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With large language models, robots can understand language more flexibly and more capable than ever before. This survey reviews recent literature and situates it into a spectrum with two poles: 1) mapping between language and some manually defined formal representation of meaning, and 2) mapping between language and high-dimensional vector spaces that translate directly to low-level robot policy. Using a formal representation allows the meaning of the language to be precisely represented, limits the size of the learning problem, and leads to a framework for interpretability and formal safety guarantees. Methods that embed language and perceptual data into high-dimensional spaces avoid this manually specified symbolic structure and thus have the potential to be more general when fed enough data but require more data and computing to train. We discuss the benefits and tradeoffs of each approach and finish by providing directions for future work that achieves the best of both worlds., Comment: IJCAI 2024 Survey Track

A critical component when developing question-answering AIs is an adversarial dataset that challenges models to adapt to the complex syntax and reasoning underlying our natural language. Present techniques for procedurally generating adversarial texts are not robust enough for training on complex tasks such as answering multi-sentence trivia questions. We instead turn to human-generated data by introducing an interface for collecting adversarial human-written trivia questions. Our interface is aimed towards question writers and players of Quiz Bowl, a buzzer-based trivia competition where paragraph-long questions consist of a sequence of clues of decreasing difficulty. To incentivize usage, a suite of machine learning-based tools in our interface assist humans in writing questions that are more challenging to answer for Quiz Bowl players and computers alike. Not only does our interface gather training data for the groundbreaking Quiz Bowl AI project QANTA, but it is also a proof-of-concept of future adversarial data collection for question-answering systems. The results of performance-testing our interface with ten originally-composed questions indicate that, despite some flaws, our interface's novel question-writing features as well as its real-time exposure of useful responses from our machine models could facilitate and enhance the collection of adversarial questions., Comment: 17 pages, 1 figure, 1 table

BackgroundUnbalanced translocations can cause developmental delay (DD), intellectual disability (ID), growth problems, dysmorphic features, and congenital anomalies. They may arise de novo or may be inherited from a parent carrying a balanced rearrangement. It is estimated that 1/500 people is a balanced translocation carrier. The outcomes of different chromosomal rearrangements have the potential to reveal the functional consequences of partial trisomy or partial monosomy and can help guide genetic counseling for balanced carriers, and other young patients diagnosed with similar imbalances.MethodsWe performed clinical phenotyping and cytogenetic analyses of two siblings with a history of developmental delay (DD), intellectual disability (ID) and dysmorphic features.ResultsThe proband, a 38-year-old female, has a history of short stature, dysmorphic features and aortic coarctation. She underwent chromosomal microarray analysis, which identified partial monosomy of 4q and partial trisomy of 10p. Her brother, a 37-year-old male, has a history of more severe DD, behavioral problems, dysmorphic features, and congenital anomalies. Subsequently, karyotype confirmed two different unbalanced translocations in the siblings: 46,XX,der(4)t(4;10)(q33;p15.1) and 46,XY,der(10)t(4;10)(q33;p15.1), respectively. These chromosomal rearrangements represent two possible outcomes from a parent who is a carrier for a balanced translocation 46,XX,t(4;10)(q33;p15.1).ConclusionTo our knowledge, this 4q and 10p translocation has not been described in literature. In this report we compare clinical features due to the composite effects of partial monosomy 4q with partial trisomy 10p and partial trisomy 4q with partial monosomy 10p. These findings speak to the relevance of old and new genomic testing, the viability of these segregation outcomes, and need for genetic counseling.

The proportional-derivative (PD) control is investigated for discrete-time positive linear systems in this paper, which is a fundamental research problem in positive systems theory. Based on positive systems theory and Lyapunov theory, this paper proposes a systematic approach on the PD controller design for positive stabilization. The analyses and methods presented in this paper preserve both the necessity and sufficiency of the control problem, and the convergence of the algorithm is also analyzed. Finally, a numerical example on pest population control is employed in the simulation to verify the results.

Grounding navigational commands to linear temporal logic (LTL) leverages its unambiguous semantics for reasoning about long-horizon tasks and verifying the satisfaction of temporal constraints. Existing approaches require training data from the specific environment and landmarks that will be used in natural language to understand commands in those environments. We propose Lang2LTL, a modular system and a software package that leverages large language models (LLMs) to ground temporal navigational commands to LTL specifications in environments without prior language data. We comprehensively evaluate Lang2LTL for five well-defined generalization behaviors. Lang2LTL demonstrates the state-of-the-art ability of a single model to ground navigational commands to diverse temporal specifications in 21 city-scaled environments. Finally, we demonstrate a physical robot using Lang2LTL can follow 52 semantically diverse navigational commands in two indoor environments., Comment: Conference on Robot Learning 2023

In the context of mobile sensing environments, various sensors on mobile devices continually generate a vast amount of data. Analyzing this ever-increasing data presents several challenges, including limited access to annotated data and a constantly changing environment. Recent advancements in self-supervised learning have been utilized as a pre-training step to enhance the performance of conventional supervised models to address the absence of labelled datasets. This research examines the impact of using a self-supervised representation learning model for time series classification tasks in which data is incrementally available. We proposed and evaluated a workflow in which a model learns to extract informative features using a corpus of unlabeled time series data and then conducts classification on labelled data using features extracted by the model. We analyzed the effect of varying the size, distribution, and source of the unlabeled data on the final classification performance across four public datasets, including various types of sensors in diverse applications., Comment: 6 pages, accepted in the 24th IEEE International Conference on Mobile Data Management (MDM2023)

