Miracle-dz/UniFAR-FTUs huggingface dataset:
BAYESIAN INFERENCE AND MODEL CHOICE IN A HIDDEN STOCHASTIC TWO-COMPARTMENT MODEL OF HEMATOPOIETIC STEM CELL FATE DECISIONS 1

Despite rapid advances in experimental cell biology, the in vivo behavior of hematopoietic stem cells (HSC) cannot be directly observed and mea-sured. Previously we modeled feline hematopoiesis using a two-compartment hidden Markov process that had birth and emigration events in the ﬁrst compartment. Here we perform Bayesian statistical inference on models which contain two additional events in the ﬁrst compartment in order to determine if HSC fate decisions are linked to cell division or occur independently. Pareto Optimal Model Assessment approach is used to cross check the estimates from Bayesian inference. Our results show that HSC must divide symmetrically (i.e., produce two HSC daughter cells) in order to maintain hematopoiesis. We then demonstrate that the augmented model that adds asymmetric division events provides a better ﬁt to the competitive transplantation data, and we thus provide evidence that HSC fate determination in vivo occurs both in association with cell division and at a separate point in time. Last we show that assuming each cat has a unique set of parameters leads to either a signiﬁcant decrease or a nonsigniﬁcant increase in model ﬁt, suggesting that the kinetic parameters for HSC are not unique attributes of individual animals, but shared within a species.
Overview of the Experimental Works on Steel Reinforced Polymer Systems

The paper presents a brief overview of the experimental works carried out over the last eleven years on SRP systems, to provide an updated knowledge on the behaviour of SRP strengthened structures and identify areas of future research. First, the studies devoted to the mechanical characterization under tensile loading, to the durability of steel textiles and to the shear bond performance on concrete/masonry substrates are shown. Then, some applications of SRP systems to medium/large-scale structural members made of either reinforced concrete or masonry are described.
Students’ Re-imaginations of the Future of Higher Education in Cameroon

The unprecedented changes in our society are re-shaping the future of higher education; therefore, re-imagining the future of higher education is a constructive process of development. This study is aimed at exploring the re-imaginations of students in higher education from their own analysis of their previous and current experiences at three key stages: before entry, participation, and graduation. This exploratory study collected qualitative data from 68 current students through synchronous online focused group discussions. Analysis revealed that students’ experiences position higher education in Cameroon as invisible at lower levels, providing limited opportunities and promoting dependency upon graduation. However, they hope that the future of higher education will be characterized by a strong link between higher education and lower levels that will lead to the acquisition of practical skills relevant to the context and will leave graduates self-reliant. Recommendations that go beyond developing a policy to implementation and action are equally discussed.
Application of Two-Stage Data Pre-processing Approach for Software Fault Prediction

: Software fault prediction is a valuable exercise in software quality assurance to better allocate limited testing resources. Classification is one of the effective strategies for predicting software errors. The classification models are trained based on data sets obtained by historical repositories of mining software. In this project, a new Two-stage data preprocessing approach is applied with classification models such as Naive Bayes, Decision Tree, Knn Classifier and SVM to improve the prediction accuracy of each classification model. The data preprocessing approach in two stages incorporates both the selection of features and the reduction of instances. Specifically, in the feature selection stage, first relevance analysis is done, second, a threshold-based clustering method is proposed, termed novel threshold-based clustering algorithm, to drive redundancy control. In the instance reduction stage, random sampling is applied to maintain the balance between defective and non defective instances. To demonstrate this project chose real-world software project dataset, such as Eclipse.
Tree Volume and Taper of Loblolly Pine Varies by Stand Origin 1

