搜索结果：Bioscience reports

Purpose: We investigated the utilization of privacy-preserving, locally-deployed, open-source Large Language Models (LLMs) to extract diagnostic information from free-text cardiovascular magnetic resonance (CMR) reports. Materials and Methods: We evaluated nine open-source LLMs on their ability to identify diagnoses and classify patients into various cardiac diagnostic categories based on descriptive findings in 109 clinical CMR reports. Performance was quantified using standard classification metrics including accuracy, precision, recall, and F1 score. We also employed confusion matrices to examine patterns of misclassification across models. Results: Most open-source LLMs demonstrated exceptional performance in classifying reports into different diagnostic categories. Google's Gemma2 model achieved the highest average F1 score of 0.98, followed by Qwen2.5:32B and DeepseekR1-32B with F1 scores of 0.96 and 0.95, respectively. All other evaluated models attained average scores above 0.93, with Mistral and DeepseekR1-7B being the only exceptions. The top four LLMs outperformed our board-certified cardiologist (F1 score of 0.94) across all evaluation metrics in analyzing CMR reports.

Timely identification of issue reports reflecting software vulnerabilities is crucial, particularly for Internet-of-Things (IoT) where analysis is slower than non-IoT systems. While Machine Learning (ML) and Large Language Models (LLMs) detect vulnerability-indicating issues in non-IoT systems, their IoT use remains unexplored. We are the first to tackle this problem by proposing two approaches: (1) combining ML and LLMs with Natural Language Processing (NLP) techniques to detect vulnerability-indicating issues of 21 Eclipse IoT projects and (2) fine-tuning a pre-trained BERT Masked Language Model (MLM) on 11,000 GitHub issues for classifying \vul. Our best performance belongs to a Support Vector Machine (SVM) trained on BERT NLP features, achieving an Area Under the receiver operator characteristic Curve (AUC) of 0.65. The fine-tuned BERT achieves 0.26 accuracy, emphasizing the importance of exposing all data during training. Our contributions set the stage for accurately detecting IoT vulnerabilities from issue reports, similar to non-IoT systems.

In this study, we investigate student performance using grades and grade anomalies across periods before, during, and after COVID-19 remote instruction in courses for bioscience and health-related majors. Additionally, we explore gender equity in these courses using these measures. We define grade anomaly as the difference between a student's grade in a course of interest and their overall grade point average (GPA) across all other courses taken up to that point. If a student's grade in a course is lower than their GPA in all other courses, we refer to this as a grade penalty. Students received grade penalties in all courses studied, consisting of twelve courses taken by the majority of bioscience and health-related majors. Overall, we found that both grades and grade penalties improved during remote instruction but deteriorated after remote instruction. Additionally, we find more pronounced gender differences in grade anomalies than in grades. We hypothesize that women's decisions to pursue STEM careers may be more influenced by the grade penalties they receive in required science courses than men's, as women tend to experience larger penalties across all periods studied. Furtherm

The rapid adoption of generative artificial intelligence (GenAI) in the biosciences is transforming biotechnology, medicine, and synthetic biology. Yet this advancement is intrinsically linked to new vulnerabilities, as GenAI lowers the barrier to misuse and introduces novel biosecurity threats, such as generating synthetic viral proteins or toxins. These dual-use risks are often overlooked, as existing safety guardrails remain fragile and can be circumvented through deceptive prompts or jailbreak techniques. In this Perspective, we first outline the current state of GenAI in the biosciences and emerging threat vectors ranging from jailbreak attacks and privacy risks to the dual-use challenges posed by autonomous AI agents. We then examine urgent gaps in regulation and oversight, drawing on insights from 130 expert interviews across academia, government, industry, and policy. A large majority ($\approx 76$\%) expressed concern over AI misuse in biology, and 74\% called for the development of new governance frameworks. Finally, we explore technical pathways to mitigation, advocating a multi-layered approach to GenAI safety. These defenses include rigorous data filtering, alignment w