Understanding how genetic variants impact molecular phenotypes is a key goal of functional genomics, currently hindered by reliance on a single haploid reference genome. Here, we present the EN-TEx resource of 1,635 open-access datasets from four donors (∼30 tissues × ∼15 assays). The datasets are mapped to matched, diploid genomes with long-read phasing and structural variants, instantiating a catalog of >1 million allele-specific loci. These loci exhibit coordinated activity along haplotypes and are less conserved than corresponding, non-allele-specific ones. Surprisingly, a deep-learning transformer model can predict the allele-specific activity based only on local nucleotide-sequence context, highlighting the importance of transcription-factor-binding motifs particularly sensitive to variants. Furthermore, combining EN-TEx with existing genome annotations reveals strong associations between allele-specific and GWAS loci. It also enables models for transferring known eQTLs to difficult-to-profile tissues (e.g., from skin to heart). Overall, EN-TEx provides rich data and generalizable models for more accurate personal functional genomics.

IntroductionWe examined the relationship between current tobacco use and functionally important respiratory symptoms.MethodsLongitudinal cohort study of 16 295 US adults without COPD in Waves 2-3 (W2-3, 2014-2016) of the Population Assessment of Tobacco and Health Study. Exposure-Ten mutually exclusive categories of tobacco use including single product, multiple product, former, and never use (reference). Outcome-Seven questions assessing wheezing/cough were summed to create a respiratory symptom index; cutoffs of ≥2 and ≥3 were associated with functional limitations and poorer health. Multivariable regressions examined both cutoffs cross-sectionally and change over approximately 12 months, adjusting for confounders.ResultsAll tobacco use categories featuring cigarettes (>2/3's of users) were associated with higher risk (vs. never users) for functionally important respiratory symptoms at W2, for example, at symptom severity ≥ 3, risk ratio for exclusive cigarette use was 2.34 [95% CI, 1.92, 2.85] and for worsening symptoms at W3 was 2.80 [2.08, 3.76]. There was largely no increased symptom risk for exclusive use of cigars, smokeless tobacco, hookah, or e-cigarettes (adjustment for pack-years and marijuana attenuated the cross-sectional e-cigarette association from 1.53(95% CI 0.98, 2.40) to 1.05 (0.67, 1.63); RRs for these products were also significantly lower compared to exclusive use of cigarettes. The longitudinal e-cigarette-respiratory symptom association was sensitive to the respiratory index cutoff level; exclusive e-cigarette use was associated with worsening symptoms at an index cutoff ≥ 2 (RR = 1.63 [1.02, 2.59]) and with symptom improvement at an index cutoff of ≥ 3 (RR = 1.64 [1.04, 2.58]).ConclusionsPast and current cigarette smoking drove functionally important respiratory symptoms, while exclusive use of other tobacco products was largely not associated. However, the relationship between e-cigarette use and symptoms was sensitive to adjustment for

Coeliac disease (CD) is a chronic immune-mediated enteropathy triggered by ingestion of gluten. The aim of this study was to investigate if the salivary glands as a component of the mucosal immune system are involved in CD, leading to sialadenitis and salivary gland dysfunction and associated oral manifestations. Twenty patients with CD aged 49.2 (SD 15.5 years) and 20 age- and gender-matched healthy controls underwent an interview regarding general and oral health, serological analysis, a clinical oral examination including bitewing radiographs, Candida smear, assessment of salivary mutans streptococci and lactobacilli levels, unstimulated and chewing-stimulated whole and parotid saliva flow rates, analysis of secretory IgA, and a labial salivary gland biopsy. Xerostomia, mucosal lesions, dry/cracked lips and focal lymphocytic sialadenitis were more prevalent and extensive in patients with CD than in healthy controls. Moreover, the patients had less gingival inflammation and higher whole saliva flow rates than the healthy controls, but did not differ regarding dental health and levels of cariogenic bacteria and Candida. The major salivary gland function appears unaffected, contributing to maintenance of a balanced microbiota and oral health in CD patients. Xerostomia and labial dryness may be related to minor salivary gland inflammation and subsequent impaired mucosal lubrication.

Any pair of consecutive B-smooth integers for a given smoothness bound B corresponds to a solution (x, y) of the equation x^2 - 2Dy^2 = 1 for a certain square-free, B-smooth integer D and a B-smooth integer y. This paper describes algorithms to find such twin B-smooth integers that lie in a given interval by using the structure of solutions of the above Pell equation. The problem of finding such twin smooth integers is motivated by the quest for suitable parameters to efficiently instantiate recent isogeny-based cryptosystems. While the Pell equation structure of twin B-smooth integers has previously been used to describe and compute the full set of such pairs for very small values of B, increasing B to allow for cryptographically sized solutions makes this approach utterly infeasible. We start by revisiting the Pell solution structure of the set of twin smooth integers. Instead of using it to enumerate all twin smooth pairs, we focus on identifying only those that lie in a given interval. This restriction allows us to describe algorithms that navigate the vast set of Pell solutions in a more targeted way. Experiments run with these algorithms have provided examples of twin B-smooth pairs that are larger and have smaller smoothness bound B than previously reported pairs. Unfortunately, those examples do not yet provide better parameters for cryptography, but we hope that our methods can be generalized or used as subroutines in future work to achieve that goal.

