Automaticity in processing spatial-numerical associations: Evidence from a perceptual orientation judgment task of Arabic digits in frames.

Human adults are faster to respond to small/large numerals with their left/right hand when they judge the parity of numerals, which is known as the SNARC (spatial-numerical association of response codes) effect. It has been proposed that the size of the SNARC effect depends on response latencies. The current study introduced a perceptual orientation task, where participants were asked to judge the orientation of a digit or a frame surrounding the digit. The present study first confirmed the SNARC effect with native Chinese speakers (Experiment 1) using a parity task, and then examined whether the emergence and size of the SNARC effect depended on the response latencies (Experiments 2, 3, and 4) using a perceptual orientation judgment task. Our results suggested that (a) the automatic processing of response-related numerical-spatial information occurred with Chinese-speaking participants in the parity task; (b) the SNARC effect was also found when the task did not require semantic access; and (c) the size of the effect depended on the processing speed of the task-relevant dimension. Finally, we proposed an underlying mechanism to explain the SNARC effect in the perceptual orientation judgment task

Perspiration Compensated Transdermal Alcohol Sensor for Personalized Medicine

Non-invasive continuous alcohol (ethanol) monitoring has potential applications in both population research and in clinical management of acute alcohol intoxication or chronic alcoholism. Current wearable monitors based on transdermal alcohol content (TAC) sensing have limited accessibility and blood alcohol content (BAC) quantification accuracy. In the first half of this work, we demonstrated a self-contained discreet wearable transdermal alcohol (TAC) sensor in the form of a wristband or armband. This sensor can detect vapor-phase alcohol in perspiration from 0.09 ppm (equivalent to 0.09 mg/dL sweat alcohol concentration at 25 °C under Henry’s Law equilibrium) to over 500 ppm at one-minute time resolution. Additionally, a digital sensor was employed to monitor the temperature and humidity levels inside the sensing chamber. Two male human subjects were recruited to conduct studies with alcohol consumption using calibrated prototype TAC sensors to validate the performance. Our preliminary data showed that, under well-controlled conditions, this sensor can acquire TAC curves at low doses (1-2 standard drinks). Moreover, TAC data for different doses can be easily distinguished. However, substantial interpersonal and intrapersonal variabilities in measurement data were also observed in experiments under less controlled conditions. Our observations suggest that perspiration rate might be an important contributing factor to these variabilities, which inspired us to develop a perspiration compensated TAC sensor. In the second half of this thesis, we carefully analyzed the mass transport process of ethanol and water vapors inside the sensing chamber to identify the root causes of sensor variabilities observed from our device. A mathematical model was developed to better understand the relationship between sensing current and ethanol concentration in liquid sweat. The resulted equation suggests that perspiration rate-induced sensor variabilities can potentially be compensated by two humidity measurements. Therefore, we updated the wearable TAC sensor design, integrating two additional digital temperature and humidity sensors. Internal components of the device were rearranged, reducing its overall size to 42 mm x 46 mm x 13 mm. Prototypes of the new TAC sensor were fabricated and characterized in our lab. Next, 10 repeated trials with alcohol administration (1 standard drink) were conducted by one subject to test the hypothesis on that individual. Normalized area under curve (AUC) and peak values were utilized to compare the sensor variabilities before and after compensation. Compared to TAC data without compensation, the variabilities of AUC and peak values were reduced by 45% and 64%, respectively. ANOVA f-tests were applied to test the hypothesis on this individual. The null hypothesis of the peak values has been rejected with an f statistic of 7.89 (p-value = 0.004). However, the test on AUC data yielded an f statistic of 3.35 (p-value = 0.054), indicating the null hypothesis of the AUC values was not rejected. Based on power analysis, 20 samples in total are required to draw a conclusion for AUC. Further studies with sufficient sample sizes are required to validate and characterize the impact of different perspiration rates on TAC sensors, which may inform more reproducible and accurate sensor designs in the future. In addition, the author also contributed to several other sensors and portable systems for personalized medicine and research. Two selected projects with major contributions were included in Chapter 9

Finding Maximal k-Edge-Connected Subgraphs from a Large Graph

In this paper, we study how to find maximal k-edge-connected subgraphs from a large graph. k-edge-connected subgraphs can be used to capture closely related vertices, and finding such vertex clusters is interesting in many applications, e. g., social network analysis, bioinformatics, web link research. Compared with other explicit structures for modeling vertex clusters, such as quasi-clique, k-core, which only set the requirement on vertex degrees, k-edge-connected subgraph further requires high connectivity within a subgraph (a stronger requirement), and hence defines a more closely related vertex cluster. To find maximal k-edge-connected subgraphs from a graph, a basic approach is to repeatedly apply minimum cut algorithm to the connected components of the input graph until all connected components are k-connected. However, the basic approach is very expensive if the input graph is large. To tackle the problem, we propose three major techniques: vertex reduction, edge reduction and cut pruning. These speed-up techniques are applied on top of the basic approach. We conduct extensive experiments and show that the speed-up techniques are very effective

Insights on acetate-ethanol fermentation by hydrogen-producing Ethanoligenens under acetic acid accumulation based on quantitative proteomics

Ethanoligenens, a novel ethanologenic hydrogen-producing genus, is a representative fermenter in its unique acetate-ethanol fermentation and physiology. Acetic acid accumulation is one of major factors that affect H-2-ethanol co-production. However, sufficient information is unavailable on the tolerance mechanisms of hydrogen-producing bacterium in acetic acid stress. The fermentation process of Ethanoligenens harbinense YUAN-3 was significantly slowed down in the selection stress of exogenous acetic acid. The maximum gas production rate of strain YUAN-3 decreased from 192.15 mL.(L-culture)(-1).h(-1) to 75.2 mL.(L-culture)(-1).h(-1) with increasing exogenous acetic acid from 0 mM to 30 mM, the batch fermentation period was correspondingly expanded from 66 h to 136 h. Through iTRAQ-based quantitative proteomic approach, 78, 121 and 216 proteins were differentially expressed after strain YUAN-3 was cultured in the medium supplemented with exogenous acetic acid of 10 mM, 20 mM and 30 mM. The up-regulated proteins were mainly involved in beta-alanine and pyrimidine metabolism, oxidative stress response, while the down-regulated proteins mainly participated in phosphotransferase system (PTS), fructose and mannose metabolism, phosphate uptake, ribosome, and flagellar assembly. These proteins help to maintain balance between fermentation process and alleviation of intracellular acidification in strain YUAN-3. The study indicated that response to acetic acid stress in strain YUAN-3 was a complex process, which involved multiple metabolic pathways. Reductive pyrimidine catabolic pathway played an important role in the acetic acid resistance of E. harbinense