Miracle-dz/UniFAR-FTUs huggingface dataset:
Strategies for calibrating models of biology

Computational and mathematical modelling has become a valuable tool for investigating biological systems. Modelling enables prediction of how biological components interact to deliver system-level properties and extrapolation of biological system performance to contexts and experimental conditions where this is unknown. A model's value hinges on knowing that it faithfully represents the biology under the contexts of use, or clearly ascertaining otherwise and thus motivating further model refinement. These qualities are evaluated through calibration, typically formulated as identifying model parameter values that align model and biological behaviours as measured through a metric applied to both. Calibration is critical to modelling but is often underappreciated. A failure to appropriately calibrate risks unrepresentative models that generate erroneous insights. Here, we review a suite of strategies to more rigorously challenge a model's representation of a biological system. All are motivated by features of biological systems, and illustrative examples are drawn from the modelling literature. We examine the calibration of a model against distributions of biological behaviours or outcomes, not only average values. We argue for calibration even where model parameter values are experimentally ascertained. We explore how single metrics can be non-distinguishing for complex systems, with multiple-component dynamic and interaction configurations giving rise to the same metric output. Under these conditions, calibration is insufficiently constraining and the model non-identifiable: multiple solutions to the calibration problem exist. We draw an analogy to curve fitting and argue that calibrating a biological model against a single experiment or context is akin to curve fitting against a single data point. Though useful for communicating model results, we explore how metrics that quantify heavily emergent properties may not be suitable for use in calibration. Lastly, we consider the role of sensitivity and uncertainty analysis in calibration and the interpretation of model results. Our goal in this manuscript is to encourage a deeper consideration of calibration, and how to increase its capacity to either deliver faithful models or demonstrate them otherwise.
Exposure to herbicides reduces larval sensitivity to insecticides in Spodoptera litura (Lepidoptera: Noctuidae)

Herbicides and insecticides are widely used in modern agriculture. It has been reported in various studies that application of insecticides can increase tolerance of herbivorous insects to insecticides. However, limited information exists on susceptibility to insecticides when insects are exposed to herbicides. This study was conducted to investigate the potential impact of the herbicides trifluralin and 2‐methyl‐4‐chlorophenoxyacetic acid sodium salt (MCPA‐Na) on the susceptibility of the nocturnal moth Spodoptera litura to the insecticides λ‐cyhalothrin, phoxim and bifenthrin. We found that larvae exposed to trifluralin or MCPA‐Na became significantly less susceptible to both insecticides than non‐exposed control larvae. Herbicide‐treated larvae did not show altered growth under the used test conditions. However, heads of herbicide‐treated larvae showed increased expression of the acetylcholinesterase genes SlAce1 and SlAce2. Moreover, the fat body and midgut of herbicide‐treated larvae displayed elevated expression of detoxification genes (the carboxylesterase gene SlCarE; the glutathione S‐transferase genes SlGSTe2 and SlGSTe3; the cytochrome P450 monooxygenase genes CYP6B48, CYP9A40 and CYP321B1). The CYP6B48 gene exhibited highest inducibility. In conclusion, the data of this study suggest that exposure of S. litura larvae to herbicides may stimulate detoxification mechanisms that compromise the efficacy of insecticides.
Bone marrow mesenchymal stem/stromal cells from risk-stratified acute myeloid leukemia patients are anti-inflammatory in in vivo preclinical models of hematopoietic reconstitution and severe colitis