Deploying robots in real-world domains, such as households and flexible manufacturing lines, requires the robots to be taskable on demand. Linear temporal logic (LTL) is a widely-used specification language with a compositional grammar that naturally induces commonalities across tasks. However, the majority of prior research on reinforcement learning with LTL specifications treats every new formula independently. We propose LTL-Transfer, a novel algorithm that enables subpolicy reuse across tasks by segmenting policies for training tasks into portable transition-centric skills capable of satisfying a wide array of unseen LTL specifications while respecting safety-critical constraints. Experiments in a Minecraft-inspired domain show that LTL-Transfer can satisfy over 90% of 500 unseen tasks after training on only 50 task specifications and never violating a safety constraint. We also deployed LTL-Transfer on a quadruped mobile manipulator in an analog household environment to demonstrate its ability to transfer to many fetch and delivery tasks in a zero-shot fashion.

This article focuses on the design of proportional-derivative (PD) controllers for positive linear systems in the continuous-time domain, which is a well-known open problem in positive systems theory. The main objective is to design a PD controller subject to interval gain variations, which simultaneously preserves the closed-loop system stability and positivity. The provided results fill the literature gap by considering both the PD controller structure and positivity. Although various feedback control strategies of positive systems have been developed recently, the work provides, for the first time, a systematic and tractable framework for finding nonfragile PD controller gains for positive stabilization. Finally, the theory and algorithm performance are evaluated and illustrated by numerical examples. © 2021 John Wiley & Sons Ltd.

This technical note investigates the positive consensus problem of multiagent systems with directed communication topologies, where all the agents have identical continuous-time positive linear dynamics. Existing works of such a problem mainly focus on the case, where networked communication topologies are of either undirected and incomplete graphs, or strongly connected directed graphs. In contrast to them, we study this problem in which the communication topologies of the multiagent system are described by directed graphs each containing a spanning tree, which is a more general and new scenario due to the interplay between the eigenvalues of the Laplacian matrix and the controller gains. Based on the existing results in spectral graph theory and positive linear systems theory, several necessary, and sufficient conditions on positive consensus of the directed multiagent system are derived through using linear matrix inequality techniques. A primal-dual iterative algorithm is developed for the computation of solutions. Finally, several numerical simulations are provided to illustrate the effectiveness of the proposed theoretical results. © 1963-2012 IEEE.

This article focuses on the design of proportional-derivative (PD) controllers for positive linear systems in the continuous-time domain, which is a well-known open problem in positive systems theory. The main objective is to design a PD controller subject to interval gain variations, which simultaneously preserves the closed-loop system stability and positivity. The provided results fill the literature gap by considering both the PD controller structure and positivity. Although various feedback control strategies of positive systems have been developed recently, the work provides, for the first time, a systematic and tractable framework for finding nonfragile PD controller gains for positive stabilization. Finally, the theory and algorithm performance are evaluated and illustrated by numerical examples. © 2021 John Wiley & Sons Ltd.

Evaluating the impact of genetic variants on transcriptional regulation is a central goal in biological science that has been constrained by reliance on a single reference genome. To address this, we constructed phased, diploid genomes for four cadaveric donors (using long-read sequencing) and systematically charted noncoding regulatory elements and transcriptional activity across more than 25 tissues from these donors. Integrative analysis revealed over a million variants with allele-specific activity, coordinated, locus-scale allelic imbalances, and structural variants impacting proximal chromatin structure. We relate the personal genome analysis to the ENCODE encyclopedia, annotating allele- and tissue-specific elements that are strongly enriched for variants impacting expression and disease phenotypes. These experimental and statistical approaches, and the corresponding EN-TEx resource, provide a framework for personalized functional genomics.

We envision robots that can collaborate and communicate seamlessly with humans. It is necessary for such robots to decide both what to say and how to act, while interacting with humans. To this end, we introduce a new task, dialogue object search: A robot is tasked to search for a target object (e.g. fork) in a human environment (e.g., kitchen), while engaging in a "video call" with a remote human who has additional but inexact knowledge about the target's location. That is, the robot conducts speech-based dialogue with the human, while sharing the image from its mounted camera. This task is challenging at multiple levels, from data collection, algorithm and system development,to evaluation. Despite these challenges, we believe such a task blocks the path towards more intelligent and collaborative robots. In this extended abstract, we motivate and introduce the dialogue object search task and analyze examples collected from a pilot study. We then discuss our next steps and conclude with several challenges on which we hope to receive feedback., Comment: 3 pages, 1 figure. Robotics: Science and Systems (RSS) 2021 Workshop on Robotics for People (R4P): Perspectives on Interaction, Learning and Safety. Extended Abstract

