Miracle-dz/UniFAR-FTUs huggingface dataset:
Interindividual Variability and Differential Tissue Abundance of Mitochondrial Amidoxime Reducing Component Enzymes in Humans

Mitochondrial amidoxime-reducing component (mARC) enzymes are molybdenum-containing proteins that metabolize a number of endobiotics and xenobiotics. The interindividual variability and differential tissue abundance of mARC1 and mARC2 were quantified using targeted proteomics in three types of tissue fractions: 1) pediatric liver tissue homogenates, 2) total membrane fraction of the paired liver and kidney samples from pediatric and adult donors, and 3) pooled S9 fractions of the liver, intestine, kidney, lung, and heart. The absolute levels of mARC1 and mARC2 in the pediatric liver homogenate were 40.08 ± 4.26 and 24.58 ± 4.02 pmol/mg homogenate protein, respectively, and were independent of age and sex. In the total membrane fraction of the paired liver and kidney samples, the abundance of hepatic mARC1 and mARC2 was comparable, whereas mARC2 abundance in the kidney was approximately 9-fold higher in comparison with mARC1. The analysis of the third set of samples (i.e., S9 fraction) revealed that mARC1 abundance in the kidney, intestine, and lung was 5- to 13-fold lower than the liver S9 abundance, whereas mARC2 abundance was approximately 3- and 16-fold lower in the intestine and lung than the liver S9, respectively. In contrast, the kidney mARC2 abundance in the S9 fraction was approximately 2.5-fold higher as compared with the hepatic mARC2 abundance. The abundance of mARC enzymes in the heart was below the limit of quantification (∼0.6 pmol/mg protein). The mARC enzyme abundance data presented here can be used to develop physiologically based pharmacokinetic models for the prediction of in vivo pharmacokinetics of mARC substrates. SIGNIFICANCE STATEMENT A precise targeted quantitative proteomics method was developed and applied to quantify newly discovered drug-metabolizing enzymes, mARC1 and mARC2, in pediatric and adult tissue samples. The data suggest that mARC enzymes are ubiquitously expressed in an isoform-specific manner in the human liver, kidney, intestine, and lung, and the enzyme abundance is not associated with age and sex. These data are important for developing physiologically based pharmacokinetic models for the prediction of in vivo pharmacokinetics of mARC substrates.
Treatment outside the Recommended Guidelines for Retinopathy of Prematurity (ROP): Prevalence, Characteristics, and Issues

This study aims to assess the prevalence and characteristics of preterm infants with retinopathy of prematurity (ROP) treated outside the recommended guidelines. In this retrospective monocentric cohort, we included all premature children treated in our department for ROP by laser photoablation or anti-VEGF intravitreal injection. The main outcome was treatment of both eyes for ROP less severe than pre-threshold type 1, treated outside ETROP guidelines. A total of 114 children received treatment for ROP in our department, among whom 32 (28.1%) children received treatment for indications outside the ETROP guidelines for both eyes. The indications outside the guidelines were persistent stage 2 or 3 ROP that showed no evidence of regression after 41 weeks of corrected gestational age (11 children; 34.4%), pre-plus stage (11; 34.4%), difficulties in disease staging (7; 21.9%), type 2 ROP with plus disease (2; 6.2%), and treatment due to logistical difficulties (1; 3.1%; hospitalized in neonatal units hundreds of miles away from our department, with no fundus examination possible in the neonatal unit). To resume, in our cohort, 28.1% of children received treatment for ROP less severe than pre-threshold type 1 both eyes. The main indications for off-label treatment were the persistence of active ROP during follow-up and the presence of pre-plus-stage disease. Our data suggest the need to update ROP treatment criteria to reflect real-life practices. Additional studies are required in order to evaluate the long-term benefits and side effects of treatments outside the recommended indications, and to establish revised treatment guidelines.
On differential group-delay statistics for polarization-mode dispersion emulators

