Miracle-dz/UniFAR-FTUs huggingface dataset:
IRF4 and IRGs Delineate Clinically Relevant Gene Expression Signatures in Systemic Lupus Erythematosus and Rheumatoid Arthritis

Introduction: Overactivation of the type I interferon (IFN) signature has been observed in several systemic autoimmune conditions, such as Systemic Lupus Erythematosus (SLE) or Rheumatoid Arthritis (RA). Impaired control of Interferon-Responding Genes (IRGs) expression by their regulatory mechanisms, including Interferon Regulatory Factors (IRFs), may underlie these findings and it may explain the heterogeneity observed among these conditions. In the present study we aimed to evaluate the associations between IRF4 gene expression and those of IRGs in SLE and RA patients to gain insight about its links with the IFN signature as well as to explore the potential clinical relevance of these associations. Methods: The gene expression of IRF4 and IRGs (IFI44, IFI44L, IFI6, and MX1) in peripheral blood was analyzed in 75 SLE patients, 98 RA patients, and 28 healthy controls. A group of 13 biological-naïve RA patients was prospectively followed upon TNFα-blockade. The associations among IRF4 and IRGs were evaluated by principal component analyses (PCA), correlations and network analyses. Publicly available datasets were used for replication. Results: A broad activation of IRGs was observed in autoimmune patients, although certain heterogeneity can be distinguished, whereas IRF4 was only upregulated in RA. The differential expression of IRF4 in RA was then confirmed in publicly available gene expression datasets. PCA revealed different associations among IRF4 and IRGs in each condition, which was later confirmed by correlation and network analyses. Cluster analysis identified 3 gene expression signatures on the basis of IRF4 and IRGs expression which were differentially used by SLE and RA patients. Cluster III was associated with markers of disease severity in SLE patients. Cluster II, hallmarked by IRF4 upregulation, was linked to clinical stage and mild disease course in RA. TNFα-blockade led to changes in the association between IRF4 and IRGs, whereas increasing IRF4 expression was associated with a good clinical outcome in RA. Conclusions: The differential expression of IRF4 and IRGs observed in SLE and RA can delineate gene expression signatures associated with clinical features and treatment outcomes. These results support a clinically-relevant phenomenon of shaping of the IFN signature by IRF4 in autoimmune patients.
NrnC, an RNase D-Like Protein From Agrobacterium, Is a Novel Octameric Nuclease That Specifically Degrades dsDNA but Leaves dsRNA Intact

NrnC from Agrobacterium tumefaciens (At_NrnC, UniProt accession number A9CG28) is a nuclease containing a single DEDDy domain. Here, we determined the structures of both the apo and metal-ion-bound forms of At_NrnC. Although the overall structure of the At_NrnC protomer is similar to that of the RNase D exonuclease domain, nuclease assays unexpectedly revealed that At_NrnC possesses remarkably different substrate specificity. In contrast to RNase D, which degrades both single-stranded RNA (ssRNA) and double-stranded RNA (dsRNA), At_NrnC hydrolyses ssRNA, single-stranded DNA (ssDNA), and double-stranded DNA (dsDNA) with high efficiency but does not degrade dsRNA. Crystal packing analysis and biochemical data indicated that At_NrnC forms an octameric hollow cylindrical structure that allows ssRNA, ssDNA, and dsDNA, but not dsRNA, to enter the central tunnel where the multiple active sites perform hydrolysis. This novel structural feature confers a high processivity and is responsible for the preference of At_NrnC for longer dsDNA substrates.
Effect of Linseed Supplementation on Total Longissimus Muscle Lipid Composition and Shelf-Life of Beef From Young Maremmana Bulls

