Miracle-dz/UniFAR-FTUs huggingface dataset:
MiTFM: A multi-view information fusion method based on transformer for Next Activity Prediction of Business Processes

Recent research introduces deep learning algorithms such as recurrent neural networks (RNNs) to predict the next activity, one of the most challenging tasks in predictive business process monitoring. However, the RNN-based models use only the last hidden state as a context vector, resulting in the loss of significant historical information, particularly in long sequences. Furthermore, many previous approaches rely primarily on the activities and timestamps of events, disregarding other activities and failing to capture the event log’s multi-view. To address these issues, we propose a novel method for predicting the next activity that combines a transformer network and a multi-view representation of the event log. By adding multi-view information from all attributes recorded in the event log, we hope to increase predictive accuracy. The proposed method captures long-term dependencies between the different views and fuses the multi-view information using the multi-head self-attention mechanism to predict the next activity. Experimental results on six real datasets show the effectiveness of the proposed approach compared to state-of-the-art approaches. Source codes of this paper are available on Github1.
Stability of Genomic Imprinting and X-Chromosome Inactivation in the Aging Brain

Epigenetic drift is a hallmark of aging that contributes to the irreversible decline in organismal fitness ultimately leading to aging-related diseases. Epigenetic modifications regulate the cellular memory of the epigenetic processes of genomic imprinting and X-chromosome inactivation to ensure monoallelic expression of imprinted and X-linked genes. Whether epigenetic drift affects maintenance of genomic imprinting and X-chromosome inactivation has not been comprehensively studied. Here, we investigate the allele-specific transcriptional and epigenetic signatures of the aging brain, by comparing juvenile and old hybrid mice obtained from C57BL/6J (BL6) & CAST/EiJ (CAST) reciprocal crosses, with an emphasis on the hippocampus (HCP). We confirm that the aged HCP shows an increase of DNA hydroxymethylation, a sign of epigenetic drift, and a typical aging transcriptional signature. Genomic imprinting was found to be largely unaffected with stable parent-of-origin-specific DNA methylation in HCP, but also other brain regions such as the cerebellum (CB), nucleus accumbens, hypothalamus and prefrontal cortex. Consistently, transcriptomics analysis confirmed unaltered imprinting expression in the aged HCP. An exception are three novel non-coding transcripts (B230209E15Rik, Ube2nl and A330076H08Rik) at the Prader-Willi syndrome/Angelman syndrome (PWS/AS) imprinted locus which lose strict monoallelic expression during aging. Like imprinting, X-chromosome inactivation was remarkably stable with no signs of aging-driven skewing or relaxation of monoallelic expression of X-linked genes. Our study provides a valuable resource for evaluating monoallelic expression in the aging brain and reveals that, despite epigenetic drift during aging, genomic imprinting and X-chromosome inactivation remain predominantly stable throughout the process of physiological aging in the mouse brain.
Benchmarking enrichment analysis methods with the disease pathway network

Enrichment analysis (EA) is a common approach to gain functional insights from genome-scale experiments. As a consequence, a large number of EA methods have been developed, yet it is unclear from previous studies which method is the best for a given dataset. The main issues with previous benchmarks include the complexity of correctly assigning true pathways to a test dataset, and lack of generality of the evaluation metrics, for which the rank of a single target pathway is commonly used. We here provide a generalized EA benchmark and apply it to the most widely used EA methods, representing all four categories of current approaches. The benchmark employs a new set of 82 curated gene expression datasets from DNA microarray and RNA-Seq experiments for 26 diseases, of which only 13 are cancers. In order to address the shortcomings of the single target pathway approach and to enhance the sensitivity evaluation, we present the Disease Pathway Network, in which related KEGG pathways are linked. We introduce a novel approach to evaluate pathway EA by combining sensitivity and specificity to provide a balanced evaluation of EA methods. This approach identifies Network Enrichment Analysis methods as the overall top performers compared to overlap-based methods. By using randomized gene expression datasets, we explore the null hypothesis bias of each method, revealing that most of them produce skewed p-values.
Aging modulates the effects of scene complexity on visual search and target selection in virtual environments