Test anxiety is beginning to be recognized as a significant factor affecting student performance in science, technology, engineering, and mathematics (STEM) courses, potentially contributing to gender inequity within these fields. Additionally, the management of test anxiety can improve self-efficacy, which is a construct that has been well studied in the physics context. In this study, we investigated the relationship between self-efficacy, test anxiety, and gender differences in performance in a two-semester-long introductory physics course sequence for bioscience students in which women outnumber men. Using validated survey data and grade information from students in a two-semester introductory physics course sequence, we compared the predictive power of self-efficacy and test anxiety on female and male students' performance on both low- and high-stakes assessments. We found that there were gender differences disadvantaging women in self-efficacy and test anxiety in both Physics 1 and Physics 2, as well as gender differences in high-stakes outcomes in Physics 1. There were no gender differences in low-stakes assessment scores. We also found that self-efficacy and test anxiety pr

With the growth of global maritime transportation, energy optimization has become crucial for reducing costs and ensuring operational efficiency. Shaft power is the mechanical power transmitted from the engine to the shaft and directly impacts fuel consumption, making its accurate prediction a paramount step in optimizing vessel performance. Power consumption is highly correlated with ship parameters such as speed and shaft rotation per minute, as well as weather and sea conditions. Frequent access to this operational data can improve prediction accuracy. However, obtaining high-quality sensor data is often infeasible and costly, making alternative sources such as noon reports a viable option. In this paper, we propose a transfer learning-based approach for predicting vessels shaft power, where a model is initially trained on high-frequency data from a vessel and then fine-tuned with low-frequency daily noon reports from other vessels. We tested our approach on sister vessels (identical dimensions and configurations), a similar vessel (slightly larger with a different engine), and a different vessel (distinct dimensions and configurations). The experiments showed that the mean abso

In 1984 Edward Witten proposed that an extremely dense form of matter composed of up, down, and strange quarks may be stable at zero pressure (Witten, 1984). Massive nuggets of such dense matter, if they exist, may pass through the Earth and be detectable by the seismic signals they generate (de Rujula and Glashow, 1984). With this motivation we investigated over 1 million seismic data reports to the U.S. Geological Survey for the years 1990-1993 not associated with epicentral sources. We report two results: (1) with an average of about 0.16 unassociated reports per minute after data cuts, we found a significant excess over statistical expectation for sets with ten or more reports in ten minutes; and (2) in spite of a very small a priori probability from random reports, we found one set of reports with arrival times and other features appropriate to signals from an epilinear source. This event has the properties predicted for the passage of a nugget of strange quark matter (SQM) through the earth, although there is no direct confirmation from other phenomenologies.

Screening mammography is high volume, time sensitive, and documentation heavy. Radiologists must translate subtle visual findings into consistent BI-RADS assessments, breast density categories, and structured narrative reports. While recent Vision Language Models (VLMs) enable image-to-text reporting, many rely on closed cloud systems or tightly coupled architectures that limit privacy, reproducibility, and adaptability. We present MammoWise, a local multi-model pipeline that transforms open source VLMs into mammogram report generators and multi-task classifiers. MammoWise supports any Ollama-hosted VLM and mammography dataset, and enables zero-shot, few-shot, and Chain-of-Thought prompting, with optional multimodal Retrieval Augmented Generation (RAG) using a vector database for case-specific context. We evaluate MedGemma, LLaVA-Med, and Qwen2.5-VL on VinDr-Mammo and DMID datasets, assessing report quality (BERTScore, ROUGE-L), BI-RADS classification, breast density, and key findings. Report generation is consistently strong and improves with few-shot prompting and RAG. Classification is feasible but sensitive to model and dataset choice. Parameter-efficient fine-tuning (QLoRA) of

We present a spectroscopic investigation of $^{169}\mathrm{Tm}^+$ that provides two key foundations for its use as a platform for advanced quantum applications. First, we establish the complete spectroscopic road map for optical cycling (including laser cooling) by performing high-resolution spectroscopy on $^{169}\mathrm{Tm}^+$ ions in an ion trap. We characterize the primary $313\,\mathrm{nm}$ and complementary $448/453\,\mathrm{nm}$ cycling transitions, identify the essential near-infrared repumping frequencies, and determine the magnetic-dipole hyperfine $A$ constants for all relevant levels. Second, we report a detailed characterization of a metastable state as a candidate for hosting a robust qubit, performing lifetime measurements and Zeeman-resolved microwave hyperfine spectroscopy with $\mathrm{kHz}$ precision.

