Start with the discovery : improving the process of community engagement through appreciative inquiry

As service-learning programs are becoming more popular with universities around the world, it is important to ensure that the programs’ participants maintain an ethical relationship with their community partners. Primary amongst the student groups’ ethical duties is to not impose unexpressed needs upon community partners. This paper presents an experience of a service-learning project in a larger service-learning program under the Water and Health in Limpopo (WHIL) research collaborative between the University of Venda (UNIVEN) and University of Virginia (UVA). The project takes place in the village of Tshapasha, South Africa, where an interdisciplinary team of service-learners composed of students from both UNIVEN and UVA had previously worked on a centralized water filtration project. In 2012, another interdisciplinary team of engineering, nursing, and sciences students engaged the same residents of Tshapasha in a process called appreciative inquiry (AI), which is a qualitative methodology that systematically investigates the priorities, strengths, and challenges of participant groups. As a part of the AI process, the service-learners conducted community-wide meetings and moderated nine focus groups that were formed by members’ vocations, age, and gender. The team and community discovered water supply as the community’s priority because of water’s dual value as a domestic and economic good. The interdisciplinary, service-learning team concluded that the AI process helped unify the community’s diverse set of interests into a collective set of priorities. Furthermore, the process fostered a better relationship between the service-learners and community members, one in which further collaboration would be fruitful. These findings demonstrate the use of appreciative inquiry as beneficial for the process of community engagement in service-learning

Driving The Last Mile: Characterizing and Understanding Distracted Driving Posts on Social Networks

In 2015, 391,000 people were injured due to distracted driving in the US. One of the major reasons behind distracted driving is the use of cell-phones, accounting for 14% of fatal crashes. Social media applications have enabled users to stay connected, however, the use of such applications while driving could have serious repercussions -- often leading the user to be distracted from the road and ending up in an accident. In the context of impression management, it has been discovered that individuals often take a risk (such as teens smoking cigarettes, indulging in narcotics, and participating in unsafe sex) to improve their social standing. Therefore, viewing the phenomena of posting distracted driving posts under the lens of self-presentation, it can be hypothesized that users often indulge in risk-taking behavior on social media to improve their impression among their peers. In this paper, we first try to understand the severity of such social-media-based distractions by analyzing the content posted on a popular social media site where the user is driving and is also simultaneously creating content. To this end, we build a deep learning classifier to identify publicly posted content on social media that involves the user driving. Furthermore, a framework proposed to understand factors behind voluntary risk-taking activity observes that younger individuals are more willing to perform such activities, and men (as opposed to women) are more inclined to take risks. Grounding our observations in this framework, we test these hypotheses on 173 cities across the world. We conduct spatial and temporal analysis on a city-level and understand how distracted driving content posting behavior changes due to varied demographics. We discover that the factors put forth by the framework are significant in estimating the extent of such behavior.Comment: Accepted at International Conference on Web and Social Media (ICWSM) 2020; 12 page

Brain-Targeted Intranasal Delivery of Zotepine Microemulsion : Pharmacokinetics and Pharmacodynamics

The purpose of our study was to improve the solubility, bioavailability, and efficacy of zotepine (ZTP) by brain-targeted intranasal delivery of microemulsion (ME) and its physicochemical properties, the pharmacokinetic and pharmacodynamic parameters were evaluated. The optimized ME formulations contain 10% w/w of oil (Capmul MCM C8, monoglycerides, and diglycerides of caprylic acid), 50% w/w of S-mix (Labrasol and Transcutol HP, and 40% w/w of water resulting in a globule size of 124.6 +/- 3.52 nm with low polydispersity index (PDI) (0.212 +/- 0.013) and 2.8-fold higher permeation coefficient through porcine nasal mucosa compared to pure drug). In vitro cell line studies on RPMI 2650, Beas-2B, and Neuro-2A revealed ZTP-ME as safe. ZTP-ME administered intranasally showed higher AUC(0-t24) (18.63 +/- 1.33 h x mu g/g) in the brain by approximately 4.3-fold than oral ME (4.30 +/- 0.92 h x mu g/g) and 7.7-fold than intravenous drug solutions (2.40 +/- 0.36 h x mu g/g). In vivo anti-schizophrenic activity was conducted using catalepsy test scores, the formulation showed better efficacy via the intranasal route; furthermore, there was no inflammation or hemorrhage in the nasal cavity. The results concluded that the ZTP microemulsion as a safe and effective strategy could greatly enhance brain distribution by intranasal administration.Peer reviewe

