Miracle-dz/UniFAR-FTUs huggingface dataset:
CRAM: Efficient Hardware-Based Memory Compression for Bandwidth Enhancement

This paper investigates hardware-based memory compression designs to increase the memory bandwidth. When lines are compressible, the hardware can store multiple lines in a single memory location, and retrieve all these lines in a single access, thereby increasing the effective memory bandwidth. However, relocating and packing multiple lines together depending on the compressibility causes a line to have multiple possible locations. Therefore, memory compression designs typically require metadata to specify the compressibility of the line. Unfortunately, even in the presence of dedicated metadata caches, maintaining and accessing this metadata incurs significant bandwidth overheads and can degrade performance by as much as 40%. Ideally, we want to implement memory compression while eliminating the bandwidth overheads of metadata accesses. This paper proposes CRAM, a bandwidth-efficient design for memory compression that is entirely hardware based and does not require any OS support or changes to the memory modules or interfaces. CRAM uses a novel implicit-metadata mechanism, whereby the compressibility of the line can be determined by scanning the line for a special marker word, eliminating the overheads of metadata access. CRAM is equipped with a low-cost Line Location Predictor (LLP) that can determine the location of the line with 98% accuracy. Furthermore, we also develop a scheme that can dynamically enable or disable compression based on the bandwidth cost of storing compressed lines and the bandwidth benefits of obtaining compressed lines, ensuring no degradation for workloads that do not benefit from compression. Our evaluations, over a diverse set of 27 workloads, show that CRAM provides a speedup of up to 73% (average 6%) without causing slowdown for any of the workloads, and consuming a storage overhead of less than 300 bytes at the memory controller.
Reconstruction of the Protein-Protein Interaction Network for Protein Complexes Identification by Walking on the Protein Pair Fingerprints Similarity Network

Identifying protein complexes from protein-protein interaction networks (PPINs) is important to understand the science of cellular organization and function. However, PPINs produced by high-throughput studies have high false discovery rate and only represent snapshot interaction information. Reconstructing higher quality PPINs is essential for protein complex identification. Here we present a Multi-Level PPINs reconstruction (MLPR) method for protein complexes detection. From existing PPINs, we generated full combinations of every two proteins. These protein pairs are represented as a vector which includes six different sources. Then the protein pairs with same vector are mapped to the same fingerprint ID. A fingerprint similarity network is constructed next, in which a vertex represents a protein pair fingerprint ID and each vertex is connected to its top 10 similar fingerprints by edges. After random walking on the fingerprints similarity network, each vertex got a score at the steady state. According to the score of protein pairs, we considered the top ranked ones as reliable PPI and the score as the weight of edge between two distinct proteins. Finally, we expanded clusters starting from seeded vertexes based on the new weighted reliable PPINs. Applying our method on the yeast PPINs, our algorithm achieved higher F-value in protein complexes detection than the-state-of-the-art methods. The interactions in our reconstructed PPI network have more significant biological relevance than the exiting PPI datasets, assessed by gene ontology. In addition, the performance of existing popular protein complexes detection methods are significantly improved on our reconstructed network.
Aspirin Disrupts the Crosstalk of Angiogenic and Inflammatory Cytokines between 4T1 Breast Cancer Cells and Macrophages

