Miracle-dz/UniFAR-FTUs huggingface dataset:
Predictive Value of the Cardio‐Ankle Vascular Index for Cardiovascular Events in Patients at Cardiovascular Risk

Background Arterial stiffness is an important predictor of cardiovascular events; however, indexes for measuring arterial stiffness have not been widely incorporated into routine clinical practice. This study aimed to determine whether the cardio‐ankle vascular index (CAVI), based on the blood pressure–independent stiffness parameter β and reflecting arterial stiffness from the origin of the ascending aorta, is a good predictor of cardiovascular events in patients with cardiovascular disease risk factors in a large prospective cohort. Methods and Results This multicenter prospective cohort study, commencing in May 2013, with a 5‐year follow‐up period, included patients (aged 40‒74 years) with cardiovascular disease risks. The primary outcome was the composite of cardiovascular death, nonfatal stroke, or nonfatal myocardial infarction. Among 2932 included patients, 2001 (68.3%) were men; the mean (SD) age at diagnosis was 63 (8) years. During the median follow‐up of 4.9 years, 82 participants experienced primary outcomes. The CAVI predicted the primary outcome (hazard ratio, 1.38; 95% CI, 1.16‒1.65; P<0.001). In terms of event subtypes, the CAVI was associated with cardiovascular death and stroke but not with myocardial infarction. When the CAVI was incorporated into a model with known cardiovascular disease risks for predicting cardiovascular events, the global χ2 value increased from 33.8 to 45.2 (P<0.001), and the net reclassification index was 0.254 (P=0.024). Conclusions This large cohort study demonstrated that the CAVI predicted cardiovascular events. Registration URL: https://www.clinicaltrials. gov; Unique identifier: NCT01859897.
Operationalizing a Behavioral Health Services Cascade of Care Model: Lessons Learned from a 33-Site Implementation in Juvenile Justice Community Supervision.

Michael L. Dennis Corey N. Smith Chestnut Health Systems Steven Belenko Temple University Danica Knight Texas Christian University Larkin McReynolds Columbia University/New York State Psychiatric Institute Grace Rowan New York University Richard Dembo University of South Florida Ralph DiClemente New York University Angela Robertson Mississippi State University Tisha Wiley National Institute on Drug Abuse (NIDA)
A simple, vertically resolved model of tropical disturbances with a humidity closure

Abstract A vertically resolved linear model for large-scale motions in an equatorial non-rotating atmosphere is presented. Precipitation is forced by tropospheric precipitable water, cloud—radiation interactions (CRI), and wind-induced surface heat exchange (WISHE). The dynamics of the model are based on the assumption that the vertical heating profile has the shape of the first baroclinic mode. The vertical profiles of vertical velocity, temperature perturbation, etc. are calculated using an upward radiation boundary condition. The modelled modes are of three types: fast gravity waves that resemble adiabatic modes with the vertical wavelength twice the depth of the troposphere; convectively coupled gravity modes that are damped and move with a phase speed of ≈17 m s−1; and the unstable moisture mode. The value of the model is that under a single dynamical assumption it yields the observed phase speed for the convectively coupled gravity waves that map to Kelvin waves in the equatorial beta plane case while still producing the unstable moisture mode. However, the model lacks the precipitation physics needed to produce the observed destabilization of the gravity mode.
A composition framework for change management

Change management has been a long-standing challenge for network operations. The large scale and diversity of networks, their complex dependencies, and continuous evolution through technology and software updates combined with the risk of service impact create tremendous challenges to effectively manage changes. In this paper, we use data from a large service provider and experiences of their operations teams to highlight the need for quick and easy adaptation of change management capabilities and keep up with the continuous network changes. We propose a new framework CORNET (COmposition fRamework for chaNge managEmenT) with key ideas of modularization of changes into building blocks, flexible composition into change workflows, change plan optimization, change impact verification, and automated translation of high-level change management intent into low-level implementations and mathematical models. We demonstrate the effectiveness of CORNET using real-world data collected from 4G and 5G cellular networks and virtualized services such as VPN and SDWAN running in the cloud as well as experiments conducted on a testbed of virtualized network functions. We also share our operational experiences and lessons learned from successfully using CORNET within a large service provider network over the last three years.
XLINK: QoE-driven multi-path QUIC transport in large-scale video services

