Protease Activity: Meeting Its Theranostic Potential

This themed issue provides up-to-date review and research articles covering the theranostic applications in the combined fields of protease research, diagnostics and drug development

Enabling Deep Intelligence on Embedded Systems

As deep learning for resource-constrained systems become more popular, we see an increased number of intelligent embedded systems such as IoT devices, robots, autonomous vehicles, and the plethora of portable, wearable, and mobile devices that are feature-packed with a wide variety of machine learning tasks. However, the performance of DNNs (deep neural networks) running on an embedded system is significantly limited by the platform's CPU, memory, and battery-size; and their scope is limited to simplistic inference tasks only. This dissertation proposes on-device deep learning algorithms and supporting hardware designs, enabling embedded systems to efficiently perform deep intelligent tasks (i.e., deep neural networks) that are high-memory-footprint, compute-intensive, and energy-hungry beyond their limited computing resources. We name such on-device deep intelligence on embedded systems as Embedded Deep Intelligence. Specifically, we introduce resource-aware learning strategies devised to overcome the four fundamental constraints of embedded systems imposed on the way towards Embedded Deep Intelligence, i.e., in-memory multitask learning via introducing the concept of Neural Weight Virtualization, adaptive real-time learning via introducing the concept of SubFlow, opportunistic accelerated learning via introducing the concept of Neuro.ZERO, and energy-aware intermittent learning, which tackles the problems of the small size of memory, dynamic timing constraint, low-computing capability, and limited energy, respectively. Once deployed in the field with the proposed resource-aware learning strategies, embedded systems are not only able to perform deep inference tasks on sensor data but also update and re-train their learning models at run-time without requiring any help from any external system. Such an on-device learning capability of Embedded Deep Intelligence makes an embedded intelligent system real-time, privacy-aware, secure, autonomous, untethered, responsive, and adaptive without concern for its limited resources.Doctor of Philosoph

Role of Executive Function and Alcohol-Sex Schema in the Relationship Between Alcohol Use and Sexual Assault

Heavy alcohol use and sexual assault are significant problems among women attending college. The current study examined the relationship between sexual assault and alcohol use across a four-month period and the role of executive function (EF) and alcohol-sex schema in this relationship. Participants were 176 women undergraduate students with a mean age of 19.50 years (SD = 1.30), with 85 participating in a second survey four months later. Participants completed self-report questionnaires regarding alcohol use and sexual assault, a battery of EF tasks, and a lexical decision task assessing alcohol-sex schema. Sexual assault significantly predicted alcohol use four months later. EF, specifically domains of working memory and processing speed, also significantly predicted alcohol use, even after controlling for previous alcohol use and age. Results provide information regarding EF having an additive effect on alcohol use following sexual assault, with implications for interventions on college campuses. Campus outreach programs may educate students on the risk of heavy alcohol use following sexual assault and the cognitive skills, such as information processing, which may mitigate such risks

Protease-Activated Drug Development

In this extensive review, we elucidate the importance of proteases and their role in drug development in various diseases with an emphasis on cancer. First, key proteases are introduced along with their function in disease progression. Next, we link these proteases as targets for the development of prodrugs and provide clinical examples of protease-activatable prodrugs. Finally, we provide significant design considerations needed for the development of the next generation protease-targeted and protease-activatable prodrugs

Local Municipality Public Value Co-Creation through Democratic E-Governance: A Mixed Method Analysis of Korean Municipal Government Websites

The use of technology in the public sector can improve the course of government by increasing efficiency and effectiveness, and bolster democratic principles in governance. The aforementioned can occur by employing transparency, accountability, and citizen engagement, thereby bringing the state-citizen relationship closer. Despite the crucial roles of local governments in promoting democratic practices in the e-government context, prior studies tend to have paid limited attention to e-government practices at the local level. Moreover, it was criticized that early e-government practices focused mainly from the provider’s perspectives and lost the sense of purpose. In this respect, integrating the concept of public value creation into the discussion of digital government may help this new mode of governance live up to its premises. With the gap in the current literature, this article presents a theoretical framework that portrays how the government and its citizens can interact through technology-mediated devices in the decision-making process, namely democratic egovernance, which leads to public value co-creation. Based on the theoretical ground, we analyzed municipal government websites in Korea, as its e-government system at the national level has been internationally regarded as one of the best practices. With a mixed method approach that integrated a quantitative approach to the website evaluation and qualitative analyses of in-depth interviews, we aimed to investigate the extent to which local democratic e-governance developed, and how public value was co-created through democratic e-governance in Korea. This study contributes to the literature by sharing the link between e-government studies and public value theory with substantiated evidence, and it discovered both prospects and latent challenges of public value co-creation through e-governance at the local level

