Miracle-dz/UniFAR-FTUs huggingface dataset:
Examining transportation network structures through mobile signaling data in urban China: a case study of Yixing

ABSTRACT Mobile signaling data (MSD) have become important for studying the characteristics of transportation network structures and human socioeconomic activities. However, the conversion of MSD into datasets that are suitable for transportation network analyses remains challenging, because MSD cannot directly reflect the transportation flow patterns through global positional system (GPS) data. We proposed a method of converting MSD that is suitable for complex transportation network analysis. We also visualized the distribution of transportation flow on the road based on MSD by using an application programming interface (API) of Gaode Map. Taking Yixing, China as an example, we analyzed the topology of the transportation network, identified major transportation nodes and sub-network distributions, and modelled the transportation flows on the road. Based on the results, we proposed relevant strategies to improve the provision of transportation services in the city.
PREPRINT: Using digital epidemiology methods to monitor influenza-like illness in the Netherlands in real-time: the 2017-2018 season

Introduction Despite the early development of Google Flu Trends in 2009, digital epidemiology methods have not been adopted widely, with most research focusing on the USA. In this article we demonstrate the prediction of real-time trends in influenza-like illness (ILI) in the Netherlands using search engine query data. Methods We used flu-related search query data from Google Trends in combination with traditional surveillance data from 40 general sentinel practices to build our predictive models. We introduced an artificial 4-week delay in the use of GP data in the models, in order to test the predictive performance of the search engine data. Simulating the weekly use of a prediction model across the 2017/2018 flu season we used lasso regression to fit 52 prediction models (one for each week) for weekly ILI incidence. We used rolling forecast cross-validation for lambda optimization in each model, minimizing the maximum absolute error. Results The models accurately predicted the number of ILI cases during the 2017/18 ILI epidemic in real time with a mean absolute error of 1.40 (per 10,000 population) and a maximum absolute error of 6.36. The model would also have identified the onset, peak, and end of the epidemic with reasonable accuracy The number of predictors that were retained in the prediction models was small, ranging from 3 to 5, with a single keyword (‘Griep’ = ‘Flu’) having by far the most weight in all models. Discussion This study demonstrates the feasibility of accurate real-time ILI incidence predictions in the Netherlands using internet search query data. Digital ILI monitoring strategies may be useful in countries with poor surveillance systems, or for monitoring emergent diseases, including influenza pandemics. We hope that this transparent and accessible case study inspires and supports further developments in field of digital epidemiology in Europe and beyond.
Anthelmintic Activity Ethanol Extract of Ocimum sanctum Linn. Leaves Against Ascaridia galli In Vitro

The aims of this research are to determine concentration, exposure time, interaction between concentration and exposure time of ethanol extract of Ocimum sanctum Linn. Leaves to cause death toward Ascaridia galli in vitro, and the value of LC50 and LC90 ethanol extract of Ocimum sanctum Linn. Leaves. Research design that has been used in the research was completely randomized design. This research used 200 samples of Ascaridia galli with length 7-11 cm without differentiating their sex. The concentration ethanol extract of Ocimum sanctum Linn. leaves were 1.25%, 2.5%, 5%, 10%. The control was using CMC-Na 0.5%. Each petri dish was given 40ml of solution, contained 10 worms, and were incubated at 37oC. Each treatment then being replicated four times. The observation and recording of dead worm were done at 0, 3, 6, 12 and 24 hours. Ascaridia galli were declared dead if there was no movement while disturbed by anatomy tweezers and dipped in slightly warm water (50oC). The obtained data was analyzed using ANOVA Factorial and continued with Duncan Multiple Range Test by SPSS for Windows 22. The result were 10% concentration and exposure time for 24 hours caused the most mortality toward Ascaridia galli. Interaction between concentration and exposure time resulted 10% concentration ethanol extract of Ocimum sanctum Linn. leaves in 24 hours caused the most mortality towards Ascaridia galli. Probit analysis was used to calculate the LC50 and LC90 of Ocimum sanctum Linn. leaves. The results were LC50 ethanol extract of Ocimum sanctum Linn. leaves at 6 hours was 14.8%, at 12 hours was 4.8% and at 24 hours was 3.0% and the LC90 at 24 hours was 9.1%.
Exploring white matter functional networks in children with attention-deficit/hyperactivity disorder