We present a theoretical study on differential group-delay (DGD) statistics for common models of polarization-mode dispersion (PMD) emulators. Our study will show, for the first time to our knowledge, that the statistics for the length of the PMD vector will not necessarily behave as a system with three degrees of freedom when the number of sections in the PMD emulator is low. However, when the number of sections is large, i.e., 10 sections or more, the length of the PMD vector is well described with a system with three degrees of freedom.
A depth-averaged debris-flow model that includes the effects of evolving dilatancy. II. Numerical predictions and experimental tests

We evaluate a new depth-averaged mathematical model that is designed to simulate all stages of debris-flow motion, from initiation to deposition. A companion paper shows how the model's five governing equations describe simultaneous evolution of flow thickness, solid volume fraction, basal pore-fluid pressure and two components of flow momentum. Each equation contains a source term that represents the influence of state-dependent granular dilatancy. Here, we recapitulate the equations and analyse their eigenstructure to show that they form a hyperbolic system with desirable stability properties. To solve the equations, we use a shock-capturing numerical scheme with adaptive mesh refinement, implemented in an open-source software package we call D-Claw. As tests of D-Claw, we compare model output with results from two sets of large-scale debris-flow experiments. One set focuses on flow initiation from landslides triggered by rising pore-water pressures, and the other focuses on downstream flow dynamics, runout and deposition. D-Claw performs well in predicting evolution of flow speeds, thicknesses and basal pore-fluid pressures measured in each type of experiment. Computational results illustrate the critical role of dilatancy in linking coevolution of the solid volume fraction and pore-fluid pressure, which mediates basal Coulomb friction and thereby regulates debris-flow dynamics.
MulTiNet: Multimodal Neural Networks for Glaucoma Based on Transfer Learning

Background: Being one of the most serious causes of irreversible blindness, glaucoma has many subtypes and complex symptoms. In clinic, doctors usually need to use a variety of medical images for diagnosis. Optical Coherence Tomography (OCT), Visual Field (VF) , Fundus Photosexams (FP) and Ultrasonic BioMicroscope (UBM) are widely-used and complementary techniques for diagnosing glaucoma.Methods: At present, the field of intelligent diagnosis of glaucoma is limited by two major problems. One is the small number of data sets, and the other is the low diagnostic accuracy of Single-Modal Modal. In order to solve the above two problems, we have done the following work. First, we construct DualSY glaucoma multimodal data set. The four most important subtypes of glaucoma are discussed in this article which are Primary Open Angle Glaucoma (POAG), Primary Angle Closure Glaucoma (PACG), Primary Angle Closure Suspect (PACS) and Primary Angle Closure (PAC). Each patient in the DualSY data set contains more than five medical images, as shown in the figure 4.And DualSY are labeled with image-level multi-labels. Second, We propose a new Multi-Modal classification network for glaucoma, which is a multiclass classification model with various medical images of glaucoma patients and text information as input. The network structure consists of three main branches to deal with patient metadata, domain-based glaucoma features and medical images. Transfer learning method is introduced into this paper due to the small number of medical image data sets. The flowchart is shown in Figure 5.Result: Our method on glaucoma diagnosis outperforms state-of-the-art methods. A promising average result of overall accuracy (ACC) of 94.7% is obtained. Our data set outperformed most data sets in glaucoma diagnosis with an accuracy of 87.8%. Conclusions: The results suggest that medical images such as Heidelberg OCT and three-dimensional fundus photos used in this paper can better express the high-level information of glaucoma and our modal greatly improve the accuracy of glaucoma diagnosis. At the same time, this data set has great potential, and we continue to study this data.
Technical, allocative and economic efficiencies in sugarcane production in Pakistan: A non-parametric approach

The objective of this paper is to measure the technical, allocative and economic efficiencies of sugarcane farms in Pakistan. These efficiencies are measured by data envelopment analysis method using the farm level data collected from 333 sugarcane growers of Pakistan in 2008. The results show that the average technical efficiency of the sugarcane farms is 75 percent, the average allocative efficiency is 83 percent, and the average cost efficiency is 62 percent. The results indicate that the sugarcane farms of Pakistan can reduce the costs of production by 38 percent to produce the same level of output by using the inputs in optimal proportion and by improving the technical efficiency. The results also show that the existing level of sugarcane production can potentially be increased by 33.33 percent with the same level of inputs by improving the technical efficiency of the sugarcane farms.
High gain planar antenna using optimised partially reflective surfaces

