University of California, Santa Cruz
  • Santa Cruz, California, United States
Recent publications
Moving Target Defense (MTD) prevents adversaries from being able to predict the effect of their attacks by adding uncertainty in the state of a system during runtime. In this paper, we present an MTD algorithm that randomly changes the availability of the sensor data, so that it is difficult for adversaries to tailor stealthy attacks while, at the same time, minimizing the impact of false-data injection attacks. Using tools from the design of state estimators, namely, observers, and switched systems, we formulate an optimization problem to find the probability of the switching signals that increase the visibility of stealthy attacks while decreasing the deviation caused by false data injection attacks. We show that the proposed MTD algorithm can be designed to guarantee the stability of the closed-loop system with desired performance. In addition, we formulate an optimization problem for the design of the parameters so as to minimize the impact of the attacks. The results are illustrated in two case studies, one about a generic linear time-invariant system and another about a vehicular platooning problem.
Social isolation is defined, in psychological terms, as the absence of meaningful social interactions, contacts, and relationships with family and friends, with neighbors. It can occur on an individual level and, on a broader level, within “society at large.” In the United States, three main groups of socially isolated individuals can be identified: people who reside in assisted-living facilities, nursing homes, or hospices, people suffering from “persistent loneliness” and people incarcerated in jails or prisons who are housed in involuntary solitary confinement. In this chapter, we discuss the psychological and neurobiological effects of isolation, using both animal models as well as direct studies of humans experiencing these conditions. Only by understanding the impact of isolation on the brain and the mechanisms that underlie these changes can we hope to develop interventions that prevent them from occurring in the first place. This knowledge may also contribute to the efforts of psychologists, clinicians, and community health leaders to employ evidence-based prevention programs to mitigate the risk of isolation-induced physical and psychological damage in humans.
Deep learning has achieved promising results on a broad spectrum of tasks using an end-to-end approach, and domain-specific knowledge can be used to supplement it by either constraining the solution-space, or to transform data such that relevant signals are more influential during the training process. This is especially important in the context of continuous recordings or wearable-based monitoring, where signal quality may be constrained by hardware, such as a limited number of available electrodes, or by external perturbations. In this study, a global feature extraction method for multi-periodic signals is proposed, which effectively classifies multi-cycle normal and abnormal heartbeats. The abnormal heartbeats are classed as the Left Bundle Branch Block beat (LBBB), Right Bundle Branch Block beat (RBBB), and Paced beat (P). The signal analysis process of the electrocardiogram (ECG) recordings consist of three main stages: (I) segmentation of the ECG data and partitioning of the data set; (II) generation of an overall feature map representing the “heartbeat condition” based on Empirical Mode Decomposition (EMD); and (III) a classification stage for determining the patient’s heartbeat condition. The extracted feature image is used to classify the heartbeat condition using a two-dimensional Convolutional Neural Network (CNN). This method is applied to the publicly available MIT-BIH arrhythmia database. Experimental results show that the reconstructed ECG features outperform use of the raw ECG signals. Under self-test conditions of 3 min of ECG signal, the Total Classification Accuracy (TCA) was approximately 99.01%, while the TCA without the proposed method was approximately 90.69%.
Invasive species pose the highest overall threat to seabirds, affecting the most species and the greatest impact based on the timing, scope, and severity. Invasive mammals have been proven the most harmful on seabird breeding islands, and important advances have been made towards solutions to prevent, control, or eradicate these impacts to seabirds. Biosecurity aims to prevent new invasive species from arriving, and as the most cost-effective strategy considered is a cornerstone for island restoration. Great strides have been made in eradicating invasive mammals from islands, with more than 1500 efforts on nearly 1000 islands worldwide and clearly documented benefits for seabirds. Considerable synergy exists for implementing seabird solutions on islands to achieve conservation goals for other biotas and benefits for human communities. For many seabird species, implementing tractable invasive species solutions will be a critical component of boosting resilience to projected climate change impacts.