Abstract Attention-deficit/hyperactivity disorder has been identified to involve the impairment of large-scale functional networks within grey matter, and recent studies have suggested that white matter, which also encodes neural activity, can manifest intrinsic functional organization similar to that of grey matter. However, the alterations in white matter functional networks in attention-deficit/hyperactivity disorder remain unknown. We recruited a total of 99 children, including 66 drug-naive patients and 33 typically developing controls aged from 6 to 14, to characterize the alterations in functional networks within white matter in drug-naive children with attention-deficit/hyperactivity disorder. Using clustering analysis, resting-state functional MRI data in the white matter were parsed into different networks. Intrinsic activity within each network and connectivity between networks and the associations between network activity strength and clinical symptoms were assessed. We identified eight distinct white matter functional networks: the default mode network, the somatomotor network, the dorsal attention network, the ventral attention network, the visual network, the deep frontoparietal network, the deep frontal network and the inferior corticospinal-posterior cerebellum network. The default mode, somatomotor, dorsal attention and ventral attention networks showed lower spontaneous neural activity in patients. In particular, the default mode network and the somatomotor network largely showed higher connectivity with other networks, which correlated with more severe hyperactive behaviour, while the dorsal and ventral attention networks mainly had lower connectivity with other networks, which correlated with poor attention performance. In conclusion, there are two distinct patterns of white matter functional networks in children with attention-deficit/hyperactivity disorder, with one being the hyperactivity-related hot networks including default mode network and somatomotor network and the other being inattention-related cold networks including dorsal attention and ventral attention network. These results extended upon our understanding of brain functional networks in attention-deficit/hyperactivity disorder from the perspective of white matter dysfunction.
Prediction of Heavy Rain Damage Using Deep Learning

Heavy rain damage prediction models were developed with a deep learning technique for predicting the damage to a region before heavy rain damage occurs. As a dependent variable, a damage scale comprising three categories (minor, significant, severe) was used, and meteorological data 7 days before the damage were used as independent variables. A deep neural network (DNN), convolutional neural network (CNN), and recurrent neural network (RNN), which are representative deep learning techniques, were employed for the model development. Each model was trained and tested 30 times to evaluate the predictive performance. As a result of evaluating the predicted performance, the DNN-based model and the CNN-based model showed good performance, and the RNN-based model was analyzed to have relatively low performance. For the DNN-based model, the convergence epoch of the training showed a relatively wide distribution, which may lead to difficulties in selecting an epoch suitable for practical use. Therefore, the CNN-based model would be acceptable for the heavy rain damage prediction in terms of the accuracy and robustness. These results demonstrated the applicability of deep learning in the development of the damage prediction model. The proposed prediction model can be used for disaster management as the basic data for decision making.
The mitochondrial genome sequence of a satyrid butterfly, Lethe confuse (Lepidoptera: Nymphalidae: Satyrinae)

Abstract The nearly complete mitochondrial genome (mitogenome) of Lethe confuse was sequenced and analyzed. This mitogenome is 14,945 bp in size and encodes 13 protein-coding genes, 22 transfer RNA genes, 2 ribosomal RNA genes. The most common start codon is ATN (ATA, ATG, and ATT), and the most common termination codon is TAA. In addition, three PCGs have incomplete termination codon T. The overall nucleotide composition is 38.0% of A, 7.8% of G, 42.4% of T, and 11.8% of C. The data will increase the basic information of Satyrinae phylogenetic research and can help to better understand the phylogenetic status of L. confuse in Nymphalidae.
Complete genome sequence of Bacillus velezensis JT3-1, a microbial germicide isolated from yak feces

