Miracle-dz/UniFAR-FTUs huggingface dataset:
Phytoplankton size distributions in the western North Atlantic and their seasonal variability

Phytoplankton play a major role on Earth, impacting the global distribution and cycles of carbon, oxygen, nitrogen, sulfur, and other elements, and structuring marine food webs. One fundamental trait of phytoplankton with direct biogeochemical implications is their size, as it governs metabolic and sinking rates as well as prey–predator interactions. Phytoplankton size spans approximately 3.5 orders of magnitude (when expressed as an equivalent spherical diameter), and thus measuring the full range in size distribution of phytoplankton is challenging and rarely attempted. Here, we constructed phytoplankton size spectra by merging state‐of‐the‐art cytometry and imaging cytometry measurements that were collected in the western North Atlantic Ocean, along a latitudinal gradient (36°N to 55°N) and during different phases of the annual cycle of phytoplankton. The derived spectra show a seasonal pattern that parallels changes in phytoplankton biomass, and do not always follow a commonly assumed power‐law model. Shifts in size spectra were more pronounced in the sub‐Arctic and temperate subregions, compared to the subtropical region of the study area. We evaluated the relationships between different size groups and environmental parameters to derive ecologically meaningful size groups. Finally, to simulate Ocean Color remote‐sensing algorithms of phytoplankton size, we compared temporal variations in descriptors of the size spectra (median particle size, phytoplankton size distribution exponent) with optical size proxies derived from light absorption and attenuation; good agreement was observed in the northern sections of the study area where temporal changes in community size structure were more pronounced.
Concordance of ICD-10 Codes and the Clinical Diagnosis of Alcoholic Hepatitis

INTRODUCTION: Many studies on alcoholic hepatitis (AH) use the International Classification of Disease (ICD) coding to identify patients. Data regarding the diagnostic accuracy of ICD codes for AH are limited. METHODS: A total of 151 patients with ICD-10 codes for AH were reviewed for the presence or absence of AH using standardized diagnostic criteria. RESULTS: Sixty-eight of the 151 patients met AH criteria, corresponding to a positive predictive value of 45%. Patients with AH experienced higher model for end-stage liver disease and mortality than those who did not (P < 0.05). DISCUSSION: Our results suggest ICD-10 codes are not reliable for identifying AH. Studies using the ICD codes should be interpreted cautiously.
Scale-Train: A Scalable DNN Training Framework for a Heterogeneous GPU Cloud

In order to cope with the growing scale of deep neural network (DNN) models and training data, the use of cloud computing for distributed DNN training is becoming increasingly popular. The amount of available resources in a cloud continuously changes according to users’ demands. Although distributed DNN training has a long execution time ranging from several hours to several days, existing frameworks cannot provide a dynamic scale function or have high scale in/out overhead. Therefore, it is difficult to achieve higher performance by adding graphics processing unit (GPU) nodes to a running training cluster, even when surplus GPU resources become available. In addition, the inability to dynamically reconfigure the training cluster prohibits the reform of the cluster topology when it was sub-optimally created. This paper proposes a dynamic scaling technique with which the dynamic addition and removal of new workers can be performed without suspending the ongoing training job. In addition, we propose a heterogeneity-aware straggler-proof technique so that, even when the performance of the GPUs in the cloud are uneven, a performance benefit can be guaranteed through the addition of the surplus resources. The proposed scheme improved throughput by up to a factor of 17.52 during scaling out the existing cluster of five workers to ten compared to the existing checkpoint-based scheme. Furthermore, training was continued at 95.52% of the maximum performance while being stopped for 841 seconds in Elastic Horovod, which supports dynamic scaling. Finally, even when GPUs of different performances were mixed, the error between the determined batch size and the optimal batch size was 3.37% on average.
Distributed Filtering With Dynamic Event-Triggered Communication for Networked Periodic Systems

