Miracle-dz/UniFAR-FTUs huggingface dataset:
Facilitating learning outcome assessment– development of new datasets and analysis of pre-trained language models

Student mobility reflects academic transfer from one postsecondary institution to another and facilitates students’ educational goals of obtaining multiple credentials and/or advanced training in their field. This process often relies on transfer credit assessment, based on the similarity between learning outcomes, to determine what knowledge and skills were obtained at the sending institution as well as what knowledge and skills need to still be acquired at the receiving institution. As human evaluation can be both a challenging and time-consuming process, algorithms based on natural language processing can be a reliable tool for assessing transfer credit. In this article, we propose two novel datasets in the fields of Anatomy and Computer Science. Our aim is to probe the similarity between learning outcomes utilising pre-trained embedding models and compare their performance to human-annotated results. We found that ALBERT, MPNeT and DistilRoBERTa demonstrated the best ability to predict the similarity between pairs of learning outcomes. However, Davinci - a GPT-3 model which is expected to predict better results - is only able to provide a good qualitative explanation and not an accurate similarity score. The codes and datasets are available at https://github.com/JAkriti/New-Dataset-and-Performance-of-Embedding-Models.
Linguistic Pattern Analysis in the Climate Change-Related Tweets from UK and Nigeria

To understand the global trends of human opinion on climate change in specific geographical areas, this research proposes a framework to analyse linguistic features and cultural differences in climate-related tweets. Our study combines transformer networks with linguistic feature analysis to address small dataset limitations and gain insights into cultural differences in tweets from the UK and Nigeria. Our study found that Nigerians use more leadership language and informal words in discussing climate change on Twitter compared to the UK, as these topics are treated as an issue of salience and urgency. In contrast, the UK’s discourse about climate change on Twitter is characterised by using more formal, logical, and longer words per sentence compared to Nigeria. Also, we confirm the geographical identifiability of tweets through a classification task using DistilBERT, which achieves 83% of accuracy.
BMW Tree: Large-scale, High-throughput and Modular PIFO Implementation using Balanced Multi-Way Sorting Tree

Push-In-First-Out (PIFO) queue has been extensively studied as a programmable scheduler. To achieve accurate, large-scale, and high-throughput PIFO implementation, we propose the Balanced Multi-way (BMW) Sorting Tree for real-time packet sorting. The tree is highly modularized, insertion-balanced and pipeline-friendly with autonomous nodes. Based on it, we design two simple and efficient hardware designs. The first one is a register-based (R-BMW) scheme. With a pipeline, it features an impressively high and stable throughput without any frequency reduction theoretically even under more levels. We then propose Ranking Processing Units to drive the BMW-Tree (RPU-BMW) to improve the scalability, where nodes are stored in SRAMs and dynamically loaded into/off from RPUs. As the capacity of BMW-Tree grows exponentially, only a few RPUs are needed for a large scale. The evaluation shows that when deployed on the Xilinx Alveo U200 card, R-BMW improves the throughput by 4.8x compared to the original PIFO implementation, while exhibiting a similar capacity. RPU-BMW is synthesized in GlobalFoundries 28nm process, costing a modest 0.522% (1.043mm2) chip area and 0.57MB off-chip memory to support 87k flows at 200Mpps. To our best knowledge, RPU-BMW is the first accurate PIFO implementation supporting over 80k flows at as fast as 200Mpps.
EBB: Reliable and Evolvable Express Backbone Network in Meta

We present the design, implementation, evaluation, deployment and production experiences of EBB (Express BackBone), a private WAN (Wide Area Network) connecting Meta's global data centers (DCs). Initiated in 2015, EBB now carries 100% of DC-DC traffic, witnessing remarkable growth over the years. A key design aspect of EBB is its multi-plane architecture, facilitating seamless deployment of a new control plane while ensuring operational simplicity. This architecture allows for efficient failure mitigation, standard maintenance, and capacity expansion by draining one or two planes without impacting service level objectives (SLOs). Another critical design decision is the hybrid model, combining distributed control agents and a central controller. EBB's centralized traffic engineering utilizes an MPLS-TE based solution to allocate paths periodically for different traffic classes based on service requirements, while its distributed control agents enable fast local failure recovery by pre-installing pre-computed backup paths in the data plane. We delve into our eight-year production experience, highlighting the successful deployment of multiple generations of EBB.
GGFAST: Automating Generation of Flexible Network Traffic Classifiers

