bluuebunny/crossref_metadata_2025_split huggingface dataset:
PTAS for Sparse General-valued CSPs
Authors: Mezei Balázs F. , Wrochna Marcin , Živný stanislav 
Published: December 2022

We study polynomial-time approximation schemes (PTASes) for constraint satisfaction problems (CSPs) such as Maximum Independent Set or Minimum Vertex Cover on sparse graph classes.
Baker’s approach gives a PTAS on planar graphs, excluded-minor classes, and beyond. For Max-CSPs, and even more generally, maximisation finite-valued CSPs (where constraints are arbitrary non-negative functions), Romero, Wrochna, and Živný [SODA’21] showed that the Sherali-Adams LP relaxation gives a simple PTAS for all fractionally-treewidth-fragile classes, which is the most general “sparsity” condition for which a PTAS is known. We extend these results to general-valued CSPs, which include “crisp” (or “strict”) constraints that have to be satisfied by every feasible assignment. The only condition on the crisp constraints is that their domain contains an element that is at least as feasible as all the others (but possibly less valuable).

For minimisation general-valued CSPs with crisp constraints, we present a PTAS for all
Baker
graph classes—a definition by Dvořák [SODA’20] that encompasses all classes where Baker’s technique is known to work, except for fractionally-treewidth-fragile classes. While this is standard for problems satisfying a certain monotonicity condition on crisp constraints, we show this can be relaxed to
diagonalisability
—a property of relational structures connected to logics, statistical physics, and random CSPs.
EASYR:

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econfiguration for Dynamic Deadlines in Autonomous Driving on Multicore Processors
Authors: Yi Saehanseul , Kim Tae-Wook , Kim Jong-Chan , Dutt Nikil 
Published: December 2022

The increasing computing demands of autonomous driving applications have driven the adoption of multicore processors in real-time systems, which in turn renders energy optimizations critical for reducing battery capacity and vehicle weight. A typical energy optimization method targeting traditional real-time systems finds a critical speed under a static deadline, resulting in conservative energy savings that are unable to exploit dynamic changes in the system and environment. We capture emerging dynamic deadlines arising from the vehicle’s change in velocity and driving context for an additional energy optimization opportunity. In this article, we extend the preliminary work for uniprocessors [
66
] to multicore processors, which introduces several challenges. We use the state-of-the-art real-time gang scheduling [
5
] to mitigate some of the challenges. However, it entails an NP-hard combinatorial problem in that tasks need to be grouped into gangs of tasks, gang formation, which could significantly affect the energy saving result. As such, we present EASYR, an adaptive system optimization and reconfiguration approach that generates gangs of tasks from a given directed acyclic graph for multicore processors and dynamically adapts the scheduling parameters and processor speeds to satisfy dynamic deadlines while consuming as little energy as possible. The timing constraints are also satisfied between system reconfigurations through our proposed safe mode change protocol. Our extensive experiments with randomly generated task graphs show that our gang formation heuristic performs 32% better than the state-of-the-art one. Using an autonomous driving task set from Bosch and real-world driving data, our experiments show that EASYR achieves energy reductions of up to 30.3% on average in typical driving scenarios compared with a conventional energy optimization method with the current state-of-the-art gang formation heuristic in real-time systems, demonstrating great potential for dynamic energy optimization gains by exploiting dynamic deadlines.
The Prostate Stromal Transcriptome in Aggressive and Lethal Prostate Cancer
Authors: Ma Chaoran , Zhou Yinglu , Fanelli Giuseppe Nicolò , Stopsack Konrad H. , Fiorentino Michelangelo , Zadra Giorgia , Mucci Lorelei A. , Loda Massimo , Tyekucheva Svitlana , Penney Kathryn L. 
Published: December 2022

