PANOLA: A Personal Assistant for Supporting Users in Preserving Privacy

Privacy is the right of individuals to keep personal information to themselves. When individuals use online systems, they should be given the right to decide what information they would like to share and what to keep private. When a piece of information pertains only to a single individual, preserving privacy is possible by providing the right access options to the user. However, when a piece of information pertains to multiple individuals, such as a picture of a group of friends or a collaboratively edited document, deciding how to share this information and with whom is challenging. The problem becomes more difficult when the individuals who are affected by the information have different, possibly conflicting privacy constraints. Resolving this problem requires a mechanism that takes into account the relevant individuals’ concerns to decide on the privacy configuration of information. Because these decisions need to be made frequently (i.e., per each piece of shared content), the mechanism should be automated. This article presents a personal assistant to help end-users with managing the privacy of their content. When some content that belongs to multiple users is about to be shared, the personal assistants of the users employ an auction-based privacy mechanism to regulate the privacy of the content. To do so, each personal assistant learns the preferences of its user over time and produces bids accordingly. Our proposed personal assistant is capable of assisting users with different personas and thus ensures that people benefit from it as they need it. Our evaluations over multiagent simulations with online social network content show that our proposed personal assistant enables privacy-respecting content sharing.

A 1D CNN for high accuracy classification and transfer learning in motor imagery EEG-based brain-computer interface

Objective: Brain-computer interface (BCI) aims to establish communication paths between the brain processes and external devices. Different methods have been used to extract human intentions from electroencephalography (EEG) recordings. Those based on motor imagery (MI) seem to have a great potential for future applications. These approaches rely on the extraction of EEG distinctive patterns during imagined movements. Techniques able to extract patterns from raw signals represent an important target for BCI as they do not need labor-intensive data pre-processing. Approach: We propose a new approach based on a 10-layer one-dimensional convolution neural network (1D-CNN) to classify five brain states (four MI classes plus a ‘baseline’ class) using a data augmentation algorithm and a limited number of EEG channels. In addition, we present a transfer learning method used to extract critical features from the EEG group dataset and then to customize the model to the single individual by training its outer layers with only 12-minute individual-related data. Main results: The model tested with the ‘EEG Motor Movement/Imagery Dataset’ outperforms the current state-of-the-art models by achieving a 99.38% accuracy at the group level. In addition, the transfer learning approach we present achieves an average accuracy of 99.46%. Significance: The proposed methods could foster future BCI applications relying on few-channel portable recording devices and individual-based training.

Identification of W13 in the American Miniature Horse and Shetland Pony Populations

Coat color is a trait of economic significance in horses. Variants in seven genes have been documented to cause white patterning in horses. Of the 34 variants that have been identified in KIT proto-oncogene, receptor tyrosine kinase (KIT), 27 have only been reported in a single individual or family and thus not all are routinely offered for genetic testing. Therefore, to enable proper use of marker-assisted selection, determining breed specificity for these alleles is warranted. Screening 19 unregistered all-white Shetland ponies for 16 white patterning markers identified 14 individuals whose phenotype could not be explained by testing results. In evaluating other known dominant white variants, 14 horses were heterozygous for W13. W13 was previously only reported in two quarter horses and a family of Australian miniature horses. Genotyping known white spotting variants in 30 owner-reported white animals (25 Miniature Horses and five Shetland ponies) identified two additional W13/N American Miniature Horses. The estimated allele frequency of W13 in the American Miniature Horse was 0.0063 (79 N/N, 1 W13/N) and the allele was not detected in a random sample (n = 59) of Shetland ponies. No homozygous W13 individuals were identified and W13/N ponies had a similar all-white coat with pink skin phenotype, regardless of the other white spotting variants present, demonstrating that W13 results in a Mendelian inherited dominant white phenotype and homozygosity is likely lethal. These findings document the presence of W13 in the American Miniature Horse and Shetland pony populations at a low frequency and illustrate the importance of testing for this variant in additional breeds.

Modern Folk Devils: Contemporary Constructions of Evil

The devilish has long been integral to myths, legends, and folklore, firmly located in the relationships between good and evil, and selves and others. But how are ideas of evil constructed in current times and framed by contemporary social discourses? Modern Folk Devils builds on and works with Stanley Cohen’s theory on folk devils and moral panics to discuss the constructions of evil. The authors present an array of case-studies that illustrate how the notion of folk devils nowadays comes into play and animates ideas of otherness and evil throughout the world.Examining current fears and perceived threats, this volume investigates and analyzes how and why these devils are constructed. The chapters discuss how the devilish may take on many different forms: sometimes they exist only as a potential threat, other times they are a single individual or phenomenon or a visible group, such as refugees, technocrats, Roma, hipsters, LGBT groups, and rightwing politicians. Folk devils themselves are also given a voice to offer an essential complementary perspective on how panics become exaggerated, facts distorted, and problems acutely angled. Bringing together researchers from anthropology, sociology, political studies, ethnology, and criminology, the contributions examine cases from across the world spanning from Europe to Asia and Oceania.

Better baboon break-ups: collective decision theory of complex social network fissions

Many social groups are made up of complex social networks in which each individual associates with a distinct subset of its groupmates. If social groups become larger over time, competition often leads to a permanent group fission. During such fissions, complex social networks present a collective decision problem and a multidimensional optimization problem: it is advantageous for each individual to remain with their closest allies after a fission, but impossible for every individual to do so. Here, we develop computational algorithms designed to simulate group fissions in a network-theoretic framework. We focus on three fission algorithms (democracy, community and despotism) that fall on a spectrum from a democratic to a dictatorial collective decision. We parameterize our social networks with data from wild baboons ( Papio cynocephalus ) and compare our simulated fissions with actual baboon fission events. We find that the democracy and community algorithms (egalitarian decisions where each individual influences the outcome) better maintain social networks during simulated fissions than despotic decisions (driven primarily by a single individual). We also find that egalitarian decisions are better at predicting the observed individual-level outcomes of observed fissions, although the observed fissions often disturbed their social networks more than the simulated egalitarian fissions.

