bluuebunny/crossref_metadata_2025 huggingface dataset:
Pulsatile electrical stimulation creates predictable, correctable disruptions in neural firing
Authors: Steinhardt Cynthia R. , Mitchell Diana E. , Cullen Kathleen E. , Fridman Gene Y.
Published: July 2024

Electrical stimulation is a key tool in neuroscience, both in brain mapping studies and in many therapeutic applications such as cochlear, vestibular, and retinal neural implants. Due to safety considerations, stimulation is restricted to short biphasic pulses. Despite decades of research and development, neural implants lead to varying restoration of function in patients. In this study, we use computational modeling to provide an explanation for how pulsatile stimulation affects axonal channels and therefore leads to variability in restoration of neural responses. The phenomenological explanation is transformed into equations that predict induced firing rate as a function of pulse rate, pulse amplitude, and spontaneous firing rate. We show that these equations predict simulated responses to pulsatile stimulation with a variety of parameters as well as several features of experimentally recorded primate vestibular afferent responses to pulsatile stimulation. We then discuss the implications of these effects for improving clinical stimulation paradigms and electrical stimulation-based experiments.
Adeno-associated virus delivered CXCL9 sensitizes glioblastoma to anti-PD-1 immune checkpoint blockade
Authors: von Roemeling Christina A. , Patel Jeet A. , Carpenter Savannah L. , Yegorov Oleg , Yang Changlin , Bhatia Alisha , Doonan Bently P. , Russell Rylynn , Trivedi Vrunda S. , Klippel Kelena , Ryu Daniel H. , Grippin Adam , Futch Hunter S. , Ran Yong , Hoang-Minh Lan B. , Weidert Frances L. , Golde Todd E. , Mitchell Duane A.
Published: July 2024

There are numerous mechanisms by which glioblastoma cells evade immunological detection, underscoring the need for strategic combinatorial treatments to achieve appreciable therapeutic effects. However, developing combination therapies is difficult due to dose-limiting toxicities, blood-brain-barrier, and suppressive tumor microenvironment. Glioblastoma is notoriously devoid of lymphocytes driven in part by a paucity of lymphocyte trafficking factors necessary to prompt their recruitment and activation. Herein, we develop a recombinant adeno-associated virus (AAV) gene therapy that enables focal and stable reconstitution of the tumor microenvironment with C-X-C motif ligand 9 (CXCL9), a powerful call-and-receive chemokine for lymphocytes. By manipulating local chemokine directional guidance, AAV-CXCL9 increases tumor infiltration by cytotoxic lymphocytes, sensitizing glioblastoma to anti-PD-1 immune checkpoint blockade in female preclinical tumor models. These effects are accompanied by immunologic signatures evocative of an inflamed tumor microenvironment. These findings support AAV gene therapy as an adjuvant for reconditioning glioblastoma immunogenicity given its safety profile, tropism, modularity, and off-the-shelf capability.
Glucose-6-phosphate dehydrogenase maintains redox homeostasis and biosynthesis in LKB1-deficient KRAS-driven lung cancer
Authors: Lan Taijin , Arastu Sara , Lam Jarrick , Kim Hyungsin , Wang Wenping , Wang Samuel , Bhatt Vrushank , Lopes Eduardo Cararo , Hu Zhixian , Sun Michael , Luo Xuefei , Ghergurovich Jonathan M. , Su Xiaoyang , Rabinowitz Joshua D. , White Eileen , Guo Jessie Yanxiang
Published: July 2024

Cancer cells depend on nicotinamide adenine dinucleotide phosphate (NADPH) to combat oxidative stress and support reductive biosynthesis. One major NADPH production route is the oxidative pentose phosphate pathway (committed step: glucose-6-phosphate dehydrogenase, G6PD). Alternatives exist and can compensate in some tumors. Here, using genetically-engineered lung cancer mouse models, we show that G6PD ablation significantly suppresses KrasG12D/+;Lkb1-/- (KL) but not KrasG12D/+;P53-/- (KP) lung tumorigenesis. In vivo isotope tracing and metabolomics reveal that G6PD ablation significantly impairs NADPH generation, redox balance, and de novo lipogenesis in KL but not KP lung tumors. Mechanistically, in KL tumors, G6PD ablation activates p53, suppressing tumor growth. As tumors progress, G6PD-deficient KL tumors increase an alternative NADPH source from serine-driven one carbon metabolism, rendering associated tumor-derived cell lines sensitive to serine/glycine depletion. Thus, oncogenic driver mutations determine lung cancer dependence on G6PD, whose targeting is a potential therapeutic strategy for tumors harboring KRAS and LKB1 co-mutations.
Arabidopsis and maize terminator strength is determined by GC content, polyadenylation motifs and cleavage probability
Authors: Gorjifard Sayeh , Jores Tobias , Tonnies Jackson , Mueth Nicholas A. , Bubb Kerry , Wrightsman Travis , Buckler Edward S. , Fields Stanley , Cuperus Josh T. , Queitsch Christine
Published: July 2024