ABSTRACT. Stem analysis data were used to examine volume, height-dbh, form and taper relationships for loblolly pine (Pinus taeda L.) trees grown in unthinned old- fgeld plantations, cutover-site plantations, and natural stands. Results showed signifi- cant differences in volume, height-dbh, tree form and taper relationships for loblolly pgne grown in stands from these three or- gg•ns. Thus, in order to accurately predict total and merchantable volumes as well as upper stem diameters and heights, forest managers should apply separate volume and taper equations for each stand class. South. j. Appl. For. 11(4):185-189. Loblolly pine is an important species for producing pulp and solid wood products in the South. Most of the loblolly pine acreage currently managed by foresters can be found in stands from three origins: natural stands, old-field plantations, and cutover-site plan- tations. There is evidence that tree volume and taper relationships for a particular stand origin do not vary significantly across the nat- ural range of the species. For ex- ample, Van Deusen et al. (1981) compared total volume equations derived from old-field plantation data collected throughout the
Ensemble Learning based Fishing Behavior Analysis for Vessels around Zhoushan Islands Erea

In order to improve and verify the application value of AIS data in fishing behavior analysis, three existing models were chosen to analyze the fishing paths of more than 200 boats around Zhoushan Islands. In order to confirm their operation mode, F1-score was used to evaluate related models. Experimental results shown that the Lightgbm model embodies better performance in the analysis of fishing boat behavior with higher practicality.
Analysis of Faecal Microbiota and Small ncRNAs in Autism: Detection of miRNAs and piRNAs with Possible Implications in Host–Gut Microbiota Cross-Talk

Intestinal microorganisms impact health by maintaining gut homeostasis and shaping the host immunity, while gut dysbiosis associates with many conditions, including autism, a complex neurodevelopmental disorder with multifactorial aetiology. In autism, gut dysbiosis correlates with symptom severity and is characterised by a reduced bacterial variability and a diminished beneficial commensal relationship. Microbiota can influence the expression of host microRNAs that, in turn, regulate the growth of intestinal bacteria by means of bidirectional host-gut microbiota cross-talk. We investigated possible interactions among intestinal microbes and between them and host transcriptional modulators in autism. To this purpose, we analysed, by “omics” technologies, faecal microbiome, mycobiome, and small non-coding-RNAs (particularly miRNAs and piRNAs) of children with autism and neurotypical development. Patients displayed gut dysbiosis related to a reduction of healthy gut micro- and mycobiota as well as up-regulated transcriptional modulators. The targets of dysregulated non-coding-RNAs are involved in intestinal permeability, inflammation, and autism. Furthermore, microbial families, underrepresented in patients, participate in the production of human essential metabolites negatively influencing the health condition. Here, we propose a novel approach to analyse faeces as a whole, and for the first time, we detected miRNAs and piRNAs in faecal samples of patients with autism.
Analysis on Factors Influencing Purchase Decision on Mangosteen Skin Extract Garcia

: In the last decade, business world has interesting industry, i.e. growing industry on traditional medicine. Garcia, a mangosteen skin extract product, is one of the products in traditional medicine industry marketed by PT Zena Nirmala Pratama (ZNP). Understanding consumer behaviour in purchase decision is the key of success for a company. This study aimed to analyse the influence of ZNP marketing mix and consumer aspects such as attitude, learning, motivation, and reference group towards purchase decision on mangosteen skin extract Garcia. Convenience and quota samplings were employed for sampling methods. Data were collected using questionnaires on 492 respondents of above 17 years old and had been purchasing Garcia in the last three (3) month when the study was conducted. Data acquired were analysed descriptively to describe respondent profile and Structural Equation Model (SEM) with Lisrel software was employed to assess the relation among the study variables. Descriptive analysis indicates that respondent profile (of highest percentage) consisted of female, 35-53 years old, Islam, high school graduate, married, of 4 family members in average, housewife, and IDR ±1,000,000 to 2,000,000 (USD ±77 to 154) as average expenses for food and non-food. SEM analysis indicates that significant and directly influencing variables on purchase decision were marketing mix, learning, and reference group, while non-significant variables on purchase
A Biologically Interpretable Graph Convolutional Network to Link Genetic Risk Pathways and Neuroimaging Phenotypes of Disease