This article deals with the problem of distributed filtering of periodic systems over sensor networks under event-triggered communications, based on a piecewise impulsive method. The continuous-time periodic system is approximated by several linear time-invariant subsystems through a piecewise constant method. The sensor network deployed to the periodic system is taken into account for the actual needs of monitoring or estimation. A dynamic event-triggering mechanism is designed to reduce the unnecessary data transmission over the wireless network from the sensor to the filter as well as between each filter. Distributed peak-to-peak filters with periodic time-varying parameters are designed under the event-triggering mechanism to estimate the networked continuous-time periodic systems with the bounded and local differentiable disturbance. Then, a periodic piecewise filtering error system is modeled as an impulsive periodic piecewise system, for which sufficient conditions of a guaranteed peak-to-peak performance are proposed. The gains of the filters are obtained by the method of convex linearization. The validity of the proposed results is demonstrated by a pendulum tracking system.
Logistics Service Supply Chain Model Applying Artificial Intelligence and Big Data Analysis

The logistics service chain is a specialized service chain produced along with the continuous update and development of the logistics industry. It is an inevitable direction choice that is naturally formed when the degree of logistics specialization continues to increase. This paper aims to study how to analyze and study the logistics service supply chain model based on artificial intelligence and big data analysis, and describe the data mining algorithm. This paper puts forward the problem of logistics service supply chain, which is based on big data analysis, and then elaborates around its related concepts and algorithms, and conducts case design and analysis on supplier selection. The experimental results show that the customer satisfaction of supplier C has reached 86%, the accuracy rate of supplier D's logistics operations is as high as 90%, and the logistics transportation cost of supplier B is only 0.38 yuan/ton ∗ km. In the end, the three of them were selected as company Z's transportation suppliers.
Signaling pathways of inflammation in myocardial ischemia/reperfusion injury

Abstract Primary percutaneous coronary intervention is the current standard treatment for myocardial infarction, but is associated with ischemia/reperfusion injury for which inflammation is an important part. This review summaries the recent findings in the study of pro- and anti-inflammatory signaling pathways (eg, TLR4/Myd88/NF-κB, MAPKs/NF-κB, NLRP3 inflammasome, JAK2/STAT3, and Nrf2/HO-1) in myocardial ischemia/reperfusion injury, as well as relevant drug development efforts. Despite of the vast body of literature, no concrete advances have been made in translating the new knowledge into clinical practice, but we do anticipate major breakthroughs in the foreseeable future.
Chemical Evolution of R-process Elements in Stars (CERES). I. Stellar parameters and chemical abundances from Na to Zr

Aims. The Chemical Evolution of R -process Elements in Stars (CERES) project aims to provide a homogeneous analysis of a sample of metal-poor stars ([Fe / H] < –1.5). We present the stellar parameters and the chemical abundances of elements up to Zr for a sample of 52 giant stars. Methods. We relied on a sample of high signal-to-noise UVES spectra. We determined stellar parameters from Gaia photometry and parallaxes. Chemical abundances were derived using spectrum synthesis and model atmospheres. Results. We determined chemical abundances of 26 species of 18 elements: Na, Mg, Al, Si, Ca, Sc, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Sr, Y, and Zr. For several stars, we were able to measure both neutral and ionised species, including Si, Sc, Mn, and Zr. We have roughly doubled the number of measurements of Cu for stars at [Fe / H] ≤ − 2 . 5. The homogeneity of the sample made it possible to highlight the presence of two Zn-rich stars ([Zn / Fe] ∼ + 0.7), one r -rich and the other r -poor. We report the existence of two branches in the [Zn / Fe] versus [Ni / Fe] plane and suggest that the high [Zn / Fe] branch is the result of hypernova nucleosynthesis. We discovered two stars with peculiar light neutron-capture abundance patterns: CES1237 + 1922 (also known as BS 16085-0050), which is ∼ 1dex underabundant in Sr, Y, and Zr with respect to the other stars in the sample, and CES2250-4057 (also known as HE 2247-4113), which shows a ∼ 1dex overabundance of Sr with respect to Y and Zr. hydrodynamical e ﬀ ects into account.
Type-2 Cytokines Promote the Secretion of the Eosinophil–Attractant CCL26 by Intestinal Epithelial Cells in Food-Sensitized Patients

