bluuebunny/crossref_metadata_2025_split huggingface dataset:
Benign splenic lesions in BAP1-tumor predisposition syndrome: a case series
Authors: Miranda Joao , Dave Priya , Kemel Yelena , Sheikh Rania , Zong Grace , Calderon Lina Posada , Will Marie , Liu Ying L. , Walsh Michael , Stadler Zsofia K. , Offit Kenneth , Latham Alicia , Mandelker Diana , Chen Ying-Bei , Andrieu Pamela Causa , Carlo Maria Isabel 
Published: June 2024

AbstractBAP1-Tumor Predisposition Syndrome (TPDS) is caused by germline variants in BAP1 and predisposes to solid tumors. After observation of a radiologically malignant-appearing splenic mass with benign pathology in a patient with BAP1-TPDS, we sought to retrospectively characterize splenic lesions in individuals with BAP1-TPDS seen at a comprehensive cancer center. A dedicated radiology review for splenic abnormalities was performed. We identified 37 individuals with BAP1-TPDS, 81% with a history of cancer. Of 33 individuals with abdominal imaging, 10 (30%) had splenic lesions, and none were shown to be malignant on follow-up. Splenectomy in an individual with suspected splenic angiosarcoma showed a benign vascular neoplasm with loss of nuclear staining for BAP1 in a subset of cells. Benign splenic lesions appear to be common and potentially BAP1-driven in individuals with BAP1-TPDS; confirmation of these findings could lead to more conservative management and avoidance of splenectomy.
A 3D ray traced biological neural network learning model
Authors: Yuen Brosnan , Dong Xiaodai , Lu Tao 
Published: June 2024

AbstractTraining large neural networks on big datasets requires significant computational resources and time. Transfer learning reduces training time by pre-training a base model on one dataset and transferring the knowledge to a new model for another dataset. However, current choices of transfer learning algorithms are limited because the transferred models always have to adhere to the dimensions of the base model and can not easily modify the neural architecture to solve other datasets. On the other hand, biological neural networks (BNNs) are adept at rearranging themselves to tackle completely different problems using transfer learning. Taking advantage of BNNs, we design a dynamic neural network that is transferable to any other network architecture and can accommodate many datasets. Our approach uses raytracing to connect neurons in a three-dimensional space, allowing the network to grow into any shape or size. In the Alcala dataset, our transfer learning algorithm trains the fastest across changing environments and input sizes. In addition, we show that our algorithm also outperformance the state of the art in EEG dataset. In the future, this network may be considered for implementation on real biological neural networks to decrease power consumption.
Concentrated solar CO2 reduction in H2O vapour with >1% energy conversion efficiency
Authors: Ren Yuqi , Fu Yiwei , Li Naixu , You Changjun , Huang Jie , Huang Kai , Sun Zhenkun , Zhou Jiancheng , Si Yitao , Zhu Yuanhao , Chen Wenshuai , Duan Lunbo , Liu Maochang 
Published: June 2024

AbstractH2O dissociation plays a crucial role in solar-driven catalytic CO2 methanation, demanding high temperature even for solar-to-chemical conversion efficiencies <1% with modest product selectivity. Herein, we report an oxygen-vacancy (Vo) rich CeO2 catalyst with single-atom Ni anchored around its surface Vo sites by replacing Ce atoms to promote H2O dissociation and achieve effective photothermal CO2 reduction under concentrated light irradiation. The high photon flux reduces the apparent activation energy for CH4 production and prevents Vo from depletion. The defects coordinated with single-atom Ni, significantly promote the capture of charges and local phonons at the Ni d-impurity orbitals, thereby inducing more effective H2O activation. The catalyst presents a CH4 yield of 192.75 µmol/cm2/h, with a solar-to-chemical efficiency of 1.14% and a selectivity ~100%. The mechanistic insights uncovered in this study should help further the development of H2O-activating catalysts for CO2 reduction and thereby expedite the practical utilization of solar-to-chemical technologies.
NIR-II emissive anionic copper nanoclusters with intrinsic photoredox activity in single-electron transfer
Authors: Liu Li-Juan , Zhang Mao-Mao , Deng Ziqi , Yan Liang-Liang , Lin Yang , Phillips David Lee , Yam Vivian Wing-Wah , He Jian 
Published: June 2024

