bluuebunny/crossref_metadata_2025 huggingface dataset:
Is geriatric medicine teaching homogeneous? The analysis of geriatric medicine courses at Polish undergraduate medical programmes
Authors: Kupis Robert , Perera Ian , Targowski Tomasz , Gąsowski Jerzy , Piotrowicz Karolina
Published: June 2024

Purpose

We aimed to analyse the characteristics of geriatric courses offered to undergraduate medical students in higher educational institutions (HEIs) in Poland.

Methods

We searched the official websites of the HEIs offering the medical degree programmes and directly contacted the dean’s offices and HEIs representatives to retrieve the relevant information. The documents were analysed for course content, teaching methods, duration, and recommended texts. We also checked the obtained curricula for the reference to of the learning objectives related to geriatric medicine, selected from the currently endorsed Polish educational standards (ES) provided by the Ministry of Science and Higher Education.

Results

Geriatric medicine courses were obligatory at all included HEIs (n = 19), but the courses differed in structure and content. The courses varied in duration from 11 to 60 h and were primarily lecture based. Simulation was utilized at only one HEI and e-learning at two institutions. Out of 315 learning objectives, we acknowledged only 9 as geriatric. They were not always found in all curricula. Two HEIs included self-described learning objectives in their curricula. Across all HEIs, a total of 29 recommended texts (published between 1995 and 2021) were identified, including 2 English-language texts.

Conclusion

Geriatric medicine was a mandatory subject for medical students of the included HEIs. However, there was a lack of uniformity in the offered courses. This leaves room for the development of a unified undergraduate geriatrics curriculum to effectively address diverse geriatric issues across Europe. The importance of this matter is highlighted by demographic trends and workforce challenges.
Corporate power and global value chains: current approaches for conceptualizing the power of multinationals
Authors: Kapeller Jakob , Gräbner-Radkowitsch Claudius , Hornykewycz Anna
Published: June 2024

The influential position of multinational corporations in the global economy of the twenty-first century is a particularly controversial and timely subject. This paper aims to improve our understanding of this phenomenon by focusing on one particular aspect of it: corporate power. To this end, it first puts forth a number of conceptual clarifications that help to distinguish different kinds of power and the distinct analytical levels at which power is executed. It then focuses on corporate power and studies it against the backdrop of the development of global value chains. The aim of this analysis is twofold: firstly, to review the variety of analytical tools and ontological perspectives that coin current research on corporate power, and secondly, to discuss the causes and practical consequences of asymmetrical power constellations among corporations and between corporations and other actors. This discussion is meant to facilitate a better alignment of applied research on corporate power with pluralist approaches towards rethinking economics.
A forest companies typology regarding their contribution to circular economy: a sustainability reporting-based analysis
Authors: Panori Dimitra , Papaspyropoulos Konstantinos G. , Nikolaou Ioannis E.
Published: June 2024

Relatively recently, the residuals of forest industry have been considered very important and useful to be reused through bioeconomy and circular economy practices in order to reduce the use of non-renewable natural resources. The majority of current studies have been based on interview protocols and questionnaire-based surveys to examine how managers of forest industry contribute to sustainable development and circular economy. Despite the usefulness of these surveys, there is a degree of subjectivity in the respondents' responses and an orientation to what forest companies propose to do in the circular economy. To overcome the weaknesses of previous research, this paper aims at drawing appropriate information from sustainability reporting of a sample of forest companies to provide a more unambiguous understanding of the way that forest companies meet the circular economy principles. Thus, a circular economy measurement framework is developed which is based on scoring systems and GRI guidelines to draw reliable and harmonized information from corporate sustainability reporting. Furthermore, it results in a four-type company typology according to the number of the circular economy principles and the number of items they achieve for each principle such as pioneer circular company, lagging circular company, innovative circular company and infant circular company. The suggested framework is tested on a sample of 20 forest companies with higher revenue in 2022. The findings show that half of the sampled companies can be considered as infants at the CE practices, while a positive statistical relationship is revealed between forest companies’ circular economy practices and their revenues.
Balanced Lattice Designs under Uncertain Environment
Authors: AlAita Abdulrahman , Aslam Muhammad
Published: June 2024

Balanced lattice designs are vital in numerous fields, especially in experimental design, where controlling variability among experimental units is crucial. In practical experiments, various sources of uncertainty can lead to ambiguous, vague, and imprecise data, complicating the analysis process. To address these indeterminacies, a novel approach using neutrosophic analysis within a balanced lattice design framework is proposed, termed the neutrosophic balanced lattice design (NBLD). This innovative method employs neutrosophic statistics to derive mathematical neutrosophic sums of squares and construct a neutrosophic analysis of variance (NANOVA) table. The effectiveness of the proposed NBLD is demonstrated through a numerical example, showing that it outperforms traditional methods in handling uncertainty.
Student’s motivations behind not pursuing industrial/organizational psychology
Authors: Nehvi Ayesha Farooq , Shahnawaz Mohammad Ghazi
Published: June 2024

