Issues Of Interpretation Research Articles

Role of molecular markers in identifying genetic disorders

Molecular markers are critical tools in identifying genetic disorders, allowing for precise diagnosis, risk assessment, and personalized treatment approaches. They are classified into various categories, including single nucleotide polymorphisms (SNPs), short tandem repeats (STRs), and restriction fragment length polymorphisms (RFLPs), each serving distinct roles in genetic diagnostics. SNPs are widely used in genome-wide association studies (GWAS) to identify genetic predispositions to complex diseases, while STRs are valuable in diagnosing disorders like Huntington's disease. RFLPs, though less commonly used today, remain important in specific diagnostic contexts. The application of molecular markers spans a wide range of genetic disorders, from monogenic conditions such as cystic fibrosis (CF) to complex diseases like hereditary breast and ovarian cancer syndrome and fragile X syndrome. These markers enable early detection and targeted interventions, improving patient outcomes. However, several challenges hinder their widespread adoption, including difficulties in interpreting genetic data, limited access to genetic screening, and ethical concerns related to privacy and genetic discrimination. Future directions in the use of molecular markers for genetic screening involve integrating advanced technologies like next-generation sequencing and combining molecular data with other omics approaches to provide a more comprehensive understanding of genetic disorders. Addressing the challenges of data interpretation, accessibility, and ethical issues will be crucial in expanding the utility of molecular markers in clinical practice. The advancements in molecular marker technology and their applications in detecting specific genetic disorders hold promise for improving diagnostic accuracy and personalized treatment strategies. However, ensuring that these technologies are accessible and ethically implemented will be key to their success in transforming healthcare. The ongoing evolution of molecular markers and genetic screening technologies suggests a future where early diagnosis and personalized medicine become standard care for genetic disorders.

An ontology-based approach for harmonizing metrics in bike network evaluations

The urgency to decarbonize the transportation sector has accelerated the adoption of micro-mobility solutions, with cycling network development witnessing remarkable growth. Robust and quantitative evaluation frameworks are needed to evaluate the quality of such developments. While a plethora of bike network evaluation approaches exist, their diversity creates issues of interpretability and comparability due to varying metrics and domain-specific terms. We present three contributions to address these challenges. First, we construct a formal ontology, VeloNEMO, that captures key attributes of evaluation metrics for harmonizing bike network evaluation metrics. Second, we generate a machine-readable knowledge base containing these metrics, enabling meta-analyses and resolving some of the existing terminological discrepancies. Third, we propose recommendations for transparent and comparable metric descriptions across various evaluation approaches, illustrated by exploratory metric selection scenarios for a forthcoming bike network evaluation tool. In summary, our research addresses the need for a structured and shared vocabulary for bike network evaluations. This ontology-based approach aims to improve the coherence of evaluation methods as the field of bike network planning continues to evolve, ultimately supporting decision-making for sustainable transportation planning.

An interpretable hybrid spatiotemporal fusion method for ultra-short-term photovoltaic power prediction

An interpretable hybrid spatiotemporal fusion method for ultra-short-term photovoltaic power prediction

Use of Minimal Important Difference for Patient-Reported Outcome Measures in Plastic Surgery: A Systematic Review.

The minimal important difference (MID) is vital to consider when interpreting the clinical importance of observed changes from surgical interventions assessed by patient-reported outcome measures (PROMs). There is no gold-standard for how to calculate MIDs, and uptake in plastic surgery literature is currently unknown, leading to methodological and interpretation issues. Medline and Embase were searched to identify all plastic surgery randomized controlled trials (RCTs) employing PROMs as outcomes, and MID estimation studies for PROMs used by RCTs. Included studies were assessed for uptake and application of MIDs, and MID estimation methodology and values were categorized. A total of 554 RCTs employing PROMs as outcomes were identified. Of these, 419 RCTs had the possibility of incorporating a previously published MID. Uptake rate of MIDs was 11.5% (n=48/419). The most common ways MIDs were applied was to calculate sample size (37.5%) or to determine whether results were clinically important (35.4%). A total of 99 studies estimating MID values for the most common PROMs in plastic surgery, based on our review, were analyzed. The most common estimation methodologies were receiver-operator curve analysis (49%), change difference (31%), and standard deviation (25%). Our review highlights limited uptake and application of MIDs in plastic surgery. We propose four major barriers: 1) there existed no repository of published MIDs for PROMs used in plastic surgery; 2) available MIDs are not specific to plastic surgery populations; 3) high heterogeneity in MID estimation methodology; 4) wide ranges in MID values with no superior choice.