AbstractUltrasmall copper nanoclusters have recently emerged as promising photocatalysts for organic synthesis, owing to their exceptional light absorption ability and large surface areas for efficient interactions with substrates. Despite significant advances in cluster-based visible-light photocatalysis, the types of organic transformations that copper nanoclusters can catalyze remain limited to date. Herein, we report a structurally well-defined anionic Cu40 nanocluster that emits in the second near-infrared region (NIR-II, 1000−1700 nm) after photoexcitation and can conduct single-electron transfer with fluoroalkyl iodides without the need for external ligand activation. This photoredox-active copper nanocluster efficiently catalyzes the three-component radical couplings of alkenes, fluoroalkyl iodides, and trimethylsilyl cyanide under blue-LED irradiation at room temperature. A variety of fluorine-containing electrophiles and a cyanide nucleophile can be added onto an array of alkenes, including styrenes and aliphatic olefins. Our current work demonstrates the viability of using readily accessible metal nanoclusters to establish photocatalytic systems with a high degree of practicality and reaction complexity.
Photochemically responsive polymer films enable tunable gliding flights
Authors: Yang Jianfeng , Shankar M. Ravi , Zeng Hao 
Published: June 2024

AbstractMiniaturized passive fliers based on smart materials face challenges in precise control of shape-morphing for aerodynamics and contactless modulation of diverse gliding modes. Here, we present the optical control of gliding performances in azobenzene-crosslinked liquid crystal networks films through photochemical actuation, enabling reversible and bistable shape-morphing. First, an actuator film is integrated with additive constructs to form a rotating glider, inspired by the natural maple samara, surpassing natural counterparts in reversibly optical tuning of terminal velocity, rotational rate, and circling position. We demonstrate optical modulation dispersion of landing points for the photo-responsive microfliers indoors and outdoors. Secondly, we show the scalability of polymer film geometry for miniature gliders with similar light tunability. Thirdly, we extend the material platform to other three gliding modes: Javan cucumber seed-like glider, parachute and artificial dandelion seed. The findings pave the way for distributed microflier with contactless flight dynamics control.
Development and deployment of a histopathology-based deep learning algorithm for patient prescreening in a clinical trial
Authors: Juan Ramon Albert , Parmar Chaitanya , Carrasco-Zevallos Oscar M. , Csiszer Carlos , Yip Stephen S. F. , Raciti Patricia , Stone Nicole L. , Triantos Spyros , Quiroz Michelle M. , Crowley Patrick , Batavia Ashita S. , Greshock Joel , Mansi Tommaso , Standish Kristopher A. 
Published: June 2024

AbstractAccurate identification of genetic alterations in tumors, such as Fibroblast Growth Factor Receptor, is crucial for treating with targeted therapies; however, molecular testing can delay patient care due to the time and tissue required. Successful development, validation, and deployment of an AI-based, biomarker-detection algorithm could reduce screening cost and accelerate patient recruitment. Here, we develop a deep-learning algorithm using >3000 H&E-stained whole slide images from patients with advanced urothelial cancers, optimized for high sensitivity to avoid ruling out trial-eligible patients. The algorithm is validated on a dataset of 350 patients, achieving an area under the curve of 0.75, specificity of 31.8% at 88.7% sensitivity, and projected 28.7% reduction in molecular testing. We successfully deploy the system in a non-interventional study comprising 89 global study clinical sites and demonstrate its potential to prioritize/deprioritize molecular testing resources and provide substantial cost savings in the drug development and clinical settings.
Socio-demographic and cross-country differences in attention to sustainable certifications and changes in food consumption
Authors: Mota-Gutierrez Jatziri , Sparacino Antonina , Merlino Valentina Maria , Blanc Simone , Brun Filippo , Massimelli Fabrizio , Vassallo Emanuela , Borra Danielle , Massaglia Stefano 
Published: June 2024