Industrial organizational psychology is one of the most dynamic areas of psychology yet it is usually not the first choice of graduate students when it comes to choosing a specialization. The present study is the first of its kind and aims to understand the perspective of the students behind this phenomenon. This qualitative study was done in two phases. The first phase generated initial themes based on a brainstorming session with graduate students of psychology (n = 15) at a Central University in India. The themes were analyzed and used to formulate a semi-structured interview, which formed the second phase, and in which students (n = 15) participated. Conventional content analysis was used to analyze the transcribed data following which key themes and subthemes were generated. Key themes of practicality, interest, and personality and motivation were found to be the key factors behind students not opting for industrial organizational psychology and choosing other fields like clinical psychology. The participants likely perceived a mismatch between them and the characteristics and demands of the I/O (Industrial/Organizational Psychology) in the organizations, which is seen in terms of P-E fit (person-environment fit), P-O fit (person-organisation fit), as well as P-J fit (person-job fit). Moreover, there is a subconscious need for self-presentation that motivates students to not opt for a field where they feel they might make mistakes or be criticized.
A comparative analysis of simulation approaches for predicting permeability and compressive strength in pervious concrete
Authors: Ahmad Soran Abdrahman , Ahmed Hemn Unis , Rafiq Serwan Khurshid , Jafer Frya Shawkat , Fqi Kawa Omar
Published: June 2024

Porous concrete plays a crucial role in addressing various environmental challenges and mitigating the impacts of climate change. It proves effective in reducing issues such as flooding, heat phenomena in the earth, and groundwater decline. Typically devoid of sand content, porous concrete’s key attributes lie in its permeability and compressive strength. Accurate prediction of these properties is essential for cost and time savings, ensuring precise proportions of materials in the concrete mixture. This article explores different models, including the linear model (LR), nonlinear model (NLR), and Artificial Neural Network (ANN), to predict and estimate permeability and compressive strength in porous concrete. The analysis incorporates 139 samples from various papers and experimental studies, utilizing significant parameters and variables like water-to-cement ratio, coarse aggregate content, cement content, porosity, and curing time as input variables. Statistical assessments, such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Scatter Index (SI), OBJ value, and coefficient of determination (R2), are employed to assess model performance. The results reveal that the ANN model outperforms other models in forecasting permeability and compressive strength of porous concrete. The SI and OBJ value of the ANN model are lower than those of all other models, indicating superior performance. The robust performance of the ANN model has significant implications for construction applications, ensuring precise material proportions and contributing to the durability of porous concrete structures. The success of the ANN model suggests avenues for refinement, including architecture adjustments and dataset expansion. These findings offer valuable insights into the ongoing efforts to optimize simulation techniques for predicting key properties of construction materials. On the other hand, the use of these models to optimize concrete mix design not only enhances efficiency but also significantly conserves raw materials and reduces energy consumption. These advancements contribute to lowering carbon emissions and promoting sustainable practices in the construction industry.
The stability of single-atom catalysts in thermocatalysis
Authors: Chae Sudong , Chen Yi , Yang Ji , Su Ji
Published: June 2024

Single-atom catalysts (SACs) featuring 100% atom utilization and uniform isolated active  sites have been receiving sustainable attention over the last decade, as they offer exceptional performance in various catalytic applications. The motivation behind studying SACs is to develop highly active, selective, and stable catalysts for industrial applications with desirable economic and ecological benefits. Despite their promising potential, SACs face challenges related to stability, which need to be addressed for practical implementation. In this mini-review we discuss the existing stability issues of SACs, and summarize the deactivation mechanism and behaviors, protective strategies, and regeneration methods of SACs. We highlighted the challenges and prospects of future SACs study, aiming to pave the way for their widespread application in industrial processes.
Inaccurate Equals Bad? The Effect of Motivational Performance Appraisal Politics on Organizational Citizenship Behavior
Authors: Wei Qi , Sun Jiandong , Wang Yehang
Published: June 2024

Abstract: Performance appraisal politics are prevalent in organizations and consist of personal bias appraisal politics and motivational performance appraisal politics (MPAP). Although personal bias performance appraisal politics is deleterious, MPAP is purported to bring benefits to organizations. However, few empirical studies have proved the benefits of MPAP. Based on social exchange theory, this study posited that MPAP is positively related to organizational citizenship behavior (OCB) via perceived organizational support and that core self-evaluation weakens the positive relationship between MPAP and perceived organizational support and attenuates the relationship between MPAP and OCB via perceived organizational support. Using data from a sample of 246 employees collected through a two-wave and multisource approach and structural equation model, this study found that all hypotheses were supported.
Does Helping Others Make You Happier?
Authors: Wei Yu-Chen , Chiao Yu-Ching
Published: June 2024

Abstract: This study integrates the need fulfillment and affective events theories to investigate the effects of the interaction between job involvement and organizational citizenship behavior (OCB) on employee well-being. Two-wave data, collected from 196 paired samples of employees and their supervisors, were analyzed. This study confirms that both job involvement and OCB are positively related to well-being. Furthermore, job involvement moderates the relationship between OCB and well-being. Specifically, OCB had a positive relationship with well-being when job involvement was high, but a negative one when job involvement was low. This study’s findings suggest that OCB is beneficial to employees only when they have high-level involvement in their jobs. Implications for practice and further research are also discussed.
Characteristic Slow‐Slip Events on the Superstition Hills Fault, Southern California
Authors: Vavra Ellis J. , Fialko Yuri , Rockwell Thomas , Bilham Roger , Štěpančíková Petra , Stemberk Jakub , Tábořík Petr , Stemberk Josef
Published: June 2024