In the era of single-cell sequencing, there is a growing need to extract insights from data with clustering methods. Here, we introduce Forest Fire Clustering, an efficient and interpretable method for cell-type discovery from single-cell data. Forest Fire Clustering makes minimal prior assumptions and, different from current approaches, calculates a non-parametric posterior probability that each cell is assigned a cell-type label. These posterior distributions allow for the evaluation of a label confidence for each cell and enable the computation of "label entropies," highlighting transitions along developmental trajectories. Furthermore, we show that Forest Fire Clustering can make robust, inductive inferences in an online-learning context and can readily scale to millions of cells. Finally, we demonstrate that our method outperforms state-of-the-art clustering approaches on diverse benchmarks of simulated and experimental data. Overall, Forest Fire Clustering is a useful tool for rare cell type discovery in large-scale single-cell analysis., Comment: 30 pages, 6 figures

BACKGROUND:During transcription, numerous transcription factors (TFs) bind to targets in a highly coordinated manner to control the gene expression. Alterations in groups of TF-binding profiles (i.e. "co-binding changes") can affect the co-regulating associations between TFs (i.e. "rewiring the co-regulator network"). This, in turn, can potentially drive downstream expression changes, phenotypic variation, and even disease. However, quantification of co-regulatory network rewiring has not been comprehensively studied. RESULTS:To address this, we propose DiNeR, a computational method to directly construct a differential TF co-regulation network from paired disease-to-normal ChIP-seq data. Specifically, DiNeR uses a graphical model to capture the gained and lost edges in the co-regulation network. Then, it adopts a stability-based, sparsity-tuning criterion -- by sub-sampling the complete binding profiles to remove spurious edges -- to report only significant co-regulation alterations. Finally, DiNeR highlights hubs in the resultant differential network as key TFs associated with disease. We assembled genome-wide binding profiles of 104 TFs in the K562 and GM12878 cell lines, which loosely model the transition between normal and cancerous states in chronic myeloid leukemia (CML). In total, we identified 351 significantly altered TF co-regulation pairs. In particular, we found that the co-binding of the tumor suppressor BRCA1 and RNA polymerase II, a well-known transcriptional pair in healthy cells, was disrupted in tumors. Thus, DiNeR successfully extracted hub regulators and discovered well-known risk genes. CONCLUSIONS:Our method DiNeR makes it possible to quantify changes in co-regulatory networks and identify alterations to TF co-binding patterns, highlighting key disease regulators. Our method DiNeR makes it possible to quantify changes in co-regulatory networks and identify alterations to TF co-binding patterns, highlighting key disease regulators.

RNA-binding proteins (RBPs) play key roles in post-transcriptional regulation and disease. Their binding sites cover more of the genome than coding exons; nevertheless, most noncoding variant prioritization methods only focus on transcriptional regulation. Here, we integrate the portfolio of ENCODE-RBP experiments to develop RADAR, a variant-scoring framework. RADAR uses conservation, RNA structure, network centrality, and motifs to provide an overall impact score. Then, it further incorporates tissue-specific inputs to highlight disease-specific variants. Our results demonstrate RADAR can successfully pinpoint variants, both somatic and germline, associated with RBP-function dysregulation, which cannot be found by most current prioritization methods, for example, variants affecting splicing.

Maintaining large code bases written in dynamically typed languages, such as JavaScript or Python, can be challenging due to the absence of type annotations: simple data compatibility errors proliferate, IDE support is limited, and APIs are hard to comprehend. Recent work attempts to address those issues through either static type inference or probabilistic type prediction. Unfortunately, static type inference for dynamic languages is inherently limited, while probabilistic approaches suffer from imprecision. This paper presents TypeWriter, the first combination of probabilistic type prediction with search-based refinement of predicted types. TypeWriter’s predictor learns to infer the return and argument types for functions from partially annotated code bases by combining the natural language properties of code with programming language-level information. To validate predicted types, TypeWriter invokes a gradual type checker with different combinations of the predicted types, while navigating the space of possible type combinations in a feedback-directed manner. We implement the TypeWriter approach for Python and evaluate it on two code corpora: a multi-million line code base at Facebook and a collection of 1,137 popular open-source projects. We show that TypeWriter’s type predictor achieves an F1 score of 0.64 (0.79) in the top-1 (top-5) predictions for return types, and 0.57 (0.80) for argument types, which clearly outperforms prior type prediction models. By combining predictions with search-based validation, TypeWriter can fully annotate between 14% to 44% of the files in a randomly selected corpus, while ensuring type correctness. A comparison with a static type inference tool shows that TypeWriter adds many more non-trivial types. TypeWriter currently suggests types to developers at Facebook and several thousands of types have already been accepted with minimal changes., Software Engineering

Deep learning image classification is vulnerable to adversarial attack, even if the attacker changes just a small patch of the image. We propose a defense against patch attacks based on partially occluding the image around each candidate patch location, so that a few occlusions each completely hide the patch. We demonstrate on CIFAR-10, Fashion MNIST, and MNIST that our defense provides certified security against patch attacks of a certain size., Comment: 9 pages, 5 figures