Processing task-relevant visual information is important for successfully completing many everyday tasks. Prior work demonstrated that older adults are more susceptible to distraction by salient stimuli, leading to less efficient visual search. However, these studies often relied on simple stimuli, and little is known about how aging influences visual attention in environments more representative of real-world complexity. Here, we test the hypothesis that aging impacts how the visual complexity of the environment influences visual search. We asked young and older adults to complete a virtual reality-based visual search task with three levels of increasing visual complexity. As visual complexity increased, all participants took longer to complete the task. This increase in time to completion resulted from two factors: an increased time required to transfer gaze from one target to the next as participants re-fixated task-relevant objects and longer lags between when correct targets were fixated and selected. We also found that these changes in gaze trajectory were greater in older adults. In addition, we found that short-term and working memory capacities were positively associated with multiple performance measures in the visual search task. This suggests that visual search performance could be integrated into assessments of working memory in dynamic environments.
Ongoing shuffling of protein fragments diversifies core viral functions linked to interactions with bacterial hosts

Biological modularity enhances evolutionary adaptability by allowing rearrangement of functional components. One striking example are bacterial viruses (phages). They exhibit extensive genomic modularity by being built of independent functional modules that evolve separately and combine in various ways, making them astoundingly diverse. While multiple studies have investigated genomic modularity in phages, less attention has been given to protein modularity—proteins having distinct building blocks or domains that can evolve and recombine, enhancing functional and genetic diversity. To better understand the impact of protein modularity on viral evolution, we quantified it by detecting instances of domain mosaicism, defined as a homologous fragment sharing between two otherwise unrelated proteins. We used highly sensitive homology detection to quantify domain mosaicism between pairs of 133,574 representative phage proteins and to understand its relationship with functional diversity in phage genomes. We found that diverse functional classes often shared homologous domains. This phenomenon was often linked to protein modularity, particularly in receptor-binding proteins, endolysins and DNA polymerases. We also identified multiple instances of recent diversification via exchange and gain/loss of domains in receptor-binding proteins, neck passage structures, endolysins and some members of the core replication machinery. Diversification via protein fragment exchange often transcended distant taxonomic and ecological borders. We argue that the ongoing diversification via shuffling of protein domains associated with those functions is reflective of co-evolutionary arms race and the resulting diversifying selection to overcome multiple mechanisms of bacterial resistance against phages.
IL-1β–mediated inflammatory signaling drives ineffective erythropoiesis in early-stage myelodysplastic syndromes

Myelodysplastic syndromes (MDS) are a group of incurable hematopoietic stem cell (HSC) neoplasms characterized by peripheral blood cytopenias and a high risk of progression to acute myeloid leukemia. MDS represent the final stage in a continuum of HSCs’ genetic and functional alterations and are preceded by a premalignant phase, clonal cytopenia of undetermined significance (CCUS). Dissecting the mechanisms of CCUS maintenance may uncover therapeutic targets to delay or prevent malignant transformation. Here, we demonstrate that DNMT3A and TET2 mutations, the most frequent mutations in CCUS, induce aberrant HSCs’ differentiation towards the myeloid lineage at the expense of erythropoiesis by upregulating IL-1β–mediated inflammatory signaling and that canakinumab rescues red blood cell transfusion dependence in early-stage MDS patients with driver mutations in DNMT3A and TET2. This study illuminates the biological landscape of CCUS and offers an unprecedented opportunity for MDS intervention during its initial phase, when expected survival is prolonged.
Uncovering genes involved in pollinator-driven mating system shifts and selfing syndrome evolution in Brassica rapa

