Miracle-dz/UniFAR-FTUs huggingface dataset:
Genome-Wide Search for SNP Interactions in GWAS Data: Algorithm, Feasibility, Replication Using Schizophrenia Datasets

In this study, we looked for potential gene-gene interaction in susceptibility to schizophrenia by an exhaustive searching for SNP–SNP interactions in 3 GWAS datasets (phs000021:phg000013, phs000021:phg000014, phs000167) using our recently published algorithm. The search space for SNP–SNP interaction was confined to 8 biologically plausible ways of interaction under dominant-dominant or recessive-recessive modes. First, we performed our search of all pair-wise combination of 729,454 SNPs after filtering by SNP genotype quality. All possible pairwise interactions of any 2 SNPs (5 × 1011) were exhausted to search for significant interaction which was defined by p-value of chi-square tests. Nine out the top 10 interactions, protein coding genes were partnered with non-coding RNA (ncRNA) which suggested a new alternative insight into interaction biology other than the frequently sought-after protein–protein interaction. Therefore, we extended to look for replication among the top 10,000 interaction SNP pairs and high proportion of concurrent genes forming the interaction pairs were found. The results indicated that an enrichment of signals over noise was present in the top 10,000 interactions. Then, replications of SNP–SNP interaction were confirmed for 14 SNPs-pairs in both replication datasets. Biological insight was highlighted by a potential binding between FHIT (protein coding gene) and LINC00969 (lncRNA) which showed a replicable interaction between their SNPs. Both of them were reported to have expression in brain. Our study represented an early attempt of exhaustive interaction analysis of GWAS data which also yield replicated interaction and new insight into understanding of genetic interaction in schizophrenia.
Mildly Higher Serum Prolactin Levels Are Directly Proportional to Cumulative Pregnancy Outcomes in in-vitro Fertilization/Intracytoplasmic Sperm Injection Cycles

Hyperprolactinemia has long been considered detrimental to fertility due to irregularity of ovulation. Whether mild hyperprolactinemia should be corrected before initiating an in-vitro fertilization/intracytoplasmic sperm injection cycle (IVF/ICSI) has not been determined; this study aimed to examine how different levels of prolactin affect IVF outcomes. A total of 3,009 patients with basal prolactin level <50 ng/mL undergoing IVF/ICSI cycles for tubal or male factors were recruited in this study. Patients diagnosed with anovulation owing to polycystic ovarian syndrome or hyperandrogenism were ruled out. Pregnancy outcomes were compared between patients with basal prolactin levels higher or lower than the median level of prolactin (16.05 ng/mL). Multifactor analyses were carried out among four subgroups depending on different prolactin levels. Repeated-measures analysis of variance was used to explore the relationship between the ascending trend of prolactin levels over ovarian stimulation and the corresponding cumulative pregnancy outcomes. There were significantly higher numbers of oocytes (9 vs. 8, P = 0.013) and embryos (6 vs. 5, P = 0.015) in patients with basal prolactin higher than 16.05 ng/mL. Basal prolactin higher than 30 ng/mL was positively related to cumulative clinical pregnancy, and a level higher than 40 ng/mL was a good indicator for the cumulative live birth rate. Throughout ovarian stimulation, the prognosis of pregnancy improved with increasing prolactin levels. Patients with better cumulated pregnancy outcomes had significantly higher prolactin levels as well as a profoundly increasing trend during the stimulating process than those who did not conceive. For patients who underwent the gonadotropin-releasing hormone agonist long protocol IVF/ICSI treatment, a slightly higher prolactin level during the controlled ovarian hyperstimulation protocol was a positive indicator for cumulated pregnancy/live birth rates.
A Rapid and Adaptive Alignment under Mooring Condition Using Adaptive EKF and CNN-Based Learning

Alignment of the inertial navigation system (INS) in the mooring environment should take into account the movements of the waves or wind. The alignment of the INS is performed through an extended Kalman filter (EKF) using zero velocity as a measurement. However, in the mooring condition, this is not perfect stationary, thus the measurement error covariance matrix should be adjusted. In addition, if the measurement error covariance matrix is fixed to one value, the alignment time may take longer or the performance may be reduced depending on the change in mooring conditions. To solve this problem, we propose an alignment method using adaptive Kalman filter and convolution neural network (CNN)-based learning. The proposed method was verified for the superiority of alignment time and accuracy through Monte Carlo simulation in a mooring environment.
Unanticipated Scenarios