Doping-dependent superconducting physical quantities of K-doped BaFe2_2As2_2 obtained through infrared spectroscopy

We investigated four single crystals of K-doped BaFe$_2$As$_2$ (Ba-122), Ba$_{1-x}$K$_x$Fe$_2$As$_2$ with $x$ = 0.29, 0.36, 0.40, and 0.51, using infrared spectroscopy. We explored a wide variety of doping levels, from under- to overdoped. We obtained the superfluid plasma frequencies ($\Omega_{\mathrm{sp}}$) and corresponding London penetration depths ($\lambda_{\mathrm{L}}$) from the measured optical conductivity spectra. We also extracted the electron-boson spectral density (EBSD) functions using a two-parallel charge transport channel approach in the superconducting (SC) state. From the extracted EBSD functions, the maximum SC transition temperatures ($T_c^{\mathrm{Max}}$) were determined using a generalized McMillan formula and the SC coherence lengths ($\xi_{\mathrm{SC}}$) were calculated using the timescales encoded in the EBSD functions and reported Fermi velocities. We identified some similarities and differences in the doping-dependent SC quantities between the K-doped Ba-122 and the hole-doped cuprates. We expect that the various SC quantities obtained across the wide doping range will provide helpful information for establishing the microscopic pairing mechanism in Fe-pnictide superconductors.Comment: 16 pages, 4 figures, 1 tabl

Temperature-dependent ff-electron evolution in CeCoIn5_5 via a comparative infrared study with LaCoIn5_5

We investigated CeCoIn$_5$ and LaCoIn$_5$ single crystals, which have the same HoCoGa$_5$-type tetragonal crystal structure, using infrared spectroscopy. However, while CeCoIn$_5$ has 4$f$ electrons, LaCoIn$_5$ does not. By comparing these two material systems, we extracted the temperature-dependent electronic evolution of the $f$ electrons of CeCoIn$_5$. We observed that the differences caused by the $f$ electrons are more obvious in low-energy optical spectra at low temperatures. We introduced a complex optical resistivity and obtained a magnetic optical resistivity from the difference in the optical resistivity spectra of the two material systems. From the temperature-dependent average magnetic resistivity, we found that the onset temperature of the Kondo effect is much higher than the known onset temperature of Kondo scattering ($\simeq$ 200 K) of CeCoIn$_5$. Based on momentum-dependent hybridization, the periodic Anderson model, and a maximum entropy approach, we obtained the hybridization gap distribution function of CeCoIn$_5$ and found that the resulting gap distribution function of CeCoIn$_5$ was mainly composed of two (small and large) components (or gaps). We assigned the small and large gaps to the in-plane and out-of-plane hybridization gaps, respectively. We expect that our results will provide useful information for understanding the temperature-dependent electronic evolution of $f$-electron systems near Fermi level.Comment: 23 pages, 8 figure

Portable detection of apnea and hypopnea events using bio-impedance of the chest and deep learning

Sleep apnea is one of the most common sleep-related breathing disorders. It is diagnosed through an overnight sleep study in a specialized sleep clinic. This setup is expensive and the number of beds and staff are limited, leading to a long waiting time. To enable more patients to be tested, and repeated monitoring for diagnosed patients, portable sleep monitoring devices are being developed. These devices automatically detect sleep apnea events in one or more respiration-related signals. There are multiple methods to measure respiration, with varying levels of signal quality and comfort for the patient. In this study, the potential of using the bio-impedance (bioZ) of the chest as a respiratory surrogate is analyzed. A novel portable device is presented, combined with a two-phase Long Short-Term Memory (LSTM) deep learning algorithm for automated event detection. The setup is benchmarked using simultaneous recordings of the device and the traditional polysomnography in 25 patients. The results demonstrate that using only the bioZ, an area under the precision-recall curve of 46.9% can be achieved, which is on par with automatic scoring using a polysomnography respiration channel. The sensitivity, specificity and accuracy are 58.4%, 76.2% and 72.8% respectively. This confirms the potential of using the bioZ device and deep learning algorithm for automatically detecting sleep respiration events during the night, in a portable and comfortable setup

Enhancing methodological reporting in Public Administration: The functional equivalents framework

Public administration scholarship reflects a multidisciplinary field in which many theoretical perspectives coexist. However, one of the dark sides of such theoretical pluralism is methodological fragmentation. It may be hard to assess the research quality and to engage with the findings from studies employing different methodologies, thus limiting meaningful conversations. Moreover, the constant race across social sciences to make methodologies more sophisticated may exacerbate the separation between academic and practitioner audiences. In order to counterbalance these two trends, the paper aims at increasing methodological intelligibility in our field. It does so starting from the idea that each methodology entails choices in the conventional phases of research design, data collection and data analysis, and that these choices must be reported (...