Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficacy of Lidan Tang on high-fat-diet induced gallstone in mice and possible mechanism

OBJECTIVE: To investigate efficacy of Lidan Tang (LDT) on gallstone induced by high fat diet in mice, and to study its underlying mechanism. METHODS: Mice were fed with high fat diet every day and treated with LDT (9.01 times of human clinic dosage). Mice were randomly divided into 6 groups as control group, gallstone model group (high-fat diet), positive control ursodeoxycholic acid (UDCA) group (80 mg·kg1·d, i.g.), LDT low dose group (6 kg/d, i.g.), LDT middle dose group (12 kg/d, i.g.), and LDT high dose group (24 kg/d, i. g.). The whole experiment was lasted for 4 weeks. The levels of ALT, AST, LDH, CHO, HDL-C and LDL-C in serum were measured, the pathological sections were observed by hematoxylin-eosin staining, the activities of antioxidant enzymes were measured by kits, and the proteins related to oxidative stress and lipid transport were detected by Western blot analysis. RESULTS: LDT could significantly reduce the contents of ALT and AST in serum and improve the pathological tissue of liver. LDT could significantly reduce the content of MDA and LPO, and increase
Achievement emotions and academic achievement: Reciprocal relations and the moderating influence of academic buoyancy.

Control-value theory proposes that achievement emotions impact achievement, and that achievement outcomes (i.e., success and failure) reciprocally influence the development of achievement emotions. Academic buoyancy is an adaptive response to minor academic adversity, and might, therefore, offer protection from achievement being undermined by negative achievement emotions. At present, however, there is little empirical evidence for these hypothesized relations. In this study we examined reciprocal relations between three achievement emotions (enjoyment, boredom, and anxiety) and test performance in the context of mathematics, and whether academic buoyancy moderated relations between these emotions and test performance. Data were collected from 1,242 primary school students (mean age = 9.3 years) over four waves within one school year. Achievement emotions (T1 and T3) and test performance (T2 and T4) were measured alternately. Academic buoyancy was measured at T3. A structural equation model showed negative relations of anxiety to subsequent test performance, and negative relations of test performance to subsequent anxiety. Test performance also predicted enjoyment and boredom, but not vice versa. A latent-interaction structural equation model showed buoyancy moderated relations between anxiety and test performance. Test performance was highest when anxiety was low and buoyancy high. Practitioners should consider using interventions to reduce anxiety and downstream effects on achievement.
Sustainable Development and Ecological Modernization: Boundary Discourses between “Strong” and “Weak” Approaches

The concepts of “sustainable development” and “ecological modernization” are todays’ main discourses of action on the global environmental crisis. However, the quest of priorities interpretation within concepts is raised: there is a path dependence of the hegemonic worldview of techno-economic progress that was supposed to be overcome. The objective of this text is to analyse how this dependence influenced the evolution of such concepts and their operational proposals. Methodologically, the research is based on a literature review on discourses of these concepts. A typology of “strong” and “weak” discourses highlights the possibilities of “business as usual” in its operational interpretation as well as the ways to overcome it. The results show that the confrontation between them lead to a conceptual evolution of sustainable development and ecological modernization that merges into a common agenda: the governance of ambivalence between economic and social progress and environmental frontiers. The text concludes by proposing the existence of a discursive game between “survival” and “tranquillity”. This highlights an essential tension between environmental mitigation and institutional change that has accompanied the political agenda in the past 50 years. The resulting reflexive governance as a choice implies a broad participation in decision-making processes so that environmental trade-offs are collectively discussed, and responsibilities are shared. Notwithstanding, the article claims that this essential tension further implies questioning if governance may not also be a new discourse of appeasement and political unaccountability.
Temperature, Disease, and Death in London: Analyzing Weekly Data for the Century from 1866 to 1965