Several inflammatory processes of the bowel are characterized by an accumulation of eosinophils at inflammation sites. The mechanisms that govern mucosal infiltration with eosinophils are not fully understood. In this work, we studied the colorectal polyp-confined tissue containing eosinophils and we hypothesized that intestinal epithelial cells are the cell source of eotaxin-3 or CCL26, a potent chemoattractant for eosinophils. We analyzed colorectal polyps (n=50) from pediatric patients with rectal bleeding by H&E staining and eosin staining, and different pro-inflammatory cytokines were assessed by RT-qPCR and ELISA. IgE and CCL26 were investigated by RT-qPCR, ELISA and confocal microscopy. Finally, the intracellular signaling pathway that mediates the CCL26 production was analyzed using a kinase array and immunoblotting in human intestinal Caco-2 cell line. We found a dense cell agglomeration within the polyps, with a significantly higher frequency of eosinophils than in control adjacent tissue. IL-4 and IL-13 were significantly up-regulated in polyps and CCL26 was elevated in the epithelial compartment. Experiments with Caco-2 cells showed that the type-2 cytokine IL-13 increased STAT3 and STAT6 phosphorylation and eotaxin-3 secretion. The addition of the blocking antibody Dupilumab or the inhibitor Ruxolitinib to the cytokine-stimulated Caco-2 cells diminished the CCL26 secretion to basal levels in a dose-dependent manner. In conclusion, our findings demonstrate a high frequency of eosinophils, and elevated levels of type-2 cytokines and eotaxin-3 in the inflammatory stroma of colorectal polyps from pediatric patients. Polyp epithelial cells showed to be the main cell source of CCL26, and IL-13 was the main trigger of this chemokine through the activation of the STAT3/STAT6/JAK1-2 pathway. We suggest that the epithelial compartment actively participates in the recruitment of eosinophils to the colonic polyp-confined inflammatory environment.
Blood Glucose Prediction for Type 1 Diabetes using Machine Learning Long Short-term Memory based models for blood glucose prediction

In this thesis, walk forward testing is used to evaluate the performance of two long short-term memory (LSTM) models for predicting blood glucose values for patients with type 1 diabetes. The models are compared with a support vector regression (SVR) model as well as with an auto regressive integrated moving average (ARIMA) model, both of which have been used in related research within the area. The best performing long short-term model produces results equal to those of the SVR model and it outperforms the ARIMA model for all prediction horizons. In contrast to models in related research, this LSTM model also has the ability to assign a level of confidence to each prediction, adding an edge in practical usability.
Study on Air Quality Index, Atmospheric Pollutants and Dry Deposition of PCDD/Fs in the Ambient Air near Southwest China

This study investigated the AQI (air quality index), atmospheric pollutants (PM 2.5 , NO 2 , and O 3 ), and dry deposition of PCDD/Fs in Chengdu and Chongqing, near southwest China from 2020 to 2021. The results showed that the implementation of strict epidemic prevention action led to a significant improvement in the air quality in 2020. However, the air quality index increased again in 2021 as the economic activity was recovered. In February 2020, at the height of the epidemic, the monthly average PM 2.5 and NO 2 concentrations were estimated as 50.9 µ g m –3 and 21.2 ppb, respectively in in Chengdu, and 48.9 µg m –3 and 22.9 ppb, respectively in Chongqing. In February 2021, when the economy was returned to normal, the monthly average PM 2.5 and NO 2 concentrations were 60.3 µ g m –3 and 36.2 ppb respectively in Chengdu and 52.4 µ g m –3 and 35.1 ppb, respectively in Chongqing. In addition, since O 3 concentrations were influenced by the VOCs–NO x ratio, the reduction of NO x emissions and the increase of VOC emissions during the epidemic control period caused a change in the VOCs–NO x ratio, and thus leading to an increase in O 3 concentrations. The monthly average concentration of O 3 from March 2021 to May 2021 was significantly lower than that of 2020. Furthermore, the dry deposition flux of PCDD/Fs also changed in the period before and after the epidemic. The dry deposition flux of PCDD/Fs in 2021 was significantly higher than that of 2020, which was associated with the fuel consumption of various industrial plants. This study indeed provided useful information for the contribution of scientific communities and important data bank for the future air quality control.
Multiview Facial Expression Recognition, A Survey

