Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact of Symptoms Duration in Chronic Obstructive Pulmonary Disease is There Any Meaningful Link

Aim: To examine the relationship of duration of symptoms with the disease outcomes in patients with COPD. Background: COPD patients have varying degree of symptoms. We hypothesized that duration of symptoms may be useful to understand the disease progression. Methods: This observational study carried out in England. The patients were recruited and followed up between September 1999 and December 2002 and then 2007-2008, respectively. Patient’s demographics were recorded. Medical Research Council Dyspnoea (D) score were used to measure degree of breathlessness and spirometry were utilized to evaluate the severity of disease. Patients were asked about the COPD symptoms and for how long they have these symptoms including shortness of breath, cough, and the duration of their symptoms. Results: A total of 269 subjects were recruited. Of those 227 were with the diagnosis of COPD and 42 as non COPD or control group. The data is normally distributed. The study results show that there is a strong correlation between duration of symptoms and physiological variables both at baseline and at follow up. These include age (p=0.04), pack years (p=0.02), MRC score (p=0.04) and FEV1 (p<0.001). When GOLD stages of severity were used to explore the relationship; it becomes evident that patients with higher GOLD stage had longer duration of symptoms. Conclusion: These findings indicate the possible role of considering duration of symptoms as a useful marker of disease progression and its relationship with other disease outcomes in chronic obstructive pulmonary disease patients.
Supporting diabetes self-management with ubiquitous computing technologies: a user-centered inquiry

Ubiquitous computing technologies offer opportunities to improve treatments for chronic health conditions. Type 1 diabetes is a compelling use-case for such approaches, given its severity, and need for individuals to make frequent care decisions, informed by complex data. However, current apps, typically based on effortful reflection on collected data, generally show poor adoption, lack vital cognitive and emotional support, and are poorly tailored to users’ actual diabetes decision making processes. This thesis investigates how diabetes apps can be improved from a user-centered perspective. An initial questionnaire-based study investigated how well existing diabetes apps meet user needs. Perceived benefits, limitations, and reasons for low adoption rates were identified. A talk-aloud study of detailed user interactions with diabetes logging apps was conducted to characterize the benefits and limitations of diverse UI elements for T1 diabetes management, and to more precisely identify wider problems with current interaction designs. This led to positing a refined version of Mamykina et al.’s model for diabetes self-management, to account for observed practices, whereby the previously accepted habitual and sensemaking cognitive states are augmented by a posited ‘fluid contextual reasoning’ (FCR) mode, which allows multiple contextual factors to be balanced for dynamic course correction when navigating complex situations, using previously learned knowledge. To investigate user perceptions of the levels and kinds of monitoring anticipated in next generation diabetes decision support systems, a 4-week technology probe, in which participants used multiple networked devices and external data aggregation, was used to frame requirements for user-centered development of such future systems. Integrating all of the above work, an iterative design process was undertaken to create DUETS, a card-based system to facilitate reflection by designers, users, and other stakeholders on diabetes support management systems. The resulting tool and method were then implemented and evaluated through structured sessions with stakeholder focus groups.
Characteristics of convections associated with the Tibetan Plateau vortices based on geostationary satellite data

Tibetan Plateau vortices (TPVs) are major rain producers over the Tibetan Plateau, some of which can move eastward off the plateau and trigger heavy rainfall over southwestern and eastern China. The infrared black body temperature (TBB) data, derived from geostationary meteorological satellite FY‐2E in the period of May–August of 2010–2014, is utilized to investigate the characteristics of convections associated with the moving‐off TPVs. During May–August, negative TBB anomalies appear over the tracks of the TPVs, but are located further north of the tracks in May and June than in July and August. Relative to the centre of TPVs, low TBB appears in the southeast and extends from southwest to northeast, whose values are smaller and ranges are larger before the TPVs move off than after moving off. Distributions of TBB at different evolution times show a close relationship with the characteristics of TPVs. The intensity, scales and shape of the convections linked to the TPVs are explored in the coordinates whose origin is the composite centre of the convections. Generally, convections associated with the TPVs are elliptic and stretch from southwest to northeast, with average spatial scales of approximately 3° in the zonal and 2° in the meridional direction. Convections in July and August are more intensive and lie southwest‐northeast, and those in May and June are weaker with a quasi‐zonal orientation. The horizontal scales of convections are much larger in May and July than those in June and August. Convections are usually larger and more intense before the TPVs move off than after moving off, corresponding to the changes of TPVs.
Gallium oxide-based solar-blind ultraviolet photodetectors

