Miracle-dz/UniFAR-FTUs huggingface dataset:
Genie: A Secure, Transparent Sharing and Services Platform for Genetic and Health Data

Artificial Intelligence (AI) incorporating genetic and medical information have been applied in disease risk prediction, unveiling disease mechanism, and advancing therapeutics. However, AI training relies on highly sensitive and private data which significantly limit their applications and robustness evaluation. Moreover, the data access management after sharing across organization heavily relies on legal restriction, and there is no guarantee in preventing data leaking after sharing. Here, we present Genie, a secure AI platform which allows AI models to be trained on medical data securely. The platform combines the security of Intel Software Guarded eXtensions (SGX), transparency of blockchain technology, and verifiability of open algorithms and source codes. Genie shares insights of genetic and medical data without exposing anyone's raw data. All data is instantly encrypted upon upload and contributed to the models that the user chooses. The usage of the model and the value generated from the genetic and health data will be tracked via a blockchain, giving the data transparent and immutable ownership.
Efficacy and safety of growth hormone treatment in Japanese children with small-for-gestational-age short stature in accordance with Japanese guidelines

Abstract. The efficacy and safety of recombinant human GH (rhGH) treatment were assessed in Japanese children with small-for-gestational-age short stature. A total of 88 patients were enrolled in the comparative and extension studies. At the end of the comparative study (24 mo), the mean height SD score for chronological age had significantly increased in the 0.23 mg/kg/wk and 0.47 mg/kg/wk groups with increments of 0.84 ± 0.42 and 1.50 ± 0.44 SD, respectively. In the extension study, the dose could be increased based on the pre-defined growth criteria. Increments in height SD scores over the 24 to 36 mo period at doses of 0.23 mg/kg/wk, 0.23 to 0.47 mg/kg/wk, and 0.47 mg/kg/wk were 0.25 ± 0.28, 0.46 ± 0.21, and 0.28 ± 0.16 SD, respectively. The growth effect increased following dose escalation even in the low responders in the initial 2-yr treatment at 0.23 mg/kg/wk, indicating the effectiveness of dose escalation in accordance with the Japanese guidelines. rhGH at 0.47 mg/kg/wk provided a greater degree of growth promotion after 24 mo. The safety profile appeared to be tolerable and was similar in all groups. Considering the increased insulin resistance, the recommendations of the regulatory authorities should be followed to minimize the risks of rhGH treatment.
Accessible Human-Error Interactions in AI Applications for the Blind

ACM Reference Format: Jonggi Hong. 2018. Accessible Human-Error Interactions in AI Applications for the Blind. In Adjunct Proceedings of the 2018 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the 2018 International Symposium on Wearable Computers (UbiComp/ISWC’18 Adjunct), October 8–12, 2018, Singapore, Singapore. ACM, New York, NY, USA, 7 pages. https://doi.org/10.1145/3267305.3267321
Multiplicity of Infection Is a Poor Predictor of Village-Level Plasmodium vivax and P. falciparum Population Prevalence in the Southwest Pacific

Abstract Across 8101 individuals in 46 villages, the proportion of Plasmodium spp. multiple clone infections (0%–53.8%) did not reflect prevalence by quantitative polymerase chain reaction (qPCR; 1.9%–38.4%), except for P. vivax in Solomon Islands (P < .001). Thus this parameter was not informative to identify transmission foci. In contrast, prevalence by microscopy and qPCR correlated well.
Longhorn Beetle (Coleoptera: Cerambycidae) Assemblage and the Structural Heterogeneity of Habitat at the Brazilian Atlantic Forest

Abstract The longhorn beetles play important roles in the forest ecosystem processes and their diversity is affected by many environmental disturbances worldwide.This study aimed at understanding how the structural heterogeneity of the habitat can affect the longhorn beetle assemblage in three areas of an Atlantic Forest, which suffered differential impacts in the past and are now in different successional stages.The area in the most advanced successional stage had mainly lower density of trees, but with greater availability of dead wood, especially larger diameter classes. They are important forest components that contribute to the structural diversity of the habitat providing resources for a variety of dependent species. This area has also shown the greatest richness and abundance of longhorn beetles. Our results suggest that these beetles are closely associated with the structural heterogeneity of forests and can be valuable indicators for assessing biodiversity and quality of forest habitat. It also shows that old-growth forest remnants can be the key to the maintenance of the diversity of the longhorn beetles and, consequently, of the ecosystem services they provide.
Improving Zero-Shot Translation of Low-Resource Languages