Bacillus velezensis JT3-1 is a probiotic strain isolated from feces of the domestic yak (Bos grunniens) in the Gansu province of China. It has strong antagonistic activity against Listeria monocytogenes, Staphylococcus aureus, Escherichia coli, Salmonella Typhimurium, Mannheimia haemolytica, Staphylococcus hominis, Clostridium perfringens, and Mycoplasma bovis. These properties have made the JT3-1 strain the focus of commercial interest. In this study, we describe the complete genome sequence of JT3-1, with a genome size of 3,929,799 bp, 3761 encoded genes and an average GC content of 46.50%. Whole genome sequencing of Bacillus velezensis JT3-1 will lay a good foundation for elucidation of the mechanisms of its antimicrobial activity, and for its future application.
The complete chloroplast genome of Saccharina latissima

Abstract Saccharina latissima is a brown algal (class Phaeophyceae) belonging to the family Laminariaceae. We reported the de novo assembly and the annotation of the complete chloroplast genome of S. latissima. The circled cpDNA of S. latissima is 130,619 bp in length with a large and a small single-copy region (LSC and SSC), separated by two copies of inverted repeats (IRa and IRb). The genome contains 139 protein-coding genes (PCGs), 3 kinds of ribosomal RNAs (rRNAs), and 29 transfer RNAs (tRNAs) genes that are typical of Saccharina cpDNA. A phylogenetic analysis strongly supported the close phylogenetic affinity of S. latissima and Saccharina japonica. The complete cpDNA of S. latissima will provide valuable molecular data for further analysis of evolutionary and conservation genetic resources.
A novel approach for optimization of transportation problem in chaos environment

Nature is characterized by its chaotic behavior. Mathematics is considered one of the appropriate tools to achieve the best definition of possible its chaos variables and process. Classical mathematics deals with the numbers as static and meaningless, but chaos mathematics deals with it as dynamic evolutionary, and value- added. This paper attempts to introduce the transportation problem representation in chaos environment and also the necessity of the model is investigated. An approach for determining the chaos best solution is proposed briefly. The advantage of the proposed approach is accomplished with the associated ordinary number and the number of iterations arriving to the best solution is reduced. A numerical example is given to illustrate the utility, effectiveness and applicability of the approach for the problem.
The molecular characterization of complete mitochondrial genome of spotted snakehead fish, Channa punctata (Bloch 1793)

Abstract The complete mitochondrial genome of spotted snakehead fish, Channa punctata (Bloch 1793) was determined by MiSeq platform. The genome was 16,409 bp in length, in which canonical 13 protein-coding genes, 22 tRNAs, 2 rRNAs, and a control region (D-Loop) were encoded. Relatively higher overall A + T contents (53.70%) was identified compared with G + C contents (46.30%). Among 13 protein-coding genes, only one unusual start condon (GTG) was identified in COX1, whereas six genes including COXII, COXIII, NAD2, NAD3, NAD4, and Cytb were ended with the incomplete stop codons (TA-/T–). Based on the currently identified mitogenomes of Channa fish species, C. punctata was most closely related to Channa gachua with 82% sequence identity.
Population structure of Poecilia mexicana (native) and Poeciliopsis gracilis (non-native) in a subtropical river

Despite the increase of exotic species in bodies of water, studies on the population structure of coexisting native and exotic fish species are rare. We evaluated the population structure of the native Poecilia mexicana and non-native Poeciliopsis gracilis species in an environmental gradient and its relation to the habitat characteristics in the Jalpan River, located in the state of Queretaro, Mexico. We calculated size structure, sex ratio, gonad stage, and condition factor for both species. Visual-based habitat quality, riparian quality, and physical and chemical characteristics of water were also evaluated. A total of 322 individuals of P. gracilis and 762 of P. mexicana were captured with different fishing nets due to the habitat heterogeneity. Study sites were classified into three categories of habitat conditions: poor, marginal, and sub-optimal. P. mexicana females showed a complete structure of sizes in the poor habitat category. P. gracilis did not show a complete structure in any habitat category. The sub-optimal category of habitat conditions had the lowest number of specimens for both species. Poor category showed a sex ratio of 1:1 (females: males) for P. mexicana while in P. gracilis , females were more abundant than males (sex ratio 5:1). Fulton's factor was higher for P. mexicana than for P. gracilis . Our results showed that habitat quality of the river affected size structure, stages of development, and sex ratio of the two species, but did not show conclusive evidence of favored species or reproductive competition.
A theory of financial inclusion and income inequality

