Falling a House of Cards: Rediscovering a Humanist Language in an Age of Neuroreduction

Through Falling a house of cards: Rediscovering a humanist language in an age of neuroreduction, I argue that the language games specific to both neuroscience research and psychological treatment have becoming nonsensically intertwined, leading to commodification of treatment and patient abuse. I first examine the historical perspective of reductionism in science that enabled this linguistic blending, starting with Descartes and tracing its lineage to the modern times. I then expound on the nonsensical nature of modern neuroscience through a Wittgensteinian lens, and finally conclude with an examination of the social consequences produced by this linguistic confusion. The primary critique leveled is an argument coming from the field of mereology and entitled the mereological fallacy, where concepts of the whole creature are inappropriately and nonsensically attributed to specific parts of that creature. Ultimately, my thesis stands as an argument against the translational use of reductionism from research paradigms to treatment protocols

The acute phase protein, haptoglobin : a potential parameter in welfare assessment?

Physiological parameters are important measures in animal welfare assessment. To assess the amount of stress an animal experiences, stress hormones like cortisol are frequently used. However, measuring cortisol has major disadvantages due to its rapid reactivity and decline and many influencing factors. Other potential alternative markers are acute phase proteins, since stress is known to affect the immune system. A pilot study was conducted to investigate the response of the acute phase protein, plasma haptoglobine (HP), in pigs subjected to a stressor (food deprivation) and to examine the correlation between HP levels and average daily growth (ADG). Forty grower pigs (25.1 ± 4.4 kg, mean ± SD) (sex and former pen mates balanced), were allocated to 4 conventional pens, 2 treatment (T) and 2 control (C) groups (10 pigs per pen). After 10 days of adaptation the experiment started and ran for 3 weeks. In the 2nd week, T groups were repeatedly subjected to an 8-hour food deprivation (day 1, 3, 5 and 7 of week 2), C groups had normal, unrestricted, access to food. Pigs were weighed twice a week and blood was collected once a week (every 5th day). Mean levels of plasma HP of C and T groups showed large variation between individuals (C groups, week 2: 1.84 ± 3.11 mg/ml; T groups, week 2: 1.40 ± 1.16 mg/ml). No significant differences (Kruskal-Wallis test) in HP levels or growth were found between the C and T groups or between the different weeks within the T groups. Significant negative weak to moderate correlations were found between ADG and HP levels (HP week 1 and ADG week 1: rs = -0.47, p=0.005; HP week 2 and ADG total; rs= -0.60, p=0.015; HP week 3 and ADG total: rs = -0.43, p=0.025; average HP total and ADG total: rs= -0.41, p=0.017). Large variations in HP levels between individuals were shown and no effect of treatment on HP levels or growth was found. Possibly, food deprivation had no apparent stress eliciting effect. Despite these results, interesting correlations between the level of HP and ADG were found, corroborating the inverse relationship between the acute phase response and growth. To further investigate the relation of the acute phase response and stress a successive experiment will be conducted in which we apply a stronger stressor (mixing pigs) and combine the physiological data with behavior

Who Renews? Who Leaves? Identifying Customer Churn in a Telecom Company Using Big Data Techniques

Within the context of the telecom industry, this teaching case is an active learning analytics exercise to help students build hands-on expertise on how to utilize Big Data to solve a business problem. Particularly, the case utilizes an analytics method to help develop a customer retention strategy to mitigate against an increasing customer churn problem in a telecom company. Traditionally, the forecast of customer churn uses various demographic and cell phone usage data. Big Data techniques permit a much finer granularity in the prediction of churn by analyzing specific activities a customer undertakes before churning. The authors help students to understand how data from customer interactions with the company through multiple channels can be combined to create a “session.” Subsequently, the authors demonstrate the use of effective visualization to identify the most relevant paths to customer churn. The Teradata Aster Big Data platform is used in developing this case study

Chemotactic smoothing of collective migration

Collective migration -- the directed, coordinated motion of many self-propelled agents -- is a fascinating emergent behavior exhibited by active matter that has key functional implications for biological systems. Extensive studies have elucidated the different ways in which this phenomenon may arise. Nevertheless, how collective migration can persist when a population is confronted with perturbations, which inevitably arise in complex settings, is poorly understood. Here, by combining experiments and simulations, we describe a mechanism by which collectively migrating populations smooth out large-scale perturbations in their overall morphology, enabling their constituents to continue to migrate together. We focus on the canonical example of chemotactic migration of Escherichia coli, in which fronts of cells move via directed motion, or chemotaxis, in response to a self-generated nutrient gradient. We identify two distinct modes in which chemotaxis influences the morphology of the population: cells in different locations along a front migrate at different velocities due to spatial variations in (i) the local nutrient gradient and in (ii) the ability of cells to sense and respond to the local nutrient gradient. While the first mode is destabilizing, the second mode is stabilizing and dominates, ultimately driving smoothing of the overall population and enabling continued collective migration. This process is autonomous, arising without any external intervention; instead, it is a population-scale consequence of the manner in which individual cells transduce external signals. Our findings thus provide insights to predict, and potentially control, the collective migration and morphology of cell populations and diverse other forms of active matter

Solvable model for chimera states of coupled oscillators

Networks of identical, symmetrically coupled oscillators can spontaneously split into synchronized and desynchronized sub-populations. Such chimera states were discovered in 2002, but are not well understood theoretically. Here we obtain the first exact results about the stability, dynamics, and bifurcations of chimera states by analyzing a minimal model consisting of two interacting populations of oscillators. Along with a completely synchronous state, the system displays stable chimeras, breathing chimeras, and saddle-node, Hopf and homoclinic bifurcations of chimeras.Comment: 4 pages, 4 figures. This version corrects a previous error in Figure 3, where the sign of the phase angle psi was inconsistent with Equation 1

