bluuebunny/crossref_metadata_2025_split huggingface dataset:
Macroscale Patterns of Dissolved Organic Matter Thermodynamic Properties Across Diverse River Systems
Authors: Stegen James , Garayburu-Caruso Vanessa , Sengupta Aditi , Dodds Walter , Fansler Sarah , Chu Rosalie , Danczak Robert , Garcia Marci , Goldman Amy , Graham Emily , Kaufman Matthew , Ren Huiying , Renteria Lupita , Sandborn Daniel , Song Hyun-Seob , Willi Katie , Ross Matt , Torgeson Joshua , Toyoda Jason 
Published: October 2023

Abstract

Thermodynamic properties of dissolved organic matter (DOM) influence river biogeochemistry. Using these properties to predict biogeochemical rates across rivers requires knowledge of how they vary. We employed mass spectrometry to quantify three DOM thermodynamic properties mechanistically linked to microbial respiration and biomass growth. We estimated these properties for each identified molecule in over 500 water and sediment samples from freshwater and saline rivers spanning diverse biomes. DOM thermodynamic properties exhibited continental-scale spatial gradients associated with climate and land-cover. Theory predicts relationships among the three properties. Observed inter-property relationships diverged from these predictions, but were consistent across continents. We infer that while there is variation in DOM thermodynamic properties across rivers--associated with climate and land cover--quantitative shifts in any one property are mechanistically linked to shifts in the other properties, leading to highly constrained inter-property relationships that persist across diverse rivers. Given the broad extent of sampled rivers, we suggest the observed inter-property relationships may hold across all rivers. Models that predict river biogeochemistry via DOM thermodynamic properties can use patterns observed here as inputs and constraints.
Development and Delphi consensus validation of the Medication-Related Fall Screening and Scoring tool.
Authors: Saeed Dima , Carter Gillian , Miller Ruth , Darcy Carmel , Miller Karen , Madden Kevin , McKee Hilary , Agnew Jayne , Crawford Paula , Parsons Carole 
Published: October 2023

Abstract

Introduction:
Falls are a significant public health problem and constitute a major cause of injuries and mortality. Risk factors for falls are multifactorial and include medication use.
Aim
To develop a medication-related fall (MRF) screening and scoring tool and to determine its content validity.
Methods
The MRF tool was developed from clinical practice guidelines addressing medication-related problems and additional medications identified by specialist pharmacists across a region of the United Kingdom (Northern Ireland (NI)). Medication classes were categorised according to their ‘potential to cause falls’ as: high-risk (three points), moderate-risk (two points) or low-risk (one point). The overall medication-related falls risk for the patient was determined by summing the scores for all medications. The MRF was validated using Delphi consensus methodology, whereby three iterative rounds of surveys were conducted using SurveyMonkey®. Twenty-two experts from 10 countries determined their agreement with the falls risk associated with each medication on a 5-point Likert scale. Only medications with at least 75% of respondents agreeing or strongly agreeing were retained in the next round.
Results
Consensus was reached for 19 medications/medication classes to be included in the final version of the MRF tool; ten were classified as high-risk, eight as moderate-risk and one as low-risk.
Conclusion
The MRF tool is simple and has the potential to be integrated into medicines optimisation to reduce falls risk and negative fall-related outcomes. The score from the MRF tool can be used to as a clinical parameter to assess the need for medication review and clinical interventions.
Interventions to improve symptomatology in patients with hypothyroidism and persistent symptoms: A systematic review.
Authors: Hidalgo Jessica , Lincango Eddy P , Codova-Madera Sandra , Ruiz-Arellanos Kim , Wenczenovicz Camila , Ponce Oscar J. , Alvarez-Villalobos Neri , Rao Arbaaz , Ospina Naykky M. Singh , Maraka Spyridoula , Acosta Gonzalo J , Brito Juan Pablo 
Published: October 2023

