bluuebunny/crossref_metadata_2025 huggingface dataset:
Enhancing environmental policy through evidence synthesis: a review of the Environmental Evidence for the Future (EEF) Initiative
Authors: Monk Kathryn Anne
Published: March 2024

The Environmental Evidence for the Future (EEF) Initiative emerged in response to the challenges and opportunities presented by the UK’s decision to leave the European Union and its associated Environmental Frameworks. The Natural Environment Research Council (NERC), working closely with the Collaboration for Environmental Evidence (CEE) and UK stakeholders, developed the initiative to identify and address crucial evidence gaps, offering a long-term vision for environmental policy and sustainability. The EEF Initiative progressed through three stages: strategic priority identification, NERC panel award selection, and the production of Systematic Maps of existing evidence. The first stage involved collaborative workshops across the UK to identify key knowledge gaps in environmental science. The subsequent prioritisation resulted in 65 challenges across 10 thematic areas. The second stage saw NERC initiating, with CEE support, an open call for research proposals emphasising the use of evidence synthesis methodology. The selection process, balancing topic importance and applicant expertise, led to funding for five projects. The final stage involved the production of Systematic Maps of existing evidence based on the CEE Guidelines and Standards, providing a structured overview of existing literature on specific topics. The EEF Initiative demonstrated effective collaboration between UKRI (NERC), an independent non-profit (CEE), academia, and government agencies, addressing critical environmental challenges through rigorous evidence synthesis methodologies. The programme enhanced understanding and utilisation of these methodologies within the research community. Key lessons include the importance of inclusive priority-setting, differentiation between broad policy questions and specific Systematic Map questions, recognition of the value of Systematic Maps, and the role of experience in evidence synthesis teams. As policymakers and researchers navigate environmental policies in a resource-constrained environment, the EEF Initiative highlights the cost-effectiveness and efficiency of systematic mapping and review processes for evidence-based decision-making. The success of funding through NERC sets a precedent for future thematic evidence focused programmes, emphasising the need for continued support in developing synthesis skills among researchers and encouraging direct government commissions for targeted and responsive evidence. The EEF Initiative serves as a model for effective collaboration, providing valuable insights into addressing evidence gaps and shaping evidence-based policymaking in an ever-evolving environmental landscape.
A personal social knowledge network (PSKN) facilitates learners’ wayfinding and its differences in behavior patterns between high and low performers in connectivist learning
Authors: Duan Jinju , Xie Kui , Zhao Qiuhua
Published: March 2024

Wayfinding, which is a part of learning in connectivist learning, involves consolidating a wide variety of resources and information and building connections among them. However, learners often encounter difficulties in wayfinding, and are lost without technological support in connectivist learning. This study examined the wayfinding processes occurring within a network of learners in a personal social knowledge network (PSKN), explored differences in behavior patterns between high and low performers in PSKN. The results reveal the diversity and complexity of wayfinding in a PSKN, including finding and connecting nodes, forming cognitive maps, finding and filtering information, and creating new nodes. Moreover, the characteristics of wayfinding in the PSKN differed across participants, and high- and low-performing participants demonstrated different and unique wayfinding behavioral patterns, which provided a basis for comprehensive analyses of wayfinding. These findings can be used to provide instructional support and network navigation in connectivist learning for learners at various performance levels. The proposed PSKN shows promise in facilitate wayfinding including finding nodes and connecting nodes, as well as relations between knowledge nodes and the course base demonstrated by PSKN, providing great convenience for learners to form cognitive maps based on the node sequence. Compared with current studies, this research focuses on diversified interaction data and resource behavior rather than teaching videos and quizzes or exercises as the main resources and considering that course and technological factors influence the ways in which learners access resources in connectivist learning.
3R-Refinement principles: elevating rodent well-being and research quality
Authors: Rinwa Puneet , Eriksson Marie , Cotgreave Ian , Bäckberg Matilda
Published: March 2024

This review article delves into the details of the 3R-Refinement principles as a vital framework for ethically sound rodent research laboratory. It highlights the core objective of the refinement protocol, namely, to enhance the well-being of laboratory animals while simultaneously improving the scientific validity of research outcomes. Through an exploration of key components of the refinement principles, the article outlines how these ethics should be implemented at various stages of animal experiments. It emphasizes the significance of enriched housing environments that reduce stress and encourage natural behaviors, non-restraint methods in handling and training, refined dosing and sampling techniques that prioritize animal comfort, the critical role of optimal pain management and the importance of regular animal welfare assessment in maintaining the rodents well-being. Additionally, the advantages of collaboration with animal care and ethics committees are also mentioned. The other half of the article explains the extensive benefits of the 3R-Refinement protocol such as heightened animal welfare, enhanced research quality, reduced variability, and positive feedback from researchers and animal care staff. Furthermore, it addresses avenues for promoting the adoption of the protocol, such as disseminating best practices, conducting training programs, and engaging with regulatory bodies. Overall, this article highlights the significance of 3R-Refinement protocol in aligning scientific advancement with ethical considerations along with shaping a more compassionate and responsible future for animal research.
Randomized Controlled Trial of Virtually Delivered Cognitive Behavioral Therapy for Insomnia to Address Perceived Cancer-Related Cognitive Impairment in Cancer Survivors
Authors: Garland Sheila N. , Tulk Joshua , Savard Josée , Rash Joshua A. , Browne Sondria , Urquhart Robin , Seal Melanie , Thoms John , Laing Kara
Published: March 2024

PURPOSE

Comorbid insomnia and cancer-related cognitive impairment (CRCI) are experienced by up to 26% of individuals diagnosed with cancer. This study examined the efficacy and durability of cognitive behavioral therapy for insomnia (CBT-I) on perceived CRCI in cancer survivors.