Background Shifts in pollinator occurrence and their pollen transport effectiveness drive the evolution of mating systems in flowering plants. A decline in pollinator numbers can lead to the phenotypic evolution of floral traits favoring self-pollination (selfing syndrome). Understanding the genomic basis involved in such shifts of the mating system is crucial for predicting a species’ persistence or extinction under changing biotic and abiotic stressors in natural populations. We investigated loci showing high genetic differentiation before and after selection in fast-cycling Brassica rapa that were associated with rapid phenotypic evolution toward the selfing syndrome (reduction in petal width, stamen and pistil length, and herkogamy). Combining a genotyping-by-sequencing (GBS) approach with a genome-wide association study (GWAS), we shed light onto the genetic basis associated with the mating system shift over nine generations of pollination by Episyphrus balteatus (hoverflies), an abundant pollinator of B. rapa. Moreover, we functionally validated the involvement of candidate genes associated with changes in floral morphology by studying corresponding mutants in the model plant Arabidopsis thaliana. Results We found that the mating system of B. rapa shifted from predominantly outcrossing to mixed mating with high rates of autonomous selfing, accompanied by a rapid reduction of floral morphological traits and herkogamy involving many loci. We found 31 candidate genes associated with the affected traits that were involved in a wide range of functions from DNA/RNA binding to transport. Our functional validation in A. thaliana confirmed that four of the identified genes are indeed involved in regulating the size of floral organs. Interestingly, two genes, both coding for the same protein complex responsible for active DNA methylation were successfully validated and closely linked to two correlated phenotypic traits, namely pistil and stamen length. Conclusions Altogether, our study shows that hoverfly pollination leads to rapid evolutionary changes of the mating system through polygenic changes, highlighting the importance of using genomic approaches to understand pollinator-driven plant adaptation.
Identification of watermelon genes involved in the ZYMV interaction through a miRNA bioinformatics analysis and characterization of ATRIP and RBOHB

Small RNA sequencing of healthy and ZYMV-infected watermelon (21 dpi) was performed and bioinformatics analysis identified 353 miRNAs from which 22 known and 331 new miRNAs. Important information about their precursors, their length, the loci of which they originated on watermelon genome are provided. The ZYMV genome could be a target for mir396a-3p, miR8706a, and miR1886i-5p from the miRbase, but none of them was identified in the watermelon miRNAome. Furthermore, watermelon miRNAome does not contain a miRNA targeting ZYMV genome with an expectation score ≤ 3.5. There were 34 resistance genes (CC-NBS-LRR, TIR-NBS-LRR, TIR-NBS) predicted as targets of 32 miRNAs in healthy watermelon. For nine differentially expressed miRNAs the respective target genes (10 in total) were bioinformatically predicted. For cla-new_miR307 (upregulated upon ZYMV infection) and cla-miR166h-3p (downregulated upon ZYMV infection) the targets were predicted to be ClaATRIP and ClaRBOHB, respectively, with ClaATRIP downregulated and ClaRBOHB upregulated upon ZYMV infection. ALSV-mediated VIGS of ClaATRIP rendered watermelon plants more resistant to ZYMV, whereas VIGS of ClaRBOHB resulted in higher levels of ZYMV titer in watermelon. These data suggest that ClaATRIP and ClaRBOHB are a susceptibility and resistant gene, respectively. Our results provide new insights in watermelon miRNAome and could propose new strategies for generating resistant watermelon to ZYMV. Highlights Bioinformatics analysis in healthy and ZYMV-infected watermelon plants gave 353 miRNAs, 22 known and 331 new. The watermelon miRNAome identified in the present study does not appear to target the ZYMV genome (sense and reverse complement). The differential expression of ten genes of watermelon in relation to ZYMV infection was validated. The silencing of the two genes, ATRIP and RBOHB, through VIGS strongly suggested that ATRIP is a gene of susceptibility and RBOHB is a gene of resistance.
DNA damaged-induced phosphorylation of a viral replicative DNA helicase results in inhibition of DNA replication through attenuation of helicase function