Multiview Facial Expression Recognition (MFER) is a well-known interdisciplinary problem among computer science and related disciplines with promising and valuable applications. Recognizing the facial expression in pose variations, which is very common in real-world conditions, makes it very challenging. This paper aims to provide a comprehensive survey of the MFER progress, includingboth categories of traditional and deep approaches. In general, we sort each of these categories into three overall groups to meet the pose variations: Pose-Robust Features, Pose Normalization, and Pose-Specific Classification. While reviewing the traditional methods, a thorough study is proposed on the existing novel deep techniques. We also introduce the state-of-the-art and discuss the challenges, limitations, opportunities, and future trends that need to be addressed in this field. Moreover, we provide an extensive review of publicly available datasets for MFER, including the labs’ collections and the sets gathered from in the wild. Besides, we introduce the most popular protocols on each dataset to standardize comparisons in the future.
Labor Market Tightness during WWI and the Post-War Recession of 1920-1921

The U.S. economy entered the 1920s with a robust job market and high inflation but fell into a recession following the Federal Reserve’s discount rate hikes to tame inflation. Using a newly constructed data set, we study labor market dynamics during this period. We find that labor markets were tight when the Federal Reserve began tightening monetary policy, but they became loose following the tightening as the recession deepened. The demand-supply imbalance in the labor market was driven by a sharp decline in the number of job openings. We also show that the recession had an uneven effect on labor markets across sectors and by gender.
Molecular Dynamics Simulation of a tRNA-Leucine Dimer with an A3243G Heteroplasmy Mutation in Human Mitochondria Using a Secondary Structure Prediction Approach

Mitochondrial DNA mutations, such as A3243G, can affect changes in the structure of biomolecules, resulting in changes in the structure of Leucine transfer Ribose Nucleic Acid to form a dimer. Dimer structure modeling is needed to determine the properties of the structure. However, the lack of a structure template for the transfer of Ribose Nucleic Acid (tRNA) is challenging for the modeling of mutant structures of tRNA, especially mitochondrial tRNA that are susceptible to mutation. Therefore, this study predicted the structure of mitochondrial leucine tRNA and its stability through a knowledge-based method and molecular dynamics. Structural modeling and initial assessment were performed using RNAComposer and MolProbity, HNADOCK, and Discovery studios to form the dimer structure. Molecular dynamics simulations for stability analysis were performed using Amber and AmberTools20 software, showing that the conformational energy of the mutant leucine tRNA dimer structure was lower than the native structure. Moreover, the Root Mean Square Deviation (RMSD) of monomer native leucine tRNA was lower than the mutant, indicating that the dimer structure of mutant leucine tRNA is more stable than usual, and the normal leucine tRNA is more stable than the mutant.
Hybrid Precoding for Millimeter Wave MIMO: Trace Optimization Approach

The hybrid precoding problem in point-to-point millimeter wave (mmWave) multiple input multiple output (MIMO) for narrowband channel has been established as a Frobenius norm minimization problem. It is translated into trace minimization problem, and two algorithms are proposed to solve it. In the first method based on alternating minimization, we alternately determine the analog precoder and digital precoder, keeping the other constant to minimize the trace. The analog precoding subproblem with the fixed digital precoder is converted into a semi-definite programming (SDP) problem and solved by block coordinate descent (BCD) algorithm with suitable modifications. In the second method, we segregate the analog precoding and digital precoding subproblems by considering orthogonality of analog precoder. The analog precoding is further rephrased as a trace maximization problem and solved by an iterative power method by enforcing orthogonality constraint on the analog precoder. The adapted form of modified Gramm-Schmidt orthogonalization procedure is employed to impose orthogonality on the analog precoder. The proposed methods are extended for wideband channel by considering orthogonal frequency division multiplexing (OFDM). The proposed methods not only exhibit a good performance but also come with lower computational complexity when compared to existing methods with comparable performances.
Thrombin Activity in Rodent and Human Skin: Modified by Inflammation and Correlates with Innervation

