Herpetological phylogeographic analyses support a Miocene focal point of Himalayan uplift and biological diversification

The Himalaya are among the youngest and highest mountains in the world, but the exact timing of their uplift and origins of their biodiversity are still in debate. The Himalayan region is a relatively small area but with exceptional diversity and endemism. One common hypothesis to explain the rich montane diversity is uplift-driven diversification—that orogeny creates conditions favoring rapid in situ speciation of resident lineages. We test this hypothesis in the Himalayan region using amphibians and reptiles, two environmentally sensitive vertebrate groups. In addition, analysis of diversification of the herpetofauna provides an independent source of information to test competing geological hypotheses of Himalayan orogenesis. We conclude that the origins of the Himalayan herpetofauna date to the early Paleocene, but that diversification of most groups was concentrated in the Miocene. There was an increase in both rates and modes of diversification during the early to middle Miocene, together with regional interchange (dispersal) between the Himalaya and adjacent regions. Our analyses support a recently proposed stepwise geological model of Himalayan uplift beginning in the Paleocene, with a subsequent rapid increase of uplifting during the Miocene, finally giving rise to the intensification of the modern South Asian Monsoon.     

Verbosity normalized pseudo-relevance feedback in information retrieval

Document length normalization is one of the fundamental components in a retrieval model because term frequencies can readily be increased in long documents. The key hypotheses in literature regarding document length normalization are the verbosity and scope hypotheses, which imply that document length normalization should consider the distinguishing effects of verbosity and scope on term frequencies. In this article, we extend these hypotheses in a pseudo-relevance feedback setting by assuming the verbosity hypothesis on the feedback query model, which states that the verbosity of an expanded query should not be high. Furthermore, we postulate the following two effects of document verbosity on a feedback query model that easily and typically holds in modern pseudo-relevance feedback methods: 1) the verbosity-preserving effect: the query verbosity of a feedback query model is determined by feedback document verbosities; 2) the verbosity-sensitive effect: highly verbose documents more significantly and unfairly affect the resulting query model than normal documents do. By considering these effects, we propose verbosity normalized pseudo-relevance feedback, which is straightforwardly obtained by replacing original term frequencies with their verbosity-normalized term frequencies in the pseudo-relevance feedback method. The results of the experiments performed on three standard TREC collections show that the proposed verbosity normalized pseudo-relevance feedback consistently provides statistically significant improvements over conventional methods, under the settings of the relevance model and latent concept expansion.     

Modeling and validation of autoinducer-mediated bacterial gene expression in microfluidic environments

Biosensors exploiting communication within genetically engineered bacteria are becoming increasingly important for monitoring environmental changes. Currently, there are a variety of mathematical models for understanding and predicting how genetically engineered bacteria respond to molecular stimuli in these environments, but as sensors have miniaturized towards microfluidics and are subjected to complex time-varying inputs, the shortcomings of these models have become apparent. The effects of microfluidic environments such as low oxygen concentration, increased biofilm encapsulation, diffusion limited molecular distribution, and higher population densities strongly affect rate constants for gene expression not accounted for in previous models. We report a mathematical model that accurately predicts the biological response of the autoinducer N-acyl homoserine lactone-mediated green fluorescent protein expression in reporter bacteria in microfluidic environments by accommodating these rate constants. This generalized mass action model considers a chain of biomolecular events from input autoinducer chemical to fluorescent protein expression through a series of six chemical species. We have validated this model against experimental data from our own apparatus as well as prior published experimental results. Results indicate accurate prediction of dynamics (e.g., 14% peak time error from a pulse input) and with reduced mean-squared error with pulse or step inputs for a range of concentrations (10 μM–30 μM). This model can help advance the design of genetically engineered bacteria sensors and molecular communication devices.     

