External Domain Research Articles

MedShift: Automated Identification of Shift Data for Medical Image Dataset Curation.

Automated curation of noisy external data in the medical domain has long been in high demand, as AI technologies need to be validated using various sources with clean, annotated data. Identifying the variance between internal and external sources is a fundamental step in curating a high-quality dataset, as the data distributions from different sources can vary significantly and subsequently affect the performance of AI models. The primary challenges for detecting data shifts are - (1) accessing private data across healthcare institutions for manual detection and (2) the lack of automated approaches to learn efficient shift-data representation without training samples. To overcome these problems, we propose an automated pipeline called MedShift to detect top-level shift samples and evaluate the significance of shift data without sharing data between internal and external organizations. MedShift employs unsupervised anomaly detectors to learn the internal distribution and identify samples showing significant shiftness for external datasets, and then compares their performance. To quantify the effects of detected shift data, we train a multi-class classifier that learns internal domain knowledge and evaluates the classification performance for each class in external domains after dropping the shift data. We also propose a data quality metric to quantify the dissimilarity between internal and external datasets. We verify the efficacy of MedShift using musculoskeletal radiographs (MURA) and chest X-ray datasets from multiple external sources. Our experiments show that our proposed shift data detection pipeline can be beneficial for medical centers to curate high-quality datasets more efficiently. The code can be found at https://github.com/XiaoyuanGuo/MedShift. An interface introduction video to visualize our results is available at https://youtu.be/V3BF0P1sxQE.

Fluctuations of formin binding in the generation of membrane patterns

Circular actin waves that propagate on the substrate-attached membrane of Dictyostelium cells separate two distinct membrane domains from each other: an inner territory rich in phosphatidyl-(3,4,5) trisphosphate (PIP3) and an external area decorated with the PIP3-degrading 3-phosphatase PTEN. During wave propagation, the inner territory increases at the expense of the external area. Beyond a size limit, the inner territory becomes unstable, breaking into an inner and an external domain. The sharp boundary between these domains is demarcated by the insertion of an actin wave. During the conversion of inner territory to external area, the state of the membrane fluctuates, as visualized by dynamic landscapes of formin B binding. Here we analyze the formin B fluctuations in relation to three markers of the membrane state: activated Ras, PIP3, and PTEN.

Performance of the Dutch clinical prediction rule for the ambulation outcome after spinal cord injury in a middle-income country clinical setting: an external validation study in the Thai retrospective cohort.

To perform external geographic and domain validation of the clinical prediction rule (CPR) of the ambulation outcome of patients with traumatic spinal cord injury (SCI) originally developed by van Middendorp, et al. (2011) in Thais with traumatic and non-traumatic SCI. Retrospective cohort study. A tertiary rehabilitation facility in Chiang Mai, Thailand. A validation data set, including predictive (age and four neurological variables) and outcome (ambulation status) parameters was retrospectively collected from medical records of patients with traumatic and non-traumatic SCI admitted between December 2007 and December 2019. The performance of the original model was evaluated in both discrimination and calibration aspects, using an area under the receiver-operating characteristic curve (auROC) and calibration curves, respectively. Three hundred and thirty-three patients with SCI were included in the validation set. The prevalence of ambulators was 59% (197 of 333 participants). An auROC of 0.93 (95% CI 0.90-0.96) indicated excellent discrimination whereas the calibration curve demonstrated underestimation, especially in patients with AIS grade D. Performance of the CPR was decreased but acceptable in patients with non-traumatic SCI. Our external validation study demonstrated excellent discrimination but slightly underestimated calibration of the CPR of ambulation outcome after SCI. Regardless of the geographic and etiologic background of the population, the Dutch CPR could be applied to predict the ambulation outcome in patients with SCI.

Stable gravastars with Krori–Barua metric in [formula omitted] gravity

This paper deals with the physical attributes of charged gravastar in the realm of f(R,T2) gravity. The hypothetical gravastar structure is an alternative of black hole. The gravastar geometry has three sectors, namely the internal and external domains with shell region behaving as a separating layer between internal and external regions. We utilize distinct values of the equation of state parameter corresponding to each of these three regions. We construct gravastar structure incorporating a specific model of the considered theory. We formulate the field equations for a static sphere with charged isotropic matter configuration. Israel matching criteria is employed to assure smooth joining of both inner as well as outer geometries. We discuss various gravastar attributes versus shell thickness (length, entropy as well as energy) in the presence of charge. In the inner core, charge provides repulsive force counteracting the gravitational force acting inward and thus the equilibrium state of gravastar is preserved. We conclude that charged gravastar structure provides an acceptable behavior in this modified theory of gravity.