AbstractFood labeling can influence, sometimes facilitate, changes in consumer diets to support environmental sustainability and in response to climate change. However, a significant impediment to this dietary shift may arise from the consumers’ tendency to underestimate the environmental impact of their food choices and from their limited knowledge about sustainable certifications. These aspects are influenced by the characteristics and geographical affiliations of individuals. In such a context, the aim of this research has been to identify the main factors that drive the food purchasing frequency and the changes in food consumption associated with consumers’ concerns about climate change and interest in sustainable food certifications by comparing different food products and countries (Italy, France, Germany, Denmark, the USA, and China). A cross-country survey was conducted on 6500 consumers of various food products. The obtained mean scores were then compared, using generalized linear mixed-effect models, to evaluate the associations between the consumers’ food purchasing frequency, the importance of sustainable certifications, and changes in food consumption due to climate change concerns. Much of the variation in food consumption, purchasing behaviors, and interest in sustainable certifications was found to depend on such factors as age, gender, and country of origin. Indeed, Chinese consumers exhibited a heightened interest in sustainable food certifications, yet their consumption scores for all food products overall were higher. Conversely, adult and elderly Danish consumers demonstrated a decrease in the consumption of cheeses, meat, fruits, and vegetables, and their interest scores in all sustainable food certifications were lower. Despite the challenges posed by various consumers’ interests and minimal changes in food consumption patterns, our findings suggest that sustainable certifications present a promising avenue for straightforward interventions to promote the adoption of sustainable diets and to address climate change.
Exploring the genetic prediction of academic underachievement and overachievement
Authors: Kawakami Kaito , Procopio Francesca , Rimfeld Kaili , Malanchini Margherita , von Stumm Sophie , Asbury Kathryn , Plomin Robert 
Published: June 2024

AbstractAcademic underachievement refers to school performance which falls below expectations. Focusing on the pivotal first stage of education, we explored a quantitative measure of underachievement using genomically predicted achievement delta (GPAΔ), which reflects the difference between observed and expected achievement predicted by genome-wide polygenic scores. We analyzed the relationship between GPAΔ at age 7 and achievement trajectories from ages 7 to 16, using longitudinal data from 4175 participants in the Twins Early Development Study to assess empirically the extent to which students regress to their genomically predicted levels by age 16. We found that the achievement of underachievers and overachievers who deviated from their genomic predictions at age 7 regressed on average by one-third towards their genomically predicted levels. We also found that GPAΔ at age 7 was as predictive of achievement trajectories as a traditional ability-based index of underachievement. Targeting GPAΔ underachievers might prove cost-effective because such interventions seem more likely to succeed by going with the genetic flow rather than swimming upstream, helping GPAΔ underachievers reach their genetic potential as predicted by their GPS. However, this is a hypothesis that needs to be tested in intervention research investigating whether GPAΔ underachievers respond better to the intervention than other underachievers. We discuss the practicality of genomic indices in assessing underachievement.
Inhibition of lysine acetyltransferase KAT6 in ER+HER2− metastatic breast cancer: a phase 1 trial
Authors: Mukohara Toru , Park Yeon Hee , Sommerhalder David , Yonemori Kan , Hamilton Erika , Kim Sung-Bae , Kim Jee Hyun , Iwata Hiroji , Yamashita Toshinari , Layman Rachel M. , Mita Monica , Clay Timothy , Chae Yee Soo , Oakman Catherine , Yan Fengting , Kim Gun Min , Im Seock-Ah , Lindeman Geoffrey J. , Rugo Hope S. , Liyanage Marlon , Saul Michelle , Le Corre Christophe , Skoura Athanasia , Liu Li , Li Meng , LoRusso Patricia M. 
Published: June 2024