Seabirds are one of the most threatened bird groups on the planet, with approximately 30% at risk of extinction. The primary cause of population decline and extinction are non-native species introduced to islands, such as mammals, and which subsequently prey on seabirds or damage habitats. These “invasive species” are impacting 46% of seabird species and over 170 million individual seabirds globally. Of seabirds impacted, 66% are currently listed as globally threatened on the International Union for the Conservation of Nature (IUCN) Red List, highlighting the urgent need to remove the threat of invasive species to prevent seabird extinctions. In this chapter we discuss these impacts in detail, including a brief history of invasion processes that have led to this global problem. We also describe emerging invasive species threats and investigate how climate change will further exacerbate the impacts of invasive species on seabirds. We conclude this chapter with a discussion on the successful management and reduction of invasive species, which have resulted in substantial conservation gains for seabirds and whole island ecosystems worldwide.
The accurate simulation of additional interactions at the ATLAS experiment for the analysis of proton–proton collisions delivered by the Large Hadron Collider presents a significant challenge to the computing resources. During the LHC Run 2 (2015–2018), there were up to 70 inelastic interactions per bunch crossing, which need to be accounted for in Monte Carlo (MC) production. In this document, a new method to account for these additional interactions in the simulation chain is described. Instead of sampling the inelastic interactions and adding their energy deposits to a hard-scatter interaction one-by-one, the inelastic interactions are presampled, independent of the hard scatter, and stored as combined events. Consequently, for each hard-scatter interaction, only one such presampled event needs to be added as part of the simulation chain. For the Run 2 simulation chain, with an average of 35 interactions per bunch crossing, this new method provides a substantial reduction in MC production CPU needs of around 20%, while reproducing the properties of the reconstructed quantities relevant for physics analyses with good accuracy.
Microbial drug discovery programs rely heavily on accessing bacterial diversity from the environment to acquire new specialized metabolite (SM) lead compounds for the therapeutic pipeline. Therefore, knowledge of how commonly culturable bacterial taxa are distributed in nature, in addition to the degree of variation of SM production within those taxa, is critical to informing these front-end discovery efforts and making the overall sample collection and bacterial library creation process more efficient. In the current study, we employed MALDI-TOF mass spectrometry and the bioinformatics pipeline IDBac to analyze diversity within phylotype groupings and SM profiles of hundreds of bacterial isolates from two Eunapius fragilis freshwater sponges, collected 1.5 km apart. We demonstrated that within two sponge samples of the same species, the culturable bacterial populations contained significant overlap in approximate genus-level phylotypes but mostly nonoverlapping populations of isolates when grouped lower than the level of genus. Further, correlations between bacterial phylotype and SM production varied at the species level and below, suggesting SM distribution within bacterial taxa must be analyzed on a case-by-case basis. Our results suggest that two E. fragilis freshwater sponges collected in similar environments can exhibit large culturable diversity on a species-level scale, thus researchers should scrutinize the isolates with analyses that take both phylogeny and SM production into account to optimize the chemical space entering into a downstream bacterial library.
In the California Current Ecosystem, upwelled water low in dissolved iron (Fe) can limit phytoplankton growth, altering the elemental stoichiometry of the particulate matter and dissolved macronutrients. Iron-limited diatoms can increase biogenic silica (bSi) content >2-fold relative to that of particulate organic carbon (C) and nitrogen (N), which has implications for carbon export efficiency given the ballasted nature of the silica-based diatom cell wall. Understanding the molecular and physiological drivers of this altered cellular stoichiometry would foster a predictive understanding of how low Fe affects diatom carbon export. In an artificial upwelling experiment, water from 96 m depth was incubated shipboard and left untreated or amended with dissolved Fe or the Fe-binding siderophore desferrioxamine-B (+DFB) to induce Fe-limitation. After 120 h, diatoms dominated the communities in all treatments and displayed hallmark signatures of Fe-limitation in the +DFB treatment, including elevated particulate Si:C and Si:N ratios. Single-cell, taxon-resolved measurements revealed no increase in bSi content during Fe-limitation despite higher transcript abundance of silicon transporters and silicanin-1. Based on these findings we posit that the observed increase in bSi relative to C and N was primarily due to reductions in C fixation and N assimilation, driven by lower transcript expression of key Fe-dependent genes.