We propose a novel deep neural network for whole-genome imaging-genetics. Our genetics module uses hierarchical graph convolution and pooling operations that mimic the organization of a well-established gene ontology to embed subject-level data into a latent space. The ontology implicitly tracks the convergence of genetic risk across biological pathways, and an attention mechanism automatically identiﬁes the salient edges in our network. We couple the imaging and genetics data using an autoencoder and predictor, which couples the latent embeddings learned for each modality. The predictor uses these embeddings for disease diagnosis, while the decoder regularizes the model. For interpretability, we implement a Bayesian feature selection strategy to extract the discriminative biomarkers of each modality. We evaluate our framework on a population study of schizophrenia that includes two functional MRI (fMRI) paradigms and gene scores derived from Single Nucleotide Polymorphism (SNP) data. Using 10-fold cross-validation, we show that our model achieves better classiﬁcation performance than the baselines. In an exploratory analysis, we further show that the biomarkers identiﬁed by our model are reproducible and closely associated with deﬁcits in schizophrenia.
Evaluation of Novel Urban Rail Level Crossing Designs Using Driving Simulation

Rail level crossings (RLXs) are a public safety concern internationally. The design of the RLX environment has been implicated in many recent crashes. In this study we evaluated three novel RLX design concepts using a driving simulator. Participants completed four drives, each incorporating one of the RLX designs (one baseline and three novel designs) in both train coming and train not coming mode. Measures of speed and braking on approach were analyzed, along with subjective measures of workload and usability. Superior driving behavior and subjective ratings were achieved for a design that incorporated an in-vehicle device while the lowest subjective ratings were given in relation to a shared space design that incorporated a simplified crossing environment and sharing of the road environment between motorized and non-motorized users. The implications for RLX safety are discussed.
A Cluster of Interferon-g-Inducible p65 GTPases Plays a Critical Role in Host Defense against Toxoplasma gondii

Interferon-γ (IFN-γ) is essential for host defense against intracellular pathogens. Stimulation of innate immune cells by IFN-γ upregulates ∼2,000 effector genes such as immunity-related GTPases including p65 guanylate-binding protein (Gbp) family genes. We show that a cluster of Gbp genes was required for host cellular immunity against the intracellular parasite Toxoplasma gondii. We generated mice deficient for all six Gbp genes located on chromosome 3 (Gbp(chr3)) by targeted chromosome engineering. Mice lacking Gbp(chr3) were highly susceptible to T. gondii infection, resulting in increased parasite burden in immune organs. Furthermore, Gbp(chr3)-deleted macrophages were defective in IFN-γ-mediated suppression of T. gondii intracellular growth and recruitment of IFN-γ-inducible p47 GTPase Irgb6 to the parasitophorous vacuole. In addition, some members of Gbp(chr3) restored the protective response against T. gondii in Gbp(chr3)-deleted cells. Our results suggest that Gbp(chr3) play a pivotal role in anti-T. gondii host defense by controlling IFN-γ-mediated Irgb6-dependent cellular innate immunity. YAMAMOTO, Masahiro, et al. A Cluster of Interferon-γ-Inducible p65 GTPases Plays a Critical Role in Host Defense against Toxoplasma gondii. Immunity, 2012, vol. 37, no. 2, p. 302-313 DOI : 10.1016/j.immuni. 2012.06. 009
Exploring Forensic Dental Identification with Deep Learning

