Miracle-dz/UniFAR-FTUs huggingface dataset:
Conservation genomics of an endangered montane amphibian reveals low population structure, low genomic diversity and selection pressure from disease

Wildlife diseases are a major global threat to biodiversity. Boreal toads (Anaxyrus [Bufo] boreas) are a state‐endangered species in the southern Rocky Mountains of Colorado and New Mexico, and a species of concern in Wyoming, largely due to lethal skin infections caused by the amphibian chytrid fungus Batrachochytrium dendrobatidis (Bd). We performed conservation and landscape genomic analyses using single nucleotide polymorphisms from double‐digest, restriction site‐associated DNA sequencing in combination with the development of the first boreal toad (and first North American toad) reference genome to investigate population structure, genomic diversity, landscape connectivity and adaptive divergence. Genomic diversity (π = 0.00034–0.00040) and effective population sizes (Ne = 8.9–38.4) were low, likely due to post‐Pleistocene founder effects and Bd‐related population crashes over the last three decades. Population structure was also low, likely due to formerly high connectivity among a higher density of geographically proximate populations. Boreal toad gene flow was facilitated by low precipitation, cold minimum temperatures, less tree canopy, low heat load and less urbanization. We found >8X more putatively adaptive loci related to Bd intensity than to all other environmental factors combined, and evidence for genes under selection related to immune response, heart development and regulation and skin function. These data suggest boreal toads in habitats with Bd have experienced stronger selection pressure from disease than from other, broad‐scale environmental variations. These findings can be used by managers to conserve and recover the species through actions including reintroduction and supplementation of populations that have declined due to Bd.
Study on the Inspection for Shallow Area under Concrete Surface using Air-coupled Sound Wave

The hammer method is widely used for the inspection of concrete surface of tunnels or bridge girders. With this method, however, it is difficult to inspect the places where people cannot reach. Therefore, we propose a new non-contact acoustic imaging method for nondestructive inspection using as a Scanning Laser Doppler Vibrometer (SLDV) and a Long Range Acoustic Device (LRAD). In this method, the vibration speed of concrete surface, which is excited by aircoupled sound, is measured using the SLDV. The LRAD, which have high sound pressure and a sharp directivity, is used as a vibration source. We carried out experiments to confirm the effectiveness of our proposed method using a concrete test-piece. A styrofoam board (30×30×10 cm) as a substitute for a cavity was buried in the concrete test-piece at the depth of 5 cm. The area for scanning is about 50x50 cm and the number of the scanned points is 195 (15×13). It takes a few minutes to measure the velocity at all points. The sound pressure near the surface of the concrete test piece is about 110 dB. As an experimental result, the styrofoam board was clearly imaged as a high velocity anomaly of the concrete surface. In addition, we were able to get a clear image of the styrofoam board by an imaging method using the response range of the frequency for an buried object. Therefore, we can conclude that our proposed method is effective for detecting a cavity which locates below the shallow concrete surface.
A novel natural variation in the promoter of GmCHX1 regulates conditional gene expression to improve salt tolerance in soybean

Abstract Identification and characterization of soybean germplasm and gene(s)/allele(s) for salt tolerance is an effective way to develop improved varieties for saline soils. Previous studies identified GmCHX1 (Glyma03g32900) as a major salt tolerance gene in soybean, and two main functional variations were found in the promoter region (148/150 bp insertion) and the third exon with a retrotransposon insertion (3.78 kb). In the current study, we identified four salt-tolerant soybean lines, including PI 483460B (Glycine soja), carrying the previously identified salt-sensitive variations at GmCHX1, suggesting new gene(s) or new functional allele(s) of GmCHX1 in these soybean lines. Subsequently, we conducted quantitative trait locus (QTL) mapping in a recombinant-inbred line population (Williams 82 (salt-sensitive) × PI 483460B) to identify the new salt tolerance loci/alleles. A new locus, qSalt_Gm18, was mapped on chromosome 18 associated with leaf scorch score. Another major QTL, qSalt_Gm03, was identified to be associated with chlorophyll content ratio and leaf scorch score in the same chromosomal region of GmCHX1 on chromosome 3. Novel variations in a STRE (stress response element) cis-element in the promoter region of GmCHX1 were found to regulate the salt-inducible expression of the gene in these four newly identified salt-tolerant lines including PI 483460B. This new allele of GmCHX1 with salt-inducible expression pattern provides an energy cost efficient (conditional gene expression) strategy to protect soybean yield in saline soils without yield penalty under non-stress conditions. Our results suggest that there might be no other major salt tolerance locus similar to GmCHX1 in soybean germplasm, and further improvement of salt tolerance in soybean may rely on gene-editing techniques instead of looking for natural variations.
Research on Pollution Characteristics of Nutriment Elements and Heavy Metals in Sediment of Lake Kuncheng

