Miracle-dz/UniFAR-FTUs huggingface dataset:
The International Exascale Software Project Roadmap

Jack Dongarra, Pete Beckman, Terry Moore, Patrick Aerts, Giovanni Aloisio, Jean-­‐Claude Andre, David Barkai, Jean-­‐Yves Berthou, Taisuke Boku, Bertrand Braunschweig, Franck Cappello, Barbara Chapman, Xuebin Chi, Alok Choudhary, Sudip Dosanjh, Thom Dunning, Sandro Fiore, Al Geist, Bill Gropp, Robert Harrison, Mark Hereld, Michael Heroux, Adolfy Hoisie, Koh Hotta, Yutaka Ishikawa, Zhong Jin, Fred Johnson, Sanjay Kale, Richard Kenway, David Keyes, Bill Kramer, Jesus Labarta, Alain Lichnewsky, Thomas Lippert, Bob Lucas, Barney Maccabe, Satoshi Matsuoka, Paul Messina, Peter Michielse, Bernd Mohr, Matthias Mueller, Wolfgang Nagel, Hiroshi Nakashima, Michael E. Papka, Dan Reed, Mitsuhisa Sato, Ed Seidel, John Shalf, David Skinner, Marc Snir, Thomas Sterling, Rick Stevens, Fred Streitz, Bob Sugar, Shinji Sumimoto, William Tang, John Taylor, Rajeev Thakur, Anne Trefethen, Mateo Valero, Aad van der Steen, Jeffrey Vetter, Peg Williams, Robert Wisniewski, and Kathy Yelick Abstract Over the last twenty years, the open source community has provided more and more software on which the world’s High Performance Computing (HPC) systems depend for performance and productivity. The community has invested millions of dollars and years of effort to build key components. But although the investments in these separate software elements have been tremendously valuable, a great deal of productivity has also been lost because of the lack of planning, coordination, and key integration of technologies necessary to make them work together smoothly and efficiently, both within individual PetaScale systems and between different systems. It seems clear that this completely uncoordinated development model will not provide the software needed to support the unprecedented parallelism required for peta/exascale computation on millions of cores, or the flexibility required to exploit new hardware models and features, such as transactional memory, speculative execution, and GPUs. This report describes the work of the community to prepare for the challenges of exascale computing, ultimately combing their efforts in a coordinated International Exascale Software Project.
PEFT-SP: Parameter-Efficient Fine-Tuning on Large Protein Language Models Improves Signal Peptide Prediction

Signal peptides (SP) play a crucial role in protein translocation in cells. The development of large protein language models (PLMs) provides a new opportunity for SP prediction, especially for the categories with limited annotated data. We present a Parameter-Efficient Fine-Tuning (PEFT) framework for SP prediction, PEFT-SP, to effectively utilize pre-trained PLMs. We implanted low-rank adaptation (LoRA) into ESM-2 models to better leverage the protein sequence evolutionary knowledge of PLMs. Experiments show that PEFT-SP using LoRA enhances state-of-the-art results, leading to a maximum MCC2 gain of 0.372 for SPs with small training samples and an overall MCC2 gain of 0.048. Furthermore, we also employed two other PEFT methods, i.e., Prompt Tunning and Adapter Tuning, into ESM-2 for SP prediction. More elaborate experiments show that PEFT-SP using Adapter Tuning can also improve the state-of-the-art results with up to 0.202 MCC2 gain for SPs with small training samples and an overall MCC2 gain of 0.030. LoRA requires fewer computing resources and less memory compared to Adapter, making it possible to adapt larger and more powerful protein models for SP prediction.
A biophysically constrained brain connectivity model based on stimulation-evoked potentials