The 3’ end of a gene, often called a terminator, modulates mRNA stability, localization, translation, and polyadenylation. Here, we adapted Plant STARR-seq, a massively parallel reporter assay, to measure the activity of over 50,000 terminators from the plants Arabidopsis thaliana and Zea mays. We characterize thousands of plant terminators, including many that outperform bacterial terminators commonly used in plants. Terminator activity is species-specific, differing in tobacco leaf and maize protoplast assays. While recapitulating known biology, our results reveal the relative contributions of polyadenylation motifs to terminator strength. We built a computational model to predict terminator strength and used it to conduct in silico evolution that generated optimized synthetic terminators. Additionally, we discover alternative polyadenylation sites across tens of thousands of terminators; however, the strongest terminators tend to have a dominant cleavage site. Our results establish features of plant terminator function and identify strong naturally occurring and synthetic terminators.
Nitrifying niche in estuaries is expanded by the plastisphere
Authors: Su Xiaoxuan , Huang Xinrong , Zhang Yiyue , Yang Leyang , Wen Teng , Yang Xiaoru , Zhu Guibing , Zhang Jinbo , Tang Yijia , Li Zhaolei , Ding Jing , Li Ruilong , Pan Junliang , Chen Xinping , Huang Fuyi , Rillig Matthias C. , Zhu Yong-guan
Published: July 2024

The estuarine plastisphere, a novel ecological habitat in the Anthropocene, has garnered global concerns. Recent geochemical evidence has pointed out its potential role in influencing nitrogen biogeochemistry. However, the biogeochemical significance of the plastisphere and its mechanisms regulating nitrogen cycling remain elusive. Using 15N- and 13C-labelling coupled with metagenomics and metatranscriptomics, here we unveil that the plastisphere likely acts as an underappreciated nitrifying niche in estuarine ecosystems, exhibiting a 0.9 ~ 12-fold higher activity of bacteria-mediated nitrification compared to surrounding seawater and other biofilms (stone, wood and glass biofilms). The shift of active nitrifiers from O2-sensitive nitrifiers in the seawater to nitrifiers with versatile metabolisms in the plastisphere, combined with the potential interspecific cooperation of nitrifying substrate exchange observed among the plastisphere nitrifiers, collectively results in the unique nitrifying niche. Our findings highlight the plastisphere as an emerging nitrifying niche in estuarine environment, and deepen the mechanistic understanding of its contribution to marine biogeochemistry.
Stress-induced nuclear translocation of ONAC023 improves drought and heat tolerance through multiple processes in rice
Authors: Chang Yu , Fang Yujie , Liu Jiahan , Ye Tiantian , Li Xiaokai , Tu Haifu , Ye Ying , Wang Yao , Xiong Lizhong
Published: July 2024

Drought and heat are major abiotic stresses frequently coinciding to threaten rice production. Despite hundreds of stress-related genes being identified, only a few have been confirmed to confer resistance to multiple stresses in crops. Here we report ONAC023, a hub stress regulator that integrates the regulations of both drought and heat tolerance in rice. ONAC023 positively regulates drought and heat tolerance at both seedling and reproductive stages. Notably, the functioning of ONAC023 is obliterated without stress treatment and can be triggered by drought and heat stresses at two layers. The expression of ONAC023 is induced in response to stress stimuli. We show that overexpressed ONAC23 is translocated to the nucleus under stress and evidence from protoplasts suggests that the dephosphorylation of the remorin protein OSREM1.5 can promote this translocation. Under drought or heat stress, the nuclear ONAC023 can target and promote the expression of diverse genes, such as OsPIP2;7, PGL3, OsFKBP20-1b, and OsSF3B1, which are involved in various processes including water transport, reactive oxygen species homeostasis, and alternative splicing. These results manifest that ONAC023 is fine-tuned to positively regulate drought and heat tolerance through the integration of multiple stress-responsive processes. Our findings provide not only an underlying connection between drought and heat responses, but also a promising candidate for engineering multi-stress-resilient rice.
Sensor degradation in nuclear reactor pressure vessels: the overlooked factor in remaining useful life prediction
Authors: Hossain Raisa Bentay , Kobayashi Kazuma , Alam Syed Bahauddin
Published: July 2024

Sensor degradation poses a critical yet ‘often overlooked’ challenge in accurately predicting the remaining useful life (RUL) of nuclear reactor pressure vessels (RPVs), hindering safe and efficient plant operation. This paper introduces an approach to RUL estimation that explicitly addresses sensor degradation, a significant departure from conventional methods. We model neutron embrittlement, a dominant degradation process in RPV steel, as a Wiener process and leverage real-world surveillance capsule data for insightful parameterization. Maximum likelihood estimation is utilized to characterize the degradation dynamics in the model. A Kalman filter then seamlessly integrates the degradation model with sensor measurements, effectively compensating for degradation-induced errors and providing refined state estimates. These estimates power a robust RUL prediction framework. Our results expose the profound impact of sensor degradation on conventional RUL predictions. By directly confronting sensor degradation, our method yields substantially more accurate and reliable RUL estimates. This work marks a significant advancement in the field of materials degradation, offering a powerful tool to optimize nuclear power plant safety and longevity.
Priming antibody responses to the fusion peptide in rhesus macaques
Authors: Cottrell Christopher A. , Pratap Payal P. , Cirelli Kimberly M. , Carnathan Diane G. , Enemuo Chiamaka A. , Antanasijevic Aleksandar , Ozorowski Gabriel , Sewall Leigh M. , Gao Hongmei , Allen Joel D. , Nogal Bartek , Silva Murillo , Bhiman Jinal , Pauthner Matthias , Irvine Darrell J. , Montefiori David , Crispin Max , Burton Dennis R. , Silvestri Guido , Crotty Shane , Ward Andrew B.
Published: July 2024

