Century-long cod otolith biochronology reveals individual growth plasticity in response to temperature

Otolith biochronologies combine growth records from individual fish to produce long-term growth sequences, which can help to disentangle individual from population-level responses to environmental variability. This study assessed individual thermal plasticity of Atlantic cod (Gadus morhua) growth in Icelandic waters based on measurements of otolith increments. We applied linear mixed-effects models and developed a century-long growth biochronology (1908-2014). We demonstrated interannual and cohort-specific changes in the growth of Icelandic cod over the last century which were mainly driven by temperature variation. Temperature had contrasting relationships with growth-positive for the fish during the youngest ages and negative during the oldest ages. We decomposed the effects of temperature on growth observed at the population level into within-individual effects and among-individual effects and detected significant individual variation in the thermal plasticity of growth. Variance in the individual plasticity differed across cohorts and may be related to the mean environmental conditions experienced by the group. Our results underscore the complexity of the relationships between climatic conditions and the growth of fish at both the population and individual level, and highlight the need to distinguish between average population responses and growth plasticity of the individuals for accurate growth predictions

Polyhedra Circuits and Their Applications

To better compute the volume and count the lattice points in geometric objects, we propose polyhedral circuits. Each polyhedral circuit characterizes a geometric region in Rd . They can be applied to represent a rich class of geometric objects, which include all polyhedra and the union of a finite number of polyhedron. They can be also used to approximate a large class of d-dimensional manifolds in Rd . Barvinok [3] developed polynomial time algorithms to compute the volume of a rational polyhedron, and to count the number of lattice points in a rational polyhedron in Rd with a fixed dimensional number d. Let d be a fixed dimensional number, TV(d,n) be polynomial time in n to compute the volume of a rational polyhedron, TL(d,n) be polynomial time in n to count the number of lattice points in a rational polyhedron, where n is the total number of linear inequalities from input polyhedra, and TI(d,n) be polynomial time in n to solve integer linear programming problem with n be the total number of input linear inequalities. We develop algorithms to count the number of lattice points in geometric region determined by a polyhedral circuit in O(nd⋅rd(n)⋅TV(d,n)) time and to compute the volume of geometric region determined by a polyhedral circuit in O(n⋅rd(n)⋅TI(d,n)+rd(n)TL(d,n)) time, where rd(n) is the maximum number of atomic regions that n hyperplanes partition Rd . The applications to continuous polyhedra maximum coverage problem, polyhedra maximum lattice coverage problem, polyhedra (1−β) -lattice set cover problem, and (1−β) -continuous polyhedra set cover problem are discussed. We also show the NP-hardness of the geometric version of maximum coverage problem and set cover problem when each set is represented as union of polyhedra

Unifying generative and discriminative learning principles

<p>Abstract</p> <p>Background</p> <p>The recognition of functional binding sites in genomic DNA remains one of the fundamental challenges of genome research. During the last decades, a plethora of different and well-adapted models has been developed, but only little attention has been payed to the development of different and similarly well-adapted learning principles. Only recently it was noticed that discriminative learning principles can be superior over generative ones in diverse bioinformatics applications, too.</p> <p>Results</p> <p>Here, we propose a generalization of generative and discriminative learning principles containing the maximum likelihood, maximum a posteriori, maximum conditional likelihood, maximum supervised posterior, generative-discriminative trade-off, and penalized generative-discriminative trade-off learning principles as special cases, and we illustrate its efficacy for the recognition of vertebrate transcription factor binding sites.</p> <p>Conclusions</p> <p>We find that the proposed learning principle helps to improve the recognition of transcription factor binding sites, enabling better computational approaches for extracting as much information as possible from valuable wet-lab data. We make all implementations available in the open-source library Jstacs so that this learning principle can be easily applied to other classification problems in the field of genome and epigenome analysis.</p

Novel derivative of aminobenzenesulfonamide (3c) induces apoptosis in colorectal cancer cells through ROS generation and inhibits cell migration

Background: Colorectal cancer (CRC) is the 3rd most common type of cancer worldwide. New anti-cancer agents are needed for treating late stage colorectal cancer as most of the deaths occur due to cancer metastasis. A recently developed compound, 3c has shown to have potent antitumor effect; however the mechanism underlying the antitumor effect remains unknown. Methods: 3c-induced inhibition of proliferation was measured in the absence and presence NAC using MTT in HT-29 and SW620 cells and xCELLigence RTCA DP instrument. 3c-induced apoptotic studies were performed using flow cytometry. 3c-induced redox alterations were measured by ROS production using fluorescence plate reader and flow cytometry and mitochondrial membrane potential by flow cytometry; NADPH and GSH levels were determined by colorimetric assays. Bcl2 family protein expression and cytochrome c release and PARP activation was done by western blotting. Caspase activation was measured by ELISA. Cell migration assay was done using the real time xCELLigence RTCA DP system in SW620 cells and wound healing assay in HT-29. Results: Many anticancer therapeutics exert their effects by inducing reactive oxygen species (ROS). In this study, we demonstrate that 3c-induced inhibition of cell proliferation is reversed by the antioxidant, N-acetylcysteine, suggesting that 3c acts via increased production of ROS in HT-29 cells. This was confirmed by the direct measurement of ROS in 3c-treated colorectal cancer cells. Additionally, treatment with 3c resulted in decreased NADPH and glutathione levels in HT-29 cells. Further, investigation of the apoptotic pathway showed increased release of cytochrome c resulting in the activation of caspase-9, which in turn activated caspase-3 and −6. 3c also (i) increased p53 and Bax expression, (ii) decreased Bcl2 and BclxL expression and (iii) induced PARP cleavage in human colorectal cancer cells. Confirming our observations, NAC significantly inhibited induction of apoptosis, ROS production, cytochrome c release and PARP cleavage. The results further demonstrate that 3c inhibits cell migration by modulating EMT markers and inhibiting TGFβ-induced phosphorylation of Smad2 and Samd3. Conclusions: Our findings thus demonstrate that 3c disrupts redox balance in colorectal cancer cells and support the notion that this agent may be effective for the treatment of colorectal cancer