Fungi are the major decomposers of lignocellulosic material in the natural world, and our ability to harness the enzymes that fungi use for degrading plant biomass in their environment have revolutionized the food, fuel, textile and pharmaceutical industries. These enzymes are used to alter naturally occurring polymers from plant materials into everyday products like corn starch based sweeteners, renewable cellulosic biofuels, better performing clothing detergents, or pharmaceuticals. What makes many of these industries feasible is the high capacity of secretion that is inherent to many filamentous fungal organisms. While yeast strains are able to secrete up to 3 g/L of human serum albumin, filamentous hosts have been shown to secrete upwards of 100g/L of cellulase proteins into the supernatant. Thus, many of these industries are fundamentally interested in generating a high producing strain of their protein of interest, and generating a highly productive filamentous fungal host could provide a potential source for these secreted proteins.Unfortunately, the efficient production of well folded and functional protein becomes the major cost limitation of many of these industries. While the essential components of the secretory pathway in fungi are well studied, much less is publicly known about how to engineer a filamentous fungal host for more efficient protein production. There are many reasons for this: (1) known hypersecreting strains have poor genetic amenability, (2) these strains have very few established tools for determining causal alleles and (3), the intellectual property culture that revolves around many of these industrial strains prevents the dissemination of much of this knowledge. In order to address our knowledge gap in filamentous fungal hyper-production, we perform five tasks in this work, (1) we establish Neurospora crassa as a filamentous host for protein production through an iterative mutagenesis process using a quantitative screen to select for

Maintaining large code bases written in dynamically typed languages, such as JavaScript or Python, can be challenging due to the absence of type annotations: simple data compatibility errors proliferate, IDE support is limited, and APIs are hard to comprehend. Recent work attempts to address those issues through either static type inference or probabilistic type prediction. Unfortunately, static type inference for dynamic languages is inherently limited, while probabilistic approaches suffer from imprecision. This paper presents TypeWriter, the first combination of probabilistic type prediction with search-based refinement of predicted types. TypeWriter's predictor learns to infer the return and argument types for functions from partially annotated code bases by combining the natural language properties of code with programming language-level information. To validate predicted types, TypeWriter invokes a gradual type checker with different combinations of the predicted types, while navigating the space of possible type combinations in a feedback-directed manner. We implement the TypeWriter approach for Python and evaluate it on two code corpora: a multi-million line code base at Facebook and a collection of 1,137 popular open-source projects. We show that TypeWriter's type predictor achieves an F1 score of 0.64 (0.79) in the top-1 (top-5) predictions for return types, and 0.57 (0.80) for argument types, which clearly outperforms prior type prediction models. By combining predictions with search-based validation, TypeWriter can fully annotate between 14% to 44% of the files in a randomly selected corpus, while ensuring type correctness. A comparison with a static type inference tool shows that TypeWriter adds many more non-trivial types. TypeWriter currently suggests types to developers at Facebook and several thousands of types have already been accepted with minimal changes.

Ubiquitous personal monitoring of health has rapidly developed in recent years. Technology is playing a major role in the improvement of quality of life. Much of the total expense on healthcare is attributed to chronic illnesses and a large portion can be eliminated through continuous personal monitoring solutions. While pervasive technologies are available now, they need to be as unobtrusive as possible and require as little human intervention as possible. Broad ranges of electronic devices will monitor the human condition and create alert notifications to the individual or care providers.One new modality in this world of technological advances that can provide major benefit, is a pressure sensitive bedsheet. The resulting pressure images can be analyzed to ascertain information about the person lying on the bed. Questions can be asked, such as what posture is the person sleeping in, or what movement is there? Can we identify and track bodyparts? Can respiration and vital signs be measured? What medical conditions can be detected?This work investigates some of these questions and contains various methods of analyses through static images and dynamic sequences of motion. Furthermore, methods of sampling strategies are presented with the aim of optimizing the data flow. This work introduces this current research to achieve the aim of continuous personal monitoring using pressure sensitive material.

Transcriptional activation domains (TADs) are difficult to predict and identify, since they are not conserved and have little consensus. Here, we describe a yeast-based screening method that is able to identify individual amino acid residues involved in transcriptional activation in a high throughput manner. A plant transcriptional activator, PIF3 (phytochrome interacting factor 3), was fused to the yeast GAL4-DNA-binding Domain (BD), driving expression of the URA3 (Orotidine 5'-phosphate decarboxylase) reporter, and used for negative selection on 5-fluroorotic acid (5FOA). Randomly mutagenized variants of PIF3 were then selected for a loss or reduction in transcriptional activation activity by survival on FOA. In the process, we developed a strategy to eliminate false positives from negative selection that can be used for both reverse-1- and 2-hybrid screens. With this method we were able to identify two distinct regions in PIF3 with transcriptional activation activity, both of which are functionally conserved in PIF1, PIF4, and PIF5. Both are collectively necessary for full PIF3 transcriptional activity, but neither is sufficient to induce transcription autonomously. We also found that the TAD appear to overlap physically with other PIF3 functions, such as phyB binding activity and consequent phosphorylation. Our protocol should provide a valuable tool for identifying, analyzing and characterizing novel TADs in eukaryotic transcription factors, and thus potentially contribute to the unraveling of the mechanism underlying transcriptional activation.