METHODS

Atlantic Canadian cancer survivors with insomnia and CRCI were randomly assigned to receive seven weekly virtual CBT-I sessions (n = 63) or placed in a waitlist control group (n = 69) to receive treatment after the waiting period. Participants completed assessments at baseline, 1 month (mid-treatment), and 2 months (post-treatment). Age- and education-adjusted mixed-effects models using intention-to-treat principles assessed change at post-treatment. Data from both groups were then pooled to assess the durability of effects at 3 and 6 months. A mediation analysis examined whether change in insomnia symptoms mediated the effect of CBT-I on cognitive outcomes.

RESULTS

The mean age of the sample was 60 years, 77% were women, and breast cancer was the most common diagnosis (41%). The treatment group reported an 11.35-point reduction in insomnia severity, compared with a 2.67-point reduction in the waitlist control group ( P < .001). The treatment group had a greater overall improvement than the waitlist control on perceived cognitive impairment ( P < .001; d = 0.75), cognitive abilities ( P < .001; d = 0.92), and impact on quality of life ( P < .001; d = 1.01). These improvements were maintained at follow-up. Change in insomnia symptoms fully mediated the effect of CBT-I on subjective cognitive outcomes.

CONCLUSION

Treating insomnia with CBT-I produces clinically meaningful and durable improvements in CRCI. There is an urgent need increase access to evidence-based treatment for insomnia in cancer centers and the community.
Follicle-Stimulating Hormone and Diabetes in Postmenopausal Women: A Systematic Review and Meta-Analysis
Authors: Saei Ghare Naz Marzieh , Farhadi-Azar Mahbanoo , Noroozzadeh Mahsa , Farahmand Maryam , Ramezani Tehrani Fahimeh
Published: March 2024

Context

The co-occurrence of hormonal changes during menopause and the risk of cardio-metabolic disorders has been well studied.

Objective

We explored the association of circulating levels of follicle-stimulating hormone (FSH) with diabetes (DM) among postmenopausal women.

Method

In this systematic review and meta-analysis, the search was performed in PubMed, Scopus, Web of Sciences, Epistemonikos, and Cochrane Library up to September 2023. Risk of bias was assessed by Newcastle-Ottawa Quality Assessment Scale. Pooled estimates of mean differences in FSH levels were compared between postmenopausal women with and without DM. Correlations between FSH and fasting blood glucose (FBG)/insulin/homeostatic model assessment for insulin resistance (HOMA-IR) as well as pooled effect sizes with their 95% CIs for risk of DM were calculated.

Results

In this study, 14 articles, including 7878 postmenopausal women, met eligibility criteria. Most of the included studies had a low/moderate risk of bias. Women with DM had significantly lower FSH levels than those without DM (standardized mean difference [SMD] −0.751 [95% CI, −1.129 to −.372], I2 = 82.46%, n = 1416). The pooled effect size for diabetes was 0.861 (95% CI, 0.740-1.001; I2 = 80.11%). The pooled risk estimate for DM based on the categorical FSH levels (high vs low) was (HR = 0.550; 95% CI, 0.356-0.850, I2 = 0). The significant inverse correlation was found between FSH levels and glycemic parameters: FBG (r= −0.285 [95% CI −0.441 to −0.113]; n = 1229), HOMA-IR (r = −0.241[−0.378 to −0.0924]; n = 1229) and insulin (r = −0.337 [−0.434 to −0.232]; n = 959)]. There were no statistically significant differences between estradiol levels among diabetic and nondiabetic groups; however, the SMD for luteinizing hormone was similar to that reported for FSH.

Conclusion

The available data indicated an indirect association between FSH levels and glucose disturbances among postmenopausal women, notwithstanding heterogeneity among included studies, and the complexity of various influential factors needs to be considered. Further efforts should be made to clarify the underlying mechanisms.
Serial Diurnal Salivary Cortisol Profiles in 667 Pregnant Women—Association With Cardiometabolic Complications
Authors: Schowe Alicia M , Czamara Darina , Lahti-Pulkkinen Marius , Girchenko Polina , Castro-Quintas Águeda , Fañanas Lourdes , Binder Elisabeth B , Räikkönen Katri
Published: March 2024

Context

Maternal obesity, hypertensive pregnancy disorders, and gestational diabetes (GDM) are linked to an increased risk of negative offspring health outcomes. This association may be mediated by maternal hypothalamic-pituitary-adrenal axis (HPA axis) activity, resulting in elevated maternal cortisol levels and fetal exposure, but evidence remains scarce.

Objective

We (1) examined maternal diurnal cortisol profiles longitudinally across gestation, and (2) explored associations with maternal cardiometabolic complications.