Applying Blockchain consensus mechanisms to Network Service Federation: Analysis and performance evaluation

Cutting edge (vertical) applications may swiftly saturate service providers’ virtualized infrastructure. In 5G and beyond, service providers seek out for innovative solutions such as Network Service Federation (NSF) which allows orchestration of external domain services/resources to provide a zero-downtime end-to-end vertical service. Distributed ledger technologies, such as Blockchain, are used to enhance the federation process. In this article, we propose and evaluate the application of Blockchain technology for NSF. We evaluate four different consensus mechanisms: Proof-of-Work (PoW), Proof-of-Authority (PoA), Practical Byzantine-Fault Tolerant (PBFT), and Proof-of-Stake (PoS). The experimental evaluation is executed using Ethereum, Tendermint and Cosmos platforms. Results show that the evaluated consensus mechanisms enable the use of NSF for both latency-sensitive and security stringent vertical applications.

Visual Analytics of Co-Occurrences to Discover Subspaces in Structured Data

We present an approach that shows all relevant subspaces of categorical data condensed in a single picture. We model the categorical values of the attributes as co-occurrences with data partitions generated from structured data using pattern mining. We show that these co-occurrences are a-priori , allowing us to greatly reduce the search space, effectively generating the condensed picture where conventional approaches filter out several subspaces as these are deemed insignificant. The task of identifying interesting subspaces is common but difficult due to exponential search spaces and the curse of dimensionality. One application of such a task might be identifying a cohort of patients defined by attributes such as gender, age, and diabetes type that share a common patient history, which is modeled as event sequences. Filtering the data by these attributes is common but cumbersome and often does not allow a comparison of subspaces. We contribute a powerful multi-dimensional pattern exploration approach (MDPE-approach) agnostic to the structured data type that models multiple attributes and their characteristics as co-occurrences, allowing the user to identify and compare thousands of subspaces of interest in a single picture. In our MDPE-approach, we introduce two methods to dramatically reduce the search space, outputting only the boundaries of the search space in the form of two tables. We implement the MDPE-approach in an interactive visual interface (MDPE-vis) that provides a scalable, pixel-based visualization design allowing the identification, comparison, and sense-making of subspaces in structured data. Our case studies using a gold-standard dataset and external domain experts confirm our approach’s and implementation’s applicability. A third use case sheds light on the scalability of our approach and a user study with 15 participants underlines its usefulness and power.

The anti-SARS-CoV-2 activity of novel 9, 10-dihydrophenanthrene derivatives: an insight into molecular docking, ADMET analysis, and molecular dynamics simulation

Originating in Wuhan, the COVID-19 pandemic wave has had a profound impact on the global healthcare system. In this study, we used a 2D QSAR technique, ADMET analysis, molecular docking, and dynamic simulations to sort and evaluate the performance of thirty-nine bioactive analogues of 9,10-dihydrophenanthrene. The primary goal of the study is to use computational approaches to create a greater variety of structural references for the creation of more potent SARS-CoV-2 3Clpro inhibitors. This strategy is to speed up the process of finding active chemicals. Molecular descriptors were calculated using ‘PaDEL’ and ‘ChemDes’ software, and then redundant and non-significant descriptors were eliminated by a module in ‘QSARINS ver. 2.2.2’. Subsequently, two statistically robust QSAR models were developed by applying multiple linear regression (MLR) methods. The correlation coefficients obtained by the two models are 0.89 and 0.82, respectively. These models were then subjected to internal and external validation tests, Y-randomization, and applicability domain analysis. The best model developed is applied to designate new molecules with good inhibitory activity values against severe acute respiratory syndrome coronavirus 2 (SARS CoV-2). We also examined various pharmacokinetic properties using ADMET analysis. Then, through molecular docking simulations, we used the crystal structure of the main protease of SARS-CoV-2 (3CLpro/Mpro) in a complex with the covalent inhibitor "Narlaprevir" (PDB ID: 7JYC). We also supported our molecular docking predictions with an extended molecular dynamics simulation of a docked ligand-protein complex. We hope that the results obtained in this study can be used as good anti-SARS-CoV-2 inhibitors.