Acute myeloid leukemia represents a heterogeneous group of disorders characterized by the rapid expansion of immature myeloid cells (blasts) in the bone marrow. There is wide disease heterogeneity and patient risk-stratification principally relies on cytogenetic-molecular data. Bone-marrow mesenchymal stem/stromal cells are key components of the hematopoietic niche thought to contribute to leukemia pathogenesis. We recently reported a correlation between behaviors of bone-marrow mesenchymal stem/stromal cells from acute myeloid leukemia patients in vitro and overall survival. To elaborate on this finding, we investigated the capacity of bone-marrow mesenchymal stem/stromal cells from risk-stratified de novo acute myeloid leukemia patients to regulate the homeostasis of healthy CD34+ hematopoietic stem/progenitor cells, and to exert anti-inflammatory effects in vivo in a pre-clinical model of severe acute colitis. We report that regardless of risk-group, bone-marrow mesenchymal stem/stromal cells from acute myeloid leukemia patients support, similar to those derived from healthy bone-marrow the survival, proliferation, differentiation and clonogenecity of CD34+ cells in vitro, and the in vivo immune deficient mice repopulating assays. Additionally, bone-marrow mesenchymal stem/stromal cells from acute myeloid leukemia patients were capable of reversing the inflammatory phenotype in preclinical models of acute severe colitis, showing a greater anti-inflammatory capacity compared to those derived from healthy bone marrow. Collectively, bone-marrow mesenchymal stem/stromal cells from acute myeloid leukemia patients retain the capacity to support healthy hematopoietic stem/progenitor cells and can suppress inflammation in vivo.
A National Multicity Analysis of the Causal Effect of Local Pollution, NO2, and PM2.5 on Mortality

Background: Studies have long associated PM2.5 with daily mortality, but few applied causal-modeling methods, or at low exposures. Short-term exposure to NO2, a marker of local traffic, has also been associated with mortality but is less studied. We previously found a causal effect between local air pollution and mortality in Boston. Objectives: We aimed to estimate the causal effects of local pollution, PM2.5, and NO2 on mortality in 135 U.S. cities. Methods: We used three methods which, under different assumptions, provide causal marginal estimates of effect: a marginal structural model, an instrumental variable analysis, and a negative exposure control. The instrumental approach used planetary boundary layer, wind speed, and air pressure as instruments for concentrations of local pollutants; the marginal structural model separated the effects of NO2 from the effects of PM2.5, and the negative exposure control provided protection against unmeasured confounders. Results: In 7.3 million deaths, the instrumental approach estimated that mortality increased 1.5% [95% confidence interval (CI): 1.1%, 2.0%] per 10 μg/m3 increase in local pollution indexed as PM2.5. The negative control exposure was not associated with mortality. Restricting our analysis to days with PM2.5 below 25 μg/m3, we found a 1.70% (95% CI 1.11%, 2.29%) increase. With marginal structural models, we found positive significant increases in deaths with both PM2.5 and NO2. On days with PM2.5 below 25 μg/m3, we found a 0.83% (95% CI 0.39%, 1.27%) increase. Including negative exposure controls changed estimates minimally. Conclusions: Causal-modeling techniques, each subject to different assumptions, demonstrated causal effects of locally generated pollutants on daily deaths with effects at concentrations below the current EPA daily PM2.5 standard. https://doi.org/10.1289/EHP2732
Protocol for the REVISIT-BPD Trial, a Randomized Controlled Trial Testing the Effectiveness of an Internet-Based Self-Management Intervention in the Treatment of Borderline Personality Disorder (BPD)

Introduction: Borderline Personality Disorder (BPD) is a prevalent condition that is often under-treated. This is partly because very few psychotherapists offer treatment for this disabling disorder. Internet-based self-management interventions could contribute to reducing the treatment gap but—mainly due to safety concerns—these have never been tested for BPD in controlled trials. Methods: Patients with BPD will be recruited primarily via the internet and randomized to two groups: care as usual (CAU) alone) or the self-management intervention priovi® in addition to CAU. At the end of the diagnostic interview, all participants will discuss an emergency plan. The main outcome measure is the clinician-rated symptom severity using the BPD Severity Index (BPDSI). Secondary outcome measures include a range of self-reported scales, an SCID-diagnosis of BPD and several safety parameters including serious adverse events (e.g., a life-threatening event, hospitalization or suicide attempt). Discussion: This trial will evaluate the effectiveness of the self-management intervention, priovi, in reducing symptoms of BPD. It will also assess the safety of its use in this target population. If successful, this intervention would be the first comprehensive internet intervention for the treatment of BPD and complement the wide range of internet interventions effective in treating other mental disorders, particularly depression and anxiety disorders. Trial Registration: NCT03418142 (clinicaltrials.gov) on January 23rd 2018. Trial status: recruiting, currently N = 108 (July 2018).
School Attachment and Violent Attitudes Preventing Future Violent Behavior Among Youth