Abstract

Background
Levothyroxine (LT4) monotherapy is the standard treatment for hypothyroidism; however, 10–15% of patients have persistent hypothyroid symptoms despite normalizing thyroid hormone levels with LT4. This study aims to summarize the best available evidence on interventions to improve symptomatology in patients with hypothyroidism and persistent symptoms.
Methods
A systematic search was conducted in March 2022 for randomized controlled trials (RCTs) and observational studies on interventions for adult patients with persistent hypothyroid symptoms despite biochemical euthyroidism on thyroid hormone replacement.
Results
Of 277 articles, seven fulfilled the inclusion criteria. 455 participants were included. Most intervention participants were female (78.6%) with a mean age of 47.5 years. Five clinical trials evaluating ginger (vs. starch), L-carnitine (vs. placebo), combination LT4 and liothyronine (LT3) (vs. LT4 or placebo), and surgery for patients with serum antithyroid peroxidase (TPO Ab) titers greater than 1000 IU/ml (vs. LT4) found inconsistent improvement in hypothyroidism related symptoms and general health. Of these clinical trials, the use of ginger and surgery showed the largest improvements in fatigue scores. One observational study comparing thyroidectomy vs observation found no impact difference on general health. Lastly, another observational study evaluating combination LT4/LT3 (vs. LT4 monotherapy) found improvement in fatigue and QoL. There were 31 (12%) adverse events in the intervention group and 18 (10.8%) in the comparator group.
Conclusions
There is no high-quality evidence supporting any intervention for persistent symptoms in hypothyroidism. Available evidence, limited by the risk of bias, inconsistency, and heterogeneity, suggests that some persistent symptoms, particularly fatigue, could improve with ginger and thyroidectomy.
Trial registry number:PROSPERO (CRD42022318720)
The Invisible Embedded “Values” Within Large Language Models: Implications for Mental Health Use
Authors: Hadar-Shoval Dorit , Asraf Kfir , Mizrachi Yonathan , Haber Yuval , Elyoseph Zohar 
Published: October 2023

Abstract

Values are an integral part of any mental health intervention, profoundly shaping definitions of psychopathology and treatment approaches. As large language models (LLMs) hold promises for mental health applications, it is prudent to evaluate their embedded “values-like” abilities prior to implementation. This study uses Schwartz's Theory of Basic Values (STBV) to quantify and compare the motivational “values-like” abilities underpinning four leading LLMs. The results suggest that Schwartz’s theory can reliably and validly measure “values-like” abilities within LLMs. However, apparent divergence from published human values data emerged, with each LLM exhibiting a distinct motivational profile, potentially reflecting opaque alignment choices. Such apparent mismatches with human values diversity might negatively impact global LLM mental health implementations. The appropriate transparency and refinement of alignment processes may be vital for instilling comprehensive human values into LLMs before this sensitive implementation in mental healthcare. Overall, the study provides a framework for rigorously evaluating and improving LLMs’ embodiment of diverse cultural values to promote mental health equity.
Influence of Spiral Airway Structure Parameters of Dual-Airway Diesel Engine on Airway Performance
Authors: bao guangyuan , he chao , li jiaqiang , liu xueyuan 
Published: October 2023

Abstract
Numerical simulations of the airflow within the intake system and in-cylinder flow of the D19TCI automotive diesel engine were conducted using Computational Fluid Dynamics (CFD) software. The primary focus of this investigation was to examine the influence of the structural design parameters of the diesel engine's spiral intake manifold on the cylinder's swirl ratio. Through the manipulation of three key structural design parameters of the spiral intake manifold, we explored their impact on the cylinder's swirl ratio. The study utilized the spiral airflow deflection angle (θ), spiral chamber height (H), and eccentricity of the spiral intake manifold (∆a) as the three influential factors, each set at three levels. The swirl ratio and intake volume, representing two essential airflow characteristics, served as the evaluation criteria in an orthogonal experiment and subsequent linear regression analysis.The findings reveal that individually altering the three structural design parameters of the spiral intake manifold to values of -15° for the swirl angle, 6.28 mm for the spiral chamber height, and 3 mm for the eccentricity results in the maximum cylinder swirl ratio. This relationship adheres to the linear regression equation: Swirl Ratio = 0.754 - 0.005Swirl Angle + 0.165Spiral Chamber Height + 0.088*Eccentricity.
Integrative analysis of the role of CCT3 in human pan-cancer
Authors: Lin Shaochong , Zhang Qian , Wu Di , Cheng Yanfei , Yao Yuanqing 
Published: October 2023