The Superstition Hills Fault (SHF) exhibits a rich spectrum of slip modes, including M 6+ earthquakes, afterslip, quasi‐steady creep, and both triggered and spontaneous slow slip events (SSEs). Following 13 years of quiescence, creepmeters recorded 25 mm of slip during 16–19 May 2023. Additional sub‐events brought the total slip to 41 mm. The event nucleated on the northern SHF in early‐May and propagated bi‐laterally at rates on the order of kilometers per day. Surface offsets reveal a bi‐modal slip distribution, with slip on the northern section of the fault being less localized and lower amplitude compared to the southern section. Kinematic slip models confirm systematic variations in the slip distribution along‐strike and with depth and suggest that slip is largely confined to the shallow sedimentary layer. Observations and models of the 2023 SSE bear a strong similarity to previous slip episodes in 1999, 2006, and 2010, suggesting a characteristic behavior.
Revisiting Winter Southern Ocean CO2 Uptake Based on CALIPSO Observations
Authors: Huang Hao , Zhang Ke , Zhang Zhaoru , Smith Walker , He Jianfeng , Liu Na , Le Chengfeng
Published: June 2024

The absorption of atmospheric carbon dioxide (CO2) in the Southern Ocean represents a critical component of the global oceanic carbon budget. Previous assessments of air‐sea carbon flux variations and long‐term trends in polar regions during winter have faced limitations due to scarce field data and the lack of ocean color satellite imagery, causing uncertainties in estimating CO2 flux estimation. This study utilized the Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation satellite to construct a continuous 16‐year (2006–2021) time series of sea surface partial pressure of CO2 (pCO2) in the Southern Ocean. Our findings revealed that the polar region in South Ocean acts as a carbon sink in winter, with CO2 flux of ∼30 TgC in high‐latitude areas (South of 50°S). This work highlights the efficacy of active remote sensing for monitoring sea surface pCO2 and contributes insights into the dynamic carbonate systems of the Southern Ocean.
Evaluating Cosmic Ray Neutron Sensor Estimates of Snow Water Equivalent in a Prairie Environment Using UAV Lidar
Authors: Woodley M. , Kim H. , Sproles E. , Eberly J. , Tuttle S.
Published: June 2024

Monitoring snow cover in prairie environments is important for understanding water and energy fluxes, agricultural production, and flooding, but difficult due to shallow snowpack and considerable snow heterogeneity. Cosmic ray neutron sensors (CRNS) are sensitive to snow within a radius of 150–250 m, which allows for continuous estimation of snow water equivalent (SWE) over a large footprint and may better represent area‐averaged snow cover in prairies than conventional SWE instruments, such as snow pillows. A CRNS was installed at Montana State University's Central Agricultural Research Center (CARC; 47.06°, −109.95°) in Moccasin, MT in coordination with NASA's SnowEx 2021 field campaign. This work assesses the feasibility of a CRNS for SWE monitoring in prairies by comparing CRNS SWE estimates to spatially distributed SWE derived from uninhabited aerial vehicle lidar snow depths within the sensor's footprint and manual snow pit measurements. Lidar observations show snow cover was highly spatially variable, with the largest snow accumulation near barriers and the least in barren fields. Additionally, we evaluate our CRNS SWE estimates using Ultra Rapid Neutron Only Simulation (URANOS) Monte Carlo simulations. Comparisons of SWE estimates derived from lidar, CRNS, and URANOS for shallow snowpack at the site yielded root mean square values of about 2 mm (approximately 30% of the mean SWE). These results suggest that the CRNS is effective at integrating over significant spatial variability within its footprint at this site. However, the spatial distribution of snow exerts a strong influence on the CRNS signal and must be considered when interpreting CRNS observations.
Salinity Profiles in Coastal Aquifers: A Characterization Framework for Field Measurements
Authors: Huang Xinran , Werner Adrian D. , Solórzano‐Rivas S. Cristina , Jazayeri Amir
Published: June 2024