Gallium oxide (Ga2O3) is an emerging ultrawide bandgap (UWBG) semiconducting material as a key building block for the applications of power electronics, solar blind photodetectors and deep ultraviolet optoelectronics beyond existing technologies. To date, solar-blind photodetectors based on Ga2O3 in the various forms of bulk crystals, epitaxial thin films, nanostructures, and heterostructures have been demonstrated with either high performance or multiple functionalities, however, several remaining challenges require proper solutions for practical applications. In this topic review, we summarized recent advances in processing and device performance of solar photodetectors based on Ga2O3 and the associated physical mechanisms behind according to the architecture of photodetectors. The feasibility of p-type doping, the defect behavior, and radiation effects on the device performance have been discussed. The demonstration of novel and advanced architectures such as phototransistors, highly narrow-band photodetectors, photodetector arrays, and integrated NEMS resonance oscillators for real-time ultraviolet light detection are included. This review may provide better understanding on the optoelectronics properties of the Ga2O3 emerging material to fully exploit its promising optoelectronic applications in deep ultraviolet spectral region.
Low-Light Image Enhancement With Semi-Decoupled Decomposition

Low-light image enhancement is important for high-quality image display and other visual applications. However, it is a challenging task as the enhancement is expected to improve the visibility of an image while keeping its visual naturalness. Retinex-based methods have well been recognized as a representative technique for this task, but they still have the following limitations. First, due to less-effective image decomposition or strong imaging noise, various artifacts can still be brought into enhanced results. Second, although the priori information can be explored to partially solve the first issue, it requires to carefully model the priori by a regularization term and usually makes the optimization process complicated. In this paper, we address these issues by proposing a novel Retinex-based low-light image enhancement method, in which the Retinex image decomposition is achieved in an efficient semi-decoupled way. Specifically, the illumination layer <inline-formula><tex-math notation="LaTeX">$I$</tex-math></inline-formula> is gradually estimated only with the input image <inline-formula><tex-math notation="LaTeX">$S$</tex-math></inline-formula> based on the proposed Gaussian Total Variation model, while the reflectance layer <inline-formula><tex-math notation="LaTeX">$R$</tex-math></inline-formula> is jointly estimated by <inline-formula><tex-math notation="LaTeX">$S$</tex-math></inline-formula> and the intermediate <inline-formula><tex-math notation="LaTeX">$I$</tex-math></inline-formula>. In addition, the imaging noise can be simultaneously suppressed during the estimation of <inline-formula><tex-math notation="LaTeX">$R$</tex-math></inline-formula>. Experimental results on several public datasets demonstrate that our method produces images with both higher visibility and better visual quality, which outperforms the state-of-the-art low-light enhancement methods in terms of several objective and subjective evaluation metrics.
ALDH1+ stem cells demonstrate more stem cell-like characteristics than CD44+/CD24–/low stem cells in different molecular subtypes of breast cancer

Background CD44+/CD24–/low and ALDH1+ are both markers of breast cancer stem cell (BCSC), we compared the biological characteristics of CD44+/CD24–/low and ALDH1+ BCSCs in the four molecular subtypes of breast cancer. Methods Four fresh blocks of breast cancer tissue were obtained from patients with Luminal A, Luminal B, human epidermal receptor-2 (HER2)-overexpression and triple-negative breast cancer, respectively. Flow cytometry was used to sort CD44+/CD24–/low and ALDH1+ BCSCs. The mammosphere (MS) formation experiment and NOD/SCID mouse xenograft experiment were performed to compare the self-renewal and tumorigenic abilities of CD44+/CD24–/low with those of ALDH1+ BCSCs. Results The proportions of CD44+/CD24–/low BCSC and ALDH1+ BCSC in Luminal A, Luminal B, HER2 overexpression and triple-negative subtypes were 12.1% and 8.7%, 2.7% and 5.7%, 0.8% and 8.7%, 0.7% and 4.5%, respectively. The MS formation experiment demonstrated that ALDH1+ BCSC formed significantly more MSs than CD44+/CD24–/low BCSC in the four molecular subtypes (P<0.001). The NOD/SCID mouse xenograft experiment demonstrated that the sizes of grafted tumors formed by ALDH1+ BCSC were larger than those formed by CD44+/CD24–/low BCSC in the four molecular subtypes (P<0.05). Conclusions In the four molecular subtypes of breast cancer, ALDH1+ BCSC demonstrated better self-renewal and tumorigenic abilities than CD44+/CD24–/low BCSC.
Adaptive Filtering Technique for Chronic Wound Analysis under Tele-Wound Network