Twenty young Maremmana bulls were randomly allotted to two dietary groups during a finishing period (~90 days): C diet (without lipid supplementation), and L diet (with linseed supplementation, 20% concentrate feed). The aim was to investigate the effects of dietary linseed supplementation on the intramuscular fat composition, and the shelf-life of minced beef. The L diet did not alter the dressing rate and daily weight gain, whereas the C18:3n-3 content in the intramuscular fat significantly increased (from 0.85 to 1.78 g/100 g of total fatty acid, +109%), leading to a reduction in a n-6/n-3 ratio below four and an increase in the overall proportion of long chain n-3 fatty acids in beef lipids. These effects were principally related to differences in the distribution of fatty acids between the neutral and polar lipids. The results demonstrated that linseed supplementation modified the lipid composition of beef, without negatively impacting overall productivity, in the period from weaning the bull until its slaughter. However, the total amount of n-3 fatty acids in the beef of young bulls on the L diet, was not sufficient for the aliquot of humans′ n-3 fatty acid requirements. This was mainly related to the low levels of fat in the beef. Comparing beef samples from animals on the L diet, with samples from animals on the C diet, the higher levels of n-3 polyunsaturated fatty acids led to a significant reduction of shelf-life starting after 2 days of storage at 4°C, because of fatty acid and cholesterol oxidation. The L diet group samples showed a higher level of TBARS (+80%) and COPs (+73%), two important parameters to estimate the oxidation level of beef. This suggests that enriching beef with n-3 fatty acids should be coupled with packaging techniques that consider the higher susceptibility to lipid oxidation. In conclusion, dietary linseed supplementation led to a higher proportion of n-3 fatty acids in beef lipids, however, the natural content of antioxidant substances was not able to protect intramuscular fat from oxidation during storage at 4°C.
The Sensitivity of Subsurface Microbes to Ocean Warming Accentuates Future Declines in Particulate Carbon Export

Under future warming Earth System Models (ESMs) project a decrease in the magnitude of downward particulate organic carbon (POC) export, suggesting the potential for carbon storage in the deep ocean will be reduced. Projections of POC export can also be quantified using an alternative physiologically-based approach, the Metabolic Theory of Ecology (MTE). MTE employs an activation energy (Ea) describing organismal metabolic sensitivity to temperature change, but does not consider changes in ocean chemistry or physics. Many ESMs incorporate temperature dependent functions, where rates (e.g. respiration) scale with temperature. Temperature sensitivity describes how temperature dependence varies across metabolic rates or species. ESMs acknowledge temperature sensitivity between rates (e.g. between heterotrophic and autotropic processes), but due to a lack of empirical data cannot parameterise for variation within rates, such as differences within species or biogeochemical provinces. Here we investigate how varying temperature sensitivity affects heterotrophic microbial respiration and hence future POC export. Using satellite-derived data and ESM temperature projections we applied microbial MTE, with varying temperature sensitivity, to estimates of global POC export. In line with observations from polar regions and the deep ocean we imposed an elevated temperature sensitivity (Ea = 1.0 eV) to cooler regions; firstly to the Southern Ocean (south of 40 S) and secondly where temperature at 100 m depth < 13 C. Elsewhere in both these scenarios Ea was set to 0.7 eV (moderate sensitivity/classic MTE). Imposing high temperature sensitivity in cool regions resulted in projected declines in export of 17  1 % (< 40 S) and 23  1 % (< 13 C) by 2100 relative to the present day. Hence varying microbial temperature sensitivity resulted in at least twofold greater declines in POC export than suggested by classic MTE derived in this study (12  1 %, Ea = 0.7 eV globally) or ESMs (1-12 %). The sparse observational data currently available suggests metabolic temperature sensitivity of organisms likely differs depending on the oceanic province they reside in. We advocate temperature sensitivity to be incorporated in biogeochemical models to improve projections of future carbon export, which could be currently underestimating future POC export.
Novel Plastid-Nuclear Genome Combinations Enhance Resistance to Citrus Canker in Cybrid Grapefruit