商业模式、创新双元性与企业绩效的关系研究

本文旨在探讨商业模式如何通过创新双元性的中介作用对企业绩效产生影响。为此,本文提出了商业模式、创新双元性与企业绩效三者之间关系的研究假设,并通过应用层次回归分析方法对来自173家制造业企业的问卷调查数据进行实证分析检验了这些假设。研究结果表明:效率为中心的商业模式设计与新颖为中心的商业模式设计都对创新双元性具有显著的正向影响;创新双元性对企业绩效具有显著的正向影响;创新双元性分别在效率为中心的商业模式设计、新颖为中心的商业模式设计对企业绩效的影响中起部分中介作用。这些结论因揭示了商业模式对于企业绩效的间接影响而拓展了商业模式与企业绩效关系理论,同时对于企业建立商业模式模仿隔离机制具有一定指导意义。     

H2 and H∞ optimal model reduction using genetic algorithms

The mathematical modeling of most physical systems, such as aerospace systems, heat processes, telecommunication systems, transmission lines and chemical reactors, results in complex high order models. The complexity of the models imposes a lot of difficulties in analysis, simulation and control designs. Several analytical model reduction techniques have been proposed in literature over the past few decades to reduce these difficulties. However, most of the optimal techniques follow computationally demanding, time consuming, iterative procedures that usually result in non-robustly stable models with poor frequency response resemblance to the original high order model in some frequency ranges. Genetic Algorithm (GA) has proved to be an excellent optimization tool in the past few years. Therefore, the aim of this paper will be to use GA to solve H₂ and H_∞ norm model reduction problems, and help obtain globally optimized nominal models.     

Resource constraints as triggers of radical innovation: Longitudinal evidence from the manufacturing sector

Despite the significant role of radical innovation as a driver of firm growth and performance, the consequences of resource constraints for radical innovation outcomes remain unknown. Our paper addresses this gap. We combine arguments from entrepreneurship theory and the theory of recombinative innovation to construct an overarching theoretical framework that argues why resource constraints can promote, rather than impede, radical innovation. We then build hypotheses on two specific resource constraints, knowledge and financial, and test these by a lagged-variable random-effects Tobit model with longitudinal data from an exceptionally large and detailed innovation survey. Controlling for absorptive capacity, firm age, and firm growth, we find full support for the hypothesis that knowledge constraints spur radical innovation and partial support for the hypothesis that financial constraints spur radical innovation. We discuss the theoretical significance of these findings and point to managerial implications and paths for future research.     

Potential application of a knowledgebase of iron metabolism of Acidithiobacillus ferrooxidans as an alternative platform

BackgroundAcidithiobacillus ferrooxidans is a facultative anaerobe that depends on ferrous ion oxidation as well as reduced sulfur oxidation to obtain energy and is widely applied in metallurgy, environmental protection, and soil remediation. With the accumulation of experimental data, metabolic mechanisms, kinetic models, and several databases have been established. However, scattered data are not conducive to understanding A. ferrooxidans that necessitates updated information informed by systems biology.ResultsHere, we constructed a knowledgebase of iron metabolism of A. ferrooxidans (KIMAf) system by integrating public databases and reviewing the literature, including the database of bioleaching substrates (DBS), the database of bioleaching metallic ion-related proteins (MIRP), the A. ferrooxidans bioinformation database (Af-info), and the database for dynamics model of bioleaching (DDMB). The DBS and MIRP incorporate common bioleaching substrates and metal ion-related proteins. Af-info and DDMB integrate nucleotide, gene, protein, and kinetic model information. Statistical analysis was performed to elucidate the distribution of isolated A. ferrooxidans strains, evolutionary and metabolic advances, and the development of bioleaching models.ConclusionsThis comprehensive system provides researchers with a platform of available iron metabolism-related resources of A. ferrooxidans and facilitates its application.How to citeZhou Z, Ma W, Liu Y, et al. Potential application of a knowledgebase of iron metabolism of Acidithiobacillus ferrooxidans as an alternative platform. Electron J Biotechnol 2021;51; https://doi.org/10.1016/j.ejbt.2021.04.003     