AbstractProstate cancer has a heterogeneous prognosis. Most previous studies have focused on the identification of prognostic biomarkers in the prostate cancer tumor. However, it is increasingly recognized that the tumor microenvironment contributes to prostate cancer aggressiveness and progression. We therefore examined whole transcriptome expression of the prostate stroma and associations with aggressive and lethal prostate cancer. We performed RNA sequencing (Illumina TruSeq Exome Capture) of 272 tumor-adjacent and 120 benign-adjacent macrodissected prostate stromal samples from 293 men with prostate cancer from the Health Professionals Follow-up Study and Physicians’ Health Study. We performed differential expression analysis comparing gene expression and pathways by Gleason score and lethal outcome. We also tested a previously developed stromal gene signature of Gleason score in these datasets. Comparing high- with low-Gleason score cancers, 26 genes (P &lt; 0.001) and 12 pathways (FDR &lt; 0.20) were significantly differentially expressed in tumor-adjacent stroma, including pathways related to stroma composition remodeling and DNA repair, with 73 genes and 65 pathways significant in benign-adjacent stroma. Comparing lethal with nonlethal prostate cancer, 11 genes were differentially expressed in tumor-adjacent and 15 genes in benign-adjacent stroma, and pathways involved in inflammatory response were differentially enriched in both tumor and benign-adjacent stroma. In addition, our previously identified Gleason stromal gene signature was validated to be associated with Gleason score in these data.Implications: Our study uncovers stroma-specific genes and pathways that are differentially enriched with high Gleason score and lethal prostate cancer, demonstrating that the molecular investigation of the tumor microenvironment can provide additional information about prostate cancer prognosis.
Modulation of limbic resting-state networks by subthalamic nucleus deep brain stimulation
Authors: Eraifej John , Cabral Joana , Fernandes Henrique M. , Kahan Joshua , He Shenghong , Mancini Laura , Thornton John , White Mark , Yousry Tarek , Zrinzo Ludvic , Akram Harith , Limousin Patricia , Foltynie Tom , Aziz Tipu Z. , Deco Gustavo , Kringelbach Morten , Green Alexander L. 
Published: December 2022

Abstract
Beyond the established effects of subthalamic nucleus deep brain stimulation (STN-DBS) in reducing motor symptoms in Parkinson’s disease, recent evidence has highlighted the effect on non-motor symptoms. However, the impact of STN-DBS on disseminated networks remains unclear. This study aimed to perform a quantitative evaluation of network-specific modulation induced by STN-DBS using Leading Eigenvector Dynamics Analysis (LEiDA). We calculated the occupancy of resting-state networks (RSNs) in functional MRI data from 10 patients with Parkinson’s disease implanted with STN-DBS and statistically compared between ON and OFF conditions. STN-DBS was found to specifically modulate the occupancy of networks overlapping with limbic RSNs. STN-DBS significantly increased the occupancy of an orbitofrontal limbic subsystem with respect to both DBS OFF (p = 0.0057) and 49 age-matched healthy controls (p = 0.0033). Occupancy of a diffuse limbic RSN was increased with STN-DBS OFF when compared with healthy controls (p = 0.021), but not when STN-DBS was ON, which indicates rebalancing of this network. These results highlight the modulatory effect of STN-DBS on components of the limbic system, particularly within the orbitofrontal cortex, a structure associated with reward processing. These results reinforce the value of quantitative biomarkers of RSN activity in evaluating the disseminated impact of brain stimulation techniques and the personalization of therapeutic strategies.
Dynamic structure of motor cortical neuron coactivity carries behaviorally relevant information
Authors: Sundiang Marina , Hatsopoulos Nicholas G. , MacLean Jason N. 
Published: December 2022

