Multiply Robust Causal Inference with Double Negative Control Adjustment for Categorical Unmeasured Confounding

Unmeasured confounding is a threat to causal inference in observational studies. In recent years, use of negative controls to mitigate unmeasured confounding has gained increasing recognition and popularity. Negative controls have a longstanding tradition in laboratory sciences and epidemiology to rule out non-causal explanations, although they have been used primarily for bias detection. Recently, Miao et al. (2018) have described sufficient conditions under which a pair of negative control exposure and outcome variables can be used to nonparametrically identify the average treatment effect (ATE) from observational data subject to uncontrolled confounding. In this paper, we establish nonparametric identification of the ATE under weaker conditions in the case of categorical unmeasured confounding and negative control variables. We also provide a general semiparametric framework for obtaining inferences about the ATE while leveraging information about a possibly large number of measured covariates. In particular, we derive the semiparametric efficiency bound in the nonparametric model, and we propose multiply robust and locally efficient estimators when nonparametric estimation may not be feasible. We assess the finite sample performance of our methods in extensive simulation studies. Finally, we illustrate our methods with an application to the postlicensure surveillance of vaccine safety among children

A Conceptual Model for User-system Collaboration: Enhancing Usability of Complex Information Systems

A variety of organizations around the world use complex information systems (e.g., enterprise resource planning and supply chain management systems). However, poor usability caused by system complexity continues to frustrate users and damage the reputation of these systems. In this study, we address usability issues with complex information systems from the human-computer collaboration perspective by modeling user-system interaction as a joint activity between the system and its users. We propose a conceptual model for user-system collaboration, elaborate on the components in the model and the relationships between the components, derive the required capabilities for collaborative information systems, and establish conceptual relationships between system collaborative behaviors and usability. We use empirical evidence gathered from a qualitative field study on ERP systems to illustrate the model and the possible impact of system collaborativeness (i.e., the presence or absence of collaborative capabilities) on usability. We do so to provide a strong conceptual foundation for modeling user-system collaboration and to encourage designers to employ the collaboration metaphor during system design and, thus, help them develop future complex information systems with better usability

Studying the Structure of Terrorist Networks: A Web Structural Mining Approach

Because terrorist organizations often operate in network forms where individual terrorists collaborate with each other to carry out attacks, we could gain valuable knowledge about the terrorist organizations by studying structural properties of such terrorist networks. However, previous studies of terrorist network structure have generated little actionable results. This is due to the difficulty in collecting and accessing reliable data and the lack of advanced network analysis methodologies in the field. To address these problems, we introduced the Web structural mining technique into the terrorist network analysis field which, to the best our knowledge, has never been done before. We employed the proposed technique on a Global Salafi Jihad network dataset collected through a large scale empirical study. Results from our analysis not only provide insights for terrorism research community but also support decision making in law-reinforcement, intelligence, and security domains to make our nation safer

Data Science for Social Good

Data science has been described as the fourth paradigm for scientific discovery. The latest wave of data science research, pertaining to machine learning and artificial intelligence (AI), is growing exponentially and garnering millions of annual citations. However, this growth has been accompanied by a diminishing emphasis on social good challenges - our analysis reveals that the proportion of data science research focusing on social good is less than it has ever been. At the same time, the proliferation of machine learning and generative AI have sparked debates about the socio-technical prospects and challenges associated with data science for human flourishing, organizations, and society. Against this backdrop, we present a framework for "data science for social good" (DSSG) research that considers the interplay between relevant data science research genres, social good challenges, and different levels of socio-technical abstraction. We perform an analysis of the literature to empirically demonstrate the paucity of work on DSSG in information systems (and other related disciplines) and highlight current impediments. We then use our proposed framework to introduce the articles appearing in the special issue. We hope that this article and the special issue will spur future DSSG research and help reverse the alarming trend across data science research over the past 30-plus years in which social good challenges are garnering proportionately less attention with each passing day

Carbon Dioxide Gas Sensors and Method of Manufacturing and Using Same

A gas sensor includes a substrate and a pair of interdigitated metal electrodes selected from the group consisting of Pt, Pd, Au, Ir, Ag, Ru, Rh, In, and Os. The electrodes each include an upper surface. A first solid electrolyte resides between the interdigitated electrodes and partially engages the upper surfaces of the electrodes. The first solid electrolyte is selected from the group consisting of NASICON, LISICON, KSICON, and .beta.''-Alumina (beta prime-prime alumina in which when prepared as an electrolyte is complexed with a mobile ion selected from the group consisting of Na.sup.+, K.sup.+, Li.sup.+, Ag.sup.+, H.sup.+, Pb.sup.2+, Sr.sup.2+ or Ba.sup.2+). A second electrolyte partially engages the upper surfaces of the electrodes and engages the first solid electrolyte in at least one point. The second electrolyte is selected from the group of compounds consisting of Na.sup.+, K.sup.+, Li.sup.+, Ag.sup.+, H.sup.+, Pb.sup.2+, Sr.sup.2+ or Ba.sup.2+ ions or combinations thereof