The freshwater‐seawater mixing zone (“interface”) is affected by tides, seawater intrusion, submarine groundwater discharge and aquifer heterogeneities. Our current understanding is based primarily on theoretical studies involving numerical simulation and sand‐tank experimentation, with limited field analyses of interface characteristics in coastal aquifers that serve as important freshwater reservoirs. The most common field evidence for interface characteristics is derived from electrical conductivity (EC) profiles, often obtained from long‐screen wells. However, the EC profiles from long‐screen wells have been assessed previously using only simple statistics like the depth to 50% of seawater concentration. This study introduces a methodology for characterizing EC profiles using curve fitting and objective assessment of multiple interface attributes, with application to 170 EC profiles from the Lower Burdekin Delta, Australia. The adopted fitting function showed an excellent match (mean R2 = 0.99). Fitting parameters were linked to the curvatures of transitions from fresh to brackish water and from brackish water to seawater, the elevation and gradient of the mid‐salinity value, and the thickness of the mixing zone. Field EC profiles showed approximately linear relationships between head changes averaged over the previous 5 months and the elevation of 5 mS/cm isochlor for some wells. The slope of this relationship was approximately one, which is approximately 1/40th of the value arising from the Ghyben‐Herzberg relation. The framework for characterizing salinity profiles presented in this article offers a new, objective approach for investigating the interface, providing future studies with a technique for examining relationships between interface behavior and key controlling forces.
Ketamine induced synaptic plasticity operates independently of long-term potentiation
Authors: Piazza Michelle K. , Kavalali Ege T. , Monteggia Lisa M.
Published: June 2024

Synaptic plasticity occurs via multiple mechanisms to regulate synaptic efficacy. Homeostatic and Hebbian plasticity are two such mechanisms by which neuronal synapses can be altered. Although these two processes are mechanistically distinct, they converge on downstream regulation of AMPA receptor activity to modify glutamatergic neurotransmission. However, much remains to be explored regarding how these two prominent forms of plasticity interact. Ketamine, a rapidly acting antidepressant, increases glutamatergic transmission via pharmacologically-induced homeostatic plasticity. Here, we demonstrate that Hebbian plasticity mechanisms are still intact in synapses that have undergone homeostatic scaling by ketamine after either systemic injection or perfusion onto hippocampal brain slices. We also investigated this relationship in the context of stress induced by chronic exposure to corticosterone (CORT) to better model the circumstances under which ketamine may be used as an antidepressant. We found that CORT induced an anhedonia-like behavioral phenotype in mice but did not impair long-term potentiation (LTP) induction. Furthermore, corticosterone exposure does not impact the intersection of homeostatic and Hebbian plasticity mechanisms, as synapses from CORT-exposed mice also demonstrated intact ketamine-induced plasticity and LTP in succession. These results provide a mechanistic explanation for how ketamine used for the treatment of depression does not impair the integrity of learning and memory processes encoded by mechanisms such as LTP.
Dependence on mitochondrial respiration of malignant T cells reveals a new therapeutic target for angioimmunoblastic T-cell lymphoma
Authors: Krug Adrien , Mhaidly Rana , Tosolini Marie , Mondragon Laura , Tari Gamze , Turtos Adriana Martinez , Paul-Bellon Rachel , Asnafi Vahid , Marchetti Sandrine , Di Mascio Léa , Travert Marion , Bost Frédéric , Bachy Emmanuel , Argüello Rafael J. , Fournié Jean-Jacques , Gaulard Philippe , Lemonnier François , Ricci Jean-Ehrland , Verhoeyen Els
Published: June 2024

Cancer metabolic reprogramming has been recognized as one of the cancer hallmarks that promote cell proliferation, survival, as well as therapeutic resistance. Up-to-date regulation of metabolism in T-cell lymphoma is poorly understood. In particular, for human angioimmunoblastic T-cell lymphoma (AITL) the metabolic profile is not known. Metabolic intervention could help identify new treatment options for this cancer with very poor outcomes and no effective medication. Transcriptomic analysis of AITL tumor cells, identified that these cells use preferentially mitochondrial metabolism. By using our preclinical AITL mouse model, mimicking closely human AITL features, we confirmed that T follicular helper (Tfh) tumor cells exhibit a strong enrichment of mitochondrial metabolic signatures. Consistent with these results, disruption of mitochondrial metabolism using metformin or a mitochondrial complex I inhibitor such as IACS improved the survival of AITL lymphoma-bearing mice. Additionally, we confirmed a selective elimination of the malignant human AITL T cells in patient biopsies upon mitochondrial respiration inhibition. Moreover, we confirmed that diabetic patients suffering from T-cell lymphoma, treated with metformin survived longer as compared to patients receiving alternative treatments. Taking together, our findings suggest that targeting the mitochondrial metabolic pathway could be a clinically efficient approach to inhibit aggressive cancers such as peripheral T-cell lymphoma.
The spectrum of giant cell arteritis through a rheumatology lens
Authors: Khalique Muhammad Irfan , Arjunan Mousindha , Wood Samuel , Mackie Sarah L.
Published: June 2024