Immunodominance of antibodies targeting non-neutralizing epitopes and the high level of somatic hypermutation within germinal centers (GCs) required for most HIV broadly neutralizing antibodies (bnAbs) are major impediments to the development of an effective HIV vaccine. Rational protein vaccine design and non-conventional immunization strategies are potential avenues to overcome these hurdles. Here, we report using implantable osmotic pumps to continuously deliver a series of epitope-targeted immunogens to rhesus macaques over the course of six months to prime and elicit antibody responses against the conserved fusion peptide (FP). GC responses and antibody specificities were tracked longitudinally using lymph node fine-needle aspirates and electron microscopy polyclonal epitope mapping (EMPEM), respectively, to show antibody responses to the FP/N611 glycan hole region were primed, although exhibited limited neutralization breadth. Application of cryoEMPEM delineated key residues for on-target and off-target responses that can drive the next round of structure-based vaccine design.
Neutrophil-derived migrasomes are an essential part of the coagulation system
Authors: Jiang Dong , Jiao Lin , Li Qing , Xie Renxiang , Jia Haohao , Wang ShiHui , Chen Yining , Liu Siyuan , Huang Dandan , Zheng Jiajia , Song Wenhao , Li Ying , Chen JianFeng , Li Jinsong , Ying Binwu , Yu Li
Published: July 2024

Migrasomes are organelles that are generated by migrating cells. Here we report the key role of neutrophil-derived migrasomes in haemostasis. We found that a large number of neutrophil-derived migrasomes exist in the blood of mice and humans. Compared with neutrophil cell bodies and platelets, these migrasomes adsorb and enrich coagulation factors on the surface. Moreover, they are highly enriched with adhesion molecules, which enable them to preferentially accumulate at sites of injury, where they trigger platelet activation and clot formation. Depletion of neutrophils, or genetic reduction of the number of these migrasomes, significantly decreases platelet plug formation and impairs coagulation. These defects can be rescued by intravenous injection of purified neutrophil-derived migrasomes. Our study reveals neutrophil-derived migrasomes as a previously unrecognized essential component of the haemostasis system, which may shed light on the cause of various coagulation disorders and open therapeutic possibilities.
Emergence of lake conditions that exceed natural temperature variability
Authors: Huang Lei , Woolway R. Iestyn , Timmermann Axel , Lee Sun-Seon , Rodgers Keith B. , Yamaguchi Ryohei
Published: July 2024

Lake surface temperatures are projected to increase under climate change, which could trigger shifts in the future distribution of thermally sensitive aquatic species. Of particular concern for lake ecosystems are when temperatures increase outside the range of natural variability, without analogue either today or in the past. However, our knowledge of when such no-analogue conditions will appear remains uncertain. Here, using daily outputs from a large ensemble of SSP3-7.0 Earth system model projections, we show that these conditions will emerge at the surface of many northern lakes under a global warming of 4.0 °C above pre-industrial conditions. No-analogue conditions will occur sooner, under 2.4 °C of warming, at lower latitudes, primarily due to a weaker range of natural variability, which increases the likelihood of the upper natural limit of lake temperature being exceeded. Similar patterns are also projected in subsurface water, with no-analogue conditions occurring first at low latitudes and occurring last, if at all, at higher latitudes. Our study suggests that global warming will induce changes across the water column, particularly at low latitudes, leading to the emergence of unparalleled climates with no modern counterparts, probably affecting their habitability and leading to rearrangements of freshwater habitats this century.
Precise in vivo RNA base editing with a wobble-enhanced circular CLUSTER guide RNA
Authors: Reautschnig Philipp , Fruhner Carolin , Wahn Nicolai , Wiegand Charlotte P. , Kragness Sabrina , Yung John F. , Hofacker Daniel T. , Fisk Jenna , Eidelman Michelle , Waffenschmidt Nils , Feige Maximilian , Pfeiffer Laura S. , Schulz Annika E. , Füll Yvonne , Levanon Erez Y. , Mandel Gail , Stafforst Thorsten
Published: July 2024

Recruiting the endogenous editing enzyme adenosine deaminase acting on RNA (ADAR) with tailored guide RNAs for adenosine-to-inosine (A-to-I) RNA base editing is promising for safely manipulating genetic information at the RNA level. However, the precision and efficiency of editing are often compromised by bystander off-target editing. Here, we find that in 5′-UAN triplets, which dominate bystander editing, G•U wobble base pairs effectively mitigate off-target events while maintaining high on-target efficiency. This strategy is universally applicable to existing A-to-I RNA base-editing systems and complements other suppression methods such as G•A mismatches and uridine (U) depletion. Combining wobble base pairing with a circularized format of the CLUSTER approach achieves highly precise and efficient editing (up to 87%) of a disease-relevant mutation in the Mecp2 transcript in cell culture. Virus-mediated delivery of the guide RNA alone realizes functional MeCP2 protein restoration in the central nervous system of a murine Rett syndrome model with editing yields of up to 19% and excellent bystander control in vivo.
Disruption of neural periodicity predicts clinical response after deep brain stimulation for obsessive-compulsive disorder
Authors: Provenza Nicole R. , Reddy Sandesh , Allam Anthony K. , Rajesh Sameer V. , Diab Nabeel , Reyes Gabriel , Caston Rose M. , Katlowitz Kalman A. , Gandhi Ajay D. , Bechtold Raphael A. , Dang Huy Q. , Najera Ricardo A. , Giridharan Nisha , Kabotyanski Katherine E. , Momin Faiza , Hasen Mohammed , Banks Garrett P. , Mickey Brian J. , Kious Brent M. , Shofty Ben , Hayden Benjamin Y. , Herron Jeffrey A. , Storch Eric A. , Patel Ankit B. , Goodman Wayne K. , Sheth Sameer A.
Published: July 2024