Mosaicism in clinical genetics

Genetic mosaicism is the state in which there are two or more different sets of cells in a single individual because of one or more postzygotic mutations, and its importance in clinical genetics has long been recognized (Hall, Am J Hum Genet43: 355 [1988]). In this Perspective, a paper in this special issue on mosaicism from Cook et al. (Cold Spring Harb Mol Case Studies7: a006125 [2021]) is discussed.

Multi-omics reveal differentiation and maintenance of dimorphic flowers in an alpine plant on the Qinghai-Tibet Plateau

 Dimorphic flowers growing on a single individual plant play a critical role in extreme adaption and reproductive assurance in plants and have high ecological and evolutionary significance. However, the omics bases underlying such a differentiation and maintenance remain largely unknown. We aimed to investigate this through genomic, transcriptome and metabolomic analyses of dimorphic flowers in an alpine biennial, Sinoswertia tetraptera (Gentianaceae).  A high-quality chromosome-level genome sequence (903 Mb) was first assembled for S. tetraptera with 31,359 protein-coding genes annotated. Two rounds of recent independent whole-genome duplication (WGD) were revealed. More than 10% of the novel genes from the recent species-specific WGD were found to be differentially expressed in the two types of flowers, and this may have helped contribute to the origin of this innovative trait.  Other contrasting gene expression between flowers included that related to flower development and color, hormones, and iridoid biosynthesis. Metabolomic analyses similarly suggested differential concentrations of both hormones and iridoids in the two types of flowers. The interactions between multiple genes may together lead to contrasting morphology and open versus closed pollination of the dimorphic flowers in this species.  A total of 56 candidate genes were identified from the known iridoid biosynthesis-related pathways. Two hub genes were found to play an essential role in transferring intermediate products between leaves and flowers during iridoid biosynthesis.

Racial Differences in the Effect of Alzheimer’s Disease on Adherence to Medication Therapy for Chronic Diseases

Multiple dementia (the presence of one or more types of dementia in a single individual) and multi-morbidity (the presence of multiple chronic diseases in an individual) present a major challenge to the U.S. healthcare system. The reduction in cognitive function associated with neurocognitive disorders such as Alzheimer’s Disease (AD) and Related Dementias (ADRD) reduce the ability of the affected individual to take care of him/herself. This can manifest as reduced adherence to medication regimens designed to manage other chronic conditions, in reduced ability to engage in healthy behavior such as exercise, or in other ways. The result is an increase in the probability of otherwise avoidable adverse health outcomes and related healthcare costs. In this study, we showcase two high impact chronic conditions common in the elderly: hypertension and type 2 diabetes mellitus (T2D). Using a 5% sample of the total Medicare population we identify groups of individuals with AD/ADRD and i) hypertension, ii) T2D or iii) both. Each group is then propensity-score-matched to similar individuals with hypertension, T2D or both but without a diagnosis of AD/ADRD. The primary explanatory variable of interest is the medication possession ratio (MPR) calculated at 1-year intervals for prescribed diabetes and/or hypertension medications. MPRs were compared between the two groups using t-tests and standardized differences each year after baseline and until death/censoring. A Cox proportional hazard model was then used to estimate differences in survival between these two groups and across race/ethnicity-related strata. Reduced adherence with time and notable race/ethnicity-related differences were identified.

Birds with multiple homes. The annual cycle of the pallid swift (Apus pallidus brehmorum)

We tracked pallid swifts (Apus pallidus brehmorum) from a single breeding colony in Gibraltar over two years. Our results show movement of birds between specific regions within the non-breeding geographical area at specific times of the year. The tracking of a single individual showed remarkable fidelity to the areas visited between years. Furthermore, two pallid swifts tracked over the entire eight-month non-breeding period, while in Africa, gave no indication of coming to land, supporting previous findings of an airborne existence in swifts outside the breeding season. In addition, the crossing of the Sahara Desert to and from breeding grounds is remarkably fast, with one individual crossing it in just over a day. We discuss our findings in the context of bird migration evolutionary strategies.

CLOUD-BASED TEMPERATURE MONITORING MECHANISM FOR COVID-19 PANDEMIC TRACKING

The unprecedented outbreak of novel coronavirus 2019 (COVID-19) globally has a huge impact to our daily life in numerous ways. To effectively minimize the spread of the virus, early symptom detection is crucial, especially in closed environment with high human traffic areas which post higher chances of human-to-human transmission. Body temperature measurement has been identified among the vital monitoring parameters. However, current available temperature monitoring mechanism is costly, limited to single individual and limited to locally without integrating to cloud and database. This led to difficulty in effective surveillance for suspicious COVID cases. Hence, the purpose of this paper is to introduce an end-to-end Internet of Things-enabled application for thermal monitoring as an early signal detection and screening method. This work integrates Raspberry Pi, thermal sensor, LCD display, buzzer, and LED light with Raspbian and Restful API for device-to-cloud communication. The system implemented is capable for user identification, body temperature remote monitoring and warning signal for fever symptoms. The result of this real-time system is capable to detect and screen the suspected contagious person in an organization effectively. Future works on integrating face recognition with machine learning and artificial intelligent enhancement.