bluuebunny/crossref_metadata_2025_split huggingface dataset:
Single-cell transcriptional profile of CD34+ hematopoietic progenitor cells from del(5q) myelodysplastic syndromes and impact of lenalidomide
Authors: Serrano Guillermo , Berastegui Nerea , Díaz-Mazkiaran Aintzane , García-Olloqui Paula , Rodriguez-Res Carmen , Huerga-Dominguez Sofia , Ainciburu Marina , Vilas-Zornoza Amaia , Martin-Uriz Patxi San , Aguirre-Ruiz Paula , Ullate-Agote Asier , Ariceta Beñat , Lamo-Espinosa Jose-Maria , Acha Pamela , Calvete Oriol , Jimenez Tamara , Molero Antonieta , Montoro Maria Julia , Díez-Campelo Maria , Valcarcel David , Solé Francisco , Alfonso-Pierola Ana , Ochoa Idoia , Prósper Felipe , Ezponda Teresa , Hernaez Mikel 
Published: June 2024

AbstractWhile myelodysplastic syndromes with del(5q) (del(5q) MDS) comprises a well-defined hematological subgroup, the molecular basis underlying its origin remains unknown. Using single cell RNA-seq (scRNA-seq) on CD34+ progenitors from del(5q) MDS patients, we have identified cells harboring the deletion, characterizing the transcriptional impact of this genetic insult on disease pathogenesis and treatment response. Interestingly, both del(5q) and non-del(5q) cells present similar transcriptional lesions, indicating that all cells, and not only those harboring the deletion, may contribute to aberrant hematopoietic differentiation. However, gene regulatory network (GRN) analyses reveal a group of regulons showing aberrant activity that could trigger altered hematopoiesis exclusively in del(5q) cells, pointing to a more prominent role of these cells in disease phenotype. In del(5q) MDS patients achieving hematological response upon lenalidomide treatment, the drug reverts several transcriptional alterations in both del(5q) and non-del(5q) cells, but other lesions remain, which may be responsible for potential future relapses. Moreover, lack of hematological response is associated with the inability of lenalidomide to reverse transcriptional alterations. Collectively, this study reveals transcriptional alterations that could contribute to the pathogenesis and treatment response of del(5q) MDS.
Guiding antibiotics towards their target using bacteriophage proteins
Authors: Zhao Xinghong , Zhong Xinyi , Yang Shinong , Deng Jiarong , Deng Kai , Huang Zhengqun , Li Yuanfeng , Yin Zhongqiong , Liu Yong , Viel Jakob H. , Wan Hongping 
Published: June 2024

AbstractNovel therapeutic strategies against difficult-to-treat bacterial infections are desperately needed, and the faster and cheaper way to get them might be by repurposing existing antibiotics. Nanodelivery systems enhance the efficacy of antibiotics by guiding them to their targets, increasing the local concentration at the site of infection. While recently described nanodelivery systems are promising, they are generally not easy to adapt to different targets, and lack biocompatibility or specificity. Here, nanodelivery systems are created that source their targeting proteins from bacteriophages. Bacteriophage receptor-binding proteins and cell-wall binding domains are conjugated to nanoparticles, for the targeted delivery of rifampicin, imipenem, and ampicillin against bacterial pathogens. They show excellent specificity against their targets, and accumulate at the site of infection to deliver their antibiotic payload. Moreover, the nanodelivery systems suppress pathogen infections more effectively than 16 to 32-fold higher doses of free antibiotics. This study demonstrates that bacteriophage sourced targeting proteins are promising candidates to guide nanodelivery systems. Their specificity, availability, and biocompatibility make them great options to guide the antibiotic nanodelivery systems that are desperately needed to combat difficult-to-treat infections.
Long-range charge transfer mechanism of the III2IV2 mycobacterial supercomplex
Authors: Riepl Daniel , Gamiz-Hernandez Ana P. , Kovalova Terezia , Król Sylwia M. , Mader Sophie L. , Sjöstrand Dan , Högbom Martin , Brzezinski Peter , Kaila Ville R. I. 
Published: June 2024