Efficient diagnosis of chronic wound depends on the quality of digital image that has reached the Tele-Medical Hub through Tele-Wound Network. However, how much ever precaution we take, while capturing the image by digital camera or by smartphone, presence of random/impulse noise would be always there to corrupt the captured image. The wound images give the vital information such as size, wound status, tissue composition and healing rate. In this paper, we have proposed adaptive filtering technique for chronic wound (CW) image analysis under Tele-wound network to improve the diagnosis. Here, the best filter has been chosen which can help to improve the diagnosis of wound. A comparative study of 16 different filters has also been performed on 72 different wound images. The experimental results are given by comparing 8 different parameters. These various parameters are Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), Signal to Noise Ratio (SNR), NegativeAbsolute Error (NAE), Maximum Difference (MD), Mean Structural Similarity Index (MSSIM), Universal Image Quality Index (UIQI) and Mean Absolute Error (MAE). Simulated results shows adaptive median provides better performances with respect to high value of PSNR (66.23), SNR (58.05) and lower value of MSE (3.01), MAE (0.29), and NAE (0.01) between original and filtered image. The proposed methodology will assist the clinicians to take better decision towards diagnosis of CW in terms of qualitative at low-resource setup. Journal of Communication, Navigation, Sensing and Services, Vol. 1, 57–76. doi: 10.13052/jconasense2246-2120.2016. 005 c © 2016 River Publishers. All rights reserved. 58 C. Chakraborty and B. Gupta
On the need for evidence-based general and family practice

As Shakespeare put it (in All's Well That Ends Well): “But thou art too fine in thy evidence; therefore stand aside. ” As today's family practitioner might update it: “How do you expect me to learn all this evidence stuff you are talking about? I am already struggling with budget managers, contracts with secondary care, and prescribing costs. I hardly have enough time to see all my patients, let alone do the educational stuff I am supposed to do. And now yon want me to look up a reference in some electronic library thing whenever someone comes in with an earache? ”
Machine learning and artificial neural network accelerated computational discoveries in materials science

Artificial intelligence (AI) has been referred to as the “fourth paradigm of science,” and as part of a coherent toolbox of data‐driven approaches, machine learning (ML) dramatically accelerates the computational discoveries. As the machinery for ML algorithms matures, significant advances have been made not only by the mainstream AI researchers, but also those work in computational materials science. The number of ML and artificial neural network (ANN) applications in the computational materials science is growing at an astounding rate. This perspective briefly reviews the state‐of‐the‐art progress in some supervised and unsupervised methods with their respective applications. The characteristics of primary ML and ANN algorithms are first described. Then, the most critical applications of AI in computational materials science such as empirical interatomic potential development, ML‐based potential, property predictions, and molecular discoveries using generative adversarial networks (GAN) are comprehensively reviewed. The central ideas underlying these ML applications are discussed, and future directions for integrating ML with computational materials science are given. Finally, a discussion on the applicability and limitations of current ML techniques and the remaining challenges are summarized.
Disease prediction in big data healthcare using extended convolutional neural network techniques

Diabetes Mellitus is one of the growing fatal diseases all over the world. It leads to complications that include heart disease, stroke, and nerve disease, kidney damage. So, Medical Professionals want a reliable prediction system to diagnose Diabetes. To predict the diabetes at earlier stage, different machine learning techniques are useful for examining the data from different sources and valuable knowledge is synopsized. So, mining the diabetes data in an efficient way is a crucial concern. In this project, a medical dataset has been accomplished to predict the diabetes. The R-Studio and Pypark software was employed as a statistical computing tool for diagnosing diabetes. The PIMA Indian database was acquired from UCI repository will be used for analysis. The dataset was studied and analyzed to build an effective model that predicts and diagnoses the diabetes disease earlier.
Cognitive stimulation therapy (CST) for people with dementia in practice: An observational study

Introduction The delivery of cognitive stimulation as a cognitive based psychosocial intervention for people with mild to moderate dementia is supported in the National Institute for Health and Care Excellence guidelines. There is a strong evidence base for its effectiveness in providing improvements in cognition and quality of life for people with dementia. However, less is known about its delivery and its impact using outcome measures when used in practice. Methods A 1-year observational study was conducted, which measured the cognition and quality of life of 89 people with dementia living in care homes and the community and were in receipt of cognitive stimulation therapy and a maintenance programme as part of their usual care in practice. Results A paired sample T-test demonstrated a significant improvement in cognition. Quality of life remained unchanged for people with mild to moderate dementia. Conclusions This study reports promising findings with demonstrated benefits for people with dementia and strengthens the evidence base supporting its use in routine care. However, attention should be given to the level of cognitive impairment of attendees. This research is relevant to the field of occupational therapy as the profession has knowledge of using psychosocial interventions and a commitment to evidence-based practice.
A Li-Ion Battery Thermal Management System Combining a Heat Pipe and Thermoelectric Cooler