Dental forensic identiﬁcation targets to identify persons with dental traces. The task is vital for the investigation of criminal scenes and mass disasters because of the resistance of dental structures and the wide-existence of dental imaging. However, no widely accepted automated solution is available for this labour-costly task. In this work, we pioneer to study deep learning for dental forensic identiﬁcation based on panoramic radiographs. We construct a comprehensive benchmark with various dental variations that can adequately reﬂect the difﬁculties of the task. By considering the task’s unique challenges, we propose FoID, a deep learning method featured by: ( i ) clinical-inspired attention localization, ( ii ) domain-speciﬁc augmentations that enable instance discriminative learning, and ( iii ) transformer-based self-attention mechanism that dynamically reasons the relative importance of attentions. We show that FoID can outperform traditional approaches by at least 22.98% in terms of Rank-1 accuracy, and outperform strong CNN baselines by at least 10.50% in terms of mean Average Precision (mAP). Moreover, extensive ablation studies verify the effectiveness of each building blocks of FoID. Our work can be a ﬁrst step towards the automated system for forensic identi-ﬁcation among large-scale multi-site databases. Also, the proposed techniques, e.g. , self-attention mechanism, can also be meaningful for other identiﬁcation tasks, e.g. , pedestrian re-identiﬁcation. Related data and codes can be found at https:/
OXIDATIVE METABOLIC PATHWAY OF LENVATINIB MEDIATED BY ALDEHYDE OXIDASE

Lenvatinib is a multi-tyrosine kinase inhibitor that inhibits vascular endothelial growth factor receptors, and being developed as an anti-cancer drug. CYPs are involved in one of the elimination pathways of lenvatinib, and mono-oxidized metabolites, such as N -oxide (M3) and desmethylated metabolite (M2), form in rats, dogs, monkeys, and humans. Meanwhile, two other oxidative metabolites are produced only in monkey and human liver S9 fractions, and their structures have been identified using high-resolution mass spectrometry as a quinolinone form of lenvatinib (M3’) and a quinolinone form of desmethylated lenvatinib (M2’). The formation of M3’ from lenvatinib occurred independently of NADPH and was effectively inhibited by typical inhibitors of aldehyde oxidase, indicating the involvement of aldehyde oxidase, but not CYPs, in this pathway. M2’ was a di-oxidized metabolite arising from a combination of mono-oxidation and desmethylation and could only be produced from M2 in an NADPH-independent manner; M2’ could not be generated from M3 or M3’. These results suggested that M2’ is formed from lenvatinib by a unique 2 step pathway through M2. Although both lenvatinib and M2 were substrates for aldehyde oxidase, an enzyme kinetic study indicated that M2 was a much more favorable substrate than lenvatinib. No inhibitory activities of lenvatinib, M2’, or M3’ and no significant
Evaluation of Heart Rate Variability by means of Laser Doppler Vibrometry measurements

Heart Rate Variability (HRV) analysis aims to study the physiological variability of the Heart Rate (HR), which is related to the health conditions of the subject. HRV is assessed measuring heart periods (HP) on a time window of >5 minutes (1)-(2). HPs are determined from signals of different nature: electrocardiogram (ECG), photoplethysmogram (PPG), phonocardiogram (PCG) or vibrocardiogram (VCG) (3)-(4)-(5). The fundamental aspect is the identification of a feature in each heartbeat that allows to accurately compute cardiac periods (such as R peaks in ECG), in order to make possible the measurement of all the typical HRV evaluations on those intervals. VCG is a non-contact technique (4), very favourable in medicine, which detects the vibrations on the skin surface (e.g. on the carotid artery) resulting from vascular blood motion consequent to electrical signal (ECG). In this paper, we propose the use of VCG for the measurement of a signal related to HRV and the use of a novel algorithm based on signal geometry (7) to detect signal peaks, in order to accurately determine cardiac periods and the Poincare plot (9)-(10). The results reported are comparable to the ones reached with the gold standard (ECG) and in literature (3)-(5). We report mean values of HP of 832±54 ms and 832±55 ms by means of ECG and VCG, respectively. Moreover, this algorithm allow us to identify particular features of ECG and VCG signals, so that in the future we will be able to evaluate specific correlations between the two.
Triaxial testing of historic masonry, test set-up and first results