Abstract
Skillful, voluntary movements are underpinned by computations performed by networks of interconnected neurons in the primary motor cortex (M1). Computations are reflected by patterns of coactivity between neurons. Using pairwise spike time statistics, coactivity can be summarized as a functional network (FN). Here, we show that the structure of FNs constructed from an instructed-delay reach task in nonhuman primates is behaviorally specific: Low-dimensional embedding and graph alignment scores show that FNs constructed from closer target reach directions are also closer in network space. Using short intervals across a trial, we constructed temporal FNs and found that temporal FNs traverse a low-dimensional subspace in a reach-specific trajectory. Alignment scores show that FNs become separable and correspondingly decodable shortly after the Instruction cue. Finally, we observe that reciprocal connections in FNs transiently decrease following the Instruction cue, consistent with the hypothesis that information external to the recorded population temporarily alters the structure of the network at this moment.
Conducting Climate Change Risk Assessments for Companies: Lessons Learned
Authors: Teale Natalie , Quiring Steven M. 
Published: December 2022

Abstract
As the private sector becomes increasingly aware of the risks associated with climate change, climatologists have been engaging with companies to assess climate change risks and opportunities. Here, we outline how we have collaborated with a Fortune 500 company to assess the physical risks of climate change to their facilities. We provide a template for a climate change report card that we generated for >100 facilities globally. This report card is designed to communicate risk to company leadership and local facility managers. We believe that by sharing our experiences, climate scientists will be able to quantify and communicate climate risk more effectively to the private sector.
Gender differences in Syrian refugees in Jordan: Psychological, self-esteem, family function, marital satisfaction, and life satisfaction
Authors: Al-Krenawi Alean , Bell Melissa M 
Published: December 2022

Aim: The current study examined gender differences in mental health, self-esteem, family function, marital satisfaction, and life satisfaction between men and women living in a refugee camp for Syrian refugees in Jordan. Method: A snowball method used by local female and male students trained to collect data according to culturally competent methods. The following research instruments were deployed: Symptoms Checklist-SCL90, Self-esteem Scale, the McMaster Family Assessment Device, Marital Satisfaction Scale, and life Satisfaction Scale. Results: A sample of 290 adults (196 women and 94 men) living in a refugee camp in Jordan participated in the study. Findings revealed that marital satisfaction, self-esteem, and life satisfaction were significantly different between males and females, indicating less subjective well-being for women. Many of the mental health symptoms in this study were more common for women than men; particularly noteworthy were somatization, obsessive compulsive behavior, interpersonal sensitivity, depression, hostility, psychoticism, and higher scores on the Global Severity Index (GSI). Conclusion: While some stressors are not gender-specific, there are unique factors that women face which place them at increased risk of mental health problems. Implications for practice include a greater understanding of the challenges and resilience mechanisms that are related to gender and culture.
Sequestered, Iatrogenic Epidermoid Implantation Cysts Associated With Retroauricular (Wilde’s) Incision
Authors: Dutta Mainak , Kadambott Sharafali , Kar Sutarthi , Arbaz Mirza , Seth Chandan 
Published: December 2022

Objective To report two patients with iatrogenic, epidermoid implantation cysts associated with the standard retroauricular (Wilde’s) incision, to establish their etiology, to explore means to prevent them, and to ensure that this experience adds to the learning curve of the surgical training of the residents. Methods Case series with the review of literature. Results One of the two patients, who had an unremarkable follow-up period after cortical mastoidectomy and cartilage tympanoplasty performed three years back, presented with a retroauricular cystic swelling visible since two months. It was hyperintense on T2-weighted magnetic resonance imaging. A sequestered cystic mass was encountered at surgery in the subcutaneous plane in association with the retroauricular scar tissue from previous surgery. In the other patient, two similar cysts (one of them sequestered) were incidental findings at revision tympanomastoid surgery. Histopathology in both instances was consistent with epidermoid cyst. Given their subcuticular location and intimate association with a pre-existing surgical scar, they were considered to be of implantation in origin. This was a potential outcome of persistent inversion of one or both skin edges following simple interrupted suturing. The reason could be a faulty surgical technique, due to improper placement of the needle with respect to skin, inadequate or uneven tissue bite, a tight knot, or failure to manually evert the apposing edges at the point(s) of suspicion. Conclusions Sequestered epidermoid implantation cysts due to persistent inversion of sutured skin edges are unusual complications. The illustrations in this case series emphasize the need to adhere to the basic principles of surgical practice. They provide a caveat to the resident surgeons and the faculties who supervise them, that inadvertent and apparently innocuous misses, even at the elementary steps of surgery (like suturing), could lead to complications that are unwanted and potentially avoidable.
Time for a Measurement Check-Up: Testing the Couple’s Satisfaction Index and the Global Measure of Sexual Satisfaction Using Structural Equation Modeling and Item Response Theory
Authors: Quinn-Nilas Christopher 
Published: December 2022