Using novel weekly mortality data for London spanning 1866-1965, we analyze the changing relationship between temperature and mortality as the city developed. Our main results show that warm weeks led to elevated mortality in the late nineteenth century, mainly due to infant deaths from digestive diseases. However, this pattern largely disappeared after WWI as infant digestive diseases became less prevalent. The resulting change in the temperature-mortality relationship meant that thousands of heat-related deaths—equal to 0.9-1.4 percent of all deaths— were averted. These findings show that improving the disease environment can dramatically alter the impact of high temperature on mortality.
Elbow contracture release

The elbow joint has a high propensity for stiffness, therefore surgical management is commonly required to provide functional range of motion. Treatment should aim to address any extrinsic or intrinsic cause for the restricted elbow range of motion and the overall management should be individualized based on the etiology for stiffness, patient’s age and functional demands. The main goal of surgical release is to provide a functional elbow range of motion. Several surgical techniques have been recommended for the release of a contracted elbow including arthroscopic or open release procedures. Open release can be performed with an anterior, posterior, lateral, medial approach or a combination of a medial and lateral approach to address all relevant pathology. Ulnohumeral Arthroplasty or Outerbridge Kashiwagi procedure can be performed either open or arthroscopically in patients with mild or moderate elbow osteoarthritis or posttraumatic arthritis. Following surgical management, rehabilitation program and dynamic splinting should be performed, usually for at least 3 months postoperatively for optimal functional results. Although satisfactory results have been reported with arthroscopic or open release procedures, each surgical technique has its own advantages and disadvantages. The optimal approach is still debatable and further study is necessary with prospective comparison trials to determine the relative risks, benefits, and outcomes of each
Plant community dynamics in an urban forest fragment of the São Paulo Metropolitan Area, Brazil

ABSTRACT We present a synthesis (2006-2012) of plant community dynamics in an area (1 ha) of the largest remnant of Atlantic Rain Forest surrounded by São Paulo city. Plants with ≥ 2.5 cm dbh, including the climbers, were sampled in 10 transects (2 × 50 m), and the smaller (> 1 m height, < 2.5 cm dbh) in 10 subtransects (1 × 50 m). We sampled 901 individuals, 125 species (38 families), 83 classified as late-successional species, 12 threatened with extinction and 56 absent in the area management plan, some Endangered (EN) species. The climbers were the most dynamics, they had the largest decrease in basal area; and the larger (> 5.0 cm dbh) and shorter (< 2.5 cm dbh) trees had the highest mortality rates of the shrub-tree synusia. Late species regeneration was predominant. The studied forest showed progression towards more mature successional stages in the monitoring period.
OCCULT PATHOLOGIES DISCLOSED IN PANORAMIC RADIOGRAPHS

Background: and maxillofacial imaging. This study was designed to detect the frequency of occult pathologies using the panoramic radiograph. Aim and Objective: the chief complaint and clinical diagnosis. Methodology: teaching dental hospital using 1032 panoramic radiographs retrieved from May 2015 to October 2015. The images were evaluated for the presence of occult pathologic findings whic chief complaint and clinical diagnosis. Statistics was calculated with simple percentage analysis was done. Result: and clinical diagnosis w incidental findings in 165 panoramic radiographs were periapical pathologies (26.67%), periodontal pathologies (23.6%), tooth anomalies (4.84%), congenitally missing teeth (5.45%), s teeth (3.63%), generalized rarefractions (9.7%), carotid artery calcifications (1.81%), cysts and tumors (7.89%), TMJ pathologies (9.1%), and styloid process elongation (3%). The study reiterates on the diagnostic utility of panoramic radiogra forensics.
MOLECULAR STUDY OF RPOB GENE IN PROTEUS MIRABILIS ISOLATED FROM URINARY TRACT INFECTION FROM DIFFERENT HOSPITALS IN BAGHDAD