A framework for the first‑person internal sensation of visual perception in mammals and a comparable circuitry for olfactory perception in Drosophila

Perception is a first-person internal sensation induced within the nervous system at the time of arrival of sensory stimuli from objects in the environment. Lack of access to the first-person properties has limited viewing perception as an emergent property and it is currently being studied using third-person observed findings from various levels. One feasible approach to understand its mechanism is to build a hypothesis for the specific conditions and required circuit features of the nodal points where the mechanistic operation of perception take place for one type of sensation in one species and to verify it for the presence of comparable circuit properties for perceiving a different sensation in a different species. The present work explains visual perception in mammalian nervous system from a first-person frame of reference and provides explanations for the homogeneity of perception of visual stimuli above flicker fusion frequency, the perception of objects at locations different from their actual position, the smooth pursuit and saccadic eye movements, the perception of object borders, and perception of pressure phosphenes. Using results from temporal resolution studies and the known details of visual cortical circuitry, explanations are provided for (a) the perception of rapidly changing visual stimuli, (b) how the perception of objects occurs in the correct orientation even though, according to the third-person view, activity from the visual stimulus reaches the cortices in an inverted manner and (c) the functional significance of well-conserved columnar organization of the visual cortex. A comparable circuitry detected in a different nervous system in a remote species-the olfactory circuitry of the fruit fly Drosophila melanogaster-provides an opportunity to explore circuit functions using genetic manipulations, which, along with high-resolution microscopic techniques and lipid membrane interaction studies, will be able to verify the structure-function details of the presented mechanism of perception

Verifying integer programming results

Software for mixed-integer linear programming can return incorrect results for a number of reasons, one being the use of inexact floating-point arithmetic. Even solvers that employ exact arithmetic may suffer from programming or algorithmic errors, motivating the desire for a way to produce independently verifiable certificates of claimed results. Due to the complex nature of state-of-the-art MIP solution algorithms, the ideal form of such a certificate is not entirely clear. This paper proposes such a certificate format designed with simplicity in mind, which is composed of a list of statements that can be sequentially verified using a limited number of inference rules. We present a supplementary verification tool for compressing and checking these certificates independently of how they were created. We report computational results on a selection of MIP instances from the literature. To this end, we have extended the exact rational version of the MIP solver SCIP to produce such certificates

Generative Embedding for Model-Based Classification of fMRI Data

Decoding models, such as those underlying multivariate classification algorithms, have been increasingly used to infer cognitive or clinical brain states from measures of brain activity obtained by functional magnetic resonance imaging (fMRI). The practicality of current classifiers, however, is restricted by two major challenges. First, due to the high data dimensionality and low sample size, algorithms struggle to separate informative from uninformative features, resulting in poor generalization performance. Second, popular discriminative methods such as support vector machines (SVMs) rarely afford mechanistic interpretability. In this paper, we address these issues by proposing a novel generative-embedding approach that incorporates neurobiologically interpretable generative models into discriminative classifiers. Our approach extends previous work on trial-by-trial classification for electrophysiological recordings to subject-by-subject classification for fMRI and offers two key advantages over conventional methods: it may provide more accurate predictions by exploiting discriminative information encoded in ‘hidden’ physiological quantities such as synaptic connection strengths; and it affords mechanistic interpretability of clinical classifications. Here, we introduce generative embedding for fMRI using a combination of dynamic causal models (DCMs) and SVMs. We propose a general procedure of DCM-based generative embedding for subject-wise classification, provide a concrete implementation, and suggest good-practice guidelines for unbiased application of generative embedding in the context of fMRI. We illustrate the utility of our approach by a clinical example in which we classify moderately aphasic patients and healthy controls using a DCM of thalamo-temporal regions during speech processing. Generative embedding achieves a near-perfect balanced classification accuracy of 98% and significantly outperforms conventional activation-based and correlation-based methods. This example demonstrates how disease states can be detected with very high accuracy and, at the same time, be interpreted mechanistically in terms of abnormalities in connectivity. We envisage that future applications of generative embedding may provide crucial advances in dissecting spectrum disorders into physiologically more well-defined subgroups