Relationship and sexual satisfaction are two central outcomes in the study of relationships and are commonly used in both academia and applied practice. However, relationship and sexual satisfaction measures infrequently undergo specific psychometric investigation. Ensuring that measures display strong psychometric performance is an important but under-tested element of replication that has come under more scrutiny lately, and adequate measurement of constructs is an important auxiliary assumption underpinning theory-testing empirical work. A measurement check-up was conducted, including Confirmatory Factor Analysis (CFA) to test factorial validity, measurement invariance to test for group comparability, and Item Response Theory (IRT) to assess the relationship between latent traits and their items/indicators. This format was used to evaluate the psychometric properties of the Couple’s Satisfaction Index (CSI) and the Global Measure of Sexual Satisfaction (GMSEX), two commonly used scales of relationship and sexual satisfaction with a sample of 640 midlife (40–59 years old) married Canadians who were recruited by Qualtrics Panels. Results of CFA suggested that both models were satisfactory. Invariance testing provided robust support for intercept invariance across all the groupings tested. IRT analysis supported the CSI and GMSEX, however, there was evidence that the GMSEX provided somewhat less information for those high on sexual satisfaction. This measurement check-up found that the CSI and GMSEX were reasonably healthy with some caveats. Implications are discussed in terms of replicability and meaning for scholars and practitioners.
Decision Curve Analysis for Personalized Treatment Choice between Multiple Options
Authors: Chalkou Konstantina , Vickers Andrew J. , Pellegrini Fabio , Manca Andrea , Salanti Georgia 
Published: December 2022

Background Decision curve analysis can be used to determine whether a personalized model for treatment benefit would lead to better clinical decisions. Decision curve analysis methods have been described to estimate treatment benefit using data from a single randomized controlled trial. Objectives Our main objective is to extend the decision curve analysis methodology to the scenario in which several treatment options exist and evidence about their effects comes from a set of trials, synthesized using network meta-analysis (NMA). Methods We describe the steps needed to estimate the net benefit of a prediction model using evidence from studies synthesized in an NMA. We show how to compare personalized versus one-size-fit-all treatment decision-making strategies, such as “treat none” or “treat all patients with a specific treatment” strategies. First, threshold values for each included treatment need to be defined (i.e., the minimum risk difference compared with control that renders a treatment worth taking). The net benefit per strategy can then be plotted for a plausible range of threshold values to reveal the most clinically useful strategy. We applied our methodology to an NMA prediction model for relapsing-remitting multiple sclerosis, which can be used to choose between natalizumab, dimethyl fumarate, glatiramer acetate, and placebo. Results We illustrated the extended decision curve analysis methodology using several threshold value combinations for each available treatment. For the examined threshold values, the “treat patients according to the prediction model” strategy performs either better than or close to the one-size-fit-all treatment strategies. However, even small differences may be important in clinical decision making. As the advantage of the personalized model was not consistent across all thresholds, improving the existing model (by including, for example, predictors that will increase discrimination) is needed before advocating its clinical usefulness. Conclusions This novel extension of decision curve analysis can be applied to NMA-based prediction models to evaluate their use to aid treatment decision making. Highlights Decision curve analysis is extended into a (network) meta-analysis framework. Personalized models predicting treatment benefit are evaluated when several treatment options are available and evidence about their effects comes from a set of trials. Detailed steps to compare personalized versus one-size-fit-all treatment decision-making strategies are outlined. This extension of decision curve analysis can be applied to (network) meta-analysis–based prediction models to evaluate their use to aid treatment decision making.
Radiomics-based machine learning models for prediction of medulloblastoma subgroups: a systematic review and meta-analysis of the diagnostic test performance
Authors: Karabacak Mert , Ozkara Burak Berksu , Ozturk Admir , Kaya Busra , Cirak Zeynep , Orak Ece , Ozcan Zeynep 
Published: December 2022