A total of 160 clinical specimens of catheterized urine collected from different hospitals in Baghdad including : Medicine city, Baghdad teaching hospital, Protect children specialist hospital and Central Child hospital, Al-Yarmook teaching hospital during the period from September 2017 to February 2018. Bacterial isolates were identified by a microscopic examination and diagnosed by using biochemical tests. These results were confirmed by identification using Api20E system. The results showed that 103 samples were positive for bacteriological culture (64%), the percentage of Proteus spp. Was 52.4% (54) urine sample and percentage of Proteus mirabilis isolates was 74% (40 samples) (74%) while Proteus vulgaris was 25.9%(14 samples). The results also indicated that the isolation rate of P. mirabilis from females was (67%), which was higher than that of males (33%). Ten isolates of P. mirabilis were selected according to their sensitivity to antibiotics. Results revealed that all isolates were highly sensitive to Ciproflaxian (100%), Streptomycin (100%), Amikacin (90%), and Norfloxacin (90%) and Gentamycin (40%). On the other hand, these isolates were highly resistant to Tetracycline (100%), Trimethoprim/ Sulphamethoxazole (60%), and Nalidixic acid (60%). Moderate level of resistance to Chloramphenicol (50%), and Tobramycin (20%). The genetic study included an extraction of genomic DNA from 10 P. mirabilis, then detection of rpoB gene responsible for phylogenic trait for these isolated which was amplified by using conventional PCR. All isolates gave positive results. The amplicons of rpoB gene of five isolates were randomly and sent for sequencing (Macrogen, Korea). Sequence analysis of rpoB genes showed 100% percent homology with genes of species P.mirabilis, registered in NCBI GenBank Database.
Performance Estimation using Deep Learning Based Facial Expression Analysis

Scientists now widely accept that it is important for nuclear accident analysis to consider human error in addition to the failure of safety device. Investigating human factors in nuclear accidents is a continuing concern within the field of nuclear safety and human engineering. To reduce human error and to improve human performance, there have been a number of notable works to estimate operator performance objectively [1]; however, previous work has focused on post accidental analysis from over 50 years ago, and only a limited number of analysis contained the required information. In the present study we propose facial expression based performance estimation system which solves these problems and provides immediate analysis nonintrusively. The study was conducted in the form of experimental simulation in nuclear accident diagnosis situations, and representative results from the experiment are presented. This work will generate fresh insight into the previous performance estimation system.
Multi-objective Methodology for Classification by Committees of Class-experts

Ensemble learning refers to the generation and combination of multiple inducers to solve a particular machine learning task [1]. Combining multiple inducers to produce a single output can be done in various ways. According to [1], such methods can be divided into two types including weighting methods and metalearning techniques. The most popular weighting method is probably that of the majority voting. An example of a meta-learning technique is that of mixture of experts. According to [2], mixture of experts is one of the most popular and interesting combining methods, which has great potential to improve performance in machine learning. A well-known ensemble generation technique involves a biobjective approach, which attempts to generate both accurate and diverse members that fulfill the theoretical requirements of good ensembles [3]. It appears that Abbass, [4], was the first to present the idea of using Pareto-based optimization to design ensembles of Artificial Neural Networks (ANNs) for classification. According to his approach, the ensemble is formed from all Pareto-optimal networks. In [5], Chandra and Yao proposed evolutionary multiobjective framework to address the trade-off between diversity and accuracy for ensemble learning. Some researchers evolved both weights and topologies (e.g., [6,7] and used network complexity as the second objective in addition to accuracy. In [6] the complexity of the ANNs is measured by either the sum of the squared weights or the sum of the absolute value of the weights, whereas in [7] the complexity is based on the number of hidden units. Several studies, such as in [8-11], used Pareto-based multiobjective optimization to find classifiers in a similar way to the technique that is used here. Namely, each objective is associated with one class. However, in contrast to the aforementioned studies, where a fixed topology ANNs have been used, here, a TWEANNbased algorithm is used to evolve the base-classifiers. Another difference is the ensemble combination approach. Here, the ensemble is defined as a mixture of experts, whereas in the aforementioned studies the majority voting method is applied. The proposed classification methodology is based on a novel two-stage approach. Fig. 1 provides an outline of the proposed classification method. Figure 1: Classification Scheme.
Block-Aware Item Similarity Models for Top-N Recommendation