A high gain planar antenna has been investigated, using an optimised partially reflecting surface (PRS) placed in front of a waveguide aperture in a ground plane. The antenna performance is initially related to the reflection characteristics of the PRS array following an approximate analysis. The array geometry is optimised using an analytical formula. The optimisation results are verified using a full wave model taking into account the edge effects. The array size for maximum antenna efficiency has also been investigated.
A comparative analysis of text embedding approach to extract named entities in Portuguese legal documents

A petição inicial é um dos componentes mais importantes de um processo civil, de modo que a automatização da análise desses documentos pode diminuir o tempo necessário para que se cumpra a fase postulatória. O corpo de qualificação das partes, por sua vez, é a seção nesse documento onde são expostas as informações a respeito das entidades envolvidas no processo. Este artigo propõe o uso de técnicas de extração de entidades nomeadas no problema de identificação e extração de informações em petições iniciais. Para tal, foi produzida uma base de dados composta por corpos de qualificação das partes de petições iniciais extraídas de processos advindos de tribunais brasileiros. Foram treinados, avaliados e comparados sete modelos BiLSTM-CRF com combinações distintas de representações vetoriais de palavras, a fim de se investigar seus efeitos na performance de um algoritmo com essa arquitetura e, dessa forma, aprimorar o reconhecimento de entidades jurídicas em textos legais. Ao contrário de outros trabalhos baseados em BiLSTM-CRF para tarefas de NER no domínio jurídico, esta pesquisa dá ênfase não às arquiteturas empregadas, mas sim aos métodos de representação de texto usados. Os experimentos executados com o corpus desenvolvido mostram que o empilhamento de incorporações de caracteres, palavras e pooled FLAIR embeddings é a combinação preferível para extrair-se o melhor desempenho possível de modelos híbridos BiLSTM-CRF.
Tumor Volume Determination Technique Using Pixel Filling and Discrete Integral Function

The subject of tumor has generated lots of interest from the medical point of view and recently from engineering/research perspective owing to advancement in medical equipment and increased computerization in many areas including medical field. This paper discusses a post segmentation tumor volume determination technique that employs mainly pixel filling, and the application of discrete integral function to determine tumor volume. The prior steps taken involve finding pixel area resolution, image field of view (FOV), then FFT-Based interpolation to achieve smaller decimation of tumor area, and finally the use of discrete integral function (DIF) to accurately determine tumor volume. The result of the work will enhance the field of oncology in general, and image guided surgery (IGS) in particular.
Deep Learning Approaches for Fashion Knowledge Extraction From Social Media: A Review

Fashion knowledge encourages people to properly dress and faces not only physiological necessity of users, but also the requirement of social practices and activities. It usually includes three jointly related aspects of: occasion, person and clothing. Nowadays, social media platforms allow users to interact with each other online to share opinions and information. The use of social media sites such as Instagram has already spread to almost every fashion brand and been evaluated as business take-off tools. With the heightened use of social media as a means of marketing communication for fashion brands, it has become necessary to empirically analyse and extract fashion knowledge from them. Thus, social brands are investing on them. In this way, they can understand the consumer’s preferences. This change is also having a significant impact on social media data analysis. To solve this issue, the Deep learning (DL) methods are proven to be effective solutions due to their automatic learning capability. However, little systematic work currently exists on how researchers have applied DL for analysing fashion knowledge from social media data. Hence, this contribution outlines DL-based techniques for social media data related to fashion domain. In this study, a review of the dataset within the fashion world and the DL methods applied on, it is presented to help out new researchers interested in this subject. In particular, five different tasks will be considered: Object Detection, that includes Clothes Landmark Detection, Clothes Parsing and Product Retrieval, Fashion Classification, Clothes Generation, Automatic Fashion Knowledge Extraction and Clothes Recommendation. Therefore, the purpose of this paper is to underline the multiple applications within the fashion world using deep learning techniques. However, this review does not cover all the methods used: in fact, only Deep Learning methods have been analyzed. This choice was made since, given the huge amount of fashion social media data that has been collected, Deep Learning methods achieve the best performance both in terms of accuracy and time. Limitations point towards unexplored areas for future investigations, serving as useful guidelines for future research directions.
Optimization of pigment production by Rhodotorula minuta URM 5197 and Rhodotorula mucilaginosa URM 7409 using yellow passion fruit peel (Passiflora edulis)