AbstractAerobic life is powered by membrane-bound redox enzymes that shuttle electrons to oxygen and transfer protons across a biological membrane. Structural studies suggest that these energy-transducing enzymes operate as higher-order supercomplexes, but their functional role remains poorly understood and highly debated. Here we resolve the functional dynamics of the 0.7 MDa III2IV2 obligate supercomplex from Mycobacterium smegmatis, a close relative of M. tuberculosis, the causative agent of tuberculosis. By combining computational, biochemical, and high-resolution (2.3 Å) cryo-electron microscopy experiments, we show how the mycobacterial supercomplex catalyses long-range charge transport from its menaquinol oxidation site to the binuclear active site for oxygen reduction. Our data reveal proton and electron pathways responsible for the charge transfer reactions, mechanistic principles of the quinone catalysis, and how unique molecular adaptations, water molecules, and lipid interactions enable the proton-coupled electron transfer (PCET) reactions. Our combined findings provide a mechanistic blueprint of mycobacterial supercomplexes and a basis for developing drugs against pathogenic bacteria.
Multiple roles for hypoxia inducible factor 1-alpha in airway epithelial cells during mucormycosis
Authors: Kavaliauskas Povilas , Gu Yiyou , Hasin Naushaba , Graf Karen T. , Alqarihi Abdullah , Shetty Amol C. , McCracken Carrie , Walsh Thomas J. , Ibrahim Ashraf S. , Bruno Vincent M. 
Published: June 2024

AbstractDuring pulmonary mucormycosis, inhaled sporangiospores adhere to, germinate, and invade airway epithelial cells to establish infection. We provide evidence that HIF1α plays dual roles in airway epithelial cells during Mucorales infection. We observed an increase in HIF1α protein accumulation and increased expression of many known HIF1α-responsive genes during in vitro infection, indicating that HIF1α signaling is activated by Mucorales infection. Inhibition of HIF1α signaling led to a substantial decrease in the ability of R. delemar to invade cultured airway epithelial cells. Transcriptome analysis revealed that R. delemar infection induces the expression of many pro-inflammatory genes whose expression was significantly reduced by HIF1α inhibition. Importantly, pharmacological inhibition of HIF1α increased survival in a mouse model of pulmonary mucormycosis without reducing fungal burden. These results suggest that HIF1α plays two opposing roles during mucormycosis: one that facilitates the ability of Mucorales to invade the host cells and one that facilitates the ability of the host to mount an innate immune response.
Hippocampal and orbitofrontal neurons contribute to complementary aspects of associative structure
Authors: Lin Huixin , Zhou Jingfeng 
Published: June 2024

AbstractThe ability to establish associations between environmental stimuli is fundamental for higher-order brain functions like state inference and generalization. Both the hippocampus and orbitofrontal cortex (OFC) play pivotal roles in this, demonstrating complex neural activity changes after associative learning. However, how precisely they contribute to representing learned associations remains unclear. Here, we train head-restrained mice to learn four ‘odor-outcome’ sequence pairs composed of several task variables—the past and current odor cues, sequence structure of ‘cue-outcome’ arrangement, and the expected outcome; and perform calcium imaging from these mice throughout learning. Sequence-splitting signals that distinguish between paired sequences are detected in both brain regions, reflecting associative memory formation. Critically, we uncover differential contents in represented associations by examining, in each area, how these task variables affect splitting signal generalization between sequence pairs. Specifically, the hippocampal splitting signals are influenced by the combination of past and current cues that define a particular sensory experience. In contrast, the OFC splitting signals are similar between sequence pairs that share the same sequence structure and expected outcome. These findings suggest that the hippocampus and OFC uniquely and complementarily organize the acquired associative structure.
Maize yield prediction and condition monitoring at the sub-county scale in Kenya: synthesis of remote sensing information and crop modeling
Authors: Kipkulei Harison K. , Bellingrath-Kimura Sonoko D. , Lana Marcos , Ghazaryan Gohar , Baatz Roland , Matavel Custodio , Boitt Mark K. , Chisanga Charles B. , Rotich Brian , Moreira Rodrigo M. , Sieber Stefan 
Published: June 2024