Deconstructing Turkcell V Iran: The First Investment Treaty Claim against Iran

Once an arbitral tribunal is constituted, the preliminary issue to be decided is the question of jurisdiction and admissibility of the claim. At the pre-merit stage, the tribunal determines whether it has jurisdiction over the disputes to proceed with the substantive issues. In investment arbitration, the jurisdictional requirements are contained in bilateral investment treaties (BITs) and the 1966 Convention on the Settlement of Investment Disputes between States and Nationals of other States (ICSID Convention) and municipal laws. This article critically analyses the first BIT claim against Iran by reconstructing the Turkcell arbitration case. The Turkcell case concerned complex questions of Iranian and international law concerning the jurisdiction of an arbitral tribunal over investment activities. The Turkcell case highlights the important issue of interpretation of treaty provisions concerning the definition of investment (ratione materiae) and investors (ratione personae).

IoT-enabled healthcare transformation leveraging deep learning for advanced patient monitoring and diagnosis

AbstractDeep learning and the Internet of Things (IoT) are revolutionizing the healthcare industry. This study explores the potential commercial transformation resulting from IoT-enabled healthcare systems that use deep learning for patient monitoring and diagnosis. Wearables, smart sensors, and internet-connected medical devices allow doctors to monitor patients' vital signs, activities, and physiological traits in real time. However, these devices generate vast and complex data, making analysis and diagnosis challenging. Deep learning models are well-suited to analyze this growing volume of medical data. Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks can automatically recognize complex patterns and relationships in sensor data, electronic health records, and patient-reported information. This capability aids clinical professionals in diagnosing illnesses, identifying warning signs, and tailoring treatments. This paper describes a Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) -based IoT-enabled healthcare system that performs feature extraction, classification, prediction, and data preparation. Additionally, it addresses interpretability issues, privacy concerns, and resource limitations of deep learning models in real-time healthcare settings. The study demonstrates the effectiveness of Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) -powered IoT-based healthcare solutions, such as real-time patient monitoring, disease detection, risk prediction, and therapy optimization. These techniques can improve the quality, cost, and outcomes of healthcare. Combining Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) with IoT can significantly enhance healthcare by improving disease detection, personalized treatment, and patient monitoring through connected devices and powerful analytics.

SANNIKI ARCHAEOLOGICAL COMPLEX: HISTORY AND STUDY PROSPECTS

This article deals with the issues of interpretation of the archaeological complex of Sanniki (Dzaytlava district, Grodno Region). The history of the study of Sanniki settlement is analysed, as well as its interpretation options in the works of researches. The results of the archaeological study conducted by the author are given.Based on analyses of new sourses, the selection of the Sanniki archaeological complex is justified.

Toward a functional classification for autism in adulthood.

Autism spectrum disorder (ASD) is a heterogeneous condition that affects development and functioning from infancy through adulthood. Efforts to parse the heterogeneity of the autism spectrum through subgroups such as Asperger's and Profound Autism have been controversial, and have consistently struggled with issues of reliability, validity, and interpretability. Nonetheless, methods for successfully identifying clinically meaningful subgroups within autism are needed to ensure that research, interventions, and services address the range of needs experienced by autistic individuals. The purpose of this study was to generate and test whether a simple set of questions, organized in a flowchart, could be used in clinical practice and research to differentiate meaningful subgroups based on individuals' level of functioning. Once generated, subgroups could also be compared to the recently proposed administrative category of Profound Autism and to groupings based on standardized adaptive measures. Ninety-seven adults with autism or related neurodevelopmental disorders participating in a longstanding longitudinal study, or their caregivers if they could not answer for themselves, completed phone interviews when the participants were ~30 years old. Information from these phone interviews was used to generate vignettes summarizing characteristics and aspects of the daily lives of each participant (e.g., language level, vocational activities, and social relationships). Three expert clinicians then used these vignettes to classify each participant based on their level of support needs. Meaningfully distinct subgroups within the sample were identified which could be reliably distinguished from one another. Implications of such categorizations and future directions are discussed.