MANAGEMENTOF CORONAVIRUSIN PAKISTAN

The COVID-19 pandemic has affected people, especially in developing countries. There are problems and shocks from its rapid proliferation and fears of further growth. The situation demands that Pakistan’s government deeply engage in planning, consultations, and fixing constitutional breaches to develop a comprehensive national health policy. The issues of a compromised healthcare system need to be urgently addressed. The best way to save lives is to have quality protective gear. It is also crucial to create international health partnerships to save lives. The policy should also focus on the growing problem of fake news. Every effort should include creating investment opportunities in digital platforms and managing external economic domains to achieve growth. Policymakers must also ensure that small businesses stay operating while health emergencies arise.This study examines the entire range of rescue operations, management, and planning but concentrates on specific factors that affect the Pakistani government’s ability to enact far-reaching policies for the people’s benefit. Considering how pervasive the impacts of the COVID-19 epidemic are, studying what could happen next is inherently more probabilistic. Related perspectives and concerned subjects may be explored in a study of the present outbreak and the policies and efforts to end it. This study uses an analytical approach to take an insight into the health governance of Pakistan and the challenges it faces.

SmartSPEC: A framework to generate customizable, semantics-based smart space datasets

This paper presents SmartSPEC, an approach to generate customizable synthetic smart space datasets using sensorized spaces in which people and events are embedded. Smart space datasets are critical to design, deploy and evaluate systems and applications under issues of heterogeneity, scalability and robustness, leading to cost-effective operation which improves the safety, comfort and convenience experienced by space occupants. However, many challenges exist in obtaining realistic smart space datasets for testing and validation, from a lack of fine-grained sensing to privacy/security concerns. SmartSPEC is a smart space simulator and data generator that leverages a semantic model augmented with user-defined constraints to represent important attributes, relationships, and external domain knowledge for a smart space. We employ machine learning (ML) approaches to extract relevant patterns from a sensorized space, which are used in an event-driven simulation strategy to generate realistic simulated data about the space (events, trajectories, sensor observation datasets, etc.). To evaluate the realism of the generated data, we develop a structured methodology and metrics to assess various aspects of smart space datasets, including trajectories of people and occupancy of spaces. Our experimental study looks at two real-world settings/datasets: an instrumented smart campus building and a city-wide GPS dataset. Our results show the realism of trajectories produced by SmartSPEC (1.4x to 4.4x more realistic than the best synthetic data baseline when compared to real-world data, depending on the scenario and configuration), as well as sensor data derived from such trajectories which adhere to the underlying semantics of the smart space as compared to synthetic sensor data baselines, even under hypothetical changes.

Multistability of gaits, the basin of attraction and its external topology in the simplest passive walking model on stairs

For the passive walking robot, the basin of attraction is very small and thin, and thus makes it a great challenge to produce stable passive dynamic walking. This paper reports systematically the effect of initial conditions on gait dynamics of the simplest passive walking model on stairs. We find two new gaits with period-3 and period-4, and they both lead to higher periodic gaits and chaos by period-doubling bifurcations. The critical period-3 unstable orbit generated via the cyclic-fold bifurcation takes part in the annihilation of chaotic attractors twice, and this double boundary crisis becomes the main cause to the falling down on stairs of the passive walker. In particular, we compute the Lyapunov exponents to verify the effectiveness of the bifurcation process of these new gaits. More importantly, through analyzing the topology of phase space, the external domain of basin of attraction is classified in detail and marked precisely with different falling patterns. We believe such findings to add significantly to the knowledge of the selection of initial values and to be potentially helpful in the design and optimization of passive walking robots.