When employing supervised machine learning to analyze network traffic, the heart of the task often lies in developing effective features for the ML to leverage. We develop GGFAST, a unified, automated framework that can build powerful classifiers for specific network traffic analysis tasks, built on interpretable features. The framework uses only packet sizes, directionality, and sequencing, facilitating analysis in a payload-agnostic fashion that remains applicable in the presence of encryption. GGFAST analyzes labeled network data to identify n-grams ("snippets") in a network flow's sequence-of-message-lengths that are strongly indicative of given categories of activity. The framework then produces a classifier that, given new (unlabeled) network data, identifies the activity to associate with each flow by assessing the presence (or absence) of snippets relevant to the different categories. We demonstrate the power of our framework by building---without any case-specific tuning---highly accurate analyzers for multiple types of network analysis problems. These span traffic classification (L7 protocol identification), finding DNS-over-HTTPS in TLS flows, and identifying specific RDP and SSH authentication methods. Finally, we demonstrate how, given ciphersuite specifics, we can transform a GGFAST analyzer developed for a given type of traffic to automatically detect instances of that activity when tunneled within SSH or TLS.
Effect of atrial fibrillation on plasma galectin-3 and soluble CD40 ligand levels in patients with ischemic cardiomyopathy

Objective To reveal the significance of plasma galectin-3 and soluble CD40 ligand (sCD40L) levels in patients with ischemic cardiomyopathy (ICM) combined with atrial fibrillation. Methods In this case–control study, the case group comprised 60 patients with ICM combined with atrial fibrillation and the control group comprised patients with ICM without atrial fibrillation. Plasma galectin-3 and sCD40L levels, left atrial diameter (LAD), left ventricular ejection fraction (LVEF), and left ventricular diameter (LVD) were compared. Results The plasma galectin-3 and sCD40L levels, LAD, and LVD were higher and the LVEF was lower in the case than control group. In the case group, the plasma galectin-3 and sCD40L levels were positively correlated with the LAD and LVD but negatively correlated with the LVEF. The area under the receiver operating characteristic curve of the plasma galectin-3 and sCD40L levels in the diagnosis of ICM combined with atrial fibrillation was 0.857 (95% confidence interval, 0.792–0.923) and 0.724 (95% confidence interval, 0.634–0.814), respectively. Conclusion The plasma galectin-3 and sCD40L levels are significantly elevated in patients with ICM combined with atrial fibrillation. Although both may have predictive value in the diagnosis of ICM combined with atrial fibrillation, galectin-3 may have the higher predictive value.
Murphy: Performance Diagnosis of Distributed Cloud Applications

Modern cloud-based applications have complex inter-dependencies on both distributed application components as well as network infrastructure, making it difficult to reason about their performance. As a result, a rich body of work seeks to automate performance diagnosis of enterprise networks and such cloud applications. However, existing methods either ignore inter-dependencies which results in poor accuracy, or require causal acyclic dependencies which cannot model common enterprise environments. We describe the design and implementation of Murphy, an automated performance diagnosis system, that can work with commonly available telemetry in practical enterprise environments, while achieving high accuracy. Murphy utilizes loosely-defined associations between entities obtained from commonly available monitoring data. Its learning algorithm is based on a Markov Random Field (MRF) that can take advantage of such loose associations to reason about how entities affect each other in the context of a specific incident. We evaluate Murphy in an emulated microservice environment and in real incidents from a large enterprise. Compared to past work, Murphy is able to reduce diagnosis error by ≈ 1.35× in restrictive environments supported by past work, and by ≥ 4.7× in more general environments.
Observations of cross-shore sediment transport and formulation of the undertow