The objective of this work was to optimize the production of pigments by Rhodotorula minuta and Rhodotorula mucilaginosa through submerged fermentation, using yellow passion fruit peel (Passiflora edulis), as the only substrate. The independent variables evaluated to optimize were: yellow passion fruit peel (YPFP), in grams (g) as culture medium, pH and fermentation time, in days. The study of pigment production and its optimization was carried out using the Doehlert matrix, with fifteen experimental conditions, of which thirteen had different combinations and two repeated the central point. The fixed variables were 30ºC and 150 rpm. Data analysis was performed using the Statistica software version 10.0. The largest amount of total pigments and total carotenoids produced by R. minuta was 28±0.01 mg/L and 72.8±0.026 µg/g, respectively; while for R. mucilaginosa the production of total pigments was 37±0.002 mg/L and total carotenoids 236.8±0.013 µg/g. The optimum production point of total pigments for R. minuta was 2.3g of YPFP, pH 6.5 and 5 days and for R. mucilaginosa 2.5g of YPFP pH 6 and 5 days. In samples of total pigments, the presence of 0.29mg/L of β-carotene for R. minuta and 0.83 mg/L for R. mucilaginosa was identified. It is possible to conclude that yellow passion fruit peel can be used as a nutrient source for Rhodotorula spp growth and pigment production with total carotenoids and β-carotene in its composition.
Differentially-Fed Patch Antenna with Electrically Large Property Based on TM03 Mode Using SISL Technology

The proposed SISL differential-fed patch antenna based on TM03 mode in this paper realize the improvement of the realized gain and the sidelobe level. Differential feed is used to suppress even-order modes to achieve effective broadside radiation. The reverse current step of the TM30 mode is broken by introducing the transverse slots. As the current path increases, the resonance frequency decreases, the compressibility increases so the directivity increase. The simulated results shows that a fractional bandwidth of 21.1% from 21.51 to 26.59 GHz and the peak realized gain without transition is 10.3 dBi and the side lobe level is reduced to −7 dB. Morever, the cross-polarization is better than −40 dB.
A simple realisation scheme for OTA-C universal biquadratic filter

A simple scheme for realising four standard second-order voltage transfer functions, viz., low-pass, highpass, bandpass, and band-elimination is presented. It is implemented by using only operational transconductance amplifiers and capacitors to realise two universal biquadratic filters (UBFs), i.e. UBF-I and UBF-II. The UBF-I can also be converted into a sinusoidal oscillator by simply grounding a suitable node. The circuits have the attractive features of wide-range electronic tunability of its pole frequency, bandwidth and pole Q; these can also be programmed digitally. In addition, the filters enjoy excellent sensitivity performance and are suitable for monolithic MOS or Bi-MOS implementation. The effects of non-ideal buffer on the performance of the circuits are studied.
The Effect of Salt on the Gelling Properties and Protein Phosphorylation of Surimi-Crabmeat Mixed Gels