Thrombin is present in peripheral nerves and is involved in the pathogenesis of neuropathy. We evaluated thrombin activity in skin punch biopsies taken from the paws of male mice and rats and from the legs of patients with suspected small-fiber neuropathy (SFN). In mice, inflammation was induced focally by subcutaneous adjuvant injection to one paw and systemically by intraperitoneal lipopolysaccharides (LPS) administration. One day following injection, thrombin activity increased in the skin of the injected compared with the contralateral and non-injected control paws (p = 0.0009). One week following injection, thrombin increased in both injected and contralateral paws compared with the controls (p = 0.026), coupled with increased heat-sensitivity (p = 0.009). Thrombin activity in the footpad skin was significantly increased one week after systemic administration of LPS compared with the controls (p = 0.023). This was not accompanied by increased heat sensitivity. In human skin, a correlation was found between nerve fiber density and thrombin activity. In addition, a lower thrombin activity was measured in patients with evidence of systemic inflammation compared with the controls (p = 0.0035). These results support the modification of skin thrombin activity by regional and systemic inflammation as well as a correlation with nerve fiber density. Skin thrombin activity measurments may aid in the diagnosis and treatment of SFN.
Miniaturized dual‐band PCB inverted F/L antenna for nano‐satellite application

This article presents the design and implementation of a miniaturized dual‐band PCB inverted F/L antenna, with right‐handed circular polarization (CP) for Nano‐satellite application. This antenna has been designed in UHF/VHF amateur radio frequency bands, 144 and 435 MHz, which have been allocated for these applications. The proposed design is composed of four spiral‐shaped lines, printed on a FR‐4 substrate. Dimensions of each antenna are only 6.8 × 23.38 cm2, and they have been driven by 90 degrees phase difference in order to generate CP. Bandwidth and axial ratio of the antenna is about 1% and 1.68 dB in VHF, and is about 4% and 0.37 dB in UHF, respectively. A prototype of proposed antenna has been manufactured and tested. It will be shown that the simulation results are in good agreement with the measurements.
Soil Penetration Resistance Influenced by Eucalypt Straw Management under Mechanized Harvesting

This study aimed to evaluate the impacts of mechanized harvesting and soil tillage on soil penetration resistance (PR), influenced by the eucalypt straw management under sandy clay Oxisol in Southern Brazil. The study was conducted in a eucalyptus production area under Oxisol in Paraná State, Brazil. The treatments consisted of two harvesting systems: harvester + forwarder (HF) and feller + skidder (FS) both applied in areas under coppicing and stand renewal eucalypt cultivation systems. For stand renewal areas, eucalypt straw was managed on the soil surface at levels of 100, 50, and 0% before soil tillage. PR and soil moisture measurements were made in points distributed in regular grid for all treatments. This grid also was used to evaluate the geospatial behavior of PR in the stand renewal areas. During the measuring of PR, the averages (± confidence interval) of soil moisture up to 0–0.60 m depth were 0.20 ± 0.01 and 0.24 ± 0.01 in coppicing and stand renewal areas, respectively. In areas under coppicing, the PR mean ± confidence interval at 0–0.05 m layer in HF (1.28 ± 0.24 MPa) was lower than in FS treatment (2.11 ± 0.44 MPa). However, the PR values were similar between treatments in stand renewal areas, regardless of the forest straw level on the soil surface. For both harvesting systems, there was a lack of spatial dependence of PR up to 0.40 m soil depth, indicating some physical and mechanical homogenization induced by the soil tillage in the layer. Eucalypt straw contributed to mitigating effects of harvest traffic on PR level in coppicing forest systems. However, different levels of eucalypt straw managed before soil tillage did not influence PR levels in stand renewal forest systems.