approved: H. Tuba Özkan-Haller A new set of data from a large-scale sand bar migration experiment is presented. During this experiment, two sandbars were generated. One of the bar was generated by the action of undertow, and sediment moved offshore. The other bar was generated by the shoreward movement of sediment coming from the first bar. The principal mechanism responsible for shoreward movement is associated with effects of velocity asymmetry. Analysis of bathymetric surveys and suspended sediment concentration data reveals that suspended load contributed to a large extent to the formation of the first bar. Bed load was also important, and was moving in the same direction as suspended load. For the second bar, shoreward sediment movement occurred as bedload. Suspended load was moving in the opposite direction as bed load. This difference in sediment movement is explained by the predominance of the undertow in the suspended sediment flux. Two models were tested to reproduce the observed sediment transport, a wave-averaged (energetics) model and a wave-resolved model. After proper calibration, both models yielded satisfactory results. Calibration efforts highlighted the need for robust models of sediment pickup functions and sediment eddy diffusivity. They also showed the need for a deterministic undertow models. Formulations of the undertow are presented. These formulations are valid for all relative water depths, and include mean current advective terms. These formulations show that the forcing of the undertow is depth-uniform, assuming linear water wave theory. The model is tested against four datasets to evaluate the possibility of a deterministic model. Although a constant eddy viscosity would yield such a model, no universal parametrization could be determined.
Mechanism of stepwise electron transfer in six-transmembrane epithelial antigen of the prostate (STEAP) 1 and 2

Six transmembrane epithelial antigen of the prostate (STEAP) is a family of membrane-embedded hemoproteins with four members, STEAP1-4, all of which have a transmembrane domain (TMD) that chelates a heme prosthetic group. STEAP2-4, but not STEAP1, have an intracellular oxidoreductase domain (OxRD) so that an electron transfer chain composed of NADPH, FAD, and heme is established to mediate electron transfer across cell membranes. However, it is not known whether STEAP1 can establish a physiologically relevant electron transfer chain. Here we show that reduced FAD binds to STEAP1 and enables reduction of the heme. We also show that a soluble cytochrome b5 reductase can dock on STEAP1 and serve as a surrogate OxRD to reduce the heme. These results provide the first evidence that STEAP1 can support a cross-membrane electron transfer chain. It is not clear whether FAD, which relays electrons from NADPH to heme and interacts with both OxRD and TMD, remains constantly bound to the STEAPs. We found that FAD reduced by STEAP2 can be utilized by STEAP1, supporting the hypothesis that FAD is diffusible rather than staying bound to STEAP2. We determined the structure of human STEAP2 in complex with NADP+ and FAD to an overall resolution of 3.2 Å by cryo-electron microscopy. The structure of STEAP2 shows that the two cofactors bind similarly to those in the STEAP4 structure and thus a diffusible FAD is likely a general feature of the electron transfer mechanism in the STEAPs. The structure of STEAP2 also shows that its extracellular regions are less structured than those of STEAP4 or STEAP1, and further experiments show that STEAP2 reduces Fe3+-NTA with a rate significantly slower than STEAP1. These results establish a solid foundation for understanding the function and mechanisms of STEAP family of proteins.
Mechano-signaling of prostate tumor initiating cells facilitates their tropism to stiff metastatic niche

Analysis of clinical datasets indicate that cancer stem-like cells/tumor-initiating cells (CSCs/TICs) derived from prostate cancer (PCa) patients display an elevated expression of genes for cell-matrix interactions, cell adhesion proteins and of the putative mechanotransducer TAZ. Here we combined measurements on the cellular mechano-responses to matrix stiffness, including cell-generated forces, zebrafish and PDX-derived organoid models, to show that mechanotransduction serves as a key determinant for PCa CSC maintenance during metastatic onset. The β1-integrin-ILK-CDC42-N-Wasp dependent cytoskeletal tension and TAZ nucleus-translocation mediate this mechano-signaling axis. As a result, expression of the stemness genes NANOG and OCT4 are induced, leading to metastatic tumor initiation. It is further demonstrated that pharmaceutical perturbation of this mechano-signaling using a novel YAP/TAZ inhibitor K975 constrains PCa metastasis in zebrafish, and development of PDX-derived organoids. Our data highlights the essential role of mechanotransduction in PCa aggressiveness, thereby underlying this pathway as a therapeutic target for future studies.
NeoBFT: Accelerating Byzantine Fault Tolerance Using Authenticated In-Network Ordering