AbstractInhibition of histone lysine acetyltransferases (KATs) KAT6A and KAT6B has shown antitumor activity in estrogen receptor-positive (ER+) breast cancer preclinical models. PF-07248144 is a selective catalytic inhibitor of KAT6A and KAT6B. In the present study, we report the safety, pharmacokinetics (PK), pharmacodynamics, efficacy and biomarker results from the first-in-human, phase 1 dose escalation and dose expansion study (n = 107) of PF-07248144 monotherapy and fulvestrant combination in heavily pretreated ER+ human epidermal growth factor receptor-negative (HER2−) metastatic breast cancer (mBC). The primary objectives of assessing the safety and tolerability and determining the recommended dose for expansion of PF-07248144, as monotherapy and in combination with fulvestrant, were met. Secondary endpoints included characterization of PK and evaluation of antitumor activity, including objective response rate (ORR) and progression-free survival (PFS). Common treatment-related adverse events (any grade; grades 3–4) included dysgeusia (83.2%, 0%), neutropenia (59.8%, 35.5%) and anemia (48.6%, 13.1%). Exposure was approximately dose proportional. Antitumor activity was observed as monotherapy. For the PF-07248144–fulvestrant combination (n = 43), the ORR (95% confidence interval (CI)) was 30.2% (95% CI = 17.2–46.1%) and the median PFS was 10.7 (5.3–not evaluable) months. PF-07248144 demonstrated a tolerable safety profile and durable antitumor activity in heavily pretreated ER+HER2− mBC. These findings establish KAT6A and KAT6B as druggable cancer targets, provide clinical proof of concept and reveal a potential avenue to treat mBC. clinicaltrial.gov registration: NCT04606446.
Pembrolizumab for advanced urothelial carcinoma: exploratory ctDNA biomarker analyses of the KEYNOTE-361 phase 3 trial
Authors: Powles Thomas , Chang Yen-Hwa , Yamamoto Yoshiaki , Munoz Jose , Reyes-Cosmelli Felipe , Peer Avivit , Cohen Graham , Yu Evan Y. , Lorch Anja , Bavle Abhishek , Homet Moreno Blanca , Markensohn Julia , Edmondson Mackenzie , Chen Cai , Cristescu Razvan , Peña Carol , Lunceford Jared , Gunduz Seyda 
Published: June 2024

AbstractCirculating tumor DNA (ctDNA) is emerging as a potential biomarker in early-stage urothelial cancer, but its utility in metastatic disease remains unknown. In the phase 3 KEYNOTE-361 study, pembrolizumab with and without chemotherapy was compared with chemotherapy alone in patients with metastatic urothelial cancer. The study did not meet prespecified efficacy thresholds for statistical significance. To identify potential biomarkers of response, we retrospectively evaluated the association of pre- and posttreatment ctDNA with clinical outcomes in a subset of patients who received pembrolizumab (n = 130) or chemotherapy (n = 130) in KEYNOTE-361. Baseline ctDNA was associated with best overall response (BOR; P = 0.009), progression-free survival (P < 0.001) and overall survival (OS; P < 0.001) for pembrolizumab but not for chemotherapy (all; P > 0.05). Chemotherapy induced larger ctDNA decreases from baseline to treatment cycle 2 than pembrolizumab; however, change with pembrolizumab (n = 87) was more associated with BOR (P = 4.39 × 10−5) and OS (P = 7.07 × 10−5) than chemotherapy (n = 102; BOR: P = 1.01 × 10−4; OS: P = 0.018). Tumor tissue-informed versions of ctDNA change metrics were most associated with clinical outcomes but did not show a statistically significant independent value for explaining OS beyond radiographic change by RECIST v.1.1 when jointly modeled (pembrolizumab P = 0.364; chemotherapy P = 0.823). These results suggest distinct patterns in early ctDNA changes with immunotherapy and chemotherapy and differences in their association with long-term outcomes, which provide preliminary insights into the utility of liquid biopsies for treatment monitoring in metastatic urothelial cancer. Clinical trial registration: NCT02853305.
Neonatal mortality rate and determinants among births of mothers at extreme ages of reproductive life in low and middle income countries
Authors: Tamir Tadesse Tarik 
Published: June 2024