The vertical distributions of nutriment elements(TN,TP and TOC) and heavy metals(Pb,Cu,Zn,Cd,Cr) in the columnar sediments of 9 sampling sites in Lake Kuncheng were determined.The results showed that the contents of all the elements lowered continuously with the increase of the sediment depth.The environmental behavior of TOC was similar to TN's,which accounts for that the aggrandizement of TN is accompanied by the organic substances;there was a positive correlation between TOC and heavy metals,which indicated that the pollution of heavy metals was disturbed markedly by human beings.The organic substances in the sediments had obvious dual sources.By evaluating the sediments,it showed that organic pollution of the lake was in the state of clear and less clear,and the results of index of geo-accumulation(I_(geo)) evaluation showed that pollution mainly focused on the surface and the dominant contaminant was Zn,and there were basically no pollution of Cd and Cr.
Challenges simulating the AMOC in climate models

The latest assessment report from the Intergovernmental Panel on Climate Change concluded that the Atlantic Meridional Overturning Circulation (AMOC) was very likely to decline over the twenty-first century under all emissions scenarios; however, there was low confidence in the magnitude of the decline. Recent research has highlighted that model biases in the mean climate state can affect the AMOC in its mean state, variability and its response to climate change. Hence, understanding and reducing these model biases is critical for reducing uncertainty in the future changes of the AMOC and in its impacts on the wider climate. We discuss how model biases, in particular salinity biases, influence the AMOC and deep convection. We then focus on biases in the UK HadGEM3-GC3-1 climate model and how these biases change with resolution. We also discuss ongoing model development activities that affect these biases, and highlight priorities for improved representation of processes, such as the position of the North Atlantic Current, transports in narrow boundary current, resolution (or improved parameterization) of eddies and spurious numerical mixing in overflows. This article is part of a discussion meeting issue ‘Atlantic overturning: new observations and challenges’.
Predictors of fatal and nonfatal overdose after prescription of opioids for chronic pain: a systematic review and meta-analysis of observational studies

Background: Higher doses of opioids, mental health comorbidities, co-prescription of sedatives, lower socioeconomic status and a history of opioid overdose have been reported as risk factors for opioid overdose; however, the magnitude of these associations and their credibility are unclear. We sought to identify predictors of fatal and nonfatal overdose from prescription opioids. Methods: We systematically searched MEDLINE, Embase, CINAHL, PsycINFO and Web of Science up to Oct. 30, 2022, for observational studies that explored predictors of opioid overdose after their prescription for chronic pain. We performed random-effects meta-analyses for all predictors reported by 2 or more studies using odds ratios (ORs) and 95% confidence intervals (CIs). Results: Twenty-eight studies (23 963 716 patients) reported the association of 103 predictors with fatal or nonfatal opioid overdose. Moderate- to high-certainty evidence supported large relative associations with history of overdose (OR 5.85, 95% CI 3.78–9.04), higher opioid dose (OR 2.57, 95% CI 2.08–3.18 per 90-mg increment), 3 or more prescribers (OR 4.68, 95% CI 3.57–6.12), 4 or more dispensing pharmacies (OR 4.92, 95% CI 4.35–5.57), prescription of fentanyl (OR 2.80, 95% CI 2.30–3.41), current substance use disorder (OR 2.62, 95% CI 2.09–3.27), any mental health diagnosis (OR 2.12, 95% CI 1.73–2.61), depression (OR 2.22, 95% CI 1.57–3.14), bipolar disorder (OR 2.07, 95% CI 1.77–2.41) or pancreatitis (OR 2.00, 95% CI 1.52–2.64), with absolute risks among patients with the predictor ranging from 2–6 per 1000 for fatal overdose and 4–12 per 1000 for nonfatal overdose. Interpretation: We identified 10 predictors that were strongly associated with opioid overdose. Awareness of these predictors may facilitate shared decision-making regarding prescribing opioids for chronic pain and inform harm-reduction strategies Systematic review registration: Open Science Framework (https://osf.io/vznxj/)
BehavIoT: Measuring Smart Home IoT Behavior Using Network-Inferred Behavior Models

Smart home IoT platforms are typically closed systems, meaning that there is poor visibility into device behavior. Understanding device behavior is important not only for determining whether devices are functioning as expected, but also can reveal implications for privacy (e.g., surreptitious audio/video recording), security (e.g., device compromise), and safety (e.g., denial of service on a baby monitor). While there has been some work on identifying devices and a handful of activities, an open question is what is the extent to which we can automatically model the entire behavior of an IoT deployment, and how it changes over time, without any privileged access to IoT devices or platform messages. In this work, we demonstrate that the vast majority of IoT behavior can indeed be modeled, using a novel multi-dimensional approach that relies only on the (often encrypted) network traffic exchanged by IoT devices. Our key insight is that IoT behavior (including cross-device interactions) can often be captured using relatively simple models such as timers (for periodic behavior) and probabilistic state-machines (for user-initiated behavior and devices interactions) during a limited observation phase. We then propose deviation metrics that can identify when the behavior of an IoT device or an IoT system changes over time. Our models and metrics successfully identify several notable changes in our IoT deployment, including a camera that changed locations, network outages that impact connectivity, and device malfunctions.
ASSESSMENT OF INDOOR ENVIRONMENT APPLYNIG COPRAS METHOD WITH ATTRIBUTES DETERMINED IN INTERVALS