Abstract
Increasing evidence revealed that the carcinogenic effects of chaperonin containing CCT3 in multiple tumors, but there is no pan-cancer analysis of CCT3. In this study, we utilized a series of bioinformatics tools to systematically reveal the expression status, prognostic value, methylation level, immune infiltration relevance and functional mechanisms of CCT3 in thirty-three TCGA tumors in an attempt to provide an in-depth and comprehensive view of the role of CCT3 in malignancies. We found that the dysregulation of CCT3 was manifested at multiple levels, such as transcriptome, protein and promoter region methylation status. In general, the upregulation of CCT3 has been widely observed in most tumor tissues compared with normal tissues, which was closely related to the clinical features of patients. We also discovered that the high CCT3 expression indicated poor overall OS and RFS in ACC, CESC and KIRP. CCT3 was significantly correlated with immune infiltrating cells and immune checkpoints in pan-cancer. Several cancer-related pathways and a novel CCT3-related ceRNA network were eventually identified, providing insights for future studies. In summary, the pan-cancer analysis confirmed that CCT3 could be a promising biological target for assessing the prognosis and immunotherapy of cancers.
Evaluating Event Embeddings on Commodity Future Prices
Authors: Steel Benjamin , Ruths Derek 
Published: October 2023

Abstract
Using news text-as-data to predict complex socio-political and economic measures is difficult. Modern pre-training natural language processing techniques can deliver intuitive and sophisticated methods that produce cogent results, but they necessitate arduous initial work before they can be widely applied. Here we show that pre-trained event embeddings allow accessible and powerful latent space modelling for time series data such as market prices. We evaluate commodity futures prediction, such as oil and agricultural futures, using neural time series modelling. We show that event embeddings can outperform sentiment on benchmark socio-economic measures.
Patterns, causes and policy implications of regional differences in COVID-19 mortality in the United States: A secondary analysis of excess mortality estimates
Authors: Stoto Michael A. , Schlageter Samantha , degl’Innocenti Duccio Gamannossi , Zollo Fabiana , Kraemer John D 
Published: October 2023

Abstract
In 2021 the “Red Covid,” narrative emerged: states and counties that are predominantly Republican and/or supported Trump experienced widespread vaccine hesitancy and eventually opposed mask and vaccine mandates. Our analysis of excess mortality quantifies this effect. After the first wave, death rates in the South were more than double those in the Northeast; 45% of deaths were in the South, with 38% of the population. Although there have been many correlational studies of this phenomenon, our analysis uses excess mortality estimates, aggregated by Census region and six pandemic phases, to establish the magnitude of this effect. We show that, if every region had the same mortality rate as the lowest regional rate in each period, more than 418,763 COVID-19 deaths were in this sense "avoidable." The analysis also shows that the U.S. has been a divided nation at almost since the start of the pandemic. We estimate that 198,642 deaths could have been averted during the Summer of 2020 and the Alpha wave, before vaccines were widely available. This strongly suggests that implementation of and adherence to NPIs made a difference. Similarly, while vaccination is a personal choice, as many Republicans argue, our estimates of 221,360 deaths averted after vaccines became available demonstrate the population-level consequences of that choice. Beyond the substantive results, this analysis demonstrates the importance of excess mortality estimates as part of the public health surveillance toolkit. Official mortality counts rely on complete recording of COVID-19 as a cause of death, COVID-19 deaths are under reported for many reasons. Between Jan. 3, 2020 and April 30, 2022, public health officials reported 978,567 COVID-19 deaths in the U.S. According to our calculations, however, the excess mortality associated with COVID-19 totaled 1,335,292, during that period, 37% more COVID-19 deaths than reported. Moreover, the proportion of COVID-19 deaths reported as such varied markedly over time and region, and reporting was generally less complete in the regions and periods with the highest mortality rates. Analyses of recorded COVID-19 deaths, just as of cases, therefore, would attenuate regional differences.
Homomorphic Integral and Dual Rectified Convolutional Grading for Salinity Prediction During Seedling in Rice
Authors: Shiragudikar Sharada K , Bharamagoudar Geeta 
Published: October 2023