Host disease resistance is the most desirable strategy for control of citrus canker, a disease caused by a gram-negative bacterium Xanthomonas citri subsp. citri. However, no resistant commercial citrus cultivar has been identified. Cybridization, a somatic hybridization approach that combines the organelle and nuclear genomes from different species, was used to create cybrids between citrus canker resistant ‘Meiwa’ kumquat (Fortunella crassifolia Swingle snym. Citrus japonica Thunb.) and susceptible grapefruit (Citrus paradisi Macfad) cultivars. From these fusions, cybrids with grapefruit nucleus, kumquat mitochondria and kumquat chloroplasts and cybrids with grapefruit nucleus, kumquat mitochondria and grapefruit chloroplasts were generated. These cybrids showed a range of citrus canker response, but all cybrids with kumquat chloroplasts had a significantly lower number of lesions and lower Xanthomonas citri subsp. citri populations than the grapefruit controls. Cybrids with grapefruit chloroplasts had a significantly higher number of lesions than those with kumquat chloroplasts. To understand the role of chloroplasts in the cybrid disease defense, quantitative PCR was performed on both cybrid types and their parents to examine changes in gene expression during Xanthomonas citri subsp. citri infection. The results revealed chloroplast influences on nuclear gene expression, since isonuclear cybrids and ‘Marsh’ grapefruit had different gene expression profiles. In addition, only genotypes with kumquat chloroplasts showed an early up-regulation of reactive oxygen species genes upon Xanthomonas citri subsp. citri infection. These cybrids have the potential to enhance citrus canker resistance in commercial grapefruit orchards. They also serve as models for understanding the contribution of chloroplasts to plant disease response and raise the question of whether other alien chloroplast genotypes would condition similar results.
Xiaochaihutang Inhibits the Activation of Hepatic Stellate Cell Line T6 Through the Nrf2 Pathway

Xiaochaihutang (XCHT) is one of classic prescriptions in Treatise on Febrile Diseases in China which was reported to have the effect of anti-hepatic fibrosis in vivo. Activation of hepatic stellate cells (HSCs) is now well established as a central driver of fibrosis in liver injury. Nuclear factor erythroid 2-related factor 2 (Nrf2) is an important element for anti-oxidative damage which is one of the key factors responsible for occurrence. This study was to investigate the effect of XCHT compound serum on HSCs activation and focus on the Nrf2 pathway. Rats in treatment groups were given the appropriate doses of XCHT granules (5 g/kg) and Silybin (50 mg/kg) for 6 days, and the serum were obtained. The compound serum was used to intervene HSCs. The results found that XCHT compound serum significantly inhibited the proliferation of HSCT6 cells. The number of α-SMA positive stained cells in HSCT6 cells and the content of Collagen type I (collagen-I) in supernatant were significantly decreased indicating suppression of activated HSCs. Compared with the control group, the nuclear transcription of Nrf2 and the expressions of Nqo1, GCLC, and GCLM were significantly increased in XCHT group. However, the effects of XCHT were inhibited in Nrf2-siRNA transfected HSCT6 cells. These studies demonstrated that XCHT could inhibit HSCT6 cell proliferation and activation. The mechanism might be related to up-regulation of the Nrf2 pathway against oxidative stress.
Evolution of Physical-Layer Communications Research in the Post-5G Era

The evolving fifth generation new radio cellular standardization efforts at the third generation partnership project brings into focus a number of questions on relevant research problems in physical-layer communications for study by both academia and industry. To address this question, we show that the peak download data rates for both WiFi and cellular systems have been scaling exponentially with time over the last 25 years. While keeping up with the historic cellular trends will be possible in the near-term with a modest bandwidth and hardware complexity expansion, even a reasonable stretching of this road-map into the far future would require significant bandwidth accretion, perhaps possible at the millimeter wave, sub-millimeter wave, or terahertz regimes. The consequent increase in focus on systems at higher carrier frequencies necessitates a paradigm shift from the reuse of over-simplified (yet mathematically elegant) models, often inherited from sub-6 GHz systems to a more holistic view, where real measurements guide, motivate, and refine the building of relevant but possibly complicated models, solution space(s), and good solutions. To motivate the need for this shift, we illustrate how the traditional abstraction fails to correctly estimate the delay spread of millimeter wave wireless channels and hand blockage losses at higher carrier frequencies. We conclude this paper with a broad set of implications for future research prospects at the physical-layer, including key use-cases, possible research policy initiatives, and structural changes needed in telecommunications departments at universities.
Identifying Barriers to Adoption for Rust through Online Discourse