We develop a theory linking financial inclusion, defined as access to formal loans and financial assets, to income inequality. Initial inequality of households is modeled by a random variable determining initial endowments. These initial endowments can be used to invest instantaneously in human capital and financial assets. Human capital translates into income based on a strictly concave production function, suggesting optimal levels of investment. Financial assets earn yields which do not depend on the amount invested by individuals. Theoretical predictions are tested using the China Household Finance Survey (CHFS) for 2011 and 2013. Initial conditions modeled by a random variable are replaced by an actual distribution of income or assets to derive theoretical predictions regarding the proportion of the population that might benefit from financial inclusion. Financial inclusion does mitigate under-investment in education – but formal loans do not contribute. Income inequality worsens if households rely on formal or informal loans, whereas access to bank accounts improves households' prospects in the future income distribution. However, households below the 40th percentile of household income do benefit from informal loans.
Automated summarisation of SDOCT volumes using deep learning: Transfer learning vs de novo trained networks

Spectral-domain optical coherence tomography (SDOCT) is a non-invasive imaging modality that generates high-resolution volumetric images. This modality finds widespread usage in ophthalmology for the diagnosis and management of various ocular conditions. The volumes generated can contain 200 or more B-scans. Manual inspection of such large quantity of scans is time consuming and error prone in most clinical settings. Here, we present a method for the generation of visual summaries of SDOCT volumes, wherein a small set of B-scans that highlight the most clinically relevant features in a volume are extracted. The method was trained and evaluated on data acquired from age-related macular degeneration patients, and “relevance” was defined as the presence of visibly discernible structural abnormalities. The summarisation system consists of a detection module, where relevant B-scans are extracted from the volume, and a set of rules that determines which B-scans are included in the visual summary. Two deep learning approaches are presented and compared for the classification of B-scans - transfer learning and de novo learning. Both approaches performed comparably with AUCs of 0.97 and 0.96, respectively, obtained on an independent test set. The de novo network, however, was 98% smaller than the transfer learning approach, and had a run-time that was also significantly shorter.
Speech-based identification of L-DOPA ON/OFF state in Parkinson’s Disease subjects

Background Parkinson’s disease patients (PDP) are evaluated using the unified Parkinson’s disease rating scale (UP-DRS) to follow the longitudinal course of the disease. UP-DRS evaluation is performed by a neurologist, and hence its use is limited in the evaluation of short-term (daily) fluctuations. Subjects taking L-DOPA as part of treatment to reduce symptoms exhibit motor fluctuations as a common complication. Objectives The aim of the study is to assess the use of speech analysis as a proxy to continuously monitor PDP medication state. Methods We combine acoustic, prosody, and semantic features to characterize three speech tasks (picture description, reverse counting and diadochokinetic rate) of 25 PDP evaluated under different medication states: “ON” and “OFF” L-DOPA. Results Classification of medication states using features extracted from audio recordings results in cross-validated accuracy rates of 0.88, 0.84 and 0.71 for the picture description, reverse counting and diadochokinetic rate tasks, respectively. When adding feature selection and semantic features, the accuracy rates increase to 1.00, 0.96 and 0.83 respectively; thus reaching very high classification accuracy on 3 different tasks. Conclusions We show that speech-based features are highly predictive of medication state. Given that the highest performance was obtained with a very naturalistic task (picture description), our results suggest the feasibility of accurate, non-burdensome and high-frequency monitoring of medication effects.
High degree of polyphagy in a seed-eating bark beetle, Coccotrypes gedeanus (Coleoptera: Curculionidae: Scolytinae), during a community-wide fruiting event in a Bornean tropical rainforest