Recent advances in surgical neuromodulation have enabled chronic and continuous intracranial monitoring during everyday life. We used this opportunity to identify neural predictors of clinical state in 12 individuals with treatment-resistant obsessive-compulsive disorder (OCD) receiving deep brain stimulation (DBS) therapy (NCT05915741). We developed our neurobehavioral models based on continuous neural recordings in the region of the ventral striatum in an initial cohort of five patients and tested and validated them in a held-out cohort of seven additional patients. Before DBS activation, in the most symptomatic state, theta/alpha (9 Hz) power evidenced a prominent circadian pattern and a high degree of predictability. In patients with persistent symptoms (non-responders), predictability of the neural data remained consistently high. On the other hand, in patients who improved symptomatically (responders), predictability of the neural data was significantly diminished. This neural feature accurately classified clinical status even in patients with limited duration recordings, indicating generalizability that could facilitate therapeutic decision-making.
Case study on climate change effects and food security in Southeast Asia
Authors: Taniushkina Daria , Lukashevich Aleksander , Shevchenko Valeriy , Belalov Ilya S. , Sotiriadi Nazar , Narozhnaia Veronica , Kovalev Kirill , Krenke Alexander , Lazarichev Nikita , Bulkin Alexander , Maximov Yury
Published: July 2024

Agriculture, a cornerstone of human civilization, faces rising challenges from climate change, resource limitations, and stagnating yields. Precise crop production forecasts are crucial for shaping trade policies, development strategies, and humanitarian initiatives. This study introduces a comprehensive machine learning framework designed to predict crop production. We leverage CMIP5 climate projections under a moderate carbon emission scenario to evaluate the future suitability of agricultural lands and incorporate climatic data, historical agricultural trends, and fertilizer usage to project yield changes. Our integrated approach forecasts significant regional variations in crop production across Southeast Asia by 2028, identifying potential cropland utilization. Specifically, the cropland area in Indonesia, Malaysia, Philippines, and Viet Nam is projected to decline by more than 10% if no action is taken, and there is potential to mitigate that loss. Moreover, rice production is projected to decline by 19% in Viet Nam and 7% in Thailand, while the Philippines may see a 5% increase compared to 2021 levels. Our findings underscore the critical impacts of climate change and human activities on agricultural productivity, offering essential insights for policy-making and fostering international cooperation.
Multiparametric MRI–based radiomic models for early prediction of response to neoadjuvant systemic therapy in triple-negative breast cancer
Authors: Mohamed Rania M. , Panthi Bikash , Adrada Beatriz E. , Boge Medine , Candelaria Rosalind P. , Chen Huiqin , Guirguis Mary S. , Hunt Kelly K. , Huo Lei , Hwang Ken-Pin , Korkut Anil , Litton Jennifer K. , Moseley Tanya W. , Pashapoor Sanaz , Patel Miral M. , Reed Brandy , Scoggins Marion E. , Son Jong Bum , Thompson Alastair , Tripathy Debu , Valero Vicente , Wei Peng , White Jason , Whitman Gary J. , Xu Zhan , Yang Wei , Yam Clinton , Ma Jingfei , Rauch Gaiane M.
Published: July 2024

Triple-negative breast cancer (TNBC) is often treated with neoadjuvant systemic therapy (NAST). We investigated if radiomic models based on multiparametric Magnetic Resonance Imaging (MRI) obtained early during NAST predict pathologic complete response (pCR). We included 163 patients with stage I-III TNBC with multiparametric MRI at baseline and after 2 (C2) and 4 cycles of NAST. Seventy-eight patients (48%) had pCR, and 85 (52%) had non-pCR. Thirty-six multivariate models combining radiomic features from dynamic contrast-enhanced MRI and diffusion-weighted imaging had an area under the receiver operating characteristics curve (AUC) > 0.7. The top-performing model combined 35 radiomic features of relative difference between C2 and baseline; had an AUC = 0.905 in the training and AUC = 0.802 in the testing set. There was high inter-reader agreement and very similar AUC values of the pCR prediction models for the 2 readers. Our data supports multiparametric MRI-based radiomic models for early prediction of NAST response in TNBC.
Optimizing word embeddings for small datasets: a case study on patient portal messages from breast cancer patients
Authors: Song Qingyuan , Ni Congning , Warner Jeremy L. , Chen Qingxia , Song Lijun , Rosenbloom S. Trent , Malin Bradley A. , Yin Zhijun
Published: July 2024