Machine Learning-Assisted Estimation of the Photoantioxidant Activities of Bare, Mg, Cu, and Mg/Cu Dual-Doped ZnO

Green-synthesized pure zinc oxide (ZnO), Mg-doped ZnO, Cu-doped ZnO, and Mg/Cu dual-doped ZnO using aqueous leaf extract of Ziziphus mauritiana were analyzed for their antioxidant activities. In this study, the data-driven approach has been used to estimate the photoantioxidant activities of ZnO, Mg-doped ZnO, Cu-doped ZnO, and Mg/Cu dual-doped ZnO based on the experimental data and synthetic data generated through simulations. Three different machine learning models, including artificial neural network, extreme gradient boosting, and automated machine learning, were explored and compared for both data sets. These models were validated by using external validation and applicability domain methods based on the values of coefficient of determination, root mean square, and mean absolute errors. The performance of the machine learning techniques showed that photoantioxidant activities could be predicted accurately from the input variables such as types of dopants, percentage of dopants, average crystallite size, lighting condition, and concentration of antioxidants (photocatalyst). Doping and the lighting condition were found to have a more significant impact on the values of photoantioxidant activities of the ZnO, Mg-doped ZnO, Cu-doped ZnO, and Mg/Cu dual-doped ZnO in comparison to other variables. Based on three artificial neural network models, the variables for Mg doping and the lighting condition had weights with values ranging between 1.1 and 2.9.

18 million links in commit messages: purpose, evolution, and decay

Commit messages contain diverse and valuable types of knowledge in all aspects of software maintenance and evolution. Links are an example of such knowledge. Previous work on "9.6 million links in source code comments" showed that links are prone to decay, become outdated, and lack bidirectional traceability. We conducted a large-scale study of 18,201,165 links from commits in 23,110 GitHub repositories to investigate whether they suffer the same fate. Results show that referencing external resources is prevalent and that the most frequent domains other than github.com are the external domains of Stack Overflow and Google Code. Similarly, links serve as source code context to commit messages, with inaccessible links being frequent. Although repeatedly referencing links is rare (4%), 14% of links that are prone to evolve become unavailable over time; e.g., tutorials or articles and software homepages become unavailable over time. Furthermore, we find that 70% of the distinct links suffer from decay; the domains that occur the most frequently are related to Subversion repositories. We summarize that links in commits share the same fate as links in code, opening up avenues for future work.

Impact of Decalcification, Cold Ischemia, and Deglycosylation on Performance of Programmed Cell Death Ligand-1 Antibodies With Different Binding Epitopes: Comparison of 7 Clones

Programmed cell death ligand-1 (PD-L1) expression levels in patients’ tumors have demonstrated clinical utility across many cancer types and are used to determine treatment eligibility. Several independently developed PD-L1 immunohistochemical (IHC) predictive assays are commercially available and have demonstrated different levels of staining between assays, generating interest in understanding the similarities and differences between assays. Previously, we identified epitopes in the internal and external domains of PD-L1, bound by antibodies in routine clinical use (SP263, SP142, 22C3, and 28-8). Variance in performance of assays utilizing these antibodies, observed following exposure to preanalytical factors such as decalcification, cold ischemia, and duration of fixation, encouraged additional investigation of antibody-binding sites, to understand whether binding site structures/conformations contribute to differential PD-L1 IHC assay staining. We proceeded to further investigate the epitopes on PD-L1 bound by these antibodies, alongside the major clones utilized in laboratory-developed tests (E1L3N, QR1, and 73-10). Characterization of QR1 and 73-10 clones demonstrated that both bind the PD-L1 C-terminal internal domain, similar to SP263/SP142. Our results also demonstrate that under suboptimal decalcification or fixation conditions, the performance of internal domain antibodies is less detrimentally affected than that of external domain antibodies 22C3/28-8. Furthermore, we show that the binding sites of external domain antibodies are susceptible to deglycosylation and conformational structural changes, which directly result in IHC staining reduction or loss. The binding sites of internal domain antibodies were unaffected by deglycosylation or conformational structural change. This study demonstrates that the location and conformation of binding sites, recognized by antibodies employed in PD-L1 diagnostic assays, differ significantly and exhibit differing degrees of robustness. These findings should reinforce the need for vigilance when performing clinical testing with different PD-L1 IHC assays, particularly in the control of cold ischemia and the selection of fixation and decalcification conditions.