Background Single-pulse electrical stimulation (SPES) is an established technique used to map functional effective connectivity networks in treatment-refractory epilepsy patients undergoing intracranial-electroencephalography monitoring. While the connectivity path between stimulation and recording sites has been explored through the integration of structural connectivity, there are substantial gaps, such that new modeling approaches may advance our understanding of connectivity derived from SPES studies. New Method Using intracranial electrophysiology data recorded from a single patient undergoing sEEG evaluation, we employ an automated detection method to identify early response components, C1, from pulse-evoked potentials (PEPs) induced by SPES. C1 components were utilized for a novel topology optimization method, modeling 3D conductivity propagation from stimulation sites. Additionally, PEP features were compared with tractography metrics, and model results were analyzed with respect to anatomical features. Results The proposed optimization model resolved conductivity paths with low error. Specific electrode contacts displaying high error correlated with anatomical complexities. The C1 component strongly correlates with additional PEP features and displayed stable, weak correlations with tractography measures. Comparison with existing methods Existing methods for estimating conductivity propagation are imaging-based and thus rely on anatomical inferences. Conclusions These results demonstrate that informing topology optimization methods with human intracranial SPES data is a feasible method for generating 3D conductivity maps linking electrical pathways with functional neural ensembles. PEP-estimated effective connectivity is correlated with but distinguished from structural connectivity. Modeled conductivity resolves connectivity pathways in the absence of anatomical priors.
DeepGSEA: Explainable Deep Gene Set Enrichment Analysis for Single-cell Transcriptomic Data

Gene set enrichment (GSE) analysis allows for an interpretation of gene expression through pre-defined gene set databases and is a critical step in understanding different phenotypes. With the rapid development of single-cell RNA sequencing (scRNA-seq) technology, GSE analysis can be performed on fine-grained gene expression data to gain a nuanced understanding of phenotypes of interest. However, due to the extreme heterogeneity of single-cell gene expression, current statistical GSE analysis methods sometimes fail to identify enriched gene sets. Meanwhile, deep learning has gained traction in specific applications like clustering and trajectory inference in single-cell studies due to its prowess in capturing complex data patterns. However, its use in GSE analysis remains limited, primarily due to interpretability challenges. In this paper, we present DeepGSEA, an explainable deep gene set enrichment analysis approach which leverages the expressiveness of interpretable, prototype-based neural networks to provide an in-depth analysis of GSE. DeepGSEA learns the ability to capture GSE information through our designed classification tasks, and significance tests can be performed on each gene set, enabling the identification of enriched sets. The underlying distribution of a gene set learned by DeepGSEA can be explicitly visualized using the encoded cell and cellular prototype embeddings. We demonstrate the expressiveness of DeepGSEA over commonly used GSE analysis methods by examining their sensitivity and specificity with four simulation studies. In addition, we test our model on three real scRNA-seq datasets and illustrate the interpretability of DeepGSEA by showing how its results can be explained. The source code of DeepGSEA is available at https://github.com/Teddy-XiongGZ/DeepGSEA.
HiCMC: High-Efficiency Contact Matrix Compressor

Chromosome organization plays an important role in biological processes such as replication, regulation, and transcription. One way to study the relationship between chromosome structure and its biological functions is through Hi-C studies, a genome-wide method for capturing chromosome conformations. Such studies generate vast amounts of data. The problem is exacerbated by the fact that chromosome organization is dynamic, requiring snapshots at different points in time, further increasing the amount of data to be stored. We present a novel approach called the High-Efficiency Contact Matrix Compressor (HiCMC) for efficient compression of Hi-C data. By modeling the underlying structures found in the contact matrix, such as compartments and domains, HiCMC outperforms CMC by approximately 8% and more than 50% against cooler, LZMA, and bzip2 over the state of the art across multiple cell lines and resolutions. In addition, the domain information that is embedded in the data can be used to speed up downstream analysis. HiCMC is available at https://github.com/sXperfect/hicmc.
‘Optical Von-Frey’ method to determine nociceptive thresholds: a novel paradigm for preclinical pain assessment and analgesic screening