The paper considers the problem of microclimate in spaces and its effect on human health. Based on the measurements made, actual indoor environment at the room is described. The causes of deviation of indoor climate from the standards are identified and rooms are suggested. By using COPRAS-G method, the best room was chosen from the available alternatives described by values determined in intervals. Multicriteria selection was based on the data on the indoor climate obtained in the research.
How to Operate a Meta-Telescope in your Spare Time

Unsolicited traffic sent to advertised network space that does not host active services provides insights about misconfigurations as well as potentially malicious activities, including the spread of Botnets, DDoS campaigns, and exploitation of vulnerabilities. Network telescopes have been used for many years to monitor such unsolicited traffic. Unfortunately, they are limi the available address space for such tasks and, thus, limited to specific geographic and/or network regions. In this paper, we introduce a novel concept to broadly capture unsolicited Internet traffic, which we call a "meta-telescope". A meta-telescope is based on the intuition that, with the availability of appropriate vantage points, one can (i) infer which address blocks on the Internet are unused and (ii) capture traffic towards them-both without having control of such address blocks. From this intuition, we develop and evaluate a methodology for identifying unlikely to be used Internet address space and build a meta-telescope that has very desirable properties, such as broad coverage of dark space both in terms of size and topological placement. Such meta-telescope identifies and captures unsolicited traffic to more than 350k /24 blocks in more than 7k ASes. Through the analysis of background radiation towards these networks, we also highlight that unsolicited traffic differs by destination network/geographic region as well as by network type. Finally, we discuss our experience and challenges when operating a meta-telescope in the wild.
Localizing Traffic Differentiation

Network neutrality is important for users, content providers, policymakers, and regulators interested in understanding how network providers differentiate performance. When determining whether a network differentiates against certain traffic, it is important to have strong evidence, especially given that traffic differentiation is illegal in certain countries. In prior work, WeHe detects differentiation via end-to-end throughput measurements between a client and server but does not isolate the network responsible for it. Differentiation can occur anywhere on the network path between endpoints; thus, further evidence is needed to attribute differentiation to a specific network. We present a system, WeHeY, built atop WeHe, that can localize traffic differentiation, i.e., obtain concrete evidence that the differentiation happened within the client's ISP. Our system builds on ideas from network performance tomography; the challenge we solve is that TCP congestion control creates an adversarial environment for performance tomography (because it can significantly reduce the performance correlation on which tomography fundamentally relies). We evaluate our system via measurements "in the wild,'' as well as in emulated scenarios with a wide-area testbed; we further explore its limits via simulations and show that it accurately localizes traffic differentiation across a wide range of network conditions. WeHeY's source code is publicly available athttps://nal-epfl.github. io/WeHeY.
Not only E.T. Phones Home: Analysing the Native User Tracking of Mobile Browsers

Contemporary browsers constitute a critical component of our everyday interactions with the Web. Similar to a small, but powerful operating system, a browser is responsible to fetch and run web apps locally, on the user's (mobile) device. Even though in the last few years, there has been an increased interest for tools and mechanisms to block potentially malicious behaviours of web domains against the users' privacy (e.g., ad blockers, incognito browsing mode, etc.), it is still unclear if the user can browse the Web in private. In this paper, we analyse the natively generated network traffic of 15 mobile browser apps under different configurations to investigate if the users are capable of browsing the Web privately, without sharing their browsing history with remote servers. We develop a novel framework (Panoptes) to instrument and monitor separately the mobile browser traffic generated by (a) the web engine and (b) natively by the mobile app. By crawling a set of websites via Panoptes, and analyzing the native traffic of browsers, we find that there are browsers (i) who persistently track their users, and (ii) browsers that report to remote servers (geolocated outside EU), the exact page and content the user is browsing at that moment. Finally, we see browsers communicating with third-party ad servers while leaking personal and device identifiers.
The role of soluble surfactant in the linear instability of a film coating inside a tube