Past research has revealed the propagation of dense, asymmetric ionization zones in both high and low current magnetron discharges. Here we report about the direction reversal of ionization zone propagation as observed with fast cameras. At high currents, zones move in the E B direction with velocities of 103 to 104 m/s. However at lower currents, ionization zones are observed to move in the opposite, the -E B direction, with velocities ~; 103 m/s. It is proposed that the direction reversal is associated with the local balance of ionization and supply of neutrals in the ionization zone.

Organic cation transporter 1, OCT1 (SLC22A1), is the major hepatic uptake transporter for metformin, the most prescribed antidiabetic drug. However, its endogenous role is poorly understood. Here we show that similar to metformin treatment, loss of Oct1 caused an increase in the ratio of AMP to ATP, activated the energy sensor AMP-activated kinase (AMPK), and substantially reduced triglyceride (TG) levels in livers from healthy and leptin-deficient mice. Conversely, livers of human OCT1 transgenic mice fed high-fat diets were enlarged with high TG levels. Metabolomic and isotopic uptake methods identified thiamine as a principal endogenous substrate of OCT1. Thiamine deficiency enhanced the phosphorylation of AMPK and its downstream target, acetyl-CoA carboxylase. Metformin and the biguanide analog, phenformin, competitively inhibited OCT1-mediated thiamine uptake. Acute administration of metformin to wild-type mice reduced intestinal accumulation of thiamine. These findings suggest that OCT1 plays a role in hepatic steatosis through modulation of energy status. The studies implicate OCT1 as well as metformin in thiamine disposition, suggesting an intriguing and parallel mechanism for metformin and its major hepatic transporter in metabolic function.

A robust algorithm for non-negative matrix factorization (NMF) is presented in this paper with the purpose of dealing with large-scale data, where the separability assumption is satisfied. In particular, we modify the Linear Programming (LP) algorithm of [9] by introducing a reduced set of constraints for exact NMF. In contrast to the previous approaches, the proposed algorithm does not require the knowledge of factorization rank (extreme rays [3] or topics [7]). Furthermore, motivated by a similar problem arising in the context of metabolic network analysis [13], we consider an entirely different regime where the number of extreme rays or topics can be much larger than the dimension of the data vectors. The performance of the algorithm for different synthetic data sets are provided., Comment: Appeared in IEEE GlobalSIP, 2013, TX, Austin

Current single-locus-based analyses and candidate disease gene prediction methodologies used in genome-wide association studies (GWAS) do not capitalize on the wealth of the underlying genetic data, nor functional data available from molecular biology. Here, we analyzed GWAS data from the Wellcome Trust Case Control Consortium (WTCCC) on coronary artery disease (CAD). Gentrepid uses a multiple-locus-based approach, drawing on protein pathway- or domain-based data to make predictions. Known disease genes may be used as additional information (seeded method) or predictions can be based entirely on GWAS single nucleotide polymorphisms (SNPs) (ab initio method). We looked in detail at specific predictions made by Gentrepid for CAD and compared these with known genetic data and the scientific literature. Gentrepid was able to extract known disease genes from the candidate search space and predict plausible novel disease genes from both known and novel WTCCC-implicated loci. The disease gene candidates are consistent with known biological information. The results demonstrate that this computational approach is feasible and a valuable discovery tool for geneticists.

Genome-wide association studies have identified numerous genetic loci for spirometic measures of pulmonary function, forced expiratory volume in one second (FEV1), and its ratio to forced vital capacity (FEV1/FVC). Given that cigarette smoking adversely affects pulmonary function, we conducted genome-wide joint meta-analyses (JMA) of single nucleotide polymorphism (SNP) and SNP-by-smoking (ever-smoking or pack-years) associations on FEV1 and FEV1/FVC across 19 studies (total N = 50,047). We identified three novel loci not previously associated with pulmonary function. SNPs in or near DNER (smallest PJMA = 5.00×10−11), HLA-DQB1 and HLA-DQA2 (smallest PJMA = 4.35×10−9), and KCNJ2 and SOX9 (smallest PJMA = 1.28×10−8) were associated with FEV1/FVC or FEV1 in meta-analysis models including SNP main effects, smoking main effects, and SNP-by-smoking (ever-smoking or pack-years) interaction. The HLA region has been widely implicated for autoimmune and lung phenotypes, unlike the other novel loci, which have not been widely implicated. We evaluated DNER, KCNJ2, and SOX9 and found them to be expressed in human lung tissue. DNER and SOX9 further showed evidence of differential expression in human airway epithelium in smokers compared to non-smokers. Our findings demonstrated that joint testing of SNP and SNP-by-environment interaction identified novel loci associated with complex traits that are missed when considering only the genetic main effects.

Serotonergic regulation of feeding behavior has been studied intensively, both for an understanding of the basic neurocircuitry of energy balance in various organisms and as a therapeutic target for human obesity. However, its underlying molecular mechanisms remain poorly understood. Here, we show that neural serotonin signaling in C. elegans modulates feeding behavior through inhibition of AMP-activated kinase (AMPK) in interneurons expressing the C. elegans counterpart of human SIM1, a transcription factor associated with obesity. In turn, glutamatergic signaling links these interneurons to pharyngeal neurons implicated in feeding behavior. We show that AMPK-mediated regulation of glutamatergic release is conserved in rat hippocampal neurons. These findings reveal cellular and molecular mediators of serotonergic signaling.