Brain-Targeted Intranasal Delivery of Zotepine Microemulsion: Pharmacokinetics and Pharmacodynamics

The purpose of our study was to improve the solubility, bioavailability, and efficacy of zotepine (ZTP) by brain-targeted intranasal delivery of microemulsion (ME) and its physicochemical properties, the pharmacokinetic and pharmacodynamic parameters were evaluated. The optimized ME formulations contain 10% w/w of oil (Capmul MCM C8, monoglycerides, and diglycerides of caprylic acid), 50% w/w of Smix (Labrasol and Transcutol HP, and 40% w/w of water resulting in a globule size of 124.6 ± 3.52 nm with low polydispersity index (PDI) (0.212 ± 0.013) and 2.8-fold higher permeation coefficient through porcine nasal mucosa compared to pure drug). In vitro cell line studies on RPMI 2650, Beas-2B, and Neuro-2A revealed ZTP-ME as safe. ZTP-ME administered intranasally showed higher AUC0–t24 (18.63 ± 1.33 h × µg/g) in the brain by approximately 4.3-fold than oral ME (4.30 ± 0.92 h × µg/g) and 7.7-fold than intravenous drug solutions (2.40 ± 0.36 h × µg/g). In vivo anti-schizophrenic activity was conducted using catalepsy test scores, the formulation showed better efficacy via the intranasal route; furthermore, there was no inflammation or hemorrhage in the nasal cavity. The results concluded that the ZTP microemulsion as a safe and effective strategy could greatly enhance brain distribution by intranasal administration

Effects of type of substrate and dilution rate on fermentation in serial rumen mixed cultures.

Forages and concentrates have consistently distinct patterns of fermentation in the rumen, with forages producing more methane (CH4) per unit of digested organic matter (OM) and higher acetate to propionate ratio than concentrates. A mechanism based on the Monod function of microbial growth has been proposed to explain the distinct fermentation pattern of forages and concentrates, where greater dilution rates and lower pH associated with concentrate feeding increase dihydrogen (H2) concentration through increasing methanogens growth rate and decreasing methanogens theoretically maximal growth rate, respectively. Increased H2 concentration would in turn inhibit H2 production, decreasing methanogenesis, inhibit H2-producing pathways such as acetate production via pyruvate oxidative decarboxylation, and stimulate H2-incorporating pathways such as propionate production. We examined the hypothesis that equalizing dilution rates in serial rumen cultures would result in a similar fermentation profile of a high forage and a high concentrate substrate. Under a 2 × 3 factorial arrangement, a high forage and a high concentrate substrate were incubated at dilution rates of 0.14, 0.28, or 0.56 h-1 in eight transfers of serial rumen cultures. Each treatment was replicated thrice, and the experiment repeated in two different months. The high concentrate substrate accumulated considerably more H2 and formate and produced less CH4 than the high forage substrate. Methanogens were nearly washed-out with high concentrate and increased their initial numbers with high forage. The effect of dilution rate was minor in comparison to the effect of the type of substrate. Accumulation of H2 and formate with high concentrate inhibited acetate and probably H2 and formate production, and stimulated butyrate, rather than propionate, as an electron sink alternative to CH4. All three dilution rates are considered high and selected for rapidly growing bacteria. The archaeal community composition varied widely and inconsistently. Lactate accumulated with both substrates, likely favored by microbial growth kinetics rather than by H2 accumulation thermodynamically stimulating electron disposal from NADH into pyruvate reduction. In this study, the type of substrate had a major effect on rumen fermentation largely independent of dilution rate and pH