Top-N recommendations have been studied extensively. Promising results have been achieved by recent item-based collaborative filtering (ICF) methods. The key to ICF lies in the estimation of item similarities. Observing the block-diagonal structure of the item similarities in practice, we propose a block-diagonal regularization (BDR) over item similarities for ICF. The intuitions behind BDR are as follows: (1) with BDR, item clustering is embedded into the learning of ICF methods; (2) BDR induces sparsity of item similarities, which guarantees recommendation efficiency; and (3) BDR captures in-block transitivity to overcome rating sparsity. By regularizing the item similarity matrix of item similarity models with BDR, we obtain a block-aware item similarity model. Our experimental evaluations on a large number of datasets show that the block-diagonal structure is crucial to the performance of top-N recommendation.
Collaborative Machine Learning with Incentive-Aware Model Rewards

Collaborative machine learning (ML) is an appealing paradigm to build high-quality ML models by training on the aggregated data from many parties. However, these parties are only willing to share their data when given enough incentives, such as a guaranteed fair reward based on their contributions. This motivates the need for measuring a party's contribution and designing an incentive-aware reward scheme accordingly. This paper proposes to value a party's reward based on Shapley value and information gain on model parameters given its data. Subsequently, we give each party a model as a reward. To formally incentivize the collaboration, we define some desirable properties (e.g., fairness and stability) which are inspired by cooperative game theory but adapted for our model reward that is uniquely freely replicable. Then, we propose a novel model reward scheme to satisfy fairness and trade off between the desirable properties via an adjustable parameter. The value of each party's model reward determined by our scheme is attained by injecting Gaussian noise to the aggregated training data with an optimized noise variance. We empirically demonstrate interesting properties of our scheme and evaluate its performance using synthetic and real-world datasets.
Laboratory markers included in the Corona Score can identify false negative results on COVID-19 RT-PCR in the emergency room

After December 2019 outbreak in China, the novel Coronavirus infection (COVID-19) has very quickly overflowed worldwide. Infection causes a clinical syndrome encompassing a wide range of clinical features, from asymptomatic or oligosymptomatic course to acute respiratory distress and death. In a very recent work we preliminarily observed that several laboratory tests have been shown as characteristically altered in COVID-19. We aimed to use the Corona score, a validated point-based algorithm to predict the likelihood of COVID-19 infection in patients presenting at the Emergency rooms. This approach combines chest images-relative score and several laboratory parameters to classify emergency room patients. Corona score accuracy was satisfactory, increasing the detection of positive patients’ rate.
Effects of Salt Stress on Growth and Flowering of Ornamental Annual Species

ABSTRACT In 2008, a series of experiments were performed at the Institute of Ornamental Plants—Sofia in greenhouse conditions that tested the influence of salt stress on species of annual plants—Tagetes patula and Ageratum mexicanum. The plants were cultivated in pots under normal soil conditions and salt stress was induced by adding solutions with different concentrations of NaCl (0.4%, 1.2% and 2.0%). The following results were obtained: • The suppression of plant growth increases with the increase of NaCl concentration. • The plants, treated with NaCl, have earlier and shorter blooming period than non-treated plants.
INTAKE RATES AND THE TIMING OF CROSSBILL REPRODUCTION

I measured the foraging rates of breeding and nonbreeding populations of White-winged Crossbills (Loxia leucoptera) and Red Crossbills (L. curvirostra) throughout the year in the northeastern United States and adjacent Canada. I compared actual food intake rates (80 samples from 26 populations) to those predicted for five hypotheses that pertain to the timing of reproduction. To form eggs, crossbills required greater intake rates to meet energy demands than to meet protein requirements. Intake rates predicted to be necessary for the nestling stage (first five days after eggs hatch) were greater than those required for egg formation. Nearly all breeding and nonbreeding populations had intake rates adequate for egg formation. Thus, the ability to form eggs may only infrequently limit nesting. Cross- bills nested when intake was sufficient for the nestling stage, but ceased nesting if intake rates were declining so that they would be inadequate for the nestling stage. When intake rates were less than those required for the nestling stage, crossbills nested if intake rates were increasing and would be sufficient for the nestling stage. The timing of crossbill re- production was most consistent with the hypothesis that crossbills bred whenever intake was sufficient for egg formation and would be sufficient three weeks later during the nestling stage. That is, crossbills bred as if they were sensitive to both net intake and rates of change in intake. Intake rates may provide a proximate cue for crossbills to nest, but other factors such as maturing cone crops and changing day length probably affect breeding readiness. Received 19 August 1988, accepted 12 December 1989.
E-LAS: Design and Analysis of Completion-Time Agnostic Scheduling for Distributed Deep Learning Cluster