Mission critical systems deployed in data centers today are facing more sophisticated failures. Byzantine fault-tolerant (BFT) protocols are capable of masking these types of failures, but are rarely deployed due to their performance cost and complexity. In this work, we propose a new approach to designing high performance BFT protocols in data centers. By re-examining the ordering responsibility between the network and the BFT protocol, we advocate a new abstraction offered by the data center network infrastructure. Concretely, we design a new authenticated ordered multicast primitive (aom) that provides transferable authentication and non-equivocation guarantees. Feasibility of the design is demonstrated by two hardware implementations of aom- one using HMAC and the other using public key cryptography for authentication - on new-generation programmable switches. We then co-design a new BFT protocol, NeoBFT, that leverages the guarantees of aom to eliminate cross-replica coordination and authentication in the common case. Evaluation results show that NeoBFT outperforms state-of-the-art protocols on both latency and throughput metrics by a wide margin, demonstrating the benefit of our new network ordering abstraction for BFT systems.
Inhibiting NINJ1-dependent plasma membrane rupture protects against inflammasome-induced blood coagulation and inflammation

Systemic blood coagulation accompanies inflammation during severe infection like sepsis and COVID. We’ve previously established a link between pyroptosis, a vital defense mechanism against infection, and coagulopathy. During pyroptosis, the formation of gasdermin-D (GSDMD) pores on the plasma membrane leads to the release of tissue factor (TF)-positive microvesicles (MVs) that are procoagulant. Mice lacking GSDMD release fewer TF MVs. However, the specific mechanisms leading from activation of GSDMD to MV release remain unclear. Plasma membrane rupture (PMR) in pyroptosis was recently reported to be actively mediated by the transmembrane protein Ninjurin-1 (NINJ1). Here we show that NINJ1 promotes procoagulant MV release during pyroptosis. Haploinsuffciency or glycine inhibition of NINJ1 limited the release of procoagulant MVs and inflammatory cytokines and protected against blood coagulation and lethality triggered by bacterial flagellin. Our findings suggest a crucial role for NINJ1-dependent PMR in inflammasome-induced blood coagulation and inflammation.
Liquid Crystalline Layering and Divalent Cations Cooperatively Enhance DNA Condensation

The layered liquid crystalline (LC) phases formed by DNA molecules which include rigid and flexible segments (‘gapped DNA’) enable the study of both end-to-end stacking and side-to-side lateral interactions that drive the condensation of DNA molecules. The resulting layer structure exhibits long-range inter-layer and intra-layer positional correlations. Using synchrotron small-angle x-ray scattering (SAXS) measurements, we investigate the impact of divalent Mg2+ cations on the stability of the inter- and intra-layer DNA ordering as a function of temperature between 5-65 °C and for different terminal base pairings at the blunt ends of the gapped DNA constructs, which mediate the strength of the attractive end-to-end interaction. We demonstrate that the stabilities at a fixed DNA concentration of both inter-layer and intra-layer order are significantly enhanced even at a few mM Mg2+ concentration. The stability continues to increase up to ∼30 mM Mg2+ concentration, but at higher (∼100 mM) Mg2+ content repulsion between positive ions counteracts and reverses the increase. On the other hand, sufficiently strong base-stacking interactions promote intra-layer order even in the absence of multivalent cations, which demonstrates the impact of liquid crystal layering on the DNA condensation process. We discuss the implications of these results in terms cation-mediated DNA-DNA attraction.
Pangenome evaluation of gene essentiality in Streptococcus pyogenes