Features, Causes and Consequences of Splanchnic Sequestration of Amino Acid in Old Rats

RATIONALE: In elderly subjects, splanchnic extraction of amino acids (AA) increases during meals in a process known as splanchnic sequestration of amino acids (SSAA). This process potentially contributes to the age-related progressive decline in muscle mass via reduced peripheral availability of dietary AA. SSAA mechanisms are unknown but may involve an increased net utilization of ingested AA in the splanchnic area. OBJECTIVES: Using stable isotope methodology in fed adult and old rats to provide insight into age-related SSAA using three hypotheses: 1) an increase in protein synthesis in the gut and/or the liver, 2) an increase in AA oxidation related to an increased ureagenesis, and 3) Kupffer cell (KC) activation consequently to age-related low-grade inflammation. FINDINGS: Splanchnic extraction of Leu (SPELeu) was doubled in old rats compared to adult rats and was not changed after KC inactivation. No age-related effects on gut and liver protein synthesis were observed, but urea synthesis was lower in old rats and negatively correlated to liver Arg utilization. Net whole-body protein synthesis and arterial AA levels were lower in old rats and correlated negatively with SPELeu. CONCLUSION: SSAA is not the consequence of age-related alterations in ureagenesis, gut or liver protein synthesis or of KC activity. However, SSAA may be related to reduced net whole-body protein synthesis and consequently to the reduced lean body mass that occurs during aging

Deep learning methods for Electrocorticographic data analyses

Electrocorticography (ECoG) records brain activity from the cortical surface. ECoG data analyses has led to significant advancements in neuroscientific research, particularly in two major domains: functional mapping to understand the cortical organization of human brain; and brain-machine interfaces (BMIs) that decode intent from neural data. Designing high performance BMIs is an active area of interest. A discrete BMI design primarily involves decoding specific targets from features extracted from ECoG data. Majority of ECoG based research studies use spectral features i.e. powers in specific frequency bands; which are based on empirical observations. However, given the non-stationarity and variability of neural signals, features extracted in a data driven way could lead to more robust BMIs. In addition to efficient feature extraction and decoder training, the choice of targets presented to BMI user can greatly affect the bit-rate or throughput of the BMI. The ability to record ECoG data in the order of days in epilepsy monitorig units (EMU); in synchrony with behavioral data through non-invasive sensors like Kinect; has resulted in deluge of large-scale, coarsely labelled datasets. Deep learning architectures are being explored to tackle this big-data problem and extract meaningful spatio-temporal patterns from these data. However, much of the existing research has been relying on architectures that found success in other domains such as computer vision and audio. A more systematic approach towards neural network architecture designs for analyzing large-scale ECoG datasets, that embeds domain knowledge from neuroscience and neurophysiology, is necessary. The contributions of this thesis are three-fold. Firstly, I show that we can increase the throughput of a speech-based BMI by using targets that generate maximal separation in neural space. Secondly, I show that data-driven features can be learnt in an unsupervised fashion using autoencoders and are more robust compared to linear dimensionality reductions methods like PCA. These features also aid in funcional mapping by identifying functionally similar electrodes in unsupervised fashion. Lastly, I show that cross-subject model can be learnt to decode finger flexions by learning common latent spaces that map activity from different subjects onto a single latent space. By learning temporal dynamics using a recurrent neural network, from this common space, we show that we can decode continuous behvaiors from ECoG. By analyzing the architectural design decisions on smaller, well-structured and labelled datasets, we can have a smarter approach towards developing deep learning toolkits for larger, coarsely labelled or unlabelled datasets. The methods described in this thesis will aid neuroscientists in ECoG data analysis, clinicians by providing data driven functional mapping methods, and neural engineers by providing more robust machine learning pipelines for BMI design