Treatment of giant cell arteritis (GCA) aims initially to prevent acute visual loss, and subsequently to optimise long-term quality of life. Initial prevention of acute visual loss in GCA is well-standardised with high-dose glucocorticoid therapy but in the longer term optimising quality of life requires tailoring of treatment to the individual. The licensing of the IL-6 receptor inhibitor tocilizumab combined with advances in vascular imaging have resulted in many changes to diagnostic and therapeutic practice. Firstly, GCA is a systemic disease that may involve multiple vascular territories and present in diverse ways. Broadening of the “spectrum” of what is called GCA has been crystallised in the 2022 GCA classification criteria. Secondly, the vascular inflammation of GCA frequently co-exists with the extracapsular musculoskeletal inflammation of the related disease, polymyalgia rheumatica (PMR). Thirdly, GCA care must often be delivered across multiple specialities and healthcare organisations requiring effective interprofessional communication. Fourthly, both GCA and PMR may follow a chronic or multiphasic disease course; long-term management must be tailored to the individual patient’s needs. In this article we focus on some areas of current rheumatology practice that ophthalmologists need to be aware of, including comprehensive assessment of extra-ocular symptoms, physical signs and laboratory markers; advanced imaging techniques; and implications for multi-speciality collaboration.
Climate and urbanization drive changes in the habitat suitability of Schistosoma mansoni competent snails in Brazil
Authors: Glidden Caroline K. , Singleton Alyson L. , Chamberlin Andrew , Tuan Roseli , Palasio Raquel G. S. , Caldeira Roberta Lima , Monteiro Antônio Miguel V. , Lwiza Kamazima M. M. , Liu Ping , Silva Vivian , Athni Tejas S. , Sokolow Susanne H. , Mordecai Erin A. , De Leo Giulio A.
Published: June 2024

Schistosomiasis is a neglected tropical disease caused by Schistosoma parasites. Schistosoma are obligate parasites of freshwater Biomphalaria and Bulinus snails, thus controlling snail populations is critical to reducing transmission risk. As snails are sensitive to environmental conditions, we expect their distribution is significantly impacted by global change. Here, we used machine learning, remote sensing, and 30 years of snail occurrence records to map the historical and current distribution of forward-transmitting Biomphalaria hosts throughout Brazil. We identified key features influencing the distribution of suitable habitat and determined how Biomphalaria habitat has changed with climate and urbanization over the last three decades. Our models show that climate change has driven broad shifts in snail host range, whereas expansion of urban and peri-urban areas has driven localized increases in habitat suitability. Elucidating change in Biomphalaria distribution—while accounting for non-linearities that are difficult to detect from local case studies—can help inform schistosomiasis control strategies.
Camrelizumab-based induction chemoimmunotherapy in locally advanced stage hypopharyngeal carcinoma: phase II clinical trial
Authors: Gong Hongli , Tian Shu , Ding Hao , Tao Lei , Wang Li , Wang Jie , Wang Tian , Yuan Xiaohui , Heng Yu , Zhang Ming , Shi Yong , Xu Chengzhi , Wu Chunping , Wang Shengzi , Zhou Liang
Published: June 2024

This phase II trial aimed to determine the efficacy and safety of induction chemoimmunotherapy of camrelizumab plus modified TPF in locally advanced hypopharyngeal squamous cell carcinoma (LA HSCC) (NCT04156698). The primary endpoint was objective response rate (ORR), and secondary endpoints were 3-year overall survival (OS), progression-free survival (PFS), larynx preservation rate (LPR), and metastasis-free survival (MFS). Patients (cT3-4aN0-2M0), regardless of sex, received induction chemoimmunotherapy for three cycles: camrelizumab 200 mg d1, docetaxel 75 mg/m2 d1, cisplatin 25 mg/m2 d1-3, and capecitabine 800 mg/m2 bid d1-14, q21d. Patients were assigned to radioimmunotherapy if they had a complete or partial response, those with stable or progressive disease underwent surgery and adjuvant (chemo)radiotherapy. Camrelizumab was maintained post-radioimmunotherapy. Fifty-one patients were enrolled with a median follow-up duration of 23.7 months. After induction therapy, the ORR was 82.4% (42/51), meeting the prespecified endpoint. Grade 3/4 adverse events occurred in 26 patients, and no treatment-related death occurred. As three-year outcomes were immature, two-year OS, PFS and LPR were reported. As no distant metastatic event had occurred, MFS was not reported here. The two-year OS, PFS, and LPR rates were 83.0%, 77.1%, and 70.0%, respectively. The induction chemoimmunotherapy of camrelizumab plus TPF showed a high ORR rate with an acceptable safety profile in LA HSCC.
A robust model for cell type-specific interindividual variation in single-cell RNA sequencing data
Authors: Chen Minhui , Dahl Andy
Published: June 2024

Single-cell RNA sequencing (scRNA-seq) has been widely used to characterize cell types based on their average gene expression profiles. However, most studies do not consider cell type-specific variation across donors. Modelling this cell type-specific inter-individual variation could help elucidate cell type-specific biology and inform genes and cell types underlying complex traits. We therefore develop a new model to detect and quantify cell type-specific variation across individuals called CTMM (Cell Type-specific linear Mixed Model). We use extensive simulations to show that CTMM is powerful and unbiased in realistic settings. We also derive calibrated tests for cell type-specific interindividual variation, which is challenging given the modest sample sizes in scRNA-seq. We apply CTMM to scRNA-seq data from human induced pluripotent stem cells to characterize the transcriptomic variation across donors as cells differentiate into endoderm. We find that almost 100% of transcriptome-wide variability between donors is differentiation stage-specific. CTMM also identifies individual genes with statistically significant stage-specific variability across samples, including 85 genes that do not have significant stage-specific mean expression. Finally, we extend CTMM to partition interindividual covariance between stages, which recapitulates the overall differentiation trajectory. Overall, CTMM is a powerful tool to illuminate cell type-specific biology in scRNA-seq.
Small molecule in situ resin capture provides a compound first approach to natural product discovery
Authors: Bogdanov Alexander , Salib Mariam N. , Chase Alexander B. , Hammerlindl Heinz , Muskat Mitchell N. , Luedtke Stephanie , da Silva Elany Barbosa , O’Donoghue Anthony J. , Wu Lani F. , Altschuler Steven J. , Molinski Tadeusz F. , Jensen Paul R.
Published: June 2024