Influence of different electrode placement patterns on electromagnetic interference generated by Left Ventricular Assist Devices (LVADs) during electrocardiogram performance

Abstract Background Heart failure (HF), with an incidence of approximately 3/1000 person-years, often progresses to advanced stages where symptoms persist despite treatment. In such cases, mechanical circulatory assist devices, particularly left ventricular assist devices (LVADs) in ambulatory patients, enhance survival and quality of life. These LVADs generate electromagnetic interference, resulting in noise artefacts in conventional electrocardiograms (ECGs). The conventional ECG should be configured with a low-pass filter of 150 Hz and a high-pass filter of 0.05 Hz, as recommended by the American Heart Association/American College of Cardiology/Heart Rhythm Society. LVADs, such as the HeartMate 3 (Abbott®), typically operate at a rotational speed of 5000 to 6000 rpm, corresponding to oscillatory frequencies of 83.3 to 100 Hz. Objective To assess the impact of various surface electrode placement strategies and modifications to the low-pass filter on the electromagnetic interference produced by LVADs. Methods We conducted an observational study involving seven patients with HeartMate 3 adjusted to various rotational speeds (refer to Table 1). Three distinct electrode placement schemes (AHA, Mason Likar, and modified Torso) were employed for the frontal plane leads. The horizontal plane leads remained unmodified. Each patient underwent a total of 6 ECGs, corresponding to each of the aforementioned schemes. ECG recordings were performed using a high-pass filter set at 0.05 Hz and a low-pass filter set at 150 Hz. Additionally, a set of ECGs was obtained by modifying the low-pass filter to 40 Hz for further analysis. Results Figure 1 displays ECGs obtained from a male with a HeartMate 3 operating at 4600 rpm or 76.6 Hz. This device employs an algorithm to generate an "artificial pulse" by cyclically accelerating and decelerating the pump speed by 2000 rpm every 2 seconds, resulting in rotational speeds of 2600 rpm and 6600 rpm (43.3 Hz and 110 Hz, respectively). No specific placement scheme demonstrated consistent noise reduction across all cases, likely due to inter-individual variability resulting from factors such as the final position of the LVAD the rpm in each case, and the thoracic impedance of each patient. However, the modification of the low-pass filter to 40 Hz yielded a noteworthy improvement in noise reduction across all cases. In our limited sample, this adjustment did not pose issues for ECG interpretation. Nevertheless, it is essential to acknowledge that excessively low filter settings may not only eliminate noise but also potentially discard clinically relevant high-frequency signals (e.g., pacemaker spikes, large amplitude Rs, QRS notches). Conclusions Individualizing the electrode placement scheme is crucial for minimizing noise generated by LVADs. The adjustment of the low-pass filter to 40 Hz could generally prove beneficial in reducing artifact generation, despite the clinical limitations it may entail.

Transforming Cancer Care: The Impact of AI-Driven Strategies.

AI is a critical component in healthcare, especially in the application of precision medicine where patients' characteristics, including genetic makeup, determine the treatment options that should be implemented. AI sorts big data, predicting people's reactions to specific treatments, the right combinations of drugs, and possible side effects, therefore increasing the efficiency of the treatment process and decreasing negative outcomes. This article briefly presents the ethical issues and concerns that might arise due to the integration of AI in society, such as the privacy of data, the issues of bias in the algorithms, and the issues of interpretability of the AI systems. Nevertheless, there is no doubt that AI can bring qualitative changes in cancer care based on its potential to enhance patient prognosis and reduce health care costs, as well as become a defining feature of the standard of care.