The goal of this study was to characterize a model that specifically activates peripheral nociceptors, allowing pure nociceptive thresholds to be monitored over a range of conditions including pathology or in screening analgesic compounds. Transgenic mice expressing channelrhodopsin-2 (ChR2) in cell populations positive for the transient receptor potential cation channel subfamily V member 1 (TRPV1) gene were bred to enable peripheral nociceptor photostimulation. Preliminary experiments confirmed the expected localisation pattern of ChR2 positive profiles in the dorsal root ganglion and superficial dorsal horn, mirroring TPRV1 expression. Brief hindpaw photostimulation with 470nm light caused hindpaw withdrawal and nocifensive behaviours in ChR2 positive animals but not control ChR2 negative animals. Using a simplified up/down approach, ‘optical’ nociceptive thresholds were assessed with a 5-intensity hindpaw photostimulation paradigm, establishing the minimum intensity required to produce a withdrawal response (optical threshold). All testing was also video recorded and analysed post-hoc to assess additional photostimulation evoked behaviours. Repeated testing over several days showed optical nociceptive thresholds and response duration were similar, supporting the stability of these variables across a timeframe relevant to onset of pathology or drug administration. Optical nociceptive thresholds were also assessed following morphine administration (30 mg/kg), which significantly raised thresholds, highlighting analgesic screening utility of this model. Together, these findings demonstrate the peripheral photostimulation with optical thresholding is a useful addition to the preclinical nociception assessment toolkit, with the key advantage of inducing a purely nociceptive response to a non-invasive, non-tissue damaging stimulus.
Cerebral thickness reveals sex differences in verbal and visuospatial memory

Although sex differences in behavior/cognition and brain have previously been reported and reviewed, a sex difference of verbal and visuospatial memory and the underlying neural basis still need to be explored further. In this study, we used a machine learning model to explore a sex difference of the association between brain structure and verbal/visuospatial memory based on a community older cohort (n = 1153, age ranged from 50.42 to 86.67 years). We found females outperformed males in verbal memory (t = -5.431, p < 0.001), while males outperformed females in visuospatial memory (t = 3.201, p = 0.001). The key regions related to verbal memory in females include medial temporal cortex, orbitofrontal cortex, and some regions around insula. According to the overlapping with Yeo’s 7 networks, those regions are more located in limbic, dorsal attention and default model networks, and they are also associated with face recognition and perception processes. The key regions related to visuospatial memory, which is outstanding in males, include lateral prefrontal cortex, anterior cingulate gyrus, and some occipital regions. They overlapped more with dorsal attention, frontoparietal and visual networks, and associated with object recognition processes. These findings imply the memory performance advantage of females and males is related to the different memory preprocessing tendencies and its associated network.
Probing the Evolution of Genes Associated With DNA Methylation in Listeria monocytogenes

In the last decade, there have been increased reports of atypical Listeria and the discovery of new species. There are public health concerns that new strains may come with increased pathogenicity. Hence, this study aimed to establish the prevalence, evolutionary lineage and ancestry of a Listeria monocytogenes collection that includes isolates that harbour a unique set of methylase genes. The addition of methyl groups to DNA can interfere with transcription. Allelic-specific lineage analysis and ribotyping with southern hybridization were carried out after which further phylogenetic analysis was performed in silico. Results show that all the methylase strains belonged to Lineage I and were serotypes 4b or 4d. All designated ancestral strains also belonged to Lineage 1. A Listeria monocytogenes plasmid from a serotype 1/2a (Lineage II) contained sequences homologous to that of Lineage I isolates. The methylase nucleotide sequence in the strains studied appears to be highly conserved in Listeria monocytogenes and not yet orthologous among other bacterial genera. It is of epidemiological interest and public benefit if wider or continuous surveillance is carried out to ascertain if these rare strains are linked with increased pathogenesis, food type or geographical region.
Coexistència de dos regulons LexA a Pseudomonas putida