Culture-based microbial natural product discovery strategies fail to realize the extraordinary biosynthetic potential detected across earth’s microbiomes. Here we introduce Small Molecule In situ Resin Capture (SMIRC), a culture-independent method to obtain natural products directly from the environments in which they are produced. We use SMIRC to capture numerous compounds including two new carbon skeletons that were characterized using NMR and contain structural features that are, to the best of our knowledge, unprecedented among natural products. Applications across diverse marine habitats reveal biome-specific metabolomic signatures and levels of chemical diversity in concordance with sequence-based predictions. Expanded deployments, in situ cultivation, and metagenomics facilitate compound discovery, enhance yields, and link compounds to candidate producing organisms, although microbial community complexity creates challenges for the later. This compound-first approach to natural product discovery provides access to poorly explored chemical space and has implications for drug discovery and the detection of chemically mediated biotic interactions.
Nanotransistor-based gas sensing with record-high sensitivity enabled by electron trapping effect in nanoparticles
Authors: Hu Qitao , Solomon Paul , Österlund Lars , Zhang Zhen
Published: June 2024

Highly sensitive, low-power, and chip-scale H2 gas sensors are of great interest to both academia and industry. Field-effect transistors (FETs) functionalized with Pd nanoparticles (PdNPs) have recently emerged as promising candidates for such H2 sensors. However, their sensitivity is limited by weak capacitive coupling between PdNPs and the FET channel. Herein we report a nanoscale FET gas sensor, where electrons can tunnel between the channel and PdNPs and thus equilibrate them. Gas reaction with PdNPs perturbs the equilibrium, and therefore triggers electron transfer between the channel and PdNPs via trapping or de-trapping with the PdNPs to form a new balance. This direct communication between the gas reaction and the channel enables the most efficient signal transduction. Record-high responses to 1–1000 ppm H2 at room temperature with detection limit in the low ppb regime and ultra-low power consumption of $$\sim$$
~
300 nW are demonstrated. The same mechanism could potentially be used for ultrasensitive detection of other gases. Our results present a supersensitive FET gas sensor based on electron trapping effect in nanoparticles.
Mapping of valley splitting by conveyor-mode spin-coherent electron shuttling
Authors: Volmer Mats , Struck Tom , Sala Arnau , Chen Bingjie , Oberländer Max , Offermann Tobias , Xue Ran , Visser Lino , Tu Jhih-Sian , Trellenkamp Stefan , Cywiński Łukasz , Bluhm Hendrik , Schreiber Lars R.
Published: June 2024

In Si/SiGe heterostructures, the low-lying excited valley state seriously limits the operability and scalability of electron spin qubits. For characterizing and understanding the local variations in valley splitting, fast probing methods with high spatial and energy resolution are lacking. Leveraging the spatial control granted by conveyor-mode spin-coherent electron shuttling, we introduce a method for two-dimensional mapping of the local valley splitting by detecting magnetic field-dependent anticrossings of ground and excited valley states using entangled electron spin-pairs as a probe. The method has sub-μeV energy accuracy and a nanometer lateral resolution. The histogram of valley splittings spanning a large area of 210 nm by 18 nm matches well with statistics obtained by the established but time-consuming magnetospectroscopy method. For the specific heterostructure, we find a nearly Gaussian distribution of valley splittings and a correlation length similar to the quantum dot size. Our mapping method may become a valuable tool for engineering Si/SiGe heterostructures for scalable quantum computing.
Microbial lysates repurposed as liquid egg substitutes
Authors: Choi Kyeong Rok , Ahn Da-Hee , Jung Seok Yeong , Lee Yu Hyun , Lee Sang Yup
Published: June 2024

Microbial lysates, rich in protein and essential nutrients, demonstrate remarkable capabilities in forming gels and stable foams when heated and whisked, similar to liquid eggs. These characteristics make them an excellent alternative to animal-derived liquid eggs, contributing to sustainable food production and consumption while maintaining high nutritional value. Their versatility positions microbial lysates as promising ingredients in culinary applications, offering a sustainable and nutritious substitute.
EmoWear: Wearable Physiological and Motion Dataset for Emotion Recognition and Context Awareness
Authors: Rahmani Mohammad Hasan , Symons Michelle , Sobhani Omid , Berkvens Rafael , Weyn Maarten
Published: June 2024