The tumor microenvironment is rich in multiple cell types that influence tumor development. Macrophages infiltrate tumors, where they are the most abundant immune cell population and secrete a number of cytokines. Aspirin acts as a chemopreventive agent against cancer development. This study investigated whether aspirin regulates crosstalk between breast cancer cells and macrophages. To study these interactions in a tumor microenvironment, a conditioned media was employed using 4T1 breast cancer cells cultured in RAW 264.7 cell-conditioned medium (RAW-CM), and a cocultured model of both cells was used. When 4T1 cells were cultured in the RAW-CM, there were increases in cell viability and secretion of the cytokines VEGF, PAI-1, TNF-α, and IL-6. Treatment with aspirin inhibited 4T1 cell growth and migration and MCP-1, PAI-1, and IL-6 production. In the coculture of both cells, aspirin inhibited secretion of MCP-1, IL-6, and TGF-β. Furthermore, aspirin significantly decreased the M2 macrophage marker CD206, but increased M1 marker CD11c expression. In summary, aspirin treatment inhibited the crosstalk of 4T1 and RAW 264.7 cells through regulation of angiogenic and inflammatory mediator production and influenced the M1/M2 macrophage subtype. This highlighted that aspirin suppresses the tumor favorable microenvironment and could be a promising agent against triple-negative breast cancer.
Rank Minimization for Snapshot Compressive Imaging

Snapshot compressive imaging (SCI) refers to compressive imaging systems where multiple frames are mapped into a single measurement, with video compressive imaging and hyperspectral compressive imaging as two representative applications. Though exciting results of high-speed videos and hyperspectral images have been demonstrated, the poor reconstruction quality precludes SCI from wide applications. This paper aims to boost the reconstruction quality of SCI via exploiting the high-dimensional structure in the desired signal. We build a joint model to integrate the nonlocal self-similarity of video/hyperspectral frames and the rank minimization approach with the SCI sensing process. Following this, an alternating minimization algorithm is developed to solve this non-convex problem. We further investigate the special structure of the sampling process in SCI to tackle the computational workload and memory issues in SCI reconstruction. Both simulation and real data (captured by four different SCI cameras) results demonstrate that our proposed algorithm leads to significant improvements compared with current state-of-the-art algorithms. We hope our results will encourage the researchers and engineers to pursue further in compressive imaging for real applications.
Deep Transfer Learning for Cross-domain Activity Recognition

Human activity recognition plays an important role in people's daily life. However, it is often expensive and time-consuming to acquire sufficient labeled activity data. To solve this problem, transfer learning leverages the labeled samples from the source domain to annotate the target domain which has few or none labels. Unfortunately, when there are several source domains available, it is difficult to select the right source domains for transfer. The right source domain means that it has the most similar properties with the target domain, thus their similarity is higher, which can facilitate transfer learning. Choosing the right source domain helps the algorithm perform well and prevents the negative transfer. In this paper, we propose an effective Unsupervised Source Selection algorithm for Activity Recognition (USSAR). USSAR is able to select the most similar K source domains from a list of available domains. After this, we propose an effective Transfer Neural Network to perform knowledge transfer for Activity Recognition (TNNAR). TNNAR could capture both the time and spatial relationship between activities while transferring knowledge. Experiments on three public activity recognition datasets demonstrate that: 1) The USSAR algorithm is effective in selecting the best source domains. 2) The TNNAR method can reach high accuracy when performing activity knowledge transfer.
Differentiation between metastatic and tumour-free cervical lymph nodes in patients with papillary thyroid carcinoma by grey-scale sonographic texture analysis

Purpose Papillary thyroid carcinoma (PTC) is the most common thyroid cancer, and cervical lymph nodes (LNs) are the most common extrathyroid metastatic involvement. Early detection and reliable diagnosis of LNs can lead to improved cure rates and management costs. This study explored the potential of texture analysis for texture-based classification of tumour-free and metastatic cervical LNs of PTC in ultrasound imaging. Material and methods A total of 274 LNs (137 tumour-free and 137 metastatic) were explored using the texture analysis (TA) method. Up to 300 features were extracted for texture analysis in three normalisations (default, 3sigma, and 1-99%). Linear discriminant analysis was employed to transform raw data to lower-dimensional spaces and increase discriminative power. The features were classified by the first nearest neighbour classifier. Results Normalisation reflected improvement on the performance of the classifier; hence, the features under 3sigma normalisation schemes through FFPA (fusion Fisher plus the probability of classification error [POE] + average correlation coefficients [ACC]) features indicated high performance in classifying tumour-free and metastatic LNs with a sensitivity of 99.27%, specificity of 98.54%, accuracy of 98.90%, positive predictive value of 98.55%, and negative predictive value of 99.26%. The area under the receiver operating characteristic curve was 0.996. Conclusions TA was determined to be a reliable method with the potential for characterisation. This method can be applied by physicians to differentiate between tumour-free and metastatic LNs in patients with PTC in conventional ultrasound imaging.
Development of an Activity-Dependent Epidural Stimulation System in Freely Moving Spinal Cord Injured Rats: A Proof of Concept Study