In the face of an infectious disease, a key epidemiological measure is the basic reproduction number, which quantifies the average secondary infections caused by a single case in a susceptible population. In practice, the effective reproduction number, denoted as $R_t$, is widely used to assess the transmissibility of the disease at a given time $t$. Real-time estimating this metric is vital for understanding and managing disease outbreaks. Traditional statistical inference often relies on two assumptions. One is that samples are assumed to be drawn from a homogeneous population distribution, neglecting significant variations in individual transmission rates. The other is the ideal case reporting assumption, disregarding time delays between infection and reporting. In this paper, we thoroughly investigate these critical factors and assess their impact on estimating $R_t$. We first introduce negative binomial and Weibull distributions to characterize transmission rates and reporting delays, respectively, based on which observation and state equations are formulated. Then, we employ a Bayesian filtering for estimating $R_t$. Finally, validation using synthetic and empirical data demo

In year 2006 Bio-Linux with the work of Tim Booth and team gives its rising and provide an operating system that was and still specialized in providing a bioinformatic specific software environment for the working needs in this corner of bioscience. It is shown that Bio-Linux is affected by a 2 year release cycle and with this the final releases of Bio-Linux will not have the latest bioinformatic software on board. The paper shows how to get around this huge time gap and bring new software for Bio-Linux on board through a process that is called backporting. A summary of within the work to this paper just backported bioinformatic tools is given. A describtion of a workflow for continuously integration of the newest bioinformatic tools gives an outlook to further concrete planned developments and the influence of speeding up scientific progress.

The primary use of the LILLYPUT 3 accelerator at the Nondestructive Testing Laboratory at Wroclaw Technology Park is X-ray radiography for nondestructive testing, including R&D of novel techniques for industrial and medical imaging. The scope of possible applications could be greatly extended by providing a system for irradiation with electron beam. The purpose of this work was to design such a system, especially for high dose rate, small field irradiations under cryogenic conditions for material and bioscience research. In this work, two possible solutions, based either on beam scanning or scattering and collimation, were studied and compared. It was found that under existing conditions efficiency of both systems would be comparable. The latter one was adopted due to its simplicity and much lower cost. The system design was optimized by means of detailed Monte Carlo modeling. The system is being currently fabricated at National Centre for Nuclear Research in Świerk.

The increasing use of artificial intelligence (AI) in news production raises important questions about how audiences perceive and respond to AI-generated journalism. This preregistered survey experiment (N = 599, German-speaking Switzerland) examines (i) perceptions of article quality (measured as credibility, readability, and expertise) across news excerpts that were human-written, AI-assisted, or fully AI-generated, and (ii) self-reported intentions to engage following disclosure of AI involvement. Participants rated two short news excerpts before learning how they had been produced. Articles across all conditions were evaluated similarly in perceived quality. After disclosure, participants in the AI-assisted and AI-generated conditions reported a higher willingness to continue reading their assigned articles compared to the control group, but future willingness to read AI-generated news did not differ across conditions. Overall, the findings suggest that readers assess AI-generated and human-written news comparably in quality, while disclosure of AI use can momentarily increase curiosity or interest without yet changing longer-term reading intentions.