In 1998 the Reyntjens Laboratory acquired a big size triaxial cell testing device for the testing of heterogeneous materials. The goal of the triaxial test set-up is to gather information about the material behaviour under a multi-axial stress state. In that it simulates the confining pressure of the surrounding material. In 1998 and 1999 the test set-up was developed. Meanwhile the first test results on the triaxial behaviour of lime mortar and masonry have been acquired. In the case of masonry cores, special attention is paid to the resulting biaxial yield-criterion. as this is of major importance for an assessment of the structural safety of the masonry. The study on lime mortar aims at determining its deformability in the case of a triaxial compression state. Especially the influence of the pore structure is evaluated. The research goal is the better understanding of the mechanical behaviour of ancient masonry, seeking for scientific explanation about the relative high mechanical strength of masonry built up with lime mortar that is considered to be a weak material. The test set-up is illustrated, the first test results and the resulting biaxial yield criterion for masonry based on cores are presented. Triaxial tests on both hydrated and hydraulic lime mortars as well as the mutual influence between the triaxial behaviour of the mortar and its pore structure are studied and discussed.
Social water assessment protocol: a step towards connecting mining, water and human rights

The human right to water has recently been recognized by both the United Nations General Assembly and the Human Rights Council. As the mining industry interacts with water on multiple levels, it is important that these interactions respect the human right to water. Currently, a disconnect exists between mine site water management practices and the recognition of water from a human rights perspective. It has been argued that the Minerals Council of Australia Water Accounting Framework can be used to strengthen the connection between water management and human rights. This article extends this connection through the use of a Social Water Assessment Protocol (SWAP). The SWAP is a scoping tool consisting of a set of questions classified into taxonomic themes under leading topics with suggested sources of data that enable mine sites to better understand the local water context in which they operate. Three of the themes contained in the SWAP – gender, Indigenous peoples and health – are discussed to demonstrate how the protocol may be useful in assisting mining companies to consider their impacts on the human right to water.
A UTO D IST : A C OMPOSABLE AND A UTOMATED S YNCHRONIZATION S YSTEM FOR D ISTRIBUTED D EEP L EARNING

Efficient data-parallel distributed training has been a key driver behind recent innovations in deep learning (DL). However, achieving satisfactory distributed performance involves making difficult system-level decisions related to diverse synchronization aspects. We present AutoDist, which automatically composes parallel synchronization strategies for DL models by rewriting their original dataflow graphs into parallel versions. Unlike existing training systems with fixed strategies, AutoDist adaptively composes strategies by jointly optimizing multiple aspects, each applied to different parts of the DL model. Compared to other graph rewriting systems, AutoDist deliberately breaks seemingly distinct synchronization optimizations into atomic graph rewriting kernels, and allows mechanically assembling them to express new strategies that extrapolate to new models and clusters. We show that AutoDist can find high-performance strategies quickly, and enables model training 1.2x to 1.6x faster than hand-optimized baselines. Critically, AutoDist does not require manual tuning when faced with new DL models or cluster configurations.
Impact load of a supply vessel

This paper presents a set of simplified analytical methods for estimation of impact loads from a supply vessel. For a how collision, the method of Yang and Caldwell (1988) is modified to take into account the effects of waterline angle, besides discussions on some parameters involved in this method. A simple formula is proposed which is determined by two parameters characterizing the cross-section of a bow. For a side collision, a modified beam model and a denting model of Wang and Ohtsubo (1997) are introduced for side shell and for deck plate, respectively. The impact load in a collision accident is obtained by summing up contributions from all the deformed structures. Calculations based on this approach compare satisfactorily with existing model tests. Through application of this procedure, collision of a typical supply vessel with an offshore installation is analyzed. A diagram is obtained describing the maximum impact load of this vessel as a function of the collision velocity, the draft of the vessel and the collision location. It is found that the impact load of this supply vessel is nearly not dependent on the draft of the vessel.