Purpose: Extensive pre-clinical and clinical experimentation has yielded data on the robustness and versatility of epidural stimulation (ES) strategies to activate spinal neural circuitry to produce functional benefits. Increasing studies are now reporting that closed-loop electrical stimulation delivery methods significantly enhance the neuromodulation effects of stimulation, to in turn, improve physiological outcomes of the intervention. No studies have yet explored the feasibility and usage of closed-loop systems to neuromodulate the cervical spinal cord using ES. Methods: We developed an activity-dependent system that utilizes electromyography (EMG) activity to trigger epidural stimulation (tES) of the cervical spinal cord in awake, freely moving rats. Experiments were performed on rats that were implanted with chronic forelimb EMG and cervical epidural implants, with (n = 7) and without (n = 2) a complete C4 spinal hemisection. Results: Our results show that the EMG triggered activity-dependent system can be reliably applied and reproduced for: (i) stimulating multiple rats simultaneously throughout the night during free home-cage activity and (ii) use as a mobile system for testing and training during various short-term behavioral testing conditions. The system was able to consistently generate stimulation pulse trains in response to attempted EMG activity that crossed a user-defined threshold in all rats for all experiments, including the overnight experiments that lasts for 7 h/session for 6 days/week through the 3-month period. Conclusion: The developed closed-loop system can be considered to represent a class of bidirectional neural prostheses via a circuit that enables two-way interactions between neural activity (real-time processing of EMG activity) and external devices (such as a stimulator). It can operate autonomously for extended periods of time in unrestrained rats, allowing its use as a long-term therapeutic tool. It can also enable us to study the long-term physiological effects of incorporating electrical stimulation techniques into the nervous system. The system can also be experimented for connecting several neural systems into a Brainet by combining neural signals from multiple rats dynamically and in real-time so as to enhance motor performance. Studies are ongoing in our laboratory to test the usefulness of this system in the recovery of hand function after cervical spinal cord injuries.
Regulatory Role of CD4+ T Cells in Myocarditis

Myocarditis is an important cause of heart failure in young patients. Autoreactive, most often, infection-triggered CD4+ T cells were confirmed to be critical for myocarditis induction. Due to a defect in clonal deletion of heart-reactive CD4+ T cells in the thymus of mice and humans, significant numbers of heart-specific autoreactive CD4+ T cells circulate in the blood. Normally, regulatory T cells maintain peripheral tolerance and prevent spontaneous myocarditis development. In the presence of tissue damage and innate immune activation, however, activated self-antigen-loaded dendritic cells promote CD4+ effector T cell expansion and myocarditis. So far, a direct pathogenic role has been described for both activated Th17 and Th1 effector CD4+ T cell subsets, though Th1 effector T cell-derived interferon-gamma was shown to limit myocarditis severity and prevent transition to inflammatory dilated cardiomyopathy. Interestingly, recent observations point out that various CD4+ T cell subsets demonstrate high plasticity in maintaining immune homeostasis and modulating disease phenotypes in myocarditis. These subsets include Th1 and Th17 effector cells and regulatory T cells, despite the fact that there are still sparse and controversial data on the specific role of FOXP3-expressing Treg in myocarditis. Understanding the specific roles of these T cell populations at different stages of the disease progression might provide a key for the development of successful therapeutic strategies.
Fecal Microbiota Transplantation Is Associated With Reduced Morbidity and Mortality in Porcine Circovirus Associated Disease

