Information technologies and civic engagement: Perspectives from librarianship and planning

Urban planning and librarianship are parallel fields in many ways. Both have theoretical and practical underpinnings driving each discipline; are interdisciplinary in focus; and both professions gather and disseminate information to stakeholders. Essential to the success of each discipline is an engaged user population. The authors use a case study from the Village of Oak Park to examine and reflect upon the effect of the Internet and other technologies in the public\u27s ability to participate in an open planning process. The Village of Oak Park is known for its architectural heritage and outspoken community. Within its 4.5 square miles live a diverse population of approximately 52,000 people from different cultures, races, ethnicities, professions, lifestyles, religions, ages, and incomes where a majority of the population have some advanced education and over 80% of the households report home Internet use. How does a community like the Village of Oak Park engage in the planning process? What role does information play in this process? Can modern Information Technologies facilitate civic engagement? These questions are examined during a year-long case study conducted by the University of Illinois at Chicago (UIC) in cooperation with the Village of Oak Park (VOP). Specifically, the investigators examined how information is gathered and used in the planning process and how the introduction of Information Technology (IT) tools influenced public participation and introduced many citizens to e-government through the planning process. The use of these tools is compared to the traditional planning process. Data from the project is presented to demonstrate a different kind of role librarians play in the research process. Last, the authors discuss the role of privacy in the gathering and sharing of information with the reliability of that information and the impact of privacy on the public planning process

Identification of Distinct Characteristics of Antibiofilm Peptides and Prospection of Diverse Sources for Efficacious Sequences

A majority of microbial infections are associated with biofilms. Targeting biofilms is considered an effective strategy to limit microbial virulence while minimizing the development of antibiotic resistance. Toward this need, antibiofilm peptides are an attractive arsenal since they are bestowed with properties orthogonal to small molecule drugs. In this work, we developed machine learning models to identify the distinguishing characteristics of known antibiofilm peptides, and to mine peptide databases from diverse habitats to classify new peptides with potential antibiofilm activities. Additionally, we used the reported minimum inhibitory/eradication concentration (MBIC/MBEC) of the antibiofilm peptides to create a regression model on top of the classification model to predict the effectiveness of new antibiofilm peptides. We used a positive dataset containing 242 antibiofilm peptides, and a negative dataset which, unlike previous datasets, contains peptides that are likely to promote biofilm formation. Our model achieved a classification accuracy greater than 98% and harmonic mean of precision-recall (F1) and Matthews correlation coefficient (MCC) scores greater than 0.90; the regression model achieved an MCC score greater than 0.81. We utilized our classification-regression pipeline to evaluate 135,015 peptides from diverse sources for potential antibiofilm activity, and we identified 185 candidates that are likely to be effective against preformed biofilms at micromolar concentrations. Structural analysis of the top 37 hits revealed a larger distribution of helices and coils than sheets, and common functional motifs. Sequence alignment of these hits with known antibiofilm peptides revealed that, while some of the hits showed relatively high sequence similarity with known peptides, some others did not indicate the presence of antibiofilm activity in novel sources or sequences. Further, some of the hits had previously recognized therapeutic properties or host defense traits suggestive of drug repurposing applications. Taken together, this work demonstrates a new in silico approach to predicting antibiofilm efficacy, and identifies promising new candidates for biofilm eradication

Electrochemical Behavior of Biomedical Titanium Alloys Coated with Diamond Carbon in Hanks’ Solution

Biomedical implants in the knee and hip are frequent failures because of corrosion and stress on the joints. To solve this important problem, metal implants can be coated with diamond carbon, and this coating plays a critical role in providing an increased resistance to implants toward corrosion. In this study, we have employed diamond carbon coating over Ti-6Al-4V and Ti-13Nb-13Zr alloys using hot filament chemical vapor deposition method which is well-established coating process that significantly improves the resistance toward corrosion, wears and hardness. The diamond carbon-coated Ti-13Nb-13Zr alloy showed an increased microhardness in the range of 850 HV. Electrochemical impedance spectroscopy and polarization studies in SBF solution (simulated body fluid solution) were carried out to understand the in vitro behavior of uncoated as well as coated titanium alloys. The experimental results showed that the corrosion resistance of Ti-13Nb-13Zr alloy is relatively higher when compared with diamond carbon-coated Ti-6Al-4V alloys due to the presence of β phase in the Ti-13Nb-13Zr alloy. Electrochemical impedance results showed that the diamond carbon-coated alloys behave as an ideal capacitor in the body fluid solution. Moreover, the stability in mechanical properties during the corrosion process was maintained for diamond carbon-coated titanium alloys

