Miracle-dz/UniFAR-FTUs huggingface dataset:
“Internal tide pools” prolong kelp forest hypoxic events

This study uses field observations within a single kelp bed in southern Monterey Bay, California, to evaluate the retention of cold, hypoxic water within depressions in the rocky reef following relaxation of internal wave events. Just as tide pools in the rocky intertidal zone persist in depressions following the relaxation of surface waves and tides, “internal tide pools” persist in depressions in the subtidal reef following the relaxation of internal waves. When internal waves contain low dissolved oxygen (DO) water, the duration of hypoxic events can be extended for organisms within the pooling zone. Additionally, we suggest that internal wave run‐up can cause the early arrival of cold hypoxic water in these same areas, thereby extending hypoxic events further. Together, internal tide pools and wave run‐up extend event durations by 20% on average. However, some events are extended up to 160% of the regional‐scale event length, which may be more ecologically relevant than the mean extension. When internal waves contain other environmental covariates (e.g., low pH, nutrients), internal tide pools are expected to create a similar time extension and patchy landscape in these quantities. We attribute the slowing of the relaxation of sub‐thermocline water within pooling zones to retention within the reef itself rather than a drag effect of the kelp bed. Following hypoxic internal bores, internal tide pools cause a period of extreme spatial variability in the DO and temperature field which is likely to impact the distribution and behavior of local organisms.
Ethical concerns and the reach of markets : A choice experiment on Americans ’ views about paying kidney donors *

Regulation and public policies are often the result of competition and compromise between different views and interests. In several cases, strongly held moral beliefs voiced by societal groups lead lawmakers to prohibit certain transactions or to prevent them from occurring through markets. However, there is limited evidence about the specific nature of the general population’s opposition to using prices in such contentious transactions. We conducted a choice experiment on a representative sample of Americans to examine preferences for legalizing payments to kidney donors. We found strong polarization, with many participants in favor or against payments regardless of potential supply gains. However, about 20% of respondents would switch to supporting payments for large enough supply gains. Preferences for compensation have strong moral foundations. Respondents especially reject direct payments by patients, which they find would violate principles of fairness. We corroborate the interpretation of our findings with the analysis of a costly decision to donate money to a foundation that supports donor compensation.
Nanoscale dysregulation of collagen structure-function disrupts mechano-homeostasis and mediates pulmonary fibrosis

Matrix stiffening with downstream activation of mechanosensitive pathways is strongly implicated in progressive fibrosis; however, pathologic changes in extracellular matrix (ECM) that initiate mechano-homeostasis dysregulation are not defined in human disease. By integrated multiscale biomechanical and biological analyses of idiopathic pulmonary fibrosis lung tissue, we identify that increased tissue stiffness is a function of dysregulated post-translational collagen cross-linking rather than any collagen concentration increase whilst at the nanometre-scale collagen fibrils are structurally and functionally abnormal with increased stiffness, reduced swelling ratio, and reduced diameter. In ex vivo and animal models of lung fibrosis, dual inhibition of lysyl oxidase-like (LOXL) 2 and LOXL3 was sufficient to normalise collagen fibrillogenesis, reduce tissue stiffness, and improve lung function in vivo. Thus, in human fibrosis, altered collagen architecture is a key determinant of abnormal ECM structure-function, and inhibition of pyridinoline cross-linking can maintain mechano-homeostasis to limit the self-sustaining effects of ECM on progressive fibrosis.
Securing Input Data of Deep Learning Inference Systems via Partitioned Enclave Execution

Deep learning systems have been widely deployed as backend engines of artificial intelligence (AI) services for their approaching-human performance in cognitive tasks. However, end users always have some concerns about the confidentiality of their provisioned input data, even for those reputable AI service providers. Accidental disclosures of sensitive user data might unexpectedly happen due to security breaches, exploited vulnerabilities, neglect, or insiders. In this paper, we systematically investigate the potential information exposure in deep learning based AI inference systems. Based on our observation, we develop DeepEnclave, a privacy-enhancing system to mitigate sensitive information disclosure in deep learning inference pipelines. The key innovation is to partition deep learning models and leverage secure enclave techniques on cloud infrastructures to cryptographically protect the confidentiality and integrity of user inputs. We formulate the information exposure problem as a reconstruction privacy attack and quantify the adversary's capabilities with different attack strategies. Our comprehensive security analysis and performance measurement can act as a guideline for end users to determine their principle of partitioning deep neural networks, thus to achieve maximum privacy guarantee with acceptable performance overhead.
Adaptive threshold parameter estimation with recursive differential grouping for problem decomposition