Porcine circovirus associated disease (PCVAD) is a term used to describe the multi-factorial disease syndromes caused by porcine circovirus type 2 (PCV-2), which can be reproduced in an experimental setting through the co-infection of pigs with PCV-2 and porcine reproductive and respiratory syndrome virus (PRRSV). The resulting PCVAD-affected pigs represent a subpopulation within the co-infected group. In co-infection studies, the presence of increased microbiome diversity is linked to a reduction in clinical signs. In this study, fecal microbiota transplantation (FMT) was investigated as a means to prevent PCVAD in pigs co-infected with PRRSV and PCV-2d. The sources of the FMT material were high-parity sows with a documented history of high health status and robust litter characteristics. The analysis of the donated FMT material showed the absence of common pathogens along with the presence of diverse microbial phyla and families. One group of pigs (n = 10) was administered the FMT while a control group (n = 10) was administered a sterile mock-transplant. Over the 42-day post-infection period, the FMT group showed fewer PCVAD-affected pigs, as evidenced by a significant reduction in morbidity and mortality in transplanted pigs, along with increased antibody levels. Overall, this study provides evidence that FMT decreases the severity of clinical signs following co-infection with PRRSV and PCV-2 by reducing the prevalence of PCVAD.
Asthma is not associated with the need for surgery in Crohn’s disease when controlling for smoking status: a population-based cohort study

Purpose Growing evidence suggests asthma and Crohn’s disease commonly cooccur. However, the impact of asthma on the prognosis of Crohn’s disease is unknown. The aim of our study was to assess the effect of asthma on the need for intestinal resection in patients with Crohn’s disease while adjusting for smoking status, imputed from a smaller, secondary data set. Patients and methods Using health administrative data from a universally funded healthcare plan in Alberta, Canada, we conducted a cohort study to assess the effect of asthma on the need for surgery in patients with Crohn’s disease diagnosed between 2002 and 2008 (N=2,113). Validated algorithms were used to identify incident cases of Crohn’s disease, cooccurring asthma, and intestinal resection. The association between asthma and intestinal resection was estimated using multivariable Cox proportional hazards regression. Smoking status was imputed using a novel method using martingale residuals, derived from a data set of 485 patients enrolled in the Alberta Inflammatory Bowel Disease Consortium (2007 to 2014) who completed environmental questionnaires. All analyses were adjusted for age, sex, rural/urban status, and mean neighborhood income quintile. Results Asthma did not increase the risk of surgery in the health administrative data when not adjusting for smoking status (HR 1.03, 95% CI 0.81 to 1.29). The association remained nonsignificant after imputing smoking status in the health administrative data (HR 1.03, 95% CI 0.81 to 1.29). Conclusion Although asthma is associated with an increased risk of Crohn’s disease, co-occurring asthma is not associated with the risk of surgery in these patients. This null association persisted after adjusting for smoking status. This study described a novel method to adjust for confounding (smoking status) in time-to-event analyses, even when the confounding variable is unmeasured in health administrative data.
Tauroursodeoxycholic Acid Alleviates H2O2-Induced Oxidative Stress and Apoptosis via Suppressing Endoplasmic Reticulum Stress in Neonatal Rat Cardiomyocytes