Methods

Women in the InTraUterine sampling in early pregnancy (ITU) study (n = 667) provided 7 salivary cortisol samples from awakening to bedtime up to 3 times during pregnancy (median gestational week 19.3, 25.7, and 38.1; n = 9356 samples). Changes in cortisol awakening response (CAR) and diurnal slope (indicative of HPA axis activity) and their associations with maternal body mass index (BMI), hypertensive pregnancy disorders and GDM were examined using linear mixed models.

Results

The CAR declined in 60% to 67% of women, and the diurnal slope attenuated from early to late pregnancy (b = 0.006; P = .001). Higher BMI was associated with less decline in CAR (b = 0.031; P = .0004) and less attenuation in diurnal slope from early to late pregnancy (b = −0.001; P = .006). Hypertensive pregnancy disorders and GDM were not significantly associated with diurnal cortisol profiles.

Conclusion

The attenuation in CAR and diurnal slope support HPA axis hyporesponsivity during pregnancy. Less attenuation of both markers in women with a higher BMI may indicate reduced adaption of the HPA axis to pregnancy, presenting a mechanistic link to offspring health outcomes.
The Iconic-Symbolic Spectrum
Authors: Greenberg Gabriel
Published: March 2024

It is common to distinguish two great families of representation. Symbolic representations include logical and mathematical symbols, words, and complex linguistic expressions. Iconic representations include dials, diagrams, maps, pictures, 3-dimensional models, and depictive gestures. This essay describes and motivates a new way of distinguishing iconic from symbolic representation. It locates the difference not in the signs themselves, nor in the contents they express, but in the semantic rules by which signs are associated with contents. The two kinds of rule have divergent forms, occupying opposite poles on a spectrum of naturalness. Symbolic rules are composed entirely of primitive juxtapositions of sign types with contents, while iconic rules determine contents entirely by uniform natural relations with sign types. This distinction is marked explicitly in the formal semantics of familiar sign systems, both for atomic first-order representations, like words, pixel colors, and dials, and for complex second-order representations, like sentences, diagrams, and pictures.
Role of Copeptin in Predicting Postoperative Hyponatremia and Hypernatremia in Patients Undergoing Endoscopic Pituitary Adenoma Surgery
Authors: Binu Anand , HV Easwer , Nair Prakash , Stanley Antony , Patel Biren Khimji , Urulangodi Madhusoodanan , Mandagini Geetha , Jose Tania
Published: March 2024

BACKGROUND AND OBJECTIVES:

Arginine vasopressin (AVP) is an important hormone responsible for maintaining sodium homeostasis after pituitary surgery. The measurement of AVP levels is difficult because of its short half-life (t1/2). Copeptin is a preprohormone of AVP, and it is a more stable peptide, which can be used as surrogate marker for AVP. This study aims to assess the role of copeptin as a predictor of postoperative hyponatremia and hypernatremia in patients undergoing endoscopic pituitary adenoma surgery.

METHODS:

This prospective study included 50 patients who underwent endoscopic pituitary adenoma surgery. Serum copeptin levels of these patients were assessed (1) preoperatively (C1), (2) at extubation (C2), and (3) postoperative day 4 (C3). Perioperative data regarding fluid and sodium balance were collected from patients. Statistical analysis was done using the above data.

RESULTS:

The copeptin values were assessed against the sodium disturbances. 100% of patients who developed transient diabetes insipidus had a relative decrease in C2 from C1 (P - .0002). 88% of patients who developed early hyponatremia had a relative increase in C2 as compared with C1 (P < .01). 75% of patients who developed delayed hyponatremia had a relative increase in C3 as compared with C1 (P = .003).

CONCLUSION:

A relative increase or decrease in early change in copeptin (C2-C1) can predict development of early hyponatremia or transient central diabetes insipidus, respectively. A relative increase in delayed change in copeptin (C3-C1) can predict development of delayed hyponatremia.
Microsurgical Resection of Brainstem Cavernous Malformations in Older Adults: A Multicenter, 30-Year Experience
Authors: Catapano Joshua S. , Koester Stefan W. , Rumalla Kavelin , Lamorie-Foote Krista , Winkler Ethan A. , Benner Dimitri , Scherschinski Lea , Baranoski Jacob F. , Cole Tyler S. , Rudy Robert F. , Graffeo Christopher S. , Srinivasan Visish M. , Spetzler Robert F. , Lawton Michael T.
Published: March 2024

BACKGROUND AND OBJECTIVES:

Microsurgical resection is the only curative intervention for symptomatic brainstem cavernous malformations (BSCMs), but the management of these lesions in older adults (≥65 years) is not well described. This study sought to address this gap by examining the safety and efficacy of BSCM resection in a cohort of older adults.

METHODS:

Records of patients who underwent BSCM resection over a 30-year period were reviewed retrospectively. Baseline characteristics and outcomes were compared between older (≥65 years) and younger (<65 years) patients.

