Bioecological Theory and Risk Management: A Model for School Risk Planning

With the vulnerability, unpredictability, and ambiguity of the situation, schools all over the world have faced a variety of restrictions and unprecedented risks that caused some to cease operations and classes permanently or for an extended period of time. The concept of risk has become closely associated with every school process and structure so as to aid them in adapting to the current situation. This paper explores the concept of risk management and risks planning through the lens of school management and the Plan-Do-Check-Act (PDCA) Cycle. Furthermore, the researchers link the permeation of the direct and indirect effects of risks in the school system by reflecting on the layers of the school’s bioecological nest as adapted from the Bioecological Theory of Urie Bronfenbrenner. Finally, this paper suggests a model for risk planning that can help school administrators and leaders in managing risks and aid future researchers in studying concepts related to risk management

OpenFed: A Comprehensive and Versatile Open-Source Federated Learning Framework

Recent developments in Artificial Intelligence techniques have enabled their successful application across a spectrum of commercial and industrial settings. However, these techniques require large volumes of data to be aggregated in a centralized manner, forestalling their applicability to scenarios wherein the data is sensitive or the cost of data transmission is prohibitive. Federated Learning alleviates these problems by decentralizing model training, thereby removing the need for data transfer and aggregation. To advance the adoption of Federated Learning, more research and development needs to be conducted to address some important open questions. In this work, we propose OpenFed, an open-source software framework for end-to-end Federated Learning. OpenFed reduces the barrier to entry for both researchers and downstream users of Federated Learning by the targeted removal of existing pain points. For researchers, OpenFed provides a framework wherein new methods can be easily implemented and fairly evaluated against an extensive suite of benchmarks. For downstream users, OpenFed allows Federated Learning to be plug and play within different subject-matter contexts, removing the need for deep expertise in Federated Learning.Comment: 18 pages, 3 figures, 1 tabl

Reconstruction of Phonated Speech from Whispers Using Formant-Derived Plausible Pitch Modulation

Whispering is a natural, unphonated, secondary aspect of speech communications for most people. However, it is the primary mechanism of communications for some speakers who have impaired voice production mechanisms, such as partial laryngectomees, as well as for those prescribed voice rest, which often follows surgery or damage to the larynx. Unlike most people, who choose when to whisper and when not to, these speakers may have little choice but to rely on whispers for much of their daily vocal interaction. Even though most speakers will whisper at times, and some speakers can only whisper, the majority of today’s computational speech technology systems assume or require phonated speech. This article considers conversion of whispers into natural-sounding phonated speech as a noninvasive prosthetic aid for people with voice impairments who can only whisper. As a by-product, the technique is also useful for unimpaired speakers who choose to whisper. Speech reconstruction systems can be classified into those requiring training and those that do not. Among the latter, a recent parametric reconstruction framework is explored and then enhanced through a refined estimation of plausible pitch from weighted formant differences. The improved reconstruction framework, with proposed formant-derived artificial pitch modulation, is validated through subjective and objective comparison tests alongside state-of-the-art alternatives

Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions

Breast cancer has reached the highest incidence rate worldwide among all malignancies since 2020. Breast imaging plays a significant role in early diagnosis and intervention to improve the outcome of breast cancer patients. In the past decade, deep learning has shown remarkable progress in breast cancer imaging analysis, holding great promise in interpreting the rich information and complex context of breast imaging modalities. Considering the rapid improvement in the deep learning technology and the increasing severity of breast cancer, it is critical to summarize past progress and identify future challenges to be addressed. In this paper, we provide an extensive survey of deep learning-based breast cancer imaging research, covering studies on mammogram, ultrasound, magnetic resonance imaging, and digital pathology images over the past decade. The major deep learning methods, publicly available datasets, and applications on imaging-based screening, diagnosis, treatment response prediction, and prognosis are described in detail. Drawn from the findings of this survey, we present a comprehensive discussion of the challenges and potential avenues for future research in deep learning-based breast cancer imaging.Comment: Survey, 41 page

A Unified Framework for GPS Code and Carrier-Phase Multipath Mitigation Using Support Vector Regression

