The International Decision-Making and Travel Behavior of Graduates Participating in Working Holiday

After graduation, most graduates find themselves at a significant stage in their life as they have to decide between “further study” and “working.” When faced with this confusion and uncertainty, a “working holiday” combining travel and work has coincidentally becomes a third option. This study employed a qualitative approach through literature review, in-depth interviews, and semi-structured interviews. The research revealed that graduates are influenced by “internal personal thinking” and “external driving forces” when they embark on a working holiday. The former includes negative obstructions and positive stimulus. The latter factor’s stimulus includes attraction of natural landscapes, history and culture, learning foreign languages, safety concerns, difficulties in visa application, and the opportunity to obtain a salaried job. The process of embarking on a working holiday was complex and unpredictable. The traveling behavior of working holiday destinations included short-distance leisure behavior and long-distance traveling behavior. In terms of the influences of short-distance leisure behavior, graduates preferred being employed by service industries that had less working hours, flexible work arrangements and included the purchase of preferential price tickets. Graduates’ long-distance traveling behavior was affected by the work they performed. The travel time was different between various industries

MAEEG: Masked Auto-encoder for EEG Representation Learning

Decoding information from bio-signals such as EEG, using machine learning has been a challenge due to the small data-sets and difficulty to obtain labels. We propose a reconstruction-based self-supervised learning model, the masked auto-encoder for EEG (MAEEG), for learning EEG representations by learning to reconstruct the masked EEG features using a transformer architecture. We found that MAEEG can learn representations that significantly improve sleep stage classification (~5% accuracy increase) when only a small number of labels are given. We also found that input sample lengths and different ways of masking during reconstruction-based SSL pretraining have a huge effect on downstream model performance. Specifically, learning to reconstruct a larger proportion and more concentrated masked signal results in better performance on sleep classification. Our findings provide insight into how reconstruction-based SSL could help representation learning for EEG.Comment: 10 pages, 5 figures, accepted by Workshop on Learning from Time Series for Health, NeurIPS2022 as poster presentatio

Qubit Mapping Toward Quantum Advantage

Qubit Mapping is a pivotal stage in quantum compilation flow. Its goal is to convert logical circuits into physical circuits so that a quantum algorithm can be executed on real-world non-fully connected quantum devices. Qubit Mapping techniques nowadays still lack the key to quantum advantage, scalability. Several studies have proved that at least thousands of logical qubits are required to achieve quantum computational advantage. However, to our best knowledge, there is no previous research with the ability to solve the qubit mapping problem with the necessary number of qubits for quantum advantage in a reasonable time. In this work, we provide the first qubit mapping framework with the scalability to achieve quantum advantage while accomplishing a fairly good performance. The framework also boasts its flexibility for quantum circuits of different characteristics. Experimental results show that the proposed mapping method outperforms the state-of-the-art methods on quantum circuit benchmarks by improving over 5% of the cost complexity in one-tenth of the program running time. Moreover, we demonstrate the scalability of our method by accomplishing mapping of an 11,969-qubit Quantum Fourier Transform within five hours

A quantitative analysis of monochromaticity in genetic interaction networks

<p>Abstract</p> <p>Background</p> <p>A genetic interaction refers to the deviation of phenotypes from the expected when perturbing two genes simultaneously. Studying genetic interactions help clarify relationships between genes, such as compensation and masking, and identify gene groups of functional modules. Recently, several genome-scale experiments for measuring quantitative (positive and negative) genetic interactions have been conducted. The results revealed that genes in the same module usually interact with each other in a consistent way (pure positive or negative); this phenomenon was designated as monochromaticity. Monochromaticity might be the underlying principle that can be utilized to unveil the modularity of cellular networks. However, no appropriate quantitative measurement for this phenomenon has been proposed.</p> <p>Results</p> <p>In this study, we propose the monochromatic index (MCI), which is able to quantitatively evaluate the monochromaticity of potential functional modules of genes, and the MCI was used to study genetic landscapes in different cellular subsystems. We demonstrated that MCI not only amend the deficiencies of MP-score but also properly incorporate the background effect. The results showed that not only within-complex but also between-complex connections present significant monochromatic tendency. Furthermore, we also found that significantly higher proportion of protein complexes are connected by negative genetic interactions in metabolic network, while transcription and translation system adopts relatively even number of positive and negative genetic interactions to link protein complexes.</p> <p>Conclusion</p> <p>In summary, we demonstrate that MCI improves deficiencies suffered by MP-score, and can be used to evaluate monochromaticity in a quantitative manner. In addition, it also helps to unveil features of genetic landscapes in different cellular subsystems. Moreover, MCI can be easily applied to data produced by different types of genetic interaction methodologies such as Synthetic Genetic Array (SGA), and epistatic miniarray profile (E-MAP).</p

Bacteremic pneumonia caused by Nocardia veterana in an HIV-infected patient

SummaryDisseminated Nocardia veterana infection has rarely been reported. We describe the first reported case of N. veterana bacteremic pneumonia in an HIV-infected patient. The isolate was confirmed by 16S rRNA sequencing analysis. The patient initially responded well to trimethoprim–sulfamethoxazole treatment (minimum inhibitory concentration 0.25μg/ml), but died of ventilator-associated pneumonia

Highly efficient mode-locked and Q-switched Er3+-doped fiber lasers using a gold nanorod saturable absorber