SEPTEMBER/OCTOBER 2020 | IEEE SOFTWARE 3 IEEE Software To be the best source of reliable, useful, peer-reviewed information for leading software practitioners— Mission Statement the developers and managers who want to keep up with rapid technology change. MOST SOFTWARE-EVALUATION TECHNIQUES, especially those that evaluate the fitness of a system’s software architecture to support the system for its shortand long-term goals, employ an approach where the systems business and engineering stakeholders are encouraged to come up with unanticipated scenarios. Then, the system is evaluated to enumerate the risks if those scenarios should materialize. System stakeholders, development teams, business stakeholders, and users are asked to envision response measures that are outside the limits of current expectations. These may include increased load, unanticipated failures, increased availability and latency expectations, scaling from thousands to millions of concurrent users, and using the system in scenarios for which it was not developed. The architecture tradeoff analysis method (ATAM) developed by the Carnegie Mellon University Software Engineering Institute is one example of such a software-evaluation technique.1 Some development teams struggle when these unanticipated scenarios are generated and discussed. They fear that they will be required to architect the system for these unanticipated demands, and they may not have the resources or need to make design decisions to support such extreme scenarios. Others embrace this exercise as a challenge and include in their architecting and technology-selection discussions what it would mean to change the system to support these scenarios if they should materialize. Some even make tradeoffs that would maximize their ability to evolve quickly if necessary. Software engineering teams have been given the rare, albeit challenging, opportunity to put their systems to the test with a uniquely unanticipated scenario in the wake of the coronavirus pandemic. The most effective response to prevent the virus from spreading has been social distancing through the physical separation of individuals, in particular, in crowded community settings. Overnight, organizations advised their employees to work from home whenever possible as their response to COVID-19. As I write this article, we are exactly four months into working from home. Enough time has lapsed to reflect that software systems had a significant role in shaping the success and addressing the challenges of shifting overnight to a completely unanticipated mode of interaction at our schools, offices, businesses, family gatherings, and extracurricular functions.
Node Deployment of Marine Monitoring Networks: A Multiobjective Optimization Scheme

The increasing demands for real-time marine monitoring call for the wide deployment of Marine Monitoring Networks (MMNs). The low-rate underwater communications over a long distance, long propagation delay of underwater acoustic channel, and high deployment costs of marine sensors in a large-scale three-dimensional space bring great challenges in the network deployment and management of MMN. In this paper, we first propose a multitier, hierarchical network architecture of MMN with the support of edge computing (HMMN-EC) to enable efficient monitoring services in a harsh marine environment, taking into consideration the salient features of marine communications. Specifically, HMMN-EC is composed of three subnetworks, i.e., underwater acoustic subnetwork, the sea-surface wireless subnetwork, and the air wireless subnetwork, with a diversity of network nodes with different capabilities. We then jointly investigate the deployment diverse network nodes with various constraints in different subnetworks of HMMN-EC. To this end, we formulate a Multiobjective Optimization (MO) problem to minimize the network deployment cost while achieving the maximal network lifetime, subject to the limited energy of different marine nodes and the complex deployment environment. To solve the formulated problem, we present an Ant-Colony-based Efficient Topology Optimization (AC-ETO) algorithm to find the optimal locations of nodes in different subnetworks of MMN in a large-scale deployment. The time complexity of the proposed algorithm is also analyzed. Finally, extensive simulations are carried out to validate the superior performance of the proposed algorithm compared with some existing solutions.
Weighted Kernel Filter Based Anti-Air Object Tracking for Thermal Infrared Systems

Visual object tracking is an important component of surveillance systems and many high-performance methods have been developed. However, these tracking methods tend to be optimized for the Red/Green/Blue (RGB) domain and are thus not suitable for use with the infrared (IR) domain. To overcome this disadvantage, many researchers have constructed datasets for IR analysis, including those developed for The Thermal Infrared Visual Object Tracking (VOT-TIR) challenges. As a consequence, many state-of-the-art trackers for the IR domain have been proposed, but there remains a need for reliable IR-based trackers for anti-air surveillance systems, including the construction of a new IR dataset for this purpose. In this paper, we collect various anti-air thermal-wave IR (TIR) images from an electro-optical surveillance system to create a new dataset. We also present a framework based on an end-to-end convolutional neural network that learns object tracking in the IR domain for anti-air targets such as unmanned aerial vehicles (UAVs) and drones. More specifically, we adopt a Siamese network for feature extraction and three region proposal networks for the classification and regression branches. In the inference phase, the proposed network is formulated as a detection-by-tracking method, and kernel filters for the template branch that are continuously updated for every frame are introduced. The proposed network is able to learn robust structural information for the targets during offline training, and the kernel filters can robustly track the targets, demonstrating enhanced performance. Experimental results from the new IR dataset reveal that the proposed method achieves outstanding performance, with a real-time processing speed of 40 frames per second.
Transduction Mechanisms, Micro-Structuring Techniques, and Applications of Electronic Skin Pressure Sensors: A Review of Recent Advances