El sistema SOS es una xarxa multigenica controlada negativament per la proteina LexA, i esta format per un conjunt de gens implicats en el manteniment de la viabilitat cel·lular davant de lesions en el DNA. Aquest sistema es troba en la majoria d'especies bacterianes, malgrat que existeixen diferencies tant en la sequencia d'unio de la proteina LexA, com en el contingut genetic del regulo. En la present memoria es descriu el sistema SOS de Pseudomonas putida, un bacteri gramnegatiu pertanyent al grup Gamma. Primerament s'han clonat els dos gens lexA, anomenats lexA1 i lexA2, i s'han obtingut els seus productes genics mitjancant sobreexpressio i purificacio per columnes d'afinitat. Ambdues proteines s'han utilitzat en assaigs de mobilitat electroforetica (EMSA) amb els promotors de cada un dels gens lexA. Aixi s'ha pogut identificar la sequencia d'unio de la proteina LexA1 (CTGTN8ACAG) i de la proteina LexA2 (AGTACN4GTGCT). Posteriorment, utilitzant RT-PCR, s'ha vist com el gen lexA2 constitueix una unica unitat transcripcional amb els gens que el segueixen, formant el casset lexA2-imuA-imuB-dnaE2. Aquest casset s'ha vist que es induible per danys en el DNA, i que es troba ampliament distribuit en el domini Bacteria. Seguidament, s'han obtingut dues soques mutants defectives pels gens lexA1 i lexA2, i s'ha analitzat l'expressio genica de cada una d'elles, respecte la soca salvatge, utilitzant xips de DNA (microarrays). Els resultats obtinguts han demostrat que la proteina LexA1 controla la majoria de gens del sistema SOS, que a mes corresponen amb la resposta convencional del seu grup filogenetic; mentre que la proteina LexA2 nomes regula l'expressio de la seva propia unitat transcripcional, i la d'un gen (PP3901) pertanyent a un profag resident de P. putida. A mes, aquest gen tambe es troba controlat per la proteina LexA1, essent l'unic que comparteix les dues regulacions. L'obtencio d'un mutant defectiu pel gen PP3901 ha demostrat que l'expressio d'aquest es necessaria per a la transcripcio dels gens del profag resident. L'expressio d'aquest profag, pero, no origina cap efecte deleteri apreciable sobre el creixement de P. putida.
Systematic elucidation of independently modulated genes in Lactiplantibacillus plantarum reveals a trade‐off between secondary and primary metabolism

Lactobacillus plantarum is a probiotic bacteria widely used in food and health industries, but its gene regulatory information is limited in existing databases, which impedes the research of its physiology and its applications. To obtain a better understanding of the transcriptional regulatory network of L. plantarum, independent component analysis (ICA) of its transcriptomes was used to derive 45 sets of independently modulated genes (iModulons). Those iModulons were annotated for associated transcription factors (TFs) and functional pathways, and active iModulons in response to different growth conditions were identified and characterized in detail. Eventually, the analysis of iModulon activities reveals a trade-off between regulatory activities of secondary and primary metabolism in L. plantarum.
Auxetic patch material exhibits systolic thickening and restores pump function in a finite element model of acute myocardial infarction repair

Passive mechanical reinforcement of the infarcted heart has been shown to counteract infarct expansion and left ventricular (LV) functional degradation. However, traditional patch plasty of the ischemic region also restricts diastolic filling, reducing cardiac output. These negative side-effects can be minimized with strategic modification of the standard patch, suggesting further functional improvements could be possible through a broader exploration of patch materials. This study examines the potential advantages of a patch graft with auxetic properties, having a negative Poisson’s ratio (ν < 0). For preliminary evaluation, an established finite element model of LV biomechanics pre- and post-acute infarction, originally developed for modeling patch plasty with non-auxetic, or meiotic, materials, was modified to simulate epicardial implantation of auxetic patch materials. A homogeneous auxetic patch graft (ν = -0.2) exhibited radial thickening during systole, driven by tension on the patch from the neighboring contracting myocardium; but the patch thickness expanded away from the LV cavity and therefore did not improve ventricular mechanics compared to a standard meiotic patch graft (ν = 0.4). Alternatively, a heterogeneous auxetic patch design with an outer reinforcement layer caused inwardly-directed systolic thickening that restored the LV pressure-volume relationship toward baseline function, without adversely affecting fiber stress in the border zone or remote myocardium compared to the meiotic patch. This computational modeling study demonstrates the potential to harness auxetic mechanical properties for improving LV pump function in the setting of acute myocardial infarction, motivating further experimental validation of auxetic metamaterial patch devices for surgical repair of injured myocardium.
Motivating Cybersecurity Awareness within an Organisation An explorative study from an awareness practitioner’s perspective