The effects of different salt additions (1.0%, 1.5%, 2.0%, 2.5%, 3.0%, and 3.5%) on the gelling properties and protein phosphorylation of the mixed gels (MG) formed by silver carp (Hypophthalmichthys molitrix) surimi with 10% crabmeat were investigated. The MG’s breaking force, deformation, gel strength, and water-holding capacity (WHC) increased as the salt concentration increased. The intrinsic fluorescence intensity of the samples initially decreased and then increased, reaching the lowest when the NaCl concentration was 2.5%. The result of SDS–polyacrylamide gel electrophoresis indicated that large aggregates were formed by protein–protein interaction in the MG containing 2.5% or 3.0% NaCl, decreasing the protein band intensity. It was also found that with the addition of NaCl, the phosphorus content initially increased and then decreased, reaching the maximum when the NaCl concentration was 2% or 2.5%, which was similar to the changing trend of actin band intensity reported in the results of Western blot. These results revealed that the amount of salt used had a significant effect on the degree of phosphorylation of the MG protein. The increase in phosphorylation was linked to improved gelling properties, which could lead to new ideas for manufacturing low-salt surimi products in the future.
Effect of non-uniform traffic distributions on performance of a cellular CDMA system

This paper presents a study of the effect of nonuniform user traffic distributions on the performance of a CDMA cellular system. While, there have been many studies showing the capacity gains of CDMA over narrowband TDMA systems, most of these studies assume a uniform distribution of the user population across the coverage area. When compared to the more general case of non-uniform user distributions, the uniform user distribution leads to lower multi-user interference and consequently higher system capacity. Hence, use of these models can lead to overestimation of system capacity. Three non-uniform user density models are developed and used to analyze the system's performance in typical CDMA implementation scenarios. The results indicate that intra-cell multi-user interference dominates the total interference levels in most cases. As expected cell splitting is shown to improve the system performance. The most significant improvements in performance with cell-splitting are seen for a Gaussian user density model (when compared to linear or exponential user density models). The results provide additional insight into the performance of CDMA systems, and are useful for the purposes of network planning and resource allocation.
Two color QWIP and extended wavebands

Since 2002, the THALES Group has been manufacturing sensitive arrays using QWIP technology based on GaAs and related III-V compounds, at THALES Research and Technology Laboratory. The QWIP technology allows the realization of large staring arrays for Thermal Imagers (TI) working in the long-wave infrared (LWIR) band (8-12 μm). In the past researchers claimed many advantages of QWIPs. Uniformity was one of these and has been the key parameter for the production to start. The 640x512 LWIR focal plane arrays (FPAs) with 20μm pitch was the demonstration that state of the art performances can be achieved even with small pixels. This opened the field for the realization of usable and affordable megapixel FPAs. Thales Research & Technology (TRT) has been developing third generation GaAs LWIR QWIP arrays for volume manufacture of high performance low cost thermal imaging cameras. In the past, another widely claimed advantage for QWIPs was the so-called band-gap engineering and versatility of the III-V processing allowing the custom design of quantum structures to fulfil the requirements of specific applications such as very long wavelength (VLWIR) or multispectral detection. In this presentation, we present the performances of both our first 384x288, 25 μm pitch, MWIR (3-5μm) / LWIR (8-9 μm) dual-band FPAs, and the current status of QWIPs for MWIR (< 5μm) and VLWIR (>15μm) arrays.
Modeling Energy-Aware Photoplethysmography Hardware for Personalized Health Care Applications Across Skin Phototypes

Photoplethysmography (PPG) sensing is a popular optical method used to determine blood oxygen saturation and heart rate based on light reflected from a patient's skin. Both health metrics are useful in detecting COVID-19 in asymptomatic patients but remain impacted by physiological differences in individuals. In the context of wearable self-powered devices, PPG sensing is relatively high-power compared to available on-body energy. This paper presents a PPG sensing model, with a transimpedance amplifier (TIA) to demonstrate power, signaling, and design tradeoffs, and a photodiode model that includes the impact of a patient's skin phototype on reflected light and PPG sensing accuracy. It also presents preliminary measured results from on-body testing with existing hardware to verify the power and ability to extract a PPG signal at those power levels. This model demonstrates the need to first identify the minimum allowable photodiode current that can produce accurate results for each skin phototype and then determine user-specific circuit knobs to achieve personalized PPG sensing with optimized power consumption.