Patient portal messages often relate to specific clinical phenomena (e.g., patients undergoing treatment for breast cancer) and, as a result, have received increasing attention in biomedical research. These messages require natural language processing and, while word embedding models, such as word2vec, have the potential to extract meaningful signals from text, they are not readily applicable to patient portal messages. This is because embedding models typically require millions of training samples to sufficiently represent semantics, while the volume of patient portal messages associated with a particular clinical phenomenon is often relatively small. We introduce a novel adaptation of the word2vec model, PK-word2vec (where PK stands for prior knowledge), for small-scale messages. PK-word2vec incorporates the most similar terms for medical words (including problems, treatments, and tests) and non-medical words from two pre-trained embedding models as prior knowledge to improve the training process. We applied PK-word2vec in a case study of patient portal messages in the Vanderbilt University Medical Center electric health record system sent by patients diagnosed with breast cancer from December 2004 to November 2017. We evaluated the model through a set of 1000 tasks, each of which compared the relevance of a given word to a group of the five most similar words generated by PK-word2vec and a group of the five most similar words generated by the standard word2vec model. We recruited 200 Amazon Mechanical Turk (AMT) workers and 7 medical students to perform the tasks. The dataset was composed of 1389 patient records and included 137,554 messages with 10,683 unique words. Prior knowledge was available for 7981 non-medical and 1116 medical words. In over 90% of the tasks, both reviewers indicated PK-word2vec generated more similar words than standard word2vec (p = 0.01).The difference in the evaluation by AMT workers versus medical students was negligible for all comparisons of tasks’ choices between the two groups of reviewers ($${\text{p}} = 0.774$$

p
=
0.774

under a paired t-test). PK-word2vec can effectively learn word representations from a small message corpus, marking a significant advancement in processing patient portal messages.
Establishment of a corneal ulcer prognostic model based on machine learning
Authors: Wang Meng-Tong , Cai You-Ran , Jang Vlon , Meng Hong-Jian , Sun Ling-Bo , Deng Li-Min , Liu Yu-Wen , Zou Wen-Jin
Published: July 2024

Corneal infection is a major public health concern worldwide and the most common cause of unilateral corneal blindness. Toxic effects of different microorganisms, such as bacteria and fungi, worsen keratitis leading to corneal perforation even with optimal drug treatment. The cornea forms the main refractive surface of the eye. Diseases affecting the cornea can cause severe visual impairment. Therefore, it is crucial to analyze the risk of corneal perforation and visual impairment in corneal ulcer patients for making early treatment strategies. The modeling of a fully automated prognostic model system was performed in two parts. In the first part, the dataset contained 4973 slit lamp images of corneal ulcer patients in three centers. A deep learning model was developed and tested for segmenting and classifying five lesions (corneal ulcer, corneal scar, hypopyon, corneal descementocele, and corneal neovascularization) in the eyes of corneal ulcer patients. Further, hierarchical quantification was carried out based on policy rules. In the second part, the dataset included clinical data (name, gender, age, best corrected visual acuity, and type of corneal ulcer) of 240 patients with corneal ulcers and respective 1010 slit lamp images under two light sources (natural light and cobalt blue light). The slit lamp images were then quantified hierarchically according to the policy rules developed in the first part of the modeling. Combining the above clinical data, the features were used to build the final prognostic model system for corneal ulcer perforation outcome and visual impairment using machine learning algorithms such as XGBoost, LightGBM. The ROC curve area (AUC value) evaluated the model’s performance. For segmentation of the five lesions, the accuracy rates of hypopyon, descemetocele, corneal ulcer under blue light, and corneal neovascularization were 96.86, 91.64, 90.51, and 93.97, respectively. For the corneal scar lesion classification, the accuracy rate of the final model was 69.76. The XGBoost model performed the best in predicting the 1-month prognosis of patients, with an AUC of 0.81 (95% CI 0.63–1.00) for ulcer perforation and an AUC of 0.77 (95% CI 0.63–0.91) for visual impairment. In predicting the 3-month prognosis of patients, the XGBoost model received the best AUC of 0.97 (95% CI 0.92–1.00) for ulcer perforation, while the LightGBM model achieved the best performance with an AUC of 0.98 (95% CI 0.94–1.00) for visual impairment.
Broadening bandgaps in a multi-resonant piezoelectric metamaterial plate via bandgap merging phenomena
Authors: Li Yuhao , Liu Zhiyuan , Zhou Hao , Yi Kaijun , Zhu Rui
Published: July 2024

Locally resonant metamaterials usually have narrow bandgaps, which significantly limits their applications in realistic engineering environments. In this paper, an optimization method based on the genetic algorithm is proposed to broaden bandgaps in multi-resonant piezoelectric metamaterial through the merging of multiple separated bandgaps. Using the effective medium theory, the equivalent bending stiffness and dispersion relationship of a metamaterial plate are first obtained. Then, the criteria for determining the bandgap ranges for the two cases with and without damping are provided and analyzed. Furthermore, based on the bandgap merging phenomena, an optimization method for widening the bandgap is proposed based on the genetic algorithm. By investigating the bandgap widening effects in cases without and with damping, it is found that, when there is no damping, the bandgap can only be slightly widened; while after introducing damping into the transfer functions, the bandgap can be significantly widened by more than 200%. The bandgap widening effects are verified by comparing with finite element simulation results.
Setting the morphologic quality limits enabling accurate classification of charred archaeological grape seeds
Authors: Landa Vlad , Shapira Yekaterina , Eliyahu-Behar Adi , Ben-Arie Reut Levitan , Weiss Ehud , Reuveni Yuval , Drori Elyashiv
Published: July 2024