A major function of the DNA damage responses (DDRs) that act during the replicative phase of the cell cycle is to inhibit initiation and elongation of DNA replication. The polyomavirus SV40 is an important model system for studying human DNA replication and DDRs due to its heavy reliance on host factors for viral DNA replication, and the arrest of SV40 DNA replication in response to DDR activation. The inhibition of SV40 DNA replication following DDR activation is associated with enhanced DDR kinase phosphorylation of SV40 Large T-antigen (LT), the viral origin-binding protein and DNA helicase. NetPhos prediction of LT phosphorylation on multiple sites were confirmed by mass spectroscopy, including a highly conserved DDR kinase site, T518. In cell-based DNA replication assays expression of the phosphomimetic mutant form of LT at T518 (T518D) resulted in dramatically decreased levels of SV40 DNA replication; while LT-dependent transcriptional activation was unaffected. WT and LT T518D were subsequently expressed, purified, and analyzed in vitro for assessment of biochemical function. In concordance with the cell-based data, reactions using SV40 LT-T518D, but not T518A, showed dramatic inhibition of SV40 DNA replication. Importantly, the LT T518D mutation did not affect critical LT protein interactions or its ATPase function, but showed decreased helicase activity on long, but not very short, DNA templates. These results suggest that DDR phosphorylation at T518 inhibits SV40 DNA replication by impeding LT helicase activity, thereby slowing the DNA replication fork. This is consistent with the slowing of cellular replication forks following DDR and may provide a paradigm for another mechanism for how DNA replication forks can be slowed in response to DDR, by phosphorylation of DNA helicases.
Induced pluripotent stem cell derived pericytes respond to endogenous mediators of proliferation and contractility

Background Pericytes are multifunctional contractile cells that reside on capillaries. Pericytes are critical regulators of cerebral blood flow and blood-brain barrier function, and pericyte dysfunction may contribute to the pathophysiology of human neurological diseases including Alzheimers disease, multiple sclerosis, and stroke. Induced pluripotent stem cell (iPSC)-derived pericytes (iPericytes) are a promising tool for vascular research. However, it is unclear how iPericytes functionally compare to primary human brain vascular pericytes (HBVPs). We differentiated iPSCs into iPericytes of either the mesoderm or neural crest lineage using established protocols. We compared iPericyte and HBVP morphologies, quantified gene expression by qPCR and bulk RNA sequencing, and visualised pericyte protein markers by immunocytochemistry. To determine whether the gene expression of neural crest iPericytes, mesoderm iPericytes or HBVPs correlated with their functional characteristics in vitro, we quantified EdU incorporation following exposure to the key pericyte mitogen, platelet derived growth factor (PDGF)-BB and, contraction and relaxation in response to the vasoconstrictor endothelin-1 or vasodilator adenosine, respectively. iPericytes were morphologically similar to HBVPs and expressed canonical pericyte markers. However, iPericytes had 1864 differentially expressed genes compared to HBVPs, while there were 797 genes differentially expressed between neural crest and mesoderm iPericytes. Consistent with the ability of HBVPs to respond to PDGF-BB signalling, PDGF-BB enhanced and PDGF receptor-beta inhibitors impaired iPericyte proliferation. Administration of endothelin-1 led to iPericyte contraction and adenosine led to iPericyte relaxation, of a magnitude similar to the response evoked in HBVPs. We determined that neural crest iPericytes were less susceptible to PDGFR beta inhibition, but responded most robustly to vasoconstrictive meditators. iPericytes express pericyte-associated genes and proteins and, exhibit an appropriate physiological response upon exposure to a key endogenous mitogen or vasoactive mediators. Therefore, the generation of functional iPericytes would be suitable for use in future investigations exploring pericyte function or dysfunction in neurological diseases.
Malaria incidence in Nairobi, Kenya and dekadal trends in NDVI and climatic variables