The EmoWear dataset provides a bridge to explore Emotion Recognition (ER) via Seismocardiography (SCG), the measurement of small cardio-respiratory induced vibrations on the chest wall through Inertial Measurement Units (IMUs). We recorded Accelerometer (ACC), Gyroscope (GYRO), Electrocardiography (ECG), Blood Volume Pulse (BVP), Respiration (RSP), Electrodermal Activity (EDA), and Skin Temperature (SKT) data from 49 participants who watched validated emotionally stimulating video clips. They self-assessed their emotional valence, arousal, and dominance, as well as extra questions about the video clips. Also, we asked the participants to walk, talk, and drink, so that researchers can detect gait, voice, and swallowing using the same IMU. We demonstrate the effectiveness of emotion stimulation with statistical methods and verify the quality of the collected signals through signal-to-noise ratio and correlation analysis. EmoWear can be used for ER via SCG, ER during gait, multi-modal ER, and the study of IMUs for context-awareness. Targeted contextual information include emotions, gait, voice activity, and drinking, all having the potential to be sensed via a single IMU.
Recommendations to minimize tooth root remodeling in patients undergoing maxillary osteotomies
Authors: Alqahtani Khalid Ayidh , Jacobs Reinhilde , Da Costa Senior Oliver , Politis Constantinus , Shaheen Eman
Published: June 2024

The purpose of this study was to report root remodeling/resorption percentages of maxillary teeth following the different maxillary osteotomies; i.e. one-piece, two-pieces, three-pieces Le Fort I, surgically assisted rapid palatal expansion (SARPE). The possibility of relationships between root remodeling and various patient- and/or treatment-related factors were further investigated. A total of 110 patients (1075 teeth) who underwent combined orthodontic and orthognathic surgery were studied retrospectively. The sample size was divided into: 30 patients in one-piece Le Fort I group, 30 patients in multi-pieces Le Fort I group, 20 patients in SARPE group and 30 patients in orthodontic group. Preoperative and 1 year postoperative cone beam computed tomography (CBCT) scans were obtained. A validated and automated method for evaluating root remodeling and resorption in three dimensions (3D) was applied. SARPE group showed the highest percentage of root remodeling. Spearman correlation coefficient revealed a positive relationship between maxillary advancement and root remodeling, with more advancement contributing to more root remodeling. On the other hand, the orthodontic group showed a negative correlation with age indicating increased root remodeling in younger patients. Based on the reported results of linear, volumetric and morphological changes of the root after 1 year, clinical recommendations were provided in the form of decision tree flowchart and tables. These recommendations can serve as a valuable resource for surgeons in estimating and managing root remodeling and resorption associated with different maxillary surgical techniques.
Constant force grinding controller for robots based on SAC optimal parameter finding algorithm
Authors: Rei Chosei , Wang Qichao , Chen Linlin , Yan Xinhua , Zhang Peng , Fu Liwei , Wang Chong , Liu Xinghui
Published: June 2024

Since conventional PID (Proportional–Integral–Derivative) controllers hardly control the robot to stabilize for constant force grinding under changing environmental conditions, it is necessary to add a compensation term to conventional PID controllers. An optimal parameter finding algorithm based on SAC (Soft-Actor-Critic) is proposed to solve the problem that the compensation term parameters are difficult to obtain, including training state action and normalization preprocessing, reward function design, and targeted deep neural network design. The algorithm is used to find the optimal controller compensation term parameters and applied to the PID controller to complete the compensation through the inverse kinematics of the robot to achieve constant force grinding control. To verify the algorithm's feasibility, a simulation model of a grinding robot with sensible force information is established, and the simulation results show that the controller trained with the algorithm can achieve constant force grinding of the robot. Finally, the robot constant force grinding experimental system platform is built for testing, which verifies the control effect of the optimal parameter finding algorithm on the robot constant force grinding and has specific environmental adaptability.
Boosting prairie dog optimizer for optimal planning of multiple wind turbine and photovoltaic distributed generators in distribution networks considering different dynamic load models
Authors: Elseify Mohamed A. , Hashim Fatma A. , Hussien Abdelazim G. , Abdel-Mawgoud Hussein , Kamel Salah
Published: June 2024

Deploying distributed generators (DGs) supplied by renewable energy resources poses a significant challenge for efficient power grid operation. The proper sizing and placement of DGs, specifically photovoltaics (PVs) and wind turbines (WTs), remain crucial due to the uncertain characteristics of renewable energy. To overcome these challenges, this study explores an enhanced version of a meta-heuristic technique called the prairie dog optimizer (PDO). The modified prairie dogs optimizer (mPDO) incorporates a novel exploration phase inspired by the slime mold algorithm (SMA) food approach. The mPDO algorithm is proposed to analyze the substantial effects of different dynamic load characteristics on the performance of the distribution networks and the designing of the PV-based and WT-based DGs. The optimization problem incorporates various operational constraints to mitigate energy loss in the distribution networks. Further, the study addresses uncertainties related to the random characteristics of PV and WT power outputs by employing appropriate probability distributions. The mPDO algorithm is evaluated using cec2020 benchmark suit test functions and rigorous statistical analysis to mathematically measure its success rate and efficacy while considering different type of optimization problems. The developed mPDO algorithm is applied to incorporate both PV and WT units, individually and simultaneously, into the IEEE 69-bus distribution network. This is achieved considering residential, commercial, industrial, and mixed time-varying voltage-dependent load demands. The efficacy of the modified algorithm is demonstrated using the standard benchmark functions, and a comparative analysis is conducted with the original PDO and other well-known algorithms, utilizing various statistical metrics. The numerical findings emphasize the significant influence of load type and time-varying generation in DG planning. Moreover, the mPDO algorithm beats the alternatives and improves distributed generators' technical advantages across all examined scenarios.
Low BCL-xL expression in triple-negative breast cancer cells favors chemotherapy efficacy, and this effect is limited by cancer-associated fibroblasts
Authors: Nocquet Lisa , Roul Julie , Lefebvre Chloé C. , Duarte Laurine , Campone Mario , Juin Philippe P. , Souazé Frédérique
Published: June 2024