Abstract
Rice is considered as one of the most sought out food crops globally. Salinity resilience plays an essential part in rice cultivation. But, uncontrolled saline level specifically at the seedling stage may unfavorable influence productivity of rice extensively. If not properly monitored, salinity stress brings about acute impairment during the seedling stage of crop growth bringing about an overall loss of 50%. Hence, it is mandatory to design an image classification and grading learning method for salinity stress analysis principally at the seedling stage and then benchmark its performance to circumvent depletion in rice yield. However, the classification using traditional method is both said to be laborious in terms of time and most of the time results in error owing to incorrect classification. To identify and classify salinity stress in rice seedlings using field images, the study reports the need of deep learning built model over traditional method of assessing rice crop's susceptibility to salt stress during the seedling stage. In this work, a method called Homomorphic Fourier Integral-based image classification and Dual Rectified Linear Convolutional (HFI-DRLC) grading learning for salinity stress level analysis in rice crops at seedling stage is proposed. Initially, an image enhancement framework that balances both field image features like as contrast, illumination and key properties that are important for identification of salinity stress level in rice crops at seedling stage. This is performed using Homomorphic Fourier Integral Filter-based Preprocessing model.
Next, Dual Rectified Linear Convolutional Neural prediction-based salinity tolerance in rice at seedling stage is designed to ensure both accuracy and precision. To investigate the effects of different improvement methods on the accuracy and precision of salinity tolerance in rice at seedling stage, comparison experiments between HFI-DRLC and traditional methods and comparison experiments with rice seedling dataset and rice seedling samplings from ICAR (Central Coastal Agricultural Research Institute, Old Goa, India) are executed. The method proposed in this study was shown to be more effective than traditional methods in terms of precision, recall, accuracy and error rate, providing a better method for salinity tolerance in rice at seedling stage.
A Collision Tumor of Clear Cell Renal Cell Carcinoma and Clear Cell Papillary Renal Cell Tumor: A Case Report and Literature Review
Authors: Lin Yingsheng , Guo Jinan , Jin Hongtao , Li Zaishang , Liu Zengqin , Xie Jing , Liu Junxu , Xiao Kefeng 
Published: October 2023

Abstract
Background Collision tumor refers to a special type of tumor in which two or more tumors of different origin occur in the same tissue or organ. There are few reports about primary renal collision tumor. We report a case of renal collision tumor composed of clear cell renal cell carcinoma (CCRCC) and clear cell papillary renal cell tumor (CCPRCT).
Case presentation A 51-year-old woman who was initially hospitalized in the respiratory department because of cough and fever. Urinary computed tomography (CT) showed two different incidental masses in the right kidney. The patient underwent radical right nephrectomy without lymph node dissection and postoperative adjuvant treatment. Pathological results of surgical specimens showed that this was a collision tumor composed of CCRCC and CCPRCT. To the best of our knowledge, this is the first case in the world. No recurrence of local or distant metastasis was found in routine follow-up 14 months after the operation.
Conclusions The coexistence of two kinds of renal malignant tumors is very rare. The microscopic features and immunohistochemical results of the tumors are helpful in the diagnosis of renal collision tumors composed of CCRCC and CCPRCT.
A long-term fulvestrant eluting implant is safe, non-toxic, and reduces the risk of breast cancer in in vivo models.
Authors: Thomas Scott , Roche Elysia , Desai Pujan , Pawlowska Nela , Bauer Diana , Gingrich David , Hsu Emily , Deitchman Amelia N. , Aweeka Fran , Munster Pamela N. 
Published: October 2023

Abstract

For individuals at high risk of developing breast cancer, interventions to mitigate this risk include surgical removal of their breasts and ovaries or five years treatment with the anti-estrogen tamoxifen or aromatase inhibitors. We hypothesized that a silicone based anti-estrogen-eluting implant placed within the breast would provide the risk reduction benefit of hormonal therapy, but without the adverse effects that limit compliance. To this end, we demonstrate that when placed adjacent to mammary tissue in the DMBA-induced rat breast cancer model a fulvestrant-eluting implant delays breast cancer with minimal systemic exposure. Using adult female sheep, fulvestrant-eluting implants were found to be safe and non-toxic when placed at the base of the udder for directed elution into the mammary tissue. At 30 days of elution, fulvestrant was found to penetrate mammary tissue forming a concentration gradient beyond 15 mm from the implant. Consistent with the small animal rat study, minimal systemic fulvestrant biodistribution was found. Together, these studies provide the proof of principle that a breast indwelling fulvestrant-eluting implant can reduce the risk of breast cancer and limit systemic exposure, while penetrating and distributing through breast tissue.
Multi-feature fusion for snowy traffic sign detection
Authors: WANG Zhanyu , LIU Lintao , DU Xuejing 
Published: October 2023