The temperature of electric vehicle batteries needs to be controlled through a thermal management system to ensure working performance, service life, and safety. In this paper, TAFEL-LAE895 100Ah ternary Li-ion batteries were used, and discharging experiments at different rates were conducted to study the surface temperature increasing characteristics of the battery. To dissipate heat, heat pipes with high thermal conductivity were used to accelerate dissipating heat on the surface of the battery. We found that the heat pipe was sufficient to keep the battery temperature within the desired range with a midlevel discharge rate. For further improvement, an additional thermoelectric cooler was needed for a high discharge rate. Simulations were completed with a battery management system based on a heat pipe and with a combined heat pipe and thermoelectric cooler, and the results were in line with the experimental results. The findings show that the combined system can effectively reduce the surface temperature of a battery within the full range of discharge rates expected in the battery used.
Theoretical Foundations of Memristor Cellular Nonlinear Networks: Stability Analysis With Dynamic Memristors

If the memristor, used in each cell of a memristive variant of the standard space-invariant Cellular Nonlinear Network (CNN), undergoes analogue memductance changes, the processing element operates as a second-order system. The Dynamic Route Map (DRM) technique, applicable to investigate first-order systems only, is no longer relevant. In this manuscript, a recently introduced methodology, generalizing the DRM technique to second-order systems, is applied to the models of Memristor CNN (M-CNN) cells, accomodating dynamic memristors. This allows to gain insights into the operating principles of these cellular structures, which make computations through the evolution of their states toward prescribed equilibria. Our analysis uncovers all possible local and global phenomena, which may emerge in the cell phase space under zero offset current for any self-feedback synaptic weight. Under these hypotheses, the dynamics of the M-CNN cell may significantly differ from those of a standard space-invariant CNN counterpart. The insertion of an offset current into each cell endows it with further properties, including monostability. The analysis method is used to demonstrate how a non-autonomous memristive array exploits the capability of its cells to feature monostability or bistability, depending upon the respective offset currents, to compute the element-wise logical AND between two binary images.
Characteristics of Black Carbon Particle-Bound Polycyclic Aromatic Hydrocarbons in Two Sites of Nanjing and Shanghai, China

Airborne polycyclic aromatic hydrocarbons (PAHs) are of great concern to human health due to their potential high toxicity. Understanding the characteristics and sources of PAHs, as well as the governing factors, is therefore critical. PAHs and refractory black carbon (rBC) are both from combustion sources. This work, for the first time, investigated exclusively the rBC-bound PAH properties by using a laser-only Aerodyne soot-particle aerosol mass spectrometer (SP-AMS). This technique offers highly time-resolved PAH results that a traditional offline measurement is unable to provide. We analyzed two datasets conducted in urban Shanghai during the fall of 2018 and in suburban Nanjing during the winter of 2017, respectively. Results show that the average concentration of PAHs in Nanjing was much higher than that in Shanghai. Nanjing PAHs contained more low molecular weight components while Shanghai PAHs contained more high molecular weight ones. PAHs in Shanghai presented two peaks in early morning and evening, while Nanjing PAHs had only one significant morning peak, but remained high throughout the nighttime. A multi-linear regression algorithm combined with positive matrix factorization (PMF) analyses on sources of PAHs reveals that the industry emissions contributed the majority of PAHs in Nanjing (~80%), while traffic emissions dominated PAHs in Shanghai (~70%). We further investigated the relationships between PAHs with various factors. PAHs in both sites tended to positively correlate with primary pollutants, including primary organic aerosol (OA) factors, and gaseous pollutants of CO, NO2 and SO2, but negatively correlated with secondary OA factors and O3. This result highlights the enhancement of rBC-bound PAHs level due to primary emissions and their oxidation loss upon atmospheric aging reactions. High concentration of PAHs seemed to frequently appear under low temperature and high relative humidity conditions, especially in Shanghai.
Emotion Dynamics and the Association With Depressive Features and Borderline Personality Disorder Traits: Unique, Specific, and Prospective Relationships

Patterns of emotional change in daily life have been consistently linked to depressive and borderline personality disorder (BPD) features. However, dynamic measures and average affect show considerable statistical overlap, and depressive and BPD features are comorbid. Moreover, the prospective nature of these relationships is unclear. We used a measurement burst design in which 202 young adults with varying levels of psychopathological features participated in a week-long experience sampling at baseline and 1-year follow-up. Taking overlap into account, we found that BPD traits were uniquely and specifically linked to higher levels of variability in negative affect (NA). For depressive features, indications were found for a specific association with inertia of NA, but these results were not robust and consistent. In fact, overall, incremental predictive power of the dynamic measures above mean affect was limited, especially for depressive features. Prospective relationships showed that psychopathological features predicted stronger emotion dynamic patterns 1 year later rather than the other way around.