Recent work on multilingual neural machine translation reported competitive performance with respect to bilingual models and surprisingly good performance even on (zero-shot) translation directions not observed at training time. We investigate here a zero-shot translation in a particularly low-resource multilingual setting. We propose a simple iterative training procedure that leverages a duality of translations directly generated by the system for the zero-shot directions. The translations produced by the system (sub-optimal since they contain mixed language from the shared vocabulary), are then used together with the original parallel data to feed and iteratively re-train the multilingual network. Over time, this allows the system to learn from its own generated and increasingly better output. Our approach shows to be effective in improving the two zero-shot directions of our multilingual model. In particular, we observed gains of about 9 BLEU points over a baseline multilingual model and up to 2.08 BLEU over a pivoting mechanism using two bilingual models. Further analysis shows that there is also a slight improvement in the non-zero-shot language directions.
Classification of Transaction Behavior in Tax Invoices Using Compositional CNN-RNN Model

Transaction behavior can be recorded by the invoice in China. The invoice records several fields including transaction content, transaction code (in accordance with the Tax Classification and Coding for Commodities and Services issued by the state) and transaction unit, etc. In this paper, we propose a compositional CNN-RNN model framework with attention mechanism to classify transaction behavior collected from tax invoices to the corresponding category based on the official transaction code, which is of great importance to tax supervision and provides a new perspective to analysis the industrial structure of the region. Preliminary experiments show that the overall accuracy of classifying transaction behavior achieves 75%.
Intervention Harvesting for Context-Dependent Examination-Bias Estimation

Accurate estimates of examination bias are crucial for unbiased learning-to-rank from implicit feedback in search engines and recommender systems, since they enable the use of Inverse Propensity Score (IPS) weighting techniques to address selection biases and missing data. Unfortunately, existing examination-bias estimators are limited to the Position-Based Model (PBM), where the examination bias may only depend on the rank of the document. To overcome this limitation, we propose a Contextual Position-Based Model (CPBM) where the examination bias may also depend on a context vector describing the query and the user. Furthermore, we propose an effective estimator for the CPBM based on intervention harvesting. A key feature of the estimator is that it does not require disruptive interventions but merely exploits natural variation resulting from the use of multiple historic ranking functions. Real-world experiments on the ArXiv search engine and semi-synthetic experiments on the Yahoo Learning-To-Rank dataset demonstrate the superior effectiveness and robustness of the new approach.
LncRNA GAS6‐AS2 promotes bladder cancer proliferation and metastasis via GAS6‐AS2/miR‐298/CDK9 axis

Long noncoding RNAs (lncRNAs) have been proved to play important roles in carcinogenesis and development of numerous cancers, but their biological functions in bladder cancer remain largely unknown. In this study, a novel lncRNA termed GAS6‐AS2 were primary identified, and its roles as well as mechanisms in regulating proliferation and metastasis of bladder cancer cells were investigated. Clinically, GAS6‐AS2 was significantly up‐regulated in bladder cancer tissues and positively correlated with tumour stages and poor prognosis. Moreover, expression of GAS6‐AS2 was also increased in bladder cancer cells compared with normal bladder cells. Further investigating the roles of GAS6‐AS2, we found GAS6‐AS2 regulated proliferation and proliferative activity of bladder cancer cells via inducing G1 phase arrest. What's more, we found that GAS6‐AS2 contributed to metastatic abilities of cells. In mechanism, GAS6‐AS2 could function as a competitive endogenous RNA (ceRNA) via direct sponging miR‐298, which further regulating the expression of CDK9. Finally, we also proved that GAS6‐AS2 knockdown suppressed tumour growth and metastasis in vivo. In conclusion, our study proved that GAS6‐AS2 could function as a ceRNA and promote the proliferation and metastasis of bladder cancer cells, which provided a novel prognostic marker for bladder cancer patients in clinic.
PKP2 and DSG2 genetic variations in Latvian arrhythmogenic right ventricular dysplasia/cardiomyopathy registry patients