Abstract This study investigates the linear instability of a thin-film coating inside a rigid tube. The flow is assumed to be inertialess and driven by an axial body force (e.g. gravity), an interfacial shearing force, or their combinations. The interface and the bulk of the film are laden with soluble surfactant. The properties of the soluble surfactant, i.e. solubility, sorption kinetics and bulk diffusivity, modulate the interfacial dynamics of the film. The influence of these properties on the linear instability of the film is comprehensively investigated via long-wave approximation analysis and numerical calculation. Two modes, namely the interface mode and the surfactant mode, are identified to dominate the instability. For a quiescent film, it is found that solubility, sorption kinetics and bulk diffusivity act to improve the uniformity of the surface surfactant and mitigate the stabilizing effect of the Marangoni force. For the film driven by the axial body/interfacial shearing force, the results reveal that solubility plays contrasting roles in the interface mode and the surfactant mode. A window with intermediate solubility is detected where the film can be linearly stabilized. Moreover, sorption kinetics is found to destabilize the perturbations with long wavelength whereas it stabilizes the perturbations with finite wavelength. The bulk diffusivity of the surfactant has a non-monotonic influence on the flow instability, and the film can be relatively stable at both strong and weak diffusivity.
Three-dimensional transition and force characteristics of low-Reynolds-number flows past a plunging airfoil

Abstract The three-dimensional (3-D) transition of the leading-edge vortex (LEV) and the force characteristics of the plunging airfoil are investigated in the chord-based Strouhal number $St_c$ range of 0.10 to 1.0 by means of experimental measurements, numerical simulations and linear stability analysis in order to understand the spanwise instabilities and the effects on the force. We find that the interaction pattern of the LEV, the LEV from a previous cycle (pLEV) and the trailing-edge vortex (TEV) is the primary mechanism that affects the 3-D transition and associated force characteristics. For $St_c \leq 0.16$, the 3-D transition is dominated by the LEV–TEV interaction. For $0.16 < St_c \leq 0.44$, the TEV lies in the middle of the LEV and the pLEV and therefore vortex interaction between them is relatively weak; as a result, the LEV remains two-dimensional up to a relatively high Reynolds number of $Re = 4000$ at $St_c = 0.32$. For $0.44 < St_{c} \leq 0.54$, and at relatively low Reynolds numbers, the pLEV and the TEV tend to form a clockwise vortex pair, which is beneficial for the high lift and stability of the LEV. For $0.49 \leq St_c$, the pLEV and TEV tend to form an anticlockwise vortex pair, which is detrimental to the lift and flow stability. In the last $St_c$ range, vortex interaction involving the LEV, the TEV and the pLEV results in an unstable period-doubling mode which has a wavelength of about two chord-lengths and the 3-D transition enhances the lift.
ArcText: A Unified Text Approach to Describing Convolutional Neural Network Architectures

Numerous Convolutional Neural Network (CNN) models have demonstrated their promising performance mostly in computer vision. The superiority of CNNs mainly relies on their complex architectures that are often manually designed with extensive human expertise. Data mining on CNN architectures can discover useful patterns and fundamental sub-comments from existing CNN architectures, providing researchers with strong prior knowledge to design CNN architectures when they have no expertise in CNNs. There have been various state-of-the-art data mining algorithms at hand, while there is rare work that has been used for this aspect. The main reason behind this is the barrier between CNN architectures and data mining algorithms. Specifically, the current CNN architecture descriptions cannot be exactly vectorized as input to data mining algorithms. In this paper, we propose a unified approach, named ArcTxt, to describing CNN architectures based on text. Particularly, three different units of ArcText and an ordering method have been elaborately designed, to uniquely describe the same architecture with sufficient information. Also, the resulted description can also be exactly converted back to the corresponding CNN architecture. ArcText bridge the gap between CNN and data mining researchers, and has the potentiality to be utilized to wider scenarios.
An Action Compiler Targeting Standard Ml

We present an action compiler that can be used in connection with an action semantics based compiler generator. Our action compiler produces code with faster execution times than code produced by other action compilers, and for some non-trivial test examples it is only a factor two slower than the code produced by the Gnu C Compiler. Targeting Standard ML makes the description of the code generation simple and easy to implement. The action compiler has been tested on a description of the Core of Standard ML and a subset of C.
Modeling of vegetation diversity and a national conservation planning: example of Russia

We show that at large scale the species number of vascular plants can be predicted to a major extent by climatically determined latent heat for evaporation and geometrical structure of landscape, described as an altitudinal difference. Application of the energy-equivalence rule across plant communities for transpiration per area unit, and using of fractal theory for the description of habitat occupation by vegetation results in a physically-based species-energy relationship. Application of averaged global constants in the relationship results in a species-area equation with known parameters. Despite its simple form, this species-energy relationship generally reproduces global patterns of vegetation diversity for scales 10 000 and 100 000 km2, and is applicable for different regions across scales from hundred to million km2. The proposed theory produces more robust results compared with the correlated-based approaches, which link plant diversity patterns to climate variables, and allows the inclusion of an evolutionary component. The final climaterichness equation for vascular plants has a simple and consistent analytical form and requires easily measurable variables.