This study investigates the morphological changes in grape pips resulting from various charring conditions. Employing high-resolution scanning combined with morphometric measurements for morphological analysis, we aimed to understand the effects of charring on grape pips. Our morphometric analysis demonstrated significant alterations in seed shape above 250 °C. The length–width ratio and the occurrence of cracks notably changed, providing a basis for assessing charring conditions. In addition, applying a machine learning classification method, we determined that accurate classification of grape varieties by the morphometric analysis method is feasible for seeds charred at up to 250 °C and 8 h. Integrating the morphometric changes and temperature ranges suitable for classification, we developed a sorting model for archaeological seeds. By projecting length–width ratios onto a curve calculated from controlled conditions, we estimated charring temperatures. Approximately 50% of archaeological seeds deviated from the model, indicating drastic charring conditions. This sorting model facilitates a stringent selection of seeds fit for classification, enhancing the accuracy of our machine learning-based methodology. In conclusion, combining machine learning with morphometric sorting enables the identification of charred grape seeds suitable for identification by the morphometric method. This comprehensive approach provides a valuable tool for future research for the identification of charred grape seeds found in archaeological contexts, enhancing our understanding of ancient viticulture practices and grape cultivation.
Observational study of missing SOFA score data frequency in RCTs relative to ICU length of stay
Authors: Molinnus Denise , Beulertz Michael , Bickenbach Johannes , Marx Gernot , Benstoem Carina
Published: July 2024

The Sequential Organ Failure Assessment, also known as SOFA score, was introduced to assess organ dysfunction of critical ill patients. However, understanding the impact of missing SOFA scores in randomized controlled trials and how this affect the validity and applicability of the SOFA score as a surrogate endpoint for predicting mortality has been a matter of interest. To address this, a secondary analysis of a systematic review was conducted to quantify the relationship between SOFA scores and the prediction of mortality in critically ill adults in randomized controlled trials (RCTs). The systematic review being referred to included 87 RCTs with a total of 12,064 critically ill patients. This analysis focused on missing SOFA score data in relation to the length of stay on the intensive care unit (ICU) and the methods used to handle missing data. SOFA score measurements from the included studies were categorized into three time frames: Early (t ≤ 4 days), Intermediate (t = 5–10 days) and Late (t > 10 days) measurement. Only one study reported a complete data set for calculating the SOFA score for an Early measurement. When considering all methods used to address missing data, 32% of studies still had missing data for Early measurements, and this percentage increased to 64% for Late measurements. These findings suggested that, over time, the number of studies with incomplete data sets has been increasing. The longer a patient is treated on the ICU, the higher the number of missing data which can impact the validity of SOFA score analyses. There was no clear trend towards a specific method for compensating missing data. An exemplary calculation demonstrated that ignoring missing data may lead to an underestimated variability of the treatment effect. This, in turn, could bias the interpretation of study results by policy- and clinical decision-makers. Overall, there are several limitations that need to be considered when using SOFA score as a surrogate endpoint for mortality. When employed as an outcome, the SOFA score is frequently missing and most studies do not adequately describe the amount or nature of missing data, or the methods used to handle missing data in the analysis.
Effect of early endothelial function improvement on subclinical target organ damage in hypertensives
Authors: Huang Xiaodong , Huang Xianwei , Pan Mandong , Lin Jiyan , Xie Liangdi
Published: July 2024

Endothelial dysfunction is acknowledged as a marker for subclinical target organ damage (STOD) in hypertension, though its therapeutic potential has not yet been clarified. This study assessed whether early endothelial function improvement (EEFI) reduced STOD in patients with essential hypertension (EH). We conducted a retrospective cohort analysis of 456 EH patients initially free from STOD. Endothelial function was assessed using brachial artery flow-mediated dilation (FMD), with values ≤ 7.1% indicating dysfunction. Patients were initially categorized by endothelial status (dysfunction: n = 180, normal: n = 276), and further divided into improved or unimproved groups based on changes within three months post-enrollment. During a median follow-up of 25 months, 177 patients developed STOD. The incidence of STOD was significantly higher in patients with initial dysfunction compared to those with normal function. Kaplan–Meier analysis indicated that the improved group had a lower cumulative incidence of STOD compared to the unimproved group (p < 0.05). Multivariable Cox regression confirmed EEFI as an independent protective factor against STOD in EH patients (p < 0.05), regardless of their baseline endothelial status, especially in those under 65 years old, non-smokers, and with low-density lipoprotein cholesterol levels ≤ 3.4 mmol/L. In conclusion, EEFI significantly reduces STOD incidence in EH patients, particularly in specific subgroups, emphasizing the need for early intervention in endothelial function to prevent STOD.
Affordable web-based foot–ankle exercise program proves effective for diabetic foot care in a randomized controlled trial with economic evaluation
Authors: Cruvinel-Júnior Ronaldo H. , Ferreira Jane S. S. P. , Veríssimo Jady L. , Monteiro Renan L. , Silva Érica Q. , Suda Eneida Y. , Sacco Isabel C. N.
Published: July 2024