Objective: The Latvian arrhythmogenic right ventricular dysplasia/cardiomyopathy (ARVD-C) registry was established to determine the genetic background of ARVD-C for analyzing discovered genetic variation frequencies in the European and Latvian populations. Methods: In total, 38 patients with suspected ARVD-C were selected. The clinical parameters were defined according to the ARVD-C guidelines, PKP2 and DSG2 gene analysis was performed using the Sanger sequencing. Additionally, large deletions/duplications were analyzed using the multiplex ligation-dependent probe amplification (MLPA) analysis. Results: Twenty symptomatic patients were enrolled in the study. Typical ARVD abnormalities were found in electrocardiography for 10 (50%) patients, in Holter monitoring for 19 (95%), in transthoracic echocardiography for 20 (100%), and in cardiac magnetic resonance for 6 (30%). Different benign genetic variations were found. Three novel, unregistered, possibly benign variations were found in the PKP2 gene: c.2489+131G>A, c.2489+72delA, and c.1035-5T>C and three in the DSG2 gene: c.404G>A, c.1107G>A, and c.379-15A>G. Two genetic variations in the PKP2 gene: c.1592T>G, c.2489+1G>A are possibly pathogenic. For the first time, variation c.1592T>G, has been discovered in the homozygote form. Using the MLPA analysis, large deletions or duplications were not found. Conclusion: The prevalence of the majority of non-pathological genetic variations is similar in the Latvian ARVD-C patients and the European population. Possibly, pathogenic variations were found only in 10% of our registry patients, which could mean that PKP2 and DSG2 are not the most commonly affected genes in the Latvian population.
Evaluation of Zika Virus-specific T-cell Responses in Immunoprivileged Organs of Infected Ifnar1-/- Mice

The Zika virus (ZIKV) can induce inflammation in immunoprivileged organs (e.g., the brain and testis), leading to the Guillain-Barré syndrome and damaging the testes. During an infection with the ZIKV, immune cells have been shown to infiltrate into the tissues. However, the cellular mechanisms that define the protection and/or immunopathogenesis of these immune cells during a ZIKV infection are still largely unknown. Herein, we describe methods to evaluate the virus-specific T-cell functionality in these immunoprivileged organs of ZIKV-infected mice. These methods include a) a ZIKV infection and vaccine inoculation in Ifnar1-/- mice; b) histopathology, immunofluorescence, and immunohistochemistry assays to detect the virus infection and inflammation in the brain, testes, and spleen; c) the preparation of a tetramer of ZIKV-derived T-cell epitopes; d) the detection of ZIKV-specific T cells in the monocytes isolated from the brain, testes, and spleen. Using these approaches, it is possible to detect the antigen-specific T cells that have infiltrated into the immunoprivileged organs and to evaluate the functions of these T cells during the infection: potential immune protection via virus clearance and/or immunopathogenesis to exacerbate the inflammation. These findings may also help to clarify the contribution of T cells induced by the immunization against ZIKV.
RNAcentral: a hub of information for non-coding RNA sequences

Abstract RNAcentral is a comprehensive database of non-coding RNA (ncRNA) sequences, collating information on ncRNA sequences of all types from a broad range of organisms. We have recently added a new genome mapping pipeline that identifies genomic locations for ncRNA sequences in 296 species. We have also added several new types of functional annotations, such as tRNA secondary structures, Gene Ontology annotations, and miRNA-target interactions. A new quality control mechanism based on Rfam family assignments identifies potential contamination, incomplete sequences, and more. The RNAcentral database has become a vital component of many workflows in the RNA community, serving as both the primary source of sequence data for academic and commercial groups, as well as a source of stable accessions for the annotation of genomic and functional features. These examples are facilitated by an improved RNAcentral web interface, which features an updated genome browser, a new sequence feature viewer, and improved text search functionality. RNAcentral is freely available at https://rnacentral.org.
3D Range Sensors Capture, Transform and Modeling of Defects