We report XLINK, a multi-path QUIC video transport solution with experiments in Taobao short videos. XLINK is designed to meet two operational challenges at the same time: (1) Optimized user-perceived quality of experience (QoE) in terms of robustness, smoothness, responsiveness, and mobility and (2) Minimized cost overhead for service providers (typically CDNs). The core of XLINK is to take the opportunity of QUIC as a user-space protocol and directly capture user-perceived video QoE intent to control multi-path scheduling and management. We overcome major hurdles such as multi-path head-of-line blocking, network heterogeneity, and rapid link variations and balance cost and performance. To the best of our knowledge, XLINK is the first large-scale experimental study of multi-path QUIC video services in production environments. We present the results of over 3 million e-commerce product short-video plays from consumers who upgraded to Taobao android app with XLINK. Our study shows that compared to single-path QUIC, XLINK achieved 19 to 50% improvement in the 99-th percentile video-chunk request completion time, 32% improvement in the 99-th percentile first-video-frame latency, 23 to 67% improvement in the re-buffering rate at the expense of 2.1% redundant traffic.
Seven years in the life of Hypergiants' off-nets

Content Hypergiants deliver the vast majority of Internet traffic to end users. In recent years, some have invested heavily in deploying services and servers inside end-user networks. With several dozen Hypergiants and thousands of servers deployed inside networks, these off-net (meaning outside the Hypergiant networks) deployments change the structure of the Internet. Previous efforts to study them have relied on proprietary data or specialized per-Hypergiant measurement techniques that neither scale nor generalize, providing a limited view of content delivery on today's Internet. In this paper, we develop a generic and easy to implement methodology to measure the expansion of Hypergiants' off-nets. Our key observation is that Hypergiants increasingly encrypt their traffic to protect their customers' privacy. Thus, we can analyze publicly available Internet-wide scans of port 443 and retrieve TLS certificates to discover which IP addresses host Hypergiant certificates in order to infer the networks hosting off-nets for the corresponding Hypergiants. Our results show that the number of networks hosting Hypergiant off-nets has tripled from 2013 to 2021, reaching 4.5k networks. The largest Hypergiants dominate these deployments, with almost all of these networks hosting an off-net for at least one -- and increasingly two or more -- of Google, Netflix, Facebook, or Akamai. These four Hypergiants have off-nets within networks that provide access to a significant fraction of end user population.
Towards Bike Type and E-Scooter Classification With Smartphone Sensors

In this paper, we present a novel approach for identifying various bike types and e-scooters using sensor readings from the cyclist’s smartphone. Bike type identification is necessary to provide context-aware navigation services that consider e-scooter- and bike-specific road conditions in route planning and improve safety and comfort by suggesting roads suitable for the cyclist’s bike type. In addition, the idea of bike type identification is useful for advertising purposes or for improving VPA (Virtual Personal Assistants) capabilities with non-intrusive, bike or e-scooter specific suggestions. We employ a CNN (Convolutional Neural Network) deep learning approach to differentiate between various bike-types and e-scooters. The evaluation includes various roads, cyclists, bike types and smartphones. The results show that bike types are identified with average F1-scores, Accuracy and AUC of up to 0.92, 0.90 and 0.98 respectively.
mmTag: a millimeter wave backscatter network

Recent advances in IoT, machine learning and cloud computing have placed a huge strain on wireless networks. In particular, many emerging applications require streaming rich content (such as videos) in real time, while they are constrained by energy sources. A wireless network which supports high data-rate while consuming low-power would be very attractive for these applications. Unfortunately, existing wireless networks do not satisfy this requirement. For example, WiFi backscatter and Bluetooth networks have very low power consumption, but their data-rate is very limited (less than a Mbps). On the other hand, modern WiFi and mmWave networks support high throughput, but have a high power consumption (more than a watt). To address this problem, we present mmTag, a novel mmWave backscatter network which enables low-power high-throughput wireless links for emerging applications. mmTag is a backscatter system which operates in the mmWave frequency bands. mmTag addresses the key challenges that prevent existing backscatter networks from operating at mmWave bands. We implemented mmTag and evaluated its performance empirically. Our results show that mmTag is capable of achieving 1 Gbps and 100 Mbps at 4.6 m and 8 m, respectively, while consuming only 2.4 nJ/bit.
Cost-effective capacity provisioning in wide area networks with Shoofly

In this work we propose Shoofly, a network design tool that minimizes hardware costs of provisioning long-haul capacity by optically bypassing network hops where conversion of signals from optical to electrical domain is unnecessary and uneconomical. Shoofly leverages optical signal quality and traffic demand telemetry from a large commercial cloud provider to identify optical bypasses in the cloud WAN that reduce the hardware cost of long-haul capacity by 40%. A key challenge is that optical bypasses cause signals to travel longer distances on fiber before re-generation, potentially reducing link capacities and resilience to optical link failures. Despite these challenges, Shoofly provisions bypass-enabled topologies that meet 8X the present-day demands using existing network hardware. Even under aggressive stochastic and deterministic link failure scenarios, these topologies save 32% of the cost of long-haul capacity.
From IP to transport and beyond: cross-layer attacks against applications