A bark beetle Coccotrypes gedeanus Eggers (Coleoptera: Curculionidae: Scolytinae) is a dominant insect seed predator of dipterocarp fruits in the lowland tropical rainforests of Southeast Asia. In this study, the host preference and host range of C. gedeanus was determined by sampling 22,216 fruits from 137 species of 59 genera belonging to 24 families in a primary lowland mixed dipterocarp forest in Borneo. Coccotrypes gedeanus adults were found in the fruits of 51 species from 19 genera belonging to 13 families, and were observed to settle in the fruits of 34 species of 11 genera belonging to 6 families to initiate breeding. Except one plant species, the rest of the 34 plant species were confirmed to bear “nut” or “drupe” type fruit. These results suggested that a population of C. gedeanus utilize seeds of various plant species simultaneously. The polyphagy of the bark beetle might be adaptive for survival in the Bornean tropical rain forests where the density of each plant species is low, and most plants produce fruits at unpredictably long intervals. Our results also suggested that the characteristics of fruit might affect the host plant preference of C. gedeanus adults and/or the growth performance of C. gedeanus larvae.
Extraction of anthocyanin from purple-fleshed potato through ultrasound-assisted extraction

In this study, the ultrasound-assisted extraction was utilized to extract anthocyanin from purple-fleshed potato, and the extraction process was optimized in the selection of various conditions of extracting agent, solid-liquid ratio, ultrasound time. In addition, single factor experiment and orthogonal experiment were carried out, which determined that the highest extraction efficiency of anthocyanin from purple-fleshed potato was achieved at the purple-fleshed potato-to-extracting agent ratio of 1:50 (g/mL), the ratio of 0.2% hydrochloric acid extracting agent to 50% ethanol at 2:3, and the ultrasound time of 15 min.
The oldest titanosaurian sauropod of the Northern Hemisphere

Volgatitan simbirskiensis, gen. et sp. nov., is described based on a series of anterior and middle caudal vertebrae from a single individual discovered in the Lower Cretaceous (upper Hauterivian, Speetoniceras versicolor ammonite Zone) marine deposits at Slantsevy Rudnik vertebrate locality near Ulyanovsk City, Russia. The new taxon is characterized by strongly procoelous anterior and middle caudal vertebrae, a long centrum of the first caudal vertebra, a strong ventral ridge in the anterior and middle caudal vertebrae, a neural arch positioned at the anterior half of the centrum, hyposphene-hypantrum articulation in the anterior caudal vertebrae, and somphospondylous bone texture. Phylogenetic analysis places the new taxon as a lithostrotian titanosaur, a basal member of the lineage leading to the Lognkosauria. This lineage previously contained only South American taxa with body mass reaching 60–70 tons. Volgatitan gen. nov. is the first European and the geologically oldest representative of this lineage. Its body mass is estimated as 17.3 tons. Discovery of Volgatitan gen. nov. suggests that the lithostrotian lineage leading to the Lognkosauria had a wider distribution in the Early Cretaceous and became extinct everywhere except South America by the end of the Early Cretaceous.
Antioxidant, anti-inflammatory, and immunostimulatory activity of polysaccharide from artemisiae argyi folium

As the traditional Chinese medicine, artemisiae argyi folium has various functions such as analgesia, antitumor, and anti-hypertension. To study antioxidant activity in vitro and cell-based anti-inflammatory activity of artemisiae argyi folium polysaccharide (AAFP). The antioxidant activity of AAFP was investigated by ABTS+ • clearance rate, DPPH•clearance rate, • OH clearance rate and FRAP assays. The lipopolysaccharide-induced RAW264.7 cell inflammation model was established to assess its anti-inflammatory activity. The results showed that the AAFP had strong antioxidant activity and the IC50 on ABTS radicals, DPPH radicals, hydroxyl radicals, and the ferric reducing antioxidant power were 74.41, 268.5, 350. 2μg/ml and the FRAP value was 163. 5μg/ml. MTT experiments showed IC50 of AAFP was 441. 5μg/ml. The quantitative polymerase chain reaction demonstrate the pro-inflammatory activity of AAFP. Immunological assay showed that AAFP significantly promoted lymphocyte proliferation. AAFP have huge potential to be developed as functional foods and medicines.