AbstractAgricultural production assessments are crucial for formulating strategies for closing yield gaps and enhancing production efficiencies. While in situ crop yield measurements can provide valuable and accurate information, such approaches are costly and lack scalability for large-scale assessments. Therefore, crop modeling and remote sensing (RS) technologies are essential for assessing crop conditions and predicting yields at larger scales. In this study, we combined RS and a crop growth model to assess phenology, evapotranspiration (ET), and yield dynamics at grid and sub-county scales in Kenya. We synthesized RS information from the Food and Agriculture Organization (FAO) Water Productivity Open-access portal (WaPOR) to retrieve sowing date information for driving the model simulations. The findings showed that grid-scale management information and progressive crop growth could be accurately derived, reducing the model output uncertainties. Performance assessment of the modeled phenology yielded satisfactory accuracies at the sub-county scale during two representative seasons. The agreement between the simulated ET and yield was improved with the combined RS-crop model approach relative to the crop model only, demonstrating the value of additional large-scale RS information. The proposed approach supports crop yield estimation in data-scarce environments and provides valuable insights for agricultural resource management enabling countermeasures, especially when shortages are perceived in advance, thus enhancing agricultural production.
Bellybutton: accessible and customizable deep-learning image segmentation
Authors: Dillavou Sam , Hanlan Jesse M. , Chieco Anthony T. , Xiao Hongyi , Fulco Sage , Turner Kevin T. , Durian Douglas J. 
Published: June 2024

AbstractThe conversion of raw images into quantifiable data can be a major hurdle and time-sink in experimental research, and typically involves identifying region(s) of interest, a process known as segmentation. Machine learning tools for image segmentation are often specific to a set of tasks, such as tracking cells, or require substantial compute or coding knowledge to train and use. Here we introduce an easy-to-use (no coding required), image segmentation method, using a 15-layer convolutional neural network that can be trained on a laptop: Bellybutton. The algorithm trains on user-provided segmentation of example images, but, as we show, just one or even a sub-selection of one training image can be sufficient in some cases. We detail the machine learning method and give three use cases where Bellybutton correctly segments images despite substantial lighting, shape, size, focus, and/or structure variation across the regions(s) of interest. Instructions for easy download and use, with further details and the datasets used in this paper are available at pypi.org/project/Bellybuttonseg.
Artificial intelligence-driven assessment of salt caverns for underground hydrogen storage in Poland
Authors: Derakhshani Reza , Lankof Leszek , GhasemiNejad Amin , Zaresefat Mojtaba 
Published: June 2024

AbstractThis study explores the feasibility of utilizing bedded salt deposits as sites for underground hydrogen storage. We introduce an innovative artificial intelligence framework that applies multi-criteria decision-making and spatial data analysis to identify the most suitable locations for storing hydrogen in salt caverns. Our approach integrates a unified platform with eight distinct machine-learning algorithms—KNN, SVM, LightGBM, XGBoost, MLP, CatBoost, GBR, and MLR—creating rock salt deposit suitability maps for hydrogen storage. The performance of these algorithms was evaluated using various metrics, including Mean Squared Error (MSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and Correlation Coefficient (R2), compared against an actual dataset. The CatBoost model demonstrated exceptional performance, achieving an R2 of 0.88, MSE of 0.0816, MAE of 0.1994, RMSE of 0.2833, and MAPE of 0.0163. The novel methodology, leveraging advanced machine learning techniques, offers a unique perspective in assessing the potential of underground hydrogen storage. This approach is a valuable asset for various stakeholders, including government bodies, geological services, renewable energy facilities, and the chemical/petrochemical industry, aiding them in identifying optimal locations for hydrogen storage.
Reactions of wintering passerines to male calls of the European cuckoo Cuculus canorus
Authors: Tryjanowski Piotr , Golawski Artur , Jankowiak Łukasz , Møller Anders Pape 
Published: June 2024