Rust is a low-level programming language known for its unique approach to memory-safe systems programming and for its steep learning curve. To understand what makes Rust difficult to adopt, we surveyed the top Reddit and Hacker News posts and comments about Rust; from these online discussions, we identified three hypotheses about Rust's barriers to adoption. We found that certain key features, idioms, and integration patterns were not easily accessible to new users.
HG-Caffe: Mobile and Embedded Neural Network GPU (OpenCL) Inference Engine with FP16 Supporting

Breakthroughs in the fields of deep learning and mobile system-on-chips are radically changing the way we use our smartphones. However, deep neural networks inference is still a challenging task for edge AI devices due to the computational overhead on mobile CPUs and a severe drain on the batteries. In this paper, we present a deep neural network inference engine named HG-Caffe, which supports GPUs with half precision. HG-Caffe provides up to 20 times speedup with GPUs compared to the original implementations. In addition to the speedup, the peak memory usage is also reduced to about 80%. With HG-Caffe, more innovative and fascinating mobile applications will be turned into reality.
Efficient, Dynamic Multi-tenant Edge Computation in EdgeOS

In the future, computing will be immersed in the world around us -- from augmented reality to autonomous vehicles to the Internet of Things. Many of these smart devices will offer services that respond in real time to their physical surroundings, requiring complex processing with strict performance guarantees. Edge clouds promise a pervasive computational infrastructure a short network hop away from end devices, but today's operating systems are a poor fit to meet the goals of scalable isolation, dense multi-tenancy, and predictable performance required by these emerging applications. In this paper we present EdgeOS, a micro-kernel based operating system that meets these goals by blending recent advances in real-time systems and network function virtualization. EdgeOS introduces a Featherweight Process model that offers lightweight isolation and supports extreme scalability even under high churn. Our architecture provides efficient communication mechanisms, and low-overhead per-client isolation. To achieve high performance networking, EdgeOS employs kernel bypass paired with the isolation properties of Featherweight Processes. We have evaluated our EdgeOS prototype for running high scale network middleboxes using the Click software router and endpoint applications using memcached. EdgeOS reduces startup latency by 170X compared to Linux processes and over five orders of magnitude compared to containers, while providing three orders of magnitude latency improvement when running 300 to 1000 edge-cloud memcached instances on one server.
The Random Forest Classifier in WEKA: Discussion and New Developments for Imbalanced Data

Data analysis and machine learning have become an integrative part of the modern scientific methodology, providing automated techniques to predict further information based on observations. One of these classification and regression techniques is the random forest approach. Those decision tree based predictors are best known for their good computational performance and scalability. However, in case of severely imbalanced training data, as often seen in medical studies' data with large control groups, the training algorithm or the sampling process has to be altered in order to improve the prediction quality for minority classes. In this work, a balanced random forest approach for WEKA is proposed. Furthermore, the prediction quality of the unmodified random forest implementation and the new balanced random forest version for WEKA are evaluated against reference implementations in R. Two-class problems on balanced data sets and imbalanced medical studies' data are investigated. A superior prediction quality using the proposed method for imbalanced data is shown compared to the other three techniques.
An Efficient Linux Kernel Implementation of Service Function Chaining for Legacy VNFs Based on IPv6 Segment Routing

We consider the IPv6 Segment Routing (SRv6) technology for Service Function Chaining of Virtual Network Functions (VNFs). Most of the VNFs are legacy VNFs (not aware of the SRv6 technology) and expect to process traditional IP packets. An SR proxy is needed to support them. We have extended the implementation of SRv6 in the Linux kernel, realizing an open source SR-proxy, referred to as SRNK (SR-Proxy Native Kernel). The performance of the proposed solution (SRNKvl) has been evaluated, identifying a poor scalability with respect to the number of VNFs to be supported in a node. Therefore we provided a second design (SRNKv2), enhancing the Linux Policy Routing framework. The performance of SRNKv2 is independent from the number of supported VNFs in a node. We compared the performance of SRNKv2 with a reference scenario not performing the encapsulation and decapsulation operation and demonstrated that the overhead of SRNKv2 is very small, on the order of 3.5%.
A nonlinear thermomechanical formulation for anisotropic volume and surface continua