Measuring the success of the Greek Taxation Information System

The transformation of many governments all around the world into new forms, namely, electronic government (e-Government), could not leave the Greek government unaffected. Therefore, it initiated an e-Government project related to national information systems and finance services, the Greek Taxation Information System (TAXIS). The purpose of this paper is to investigate the success of TAXIS from the perspective of expert employees, who work in public taxation agencies. This topic is interesting, because TAXIS is applied in a tax-driven country, under a mandatory setting. Also, it is the first time that the success of this project is examined, from the perspective of employees, using IS success models. The study adapts DeLone and McLean [DeLone, W. H., & McLean, E. R. (2003). The DeLone and McLean model of information systems success: A ten year update. Journal of Management Information Systems, 19(4), 9–30] and Seddon's [Seddon, P. B. (1997). A respecification and extension of the DeLone and McLean model of IS success. Information Systems Research, 8(3) 240–253] information systems success models. The model developed includes the constructs of information, system and service quality, perceived usefulness and user satisfaction. The results provide evidence that there are strong connections between the five success constructs. All hypothesized relationships are supported, except for the relationship between system quality and user satisfaction. The empirical evidence and discussion presented can help the Greek Government improve and fully exploit the potential of TAXIS as an innovative tool for taxation purposes.     

The Somatic Marker Hypotheses, and What the Iowa Gambling Task Does and Does not Show

Damasio's somatic marker hypothesis (SMH) is a prominent neuroscientifichypothesis about the mechanisms implementing decision-making.This paper argues that, since its inception, the SMH has notbeen clearly formulated. It is possible to identify at leasttwo different hypotheses, which make different predictions:SMH-G, which claims that somatic states generally implementpreferences and are needed to make a decision; and SMH-S, whichspecifically claims that somatic states assist decision-makingby anticipating the long-term outcomes of available options.This paper also argues that neither hypothesis is adequatelysupported empirically; the task originally proposed to testSMH is not a good test for SMH-S, and its results do not supportSMH-G either. In addition, it is not clear how SMH-G could beempirically invalidated, given its general formulation. Suggestionsare made that could help provide evidence for SMH-S, and makeSMH-G more specific.

1 Introduction

2 Two Hypotheses: SomaticMarkers as Embodied Preferences, andas a Source of Farsightedness

3 Lack of Evidence for Somatic Farsightedness

4 Does MakingDecisions Require Somatic Markers, and can itbe Shown in theLaboratory?

5 Conclusion

     

Recreating blood-brain barrier physiology and structure on chip: A novel neurovascular microfluidic bioreactor

The blood-brain barrier (BBB) is a critical structure that serves as the gatekeeper between the central nervous system and the rest of the body. It is the responsibility of the BBB to facilitate the entry of required nutrients into the brain and to exclude potentially harmful compounds; however, this complex structure has remained difficult to model faithfully in vitro. Accurate in vitro models are necessary for understanding how the BBB forms and functions, as well as for evaluating drug and toxin penetration across the barrier. Many previous models have failed to support all the cell types involved in the BBB formation and/or lacked the flow-created shear forces needed for mature tight junction formation. To address these issues and to help establish a more faithful in vitro model of the BBB, we have designed and fabricated a microfluidic device that is comprised of both a vascular chamber and a brain chamber separated by a porous membrane. This design allows for cell-to-cell communication between endothelial cells, astrocytes, and pericytes and independent perfusion of both compartments separated by the membrane. This NeuroVascular Unit (NVU) represents approximately one-millionth of the human brain, and hence, has sufficient cell mass to support a breadth of analytical measurements. The NVU has been validated with both fluorescein isothiocyanate (FITC)-dextran diffusion and transendothelial electrical resistance. The NVU has enabled in vitro modeling of the BBB using all human cell types and sampling effluent from both sides of the barrier.     

Interactive high-quality text classification

Automatic text classification (TC) is essential for information sharing and management. Its ideal goals are to achieve high-quality TC: (1) accepting almost all documents that should be accepted (i.e., high recall) and (2) rejecting almost all documents that should be rejected (i.e., high precision). Unfortunately, the ideal goals are rarely achieved, making automatic TC not suitable for those applications in which a classifier’s erroneous decision may incur high cost and/or serious problems. One way to pursue the ideal is to consult users to confirm the classifier’s decisions so that potential errors may be corrected. However, its main challenge lies on the control of the number of confirmations, which may incur heavy cognitive load on the users. We thus develop an intelligent and classifier-independent confirmation strategy ICCOM. Empirical evaluation shows that ICCOM may help various kinds of classifiers to achieve very high precision and recall by conducting fewer confirmations. The contributions are significant to the archiving and recommendation of critical information, since identification of possible TC errors (those that require confirmation) is the key to process information more properly.     