Triple negative breast cancers (TNBC) present a poor prognosis primarily due to their resistance to chemotherapy. This resistance is known to be associated with elevated expression of certain anti-apoptotic members within the proteins of the BCL-2 family (namely BCL-xL, MCL-1 and BCL-2). These regulate cell death by inhibiting pro-apoptotic protein activation through binding and sequestration and they can be selectively antagonized by BH3 mimetics. Yet the individual influences of BCL-xL, MCL-1, and BCL-2 on the sensitivity of TNBC cells to chemotherapy, and their regulation by cancer-associated fibroblasts (CAFs), major components of the tumor stroma and key contributors to therapy resistance remain to be delineated. Using gene editing or BH3 mimetics to inhibit anti-apoptotic BCL-2 family proteins in TNBC line MDA-MB-231, we show that BCL-xL and MCL-1 promote cancer cell survival through compensatory mechanisms. This cell line shows limited sensitivity to chemotherapy, in line with the clinical resistance observed in TNBC patients. We elucidate that BCL-xL plays a pivotal role in therapy response, as its depletion or pharmacological inhibition heightened chemotherapy effectiveness. Moreover, BCL-xL expression is associated with chemotherapy resistance in patient-derived tumoroids where its pharmacological inhibition enhances ex vivo response to chemotherapy. In a co-culture model of cancer cells and CAFs, we observe that even in a context where BCL-xL reduced expression renders cancer cells more susceptible to chemotherapy, those in contact with CAFs display reduced sensitivity to chemotherapy. Thus CAFs exert a profound pro-survival effect in breast cancer cells, even in a setting highly favoring cell death through combined chemotherapy and absence of the main actor of chemoresistance, BCL-xL.
Intelligent extraction of reservoir dispatching information integrating large language model and structured prompts
Authors: Yang Yangrui , Chen Sisi , Zhu Yaping , Liu Xuemei , Ma Wei , Feng Ling
Published: June 2024

Reservoir dispatching regulations are a crucial basis for reservoir operation, and using information extraction technology to extract entities and relationships from heterogeneous texts to form triples can provide structured knowledge support for professionals in making dispatch decisions and intelligent recommendations. Current information extraction technologies require manual data labeling, consuming a significant amount of time. As the number of dispatch rules increases, this method cannot meet the need for timely generation of dispatch plans during emergency flood control periods. Furthermore, utilizing natural language prompts to guide large language models in completing reservoir dispatch extraction tasks also presents challenges of cognitive load and instability in model output. Therefore, this paper proposes an entity and relationship extraction method for reservoir dispatch based on structured prompt language. Initially, a variety of labels are refined according to the extraction tasks, then organized and defined using the Backus–Naur Form (BNF) to create a structured format, thus better guiding large language models in the extraction work. Moreover, an AI agent based on this method has been developed to facilitate operation by dispatch professionals, allowing for the quick acquisition of structured data. Experimental verification has shown that, in the task of extracting entities and relationships for reservoir dispatch, this AI agent not only effectively reduces cognitive burden and the impact of instability in model output but also demonstrates high extraction performance (with F1 scores for extracting entities and relationships both above 80%), offering a new solution approach for knowledge extraction tasks in other water resource fields.
Unveiling new genetic insights in rheumatoid arthritis for drug discovery through Taxonomy3 analysis
Authors: Kozlowska Justyna , Humphryes-Kirilov Neil , Pavlovets Anastasia , Connolly Martin , Kuncheva Zhana , Horner Jonathan , Manso Ana Sousa , Murray Clare , Fox J. Craig , McCarthy Alun
Published: June 2024

Genetic support for a drug target has been shown to increase the probability of success in drug development, with the potential to reduce attrition in the pharmaceutical industry alongside discovering novel therapeutic targets. It is therefore important to maximise the detection of genetic associations that affect disease susceptibility. Conventional statistical methods such as genome-wide association studies (GWAS) only identify some of the genetic contribution to disease, so novel analytical approaches are required to extract additional insights. C4X Discovery has developed Taxonomy3, a unique method for analysing genetic datasets based on mathematics that is novel in drug discovery. When applied to a previously published rheumatoid arthritis GWAS dataset, Taxonomy3 identified many additional novel genetic signals associated with this autoimmune disease. Follow-up studies using tool compounds support the utility of the method in identifying novel biology and tractable drug targets with genetic support for further investigation.