The interaction of vortical flows with red cells in venous valve mimics

The motion of cells orthogonal to the direction of main flow is of importance in natural and engineered systems. The lateral movement of red blood cells (RBCs) distal to sudden expansion is considered to influence the formation and progression of thrombosis in venous valves, aortic aneurysms, and blood-circulating devices and is also a determining parameter for cell separation applications in flow-focusing microfluidic devices. Although it is known that the unique geometry of venous valves alters the blood flow patterns and cell distribution in venous valve sinuses, the interactions between fluid flow and RBCs have not been elucidated. Here, using a dilute cell suspension in an in vitro microfluidic model of a venous valve, we quantified the spatial distribution of RBCs by microscopy and image analysis, and using micro-particle image velocimetry and 3D computational fluid dynamics simulations, we analyzed the complex flow patterns. The results show that the local hematocrit in the valve pockets is spatially heterogeneous and is significantly different from the feed hematocrit. Above a threshold shear rate, the inertial separation of streamlines and lift forces contribute to an uneven distribution of RBCs in the vortices, the entrapment of RBCs in the vortices, and non-monotonic wall shear stresses in the valve pockets. Our experimental and computational characterization provides insights into the complex interactions between fluid flow, RBC distribution, and wall shear rates in venous valve mimics, which is of relevance to understanding the pathophysiology of thrombosis and improving cell separation efficiency

Automated Motion Tracking and Data Extraction for Red Blood Cell Biomechanics

Red blood cell biomechanics can provide us with a deeper understanding of macroscopic physiology and have the potential of being used for diagnostic purposes. In diseases like sickle cell anemia and malaria, reduced red blood cell deformability can be used as a biomarker, leading to further assays and diagnoses. A microfluidic system is useful for studying these biomechanical properties. We can observe detailed red blood cell mechanical behavior as they flow through microcapillaries using high-speed imaging and microscopy. Microfluidic devices are advantageous over traditional methods because they can serve as high-throughput tests. However, to rapidly analyze thousands of cells, there is a need for powerful image processing tools and software automation. We describe a workflow process using Image-Pro to identify and track red blood cells in a video, take measurements, and export the data for use in statistical analysis tools. The information in this protocol can be applied to large-scale blood studies where entire cell populations need to be analyzed from many cohorts of donors. © 2020 The Authors. Basic Protocol 1: Enhancing raw video for motion tracking. Basic Protocol 2: Extracting motion tracking data from enhanced video

A Facile High-Throughput Model of Surface-Independent Staphylococcus aureus Biofilms by Spontaneous Aggregation

Many microbes in their natural habitats are found in biofilm ecosystems attached to surfaces and not as free-floating (planktonic) organisms. Furthermore, it is estimated that nearly 80% of human infections are associated with biofilms. Biofilms are traditionally defined as three-dimensional, structured microbial communities that are attached to a surface and encased in a matrix of exopolymeric material. While this view of biofilm largely arises from in vitro studies under static or flow conditions, in vivo observations have indicated that this view of biofilms is essentially true only for foreign-body infections on catheters or implants where biofilms are attached to the biomaterial. In mucosal infections such as chronic wounds or cystic fibrosis or joint infections, biofilms can be found unattached to a surface and as three-dimensional aggregates. In this work, we describe a high-throughput model of aggregate biofilms of methicillin-resistant Staphylococcus aureus (MRSA) using 96-well plate hanging-drop technology. We show that MRSA forms surface-independent biofilms, distinct from surface-attached biofilms, that are rich in exopolymeric proteins, polysaccharides, and extracellular DNA (eDNA), express biofilm-related genes, and exhibit heightened antibiotic resistance. We also show that the surface-independent biofilms of clinical isolates of MRSA from cystic fibrosis and central catheter-related infections demonstrate morphological differences. Overall, our results show that biofilms can form by spontaneous aggregation without attachment to a surface, and this new in vitro system can model surface-independent biofilms that may more closely mimic the corresponding physiological niche during infection. IMPORTANCE The canonical model of biofilm formation begins with the attachment and growth of microbial cells on a surface. While these in vitro models reasonably mimic biofilms formed on foreign bodies such as catheters and implants, this is not the case for biofilms formed in cystic fibrosis and chronic wound infections, which appear to present as aggregates not attached to a surface. The hanging-drop model of biofilms of methicillin-resistant Staphylococcus aureus (MRSA), the major causative organism of skin and soft tissue infections, shows that these biofilms display morphological and antibiotic response patterns that are distinct from those of their surface-attached counterparts, and biofilm growth is consistent with their in vivo location. The simplicity and throughput of this model enable adoption to investigate other single or polymicrobial biofilms in a physiologically relevant setting

Data mining multiple stakeholders’ responses to declining schizothorax fishery in the lakes of Kashmir, India

This study documented the historical perspective of lake fisheries in Kashmir, India, estimated the trends in fish production using the Kane’s cross impact analysis and the stakeholders’ willingness to pay (WTP) for the restoration of Schizothorax fishery in the lakes of Kashmir. The cross impacts of introduction of carps in the lakes of Kashmir on Schizothorax fishery and total fish production are evaluated by Kane’s cross impact analysis. Considering the obvious shortcomings of logistic regression, Classification and Regression Trees (CART) has been used for data mining multiple stakeholders’ responses to make a case for sustainable development of the Schizothorax fishery in the lakes of Kashmir. On the whole, time spent on the lakes to earn their livelihoods emerged as the root node, as the single most important variable that determined WTP of stakeholders followed by income, type of stakeholder and age in the tree model. The CART analysis not only yielded the variables that determined the WTP but the pruned tree gave the hierarchy of the variables that determined WTP. The results of the study strongly made a case for a concerted multi-institutional action plan for the restoration of Schizothorax fishery in the lakes of Kashmir.Keywords: Modeling and Economic Theory, Environment: Valuation, Fisheries Economic