With the prosperity of deep learning, enterprises, and large platform providers, such as Microsoft, Amazon, and Google, have built and provided GPU clusters to facilitate distributed deep learning training. As deep learning training workloads are heterogeneous, with a diverse range of characteristics and resource requirements, it becomes increasingly crucial to design an efficient and optimal scheduler for distributed deep learning jobs in the GPU cluster. This paper aims to propose a simple and yet effective scheduler, called E-LAS, with the objective of reducing the averaged training completion time of deep learning jobs. Without relying on the estimation or prior knowledge of the job running time, E-LAS leverages the real-time epoch progress rate, unique for distributed deep learning training jobs, as well as the attained services from temporal and spatial domains, to guide the scheduling decisions. The theoretical analysis for E-LAS is conducted to offer a deeper understanding on the components of scheduling criteria. Furthermore, we present a placement algorithm to achieve better resource utilization without involving much implementation overhead, complementary to the scheduling algorithm. Extensive simulations have been conducted, demonstrating that E-LAS improves the averaged job completion time (JCT) by 10 × over an Apache YARN-based resource manager used in production. Moreover, E-LAS outperforms Tiresias, the state-of-the-art scheduling algorithm customized for deep learning jobs, by almost 1.5 × for the average JCT as well as queuing time.
Laser powder bed fusion for metal additive manufacturing: perspectives on recent developments

ABSTRACT While significant progress has been made in laser powder bed fusion (L-PBF) for metal additive manufacturing (AM), there is still limited large scale adoption of this advanced manufacturing technique by the industry. This paper covers the recent developments in L-PBF with discussions from the materials and process perspectives. High entropy alloys and high strength aluminium alloys have been identified as key materials development for L-PBF. Then, scanning strategies and multi-lasers applications for the process are also discussed. Other research trends and topics such as powder recycling, shape memory alloys and magnetic alloys are illustrated. The final part of this paper provides an outlook on the recent advancements while suggesting potential research topics for L-PBF.
15N/14N and 13C/12C in Weddell Sea Birds, Seals, and Fish: Implications for Diet and Trophic Structure

b13C, 6I5N, and C/N were measured for each of 247 muscle tlssue samples from 12 blrd, 4 seal, and 4 fish species collected In the Weddell Sea primanly dunng March 1986 ht3C values ranged flom -31 3 to -22 O % o and, in the case of f ~ s h and seal san~ples , varied inversely with C/N T h ~ s ~ m p l ~ e d that l ~ p i d concentratlon s~gn~f icant ly influenced these vertebrate hI3C measurements No such re la t~onshlp was found between C/N and bI5N where the latter values ranged from +4 4 to + l 1 2 % w ~ t h cons~derable overlap among many of the taxonomic groups measured Notable exceptions to thls were found in the Wilson s storm-petrel and the Kerguelan petrel, among whlch elevated bl'N values of some lndivlduals probably reflected feeding outslde of the Weddell Sea Significant feedlng on vertebrate biomass was ~ n d ~ c a t e d by the 15N enrichment of the southern giant fulmar and several snow petrel ind~viduals Some d ~ e t separation among crabeater, Antarct~c fur, leopard, and Ross seals was also evldent Nevertheless, the overlap In hI5N among most Weddell Sea vertebrates, coupled with an observed range of vertebrate values that was considerably narrower than that of t h e ~ r potent~al pley supports the hypothesis that many of these h ~ g h e r consumers share a small number of common food resources and trophlc levels