New developed technique is enhanced to recover 3D BIM (Three Dimensions Building Information Models) objects from 3D range surveying data by IR (Infra Red) camera or Laser Scanner for object defects observation. Defects detection and its characteristics have long played major role for condition and risk assessment of buildings. Defects photography by 3D range sensors from an external exposure station, if done mathematically, is useful for interpreting the photos taken by the sensors mounted at anywhere and pointed towards any parts of the building. In this paper an approach was developed to transform and 3D modeling of the buildings defects using multiple types of data and sensors. The advantage of this technique is the flexibility it provides to handle complex defects where a single technique is insufficient. The proposed approach is validated through Jacques Cartier Bridge case study were implemented on the design and inspection data and modeling the building defects by simplification the method of information visualization. Standing on the method, the Defect Information Model (DIM) is in its real shape, which decreases the difficulty and saves the time of modeling the irregular shapes of the defects.
Minimum Clinically Important Difference and Patient Acceptable Symptom State of Japanese Orthopaedic Association Score in Degenerative Cervical Myelopathy Patients

Study Design. A retrospective analysis. Objective. The aim of this study was to elucidate the psychometric properties of the original Japanese Orthopaedic Association (JOA) score, including the minimum detectable change (MDC), minimum clinically important difference (MCID), and patient-accepted symptom state (PASS). Summary of Background Data. Despite the worldwide popularity of modified JOA score (mJOA), the original JOA score is still commonly used in East Asian countries, including Japan. However, unlike mJOA score, the psychometric properties of JOA score remain poorly understood. Methods. We retrospectively reviewed a consecutive series of laminoplasty for degenerative cervical myelopathy patients in a single academic institution. Pre- and postoperative JOA scores were collected, and the recovery rate was calculated. Anchor-based methods were used to determine the cut-off values by a receiver operating characteristic (ROC) curve analysis. The patients were also asked to answer an anchor question analyzing their postoperative health transition used for the MDC and another question assessing the patient satisfaction used for the MCID and PASS. Results. A total of 101 patients were included in the analysis. The mean preoperative JOA score was 10.3 [standard deviation (SD): 2.4), and the mean postoperative JOA score was 13.4 (SD: 2.5). The mean recovery rate was 44%. A total of 68% of the patients admitted that their health condition was at least “somewhat better” than their preoperative condition, and 66% were at least “somewhat satisfied” with the treatment results. On the basis of ROC curve analyses, the MDC and MCID for JOA score in degenerative cervical myelopathy patients were calculated to be 2.5. The PASS was estimated to be 14.5, and the MCID for JOA recovery rate was 52.8%. Conclusion. The MDC, MCID, and PASS for JOA score for degenerative cervical myelopathy patients were reported by anchor-based ROC curve analyses. Level of Evidence: 4
A Comparative Approach to Classification of Locomotion and Transportation Modes Using Smartphone Sensor Data

In this paper, we have used a smartphone sensor-based benchmark Sussex-Huawei Locomotion-Transportation (SHL) dataset for rich locomotion and transportation analytics. We have shown a comparison of different sensor-based features for the identification of a specific activity level. Besides, we have proposed a "Mod technique" method, which increases the accuracy of classifier outputs in offline processing method. We have assumed that the minimum switching time from one transportation activity to another is 1-minute as a base of this technique. Based on this assumption we have been able to correct the wrong prediction of the classifier outputs in each 1-minute data frame. Our proposed method brings 4.56% accuracy increase in the Random Forest (RnF) classifier output. Our team, "Confusion Matrix" has developed this algorithm pipeline for the "Sussex-Huawei Locomotion-Transportation (SHL) recognition challenge".
Data Integration for Supporting Biomedical Knowledge Graph Creation at Large-Scale