The portrayal of crowd accidents by the media can influence public understanding and emotional response, shaping societal perceptions and potentially impacting safety measures and preparedness strategies. This paper critically examines the portrayal of crowd accidents in news coverage by analyzing the texts of 372 media reports of crowd accidents spanning 26 diverse news sources from 1900 to 2019. We investigate how media representations of crowd accidents vary across time and geographical origins. Our methodology combines lexical analysis to unveil prevailing terminologies and sentiment analysis to discern the emotional tenor of the reports. The findings reveal the prevalence of the term "stampede" over "panic" in media descriptions of crowd accidents. Notably, divergent patterns are observable when comparing Western versus South Asian media (notably India and Pakistan), unveiling a cross-cultural dimension. Moreover, the analysis detects a gradual transition from "crowd stampede" to "crowd crush" in media and Wikipedia narratives in recent years, suggesting evolving lexical sensitivities. Sentiment analysis uncovers a consistent association with fear-related language, indicative

Influenza and respiratory syncytial virus (RSV) are the leading etiological agents of seasonal acute respiratory infections (ARI) around the world. Medical doctors typically base the diagnosis of ARI on patients' symptoms alone and do not always conduct virological tests necessary to identify individual viruses, which limits the ability to study the interaction between multiple pathogens and make public health recommendations. We consider a stochastic kinetic model (SKM) for two interacting ARI pathogens circulating in a large population and an empirically motivated background process for infections with other pathogens causing similar symptoms. An extended marginal sampling approach based on the Linear Noise Approximation to the SKM integrates multiple data sources and additional model components. We infer the parameters defining the pathogens' dynamics and interaction within a Bayesian hierarchical model and explore the posterior trajectories of infections for each illness based on aggregate infection reports from six epidemic seasons collected by the state health department, and a subset of virological tests from a sentinel program at a general hospital in San Luis Potosí, Méxic

This Preliminary Design Report (PDR) describes the IsoDAR electron-antineutrino source in two volumes which are mostly site-independent and describe the cyclotron driver providing a 60 MeV, 10 mA proton beam (this Volume); and the medium energy beam transport line (MEBT) and target (Volume II). The IsoDAR driver and target will produce about 1.15e23 electron-antineutrinos over five years. Paired with a kton-scale liquid scintillator detector, it will enable a broad particle physics program including searches for new symmetries, new interactions and new particles. Here in Volume I, we describe the driver, which includes the ion source, low energy beam transport, and cyclotron. The latter features Radio-Frequency Quadrupole (RFQ) direct axial injection and represents the first accelerator purpose-built to make use of so-called vortex motion.

Extensive air showers induced from high-energy cosmic rays provide a window into understanding the most energetic phenomena in the universe. We present a new method for observing these showers using the silicon imaging detector Subaru Hyper Suprime-Cam (HSC). This method has the advantage of being able to measure individual secondary particles. When paired with a surface detector array, silicon imaging detectors like Subaru HSC will be useful for studying the properties of extensive air showers in detail. The following report outlines the first results of observing extensive air showers with Subaru HSC. The potential for reconstructing the incident direction of primary cosmic rays is demonstrated and possible interdisciplinary applications are discussed.

Early reports indicate there may be another case, but spread is thought to be localized

companies will soon need to provide similar reports as a new European directive takes effect

We fabricated ambipolar field-effect transistors (FETs) from multi-layered triclinic ReSe2, mechanically exfoliated onto a SiO2 layer grown on p-doped Si. In contrast to previous reports on thin layers (~2 to 3 layers), we extract field-effect carrier mobilities in excess of 10^2 cm^2/Vs at room temperature in crystals with nearly ~10 atomic layers. These thicker FETs also show nearly zero threshold gate voltage for conduction and high ON to OFF current ratios when compared to the FETs built from thinner layers. We also demonstrate that it is possible to utilize this ambipolarity to fabricate logical elements or digital synthesizers. For instance, we demonstrate that one can produce simple, gate-voltage tunable phase modulators with the ability to shift the phase of the input signal by either 90^o or nearly 180^o. Given that it is possible to engineer these same elements with improved architectures, for example on h-BN in order to decrease the threshold gate voltage and increase the carrier mobilities, it is possible to improve their characteristics in order to engineer ultra-thin layered logic elements based on ReSe2.