Evidence derived from social information theories support the existence of different underlying cognitive mechanisms guiding violent behavior through life. However, a few studies have examined the contribution of school variables to those cognitive mechanisms, which may help explain violent behavior later in life. The present study examines the relationship between school attachment, violent attitudes, and violent behavior over time in a sample of urban adolescents from the U.S. Midwest. We evaluated the influence of school attachment on violent attitudes and subsequent violent behavior. We used structural equation modeling to test our hypothesis in a sample of 579 participants (54.9% female, 81.3% African American). After controlling for gender and race, our results indicated that the relationship between school attachment and violent behavior over time is mediated by violent attitudes. The instrumentalization of the school context as a learning environment aiming to prevent future violent behavior is also discussed.
Beyond Hypnograms: Assessing Sleep Stability Using Acoustic and Electrical Stimulation

Conventional polysomnographic recordings reflect brain dynamics associated with sleep architecture. We hypothesized that noninvasive tools like transcranial alternating current stimulation (tACS) and acoustic stimulation (for generating event related potentials [ERPs]) would help to predict sleep stability and provide a window to actively assess brain activity during sleep.
African American Older Adults' Perceived Use of Technology for Hypertension Self-Management.

With the unprecedented growth of technology for disease prevention and management, little is known about the experience and adoption of such technology in African American older adults with hypertension. A 90-minute focus group session was used to explore African American older adults' (N = 21) experiences with using technology (mobile devices and applications) for hypertension self-management. Twenty participants reported owning smartphones and used this technology to communicate; seek, acquire, and share information; engage in entertainment; and organize and manage time. Participants expressed concerns about not being informed or trained sufficiently to integrate technology for hypertension self-management. There is a need to develop novel hypertension self-management interventions that integrate technology and training programs for this marginalized population that may help improve blood pressure control and address important clinical and public health priorities of uncontrolled hypertension. [Res Gerontol Nurs. 2018; 11(5):249-256. ].
Inferring Multiplex Diffusion Network via Multivariate Marked Hawkes Process

Understanding the diffusion in social network is an important task. However, this task is challenging since (1) the network structure is usually hidden with only observations of events like "post" or "repost" associated with each node, and (2) the interactions between nodes encompass multiple distinct patterns which in turn affect the diffusion patterns. For instance, social interactions seldom develop on a single channel, and multiple relationships can bind pairs of people due to their various common interests. Most previous work considers only one of these two challenges which is apparently unrealistic. In this paper, we study the problem of \emph{inferring multiplex network} in social networks. We propose the Multiplex Diffusion Model (MDM) which incorporates the multivariate marked Hawkes process and topic model to infer the multiplex structure of social network. A MCMC based algorithm is developed to infer the latent multiplex structure and to estimate the node-related parameters. We evaluate our model based on both synthetic and real-world datasets. The results show that our model is more effective in terms of uncovering the multiplex network structure.
Family Dynamics and Young Adults’ Well-Being: The Mediating Role of Sibling Bullying