AbstractThe reaction of birds to the nest parasite, the European cuckoo Cuculus canorus, has been the subject of extensive testing in various aspects. However, while the cuckoo is a long-distance migrant, some of its hosts are sedentary species. In this study, we aimed to investigate whether species, primarily hosts, react to the presence of the cuckoo also in the winter season. This behaviour may involve an attempt to drive the parasite away from locations that will subsequently become their breeding sites. During playback experiments conducted in the winter of 2021/2022 in Poland, we demonstrated that numerous bird species react to the male cuckoo calls in winter. These calls may be perceived as a source of danger, particularly by cuckoo hosts, who responded to this call more frequently than non-hosts and the control species (pigeon). Nonetheless, the birds’ reactions were not strong, as they did not approach the source of the call. However, our results are constrained by the limited number of cuckoo host species wintering in Poland. To better evaluate the intensity of bird responses to the male cuckoo’s call during the non-breeding season, further studies should be conducted in regions where a greater variety of species, especially those most susceptible to parasitism, overwinter.
Automated hearing loss type classification based on pure tone audiometry data
Authors: Kassjański Michał , Kulawiak Marcin , Przewoźny Tomasz , Tretiakow Dmitry , Kuryłowicz Jagoda , Molisz Andrzej , Koźmiński Krzysztof , Kwaśniewska Aleksandra , Mierzwińska-Dolny Paulina , Grono Miłosz 
Published: June 2024

AbstractHearing problems are commonly diagnosed with the use of tonal audiometry, which measures a patient’s hearing threshold in both air and bone conduction at various frequencies. Results of audiometry tests, usually represented graphically in the form of an audiogram, need to be interpreted by a professional audiologist in order to determine the exact type of hearing loss and administer proper treatment. However, the small number of professionals in the field can severely delay proper diagnosis. The presented work proposes a neural network solution for classification of tonal audiometry data. The solution, based on the Bidirectional Long Short-Term Memory architecture, has been devised and evaluated for classifying audiometry results into four classes, representing normal hearing, conductive hearing loss, mixed hearing loss, and sensorineural hearing loss. The network was trained using 15,046 test results analysed and categorised by professional audiologists. The proposed model achieves 99.33% classification accuracy on datasets outside of training. In clinical application, the model allows general practitioners to independently classify tonal audiometry results for patient referral. In addition, the proposed solution provides audiologists and otolaryngologists with access to an AI decision support system that has the potential to reduce their burden, improve diagnostic accuracy, and minimise human error.
Target reliability-based design optimization studies on cohesive soil amended with chitosan and casein for liner applications
Authors: Rasheed Romana Mariyam , Moghal Arif Ali Baig , Basha B. Munwar , Almajed Abdullah 
Published: June 2024

AbstractThe current study investigated the primary and secondary compressibility characteristics of organic clay with two biopolymers, Chitosan (Dch) and Casein (Dca) at dosages of 0.5%, 1%, 2%, and 4%. The primary compression index (Cc) values were reduced by 18% and 59% at dosage (Dch and Dca) of 4% at a consolidation pressure of 800 kPa. The secondary compression indices of chitosan and casein-treated soils fell below the normal range specified for organic soils and lay in the range of 0.01–0.017. The biopolymers also accelerated the consolidation process at all dosages (Dch) and 2% Dca. The hydraulic conductivity increased for all dosages of chitosan whereas it declined for all dosages of casein compared to untreated soil. The reliability analysis was conducted for biopolymer-treated soils and presented a rational approach toward the selection of a suitable liner. Chitosan failed to achieve a target reliability index of 3 whereas casein-amended samples attained values equal to and greater than 3 at all dosages and consolidation pressures at COV of Kmax = 20%. At all dosages, the casein-treated soils exhibited reliability index values greater than 3 up to COV of Kmax = 40% indicating the higher stability of casein mixes as a liner material.
Temporal trajectories of artificial radiocaesium 137Cs in French rivers over the nuclear era reconstructed from sediment cores
Authors: Eyrolle Frédérique , Chaboche Pierre-Alexis , Lepage Hugo , Gouin Valérie Nicoulaud , Boyer Patrick , De Vismes Anne , Seignemartin Gabrielle , Badariotti Dominique , Chabaux François , Chastanet Maxime , Claval David , Copard Yoann , Coynel Alexandra , Debret Maxime , Delus Claire , Euzen Cassandra , Gardes Thomas , Giner Franck , Gurriaran Rodolfo , Grenz Christian , Grosbois Cécile , Lestel Laurence , Losson Benoît , Mansuy-Huault Laurence , Montarges-Pelletier Emmanuelle , Morereau Amandine , Mourier Brice , Mourier David , Ollive Vincent , Papillon Laure , Schafer Jorg , Schmitt Laurent , Sempere Richard , Winiarski Thierry , Zebracki Mathilde , Evrard Olivier 
Published: June 2024

