i,j) of 1S1R devices for hardware neural network (HNN) applications. An iterative method is introduced to calculate the node voltages of the 1S1R CBA, which arises from the variable voltage drop through the wires with rcc and g i,j. Several mathematical approximations are introduced for fast and efficient calculation. The proposed method trains the HNN to have an inference accuracy of 85.9%, whereas the inference accuracy of HNN without the rcc consideration drops to 38.5%. The inference running time with the proposed method is 1% of the HSPICE‐based simulation for the given HNN structure. As the rcc increases, the inference accuracy declines due to the decreased device voltage from the target values. The worst voltage model is adopted to identify the design factors that affected the accuracy. A CBA with a size almost three times larger can be used for the HNN if the rcc is appropriately addressed under the given device conditions.
A 23‐ to 28‐GHz 5‐bit switch‐type phase shifter with 1‐bit calibration based on optimized ABCD matrix design methods for 5G MIMO system in 0.15‐μm GaAs

The fifth‐generation (5G) mobile communication system is being developed to provide ultra‐high‐speed and large‐capacity wireless communication services. However, due to the large transmission loss in the air for high‐frequency signals, it is necessary to use large‐scale array antennas to achieve adaptive control of antenna directivity to compensate for the loss. This paper reports a passive phase shifter operating at 23–28 GHz. The proposed 5‐bit switch‐type phase shifter with 1‐bit calibration is designed in a 0.15‐μm GaAs process. A new design method based on the optimized ABCD matrix and impedance matching techniques between a chip and a printed circuit board (PCB) is developed to obtain better radio‐frequency (RF) signal transmission. The proposed phase shifter features a good‐phase performance with a measured root‐mean‐square (RMS) phase error of less than 10° across 23–28 GHz. For all the phase shift states, the insertion loss is 18 ± 2 dB at 28 GHz and the RMS gain error is less than 2.1 dB over 23–28 GHz including pads and RF connector loss. The phase shifter consumes no DC power and occupies a chip area of 2 × 0.7 mm2 including all the pads.
Long-Term Use of Proton Pump Inhibitors in Cancer Patients: An Opinion Paper

Simple Summary Proton pump inhibitors are frequently used in cancer patients to alleviate some symptoms, epigastric pain or heartburn. However, acid suppression decreases the absorption of some oral-targeted anticancer treatments (tyrosine kinase inhibitors, CDK4/6 inhibitors) and induces changes in the gut microbiome. Recent data are showing that these interactions have important clinical impacts and medical oncologists and patients must be aware of these possible interactions. Abstract Multikinase inhibitors (MKIs), and particularly tyrosine kinase inhibitors (TKIs) and immune checkpoint inhibitors (CPIs), are currently some of the major breakthroughs in cancer treatment. Proton pump inhibitors (PPIs) revolutionised the treatment of acid-related diseases, but are frequently overused for epigastric pain or heartburn. However, long-term acid suppression from using PPIs may lead to safety concerns, and could have a greater impact in cancer patients undergoing therapy, like bone fractures, renal toxicities, enteric infections, and micronutrient deficiencies (iron and magnesium). Moreover, acid suppression may also affect the pharmacokinetics of drugs (at least during acid suppression) and decrease the absorption of many molecularly-targeted anticancer therapies, which are mostly weak bases with pH-dependent absorption. This type of drug-drug interaction may have detrimental effects on efficacy, with major clinical impacts described for some orally administrated targeted therapies (erlotinib, gefitinib, pazopanib, palbociclib), and conflicting results with many others, including capecitabine. Furthermore, the long-term use of PPIs results in severe alterations to the gut microbiome and recent retrospective analyses have shown that the benefit of using CPIs was suppressed in patients treated with PPIs. These very expensive drugs are of great importance because of their efficacy. As the use of PPIs is not essential, we must apply the precautionary principle. All these data should encourage medical oncologists to refrain from prescribing PPIs, explaining to patients the risks of interaction in order to prevent inappropriate prescription by another physician.
Paramyrothecium eichhorniae sp. nov., Causing Leaf Blight Disease of Water Hyacinth from Thailand