In recent years, following FAIR and open data principles, the number of available big data including biomedical data has been increased exponentially. In order to extract knowledge, these data should be curated, integrated, and semantically described. Accordingly, several semantic integration techniques have been developed; albeit effective, they may suffer from scalability in terms of different properties of big data. Even scaled-up approaches may be highly costly because tasks of semantification, curation and integration are performed independently. In order to overcome these issues, we devise ConMap, a semantic integration approach which exploits knowledge encoded in ontology in order to describe mapping rules to perform these tasks at the same time. Experimental results performed on different data sets suggest that ConMap can significantly reduce the time required for knowledge graph creation by up to 70\% of the time that is consumed following a traditional approach.
Parallel training of linear models without compromising convergence

In this paper we analyze, evaluate, and improve the performance of training generalized linear models on modern CPUs. We start with a state-of-the-art asynchronous parallel training algorithm, identify system-level performance bottlenecks, and apply optimizations that improve data parallelism, cache line locality, and cache line prefetching of the algorithm. These modifications reduce the per-epoch run-time significantly, but take a toll on algorithm convergence in terms of the required number of epochs. To alleviate these shortcomings of our systems-optimized version, we propose a novel, dynamic data partitioning scheme across threads which allows us to approach the convergence of the sequential version. The combined set of optimizations result in a consistent bottom line speedup in convergence of up to 12x compared to the initial asynchronous parallel training algorithm and up to 42x, compared to state of the art implementations (scikit-learn and h2o) on a range of multi-core CPU architectures.
Daily inhaled corticosteroids or montelukast for preschoolers with asthma or recurrent wheezing: A systematic review

Most international asthma guidelines recommend that children ≤5 years with asthma or recurrent wheezing be treated with daily low‐ moderate dose inhaled corticosteroids (ICS) as the preferred controller and leukotriene receptor antagonists (LTRA) as alternative therapy. There is no systematic review comparing the efficacy of ICS versus LTRA monotherapy in this age group.
Augmented Reality Future Step Visualization for Robust Surgical Telementoring

Introduction Surgical telementoring connects expert mentors with trainees performing urgent care in austere environments. However, such environments impose unreliable network quality, with significant latency and low bandwidth. We have developed an augmented reality telementoring system that includes future step visualization of the medical procedure. Pregenerated video instructions of the procedure are dynamically overlaid onto the trainee's view of the operating field when the network connection with a mentor is unreliable. Methods Our future step visualization uses a tablet suspended above the patient's body, through which the trainee views the operating field. Before trainee use, an expert records a “future library” of step-by-step video footage of the operation. Videos are displayed to the trainee as semitransparent graphical overlays. We conducted a study where participants completed a cricothyroidotomy under telementored guidance. Participants used one of two telementoring conditions: conventional telestrator or our system with future step visualization. During the operation, the connection between trainee and mentor was bandwidth throttled. Recorded metrics were idle time ratio, recall error, and task performance. Results Participants in the future step visualization condition had 48% smaller idle time ratio (14.5% vs. 27.9%, P < 0.001), 26% less recall error (119 vs. 161, P = 0.042), and 10% higher task performance scores (rater 1 = 90.83 vs. 81.88, P = 0.008; rater 2 = 88.54 vs. 79.17, P = 0.042) than participants in the telestrator condition. Conclusions Future step visualization in surgical telementoring is an important fallback mechanism when trainee/mentor network connection is poor, and it is a key step towards semiautonomous and then completely mentor-free medical assistance systems.
False Positive Reduction in Lung Computed Tomography Images using Convolutional Neural Networks

Recent studies have shown that lung cancer screening using annual low-dose computed tomography (CT) reduces lung cancer mortality by 20% compared to traditional chest radiography. Therefore, CT lung screening has started to be used widely all across the world. However, analyzing these images is a serious burden for radiologists. In this study, we propose a novel and simple framework that analyzes CT lung screenings using convolutional neural networks (CNNs) and reduces false positives. Our framework shows that even non-complex architectures are very powerful to classify 3D nodule data when compared to traditional methods. We also use different fusions in order to show their power and effect on the overall score. 3D CNNs are preferred over 2D CNNs because data are in 3D, and 2D convolutional operations may result in information loss. Mini-batch is used in order to overcome class-imbalance. Proposed framework has been validated according to the LUNA16 challenge evaluation and got score of 0.786, which is the average sensitivity values at seven predefined false positive (FP) points.