We perform the first analysis of methodologies for launching DNS cache poisoning: manipulation at the IP layer, hijack of the inter-domain routing and probing open ports via side channels. We evaluate these methodologies against DNS resolvers in the Internet and compare them with respect to effectiveness, applicability and stealth. Our study shows that DNS cache poisoning is a practical and pervasive threat. We then demonstrate cross-layer attacks that leverage DNS cache poisoning for attacking popular systems, ranging from security mechanisms, such as RPKI, to applications, such as VoIP. In addition to more traditional adversarial goals, most notably impersonation and Denial of Service, we show for the first time that DNS cache poisoning can even enable adversaries to bypass cryptographic defences: we demonstrate how DNS cache poisoning can facilitate BGP prefix hijacking of networks protected with RPKI even when all the other networks apply route origin validation to filter invalid BGP announcements. Our study shows that DNS plays a much more central role in the Internet security than previously assumed. We recommend mitigations for securing the applications and for preventing cache poisoning.
Bento: safely bringing network function virtualization to Tor

Tor is a powerful and important tool for providing anonymity and censorship resistance to users around the world. Yet it is surprisingly difficult to deploy new services in Tor—it is largely relegated to proxies and hidden services—or to nimbly react to new forms of attack. Conversely, “non-anonymous” Internet services are thriving like never before because of recent advances in programmable networks, such as Network Function Virtualization (NFV) which provides programmable in-network middleboxes. This paper seeks to close this gap by introducing programmable middleboxes into the Tor network. In this architecture, users can install and run sophisticated “functions” on willing Tor routers. We demonstrate a wide range of functions that improve anonymity, resilience to attack, performance of hidden services, and more. We present the design and implementation of an architecture, Bento, that protects middlebox nodes from the functions they run—and protects the functions from the middleboxes they run on. We evaluate Bento by running it on the live Tor network. We show that, with just a few lines of Python, we can significantly extend the capabilities of Tor to meet users' anonymity needs and nimbly react to new threats. We will be making our code and data publicly available.
Sailfish: accelerating cloud-scale multi-tenant multi-service gateways with programmable switches

The cloud gateway is essential in the public cloud as the central hub of cloud traffic. We show that horizontal scaling of software gateways, once sustainable for years, is no longer future-proof facing the massive scale and rapid growth of today's cloud. The root cause is the stagnant performance of the CPU core, which is prone to be overloaded by heavy hitters as traffic growth goes far beyond Moore's law. To address this, we propose \emph{Sailfish}, a cloud-scale multi-tenant multi-service gateway accelerated by programmable switches. The new challenge is that large forwarding tables due to multi-tenancy cannot be fit into the limited on-chip memories. To this end, we devise a multi-pronged approach with (1) hardware/software co-design for table sharing, (2) horizontal table splitting among gateway clusters, (3) pipeline-aware table compression for a single node. Compared with the x86 gateway of a similar price, Sailfish reduces latency by 95% (2μs), improves throughput by more than 20x in bps (3.2Tbps) and 71x in pps (1.8Gpps) with packet length < 256B. Sailfish has been deployed in Alibaba Cloud for more than two years. It is the first P4-based cloud gateway in the industry, of which a single cluster carries dozens of Tbps traffic, withstanding peak-hour traffic in large online shopping festivals.
GC content, but not nucleosome positioning, directly contributes to intron splicing efficiency in Paramecium

Eukaryotic genes are interrupted by introns that must be accurately spliced from mRNA precursors. With an average length of 25 nt, the >90,000 introns of Paramecium tetraurelia stand among the shortest introns reported in eukaryotes. The mechanisms specifying the correct recognition of these tiny introns remain poorly understood. Splicing can occur co-transcriptionally and it has been proposed that chromatin structure might influence splice site recognition. To investigate the roles of nucleosome positioning in intron recognition, we determined the nucleosome occupancy along the P. tetraurelia genome. We showed that P. tetraurelia displays a regular nucleosome array with a nucleosome repeat length of ∼151 bp, amongst the smallest periodicities reported. Our analysis revealed that introns are frequently associated with inter-nucleosomal DNA, pointing to an evolutionary constraint to locate introns at the AT-rich nucleosome edge sequences. Using accurate splicing efficiency data from cells depleted for the nonsense-mediated decay effectors, we showed that introns located at the edge of nucleosomes display higher splicing efficiency than those at the centre. However, multiple regression analysis indicated that the GC content, rather than nucleosome positioning, directly contributes to intron splicing efficiency. Our data reveal a complex link between GC content, nucleosome positioning and intron evolution in Paramecium.
Analysis of gene expression profiles to study malaria vaccine dose efficacy and immune response modulation