Abstract Paramyrothecium eichhorniae sp. nov. was observed and collected from Chiang Mai and Phetchaburi Provinces, Thailand. This new species is introduced based on morphological and molecular evidence. This fungus is characterized by its production of sporodochium conidiomata with a white setose fringe surrounding an olivaceous green to dark green slimy mass of conidia, penicillately branched conidiophores, and aseptate and cylindrical to ellipsoid conidia. Phylogenetic analyses of combined LSU rDNA, ITS rDNA, tef1, rpb2, tub2 and cmdA sequence data using maximum parsimony, maximum likelihood and Bayesian approaches placed the fungus in a strongly supported clade with other Paramyrothecium species in Stachybotryaceae (Hypocreales, Sordariomycetes). The descriptions of the species are accompanied by illustrations of morphological features, and a discussion of the related taxa is presented.
Dashboard for Machine Learning Models in Health Care

: To trust and use machine learning (ML) models in health settings, decision-makers need to understand the model's performance. Yet, there has been little agreement on what information should be visualized to present models' evaluations. This work presents an approach to construct a dashboard used to visualize supervised ML models for health care applications. The dashboard shows the models' statistical evaluations, feature importance, and sensitivity analysis.
Imaging Subsurface Fracture and Void using Wide-Band Inversion of Fresnel Interpolated Wavepath Seismic Tomography

Studying existence of fracture, void as well as fault in subsurface are important and it has wide applications. In addition, fracture and void play important role in reservoir productivity as well as in geotechnical application. The problem in imaging fracture and void in subsurface, however, is resolution. It needs high-resolution imaging method laterally as well as vertically. The seismic velocity imaging, however, usually has poor lateral resolution. Seismic tomography provides better resolution than conventional velocity analysis methods. Conventional raypath seismic tomography, however, needs dense as well as high angle and wide measurement configuration that is high cost in field data acquisition. I propose to use Fresnel interpolated wavepath (FIW) wide-band inversion. FIW is an interpolation between imaginary part of Rhytov scattering wavepath and raypath. Then, FIW is combined with wide band inversion procedure to handle sparseness configuration of measurement. By this method, smooth constraint is implemented more naturally by based on wave's spectrum. Furthermore, it can produce more stable inversion procedure. In this paper, Wide-band inversion of FIW tomography is tested into various seismic field data in order to image void, fault and fracture. The results showed that tomography based on FIW could handle the limitation of configuration angle, sparseness configuration and resulting good image. We use many difficult cases of field data such as: subsurface tunnel imaging and also void-fracture imaging in volcanic rock
Partially Separable Aspects of Spatial and Temporal Estimations in Virtual Navigation as Revealed by Adaptation

Recent studies claim that estimating the magnitude of the spatial and temporal aspects of one's self-motion shows similar characteristics, suggesting shared processing mechanisms between these two dimensions. While the estimation of other magnitude dimensions, such as size, number, and duration, exhibits negative aftereffects after prolonged exposure to the stimulus, it remains to be elucidated whether this could occur similarly in the estimation of the distance travelled and time elapsed during one's self-motion. We sought to fill this gap by examining the effects of adaptation on distance and time estimation using a virtual navigation task. We found that a negative aftereffect occurred in the distance reproduction task after repeated exposure to self-motion with a fixed travel distance. No such aftereffect occurred in the time reproduction task after repeated exposure to self-motion with a fixed elapsed time. Further, the aftereffect in distance reproduction occurred only when the distance of the adapting stimulus was fixed, suggesting that it did not reflect adaptation to time, which varied with distance. The estimation of spatial and temporal aspects of self-motion is thus processed by partially separable mechanisms, with the distance estimation being similar to the estimation of other magnitude dimensions.
A Comprehensive Evaluation of Metabolomics Data Preprocessing Methods for Deep Learning

Machine learning has greatly advanced over the past decade, owing to advances in algorithmic innovations, hardware acceleration, and benchmark datasets to train on domains such as computer vision, natural-language processing, and more recently the life sciences. In particular, the subfield of machine learning known as deep learning has found applications in genomics, proteomics, and metabolomics. However, a thorough assessment of how the data preprocessing methods required for the analysis of life science data affect the performance of deep learning is lacking. This work contributes to filling that gap by assessing the impact of commonly used as well as newly developed methods employed in data preprocessing workflows for metabolomics that span from raw data to processed data. The results from these analyses are summarized into a set of best practices that can be used by researchers as a starting point for downstream classification and reconstruction tasks using deep learning.
New Facets of Quality. A Multiple Case Study of Green Cosmetic Manufacturers