Problem decomposition plays an essential role in the success of cooperative co-evolution (CC), when used for solving large-scale optimization problems. The recently proposed recursive differential grouping (RDG) method has been shown to be very efficient, especially in terms of time complexity. However, it requires an appropriate parameter setting to estimate a threshold value in order to determine if two subsets of decision variables interact or not. Furthermore, using one global threshold value may be insufficient to identify variable interactions in components with different contribution to the fitness value. Inspired by the different grouping 2 (DG2) method, in this paper, we adaptively estimates a threshold value based on computational round-off errors for RDG. We derive an upper bound of the round-off errors, which is shown to be sufficient when identifying variable interactions across a wide range of large-scale benchmark problems. Comprehensive numerical experimental results showed that the proposed RDG2 method achieved higher decomposition accuracy than RDG and DG2. When embedded into a CC framework, it achieved statistically equal or significantly better solution quality than RDG and DG2, when used to solve the benchmark problems.
Source selection of long tail sources for federated search in an uncooperative setting

The goal of federated search is to combine results from multiple knowledge bases into a single, aggregated result list with items typically ranging from textual documents to images. These knowledge bases are also termed sources, and the process of choosing the actual subset of sources for a given query is referred to as source selection. Uncooperative setting is a scenario under which these sources do not provide information about their content in a standardized way. In our work we focus on knowledge bases with long-tail content i.e., rather specialized sources offering few relevant documents. Such sources are often neglected in favor of more popular ones, both by today's Web users and by most of the existing source selection techniques. We propose a system for source selection which i) could be utilized to automatically detect long-tail knowledge bases and ii) generates aggregated search results that tend to incorporate results of long-tail sources. Based on the current state-of-the-art, we developed components that allowed to adjust the contribution from long-tail sources. Our evaluation is conducted using the TREC 2014 Federated WebSearch dataset. Since this dataset also favors the most popular sources, systems that include many long-tail knowledge bases will yield low performance. To that end we propose a system with only a few relevant long-tail sources integrated into the list of more popular knowledge bases. Additionally, we evaluated the implications of an uncooperative setting, in which only minimal information about the sources is available to the federated search system. With this regard, a severe drop in performance is observed when the share of long-tail sources is higher than 40%. Our work is intended to steer the development of federated search systems, increasing the diversity and coverage of the aggregated search result.
Analytics for the Internet of Things

The Internet of Things (IoT) envisions a world-wide, interconnected network of smart physical entities. These physical entities generate a large amount of data in operation, and as the IoT gains momentum in terms of deployment, the combined scale of those data seems destined to continue to grow. Increasingly, applications for the IoT involve analytics. Data analytics is the process of deriving knowledge from data, generating value like actionable insights from them. This article reviews work in the IoT and big data analytics from the perspective of their utility in creating efficient, effective, and innovative applications and services for a wide spectrum of domains. We review the broad vision for the IoT as it is shaped in various communities, examine the application of data analytics across IoT domains, provide a categorisation of analytic approaches, and propose a layered taxonomy from IoT data to analytics. This taxonomy provides us with insights on the appropriateness of analytical techniques, which in turn shapes a survey of enabling technology and infrastructure for IoT analytics. Finally, we look at some tradeoffs for analytics in the IoT that can shape future research.
Dynamic control of beacon transmission rate and power with position error constraint in cooperative vehicular networks

Cooperative vehicular networks require the continuous exchange of beacon messages between neighboring vehicles to support cooperative awareness applications. The position error computed by surrounding vehicles impacts on applications capability to detect and mitigate potentially dangerous traffic situations in real-time. A challenge in cooperative safety systems is to maintain a high position accuracy, while controlling the communication channel load. In this paper, we propose a novel joint power/rate control distributed algorithm to meet the position accuracy requirements of cooperative safety applications. The beacon transmission rate is adjusted dynamically as a function of the vehicle movement status, to constrain position error computed by surrounding vehicles. Then, the transmit power is adjusted according to the channel load and the preset beacon transmission rate, to decrease packet collisions. This algorithm has been evaluated in a realistic simulation framework, considering different traffic densities for an urban scenario. The simulation results show that the proposed algorithm outperforms other basic beaconing algorithms addressed in this paper, in terms of trade-off between position accuracy, packet collisions, and warning range.
Determining the species assemblage and habitat use of cetaceans in the Svalbard Archipelago, based on observations from 2002 to 2014