Abstract
Traffic sign detection is widely used in automatic driving, assisted driving, and other intelligent transportation systems, and its detection performance is closely related to rain-driving safety. In the process of traffic sign detection, there are traffic signs of different sizes with large size variations, which will have a certain impact on the detection accuracy. For the problem that the existing target detection algorithm is poor in detecting traffic signs with small sizes, low resolution, and inconspicuous features in the image, a traffic sign detection algorithm based on improved YOLOv5s is proposed. The M-ASPP module is proposed to enhance the perceptual field without reducing the feature information and increase the feature information fusion at different scales to improve the traffic sign detection accuracy. Introduce BiFPN instead of FPN to enable the fusion of deep high-level semantic information and shallow location information to improve the recognition accuracy of the algorithm. Add a small target detection head to improve the detection accuracy of small target traffic signs. Use the NWD loss function insheathe d of the IOU loss function to reduce the small-target scale sensitivity problem and improve the similarity between small-target detection. The improved traffic sign detection algorithm has better detection performance and can maintain its detection accuracy and detection speed even in snowy environments. Compared with other detection algorithms at this stage, this method has more advantages.
Signs and symptoms of anxiety and depression in fathers, mothers, and/or caregivers of children with Autism Spectrum Disorder
Authors: García Katheree , Hernández Michele , Michelén Angely , Acevedo Noemí , Ortiz Isi 
Published: October 2023

Abstract
Purpose: This study aimed to determine the prevalence of signs and symptoms of anxiety and depression in fathers, mothers, and caregivers of children with autism spectrum disorder affiliated with Fundación Luz y Esperanza por el Autismo and Centro de Atención Integral para la Discapacidad in Santiago de los Caballeros between October 2022 and January 2023.
Methods: We conducted a descriptive-cross-sectional study involving 101 participants selected through a non-probabilistic convenience sampling. Anxiety and depression severity were assessed using the Beck Anxiety Inventory and the Beck Depression Inventory, respectively.
Results: The results indicated that 52.5% of the population experienced weakness or fatigue, 47.5% had an urge to cry, and 45.5% reported feelings of sadness. Suicidal thoughts were reported by only 2% of the participants. Regarding anxiety severity, 48.5% had minimal levels, 24.8% had mild anxiety, 20.8% had moderate anxiety, and 5.9% experienced severe anxiety. In terms of depression, 55.4% had minimal depression, 21.8% had mild depression, 17.8% had moderate depression, and 5% experienced severe depression.
Conclusions: A statistically significant results (P < 0.05) were found when analyzing the relationship between marital status and monthly income with depression severity. Nonetheless, no statistically significant relationships were observed between anxiety and depression severity and other sociodemographic variables.
Convergent plug-and-play with proximal denoiser and unconstrained regularization parameter.
Authors: Hurault Samuel , Chambolle Antonin , Leclaire Arthur , Papadakis Nicolas 
Published: October 2023

Abstract

In this work, we present new proofs of convergence for Plug-and-Play (PnP) algorithms. PnP methods are efficient iterative algorithms for solving image inverse problems where regularization is performed by plugging a pre-trained denoiser in a proximal algorithm, such as Proximal Gradient Descent (PGD) or Douglas-Rachford Splitting (DRS). Recent research has explored convergence by incorporating a denoiser that writes exactly as a proximal operator. However, the corresponding PnP algorithm has then to be run with stepsize equal to $1$. The stepsize condition for nonconvex convergence of the proximal algorithm in use then translates to restrictive conditions on the regularization parameter of the inverse problem. This can severely degrade the restoration capacity of the algorithm. In this paper, we present two remedies for this limitation. First, we provide a novel convergence proof for PnP-DRS that does not impose any restrictions on the regularization parameter. Second, we examine a relaxed version of the PGD algorithm that converges across a broader range of regularization parameters. Our experimental study, conducted on deblurring and super-resolution experiments, demonstrate that both of these solutions enhance the accuracy of image restoration.