Abstract137Cs is a long-lived man-made radionuclide introduced in the environment worldwide at the early beginning of the nuclear Era during atmospheric nuclear testing’s followed by the civil use of nuclear energy. Atmospheric fallout deposition of this major artificial radionuclide was reconstructed at the scale of French large river basins since 1945, and trajectories in French nuclearized rivers were established using sediment coring. Our results show that 137Cs contents in sediments of the studied rivers display a large spatial and temporal variability in response to the various anthropogenic pressures exerted on their catchment. The Loire, Rhone, and Rhine rivers were the most affected by atmospheric fallout from the global deposition from nuclear tests. Rhine and Rhone also received significant fallout from the Chernobyl accident in 1986 and recorded significant 137Cs concentrations in their sediments over the 1970–1985 period due to the regulatory releases from the nuclear industries. The Meuse River was notably impacted in the early 1970s by industrial releases. In contrast, the Seine River display the lowest 137Cs concentrations regardless of the period. All the rivers responded similarly over time to atmospheric fallout on their catchment, underlying a rather homogeneous resilience capacity of these river systems to this source of contamination.
An improved approach for automated cervical cell segmentation with PointRend
Authors: Zhang Baocan , Wang Wenfeng , Zhao Wei , Jiang Xiaolu , Patnaik Lalit Mohan 
Published: June 2024

AbstractRegular screening for cervical cancer is one of the best tools to reduce cancer incidence. Automated cell segmentation in screening is an essential task because it can present better understanding of the characteristics of cervical cells. The main challenge of cell cytoplasm segmentation is that many boundaries in cell clumps are extremely difficult to be identified. This paper proposes a new convolutional neural network based on Mask RCNN and PointRend module, to segment overlapping cervical cells. The PointRend head concatenates fine grained features and coarse features extracted from different feature maps to fine-tune the candidate boundary pixels of cell cytoplasm, which are crucial for precise cell segmentation. The proposed model achieves a 0.97 DSC (Dice Similarity Coefficient), 0.96 TPRp (Pixelwise True Positive Rate), 0.007 FPRp (Pixelwise False Positive Rate) and 0.006 FNRo (Object False Negative Rate) on dataset from ISBI2014. Specially, the proposed method outperforms state-of-the-art result by about$$3\%$$3%on DSC,$$1\%$$1%on TPRp and$$1.4\%$$1.4%on FNRo respectively. The performance metrics of our model on dataset from ISBI2015 are slight better than the average value of other approaches. Those results indicate that the proposed method could be effective in cytological analysis and then help experts correctly discover cervical cell lesions
Ambient air pollution exposure and COVID-19 related hospitalizations in Santiago, Chile
Authors: Dales Robert , Lukina Anna O. , Romero-Meza Rafael , Blanco-Vidal Claudia , Cakmak Sabit 
Published: June 2024

AbstractMorbidity and mortality from several diseases are increased on days of higher ambient air pollution. We carried out a daily time-series analysis with distributive lags to study the influence of short-term air pollution exposure on COVID-19 related hospitalization in Santiago, Chile between March 16 and August 31, 2020. Analyses were adjusted for temporal trends, ambient temperature, and relative humidity, and stratified by age and sex. 26,579 COVID-19 hospitalizations were recorded of which 24,501 were laboratory confirmed. The cumulative percent change in hospitalizations (95% confidence intervals) for an interquartile range increase in air pollutants were: 1.1 (0.2, 2.0) for carbon monoxide (CO), 0.30 (0.0, 0.50) for nitrogen dioxide (NO2), and 2.7 (1.9, 3.0) for particulate matter of diameter ≤ 2.5 microns (PM2.5). Associations with ozone (O3), particulate matter of diameter ≤ 10 microns (PM10) and sulfur dioxide (SO2) were not significant. The observed effect of PM2.5 was significantly greater for females and for those individuals ≥ 65 years old. This study provides evidence that daily increases in air pollution, especially PM2.5, result in a higher observed risk of hospitalization from COVID-19. Females and the elderly may be disproportionately affected.