Populations of bacterial pathogens are made of strains that often have variable gene content, termed the pangenome. Variations in the genetic makeup of a single strain can alter bacterial physiology and fitness in response to different environmental stimuli. To define biologically relevant genes within a genome, genome-wide knockout transposon mutant libraries have been used to identify genes essential for survival or virulence in a particular strain. Such phenotypic studies have been applied in four different genotypes of the major human pathogen Streptococcus pyogenes, yet challenges exist in comparing results across studies conducted in different genetic backgrounds and conditions. To advance genotype-phenotype inferences within a population genomic framework of 250 S. pyogenes reference genomes, we systematically re-analysed publicly available transposon sequencing datasets from S. pyogenes using a transposon sequencing specific analysis pipeline, Transit. Across 4 genetic backgrounds and 9 phenotypic conditions, 311 genes were highly essential for survival, corresponding to ∼22% of the core genome. Among the 311 genes, functions related to information storage, and processing were overrepresented. Genes associated with cellular processing and signalling were of significantly higher essentiality under in vivo conditions (animal models with differing disease manifestation and site of colonisation) compared to in vitro (varying types of culture media). Finally, essential operons across S. pyogenes genotypes were defined, with an increased number of essential operons detected under in vivo conditions. This study provides an extendible database to which new studies can be added, and a searchable html-based resource to direct future investigations into S. pyogenes population biology. Importance Streptococcus pyogenes is a human adapted pathogen occupying restricted ecological niches. Understanding essentiality of genes across different strains and experimental conditions is important to direct research questions and efforts to prevent the large burden of disease caused by S. pyogenes. To this end we systematically reanalysed transposon sequencing studies in S. pyogenes using transposon sequencing specific methods, integrating them into an extendible meta-analysis framework. This provides a repository of gene essentiality in S. pyogenes for the community to guide future phenotypic studies.
Morphogen gradients can convey position and time in growing tissues

During development, cells need to make fate decisions according to their position and the developmental timepoint. Morphogen gradients provide positional information, but how timing is controlled has remained elusive. Here, we show that in morphogen gradients with constant decay length, cells experience transient, hump-shaped concentration profiles if the morphogen source expands in parallel with the uniformly growing tissue. This transient signal can convey time. We further show that opposing steady-state morphogen gradients with equal decay length, as found in the vertebrate neural tube, can synchronise cell fate decisions along the entire expanding patterning axis, because the product of the two opposing concentration gradients is constant along it. In case of an increasing amplitude, cells experience a transient hump signal, while in case of constant gradient amplitudes, the concentration product declines continuously as the tissue expands — a hallmark of a depletion timer. Once the tissue reaches a critical size and the concentrations a critical value, a cell fate switch can be triggered. Timers based on morphogen gradients offer a simple mechanism for the simultaneous control of position and time and might apply in many patterning systems, as uniform growth is observed widely in development.
The marmoset default-mode network identified by deactivations in task-based fMRI studies

Understanding the default-mode network (DMN) in the common marmoset (Callithrix jacchus) has been challenging due to inconsistencies with human and marmoset DMNs. By analyzing task-negative activation in fMRI studies, we identified medial prefrontal cortical areas, rostral auditory areas, entorhinal cortex, posterior cingulate cortex area 31, hippocampus, hypothalamus, and basomedial amygdala as marmoset DMN components. Notable, medial and posterior parietal areas that were previously hypothesized to be part of the DMN were activated during visual task blocks. Seed analysis using resting-state fMRI showed strong connectivity between task-negative areas, and tracer data supported a structural network aligning with this functional DMN. These findings challenge previous definition of the marmoset DMN and reconcile many inconsistencies with the DMNs observed in humans, macaque monkeys, and even rodents. Overall, these results highlight the marmoset as a powerful model for DMN research, with potential implications for studying neuropsychiatric disorders where DMN activity and connectivity are altered.