A thermomechanical, polar continuum formulation under finite strains is proposed for anisotropic materials using a multiplicative decomposition of the deformation gradient. First, the kinematics and conservation laws for three-dimensional, polar, and nonpolar continua are obtained. Next, these kinematics and conservation laws are connected to their corresponding counterparts for surface continua, based on Kirchhoff–Love assumptions. Then the shell material models are extracted from three-dimensional material models for finite-temperature problems using established connections. The weak forms are obtained for both three-dimensional nonpolar continua and Kirchhoff–Love shells. These formulations are expressed in tensorial form so that they can be used in both curvilinear and Cartesian coordinates. They can be used to model anisotropic crystals and soft biological materials.
Rational Threshold Cryptosystems

We propose a framework for threshold cryptosystems under a permissionless-economic model in which the participants are rational profit-maximizing entities. To date, threshold cryptosystems have been considered under permissioned settings with a limited adversary. Our framework relies on an escrow service that slashes and redistributes deposits to incentivize participants to adhere desired behaviors. Today, more than ever, sophisticated escrow services can be implemented over public blockchains like Ethereum, without additional trust assumptions. The key threat to rational threshold cryptosystems is collusion---by cooperating `illegally', a subset of participants can reveal the cryptosystem's secret, which, in turn is translated to unfair profit. Our countermeasure to collusion is framing. If the escrow is notified of collusion, it rewards the framer and slashes the deposits of all other participants. We show that colluding parties find themselves in the prisoner's dilemma, where the dominant strategy is framing.
The Capacity Region of the Arbitrarily Varying MAC: With and Without Constraints

We determine both the random code capacity region and the deterministic code capacity region of the arbitrarily varying multiple access channel (AVMAC) under input and state constraints. For the AVMAC without constraints, the characterization due to Ahlswede and Cai is complete except for two cases, pointed out in the literature as an open problem. The missing piece is obtained as a special case of our results.
Practical Verifiable In-network Filtering for DDoS Defense

In light of ever-increasing scale and sophistication of modern distributed denial-of-service (DDoS) attacks, recent proposals show that in-network filtering of DDoS traffic at a handful of transit networks can handle volumetric attacks effectively. In this paper, we identify a subtle but important security risk in existing in-network filtering proposals. That is, a transit network may use the in-network filtering services as an excuse for any arbitrary packet drops made for its own benefit. For example, a malicious transit network may execute any filtering rules to discriminate against some of its neighboring networks based on its business preference while claiming that it is for the purpose of DDoS defense. We argue that this is due to the lack of verifiable filtering—i.e., no single party can check if a transit network executes the filter rules correctly as requested by the DDoS victims. To make in-network filtering a more robust defense primitive, we propose a verifiable in-network filtering system, called VIF, that exploits emerging hardware-based trusted execution environments (TEEs) and offers filtering verifiability to DDoS victims and neighboring networks. Our proof of concept demonstrates that a VIF filter implementation on commodity servers with TEE support can handle traffic at line rate (e.g., 10 Gb/s) and execute up to 3,000 filter rules. We show that VIF can scale to handle larger traffic volume (e.g., 500 Gb/s) and more complex filtering operations (e.g., 150,000 filter rules) by parallelizing the TEE-based filters. As a practical deployment model, we suggest that Internet exchange points (IXPs) are the good candidates to be early adopters of our verifiable filters due to their central locations and flexible software-defined architecture. Our large-scale simulations of two realistic attacks (i.e., DNS amplification, Mirai-based flooding) show that adopting VIF filtering service at only a small number (e.g., 5-25) of large IXPs is sufficient to handle the majority (e.g., up to 80-90%) of DDoS traffic.