ABSTRACT This study used 13 years of cetacean sighting data (2002–2014) from waters around the Svalbard Archipelago to determine key habitats for year-round resident species as well as seasonally resident species, and to explore spatial overlap between these groups via a combination of kernel density estimation and Maxent modelling. The data set consists of observations made by research vessels conducting various marine studies, coast guard ships and marine-cruise tourist operators. Data are reported from the seasonal period in which there is daylight (March-November), though 95% of the observations occurred June-September. Changes over the study period were investigated, within the limits of the data, to explore whether range shifts may be occurring. Fifteen cetacean species were reported. Among the resident ice-associated cetaceans, only white whales were reported frequently; they were seen exclusively in coastal habitats, in accordance with their known use of tidal glacier fronts for feeding in this region. Narwhal and bowhead whales were rare. Seasonally resident minke whales, fin whales, humpback whales, blue whales and sperm whales as well as small dolphins were seen frequently, in broad and somewhat overlapping habitats. Other less common seasonal residents included killer whales, northern bottlenose whales and sei whales; harbour porpoises and long-finned pilot whales were also reported, but rarely. Shifts over the study period towards higher latitudes, and into coastal environments, were observed for several seasonally resident species. These expansions are likely linked to warming ocean temperatures and a precipitous decline in sea-ice cover in the area.
Playing against Nature: causal discovery for decision making under uncertainty

We consider decision problems under uncertainty where the options available to a decision maker and the resulting outcome are related through a causal mechanism which is unknown to the decision maker. We ask how a decision maker can learn about this causal mechanism through sequential decision making as well as using current causal knowledge inside each round in order to make better choices had she not considered causal knowledge and propose a decision making procedure in which an agent holds \textit{beliefs} about her environment which are used to make a choice and are updated using the observed outcome. As proof of concept, we present an implementation of this causal decision making model and apply it in a simple scenario. We show that the model achieves a performance similar to the classic Q-learning while it also acquires a causal model of the environment.
Assessing the readability of the self-reported Strengths and Difficulties Questionnaire

The Strengths and Difficulties Questionnaire (SDQ) is one of the most widely used measures in child and adolescent mental health in clinical practice, community-based screening and research. Assessing the readability of such questionnaires is important as young people may not comprehend items above their reading ability when self-reporting. Analyses of readability in the present study indicate that the self-report SDQ might not be suitable for young people with a reading age below 13–14 years and highlight differences in readability between subscales. The findings suggest a need for caution in using the SDQ as a self-report measure for children below the age of 13, and highlight considerations of readability in measure development, selection and interpretation. Declaration of interest None.
Chondrocyte Production of Pro-Inflammatory Chemokine MCP-1 (CCL-2) and Prostaglandin E-2 Is Inhibited by Avocado/Soybean Unsaponifiables, Glucosamine, Chondroitin Sulfate Combination

Osteoarthritis (OA) is a chronic, painful disease affecting articulating joints in man and animals. It is characterized by cartilage breakdown, bone remodeling, osteophyte formation and joint inflammation. Currently used non-steroidal anti-inflammatory drugs for the management of OA are known to have deleterious side effects. To address the need for alternatives, we evaluated the anti-inflammatory effects of a combination of avocado/soybean unsaponifiables (ASU), glucosamine (GLU) and chondroitin sulfate (CS) by measuring chemokine MCP-1 (monocyte chemoattractant protein 1, CCL2) and prostaglandin E-2 (PGE2) in stimulated chondrocytes. As the only cellular constituents of cartilage, chondrocytes are the source of pro-inflammatory mediators that play critical roles in the pathogenesis of OA. Chondrocytes were incubated: with: 1) control media, 2) [ASU + GLU + CS] combination, or 3) Phenylbutazone (PBZ) for 24 hours. Cells were next stimulated with IL-1β or LPS for another 24 hrs. MCP-1 and PGE2 from supernatants were quantitated by immunoassay. Another set of chondrocytes seeded in chamber slides were stimulated with IL-1β for 1 hour and then immunostained for NF-κB. Chondrocytes stimulated with IL-1β or LPS significantly increased MCP-1 and PGE2 production which were significantly reduced after treatment with [ASU + GLU + CS]. In contrast, PBZ significantly reduced PGE2 but not MCP-1 production. IL-1β stimulation induced nuclear translocation of NF-κB, which was inhibited by pre-treatment with either [ASU + GLU + CS] or PBZ. The present study provides evidence that the production of MCP-1 by chondrocytes can be inhibited by the combination of [ASU + GLU + CS] but not by PBZ. In contrast, PGE2 production was inhibited by either treatment suggesting that the production of MCP-1 and PGE2 could be independently regulated. The finding of distinct effects of [ASU + GLU + CS] on MCP-1 and PGE2 synthesis supports a scientific rationale for a multimodal treatment approach in the management of OA.
Dynamic Interactions between Emotion Perception and Action Preparation for Reacting to Social Threat: A Combined cTBS-fMRI Study