The aim of this study was to shed light on a crucial issue through a comprehensive evaluation of the cost-effectiveness and cost-utility of a cutting-edge web-based foot–ankle therapeutic exercise program (SOPeD) designed for treating modifiable risk factors for ulcer prevention in individuals with diabetes-related peripheral neuropathy (DPN). In this randomized controlled trial, 62 participants diagnosed with DPN were assigned to the SOPeD software or received usual care for diabetic foot. Primary outcomes were DPN symptoms and severity, foot pain and function, and quality-adjusted life years (QALYs). Between-group comparisons provided 95% confidence intervals. The study also calculated incremental cost-effectiveness and cost-utility ratios (ICERs), analyzed direct costs from a healthcare perspective, and performed a sensitivity analysis to assess uncertainty. The web-based intervention effectively reduced foot pain, improved foot function and showed favorable cost-effectiveness, with ICERs ranging from (USD) $5.37–$148.71 per improvement in different outcomes. There is a high likelihood of cost-effectiveness for improving DPN symptoms and severity, foot pain, and function, even when the minimum willingness-to-pay threshold was set at $1000.00 USD. However, the intervention did not prove to be cost-effective in terms of QALYs. This study reveals SOPeD's effectiveness in reducing foot pain, improving foot function, and demonstrating cost-effectiveness in enhancing functional and clinical outcomes. SOPeD stands as a potential game-changer for modifiable risk factors for ulcers, with our findings indicating a feasible and balanced integration into public health systems. Further studies and considerations are vital for informed decisions to stakeholders and the successful implementation of this preventive program on a larger scale.

Trial Registration: ClinicalTrials.gov, NCT04011267. Registered on 8 July 2019.
Interpretable machine learning for the prediction of death risk in patients with acute diquat poisoning
Authors: Li Huiyi , Liu Zheng , Sun Wenming , Li Tiegang , Dong Xuesong
Published: July 2024

The aim of this study was to develop and validate predictive models for assessing the risk of death in patients with acute diquat (DQ) poisoning using innovative machine learning techniques. Additionally, predictive models were evaluated through the application of SHapley Additive ExPlanations (SHAP). A total of 201 consecutive patients from the emergency departments of the First Hospital and Shengjing Hospital of China Medical University admitted for deliberate oral intake of DQ from February 2018 to August 2023 were analysed. The initial clinical data of the patients with acute DQ poisoning were collected. Machine learning methods such as logistic regression, random forest, support vector machine (SVM), and gradient boosting were applied to build the prediction models. The whole sample was split into a training set and a test set at a ratio of 8:2. The performances of these models were assessed in terms of discrimination, calibration, and clinical decision curve analysis (DCA). We also used the SHAP interpretation tool to provide an intuitive explanation of the risk of death in patients with DQ poisoning. Logistic regression, random forest, SVM, and gradient boosting models were established, and the areas under the receiver operating characteristic curves (AUCs) were 0.91, 0.98, 0.96 and 0.94, respectively. The net benefits were similar across all four models. The four machine learning models can be reliable tools for predicting death risk in patients with acute DQ poisoning. Their combination with SHAP provides explanations for individualized risk prediction, increasing the model transparency.
Key propagation pathways of extreme precipitation events revealed by climate networks
Authors: Li Kaiwen , Huang Yu , Liu Kai , Wang Ming , Cai Fenying , Zhang Jianxin , Boers Niklas
Published: July 2024

The comprehensive understanding of propagation patterns of extreme precipitation events (EPEs) is essential for early warning of associated hazards such as floods and landslides. In this study, we utilize climate networks based on an event synchronization measure to investigate the propagation patterns of EPEs over the global land masses, and identify 16 major propagation pathways. We explain them in association with regional weather systems, topographic effects, and travelling Rossby wave patterns. We also demonstrate that the revealed propagation pathways carry substantial EPE predictability in certain areas, such as in the Appalachian, the Andes mountains. Our results help to improve the understanding of key propagation patterns of EPEs, where the global diversity of the propagated patterns of EPEs and corresponding potential predictability provide prior knowledge for predicting EPEs, and demonstrate the power of climate network approaches to study the spatiotemporal connectivity of extreme events in the climate system.
Identification and characterization of specific motifs in effector proteins of plant parasites using MOnSTER
Authors: Calia Giulia , Porracciolo Paola , Chen Yongpan , Kozlowski Djampa , Schuler Hannes , Cestaro Alessandro , Quentin Michaël , Favery Bruno , Danchin Etienne G. J. , Bottini Silvia
Published: July 2024

Plant pathogens cause billions of dollars of crop loss every year and are a major threat to global food security. Identifying and characterizing pathogens effectors is crucial towards their improved control. Because of their poor sequence conservation, effector identification is challenging, and current methods generate too many candidates without indication for prioritizing experimental studies. In most phyla, effectors contain specific sequence motifs which influence their localization and targets in the plant. Therefore, there is an urgent need to develop bioinformatics tools tailored for pathogen effectors. To circumvent these limitations, we have developed MOnSTER a specific tool that identifies clusters of motifs of protein sequences (CLUMPs). MOnSTER can be fed with motifs identified by de novo tools or from databases such as Pfam and InterProScan. The advantage of MOnSTER is the reduction of motif redundancy by clustering them and associating a score. This score encompasses the physicochemical properties of AAs and the motif occurrences. We built up our method to identify discriminant CLUMPs in oomycetes effectors. Consequently, we applied MOnSTER on plant parasitic nematodes and identified six CLUMPs in about 60% of the known nematode candidate parasitism proteins. Furthermore, we found co-occurrences of CLUMPs with protein domains important for invasion and pathogenicity. The potentiality of this tool goes beyond the effector characterization and can be used to easily cluster motifs and calculate the CLUMP-score on any set of protein sequences.
Deep metagenomic sequencing unveils novel SAR202 lineages and their vertical adaptation in the ocean
Authors: He Changfei , Fucich Daniel , Sosa Ana , Wang Hualong , Kan Jinjun , Liu Jihua , Xu Yongle , Jiao Nianzhi , Gonsior Michael , Chen Feng
Published: July 2024