Electronic skin (e-skin), which is an electronic surrogate of human skin, aims to recreate the multifunctionality of skin by using sensing units to detect multiple stimuli, while keeping key features of skin such as low thickness, stretchability, flexibility, and conformability. One of the most important stimuli to be detected is pressure due to its relevance in a plethora of applications, from health monitoring to functional prosthesis, robotics, and human-machine-interfaces (HMI). The performance of these e-skin pressure sensors is tailored, typically through micro-structuring techniques (such as photolithography, unconventional molds, incorporation of naturally micro-structured materials, laser engraving, amongst others) to achieve high sensitivities (commonly above 1 kPa−1), which is mostly relevant for health monitoring applications, or to extend the linearity of the behavior over a larger pressure range (from few Pa to 100 kPa), an important feature for functional prosthesis. Hence, this review intends to give a generalized view over the most relevant highlights in the development and micro-structuring of e-skin pressure sensors, while contributing to update the field with the most recent research. A special emphasis is devoted to the most employed pressure transduction mechanisms, namely capacitance, piezoelectricity, piezoresistivity, and triboelectricity, as well as to materials and novel techniques more recently explored to innovate the field and bring it a step closer to general adoption by society.
Decidable Inductive Invariants for Verification of Cryptographic Protocols with Unbounded Sessions

We develop a theory of decidable inductive invariants for an infinite-state variant of the Applied pi-calculus, with applications to automatic verification of stateful cryptographic protocols with unbounded sessions/nonces. Since in the presence of unbounded sessions any trace property becomes undecidable, we focus on (i) depth-bounded protocols, a generalisation of a class of infinite-state protocols proposed by D'Osualdo, Ong and Tiu; and (ii) downward-closed properties, which include many security properties such as absence of leaks of secrets. We study the structure of depth-bounded protocols within the framework of ideal completions for well-structured transition systems. Our main contribution is a class of expressions, called limits, that are shown sound and complete for representing infinite downward-closed sets of configurations of depth-bounded protocols. We provide direct algorithms to prove that a given limit is an inductive invariant for a protocol. Inductive invariants of this form can be inferred, and represent an independently checkable certificate of correctness. To evaluate whether the approach is viable, we provide a prototype implementation and we report on its performance on some illustrative examples.
Performance Investigation of Angle of Arrival based Localization

—By increasing the deployment of beamforming capabilities in mobile communication networks the user direction becomes readily available at base stations (BSs). Utilizing the angles of arrival (AOAs) from multiple BSs enables the calculation of the user position within one additional step using triangulation techniques.Inourwork we state the mathematical problem and derive the associated Cram´er-Rao lower bound (CRLB). Further on we compare a recently proposed linear LS estimator with a trivial triangulation technique and the corresponding CRLB in three simulation scenarios.
Blood Pressure Sensors: Materials, Fabrication Methods, Performance Evaluations and Future Perspectives

Advancements in materials science and fabrication techniques have contributed to the significant growing attention to a wide variety of sensors for digital healthcare. While the progress in this area is tremendously impressive, few wearable sensors with the capability of real-time blood pressure monitoring are approved for clinical use. One of the key obstacles in the further development of wearable sensors for medical applications is the lack of comprehensive technical evaluation of sensor materials against the expected clinical performance. Here, we present an extensive review and critical analysis of various materials applied in the design and fabrication of wearable sensors. In our unique transdisciplinary approach, we studied the fundamentals of blood pressure and examined its measuring modalities while focusing on their clinical use and sensing principles to identify material functionalities. Then, we carefully reviewed various categories of functional materials utilized in sensor building blocks allowing for comparative analysis of the performance of a wide range of materials throughout the sensor operational-life cycle. Not only this provides essential data to enhance the materials’ properties and optimize their performance, but also, it highlights new perspectives and provides suggestions to develop the next generation pressure sensors for clinical use.
Block Diagonal Hybrid Precoding and Power Allocation for QoS-Aware BDMA Downlink Transmissions

Beam Division Multiple Access (BDMA) with hybrid precoding has recently been proposed for multi-user multiple-input multiple-output (MU-MIMO) systems by simultaneously transmitting multiple digitally precoded users’ data-streams via different beams. In contrast to most existing works that assume the number of radio frequency (RF) chains must be greater than or equal to that of data-streams, this work proposes a novel BDMA downlink system by first grouping transmitting data-streams before digitally precoding data group by group. To fully harvest the benefits of this new architecture, a greedy user grouping algorithm is devised to minimize the inter-group interference while two digital precoding approaches are developed to suppress the intra-group interference by maximizing the signal-to-interference-and-noise ratio (SINR) and the signal-to-leakage-and-noise ratio (SLNR), respectively. As a result, the proposed BDMA system requires less RF chains than the total number of transmit data-streams. Furthermore, we optimize the power allocation to satisfy each user’s quality of service (QoS) requirement using the D.C. (difference of convex functions) programming technique. Simulation results confirm the effectiveness of the proposed scheme.
Development of early warning and rapid response system for patients with novel coronavirus pneumonia (COVID-19)