Mode-locked and Q-switched pulsed fiber laser sources with wavelengths of 1.55 mu m are widely used in various fields. Gold nanorods (GNRs) have been applied in biomedicine and optics owing to their biocompatibility, easy fabrication, and unique optical properties. This paper presents the analysis of a saturable absorber based on a colloidal gold nanorod (GNR) thin film for dual-function passively mode-locked and Q-switched 1.55-mu m fiber lasers. The colloidal GNR thin film possesses superior properties such as a wide operating wavelength range, large nonlinear absorption coefficient, and a picosecond-order recovery time. Its modulation depth and saturation intensity at 1.55 mu m are 7.8% and 6.55 MW/cm(2), respectively. Passive mode-locked or Q-switched laser operation is achieved by changing the number of GNR thin-film layers. The advantages of these high-quality GNRs in mode-locked and Q-switched fiber lasers with record-high slope efficiency are verified by conducting comprehensive material and laser dynamic analyses. The self-starting mode-locked fiber laser with an efficiency as high as 24.91% and passively Q-switched fiber laser with the maximum energy of 0.403 mu J are successfully demonstrated. This paper presents the novel demonstration of reconfigurable mode-locked and Q-switched all-fiber lasers by incorporating colloidal GNR thin films

Conserved charged amino acid residues in the extracellular region of sodium/iodide symporter are critical for iodide transport activity

<p>Abstract</p> <p>Background</p> <p>Sodium/iodide symporter (NIS) mediates the active transport and accumulation of iodide from the blood into the thyroid gland. His-226 located in the extracellular region of NIS has been demonstrated to be critical for iodide transport in our previous study. The conserved charged amino acid residues in the extracellular region of NIS were therefore characterized in this study.</p> <p>Methods</p> <p>Fourteen charged residues (Arg-9, Glu-79, Arg-82, Lys-86, Asp-163, His-226, Arg-228, Asp-233, Asp-237, Arg-239, Arg-241, Asp-311, Asp-322, and Asp-331) were replaced by alanine. Iodide uptake abilities of mutants were evaluated by steady-state and kinetic analysis. The three-dimensional comparative protein structure of NIS was further modeled using sodium/glucose transporter as the reference protein.</p> <p>Results</p> <p>All the NIS mutants were expressed normally in the cells and targeted correctly to the plasma membrane. However, these mutants, except R9A, displayed severe defects on the iodide uptake. Further kinetic analysis revealed that mutations at conserved positively charged amino acid residues in the extracellular region of NIS led to decrease NIS-mediated iodide uptake activity by reducing the maximal rate of iodide transport, while mutations at conserved negatively charged residues led to decrease iodide transport by increasing dissociation between NIS mutants and iodide.</p> <p>Conclusions</p> <p>This is the first report characterizing thoroughly the functional significance of conserved charged amino acid residues in the extracellular region of NIS. Our data suggested that conserved charged amino acid residues, except Arg-9, in the extracellular region of NIS were critical for iodide transport.</p

Assessing the Decision-Making Process in Human-Robot Collaboration Using a Lego-like EEG Headset

Human-robot collaboration (HRC) has become an emerging field, where the use of a robotic agent has been shifted from a supportive machine to a decision-making collaborator. A variety of factors can influence the effectiveness of decision-making processes during HRC, including the system-related (e.g., robot capability) and human-related (e.g., individual knowledgeability) factors. As a variety of contextual factors can significantly impact the human-robot decision-making process in collaborative contexts, the present study adopts a Lego-like EEG headset to collect and examine human brain activities and utilizes multiple questionnaires to evaluate participants’ cognitive perceptions toward the robot. A user study was conducted where two levels of robot capabilities (high vs. low) were manipulated to provide system recommendations. The participants were also identified into two groups based on their computational thinking (CT) ability. The EEG results revealed that different levels of CT abilities trigger different brainwaves, and the participants’ trust calibration of the robot also varies the resultant brain activities

Estimation of energy requirements for mechanically ventilated, critically ill patients using nutritional status

BACKGROUND: There is very little information on what is considered an adequate energy intake for mechanically ventilated, critically ill patients. The purpose of the present study was to determine this energy requirement by making use of patients' nutritional status. METHODS: The study was conducted in a multidisciplinary intensive care unit of Taichung Veterans General Hospital, Taiwan. Patients were hemodynamically stable and not comatose, and were requiring at least 7 days of mechanical ventilation. Fifty-four patients successfully completed this study. The resting energy expenditure was measured using indirect calorimetry. The total energy requirement was considered 120% of the measured energy expenditure. The daily nutrient intake was recorded. Nutritional status was assessed using single and multiple parameters, nitrogen balance, and medical records, and was performed within 24 hours of admission and after 7 days in the intensive care unit. RESULTS: Fifteen patients were being underfed (<90% of total energy requirement), 20 patients were in the appropriate feeding (AF) group (within ± 10% of total energy requirement), and 19 patients received overfeeding (>110% of total energy requirement). Patients in the underfeeding group received only 68.3% of their energy requirement, while the overfeeding group patients received up to 136.5% of their required calories. Only patients in the AF group had a positive nitrogen balance (0.04 ± 5.1) on day 7. AF group patients had a significantly higher Nutritional Risk Index value at day 7 than at day 1. CONCLUSION: AF patients had more improvement in nutritional status than patients in the other feeding groups. To provide at least 120% of the resting energy expenditure seemed adequate to meet the caloric energy needs of hemodynamically stable, mechanically ventilated, critically ill patients