Security awareness has been a popular topic in the last few years for both information systems researchers and organisations. News broadcasts has brought attention to the increase in cyber-attacks, with these reports noting that a significant number of these breaches have been caused by human error, linked to employee’s lack of engagement with their organisations security policies and awareness campaigns. Whilst there is existing research in human factors and the barriers of security behaviours effect on cybersecurity awareness; in practice we know very little about how employees can be motivated to engage in cybersecurity awareness programs. This study aims to explore how information security practitioners motivate interest in cybersecurity awareness. It does this through an exploratory case study approach using qualitative data collected from in-depth interviews of four cybersecurity awareness practitioners that were conducted. From an application perspective, the findings suggest that these practitioners do use a variety of techniques to motivate employee interest in cybersecurity awareness. The study identified four factors used by practitioners to motivate cybersecurity awareness which are 1) using different engaging techniques, 2) making it personable & relatable, 3) utilising leadership commitment and 4) embracing technical controls. This paper discusses these factors and implications for practitioners.
A Method for Detecting Subtitle Regions in Videos Using Video Text Candidate Images and Color Segmentation Images

In this paper, a method for detecting text regions in digital videos with telop, such as drama, movie and news programming, is proposed. The typical characteristics of telop are that it does not move, and that its edges are strong. This method takes advantage of these characteristics to produce video text candidate images. Then, this method produces the video text region images from both the video text candidate images and the color segmentation images. The video text region images and the original image are used to identify the color of the telop. Finally, text regions are detected by increasing neighboring pixels of the identied color. The experiment results show that the precision of this method was 80.36% and the recall was 77.55%, whereas the precision of the traditional method was 40.22% with the recall 75.48%. Higher accuracy was achieved by using this new method.
Turkish Journal of Electrical Engineering and Computer Turkish Journal of Electrical Engineering and Computer Sciences Sciences

: The duty of monitoring traﬀic during rush hour is diﬀicult due to the fact that modern roadways are getting more crowded every day. The automated solutions that have already been created in this area are ineffective at processing enormous amounts of data in a short amount of time, leading to ineffectiveness and inconsistent results. The YOLO (you only look once) and LSH (locality sensitive hashing) algorithms are combined with the Kafka architecture in this study to create a method for assessing traﬀic density in real-time scenarios. Our concept, which is specifically designed for vehicular networks, predicts the traﬀic density in a given location by gathering live stream data from traﬀic surveillance cameras and transforming it into frames (at a rate of 11 per minute) using the YOLOv3 algorithm, which is a crucial parameter for performing effective traﬀic diversion by suggesting alternate routes and avoiding traﬀic congestion. The predicted density is then projected onto Google Maps for the convenience of local clients. The comparative study’s results demonstrate that our strategy consistently and accurately predicts vehicular density, with an accuracy of more than 90 percent under all conditions. It also shows a significant improvement in both precision and recall, with a 4.08 percent improvement.
RESULTS OF THE ANALYSIS OF ELECTRON PRECIPITATION EVENTS OBSERVED IN THE POLAR ATMOSPHERE

Numerous Electron Precipitation Events (EPEs) were observed at high-latitude stations (Olenya, Tixie Bay, Norilsk) during long-term balloon cosmic ray measurements performed by Lebedev Physical Institute in 1957- 2002. From analysis of the data it can be concluded: (1) the energetic electron precipitation is widely extended over longitude; (2) the events typically occur 1-2 days after geomagnetic Sudden Storm Commencements. We have also examined correlation of the EPEs with electron fluxes (at the energy range >2 MeV) at geostationary orbit as well as with Dst and AE indices.
Remediation of cadmium-polluted soils with Arbuscular Mycorrhiza

As soil cadmium pollution has been more seriously in recent years,the bioremediation of cadmium pollution has received more concerns.Arbuscular mycorrhiza(AM) represents the most widely distributed mutualistic root-fungus symbiosis in terrestrial ecosystems.It's been evidenced that AM can promote plant growth,enhance plant tolerance to heavy metals,influence uptake,transport and accumulation of heavy metals by plants,and thus indicating a huge potential for phytoremediation of heavy metal-polluted soil.The direct and indirect mechanisms underlying remediation of cadmium-polluted soil using AM and the application of AM in phytoremediation were reviewed and some future research directions in this field were suggested.