SAR202 bacteria in the Chloroflexota phylum are abundant and widely distributed in the ocean. Their genome coding capacities indicate their potential roles in degrading complex and recalcitrant organic compounds in the ocean. However, our understanding of their genomic diversity, vertical distribution, and depth-related metabolisms is still limited by the number of assembled SAR202 genomes. In this study, we apply deep metagenomic sequencing (180 Gb per sample) to investigate microbial communities collected from six representative depths at the Bermuda Atlantic Time Series (BATS) station. We obtain 173 SAR202 metagenome-assembled genomes (MAGs). Intriguingly, 154 new species and 104 new genera are found based on these 173 SAR202 genomes. We add 12 new subgroups to the current SAR202 lineages. The vertical distribution of 20 SAR202 subgroups shows their niche partitioning in the euphotic, mesopelagic, and bathypelagic oceans, respectively. Deep-ocean SAR202 bacteria contain more genes and exhibit more metabolic potential for degrading complex organic substrates than those from the euphotic zone. With deep metagenomic sequencing, we uncover many new lineages of SAR202 bacteria and their potential functions which greatly deepen our understanding of their diversity, vertical profile, and contribution to the ocean’s carbon cycling, especially in the deep ocean.
Astrocytes enhance plasticity response during reversal learning
Authors: Squadrani Lorenzo , Wert-Carvajal Carlos , Müller-Komorowska Daniel , Bohmbach Kirsten , Henneberger Christian , Verzelli Pietro , Tchumatchenko Tatjana
Published: July 2024

Astrocytes play a key role in the regulation of synaptic strength and are thought to orchestrate synaptic plasticity and memory. Yet, how specifically astrocytes and their neuroactive transmitters control learning and memory is currently an open question. Recent experiments have uncovered an astrocyte-mediated feedback loop in CA1 pyramidal neurons which is started by the release of endocannabinoids by active neurons and closed by astrocytic regulation of the D-serine levels at the dendrites. D-serine is a co-agonist for the NMDA receptor regulating the strength and direction of synaptic plasticity. Activity-dependent D-serine release mediated by astrocytes is therefore a candidate for mediating between long-term synaptic depression (LTD) and potentiation (LTP) during learning. Here, we show that the mathematical description of this mechanism leads to a biophysical model of synaptic plasticity consistent with the phenomenological model known as the BCM model. The resulting mathematical framework can explain the learning deficit observed in mice upon disruption of the D-serine regulatory mechanism. It shows that D-serine enhances plasticity during reversal learning, ensuring fast responses to changes in the external environment. The model provides new testable predictions about the learning process, driving our understanding of the functional role of neuron-glia interaction in learning.
The isomer distribution of C6H6 products from the propargyl radical gas-phase recombination investigated by threshold-photoelectron spectroscopy
Authors: Hrodmarsson Helgi Rafn , Garcia Gustavo A. , Bourehil Lyna , Nahon Laurent , Gans Bérenger , Boyé-Péronne Séverine , Guillemin Jean-Claude , Loison Jean-Christophe
Published: July 2024

The resonance-stabilization of the propargyl radical (C3H3) makes it among the most important reactive intermediates in extreme environments and grants it a long enough lifetime to recombine in both terrestrial combustion media and cold molecular clouds in space. This makes the propargyl self-reaction a pivotal step in the formation of benzene, the first aromatic ring, to eventually lead to polycyclic aromatic hydrocarbons in a variety of environments. In this work, by producing propargyl radicals in a flow tube where propyne reacted with F atoms and probing the reaction products by mass-selected threshold-photoelectron spectroscopy (TPES), we identified eight C6H6 products in total, including benzene. On top of providing the first comprehensive measurements of the branching ratios of the eight identified C6H6 isomers in the propargyl self reaction products (4 mbar, 298 K conditions), this study also highlights the advantages and disadvantages of using isomer-selective TPES to identify and quantify reaction products.
Detecting dynamical causality via intervened reservoir computing
Authors: Zhao Jintong , Gan Zhongxue , Huang Ruixi , Guan Chun , Shi Jifan , Leng Siyang
Published: July 2024

An abundance of complex dynamical phenomena exists in nature and human society, requiring sophisticated analytical tools to understand and explain. Causal analysis through observational time series data is essential in comprehending complex systems when controlled experiments are not feasible or ethical. Although data-based causal discovery methods have been widely used, there is still a lack of direct ways more aligned with the intuitive definition of causality, i.e., whether interventions on one element lead to changes in the subsequent development of others. To solve this problem, we propose the method of intervened reservoir computing (IRC) based on constructing a neural network replica of the original system and applying interventions to it. This approach enables controlled trials, thus observing the intervened evolution, in the digital twins of the underlying systems. Simulated and real-world data are used to test our approach and demonstrate its accuracy in inferring causal networks. Given the importance of causality in understanding complex dynamics, we anticipate that IRC could serve as a powerful tool for various disciplines to decipher the intrinsic mechanisms of natural systems from observational data.