RESULTS:

Of 550 patients with BSCM who met inclusion criteria, 41 (7.5%) were older than 65 years. Midbrain (43.9% vs 26.1%) and medullary lesions (19.5% vs 13.6%) were more common in the older cohort than in the younger cohort (P = .01). Components of the Lawton BSCM grading system (ie, lesion size, crossing axial midpoint, developmental venous anomaly, and timing of hemorrhage) were not significantly different between cohorts (P ≥ .11). Mean (SD) Elixhauser comorbidity score was significantly higher in older patients (1.86 [1.06]) than in younger patients (0.66 [0.95]; P < .001). Older patients were significantly more likely than younger patients to have poor outcomes at final follow-up (28.9% vs 13.8%, P = .01; mean follow-up duration, 28.7 [39.1] months). However, regarding relative neurological outcome (preoperative modified Rankin Scale to final modified Rankin Scale), rate of worsening was not significantly different between older and younger patients (23.7% vs 14.9%, P = .15).

CONCLUSION:

BSCMs can be safely resected in older patients, and when each patient's unique health status and life expectancy are taken into account, these patients can have outcomes similar to younger patients.
Hypertonic Saline Solution Versus Mannitol for Brain Relaxation During Craniotomies: A Systematic Review and Updated Meta-Analysis
Authors: Menegaz de Almeida Artur , Viana Patrícia , Marinheiro Gabriel , Hoffmann Relvas Jessica , Lopes Lucca , Lima Guilherme Gustavo , Zanette Giusti João Antônio , Oliveira Paloma , Azevedo Silva Kaiser Cabral Mauro André , Carvalho Santos Renato , Medani Khalid
Published: March 2024

BACKGROUND AND OBJECTIVES:

The preferred osmotic agent used for brain relaxation during craniotomies remains unclear, either mannitol (MAN) or hypertonic saline (HTS). Hence, we sought to compare these solutions in this population.

METHODS:

MEDLINE, Embase, and Cochrane databases were systematically searched until August 02, 2023. Data were examined using the Mantel-Haenszel method and 95% CIs. Heterogeneity was assessed using I
2 statistics. Meta-regression analysis was conducted to evaluate a possible link between Brain Relaxation Score and tumor volume. R, version 4.2.3, was used for statistical analysis.

RESULTS:

A total of 16 randomized controlled trials and 1031 patients were included, of whom 631 (61%) underwent surgery for supratentorial tumor resection. Compared with MAN, HTS achieved better rates of brain relaxation (80% vs 71%; odds ratio [OR] 1.68; 95% CI 1.22-2.33; P = .001; I
2 = 0%), which was also demonstrated in the subgroup analysis of patients with supratentorial brain tumor (78% vs 65%; OR 2.02; 95% CI 1.36-2.99; P = .0005; I
2 = 0%); a minor number of patients requiring a second dose of osmotic agent (14% vs 28%; OR 0.43; 95% CI 0.27-0.69; P = .0003; I
2 = 0%); a lower fluid intake (mean difference −475.9341 mL; 95% CI −818.8952 to −132.9730; P = .007; I
2 = 88%); and lower urine output (mean difference −462.0941 mL; 95% CI −585.3020 to −338.8862; P = <.001; I
2 = 96%). Hospital length of stay and focal neurological deficits did not reach a statistically significant difference between groups.

CONCLUSION:

In this updated meta-analysis, consistent results suggest that HTS is associated with more beneficial outcomes than MAN in patients undergoing craniotomy.
Redistributive Capital Taxation Revisited
Authors: Kina Özlem , Slavík Ctirad , Yazici Hakki
Published: March 2024

This paper uses a rich quantitative model with endogenous skill acquisition to show that capital-skill complementarity provides a quantitatively significant rationale to tax capital for redistributive governments. The optimal capital income tax rate is 67 percent, while it is 61 percent in an identically calibrated model without capital-skill complementarity. The skill premium falls from 1.9 to 1.84 along the transition following the optimal reform in the capital-skill complementarity model, implying substantial indirect redistribution from skilled to unskilled workers. These results show that a redistributive government should take into account capital-skill complementarity when taxing capital. (JEL D31, H21, H23, H24, H25, J24, J31)
Home-based walking intervention for middle-aged migrant women using 360-degree virtual videos and a wearable activity tracker: A mixed-methods pilot study
Authors: Chae Duckhee , Asami Keiko , Kim Jaseon , Kim Kukhyeon , Ryu Jeeheon , Doorenbos Ardith Z.
Published: March 2024

Purpose: To sustain behavior change, an intervention strategy that considers the contribution of affect to daily physical activity behavior regulation is needed. Although virtual reality-based physical activity interventions have the potential to improve emotional status, interventions using virtual reality videos in a free-living environment are lacking. This pilot study assessed the feasibility and preliminary efficacy of a home-based intervention using 360-degree virtual videos and wearable activity trackers to improve mood and physical activity. Methods: A one-group pilot study of 12 middle-aged migrant women asked participants to watch virtual reality videos and perform moderate-intensity walking 5 days per week for 4 weeks, then complete surveys and focus group interviews. The intervention’s feasibility and preliminary efficacy were assessed by examining recruitment, retention, adherence, acceptability, mood, physical activity, and exercise self-efficacy. Results: A word-of-mouth approach was effective for recruiting participants. Although the weekly intervention adherence rate ranged from 53.5% to 83.5%, retention (92.3%) and acceptability (91.7%) were high. Participants were satisfied with the “visual status indicators,” “sense of accomplishment and confidence,” “emotional engagement and sense of presence,” “joy from exercise,” “external motivation through supervision,” “easy to control virtual reality device,” and “extra benefits” of the intervention. Participants had significant decreases in negative affect (p=.016). Positive affect, physical activity, and exercise self-efficacy showed trends toward improvement. Conclusion: This home-based intervention employing virtual reality videos and Fitbit activity trackers is feasible and shows preliminary efficacy in improving mood. Further research is warranted to evaluate its effectiveness in a more rigorous randomized controlled trial.
Factors influencing dementia preventive behaviors of older adults at high risk of dementia: Application of extended health belief model
Authors: Jung So Hee , Gu Mee Ock
Published: March 2024

