Time and Causality in the Social Sciences

This article deals with the role of time in causal models in the social sciences, in particular in structural causal modeling, in contrast to time-free models. The aim is to underline the importance of time-sensitive causal models. For this purpose, it also refers to the important discussion on time and causality in the philosophy of science, and examines how time is taken into account in demography and in economics as examples of social sciences. Temporal information is useful to the extent that it is placed in a correct causal structure, and thus further corroborating the causal mechanism or generative process explaining the phenomenon under consideration. Despite the fact that the causal ordering of variables is more relevant for explanatory purposes than the temporal order, the former should nevertheless take into account the time-patterns of causes and effects, as these are often episodes rather than single events. For this reason in particular, it is time to put time at the core of our causal models

Byzantine-Tolerant Distributed Grow-Only Sets: Specification and Applications

In order to formalize Distributed Ledger Technologies and their interconnections, a recent line of research work has formulated the notion of Distributed Ledger Object (DLO), which is a concurrent object that maintains a totally ordered sequence of records, abstracting blockchains and distributed ledgers. Through DLO, the Atomic Appends problem, intended as the need of a primitive able to append multiple records to distinct ledgers in an atomic way, is studied as a basic interconnection problem among ledgers. In this work, we propose the Distributed Grow-only Set object (DSO), which instead of maintaining a sequence of records, as in a DLO, maintains a set of records in an immutable way: only Add and Get operations are provided. This object is inspired by the Grow-only Set (G-Set) data type which is part of the Conflict-free Replicated Data Types. We formally specify the object and we provide a consensus-free Byzantine-tolerant implementation that guarantees eventual consistency. We then use our Byzantine-tolerant DSO (BDSO) implementation to provide consensus-free algorithmic solutions to the Atomic Appends and Atomic Adds (the analogous problem of atomic appends applied on G-Sets) problems, as well as to construct consensus-free Single-Writer BDLOs. We believe that the BDSO has applications beyond the above-mentioned problems

Requirement of Caprine Arthritis Encephalitis VirusvifGene forin VivoReplication

AbstractReplication ofvif-caprine arthritis encephalitis virus (CAEV) is highly attenuated in primary goat synovial membrane cells and blood-derived macrophages compared to the wild-type (wt) virus. We investigated the requirement for CAEV Vif forin vivoreplication and pathogenicity in goats by intra-articular injection of either infectious proviral DNA or viral supernatants. Wild-type CAEV DNA or virus inoculation induced persistent infection resulting in severe inflammatory arthritic lesions in the joints. We were unable to detect any sign of virus replication invif-CAEV DNA inoculated goats, whilevif-CAEV virus inoculation resulted in the seroconversion of the goats. However, virus isolation and RT-PCR analyses on blood-derived macrophage cultures remained negative throughout the experiment as well as in joint or lymphoid tissues taken at necropsy. No pathologic lesions could be observed in joint tissue sections examined at necropsy. Goats inoculated with thevif-virus demonstrated no protection against a pathogenic virus challenge. These results demonstrate that CAEV Vif is absolutely required for efficientin vivovirus replication and pathogenicity and provide additional evidence that live attenuated lentiviruses have to establish a persistent infection to induce efficient protective immunity

The issue of control in multivariate systems, A contribution of structural modelling.

This paper builds upon Judea Pearl’s directed acyclic graphs approach to causality and the tradition of structural modelling in economics and social science. The paper re-examines the issue of control in complex systems with multiple causes and outcomes, in a specific perspective of structural modelling. It begins with three-variable saturated and unsaturated models, and then examines more complex systems including models with collider and latent confounder discussed by Pearl. In particular, focusing on the causes of an outcome, the paper proposes two simple rules for selecting the variables to be controlled for when studying the direct effect of a cause on an outcome of interest or the total effect when dealing with multiple causal paths. This paper presents a model building strategy that allows a statistical model to be considered as structural. The challenge for the model builder amounts to developing an explanation through a recursive decomposition of the joint distribution of the variables congruent with background knowledge and stable with respect to specified changes of the environment

Mundos Virtuales en UNNOBA

La incorporación de Tecnologías de la Información y Comunicación (TICs) han revolucionado la forma de enseñar y aprender. Teniendo en cuenta que su evolución es cada vez mayor, este trabajo se centra en los avances realizados desde el desarrollo del Entorno Virtual de Enseñanza y Aprendizaje 3D (EV3D) en la Universidad Nacional del Noroeste de la Provincia de Buenos Aires (UNNOBA) y su conexión con el Entorno Virtual de Enseñanza y Aprendizaje (EVEA) ya oficialmente utilizado, UNNOBA Virtual