AbstractNeonatal mortality, which refers to the death of neonates during the first 28 completed days of life, is a critical global public health concern. The neonatal period is widely recognized as one of the most precarious phases in human life. Research has indicated that maternal extreme ages during reproductive years significantly impact neonatal survival, particularly in low- and middle-income countries. Consequently, this study aims to evaluate the neonatal mortality rate and determinants among neonates born to mothers at extreme reproductive ages within these countries. A secondary analysis of demographic and health surveys conducted between 2015 and 2022 in 43 low- and middle-income countries was performed. The study included a total sample of 151,685 live births. Researchers utilized a multilevel mixed-effects model to identify determinants of neonatal mortality. The measures of association were evaluated using the adjusted odds ratio within a 95% confidence interval. The neonatal mortality rate among neonates born to mothers at extreme ages of reproductive life in low- and middle-income countries was 28.96 neonatal deaths per 1000 live births (95% CI 28.13–29.82). Factors associated with higher rates of neonatal mortality include male gender, low and high birth weight, maternal education (no or low), home deliveries, multiple births, short preceding birth intervals, lack of postnatal checkups, and countries with high fertility and low literacy rates. This study sheds light on the neonatal mortality rates among neonates born to mothers at extreme ages of reproductive life in low- and middle-income countries. Notably, we found that neonatal mortality was significantly higher in this group compared to neonatal mortality rates reported regardless of maternal ages. Male babies, low and high birth-weighted babies, those born to mothers with no or low education, delivered at home, singletons, babies born with a small preceding birth interval, and those without postnatal checkups faced elevated risks of neonatal mortality. Additionally, neonates born in countries with high fertility and low literacy rates were also vulnerable. These findings underscore the urgent need for targeted interventions tailored to mothers at extreme ages. Policymakers and healthcare providers should prioritize strategies that address specific risk factors prevalent in these vulnerable populations. By doing so, we can improve neonatal outcomes and ensure the survival of these newborns during the critical neonatal period.
Robust memory of face moral values is encoded in the human caudate tail: a simultaneous EEG-fMRI study
Authors: Ataei Ali , Amini Arash , Ghazizadeh Ali 
Published: June 2024

AbstractMoral judgements about people based on their actions is a key component that guides social decision making. It is currently unknown how positive or negative moral judgments associated with a person’s face are processed and stored in the brain for a long time. Here, we investigate the long-term memory of moral values associated with human faces using simultaneous EEG-fMRI data acquisition. Results show that only a few exposures to morally charged stories of people are enough to form long-term memories a day later for a relatively large number of new faces. Event related potentials (ERPs) showed a significant differentiation of remembered good vs bad faces over centerofrontal electrode sites (value ERP). EEG-informed fMRI analysis revealed a subcortical cluster centered on the left caudate tail (CDt) as a correlate of the face value ERP. Importantly neither this analysis nor a conventional whole-brain analysis revealed any significant coding of face values in cortical areas, in particular the fusiform face area (FFA). Conversely an fMRI-informed EEG source localization using accurate subject-specific EEG head models also revealed activation in the left caudate tail. Nevertheless, the detected caudate tail region was found to be functionally connected to the FFA, suggesting FFA to be the source of face-specific information to CDt. A further psycho-physiological interaction analysis also revealed task-dependent coupling between CDt and dorsomedial prefrontal cortex (dmPFC), a region previously identified as retaining emotional working memories. These results identify CDt as a main site for encoding the long-term value memories of faces in humans suggesting that moral value of faces activates the same subcortical basal ganglia circuitry involved in processing reward value memory for objects in primates.
Enhancing ecological uncertainty predictions in pollution control games through dynamic Bayesian updating
Authors: Zhou Jiangjing , Petrosian Ovanes , Gao Hongwei 
Published: June 2024

AbstractThis study presents a dynamic Bayesian game model designed to improve predictions of ecological uncertainties leading to natural disasters. It incorporates historical signal data on ecological indicators. Participants, acting as decision-makers, receive signals about an unknown parameter-observations of a random variable’s realization values before a specific time, offering insights into ecological uncertainties. The essence of the model lies in its dynamic Bayesian updating, where beliefs about unknown parameters are refined with each new signal, enhancing predictive accuracy. The main focus of our paper is to theoretically validate this approach, by presenting a number of theorems that prove its precision and efficiency in improving uncertainty estimations. Simulation results validate the model’s effectiveness in various scenarios, highlighting its role in refining natural disaster forecasts.