Purpose: The purpose of this study was to identify the factors influencing dementia preventive behaviors of older adults at high risk of dementia based on extended health belief model. Methods: The subjects were 140 older adults at high risk of dementia living in H-gun, Gyeongsangnam-do, Republic of Korea. The data was collected from April 21 to May 28, 2021 by using structured questionnaires. The data was analyzed using t-test, ANOVA, Scheffé test, Pearson’s correlation coefficient, and hierarchical multiple regression by SPSS/WIN 24.0 program. Results: The mean score of dementia preventive behaviors of older adults at high risk of dementia was 3.47±0.49 (range 1-5). The factors influencing dementia preventive behaviors were self-efficacy (β=.82, p<.001), cues to action(β=.17, p=.013), ages 75-79 (β=0.35, p=.003; reference: ages 65-69), ages ≥80 (β=0.27, p=.021; reference: ages 65-69), which together explained 82.0% of total variance in dementia preventive behaviors (F=25.21, p<.001). Conclusion: Based on the results of this study, it is highly recommended to develop and apply the dementia prevention program that can increase self-efficacy and cues to action for improving dementia preventive behavior of older adults at high risk of dementia.
Multivariate Functional Clustering with Variable Selection and Application to Sensor Data from Engineering Systems
Authors: Jin Zhongnan , Min Jie , Hong Yili , Du Pang , Yang Qingyu
Published: March 2024

Multisensor data that track system operating behaviors are widely available nowadays from various engineering systems. Measurements from each sensor over time form a curve and can be viewed as functional data. Clustering of these multivariate functional curves is important for studying the operating patterns of systems. One complication in such applications is the possible presence of sensors whose data do not contain relevant information. Hence, it is desirable for the clustering method to equip with an automatic sensor selection procedure. Motivated by a real engineering application, we propose a functional data clustering method that simultaneously removes noninformative sensors and groups functional curves into clusters using informative sensors. Functional principal component analysis is used to transform multivariate functional data into a coefficient matrix for data reduction. We then model the transformed data by a Gaussian mixture distribution to perform model-based clustering with variable selection. Three types of penalties, the individual, variable, and group penalties, are considered to achieve automatic variable selection. Extensive simulations are conducted to assess the clustering and variable selection performance of the proposed methods. The application of the proposed methods to an engineering system with multiple sensors shows the promise of the methods and reveals interesting patterns in the sensor data.

History: Kwok-Leung Tsui served as the senior editor for this article.

Funding: The research by J. Min and Y. Hong was partially supported by the National Science Foundation [Grant CMMI-1904165] to Virginia Tech. The work by Y. Hong was partially supported by the Virginia Tech College of Science Research Equipment Fund.

Data Ethics & Reproducibility Note: The original data set is proprietary and cannot be shared. The full code to replicate the results in this paper, based on summary statistics of the original data, is available at https://github.com/jiem3/MultiFuncClustering . The code applied to a simplified version is available at https://codeocean.com/capsule/4041000/tree/v1 , which covers the data analysis and part of the simulation scenarios with a single data set under each scenario using a fixed set of hyperparameters, for reducing computation time, and at https://doi.org/10.1287/ijds.2022.0034 .
Intermediary Capital and the Credit Market
Authors: Harris Milton , Opp Christian C. , Opp Marcus M.
Published: March 2024

We propose a tractable framework to examine the role of intermediary capital in the allocation and pricing of credit. In our model, regulated financial intermediaries compete with unregulated investors, targeting distributions of heterogeneous borrowers. We derive a sufficient statistic that characterizes intermediaries’ cross-sectional lending decisions and provide a novel intermediary asset pricing equation that accounts for the endogenous segmentation of marginal investors across securities. These formulae reveal the central role of intermediaries’ shadow cost of capital in both credit allocation and pricing. Our results can concurrently rationalize a broad array of empirical facts documented in the context of credit markets.

This paper was accepted by Tomasz Piskorski, finance.