Although a common form of family violence, sibling bullying is often viewed as harmless by families and society. Consequently, it has not received as much attention in research compared with peer bullying or other types of family violence, such as interpartner violence, child abuse, and elder abuse. Considering that sibling relationships have lasting effects on children’s development, this retrospective study focused on the antecedents and outcomes of sibling bullying. Grounded in family system theory and social learning theory, it explored whether sibling bullying during childhood and adolescence mediated the relationship between negative family dynamics (i.e., sibling rivalry, interparental hostility, and parental hostility) and young adults’ well-being (i.e., sense of competence, internalized problems, self-esteem, and satisfaction with life). Data from 216 respondents were analyzed using structural equation modeling. Results revealed that young adults who reported higher levels of sibling rivalry and interparental hostility were more likely to be victims of sibling bullying. In turn, sibling bullying was associated with lower sense of competence, self-esteem, and life satisfaction, as well as more internalized problems. Parents’ hostile behaviors toward their children were not associated with sibling bullying but were negatively associated with well-being in early adulthood. Our work underlines the importance of harmonious family dynamics and the need for sibling bullying to be taken more seriously by parents and family members. From a clinical point of view, our findings suggest that clinicians and human service workers should adopt a family-level approach when trying to reduce sibling bullying and its negative outcomes by educating parents about the interdependence of family relationships.
Multiple Preambles for High Success Rate of Grant-Free Random Access With Massive MIMO

Grant-free random access (RA) with massive MIMO is a promising RA technique that provides significant benefits in increasing the channel reuse efficiency with low signaling overhead. Since user equipment (UE) detection and channel estimation in grant-free RA rely solely on the received preambles, preamble designs that enable high success rate of UE detection and channel estimation are very much in need to ensure the performance gain of grant-free RA with massive MIMO. In this paper, a super preamble consisting of multiple consecutive preambles is proposed for the high success rate of grant-free RA with massive MIMO. With the proposed approach, the success of UE detection and channel estimation for a UE depends on two conditions: 1) it is a solvable UE, where we define the UE whose super preamble is not a linear combination of the other UEs’ super preambles as a solvable UE and 2) its super preamble is detected. Accordingly, we theoretically analyze the solvable rate of the UEs with multiple preambles and propose a reliable UE detection algorithm to obtain the super preambles of the UEs by exploiting the quasi-orthogonality characteristic of massive MIMO. The theoretical analysis and simulation results show that turning a preamble into a super preamble consisting of two or three shorter preambles, the success rate of UE detection and channel estimation could be significantly increased using the proposed approach.
The Grayling Genome Reveals Selection on Gene Expression Regulation after Whole-Genome Duplication

Whole genome duplication (WGD) has been a major evolutionary driver of increased genomic complexity in vertebrates. One such event occurred in the salmonid family ∼80 million years ago (Ss4R) giving rise to a plethora of structural and regulatory duplicate-driven divergence, making salmonids an exemplary system to investigate the evolutionary consequences of WGD. Here, we present a draft genome of European grayling (Thymallus thymallus), and use this in a comparative framework to study evolution of gene regulation following WGD. Among the Ss4R duplicates identified in European grayling and Atlantic salmon, one third reflect non-neutral tissue expression evolution, with strong purifying selection, maintained over ∼50 million years. Of these, 84% reflect conserved tissue regulation under strong selective constraints and are involved in brain and neural-related functions, as well as higher-order protein-protein interactions. In contrast, 16% of the duplicates have evolved regulatory divergence in a common ancestor, suggestive of adaptive divergence following WGD. These candidates for adaptive expression divergence have elevated rates of protein coding- and promoter sequence evolution, and are enriched for immune- and metabolism ontology terms. Lastly, species-specific duplicate divergence points towards underlying differences in adaptive pressures on expression regulation in the non-anadromous grayling and anadromous Atlantic salmon. Our findings enhance our understanding of the role of WGD in genome evolution and highlights cases of functional divergence of Ss4R duplicates, possibly related to a niche shift in early salmonid evolution.
Data Shuffling in Wireless Distributed Computing via Low-Rank Optimization