Abstract Background: Coronavirus disease 2019 (COVID-19) has caused serious damage to public health. COVID-19 has no vaccine or specific therapy; its mortality rate increases significantly once patients deteriorate. Furthermore, intensive monitoring of COVID-19 is limited by insufficient medical resources and increased risks of exposure to medical staff. We therefore aim to build an early warning and rapid response system (EWRRS) to address these problems. Method: The research is designed as a prospective cohort study, to verify a dynamic and interactive evaluation system; it includes patient self-reporting, active monitoring, early alarming and treatment recommendations. Adult patients diagnosed with COVID-19 will be recruited from Sept 2020 to Aug 2021 at a tertiary contagious hospital. Patients with life expectancy <48 hours, pregnant or lactating, in immunosuppression states or end-stage diseases will be excluded. The intervention is implementation of EWRRS to detect early signs of clinical deterioration of COVID-19 patients, to provide timely and efficient treatment suggestions by the system. EWRRS can determine the classification and interactive evaluation of patient information; the determination is based on the application of 3 different scenario modules, separately driven by patients, nurses, and physicians. The primary outcome is change in disease severity category after treatment. Secondary outcomes include the proportion of patients with different disease severity types; critical deterioration events; patients who had unplanned transfers to an intensive care unit (ICU) and required critical care interventions; intervals from warning to implementation of clinical interventions; hospital mortality; length of ICU and hospital stay; workload of medical staff and risks of exposure to COVID-19. Discussion: Our hypothesis is that EWRRS provides an example of an early identification, warning, and response system for COVID-19. In addition, EWRRS can potentially be extended to use as a grading metric for general critically ill patients in an ICU setting.
The Productivity Commission’s Draft Report illustrates the benefits and risks of economic perspectives on mental healthcare

In 2018, the Australian Government instructed the Productivity Commission to examine the social and economic effects of poor mental health, as well as make recommendations on how they might be addressed. The resulting Draft Report demonstrates both the benefits and risks of an economic approach to these issues. Some of the broader systemic recommendations have merit such as the need for stepped care, coordinated crisis management and the important role of welfare, housing and the workplace in improving mental health. However, there are areas of concern, particularly for mental health services in the public sector. One is a disproportionate emphasis on prevention and intervention in the early years of life for strategies where evidence for effectiveness is limited. Another is the introduction of market-based approaches such as shadow billing or a commissioner/provider split to the funding of mental health services across Australia. Among the numerous problems of such market-driven approaches are the increased costs of additional bureaucracy and the lack of commissioner expertise in planning services or evidence-based practice. As a result, similar arrangements have been abandoned in other jurisdictions. The Commission’s final recommendations and the Australian Government’s response are awaited and the resulting changes to the mental health system, and beyond, may well be influenced by the COVID-19 pandemic. However, reform should be based on evidence and achieved without increasing administrative complexity.
What You Need to Know Before Implementing a Clinical Research Data Warehouse: Comparative Review of Integrated Data Repositories in Health Care Institutions

Background Integrated data repositories (IDRs), also referred to as clinical data warehouses, are platforms used for the integration of several data sources through specialized analytical tools that facilitate data processing and analysis. IDRs offer several opportunities for clinical data reuse, and the number of institutions implementing an IDR has grown steadily in the past decade. Objective The architectural choices of major IDRs are highly diverse and determining their differences can be overwhelming. This review aims to explore the underlying models and common features of IDRs, provide a high-level overview for those entering the field, and propose a set of guiding principles for small- to medium-sized health institutions embarking on IDR implementation. Methods We reviewed manuscripts published in peer-reviewed scientific literature between 2008 and 2020, and selected those that specifically describe IDR architectures. Of 255 shortlisted articles, we found 34 articles describing 29 different architectures. The different IDRs were analyzed for common features and classified according to their data processing and integration solution choices. Results Despite common trends in the selection of standard terminologies and data models, the IDRs examined showed heterogeneity in the underlying architecture design. We identified 4 common architecture models that use different approaches for data processing and integration. These different approaches were driven by a variety of features such as data sources, whether the IDR was for a single institution or a collaborative project, the intended primary data user, and purpose (research-only or including clinical or operational decision making). Conclusions IDR implementations are diverse and complex undertakings, which benefit from being preceded by an evaluation of requirements and definition of scope in the early planning stage. Factors such as data source diversity and intended users of the IDR influence data flow and synchronization, both of which are crucial factors in IDR architecture planning.