Hierarchical co-clustering with augmented matrices from external domains

Co-clustering simultaneously classifies row and column objects of data matrices and is considered to have better accuracy than conventional one-way clustering methods. In the era of big data, extracting classification knowledge about objects from several domains has become increasingly feasible. This study proposes a hierarchical co-clustering with augmented matrices (HICCAM), which co-clusters the row and column objects of a target matrix while utilizing the augmented data matrices of these target objects extracted from the external domains. The algorithm is designed to improve classification accuracy by transferring knowledge in augmented matrices and simultaneously improves cluster interpretability using hierarchical cluster structures. Experiments on document clustering confirmed that HICCAM achieved the highest accuracy among comparison methods with and without external knowledge. Its clusters exhibit hierarchical relationships according to their topics. In addition, we provide the experimental results with multiview synthetic datasets that demonstrate a clustering situation in which HICCAM can be effectively identified.

From Without to Within: Inner Transformation as a Pedagogy for Social Activism

Change has become the urgency of our time, as the harrowing Anthropocene and ongoing socio-political crises are challenging the future of humanity and our planet. While systemic structural forces are often (and rightfully so) the target to blame, social activism is as much about our own path of inner change as it is about attempting to create a better world externally. This aspect of social activism can easily become misunderstood as inertia, while more traditional forms of resistance aimed at subverting macro-structures are favored. In this essay, I draw from literature on spirituality and consciousness, new biology, quantum sciences, as well as nature studies and education, to shed light on a new form of social activism that calls for each person to recognize our role in co-creating the world, and emphasizes spiritual awakening as the most vital feature of human flourishing. Social change in the external domain will not last unless we have done the inner work to weed out misconceptions, biases, and wrong thinking that led us to contemporary crises in the first place. The essay discussed the elevation of human consciousness through the aid of nature and yogic sciences as the need of the hour, and as the most crucial aspects of social activism in the new era.

Knowledge graph-enhanced molecular contrastive learning with functional prompt

Deep learning models can accurately predict molecular properties and help making the search for potential drug candidates faster and more efficient. Many existing methods are purely data driven, focusing on exploiting the intrinsic topology and construction rules of molecules without any chemical prior information. The high data dependency makes them difficult to generalize to a wider chemical space and leads to a lack of interpretability of predictions. Here, to address this issue, we introduce a chemical element-oriented knowledge graph to summarize the basic knowledge of elements and their closely related functional groups. We further propose a method for knowledge graph-enhanced molecular contrastive learning with functional prompt (KANO), exploiting external fundamental domain knowledge in both pre-training and fine-tuning. Specifically, with element-oriented knowledge graph as a prior, we first design an element-guided graph augmentation in contrastive-based pre-training to explore microscopic atomic associations without violating molecular semantics. Then, we learn functional prompts in fine-tuning to evoke the downstream task-related knowledge acquired by the pre-trained model. Extensive experiments show that KANO outperforms state-of-the-art baselines on 14 molecular property prediction datasets and provides chemically sound explanations for its predictions. This work contributes to more efficient drug design by offering a high-quality knowledge prior, interpretable molecular representation and superior prediction performance.

Knowledge Adaptive Multi-Way Matching Network for Biomedical Named Entity Recognition via Machine Reading Comprehension.

Rapid and effective utilization of biomedical literature is paramount to combat diseases like COVID19. Biomedical named entity recognition (BioNER) is a fundamental task in text mining that can help physicians accelerate knowledge discovery to curb the spread of the COVID-19 epidemic. Recent approaches have shown that casting entity extraction as the machine reading comprehension task can significantly improve model performance. However, two major drawbacks impede higher success in identifying entities (1) ignoring the use of domain knowledge to capture the context beyond sentences and (2) lacking the ability to deeper understand the intent of questions. In this paper, to remedy this, we introduce and explore external domain knowledge which cannot be implicitly learned in text sequence. Previous works have focused more on text sequence and explored little of the domain knowledge. To better incorporate domain knowledge, a multi-way matching reader mechanism is devised to model representations of interaction between sequence, question and knowledge retrieved from Unified Medical Language System (UMLS). Benefiting from these, our model can better understand the intent of questions in complex contexts. Experimental results indicate that incorporating domain knowledge can help to obtain competitive results across 10 BioNER datasets, achieving absolute improvement of up to 2.02% in the f1 score.