点赞社交互动行为影响因素研究——基于微信朋友圈情境

【目的/意义】点赞是最常用的社交互动方式之一,本文旨在了解点赞行为的含义表达,探究人们执行点赞 行为的原因与机理,为社交网络互动机制优化提供依据。【方法/过程】基于社会认知理论构建微信朋友圈用户点赞 行为影响因素模型。通过问卷调查的方法收集411份样本数据,利用Smart PLS2.0工具对研究假设进行实证分析。 【结果/结论】研究结果表明：自我效能、关系维持、自我呈现、消遣娱乐以及社会影响对于用户的微信朋友圈点赞行 为具有显著的正向影响。点赞行为具有多样化的含义表达以及复杂的促进机制。     

A perspective on optical developments in microfluidic platforms for Caenorhabditis elegans research

Microfluidics offers unique ways of handling and manipulating microorganisms, which has particularly benefited Caenorhabditis elegans research. Optics plays a major role in these microfluidic platforms, not only as a read-out for the biological systems of interest but also as a vehicle for applying perturbations to biological systems. Here, we describe different areas of research in C. elegans developmental biology and behavior neuroscience enabled by microfluidics combined with the optical components. In particular, we highlight the diversity of optical tools and methods in use and the strategies implemented in microfluidics to make the devices compatible with optical techniques. We also offer some thoughts on future challenges in adapting advancements in optics to microfluidic platforms.     

Reconciling the father tongue and mother tongue hypotheses in Indo-European populations

In opposition to the mother tongue hypothesis, the father tongue hypothesis states that humans tend to speak their fathers’ language, based on a stronger correlation of languages to paternal lineages (Y-chromosome) than to maternal lineages (mitochondria). To reassess these two competing hypotheses, we conducted a genetic–linguistic study of 34 modern Indo-European (IE) populations. In this study, genetic histories of paternal and maternal migrations in these IE populations were elucidated using phylogenetic networks of Y-chromosomal and mitochondrial DNA haplogroups, respectively. Unlike previous studies, we quantitatively characterized the languages based on lexical and phonemic systems separately. We showed that genetic and linguistic distances are significantly correlated with each other and that both are correlated with geographical distances among these populations. However, when controlling for geographical factors, only the correlation between the distances of paternal and lexical characteristics, and between those of maternal and phonemic characteristics, remained. These unbalanced correlations reconciled the two seemingly conflicting hypotheses.     

Spheroid-on-chip microfluidic technology for the evaluation of the impact of continuous flow on metastatic potential in cancer models in vitro

The majority of cancer deaths are linked to tumor spread, or metastasis, but 3D in vitro metastasis models relevant to the tumor microenvironment (including interstitial fluid flow) remain an area of unmet need. Microfluidics allows us to introduce controlled flow to an in vitro cancer model to better understand the relationship between flow and metastasis. Here, we report new hybrid spheroid-on-chip in vitro models for the impact of interstitial fluid flow on cancer spread. We designed a series of reusable glass microfluidic devices to contain one spheroid in a microwell under continuous perfusion culture. Spheroids derived from established cancer cell lines were perfused with complete media at a flow rate relevant to tumor interstitial fluid flow. Spheroid viability and migratory/invasive capabilities were maintained on-chip when compared to off-chip static conditions. Importantly, using flow conditions modeled in vitro, we are the first to report flow-induced secretion of pro-metastatic factors, in this case cytokines vascular endothelial growth factor and interleukin 6. In summary, we have developed a new, streamlined spheroid-on-chip in vitro model that represents a feasible in vitro alternative to conventional murine in vivo metastasis assays, including complex tumor environmental factors, such as interstitial fluid flow, extracellular matrices, and using 3D models to model nutrient and oxygen gradients. Our device, therefore, constitutes a robust alternative to in vivo early-metastasis models for determination of novel metastasis biomarkers as well as evaluation of therapeutically relevant molecular targets not possible in in vivo murine models.     