Funding: This work was supported by Marcus och Amalia Wallenbergs minnesfond [Grant 2021.0122].
A rare case of IgG4-related disease causing inflammatory breast mass successfully treated with anti-CD20 biological therapy: a discussion of clinical case with literature review
Authors: Rifaei Sufian , Etoom Shahd , Al-Balas Mahmoud
Published: March 2024

We report a rare yet successful utilisation of anti-CD20 therapy using rituximab for treatment of a case of IgG4-related mastitis proven by clinical, serological, and histopathological evidence. This was affecting a mid-aged female who was referred to the rheumatology clinic by the breast surgeons to help assessing for the possibility of an underlying inflammatory process involving the breast tissue unilaterally. The clinical course was apparently complex with an onset of an induration in the right lateral superior quadrant of the breast with mild discomfort and heaviness sensation. This increased over a course of 2 weeks before presentation to the general surgery clinic. Subsequent investigations confirmed that the case was IgG4-related mastitis and a trial of steroids and disease modifying anti-rheumatic drugs (DMARDs) was partially helpful, but not to a full degree, mandating the utilisation of a more advanced mode of therapy, so rituximab was selected.
Management of a patient with Joubert syndrome using dynamic neuromuscular stabilisation technique to improve postural control: a case report
Authors: Devi Manju , Kumar Ashwani , Kumar Amit
Published: March 2024

Background/Aims

Joubert syndrome is a rare genetic disorder of midbrain–hindbrain malformation characterised by cerebellar ataxia, hypotonia, intellectual disability, delayed milestones, ocular and respiratory impairment. This case study presents a comprehensive approach for the intervention of a 7-year-old patient with Joubert syndrome, focusing on core stability using dynamic neuromuscular stabilisation techniques in conjunction with conventional physiotherapy.

Methods

To address core stability, an intensive regimen of dynamic neuromuscular stabilisation exercise sessions was implemented, conducted five times a week over a span of 7 months. Each session of dynamic neuromuscular stabilisation lasted for 30 minutes with an additional 30 minutes for conventional physiotherapy. Outcome measures, including the Functional Independence Measure for Children and Gross Motor Function Measure, were used to evaluate the impact of the intervention.

Results

There were significant increases in the Gross Motor Function Measure and Functional Independence Measure for Children, with initial scores of 30 and 31, advancing to 134 and 53 respectively.

Conclusions

The findings suggest that dynamic neuromuscular stabilisation, with a focus on joint centration and spinal stabilisation to maintain optimal intra-abdominal pressure, along with traditional physiotherapy, can help patients with Joubert syndrome improve their postural balance.

Implications for practice

This case study raises awareness of diagnosing and addressing rare diseases such as Joubert syndrome. Physiotherapy professionals can use this case study to create therapeutic interventions for individuals with Joubert syndrome.
Socio-economic differences in remote schoolwork during the COVID-19 pandemic: a trend analysis of the 2020 and 2021 school-closure periods using the UK Understanding Society data
Authors: Pensiero Nicola , Kelly Anthony , Bokhove Christian
Published: March 2024

In the United Kingdom, the COVID-19 pandemic in 2020 and 2021 led to two extended periods of school closures. Research on inequality of learning opportunity as a result of these closures used a single indicator of socio-economic status, neglecting important determinants of remote learning. Using data from the Understanding Society (USoc) COVID-19 surveys we analysed the levels and differentials in the uptake of remote schoolwork using parental social class, information technology (IT) availability in the home and parental working patterns to capture the distinct resources that families needed to complete remote schoolwork. This is also the first study to assess the extent to which the differentials between socio-economic groups changed between the first and second school-closure periods caused by the pandemic. We found that each of the three factors showed an independent association with the volume of remote schoolwork and that their effect was magnified by their combination. Children in families where the main parent was in an upper-class occupation, where both parents worked from home and where the children had their own IT spent more time doing remote schoolwork than other groups, particularly compared to children of single parents who work from home, children in families where the main parent was in a working-class occupation, where the child had to share IT, and where the parents did not work regularly from home. The differentials between socio-economic groups in the uptake of schoolwork were found to be stable between the two school-closure periods.
Dual-oscillator infrared electro-optic sampling with attosecond precision
Authors: Weigel Alexander , Jacob Philip , Schweinberger Wolfgang , Huber Marinus , Trubetskov Michael , Karandušovský Patrik , Hofer Christina , Buberl Theresa , Amotchkina Tatiana , Högner Maximilian , Hahner Daniel , Sulzer Philipp , Leitenstorfer Alfred , Pervak Vladimir , Krausz Ferenc , Pupeza Ioachim
Published: March 2024

Electro-optic sampling of infrared electric fields has set sensitivity and dynamic-range records in broadband molecular vibrational spectroscopy. Yet, in these works, the 1-second-scale single-trace acquisition time leads to intra-scan noise accumulation and restricts the throughput in measurements of multiple samples and of dynamic processes. We present a dual-laser-oscillator approach capturing 2800 mid-infrared waveforms per second by scanning the relative delay between the sampled waveform and the gate pulses using a modulated repetition-frequency lock. The new technique of electro-optic delay tracking (EODT) provides delay calibration with down to few-attosecond precision and provides a general route to high-precision dual-oscillator spectroscopy with picosecond delay ranges. Our work has immediate applications in, e.g., precision electric-field metrology and high-speed biosensing.
A High-Accuracy Segmentation Hybrid Method For Retinal Blood Vessel Detection In Fluorescein Angiography Images Of Real Diabetic Retinopathy Patients
Authors: Ramezanzadeh Esmat , Shoeibi Naser , Shariati Mehrdad Motamed , Rabbani Hossein , Hosseini Seyedeh Maryam , Astaneh MohammadReza Ansari , Zare Hoda , toosi seyed mohammad hosein Bahreyni
Published: March 2024