Abstract Expressions of emotion are powerful triggers for situation-appropriate responses by the observer. Of particular interest regarding the preparation of such adaptive actions are parietal and premotor cortices, given their potential for interaction with the amygdala (AMG), which is known to play a crucial role in the processing of affective information and in motor response. We set out to disentangle the respective roles of the inferior parietal lobule (IPL) and ventral premotor cortex (PMv) in humans in the processing of emotional body expressions by assessing remote effects of continuous theta burst stimulation (cTBS) in the action network and in AMG. Participants were presented with blocks of short videos showing either angry or neutral whole-body actions. The experiment consisted of three fMRI sessions: two sessions were preceded by stimulation of either right IPL (rIPL) or right PMv (rPMv); and a third session assessed baseline activity. Interestingly, whereas at baseline the left AMG did not differentiate between neutral and angry body postures, a significant difference between these conditions emerged after stimulation of either rIPL or rPMv, with much larger responses to angry than to neutral stimuli. In addition, the effects of cTBS stimulation and emotion were also observed in two other action-relevant areas, the supplementary motor area and the superior parietal cortex. Together, these results show how areas involved in action and emotion perception and in action preparation interact dynamically.
Recurrent neural network-predictions for PSO in dynamic optimization

In order to improve particle swarm optimization (PSO) to tackle dynamic optimization problems, various strategies have been introduced, e. g. , random restart, memory, and multi-swarm approaches. However, literature lacks approaches based on prediction. In this paper we propose three different PSO variants employing a prediction approach based on recurrent neural networks to adapt the swarm behavior after a change of the objective function. We compare the variants in an experimental study to a PSO algorithm that is solely based on re-randomization. The experimental study comprises the moving peaks benchmark and dynamic extensions of the Sphere, Rastrigin, and Rosenbrock functions for showing the strengths of the prediction-based PSO variants regarding convergence.
State-aware anomaly detection for industrial control systems

Anomaly detection for industrial control systems (ICS) can leverage process data to detect malicious derivations from expected process behavior. We propose state-aware anomaly detection that uses state dependent detection thresholds, which provide tighter constraints for an attacker trying to manipulate the process. In particular, our system provides: (i) estimation of system state from the knowledge of the network and the physical process (ii) a state-aware cumulative sum of residuals for monitoring the industrial control system (iii) and a novel state-aware anomaly detection technique. We implement and evaluate our anomaly detection technique on a real-world ICS. We pre-compute the process-state parameters using a big data framework for ICS and train the detector leveraging more than 120 GB of historical data from the ICS. The results show that the proposed method improves prior works by providing less time-to-detect of attacks while generating fewer false alarms.
Decontamination of Minimally-Processed Fresh Lettuce Using Reuterin Produced by Lactobacillus reuteri

Over the last years the demand for pre-washed, fresh-cut, and minimally-processed (MP) produce has increased. MP fresh vegetable are rapidly spoiled, whereas there is consumers’ concern about chemical disinfection treatments such as with chlorine. A promising antimicrobial is reuterin, a broad-spectrum-antimicrobial compound produced by food-grade Lactobacillus reuteri from glycerol. In aqueous solution, reuterin is a dynamic system consisting of 3-hydroxypropionaldehyde (3-HPA), its hydrate, its dimer as well as acrolein, which was recently identified as the main antimicrobial component of the system. Here, we tested the use of reuterin containing similar 3-HPA levels but different acrolein concentrations for decontaminating and preserving fresh-cut lettuce. Crude reuterin (CR) was produced by biotransformation of 600 mM glycerol using L. reuteri DSM 20016T. CR preparations were further incubated for 16 h at 50°C to produce enhanced reuterin (ER) with raised concentration of acrolein. Fresh-cut iceberg lettuce (Lactuca sativa) was washed using CR (1.5–1.9 mM acrolein) and ER (7.2–21.9 mM acrolein) solutions at 4°C, or sodium hypochloride (250 mg/L) and tap water, and compared with unwashed lettuce. Washed lettuce samples were packed under modified atmosphere (2% O2, 5% CO2, and 93% N2) and stored for 13 days at 4°C. Application of ER containing 12.1, 20.9, or 21.9 mM acrolein reduced the initial viable plate counts of Enterobacteriaceae (by 2.1–2.8 log CFU/g), and yeasts and molds (by 1.3–2.0 log CFU/g) when compared with unwashed samples. In contrast, reuterin solutions containing 7.2 mM acrolein, sodium hypochlorite and tap water only showed very limited and transient, or no effects on the cell loads of lettuce after washing and during storage. Visual assessment of leaves washed with ER showed acrolein concentration-dependent discoloration noticeable already after 3 days of storage for the highest acrolein concentrations. Discoloration became severe for all ER treatments after 7 days, while the other treatments preserved the aspect of washed lettuce. Our data show the predominant role of acrolein as the main antimicrobial component of the reuterin system for food biopreservation. Reuterin preparations with enhanced acrolein concentration of 12.1 mM and higher were effective to reduce plate counts of Enterobacteriaceae and yeasts and molds washed lettuce until day 7 but induced pronounced discoloration of lettuce.