Introduction: We aimed to test the mechanism of protective effects of tauroursodeoxycholic acid (TUDCA) on cardiovascular disease using cultured cardiomyocytes. Methods: Neonatal rat cardiomyocytes (NRCMs) were isolated and cultured and then the cells were divided into 4 groups based on the treatments: control group (cells treated with culture medium), H2O2/thapsigargin (TG) group (cells treated with oxidative stress and endoplasmic reticulum [ER] stress inducer), TUDCA group, and H2O2/TG + TUDCA group. The treated NRCMs were then subjected to serial analyses including flow cytometry, enzyme-linked immunosorbent assay, and Western blotting. Results: Tauroursodeoxycholic acid significantly attenuated H2O2-induced reactive oxygen species generation and lactate dehydrogenase release and restored H2O2-induced reductions of glutathione and superoxide dismutase levels in NRCMs. Tauroursodeoxycholic acid also alleviated H2O2-induced cardiomyocytes apoptosis, as well as the Bax/Bcl2 ratio compared with that of H2O2 treated alone. In addition, TUDCA suppressed TG-induced ER stress as reflected by inversing cell viability and the expression levels of glucose-regulated protein 78 kDa and C/enhancer-binding protein homologous protein. Conclusion: Our data indicated that TUDCA-mediated inhibition on H2O2-induced oxidative stress and cardiomyocytes apoptosis was through suppressing ER stress, and TUDCA possesses the potential to be developed as therapeutic tool in clinical use for cardiovascular diseases.
Past and potential future population dynamics of three grouse species using ecological and whole genome coalescent modeling

Abstract Studying demographic history of species provides insight into how the past has shaped the current levels of overall biodiversity and genetic composition of species, but also how these species may react to future perturbations. Here we investigated the demographic history of the willow grouse (Lagopus lagopus), rock ptarmigan (Lagopus muta), and black grouse (Tetrao tetrix) through the Late Pleistocene using two complementary methods and whole genome data. Species distribution modeling (SDM) allowed us to estimate the total range size during the Last Interglacial (LIG) and Last Glacial Maximum (LGM) as well as to indicate potential population subdivisions. Pairwise Sequentially Markovian Coalescent (PSMC) allowed us to assess fluctuations in effective population size across the same period. Additionally, we used SDM to forecast the effect of future climate change on the three species over the next 50 years. We found that SDM predicts the largest range size for the cold‐adapted willow grouse and rock ptarmigan during the LGM. PSMC captured intraspecific population dynamics within the last glacial period, such that the willow grouse and rock ptarmigan showed multiple bottlenecks signifying recolonization events following the termination of the LGM. We also see signals of population subdivision during the last glacial period in the black grouse, but more data are needed to strengthen this hypothesis. All three species are likely to experience range contractions under future warming, with the strongest effect on willow grouse and rock ptarmigan due to their limited potential for northward expansion. Overall, by combining these two modeling approaches, we have provided a multifaceted examination of the biogeography of these species and how they have responded to climate change in the past. These results help us understand how cold‐adapted species may respond to future climate changes.
A Cache-based Optimizer for Querying Enhanced Knowledge Bases

With recent emerging technologies such as the Internet of Things (IoT), information collection on our physical world and environment can be achieved at a much higher granularity and such detailed knowledge will play a critical role in improving the productivity, operational effectiveness, decision making, and in identifying new business models for economic growth. Efficient discovery and querying such knowledge remains a key challenge due to the limited capability and high latency of connections to the interfaces of knowledge bases, e.g., the SPARQL endpoints. In this article, we present a querying system on SPARQL endpoints for knowledge bases that performs queries faster than the state-of-the-art systems. Our system features a cache-based optimization scheme to improve querying performance by prefetching and caching the results of predicted potential queries. The evaluations on query sets from SPARQL endpoints of DBpedia and Linked GeoData showcase the effectiveness of our approach.
Towards a general mathematical theory of experimental science

In this article we lay the groundwork for a general mathematical theory of experimental science. The starting point will be the notion of verifiable statements, those assertions that can be shown to be true with an experimental test. We study the algebra of such objects and show how it is closed only under finite conjunction and countable disjunction. With simple constructions, we show that the set of possible cases distinguishable by verifiable statements is equipped with a natural Kolmogorov and second countable topology and a natural $\sigma$-algebra. This gives a clear physical meaning to those mathematical structures and provides a strong justification for their use in science. It is our hope and belief that such an approach can be extended to many areas of fundamental physics and will provide a consistent vocabulary across scientific domains.