Background Medulloblastomas are a major cause of cancer-related mortality in the pediatric population. Four molecular groups have been identified, and these molecular groups drive risk stratification, prognostic modeling, and the development of novel treatment modalities. It has been demonstrated that radiomics-based machine learning (ML) models are effective at predicting the diagnosis, molecular class, and grades of CNS tumors. Purpose To assess radiomics-based ML models’ diagnostic performance in predicting medulloblastoma subgroups and the methodological quality of the studies. Material and Methods A comprehensive literature search was performed on PubMed; the last search was conducted on 1 May 2022. Studies that predicted all four medulloblastoma subgroups in patients with histopathologically confirmed medulloblastoma and reporting area under the curve (AUC) values were included in the study. The quality assessments were conducted according to the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) and Checklist for Artificial Intelligence in Medical Imaging (CLAIM). A meta-analysis of radiomics-based ML studies’ diagnostic performance for the preoperative evaluation of medulloblastoma subgrouping was performed. Results Five studies were included in this meta-analysis. Regarding patient selection, two studies indicated an unclear risk of bias according to the QUADAS-2. The five studies had an average CLAIM score and compliance score of 23.2 and 0.57, respectively. The meta-analysis showed pooled AUCs of 0.88, 0.82, 0.83, and 0.88 for WNT, SHH, group 3, and group 4 for classification, respectively. Conclusion Radiomics-based ML studies have good classification performance in predicting medulloblastoma subgroups, with AUCs >0.80 in every subgroup. To be applied to clinical practice, they need methodological quality improvement and stability.
Bisacurone attenuates diabetic nephropathy by ameliorating oxidative stress, inflammation and apoptosis in rats
Authors: Yang F , Zhang Z , Zhang L 
Published: December 2022

Background Diabetes nephropathy (DN) is a serious diabetic problem that may progress to renal failure. The root of Curcuma longa L., often known as turmeric, provides various health benefits. Bisacurone is a bioactive terpenoid found in small amounts in turmeric that possesses anti-inflammatory and antioxidant properties. The present study focuses on the potential protective effects of bisacurone against DN via reducing renal inflammation, oxidative stress, and apoptosis. Methods Type 2 diabetes was created in rats by feeding them a high-fat/high-sugar diet for 8 weeks, followed by a low dose of streptozotocin and Bisacurone (50 and 100 μg/kg bisacurone) given for 4 weeks. Results In diabetic rats, bisacurone reduced hyperglycemia, protected against body weight (BW) loss, lowered renal markers, reduced lipid profile alterations and avoided histological abnormalities. Bisacurone treatment reduced oxidative stress by decreasing malondialdehyde (MDA) levels while enhancing antioxidant defenses through superoxide dismutase (SOD), catalase (CAT), and glutathione peroxidase (GPx) levels. Furthermore, bisacurone treatment activated the renal Nrf2/Keap1 signaling pathway but attenuated the high levels of NFκB p65, TNF-α, IL-1β, IL-6, Cox2, and iNOS. Bisacurone also reduced Bax, caspase-3, caspase-9 and cytochrome c but increased Bcl-2 in the kidneys of diabetic rats. Conclusion In the present study, bisacurone reduces DN by reducing hyperglycemia, oxidative stress, inflammation, and apoptosis, while also increasing Nrf2/HO-1 signaling.