CO2 Sensors Based on Nanocrystalline SnO2 Doped with CuO

Nanocrystalline tin oxide (SnO2) doped with copper oxide (CuO) has been found to be useful as an electrical-resistance sensory material for measuring the concentration of carbon dioxide in air. SnO2 is an n-type semiconductor that has been widely used as a sensing material for detecting such reducing gases as carbon monoxide, some of the nitrogen oxides, and hydrocarbons. Without doping, SnO2 usually does not respond to carbon dioxide and other stable gases. The discovery that the electrical resistance of CuO-doped SnO2 varies significantly with the concentration of CO2 creates opportunities for the development of relatively inexpensive CO2 sensors for detecting fires and monitoring atmospheric conditions. This discovery could also lead to research that could alter fundamental knowledge of SnO2 as a sensing material, perhaps leading to the development of SnO2-based sensing materials for measuring concentrations of oxidizing gases. Prototype CO2 sensors based on CuO-doped SnO2 have been fabricated by means of semiconductor-microfabrication and sol-gel nanomaterial-synthesis batch processes that are amendable to inexpensive implementation in mass production

Novel Carbon Dioxide Microsensor Based on Tin Oxide Nanomaterial Doped With Copper Oxide

Carbon dioxide (CO2) is one of the major indicators of fire and therefore its measurement is very important for low-false-alarm fire detection and emissions monitoring. However, only a limited number of CO2 sensing materials exist due to the high chemical stability of CO2. In this work, a novel CO2 microsensor based on nanocrystalline tin oxide (SnO2) doped with copper oxide (CuO) has been successfully demonstrated. The CuO-SnO2 based CO2 microsensors are fabricated by means of microelectromechanical systems (MEMS) technology and sol-gel nanomaterial-synthesis processes. At a doping level of CuO: SnO2 = 1:8 (molar ratio), the resistance of the sensor has a linear response to CO2 concentrations for the range of 1 to 4 percent CO2 in air at 450 C. This approach has demonstrated the use of SnO2, typically used for the detection of reducing gases, in the detection of an oxidizing gas

Chemical Sensors Based on Metal Oxide Nanostructures

This paper is an overview of sensor development based on metal oxide nanostructures. While nanostructures such as nanorods show significan t potential as enabling materials for chemical sensors, a number of s ignificant technical challenges remain. The major issues addressed in this work revolve around the ability to make workable sensors. This paper discusses efforts to address three technical barriers related t o the application of nanostructures into sensor systems: 1) Improving contact of the nanostructured materials with electrodes in a microse nsor structure; 2) Controling nanostructure crystallinity to allow co ntrol of the detection mechanism; and 3) Widening the range of gases that can be detected by using different nanostructured materials. It is concluded that while this work demonstrates useful tools for furt her development, these are just the beginning steps towards realizati on of repeatable, controlled sensor systems using oxide based nanostr uctures

Membrane integrity contributes to resistance of Cryptococcus neoformans to the cell wall inhibitor caspofungin

The fungal pathogen Cryptococcus neoformans causes up to 278 000 infections each year globally, resulting in up to 180,000 deaths annually, mostly impacting immunocompromised people. Therapeutic options for C. neoformans infections are very limited. Caspofungin, a member of the echinocandin class of antifungals, is generally well tolerated but clinically ineffective against C. neoformans. We sought to identify biological processes that can be targeted to render the cell more susceptible to echinocandins by screening the available libraries of gene deletion mutants made in the KN99α background for caspofungin sensitivity. We adapted a Candida albicans fungal biofilm assay for the growth characteristics of C. neoformans and systematically screened 4,030 individual gene deletion mutants in triplicate plate assays. We identified 25 strains that showed caspofungin sensitivity. We followed up with a dose dependence assay, and 17 of the 25 were confirmed sensitive, 5 of which were also sensitive in an agar plate assay. We made new deletion mutant strains for four of these genes

Monoamine Oxidase A (MAOA) Gene and Personality Traits from Late Adolescence through Early Adulthood: A Latent Variable Investigation.

Very few molecular genetic studies of personality traits have used longitudinal phenotypic data, therefore molecular basis for developmental change and stability of personality remains to be explored. We examined the role of the monoamine oxidase A gene (MAOA) on extraversion and neuroticism from adolescence to adulthood, using modern latent variable methods. A sample of 1,160 male and 1,180 female participants with complete genotyping data was drawn from a British national birth cohort, the MRC National Survey of Health and Development (NSHD). The predictor variable was based on a latent variable representing genetic variations of the MAOA gene measured by three SNPs (rs3788862, rs5906957, and rs979606). Latent phenotype variables were constructed using psychometric methods to represent cross-sectional and longitudinal phenotypes of extraversion and neuroticism measured at ages 16 and 26. In males, the MAOA genetic latent variable (AAG) was associated with lower extraversion score at age 16 (β = -0.167; CI: -0.289, -0.045; p = 0.007, FDRp = 0.042), as well as greater increase in extraversion score from 16 to 26 years (β = 0.197; CI: 0.067, 0.328; p = 0.003, FDRp = 0.036). No genetic association was found for neuroticism after adjustment for multiple testing. Although, we did not find statistically significant associations after multiple testing correction in females, this result needs to be interpreted with caution due to issues related to x-inactivation in females. The latent variable method is an effective way of modeling phenotype- and genetic-based variances and may therefore improve the methodology of molecular genetic studies of complex psychological traits