A Data and Model-Driven Clutter Suppression Method for Airborne Bistatic Radar Based on Deep Unfolding

Space–time adaptive processing (STAP) based on sparse recovery achieves excellent clutter suppression and target detection performance, even with a limited number of available training samples. However, most of these methods face performance degradation due to grid mismatch, which impedes their application in bistatic clutter suppression. Some gridless methods, such as atomic norm minimization (ANM), can effectively address grid mismatch issues, yet they are sensitive to parameter settings and array errors. In this article, the authors propose a data and model-driven algorithm that unfolds the iterative process of atomic norm minimization into a deep network. This approach establishes a concrete and systematic link between iterative algorithms, extensively utilized in signal processing, and deep neural networks. This methodology not only addresses the challenges associated with parameter settings in traditional optimization algorithms, but also mitigates the lack of interpretability issues commonly found in deep neural networks. Moreover, due to more rational parameter settings, the proposed algorithm achieves effective clutter suppression with fewer iterations, thereby reducing computational time. Finally, extensive simulation experiments demonstrate the effectiveness of the proposed algorithm in clutter suppression for airborne bistatic radar.

Bio-inspired computational model for direction and speed detection

This article introduces a biologically-inspired model capable of detecting both an object’s motion direction and speed, based on retinal neural mechanisms verified through biological experiments. It aims to address the interpretability issues present in current deep learning models. The proposed Motion Detection Neuron (MDN) model, inspired by early research on the retina’s direction and speed sensitivity, replicates the motion detection functions of the retina and primary visual cortex. The design of the MDN, inspired by the layered structure of the retina and incorporating various cell types and functions, has been validated through biological experimentation, providing it with robust biological interpretability. Extensive experiments have been conducted to assess the MDN’s detection accuracy and robustness against various types of noise. Additionally, to verify that the MDN not only offers enhanced biological interpretability but also maintains detection accuracy comparable to leading deep learning algorithms, we compared its performance with that of LeNet ,EfficientNet and RegNet under identical conditions. The results show that the MDN not only provides better biological interpretability and lower hardware demands but also excels in accuracy under specific conditions, comparable to advanced deep learning algorithms.

"Retail Sale" as a Mandatory Feature of the Objective Side of Illegal Retail Sale of Alcoholic and Alcohol-Containing Food Products: Issues of Interpretation and Application

The paper, based on a systematic analysis of Russian legislation, provides an interpretation of the act known as “retail sale” as a mandatory feature of the objective side of the crime provided for in Article 171.4 of the Criminal Code of the Russian Federation. The problems generated by the current wording of the disposition of the norm in question are identified. The specific characteristics of the sale are emphasized and its retail nature is described in accordance with the provisions of industry legislation. Based on existing points of view and personal vision, the author proposed an adjusted definition of the term “retail sale” in relation to Note 1 to Article 171.4 of the Criminal Code of the Russian Federation. In order to bring to criminal liability persons who have committed the “wholesale sale” of alcoholic and alcohol-containing food products, it is proposed to expand the scope of the analyzed norm by amending the disposition of the article and the note to it, formulating the act as “sale”. It is indicated that it is necessary to recognize a transaction as fictitious in cases where the sale of alcoholic and alcohol-containing food products is disguised as a gift transaction.

A quantitative analysis of fidgeting in ADHD and its relation to performance and sustained attention on a cognitive task.