Understanding the relative contributions of different spawning habitats to adult fish populations is central to effective fisheries management and species conservation. The Tarek (Alburnus tarichi) is an adfluvial cyprinid that is endemic to the alkaline-saline waters of Lake Van, Turkey. Tarek are culturally and economically important to the region, and also threatened by anthropogenic impacts, including poaching, dams, water diversions, pollution, and habitat degradation. Here we analyzed otoliths from 120 adult fish caught in Lake Van in 2016–2017 to reconstruct the age structure and natal origins of this Tarek population. Ages ranged from 2 to 10 years, with most fish belonging to the 2011–2014 cohorts (age 3–5). We analyzed strontium isotope ratios from water samples collected in 2016 and 2018 to build a baseline map and then used linear discriminant function analysis to classify Tarek to their likely natal origins. We found that adult Tarek originated from at least 7 different major tributaries of Lake Van, with a majority of fish originating from the Gevas and Engil tributaries in the south. Furthermore, the relative contributions of fish from each tributary varied among years, suggesting that a mosaic of natal habitats may be important for population stability. These results suggest that protection of all Lake Van watersheds from anthropogenic disturbance could be valuable for maintaining the stability of the Lake Van Tarek population and fishery.
Living roots and its rhizodeposition can stimulate or retard soil organic matter (SOM) decomposition via the rhizosphere priming effect (RPE). However, little is known about the RPE during non-growing seasons. In this study, we measured the RPEs of two perennial grasses (Elymus dahuricus, Stipa grandis) and two legumes (Medicago sativa, Melissilus ruthenicus) after leaf senescence and before shoot regrowth using a ¹³C natural tracer method. All four species produced positive RPEs at each sampling time, with the magnitude ranging from 40% to 238% compared to unplanted soil. Further, the inter-specific variation in the RPE during the non-growing season was partly explained by the root:shoot ratio, average root diameter and root tissue density. Overall, these results demonstrate that live roots of non-woody perennials could accelerate SOM decomposition even during the non-growing season.
In the past decade, the Florida Gulf Coast has experienced a number of severe outbreaks of Karenia brevis, a marine alga that produces harmful algal bloom (HAB) events commonly called "red tides." These recurring red tide events have produced severe ecological and economic impacts. Although satellite and water sampling information can be used to identify and track the movement of more recent red tide events, the historic patterns of red tide occurrence and severity are less known. In addition, relatively little information is available about the impact of different red tide events on fish populations, marine habitats and the fishing industry over time. This presents a challenge to fisheries managers in accounting for the impacts of red tides in fisheries stock assessments and formulating policies that anticipate and mitigate the impact of red tide events on fishing dependent businesses. This paper describes the application of fishermen's local ecological knowledge (LEK) data to improve the historic record on red tides on the west coast of Florida, and develop a red tide severity scale. In addition, this paper sheds light on the ecosystem-level insights that fishermen bring to the study of red tides. Eight years were consistently identified by interviewees, with some of the most severe red tide occurrences having increased in frequency in the last fifteen years. The paper concludes with a discussion on existing and possible future applications of the LEK data on red tides for fisheries assessment and management.
Young children's science understanding begins in everyday observations and conversations with their parents. While research on STEM learning in Latine children often suggests deficits, we used diary report methods to identify conversations as indicators of 3–5-year-old children's early strengths in science. Twenty-one Latine families from coastal California, with parent schooling averaging 9 years (range 0–16), reported family conversations about nature for 2 weeks. Families reported on average 6.95 conversations (range 1–14). Frequent topics were animals (26%), plants (21%), weather (17%), and astronomy (16%). Partial correlations (controlling for age and parent schooling) revealed that children's question-initiated conversations correlated with families' science-related home activities, and conversations about animals and astronomy. Three case studies illustrate rich conversations where children and parents engaged in science practices during everyday talk. Our study provides evidence of Latine children's early interest in a broad array of science topics and their parents' support of their interests.