Malaria is a life-threatening disease, and the Africa is still one of the most affected endemic regions despite years of policy to limit infection and transmission rates. Further, studies into the variable efficacy of the vaccine are needed to provide a better understanding of protective immunity. Thus, the current study is designed to delineate the effect of the different vaccination doses on the transcriptional profiles of subjects to determine its efficacy and understand the molecular mechanisms underlying the protection this vaccine provides. Here, we used gene expression profiles of pre and post-vaccination patients after various doses of RTS,S based on 275 and 583 samples collected from the GEO datasets. At first, exploratory data analysis based Principal component analysis (PCA) shown the distinct pattern of different doses. Subsequently, differential gene expression analysis using edgeR revealed the significantly (FDR <0.005) 158 down-regulated and 61 upregulated genes between control vs. Controlled Human Malaria Infection (CHMI) samples. Further, enrichment analysis of significant genes using Annotation and GAGE tools delineate the involvement of CCL8, CXCL10, CXCL11, XCR1, CSF3, IFNB1, IFNE, IL12B, IL22, IL6, IL27, etc.,genes which found to be upregulated after earlier doses but downregulated after the 3rd dose in cytokine-chemokine pathways. Notably, we identified 13 cytokine genes whose expression significantly varied during three doses. Eventually, these findings give insight to the dual role of cytokine responses in malaria pathogenesis and variations in their expression patterns after various doses of vaccination involved in protection.
Monomorphic ESS does not imply the stability of the corresponding polymorphic state in the replicator dynamics in matrix games under time constraints

One of the main result in the theory of classical evolutionary matrix games (Maynard Smith and Price 1973, Maynard Smith 1982) claims that monomorphic ESS condition implies the stability of the corresponding state of the polymorphic replicator dynamics (Hofbauer et al. 1979, Zeeman 1980). The picture was then refined by Cressman (1990) introducing the strong stability concept which says that if there is a monomorphic ESS then stable polymorphism is established in polymorphic populations. In this paper we demonstrate with examples that this relationship generally does not hold in three or higher dimensions if times related to the interactions vary with the strategies of the participants.
Agent-Based Model of Multicellular Spheroid Pattern Formation Driven by Synthetic Cell Adhesion Signaling Circuits

Dynamically activated differential adhesion within cell populations enables the emergence of unique patterns in heterogeneous multicellular systems. This process has previously been explored using synthetically engineered heterogenous multicell spheroid systems in which cell subpopulations engage in bidirectional intercellular signaling to regulate the expression of different cadherins. While engineered cell systems provide excellent experimental tools to observe pattern formation in cell populations, computational models may be leveraged to explore the key parameters that drive the emergence of different patterns more systematically. Here, we developed and validated two-and three-dimensional agent-based models (ABMs) of spheroid patterning for cells engineered with a bidirectional signaling circuit regulating N-and P-cadherin expression. The model was used to predict how varying initial cell seeding, cadherin induction probabilities, or homotypic adhesion strengths between cells leads to different spheroid patterns, and unsupervised machine learning techniques were used to map system parameters to unique spheroid patterns. The model was also used as a tool to design new synthetic cell signaling circuits based on a desired final multicell pattern.
The challenge of delimiting cryptic species, and a supervised machine learning solution

The diversity of biological and ecological characteristics of organisms, and the underlying genetic patterns and processes of speciation, makes the development of universally applicable genetic species delimitation methods challenging. Many approaches, like those incorporating the multispecies coalescent, sometimes delimit populations and overestimate species numbers. This issue is exacerbated in taxa with inherently high population structure due to low dispersal ability, and in cryptic species resulting from nonecological speciation. These taxa present a conundrum when delimiting species: analyses rely heavily, if not entirely, on genetic data which over split species, while other lines of evidence lump. We showcase this conundrum in the harvester Theromaster brunneus, a low dispersal taxon with a wide geographic distribution and high potential for cryptic species. Integrating morphology, mitochondrial, and sub-genomic (double-digest RADSeq and ultraconserved elements) data, we find high discordance across analyses and data types in the number of inferred species, with further evidence that multispecies coalescent approaches over split. We demonstrate the power of a supervised machine learning approach in effectively delimiting cryptic species by creating a “custom” training dataset derived from a well-studied lineage with similar biological characteristics as Theromaster. This novel approach uses known taxa with particular biological characteristics to inform unknown taxa with similar characteristics, and uses modern computational tools ideally suited for species delimitation while also considering the biology and natural history of organisms to make more biologically informed species delimitation decisions. In principle, this approach is universally applicable for species delimitation of any taxon with genetic data, particularly for cryptic species.