Global entrainment of transcriptional systems to periodic inputs

This paper addresses the problem of giving conditions for transcriptional systems to be globally entrained to external periodic inputs. By using contraction theory, a powerful tool from dynamical systems theory, it is shown that certain systems driven by external periodic signals have the property that all solutions converge to a fixed limit cycle. General results are proved, and the properties are verified in the specific case of some models of transcriptional systems. The basic mathematical results needed from contraction theory are proved in the paper, making it self-contained

An unsupervised behavioral modeling and alerting system based on passive sensing for elderly care

Artificial Intelligence in combination with the Internet of Medical Things enables remote healthcare services through networks of environmental and/or personal sensors. We present a remote healthcare service system which collects real-life data through an environmental sensor package, including binary motion, contact, pressure, and proximity sensors, installed at households of elderly people. Its aim is to keep the caregivers informed of subjects’ health-status progressive trajectory, and alert them of health-related anomalies to enable objective on-demand healthcare service delivery at scale. The system was deployed in 19 households inhabited by an elderly person with post-stroke condition in the Emilia–Romagna region in Italy, with maximal and median observation durations of 98 and 55 weeks. Among these households, 17 were multi-occupancy residences, while the other 2 housed elderly patients living alone. Subjects’ daily behavioral diaries were extracted and registered from raw sensor signals, using rule-based data pre-processing and unsupervised algorithms. Personal behavioral habits were identified and compared to typical patterns reported in behavioral science, as a quality-of-life indicator. We consider the activity patterns extracted across all users as a dictionary, and represent each patient’s behavior as a ‘Bag of Words’, based on which patients can be categorized into sub-groups for precision cohort treatment. Longitudinal trends of the behavioral progressive trajectory and sudden abnormalities of a patient were detected and reported to care providers. Due to the sparse sensor setting and the multi-occupancy living condition, the sleep profile was used as the main indicator in our system. Experimental results demonstrate the ability to report on subjects’ daily activity pattern in terms of sleep, outing, visiting, and health-status trajectories, as well as predicting/detecting 75% hospitalization sessions up to 11 days in advance. 65% of the alerts were confirmed to be semantically meaningful by the users. Furthermore, reduced social interaction (outing and visiting), and lower sleep quality could be observed during the COVID-19 lockdown period across the cohort

Annexin A5 D226K structure and dynamics: identification of a molecular switch for the large-scale conformational change of domain III

AbstractThe domain III of annexin 5 undergoes a Ca2+- and a pH-dependent conformational transition of large amplitude. Modeling of the transition pathway by computer simulations suggested that the interactions between D226 and T229 in the IIID–IIIE loop on the one hand and the H-bond interactions between W187 and T224 on the other hand, are important in this process [Sopkova et al. (2000) Biochemistry 39, 14065–14074]. In agreement with the modeling, we demonstrate in this work that the D226K mutation behaves as a molecular switch of the pH- and Ca2+-mediated conformational transition. In contrast, the hydrogen bonds between W187 and T224 seem marginal

On the neural origin of pseudoneglect: EEG-correlates of shifts in line bisection performance with manipulation of line length

Healthy participants tend to show systematic biases in spatial attention, usually to the left. However, these biases can shift rightward as a result of a number of experimental manipulations. Using electroencephalography (EEG) and a computerized line bisection task, here we investigated for the first time the neural correlates of changes in spatial attention bias induced by line-length (the so-called line-length effect). In accordance with previous studies, an overall systematic left bias (pseudoneglect) was present during long line but not during short line bisection performance. This effect of line-length on behavioral bias was associated with stronger right parieto-occipital responses to long as compared to short lines in an early time window (100–200 ms) post-stimulus onset. This early differential activation to long as compared to short lines was task-independent (present even in a non-spatial control task not requiring line bisection), suggesting that it reflects a reflexive attentional response to long lines. This was corroborated by further analyses source-localizing the line-length effect to the right temporo-parietal junction (TPJ) and revealing a positive correlation between the strength of this effect and the magnitude by which long lines (relative to short lines) drive a behavioral left bias across individuals. Therefore, stimulus-driven left bisection bias was associated with increased right hemispheric engagement of areas of the ventral attention network. This further substantiates that this network plays a key role in the genesis of spatial bias, and suggests that post-stimulus TPJ-activity at early information processing stages (around the latency of the N1 component) contributes to the left bias