The Paleocene‐Eocene Thermal Maximum (PETM) is the most pronounced global warming event of the early Paleogene related to atmospheric CO2 increases. It is characterized by negative δ¹⁸O and δ¹³C excursions recorded in sedimentary archives and a transient disruption of the marine biosphere. Sites from the U.S. Atlantic Coastal Plain show an additional small, but distinct δ¹³C excursion below the onset of the PETM, coined the “pre‐onset excursion” (POE), mimicking the PETM‐forced environmental perturbations. This study focuses on the South Dover Bridge core in Maryland, where the Paleocene‐Eocene transition is stratigraphically constrained by calcareous nannoplankton and stable isotope data, and in which the POE is well‐expressed. The site was situated in a middle neritic marine shelf setting near a major outflow of the paleo‐Potomac River system. We generated high‐resolution benthic foraminiferal assemblage, stable isotope, trace‐metal, grain‐size and clay mineralogy data. The resulting stratigraphic subdivision of this Paleocene‐Eocene transition is placed within a depth transect across the paleoshelf, highlighting that the PETM sequence is relatively expanded. The geochemical records provide detailed insights into the paleoenvironment, developing from a well‐oxygenated water column in latest Paleocene to a PETM‐ecosystem under severe biotic stress‐conditions, with shifts in food supply and temperature, and under dysoxic bottom waters in a more river‐dominated setting. Environmental changes started in the latest Paleocene and culminated atthe onset of the PETM, hinting to an intensifying trigger rather than to an instantaneous event at the Paleocene‐Eocene boundary toppling the global system.
Motivated by the contradiction between a government's hard budget constraints and artificial intelligence, this study constructs a computable general equilibrium model embedded with various fiscal and tax policies to study the impact of artificial intelligence development on the Chinese economy under government budget constraints with different intensities. This paper seeks to find a reasonable policy that takes into account China's employment, income distribution, and budget constraints to achieve common prosperity. It finds that the softer the government's budget constraints, the smaller the negative impact of artificial intelligence on the economy. More specifically, allowing the government to increase its debts and spending is more effective than tax cuts. It is suggested that if the goal is to reconcile the contradiction between hard budget constraints and artificial intelligence, fiscal and tax policy combinations, together with an improvised soft budget constraint, are required to increase the taxation of capital to an appropriate degree. In order to resolve the contradiction between the government's hard budget constraints and the development of artificial intelligence in pursuit of common prosperity, a robot tax should be levied and automation capital taxed.
With growing evidence of labor violations and exploitative working conditions in fisheries, ensuring decent work is imperative to protect fishers and fishworkers in the global seafood sector. This study provides the first evaluation of decent work in a shared, transboundary fishery – the shrimp and groundfish fishery of the Guianas-Brazil Shelf. Decent work in fisheries has gained increasing attention and research, yet gaps exist in our understanding of the elements of decent work, how we evaluate it, and how to enable decent work. To date, there has been limited analysis of decent work in a range of geographies and diverse fisheries contexts, including small-scale fisheries and transboundary fisheries. This study will address this gap by evaluating decent work, utilizing a new fishery-specific, holistic evaluation framework drawing from existing frameworks including the ILO Work in Fishing Convention (C188), the Monterey Framework for Social Responsibility, and the FAO Voluntary Guidelines for Securing Sustainable Small-Scale Fisheries. This evaluation details country-level challenges that put fishers and fishworkers at risk in their occupation, including illegal fishing, vessel safety, and worker representation. This paper concludes with recommendations, to be advanced with a transboundary, regional approach, to ensure decent work and strengthen existing progress, including 1) addressing widespread illegal activities, 2) adopting fisheries-specific standards like C188, 3) implementing and enforcing policies at the country and regional level, and 4) ensuring worker representation and participation leveraging cooperatives and collectives.
Pseudo-nitzschia species are one of the leading causes of harmful algal blooms (HABs) along the western coast of the United States. Approximately half of known Pseudo-nitzschia strains can produce domoic acid (DA), a neurotoxin that can negatively impact wildlife and fisheries and put human life at risk through amnesic shellfish poisoning. Production and accumulation of DA, a secondary metabolite synthesized during periods of low primary metabolism, is triggered by environmental stressors such as nutrient limitation. To quantify and estimate the feedbacks between DA production and environmental conditions, we designed a simple mechanistic model of Pseudo-nitzschia and domoic acid dynamics, which we validate against batch and chemostat experiments. Our results suggest that, as nutrients other than nitrogen (i.e., silicon, phosphorus, and potentially iron) become limiting, DA production increases. Under Si limitation, we found an approximate doubling in DA production relative to N limitation. Additionally, our model indicates a positive relationship between light and DA production. These results support the idea that the relationship with nutrient limitation and light is based on direct impacts on Pseudo-nitzschia biosynthesis and biomass accumulation. Because it can easily be embedded within existing coupled physical-ecosystem models, our model represents a step forward toward modeling the occurrence of Pseudo-nitzschia HABs and DA across the U.S. West Coast.