The primary objective of this research was to determine if the remotely-sensed metric, Normalised Difference Vegetation Index (NDVI) and ground-collected dekadal climatological variables were useful predictors of future malaria outbreaks in an epidemic-prone area of Nairobi, Kenya. Data collected consisted of 36 dekadal (10-day) periods for the variables rainfall, temperature and NDVI along with yearly documented malaria admissions in 2003 for Nairobi, Kenya. Linear regression models were built for malaria cases reported in Nairobi, Kenya, as the dependent variable and various time-based groupings of temperature, rainfall and NDVI data from the dekads in both the current and the previous month as the independent variables. Data from 2003 show that malaria incidence in any given month is best predicted (R2 = 0.881, p < 0.001) by the average NDVI for the 30 days including the final two dekads of the previous month and first dekad of the current month, and by the average rainfall for the 30 days including the three dekads of rainfall data from the prior month. Forecasting an outbreak in an epidemic zone would allow public health entities to plan for and disseminate resources to the general public such as antimalarials and insecticide impregnated bed nets. In addition, vector control measures could be implemented to slow the rate of transmission in the impacted population.
Suggested Practice for Drilling Boreholes for Pressuremeter Testing

A standard practice is suggested for the preparation of the borehole in which the pressuremeter probe is to be inserted. The practice covers the required diameter of the hole, the various drilling tools and methods used to place the probe, the test spacing and testing sequence, and the way to judge the quality of the borehole and of the test. This practice is an adjunct to the suggested practice for pressuremeter testing in soils.
Transforming Large-size to Lightweight Deep Neural Networks for IoT Applications: A Review

Deep Neural Networks (DNN) have gained unprecedented popularity due to their high-order performance and automated feature extraction capability. It encourages researchers to incorporate DNN in diferent Internet of Things (IoT) applications in recent years. However, the colossal requirement of computation, energy, and storage of DNN make their deployment prohibitive on resource constraint IoT devices. Therefore, several compression techniques have been proposed in recent years to reduce the energy, storage, and computation requirements of the DNN. These techniques have utilized a diferent perspective for compressing DNN with minimal accuracy compromise. It encourages us to comprehensively overview the DNN compression techniques for the IoT. This paper presents a comprehensive overview of existing literature on compressing the DNN that reduces energy consumption, storage, and computation requirements for IoT applications. We divide the existing approaches into ive broad categories, i.e., network pruning, sparse representation, bits precision, knowledge distillation, and miscellaneous, based upon the mechanism incorporated for compressing the DNN. The paper discusses the challenges associated with each category of DNN compression techniques and presents some prominent applications using IoT in conjunction with compressed DNN. Finally, we provide a quick summary of existing work under each category with the future direction in DNN compression.
Mean surface energy fluxes over the global ocean

A newly available consolidated data set from the U.S. Navy's Fleet Numerical Weather Central in Monterey, California, is utilized to investigate global energy fluxes at the sea-air interface. Sea surface temperature data from January 1949 to December 1979 were extracted from the data set along with air temperature, sea level pressure, dew point temperature, wind speed, wind direction, and cloud cover. Five degree latitude by five degree longitude spatial averaging and monthly temporal averaging were applied to the parameters in the data set for the purpose of studying long-term large-scale fluctuations. The heat balance of the global ocean surface layer is calculated using bulk flux formulations. Maps of the long-term monthly and annual means of the net surface energy flux together with the four components of the total flux (latent heat flux, sensible heat flux, incoming radiation, and outgoing radiation) for the global oceans are presented. Incoming solar radiation and latent heat flux are the two dominant components that control net surface energy fluxes. Wind speed, cloud cover, and the gradient of specific humidity are the three most important meteorological parameters in determining surface flux. Errors associated with the bulk formula calculations and the significant features of the energy balance at the ocean surface are discussed.