About Starobinsky inflation

It is believed that soon after the Planck era, space time should have a semi-classical nature. According to this, the escape from General Relativity theory is unavoidable. Two geometric counter-terms are needed to regularize the divergences which come from the expected value. These counter-terms are responsible for a higher derivative metric gravitation. Starobinsky idea was that these higher derivatives could mimic a cosmological constant. In this work it is considered numerical solutions for general Bianchi I anisotropic space-times in this higher derivative theory. The approach is ``experimental'' in the sense that there is no attempt to an analytical investigation of the results. It is shown that for zero cosmological constant $\Lambda=0$, there are sets of initial conditions which form basins of attraction that asymptote Minkowski space. The complement of this set of initial conditions form basins which are attracted to some singular solutions. It is also shown, for a cosmological constant $\Lambda> 0$ that there are basins of attraction to a specific de Sitter solution. This result is consistent with Starobinsky's initial idea. The complement of this set also forms basins that are attracted to some type of singular solution. Because the singularity is characterized by curvature scalars, it must be stressed that the basin structure obtained is a topological invariant, i.e., coordinate independent.Comment: Version accepted for publication in PRD. More references added, a few modifications and minor correction

The Formation of Kiloparsec-scale HI Holes in Dwarf Galaxies

The origin of kpc-scale holes in the atomic hydrogen (H i) distributions of some nearby dwarf irregular galaxies presents an intriguing problem. Star formation histories (SFHs) derived from resolved stars give us the unique opportunity to study past star-forming events that may have helped shape the currently visible Hi distribution. Our sample of five nearby dwarf irregular galaxies spans over an order of magnitude in both total Hi mass and absolute B-band magnitude and is at the low-mass end of previously studied systems. We use Very Large Array Hi line data to estimate the energy required to create the centrally dominant hole in each galaxy. We compare this energy estimate to the past energy released by the underlying stellar populations computed from SFHs derived from data taken with the Hubble Space Telescope. The inferred integrated stellar energy released within the characteristic ages exceeds our energy estimates for creating the holes in all cases, assuming expected efficiencies. Therefore, it appears that stellar feedback provides sufficient energy to produce the observed holes. However, we find no obvious signature of single star-forming events responsible for the observed structures when comparing the global SFHs of each galaxy in our sample to each other or to those of dwarf irregular galaxies reported in the literature. We also fail to find evidence of a central star cluster in FUV or Hα imaging. We conclude that large Hi holes are likely formed from multiple generations of star formation and only under suitable interstellar medium conditions

Ultrastable cellulosome-adhesion complex tightens under load

Challenging environments have guided nature in the development of ultrastable protein complexes. Specialized bacteria produce discrete multi-component protein networks called cellulosomes to effectively digest lignocellulosic biomass. While network assembly is enabled by protein interactions with commonplace affinities, we show that certain cellulosomal ligand-receptor interactions exhibit extreme resistance to applied force. Here, we characterize the ligand-receptor complex responsible for substrate anchoring in the Ruminococcus flavefaciens cellulosome using single-molecule force spectroscopy and steered molecular dynamics simulations. The complex withstands forces of 600-750 pN, making it one of the strongest bimolecular interactions reported, equivalent to half the mechanical strength of a covalent bond. Our findings demonstrate force activation and inter-domain stabilization of the complex, and suggest that certain network components serve as mechanical effectors for maintaining network integrity. This detailed understanding of cellulosomal network components may help in the development of biocatalysts for production of fuels and chemicals from renewable plant-derived biomass

Chaotic systems in complex phase space

This paper examines numerically the complex classical trajectories of the kicked rotor and the double pendulum. Both of these systems exhibit a transition to chaos, and this feature is studied in complex phase space. Additionally, it is shown that the short-time and long-time behaviors of these two PT-symmetric dynamical models in complex phase space exhibit strong qualitative similarities.Comment: 22 page, 16 figure

A novel approach to simulate gene-environment interactions in complex diseases

Background: Complex diseases are multifactorial traits caused by both genetic and environmental factors. They represent the major part of human diseases and include those with largest prevalence and mortality (cancer, heart disease, obesity, etc.). Despite a large amount of information that has been collected about both genetic and environmental risk factors, there are few examples of studies on their interactions in epidemiological literature. One reason can be the incomplete knowledge of the power of statistical methods designed to search for risk factors and their interactions in these data sets. An improvement in this direction would lead to a better understanding and description of gene-environment interactions. To this aim, a possible strategy is to challenge the different statistical methods against data sets where the underlying phenomenon is completely known and fully controllable, for example simulated ones. Results: We present a mathematical approach that models gene-environment interactions. By this method it is possible to generate simulated populations having gene-environment interactions of any form, involving any number of genetic and environmental factors and also allowing non-linear interactions as epistasis. In particular, we implemented a simple version of this model in a Gene-Environment iNteraction Simulator (GENS), a tool designed to simulate case-control data sets where a one gene-one environment interaction influences the disease risk. The main aim has been to allow the input of population characteristics by using standard epidemiological measures and to implement constraints to make the simulator behaviour biologically meaningful. Conclusions: By the multi-logistic model implemented in GENS it is possible to simulate case-control samples of complex disease where gene-environment interactions influence the disease risk. The user has full control of the main characteristics of the simulated population and a Monte Carlo process allows random variability. A knowledge-based approach reduces the complexity of the mathematical model by using reasonable biological constraints and makes the simulation more understandable in biological terms. Simulated data sets can be used for the assessment of novel statistical methods or for the evaluation of the statistical power when designing a study