For data integration in information ecosystems, semantic heterogeneity is a known difficulty. In this paper, we propose Shadow Theory as the philosophical foundation to address this issue. It is based on the notion of shadows in Plato's Allegory of the Cave. What we can observe are just shadows, and meanings of shadows are mental entities that only exist in viewers' cognitive structures. With enterprise customer data integration example, we proposed six design principles and algebra to support required operations., Comment: 85 pages, 31 figures

The regulation of energy homeostasis integrates diverse biological processes ranging from behavior to metabolism and is linked fundamentally to numerous disease states. To identify new molecules that can bypass homeostatic compensatory mechanisms of energy balance in intact animals, we screened for small-molecule modulators of Caenorhabditis elegans fat content. We report on several molecules that modulate fat storage without obvious deleterious effects on feeding, growth and reproduction. A subset of these compounds also altered fat storage in mammalian and insect cell culture. We found that one of the newly identified compounds exerts its effects in C. elegans through a pathway that requires previously undescribed functions of an AMP-activated kinase catalytic subunit and a transcription factor previously unassociated with fat regulation. Thus, our strategy identifies small molecules that are effective within the context of intact animals and reveals relationships between new pathways that operate across phyla to influence energy homeostasis.

Bardet-Biedl syndrome, BBS, is a rare autosomal recessive disorder with clinical presentations including polydactyly, retinopathy, hyperphagia, obesity, short stature, cognitive impairment, and developmental delays. Disruptions of BBS proteins in a variety of organisms impair cilia formation and function and the multi-organ defects of BBS have been attributed to deficiencies in various cilia-associated signaling pathways. In C. elegans, bbs genes are expressed exclusively in the sixty ciliated sensory neurons of these animals and bbs mutants exhibit sensory defects as well as body size, feeding, and metabolic abnormalities. Here we show that in contrast to many other cilia-defective mutants, C. elegans bbs mutants exhibit increased release of dense-core vesicles and organism-wide phenotypes associated with enhanced activities of insulin, neuropeptide, and biogenic amine signaling pathways. We show that the altered body size, feeding, and metabolic abnormalities of bbs mutants can be corrected to wild-type levels by abrogating the enhanced secretion of dense-core vesicles without concomitant correction of ciliary defects. These findings expand the role of BBS proteins to the regulation of dense-core-vesicle exocytosis and suggest that some features of Bardet-Biedl Syndrome may be caused by excessive neuroendocrine secretion.

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Disulfide torsional energy, a good predictor of disulfide redox potential in proteins, may be estimated by interpolation on a potential energy surface (PES) describing the twisting of diethyl disulfide through its three central dihedral angles. Here we update PES calculations at the M05-2X level of theory with the 6-31G(d) basis set. Although the surface shows no qualitative differences from an earlier MP2(full) PES, energy differences greater than 1 kJ mol–1 were seen for conformations with χ2 between –60° and 30°, or with χ3 below 60° or above 130°. This is particularly significant for highly strained disulfides that are likely to be spontaneously reduced by mechanical means. In benchmarking against the high-level G3X method, M05-2X showed significantly reduced root mean squared deviation compared with MP2(full) (1.0 versus 2.0 kJ mol–1 respectively). Results are incorporated into a web application that calculates relative torsional energies from disulfide dihedral angles (http://www.sbinf.org/applications/pes.html).

Glioblastoma multiforme (GBM) remains refractory to conventional therapy. CD133+ GBM cells have been recently isolated and characterized as chemo-/radio-resistant tumor-initiating cells and are hypothesized to be responsible for post-treatment recurrence. In order to explore the molecular properties of tumorigenic CD133+ GBM cells that resist treatment, we isolated CD133+ GBM cells from tumors that are recurrent and have previously received chemo-/radio-therapy. We found that the purified CD133+ GBM cells sorted from the CD133+ GBM spheres express SOX2 and CD44 and are capable of clonal self-renewal and dividing to produce fast-growing CD133− progeny, which form the major cell population within GBM spheres. Intracranial injection of purified CD133+, not CD133− GBM daughter cells, can lead to the development of YKL-40+ infiltrating tumors that display hypervascularity and pseudopalisading necrosis-like features in mouse brain. The molecular profile of purified CD133+ GBM cells revealed characteristics of neuroectoderm-like cells, expressing both radial glial and neural crest cell developmental genes, and portraying a slow-growing, non-differentiated, polarized/migratory, astrogliogenic, and chondrogenic phenotype. These data suggest that at least a subset of treated and recurrent GBM tumors may be seeded by CD133+ GBM cells with neural and mesenchymal properties. The data also imply that CD133+ GBM cells may be clinically indolent/quiescent prior to undergoing proliferative cell division (PCD) to produce CD133− GBM effector progeny. Identifying intrinsic and extrinsic cues, which promote CD133+ GBM cell self-renewal and PCD to support ongoing tumor regeneration may highlight novel therapeutic strategies to greatly diminish the recurrence rate of GBM.