This study aims to develop an advanced automatic method to enhance the accuracy of blood vessel segmentation in Fundus Fluorescein angiography FFA images of patients with real Diabetic retinopathy (DR) during both early and late phases. A prospective randomized study of 280 eyes of 87 DR patients at various stages was included in this study. our approach involved using four different image enhancement techniques (Histogram equalization (HE), CLAHE, RMSHE, Proposed method (CLAHE+RMSHE)) and for each enhancement except HE, four thresholding methods including the proposed method (Modified Active Contour(MAC)+ IsoData), C-mean Fuzzy, Isodata, and MAC + otsu thresholding that are commonly used in eye fundus images across different datasets. The effectiveness of segmentation was evaluated using three metrics: Dice (early: 0.84±0.05, late:0.84±0.03), Jaccard (early:0.73±0.06, late:0.74±0.05), and BF score (early:0.98±0.02, late:0.97±0.02). We found that the RMSHE method was more effective in the late phase, while the combined CLAHE+RMSHE method worked better in the early phase. The best segmentation results for both early and late phases were achieved using a blood vessel segmentation approach based on the MAC + IsoData thresholding.
Towards an ultrafast 3D imaging scanning LiDAR system: a review
Authors: Li Zhi , Han Yaqi , Wu Lican , Zang Zihan , Dai Maolin , Set Sze Yun , Yamashita Shinji , Li Qian , Fu H. Y.
Published: March 2024

Light detection and ranging (LiDAR), as a hot imaging technology in both industry and academia, has undergone rapid innovation and evolution. The current mainstream direction is towards system miniaturization and integration. There are many metrics that can be used to evaluate the performance of a LiDAR system, such as lateral resolution, ranging accuracy, stability, size, and price. Until recently, with the continuous enrichment of LiDAR application scenarios, the pursuit of imaging speed has attracted tremendous research interest. Particularly, for autonomous vehicles running on motorways or industrial automation applications, the imaging speed of LiDAR systems is a critical bottleneck. In this review, we will focus on discussing the upper speed limit of the LiDAR system. Based on the working mechanism, the limitation of optical parts on the maximum imaging speed is analyzed. The beam scanner has the greatest impact on imaging speed. We provide the working principle of current popular beam scanners used in LiDAR systems and summarize the main constraints on the scanning speed. Especially, we highlight the spectral scanning LiDAR as a new paradigm of ultrafast LiDAR. Additionally, to further improve the imaging speed, we then review the parallel detection methods, which include multiple-detector schemes and multiplexing technologies. Furthermore, we summarize the LiDAR systems with the fastest point acquisition rate reported nowadays. In the outlook, we address the current technical challenges for ultrafast LiDAR systems from different aspects and give a brief analysis of the feasibility of different approaches.
Parameter estimation in behavioral epidemic models with endogenous societal risk-response
Authors: Osi Ann , Ghaffarzadegan Navid
Published: March 2024

Behavioral epidemic models incorporating endogenous societal risk-response, where changes in risk perceptions prompt adjustments in contact rates, are crucial for predicting pandemic trajectories. Accurate parameter estimation in these models is vital for validation and precise projections. However, few studies have examined the problem of identifiability in models where disease and behavior parameters must be jointly estimated. To address this gap, we conduct simulation experiments to assess the effect on parameter estimation accuracy of a) delayed risk response, b) neglecting behavioral response in model structure, and c) integrating disease and public behavior data. Our findings reveal systematic biases in estimating behavior parameters even with comprehensive and accurate disease data and a well-structured simulation model when data are limited to the first wave. This is due to the significant delay between evolving risks and societal reactions, corresponding to the duration of a pandemic wave. Moreover, we demonstrate that conventional SEIR models, which disregard behavioral changes, may fit well in the early stages of a pandemic but exhibit significant errors after the initial peak. Furthermore, early on, relatively small data samples of public behavior, such as mobility, can significantly improve estimation accuracy. However, the marginal benefits decline as the pandemic progresses. These results highlight the challenges associated with the joint estimation of disease and behavior parameters in a behavioral epidemic model.
Amplifiers of selection for the Moran process with both Birth-death and death-Birth updating
Authors: Svoboda Jakub , Joshi Soham , Tkadlec Josef , Chatterjee Krishnendu
Published: March 2024

Populations evolve by accumulating advantageous mutations. Every population has some spatial structure that can be modeled by an underlying network. The network then influences the probability that new advantageous mutations fixate. Amplifiers of selection are networks that increase the fixation probability of advantageous mutants, as compared to the unstructured fully-connected network. Whether or not a network is an amplifier depends on the choice of the random process that governs the evolutionary dynamics. Two popular choices are Moran process with Birth-death updating and Moran process with death-Birth updating. Interestingly, while some networks are amplifiers under Birth-death updating and other networks are amplifiers under death-Birth updating, so far no spatial structures have been found that function as an amplifier under both types of updating simultaneously. In this work, we identify networks that act as amplifiers of selection under both versions of the Moran process. The amplifiers are robust, modular, and increase fixation probability for any mutant fitness advantage in a range r ∈ (1, 1.2). To complement this positive result, we also prove that for certain quantities closely related to fixation probability, it is impossible to improve them simultaneously for both versions of the Moran process. Together, our results highlight how the two versions of the Moran process differ and what they have in common.
Development of a computational model to inform environmental surveillance sampling plans for Salmonella enterica serovar Typhi in wastewater
Authors: Burnor Elisabeth , Morin Cory W. , Shirai Jeffry H. , Zhou Nicolette A. , Meschke John Scott
Published: March 2024