Scent marks deposited as semiochemical signals are a primary mode of communication for a broad range of mammal species. Such scent signals are often deposited at specific, frequently visited marking sites called latrines. Despite descriptions of widespread latrine use by numerous mammal species, detailed understanding of site visit rates and latrine function is lacking. Here we report for the first time a quantitative assessment of scent-marking behaviours that represent interpack olfactory communication by African wild dogs, Lycaon pictus, at latrines visited by multiple resident neighbouring packs, hereafter called a ‘shared marking site’ (SMS). We show that multiple packs visited specific SMSs frequently and regularly throughout the year, with a notable decrease in visits during the 3-month denning season coinciding with a contraction in range size. In addition to resident neighbouring packs, dispersing individuals visited and scent-marked at SMSs, suggesting that latrines function at least in part as sites communicating information about residence and possibly reproductive status. Further detailed investigation of the relevance of latrine use to territorial behaviour, ranging, habitat use and dispersal in this species is required, particularly as it may have direct applied conservation implications for this wide-ranging but territorial endangered species.
An ongoing pandemic SS-RNA viral infection initiated from the Chinese province has threatened people throughout the globe. Coronavirus or COVID-19 or 2019-nCoV as a contagious infection is spreading day-by-day threatening the livelihood of people. The main objective of this paper is to find out solutions for the detection of this contagious viral infection at the earliest. Computer-based artificial intelligence can be used to monitor and detect the symptoms of coronavirus. For detection of coronavirus infection, computers or smartphones can be embedded with biosensors that will perceive the information and will convert the information into digital data. In this paper, a study on the coronavirus is done and an IoT-based framework is proposed to detect the coronavirus using IoT-based sensors. The proposed approach will be able to detect the pandemic in its early stages, and better options for prevention and cure will be discussed.
The development of treatments for impaired cognition in schizophrenia has been characterized as the most important challenge facing psychiatry at the beginning of the twenty-first century. The Cognitive Neuroscience Treatment Research to Improve Cognition in Schizophrenia (CNTRICS) project was designed to build on the potential benefits of using tasks and tools from cognitive neuroscience to better understanding and treat cognitive impairments in psychosis. These benefits include: (1) the use of fine-grained tasks that measure discrete cognitive processes; (2) the ability to design tasks that distinguish between specific cognitive domain deficits and poor performance due to generalized deficits resulting from sedation, low motivation, poor test taking skills, etc.; and (3) the ability to link cognitive deficits to specific neural systems, using animal models, neuropsychology, and functional imaging. CNTRICS convened a series of meetings to identify paradigms from cognitive neuroscience that maximize these benefits and identified the steps need for translation into use in clinical populations. The Cognitive Neuroscience Test Reliability and Clinical Applications for Schizophrenia (CNTRaCS) Consortium was developed to help carry out these steps. CNTRaCS consists of investigators at five different sites across the country with diverse expertise relevant to a wide range of the cognitive systems identified as critical as part of CNTRICs. This work reports on the progress and current directions in the evaluation and optimization carried out by CNTRaCS of the tasks identified as part of the original CNTRICs process, as well as subsequent extensions into the Positive Valence systems domain of Research Domain Criteria (RDoC). We also describe the current focus of CNTRaCS, which involves taking a computational psychiatry approach to measuring cognitive and motivational function across the spectrum of psychosis. Specifically, the current iteration of CNTRaCS is using computational modeling to isolate parameters reflecting potentially more specific cognitive and visual processes that may provide greater interpretability in understanding shared and distinct impairments across psychiatric disorders.KeywordsCNTRaCSCNTRICSCognitive neurosciencePositive valence systemsSchizophrenia
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6,671 members
Sasha Tozzi
  • Department of Ocean Sciences
Marc Perry
  • Genomics Institute
Diane Gifford-Gonzalez
  • Department of Anthropology
John Pearse
  • Department of Ecology & Evolutionary Biology
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University of California