A novel term weighting scheme based on discrimination power obtained from past retrieval results

Term weighting for document ranking and retrieval has been an important research topic in information retrieval for decades. We propose a novel term weighting method based on a hypothesis that a term’s role in accumulated retrieval sessions in the past affects its general importance regardless. It utilizes availability of past retrieval results consisting of the queries that contain a particular term, retrieved documents, and their relevance judgments. A term’s evidential weight, as we propose in this paper, depends on the degree to which the mean frequency values for the relevant and non-relevant document distributions in the past are different. More precisely, it takes into account the rankings and similarity values of the relevant and non-relevant documents. Our experimental result using standard test collections shows that the proposed term weighting scheme improves conventional TF^*IDF and language model based schemes. It indicates that evidential term weights bring in a new aspect of term importance and complement the collection statistics based on TF^*IDF. We also show how the proposed term weighting scheme based on the notion of evidential weights are related to the well-known weighting schemes based on language modeling and probabilistic models.     

How do business model and health technology design influence each other? Insights from a longitudinal case study of three academic spin-offs

Academic spin-offs often lack business expertise, face uncertainties regarding their innovation and their markets, and do not have a clear idea of how their product will create value. In spite of this vagueness, academic entrepreneurs must articulate a business model and rapidly establish trustworthy relationships with potential users, purchasers and capital investors. One may thus wonder how their technology development process is influenced by the long-term expectations of their putative customers as well as the short-term requirements of capital investors? This longitudinal case study examines how the business models of three Canadian health technology spin-offs sought to address the value expectations of clinical users and capital investors, how tensions were resolved, and the impact this had on technology design. We describe the synergistic readjustments, drastic reconfiguration and mismatch between business model and technology design we observed. Our discussion highlights the mediating mechanisms by which business models and technology design influence each other, clarifying why the initial value proposition of the spin-offs was either refined or reframed. Beyond confirming the importance of differentiating business models in the health technology industry, our study suggests that it is not only who makes decision that matters, but also how stakeholders’ value expectations get embedded in a spin-off's value proposition.     

Market-level information and the diffusion of competing technologies: An exploratory analysis of the LAN industry

Market-level information diffused by print media may contribute to the legitimation of an emerging technology and thus influence the diffusion of competing technological standards. After analyzing more than 10,000 trade media abstracts from the Local Area Networks (LAN) industry published between 1981 and 2000, we found the presence of differential effects on the adoption of competing standards by two market-level information types: technology and product availability. The significance of these effects depends on the technology's order of entry and suggests that high-tech product managers should make strategic use of market-level information by appropriately focusing the content of their communications.     

Developing strategies for the information society

Abstract

Much has been written about the coming Information Society and the possible benefits and problems that could accompany its evolution. However, there is no grand design for the Information Society. Its general shape, speed, and direction are being shaped by a wide variety of forces, only some of which are technological. However, an argument can be made that the lack of a coherent strategy for the evolution of the Information Society may prove dysfunctional in the long run, because it results in ambiguity, confusion, waste, and lost opportunity in both the public and private sectors. In fact, there are many strategic choices that could be taken today which would help shape the future Information Society so as to improve the prospects for overall societal benefits. Five strategic choices in the public sector and five in the private sector are offered as examples of the kinds of actions that would appear to be appropriate today to lay the groundwork for the successful evolution of the Information Society.     

Probabilistic co-relevance for query-sensitive similarity measurement in information retrieval

Interdocument similarities are the fundamental information source required in cluster-based retrieval, which is an advanced retrieval approach that significantly improves performance during information retrieval (IR). An effective similarity metric is query-sensitive similarity, which was introduced by Tombros and Rijsbergen as method to more directly satisfy the cluster hypothesis that forms the basis of cluster-based retrieval. Although this method is reported to be effective, existing applications of query-specific similarity are still limited to vector space models wherein there is no connection to probabilistic approaches. We suggest a probabilistic framework that defines query-sensitive similarity based on probabilistic co-relevance, where the similarity between two documents is proportional to the probability that they are both co-relevant to a specific given query. We further simplify the proposed co-relevance-based similarity by decomposing it into two separate relevance models. We then formulate all the requisite components for the proposed similarity metric in terms of scoring functions used by language modeling methods. Experimental results obtained using standard TREC test collections consistently showed that the proposed query-sensitive similarity measure performs better than term-based similarity and existing query-sensitive similarity in the context of Voorhees’ nearest neighbor test (NNT).