Attention-Deficit/Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder where hyperactivity often manifests as fidgeting, a non-goal-directed motoric action. Many studies demonstrate fidgeting varies under different conditions as a self-regulating mechanism for attention and alertness during cognitively demanding tasks. Fidgeting has also been associated with reaction time variability. However, a lack of standard variables to define and quantify fidgeting can lead to discrepancies in data and interpretability issues across studies. Furthermore, little is known about fidgeting in adults with ADHD compared to youth. This study aims to design a framework to quantify meaningful fidgeting variables and to apply them to test the relation between fidgeting and performance on a cognitive task, the Flanker, in adults with ADHD. Our study included 70 adult participants diagnosed with ADHD, aged 18-50 years (30.5 ± 7.2 years). Screening included a structured clinical interview, childhood, current self and current observer ratings of ADHD symptoms. Actigraphy devices were attached to the left wrist and right ankle during completion of a cognitive control, attention task (the Flanker). Laboratory testing was subsequently completed on a single day. The relation between task performance, reaction time variability and fidgeting was examined. Our analysis revealed increased fidgeting during correct trials as defined by our new variables, consistent with previous observations. Furthermore, differences in fidgeting were observed between early and later trials while the percentage of correct trials were not significantly different. This suggests a relation between the role of fidgeting and sustaining attention. Participants with low reaction time variability, that is, those with more consistent reaction times, fidgeted more during later trials. This observation supports the theory that fidgeting aids arousal and improves sustained attention. Finally, a correlation analysis using ADHD-symptom rating scales validated the relevance of the fidget variables in relation to ADHD symptom severity. These findings suggest fidgeting may be a compensatory mechanism that aids in sustained attention for those with ADHD, although alternative explanations exist. Our study suggests that fidgeting may aid in sustained attention during the attention-demanding, cognitive control processes for adults with ADHD, with more fidgeting observed during correct trials and among participants with lower reaction time variability. Furthermore, the newly defined fidget variables were validated through a significant correlation with ADHD rating scales. By sharing our implementation of fidget variables, we hope to standardize and encourage further quantitative research into the role of fidgeting in ADHD.

A Systematic Review of Attention Models in Natural Language Processing

The role of attention models in neural networks has drawn much focus among scholars in recent years mainly because of its efficiency and versatility. Attention models are important for many Natural Language Processing applications like question answering, semantic analysis, sentiment analysis and machine translation performing significantly better than other usual approaches. These mechanisms improve neural networks capability in interpretation and tackle issues like performance decay, which affects the Recurrent Neural Networks by concentrating on significant data inputs and context. A stream of research has explored the integration of attention mechanisms to other fields, namely computer vision and graph analysis improving tasks like object detection, image captioning or node representation learning. However, for more detailed research of modern achievements and various domains and model architecture structures there is still the necessity to use the more profound survey-based investigation. This research article provides a review of previous work done on attention-based model in NLP. It also outlines the problems and provides relevant solutions using different methodologies. Future directions of the research are provided for the improvement of the attention models for NLP, discussing potential issues in terms of model performance and its capacity, as well as its interpretability. This review is intended to help further research to more universal attention-based solutions in the domain of NLP.

Recent advances and effectiveness of machine learning models for fluid dynamics in the built environment

ABSTRACT Indoor environmental quality is crucial for human health and comfort, necessitating precise and efficient computational methods to optimise indoor climate parameters. Recent advancements in machine learning (ML) and computational fluid dynamics (CFD) are promising. However, applying ML to complex building airflow presents challenges. This research aims to investigate the integration of ML with CFD in the context of built environment applications using a systematic review approach. It highlights a critical knowledge gap: the need to synthesise innovative approaches that address the limitations of indoor modelling using data-driven ML methods. The review examines contemporary literature, identifying current developments and suggesting potential future directions. It delves into the innovations in combining ML with CFD to predict thermal comfort and indoor air quality, uncovering key limitations such as the lack of high-quality experimental data for training and validation, the computational complexity of detailed CFD simulations, and the interpretability issues of ‘black-box’ ML models. The emergence of data-driven techniques in fluid mechanics offers promising prospects for modelling in the built environment. Future research should focus on incorporating physics-based rules in ML models, adapting turbulence closure models for indoor flows, and enhancing model validation using real-world datasets. The research emphasises the synergistic relationship between ML and CFD; it proposes pathways to overcome current limitations, aiming to enhance the precision and efficiency of indoor environment modelling through their integration.

Artificial Intelligence Applications for Thoracic Surgeons: "The Phenomenal Cosmic Powers of the Magic Lamp".