Intelligent mobile platforms such as smart vehicles and drones have recently become the focus of attention for onboard deployment of machine learning mechanisms to enable low latency decisions with low risk of privacy breach. However, most such machine learning algorithms are both computation-and-memory intensive, which makes it highly difficult to implement the requisite computations on a single device of limited computation, memory, and energy resources. Wireless distributed computing presents new opportunities by pooling the computation and storage resources among devices. For low-latency applications, the key bottleneck lies in the exchange of intermediate results among mobile devices for data shuffling. To improve communication efficiency, we propose a co-channel communication model and design transceivers by exploiting the locally computed intermediate values as side information. A low-rank optimization model is proposed to maximize the achieved degrees-of-freedom (DoF) by establishing the interference alignment condition for data shuffling. Unfortunately, existing approaches to approximate the rank function fail to yield satisfactory performance due to the poor structure in the formulated low-rank optimization problem. In this paper, we develop an efficient difference-of-convex-functions (DC) algorithm to solve the presented low-rank optimization problem by proposing a novel DC representation for the rank function. Numerical experiments demonstrate that the proposed DC approach can significantly improve the communication efficiency whereas the achievable DoF almost remains unchanged when the number of mobile devices grows.
Cumulative trauma, emotion reactivity and salivary cytokine levels following acute stress in healthy women

ABSTRACT Background and Objectives: To better understand how trauma leads to poor health, this study examined whether cumulative trauma and emotion reactivity contribute to pro- (IL-1β) and anti-inflammatory (IL-10) salivary cytokine levels after stress. Design: Seventy-three women, screened to be physically and mentally healthy, completed an acute stress paradigm and measures of lifetime trauma exposure. Method: Saliva was collected 10 min before (i.e., baseline) and 35 min after the onset of a 10-min stressor. State negative and positive emotion were measured at baseline and post-stress. Results: Most participants reported exposure to at least one trauma, with a mean of five. Cumulative trauma was associated with higher post-stress IL-1β and IL-1β/IL-10, but not with IL-10 or changes in emotion. Declines in positive emotion correlated with greater post-stress IL-1β. Conclusions: These findings suggest that both cumulative trauma exposure and positive emotion have implications for salivary cytokine responses to acute stress. The inclusion of healthy women strengthens internal validity, and increases confidence that observed associations between trauma and salivary cytokine responses can be attributed to trauma, rather than to confounding health problems. This study adds to the growing literature examining how trauma may connect to cytokines, and ultimately, poor health.
Inhibition of mTORC1 in pediatric low-grade glioma depletes glutathione and therapeutically synergizes with carboplatin

BACKGROUND Pediatric low-grade glioma (pLGG) often initially responds to front-line therapies such as carboplatin, but more than 50% of treated tumors eventually progress and require additional therapy. With the discovery that pLGG often contains mammalian target of rapamycin (mTOR) activation, new treatment modalities and combinations are now possible for patients. The purpose of this study was to determine if carboplatin is synergistic with the mTOR complex 1 inhibitor everolimus in pLGG. METHODS We treated 4 pLGG cell lines and 1 patient-derived xenograft line representing various pLGG genotypes, including neurofibromatosis type 1 loss, proto-oncogene B-Raf (BRAF)-KIAA1549 fusion, and BRAFV600E mutation, with carboplatin and/or everolimus and performed assays for growth, cell proliferation, and cell death. Immunohistochemistry as well as in vivo and in vitro metabolomics studies were also performed. RESULTS Carboplatin synergized with everolimus in all of our 4 pLGG cell lines (combination index <1 at Fa 0.5). Combination therapy was superior at inhibiting tumor growth in vivo. Combination treatment increased levels of apoptosis as well as gamma-H2AX phosphorylation compared with either agent alone. Everolimus treatment suppressed the conversion of glutamine and glutamate into glutathione both in vitro and in vivo. Exogenous glutathione reversed the effects of carboplatin and everolimus. CONCLUSIONS The combination of carboplatin and everolimus was effective at inducing cell death and slowing tumor growth in pLGG models. Everolimus decreased the amount of available glutathione inside the cell, preventing the detoxification of carboplatin and inducing increased DNA damage and apoptosis.