Despite the recent concern about social, environmental and economic perspectives of sustainability, the issue is relatively under-researched and very little is known about the drivers of sustainability. There is no doubt that innovation is one of those drivers. The aim of the present article is to analyse the technological (product and process) and non-technological (marketing and organisational) innovation, R&D strategy, marketing and competitiveness position of the chemical companies that are engaged in the manufacture of green, natural or conventional cosmetics with the ultimate aim of achieving high quality products and business excellence. The study reported here is qualitative in approach and uses empirical evidence from six case studies representing companies located in Catalonia, Spain, one of the main cosmetic producers of Europe. The results are discussed in the light of structural and managerial perspectives with the aim of drawing implications for both practitioners and policy-makers.
In vitro antibacterial activity of extracts and alkaloid fraction of Prosopis juliflora (Sw.) DC. leaves

In the present work, phytochemical screening and in vitro antibacterial activity of extracts of Prosopis juliflora (Sw.) DC. leaves were carried out against various bacteria like Bacillus cereus, Escherichia coli, Klebsiella pneumonia, Salmonella typhimurium, Streptococcus agalactiae and Staphylococcus aureus. Chloroform, methanol and aqueous extracts were prepared using the Soxhlet extraction method. Per cent yield was calculated for each extract. Phytochemical screening was carried out for all the extracts. Antibacterial activity of all three extracts and crude alkaloid fraction of P. juliflora leaves were carried out using disc diffusion assay. Standard antibacterial discs were used to compare the antibacterial activity of the extracts. Additionally, the antibacterial activity of extract after removal of chlorophyll was a lso evaluated. Aqueous extract showed the highest extractability. All the extracts showed the presence of alkaloids. Aqueous extract and crude alkaloid fraction of P. juliflora leaves showed the highest in vitro antibacterial activity against all the bacteria as compared to chloroform, methanol and aqueous extracts. Methanol extract of P. juliflora leaves showed significantly higher antibacterial activity against all the bacteria as compared to extract without removal of chlorophyll. Results showed that alkaloids present in the P. juliflora leaves had shown antibacterial activity. Thus, P. juliflora leaves can be a good source of antibacterial phytochemicals. Further study related to structural identification may be helpful to explore the potential of the active phytochemicals present in the P. juliflora leaves. Copyright © 2021 Ukaaz Publications. All rights reserved. Email: ukaaz@yahoo.com; Website: www.ukaazpublications. com Journal of Phytonanotechnology and Pharmaceutical Sciences 1(4): 6-9, 2021 Online ISSN:2583-0376 http://jpps.ukaazpublications. com DOI: http://dx.doi. org/10.54085/jpps.2021. 1.4. 2 Journal of Phytonanotechnology and Pharmaceutical Sciences Corresponding author: Dr. Urvesh D. Patel Associate Professor, Department of Veterinary Pharmacology and Toxicology, College of Veterinary Science and Animal Husbandry, Junagadh-362 001, Kamdhenu University, Gujarat, India E-mail: urvesh1981@yahoo.com Tel. : +91-2852670722 Original Article : Open Access
All-digital intelligent control system of LED plant light supplement lamp

An all-digital multi-parameter programmable control system of LED plant light supplement lamp is proposed.In the control system,the control box,the intelligent driver and LED lamp are designed based on CPLD.According to the actual situation,the data transfer protocol between the intelligent driver and the control box is set.Functional and timing simulation is done to the control system.The results show that the system can control red and blue color brightness of different address LED lamps to reach the preset light intensity gray value according to the light quality requirements of different plants,or of the same plants at different growth stages.Finally,the corresponding PWM control signal is output to meet the light supplement of plant.
Toward customer hyper-personalization experience — A data-driven approach

Abstract Today’s omnichannel business models incorporate physical and digital touchpoints interacting with customers. A hyper-personalization strategy relies on the organization’s capability to gather and transform customer data into personalized experiences; therefore, when a hyper-personalization organizational plan is put in place, it serves two main functions: to deliver personalized experiences and increase the number of customers receiving such experiences. For this to happen, four elements are required for a hyper-personalization strategy: data foundation, decisions, design, and distribution. While customer master data management relies on the correct identification of a customer, a real customer insight can only be achieved when three types of customer data are gathered: Identity, Contactability, and Traceability (I, C, T)- fulfilling the first element of a hyper-strategy. This article aims to identify the benefits in the total number of customers that can receive a hyper-personalization strategy when real-time touchpoints are linked to a customer Master Data Management that integrates the three types of customer data.