Multipath mitigation is a long-standing problem in global positioning system (GPS) research and is essential for improving the accuracy and precision of positioning solutions. In this work, we consider multipath error estimation as a regression problem and propose a unified framework for both code and carrier-phase multipath mitigation for ground fixed GPS stations. We use the kernel support vector machine to predict multipath errors, since it is known to potentially offer better-performance traditional models, such as neural networks. The predicted multipath error is then used to correct GPS measurements. We empirically show that the proposed method can reduce the code multipath error standard deviation up to 79% on average, which significantly outperforms other approaches in the literature. A comparative analysis of reduction of double-differential carrier-phase multipath error reveals that a 57% reduction is also achieved. Furthermore, by simulation, we also show that this method is robust to coexisting signals of phenomena (e.g., seismic signals) we wish to preserve

Bicarbonate Recycling by HIF‐1–Dependent Carbonic Anhydrase Isoforms 9 and 12 Is Critical in Maintaining Intracellular pH and Viability of Nucleus Pulposus Cells

Intervertebral disc degeneration is a ubiquitous condition closely linked to chronic low‐back pain. The health of the avascular nucleus pulposus (NP) plays a crucial role in the development of this pathology. We tested the hypothesis that a network comprising HIF‐1α, carbonic anhydrase (CA) 9 and 12 isoforms, and sodium‐coupled bicarbonate cotransporters (NBCs) buffer intracellular pH through coordinated bicarbonate recycling. Contrary to the current understanding of NP cell metabolism, analysis of metabolic‐flux data from Seahorse XF analyzer showed that CO2 hydration contributes a significant source of extracellular proton production in NP cells, with a smaller input from glycolysis. Because enzymatic hydration of CO2 is catalyzed by plasma membrane‐associated CAs we measured their expression and function in NP tissue. NP cells robustly expressed isoforms CA9/12, which were hypoxia‐inducible. In addition to increased mRNA stability under hypoxia, we observed binding of HIF‐1α to select hypoxia‐responsive elements on CA9/12 promoters using genomic chromatin immunoprecipitation. Importantly, in vitro loss of function studies and analysis of discs from NP‐specific HIF‐1α null mice confirmed the dependency of CA9/12 expression on HIF‐1α. As expected, inhibition of CA activity decreased extracellular acidification rate independent of changes in HIF activity or lactate/H+ efflux. Surprisingly, CA inhibition resulted in a concomitant decrease in intracellular pH that was mirrored by inhibition of sodium‐bicarbonate importers. These results suggested that extracellular bicarbonate generated by CA9/12 is recycled to buffer cytosolic pH fluctuations. Importantly, long‐term intracellular acidification from CA inhibition lead to compromised cell viability, suggesting that plasma‐membrane proton extrusion pathways alone are not sufficient to maintain homeostatic pH in NP cells. Taken together, our studies show for the first time that bicarbonate buffering through the HIF‐1α–CA axis is critical for NP cell survival in the hypoxic niche of the intervertebral disc. © 2017 American Society for Bone and Mineral Research.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/142506/1/jbmr3293.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/142506/2/jbmr3293-sup-0001-SuppData-S1.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/142506/3/jbmr3293_am.pd

Integrating case-finding and initial management for osteoarthritis, anxiety, and depression into primary care long-term condition reviews: results from the ENHANCE pilot trial.

Background: Multimorbidity is increasingly the norm; however, primary care remains focused on single diseases. Osteoarthritis, anxiety, and depression are frequently comorbid with other long-term conditions (LTCs), but rarely prioritized by clinicians.Objectives: To test the feasibility of a randomized controlled trial (RCT) of an intervention integrating case-finding and management for osteoarthritis, anxiety, and depression within LTC reviews.Methods: A pilot stepped-wedge RCT across 4 general practices recruited patients aged >= A5 years attending routine LTC reviews. General practice nurses provided usual LTC reviews (control period), then, following training, delivered the ENHANCE LTC review (intervention period). Questionnaires, an ENHANCE EMIS-embedded template and consultation audio-recordings, were used in the evaluation.Results: General practice recruitment and training attendance reached prespecified success criteria. Three hundred and eighteen of 466 (68%) of patients invited responded; however, more patients were recruited during the control period (206 control, 112 intervention). Eighty-two percent and 78% returned their 6-week and 6-month questionnaires, respectively. Integration of the ENHANCE LTC review into routine LTC reviews varied. Case-finding questions were generally used as intended for joint pain, but to a lesser extent for anxiety and depression. Initial management through referrals and signposting were lacking, and advice was more frequently provided for joint pain. The stepped-wedge design meant timing of the training was challenging and yielded differential recruitment.Conclusion: This pilot trial suggests that it is feasible to deliver a fully powered trial in primary care. Areas to optimize include improving the training and reconsidering the stepped-wedge design and the approach to recruitment by targeting those with greatest need