In the digital age, artificial intelligence (AI) is emerging as a transformative force in various sectors, including medicine. This article explores the potential of AI, which is akin to the magical genie of Aladdin's lamp, particularly within thoracic surgery and lung cancer management. It examines AI applications like machine learning and deep learning in achieving more precise diagnoses, preoperative risk assessment, and improved surgical outcomes. The challenges and advancements in AI integration, especially in computer vision and multi-modal models, are discussed alongside their impact on robotic surgery and operating room management. Despite its transformative potential, implementing AI in medicine faces challenges regarding data scarcity, interpretability issues, and ethical concerns. Collaboration between AI and medical communities is essential to address these challenges and unlock the full potential of AI in revolutionizing clinical practice. This article underscores the importance of further research and interdisciplinary collaboration to ensure the safe and effective deployment of AI in real-world clinical settings.

PepExplainer: An explainable deep learning model for selection-based macrocyclic peptide bioactivity prediction and optimization

Macrocyclic peptides possess unique features, making them highly promising as a drug modality. However, evaluating their bioactivity through wet lab experiments is generally resource-intensive and time-consuming. Despite advancements in artificial intelligence (AI) for bioactivity prediction, challenges remain due to limited data availability and the interpretability issues in deep learning models, often leading to less-than-ideal predictions. To address these challenges, we developed PepExplainer, an explainable graph neural network based on substructure mask explanation (SME). This model excels at deciphering amino acid substructures, translating macrocyclic peptides into detailed molecular graphs at the atomic level, and efficiently handling non-canonical amino acids and complex macrocyclic peptide structures. PepExplainer's effectiveness is enhanced by utilizing the correlation between peptide enrichment data from selection-based focused library and bioactivity data, and employing transfer learning to improve bioactivity predictions of macrocyclic peptides against IL-17C/IL-17 RE interaction. Additionally, PepExplainer underwent further validation for bioactivity prediction using an additional set of thirteen newly synthesized macrocyclic peptides. Moreover, it enabled the optimization of the IC50 of a macrocyclic peptide, reducing it from 15 nM to 5.6 nM based on the contribution score provided by PepExplainer. This achievement underscores PepExplainer's skill in deciphering complex molecular patterns, highlighting its potential to accelerate the discovery and optimization of macrocyclic peptides.

Evaluating Effects of Multilevel Interventions on Disparity in Health and Healthcare Decisions

In this paper, we introduce an analytic approach for assessing effects of multilevel interventions on disparity in health outcomes and health-related decision outcomes (i.e., a treatment decision made by a healthcare provider). We outline common challenges that are encountered in interventional health disparity research, including issues of effect scale and interpretation, choice of covariates for adjustment and its impact on effect magnitude, and the methodological challenges involved with studying decision-based outcomes. To address these challenges, we introduce total effects of interventions on disparity for the entire sample and the treated sample, and corresponding direct effects that are relevant for decision-based outcomes. We provide weighting and g-computation estimators in the presence of study attrition and sketch a simulation-based procedure for sample size determinations based on precision (e.g., confidence interval width). We validate our proposed methods through a brief simulation study and apply our approach to evaluate the RICH LIFE intervention, a multilevel healthcare intervention designed to reduce racial and ethnic disparities in hypertension control.

Deep learning in data science: Theoretical foundations, practical applications, and comparative analysis

Deep learning has emerged as a transformative technology in data science, revolutionizing various domains through its powerful capabilities. This paper explores the theoretical foundations, practical applications, and comparative analysis of deep learning models. The theoretical foundations section discusses key neural network architectures such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformers, highlighting their unique capabilities in processing different types of data. Optimization algorithms crucial for effective training, including Stochastic Gradient Descent (SGD) and Adam, are examined. Regularization techniques for preventing overfitting and enhancing generalization are also addressed. Practical applications in healthcare, finance, and retail showcase the real-world impact of deep learning. A comparative analysis of performance metrics demonstrates the superiority of deep learning models over traditional methods. Despite their advantages, deep learning models face limitations and challenges, including data dependency and interpretability issues. The paper concludes by emphasizing the ongoing research efforts to mitigate these challenges and ensure the continued advancement of deep learning in data science.