Typhoid fever–an acute febrile disease caused by infection with the bacterium Salmonella enterica serotype Typhi (S. Typhi)—continues to be a leading cause of global morbidity and mortality, particularly in developing countries with limited access to safe drinking water and adequate sanitation. Environmental surveillance, the process of detecting and enumerating disease-causing agents in wastewater, is a useful tool to monitor the circulation of typhoid fever in endemic regions. The design of environmental surveillance sampling plans and the interpretation of sampling results is complicated by a high degree of uncertainty and variability in factors that affect the final measured pathogens in wastewater samples, such as pathogen travel time through a wastewater network, pathogen dilution, decay and degradation, and laboratory processing methods. Computational models can, to an extent, assist in the design of sampling plans and aid in the evaluation of how different contributing factors affect sampling results. This study presents a computational model combining dynamic and probabilistic modeling techniques to estimate–on a spatial and temporal scale–the approximate probability of detecting S. Typhi within a wastewater system. This model may be utilized to inform environmental surveillance sampling plans and may provide useful insight into selecting appropriate sampling locations and times and interpreting results. A simulated applied modeling scenario is presented to demonstrate the model’s functionality for aiding an environmental surveillance study in a typhoid-endemic community.
Comparison of raw accelerometry data from ActiGraph, Apple Watch, Garmin, and Fitbit using a mechanical shaker table
Authors: White James W. , Finnegan Olivia L. , Tindall Nick , Nelakuditi Srihari , Brown David E. , Pate Russell R. , Welk Gregory J. , de Zambotti Massimiliano , Ghosal Rahul , Wang Yuan , Burkart Sarah , Adams Elizabeth L. , Chandrashekhar Mvs , Armstrong Bridget , Beets Michael W. , Weaver R. Glenn
Published: March 2024

The purpose of this study was to evaluate the reliability and validity of the raw accelerometry output from research-grade and consumer wearable devices compared to accelerations produced by a mechanical shaker table. Raw accelerometry data from a total of 40 devices (i.e., n = 10 ActiGraph wGT3X-BT, n = 10 Apple Watch Series 7, n = 10 Garmin Vivoactive 4S, and n = 10 Fitbit Sense) were compared to reference accelerations produced by an orbital shaker table at speeds ranging from 0.6 Hz (4.4 milligravity-mg) to 3.2 Hz (124.7mg). Two-way random effects absolute intraclass correlation coefficients (ICC) tested inter-device reliability. Pearson product moment, Lin’s concordance correlation coefficient (CCC), absolute error, mean bias, and equivalence testing were calculated to assess the validity between the raw estimates from the devices and the reference metric. Estimates from Apple, ActiGraph, Garmin, and Fitbit were reliable, with ICCs = 0.99, 0.97, 0.88, and 0.88, respectively. Estimates from ActiGraph, Apple, and Fitbit devices exhibited excellent concordance with the reference CCCs = 0.88, 0.83, and 0.85, respectively, while estimates from Garmin exhibited moderate concordance CCC = 0.59 based on the mean aggregation method. ActiGraph, Apple, and Fitbit produced similar absolute errors = 16.9mg, 21.6mg, and 22.0mg, respectively, while Garmin produced higher absolute error = 32.5mg compared to the reference. ActiGraph produced the lowest mean bias 0.0mg (95%CI = -40.0, 41.0). Equivalence testing revealed raw accelerometry data from all devices were not statistically significantly within the equivalence bounds of the shaker speed. Findings from this study provide evidence that raw accelerometry data from Apple, Garmin, and Fitbit devices can be used to reliably estimate movement; however, no estimates were statistically significantly equivalent to the reference. Future studies could explore device-agnostic and harmonization methods for estimating physical activity using the raw accelerometry signals from the consumer wearables studied herein.
Fine-tuning TrailMap: The utility of transfer learning to improve the performance of deep learning in axon segmentation of light-sheet microscopy images
Authors: Oostrom Marjolein , Muniak Michael A. , Eichler West Rogene M. , Akers Sarah , Pande Paritosh , Obiri Moses , Wang Wei , Bowyer Kasey , Wu Zhuhao , Bramer Lisa M. , Mao Tianyi , Webb-Robertson Bobbie Jo M.
Published: March 2024

Light-sheet microscopy has made possible the 3D imaging of both fixed and live biological tissue, with samples as large as the entire mouse brain. However, segmentation and quantification of that data remains a time-consuming manual undertaking. Machine learning methods promise the possibility of automating this process. This study seeks to advance the performance of prior models through optimizing transfer learning. We fine-tuned the existing TrailMap model using expert-labeled data from noradrenergic axonal structures in the mouse brain. By changing the cross-entropy weights and using augmentation, we demonstrate a generally improved adjusted F1-score over using the originally trained TrailMap model within our test datasets.