Improving risk-adjusted performance in high frequency trading using interval type-2 fuzzy logic

In this paper, we investigate the ability of higher order fuzzy systems to handle increased uncertainty, mostly induced by the market microstructure noise inherent in a high frequency trading (HFT) scenario. Whilst many former studies comparing type-1 and type-2 Fuzzy Logic Systems (FLSs) focus on error reduction or market direction accuracy, our interest is predominantly risk-adjusted performance and more in line with both trading practitioners and upcoming regulatory regimes. We propose an innovative approach to design an interval type-2 model which is based on a generalisation of the popular type-1 ANFIS model. The significance of this work stems from the contributions as a result of introducing type-2 fuzzy sets in intelligent trading algorithms, with the objective to improve the risk-adjusted performance with minimal increase in the design and computational complexity. Overall, the proposed ANFIS/T2 model scores significant performance improvements when compared to both standard ANFIS and Buy-and-Hold methods. As a further step, we identify a relationship between the increased trading performance benefits of the proposed type-2 model and higher levels of microstructure noise. The results resolve a desirable need for practitioners, researchers and regulators in the design of expert and intelligent systems for better management of risk in the field of HFT

Improving the care of people with long-term conditions in primary care: protocol for the ENHANCE pilot trial

Background:Long-term conditions (LTCs) are important determinants of quality of life and healthcare expenditure worldwide. Whilst multimorbidity is increasingly the norm in primary care, clinical guidelines and the delivery of care remain focused on single diseases, resulting in poorer clinical outcomes. Osteoarthritis, and anxiety and/or depression frequently co-occur with other LTCs, yet are seldom prioritized by the patient or clinician, resulting in higher levels of disability, poorer prognosis, and increased healthcare costs.Objective: To examine the feasibility and acceptability of an integrated approach to LTC management, tackling the underdiagnosis and under-management of osteoarthritis-related pain and anxiety and/or depression in older adults with other LTCs in primary care.Design: The ENHANCE study is a pilot stepped-wedge cluster randomized controlled trial to test the feasibility and acceptability of a nurse-led ENHANCE LTC review consultation for identifying, assessing, and managing joint pain, and anxiety and/or depression in patients attending LTCreviews. Specific objectives (process evaluation and research outcomes) will be achieved through a theoretically informed mixed-methods approach using participant self-reported questionnaires, a medical record review, an ENHANCE EMIS template, qualitative interviews, and audio recordings of the ENHANCE LTC review.Discussion: Success of the pilot trial will be measured against the level of the primary care team engagement, assessment of training delivery, and degree of patient recruitment and retention. Patient satisfaction and treatment fidelity will also be explored

A Study of the Influence of Sex on Genome Wide Methylation

Sex differences in methylation status have been observed in specific gene-disease studies and healthy methylation variation studies, but little work has been done to study the impact of sex on methylation at the genome wide locus-to-locus level or to determine methods for accounting for sex in genomic association studies. In this study we investigate the genomic sex effect on saliva DNA methylation of 197 subjects (54 females) using 20,493 CpG sites. Three methods, two-sample T-test, principle component analysis and independent component analysis, all successfully identify sex influences. The results show that sex not only influences the methylation of genes in the X chromosome but also in autosomes. 580 autosomal sites show strong differences between males and females. They are found to be highly involved in eight functional groups, including DNA transcription, RNA splicing, membrane, etc. Equally important is that we identify some methylation sites associated with not only sex, but also other phenotypes (age, smoking and drinking level, and cancer). Verification was done through an independent blood cell DNA methylation data (1298 CpG sites from a cancer panel array). The same genomic site-specific influence pattern and potential confounding effects with cancer were observed. The overlapping rate of identified sex affected genes between saliva and blood cell is 81% for X chromosome, and 8% for autosomes. Therefore, correction for sex is necessary. We propose a simple correction method based on independent component analysis, which is a data driven method and accommodates sample differences. Comparison before and after the correction suggests that the method is able to effectively remove the potentially confounding effects of sex, and leave other phenotypes untouched. As such, our method is able to disentangle the sex influence on a genome wide level, and paves the way to achieve more accurate association analyses in genome wide methylation studies