Atomic Displacements Enabling the Observation of the Anomalous Hall Effect in a Non-Collinear Antiferromagnet.

Antiferromagnets with non-collinear spin structures display various properties that make them attractive for spintronic devices. Some of the most interesting examples are an anomalous Hall effect despite negligible magnetization and a spin Hall effect with unusual spin polarization directions. However, these effects can only be observed when the sample is set predominantly into a single antiferromagnetic domain state. This can only be achieved when the compensated spin structure is perturbed and displays weak moments due to spin canting that allows for external domain control. In thin films of cubic non-collinear antiferromagnets, this imbalance waspreviously assumed to require tetragonal distortions induced by substrate strain. Here weshow that in Mn3 SnN and Mn3 GaN, spin canting is due to structural symmetry lowering induced by large displacements of the magnetic manganese atoms away from high symmetry positions. These displacements remain hidden in X-ray diffraction when only probing the lattice metric and require measurement of a large set of scattering vectors to resolve the local atomic positions. In Mn3 SnN, the induced net moments enable the observation of the anomalous Hall effect with an unusual temperature dependence, which weconjecture results from a bulk-like temperature-dependent coherent spin rotation within the Kagomeplane. This article is protected by copyright. All rights reserved.

Identifying heart disease risk factors from electronic health records using an ensemble of deep learning method

Heart disease is a leading cause of death worldwide. For decades, cardiologists have attempted to identify heart-disease risk factors to facilitate its prediction, prevention, and treatment. In recent years, electronic health records (EHRs) have become a valuable source for detecting these risk factors (e.g. smoking, obesity, and diabetes). However, challenges persist as EHRs include clinical notes in free-form and unstructured text, making it tedious for cardiologists to retrieve relevant information. To resolve this problem, we devised a deep-learning-based ensemble approach to automatically identify heart-disease risk factors from EHRs. This proposed approach can efficiently extract semantic information from EHRs and automate risk-factor identification with high performance. In particular, this approach does not require any external domain knowledge about the disease because a powerful Bidirectional Encoder Representations from Transformers (BERT) method is implemented to encode the discriminative features of clinical notes. The extracted features are then fed to conditional random fields (CRF) to identify all possible risk-factor indicators. Experimental results show that, in a scenario where no external knowledge is available, the proposed approach achieves state-of-the-art performance.

Role of inheritance during tectonic inversion of a rift system in basement-involved to salt-decoupled transition: analogue modelling and application to the Pyrenean–Biscay system

Abstract. The reactivation of former rift systems and passive margins during tectonic inversion and their incorporation into fold-and-thrust belts result in significant structural differences not only between internal and external domains, but also along-strike. The Basque–Cantabrian and Asturian systems are among the best examples to address the role of along-strike changes in rift inheritance since they show a transition from salt to basement-inherited structures divided by a transition zone separating thick- from thin-skinned structural domains. While both domains have been widely described in the literature, the transfer system separating the two has not been sufficiently investigated due to poor seismic imaging and the lack of large-scale outcrops. This contribution aims to address the linkage between basement-controlled (i.e. thick-skinned) and salt-decoupled (i.e. thin-skinned) domains and to describe how deformation is accommodated in the transitional zone between these domains. An experimental programme based on analogue models has been designed that was inspired by the transition from the thin-skinned Basque–Cantabrian Pyrenees to the east to the thick-skinned Asturian Massif to the west. As observed in nature, experimental results show that oblique structures (at low angle with the shortening direction) form in the transitional domain, and their location depends on the linkage of the active structures occurring in both surrounding thick- and thin-skinned domains at different positions. Nevertheless, their orientation and evolution are controlled by the underlying decoupling horizon (i.e. salt). The deformation in the thick-skinned domain produces significant topography over a narrow deformation area due to the lack of effective decoupling levels. On the contrary, deformation in the thin-skinned domain is more distributed due to decoupling, resulting in a wider deformation area of less topography. As a result, syn-contractional sedimentation occurs mainly in the foreland basin in front of the thick-skinned domain, whereas it is observed in the foreland but also in piggyback basins in the thin-skinned domain.