Background Automated candidate gene prediction systems allow geneticists to hone in on disease genes more rapidly by identifying the most probable candidate genes linked to the disease phenotypes under investigation. Here we assessed the ability of eight different candidate gene prediction systems to predict disease genes in intervals previously associated with type 2 diabetes by benchmarking their performance against genes implicated by recent genome-wide association studies. Results Using a search space of 9556 genes, all but one of the systems pruned the genome in favour of genes associated with moderate to highly significant SNPs. Of the 11 genes associated with highly significant SNPs identified by the genome-wide association studies, eight were flagged as likely candidates by at least one of the prediction systems. A list of candidates produced by a previous consensus approach did not match any of the genes implicated by 706 moderate to highly significant SNPs flagged by the genome-wide association studies. We prioritized genes associated with medium significance SNPs. Conclusion The study appraises the relative success of several candidate gene prediction systems against independent genetic data. Even when confronted with challengingly large intervals, the candidate gene prediction systems can successfully select likely disease genes. Furthermore, they can be used to filter statistically less-well-supported genetic data to select more likely candidates. We suggest consensus approaches fail because they penalize novel predictions made from independent underlying databases. To realize their full potential further work needs to be done on prioritization and annotation of genes.

PurposeTo elucidate the unique molecular factors and biological processes that are differentially expressed in the limbal stem cell microenvironment by comparing directly to that of its immediate adjacent structures, the cornea and conjunctiva.MethodsTotal RNA was isolated and amplified from the limbus, cornea, and conjunctiva. A gene expression profile of each tissue type was obtained by using microarray technique. The transcripts in which the expression level was at least twofold higher than that in the other two tissue types were identified. The expression levels of selected genes were confirmed by quantitative reverse transcription polymerase chain reaction (QRT-PCR). Protein expression of selected genes were confirmed by an immunohistochemistry study in normal human ocular tissue.ResultsThere were 186 preferentially overexpressed transcripts in the limbus in direct comparison to that in the cornea and conjunctiva. Many signature genes in the cornea and conjunctiva were among the preferentially expressed transcripts obtained by the microarray data. In addition, a significant number of new genes were identified, and the expression level of all nine selected genes was verified by QRT-PCR. Protein expression levels of keratin 13, tenascin c, homeodomain only protein (HOP), and TP53 apoptosis effector (PERP) were confirmed in human ocular tissues. Functional analysis of the preferentially expressed genes in the limbus reviewed that melanin metabolism and cell-cell adhesion were among the noticeable biological processes. Chromosomal distribution of the overexpressed genes in the limbus was disproportional to that of all known human genes.ConclusionsThese findings may shed light on the unique molecular components and regulation of limbal stem cells and their niche.

Kristin Turner, musical director & coordinating conductor.; With Peter Nahm, violin, Jasmine Lewis, viola, and Linda Hood, piano.; Includes a list of the personnel of the East Central Indiana Youth Symphony Orchestra.; Includes lists of the Ball State University section coaches and the members of the ECIYSO Board., Series LX, Number 227., This archival material has been provided for educational purposes. Ball State University Libraries recognizes that some historic items may include offensive content. Our statement regarding objectionable content is available at: https://dmr.bsu.edu/digital/about

Linkage analysis is a successful procedure to associate diseases with specific genomic regions. These regions are often large, containing hundreds of genes, which make experimental methods employed to identify the disease gene arduous and expensive. We present two methods to prioritize candidates for further experimental study: Common Pathway Scanning (CPS) and Common Module Profiling (CMP). CPS is based on the assumption that common phenotypes are associated with dysfunction in proteins that participate in the same complex or pathway. CPS applies network data derived from protein–protein interaction (PPI) and pathway databases to identify relationships between genes. CMP identifies likely candidates using a domain-dependent sequence similarity approach, based on the hypothesis that disruption of genes of similar function will lead to the same phenotype. Both algorithms use two forms of input data: known disease genes or multiple disease loci. When using known disease genes as input, our combined methods have a sensitivity of 0.52 and a specificity of 0.97 and reduce the candidate list by 13-fold. Using multiple loci, our methods successfully identify disease genes for all benchmark diseases with a sensitivity of 0.84 and a specificity of 0.63. Our combined approach prioritizes good candidates and will accelerate the disease gene discovery process.

Bridget-Michaele Reischl, music director.; John Ginocchio, conductor.; With Jeffy Yaun, violin, Jason Liu, violin, and Jarod Wilkerson, cello.; Includes a list of the personnel of the East Central Indiana Youth Symphony Orchestra, with principals.; Includes a list of the educational staff of the East Central Indiana Youth Symphony Orchestra., Series LIX, Number 126., This archival material has been provided for educational purposes. Ball State University Libraries recognizes that some historic items may include offensive content. Our statement regarding objectionable content is available at: https://dmr.bsu.edu/digital/about

Kristin Turner, musical director & coordinating conductor.; With members of the Preparatory Strings.; Includes lists of the personnel of the East Central Indiana Youth Symphony Orchestra and the Preparatory Strings.; Includes a list of the Ball State University section coaches., Series LX, Number 104., This archival material has been provided for educational purposes. Ball State University Libraries recognizes that some historic items may include offensive content. Our statement regarding objectionable content is available at: https://dmr.bsu.edu/digital/about