Miracle-dz/UniFAR-FTUs huggingface dataset:
Increased interaction between connexin43 and microtubules is critical for glioblastoma stem-like cell maintenance and tumorigenicity

Glioblastoma (GBM) is the most common primary tumor of the central nervous system. One major challenge in GBM treatment is the resistance to chemotherapy and radiotherapy observed in subpopulations of cancer cells, including GBM stem-like cells (GSCs). These cells hold the ability to self-renew or differentiate following treatment, participating in tumor recurrence. The gap junction protein connexin43 (Cx43) has complex roles in oncogenesis and we have previously demonstrated an association between Cx43 and GBM chemotherapy resistance. Here, we report, for the first time, increased direct interaction between non-junctional Cx43 with microtubules in the cytoplasm of GSCs. We hypothesize that non-junctional Cx43/microtubule complexing is critical for GSC maintenance and survival and sought to specifically disrupt this interaction while maintaining other Cx43 functions, such as gap junction formation. Using a Cx43 mimetic peptide of the carboxyl terminal tubulin-binding domain of Cx43 (JM2), we successfully ablated Cx43 interaction with microtubules in GSCs. Importantly, administration of JM2 significantly decreased GSC survival in vitro, and limited GSC-derived tumor growth in vivo. Together, these results identify JM2 as a novel peptide drug to ablate GSCs in GBM treatment.
An accessible digital imaging workflow for multiplexed quantitative analysis of adult eye phenotypes in Drosophila melanogaster

The compound eye of Drosophila melanogaster has long been a model for studying genetics, development, neurodegeneration, and heterochromatin. Imaging and morphometry of adult Drosophila and other insects is hampered by the low throughput, narrow focal plane, and small image sensors typical of stereomicroscope cameras. In educational environments, where data collection may be distributed among many individuals over extended time periods, these limitations are compounded by inter-operator variability in lighting, sample positioning, focus, and post-acquisition processing. Our goal was to develop an affordable and student-friendly method for multiplexed quantitative analysis of adult Drosophila melanogaster phenotypes. Here we report a system for efficient data collection and analysis of up to 60 adult flies in a single image with standardized conditions that eliminate inter-operator variability and enable precise quantitative comparison of adult morphological phenotypes. Semi-automated data analysis using ImageJ and R reduces image manipulations, facilitates reproducibility, and can be adapted to emerging automated segmentation methods. This collection of classroom-friendly methods doubles as a hands-on introduction to imaging, data visualization, and statistical analysis for students.
Scarcity of fixed carbon transfer in a model microbial phototroph-heterotroph interaction

While the green alga Chlamydomonas reinhardtii has long served as a reference organism, few studies have interrogated its role as a primary producer in microbial interactions. Here, we quantitatively investigated C. reinhardtii’s capacity to support a heterotrophic microbe using the established coculture system with Mesorhizobium japonicum, a vitamin B12-producing α- proteobacterium. Using stable isotope probing and nanoscale secondary ion mass spectrometry (nanoSIMS), we tracked the flow of photosynthetic fixed carbon and consequent bacterial biomass synthesis under continuous and diel light with single-cell resolution. We found that more 13C fixed by the alga was taken up by bacterial cells under continuous light, invalidating the hypothesis that the alga’s fermentative degradation of starch reserves during the night would boost M. japonicum heterotrophy. 15NH4 assimilation rates and changes in cell size revealed that the carbon transferred was insufficient for balanced growth of M. japonicum cells, which instead underwent reductive division. However, despite this sign of starvation, M. japonicum still supported a B12-dependent C. reinhardtii mutant. Finally, we showed that bacterial proliferation could be supported solely by the algal lysis that occurred in coculture, highlighting the role of necromass in carbon cycling. Collectively, these results reveal the scarcity of fixed carbon in this microbial trophic relationship, demonstrate B12 exchange even during bacterial starvation, and underscore the importance of quantitative approaches for assessing metabolic coupling in algal-bacterial interactions.
A Bayesian overhaul of thermal tolerance landscape models: Predicting ectotherm lethality buildup and survival amid heatwaves

1. In the face of escalating heatwaves, accurately forecasting ectotherm population mortality is a pressing ecological challenge. Current Thermal Tolerance Landscape (TTL) models, while surpassing single-threshold metrics by incorporating individual survival times, are constrained by frequentist regression parametrization reliant on constant-temperature experiments, omitting probabilistic outcomes. 2. This study addresses these limitations by pioneering the application of Approximate Bayesian Computation-Sequential Monte Carlo (ABC-SMC) to analyze survival data from Baltic Mytilus mussels subjected to both microcosm (constant temperature) and mesocosm (dynamic temperature) heatwave regimes. 3. The ABC-SMC yields probabilistic predictions of individual lethality buildup and population survival trajectories, closely aligned with observed survival data across both experimental conditions. Informed by more realistic dynamic data, the TTL model predicts local mussel resilience against the most extreme summer heatwaves projected for this century, albeit with considerations for sublethal impacts and potential recruitment declines. 4. Our approach can enhance the predictive accuracy concerning the sensitivity of key marine populations amidst intensifying heatwaves, addressing the urgent need for accurate modeling tools to inform conservation practices and ecosystem management, ultimately aiding in the preservation of marine biodiversity.
Planar, Spiral, and Concentric Traveling Waves Distinguish Cognitive States in Human Memory

A fundamental challenge in neuroscience is explaining how widespread brain regions flexibly interact to support behaviors. We hypothesize that a mechanism of neural coordination is brain oscillations that propagate as traveling waves across the cortex in distinctive patterns that control when and where different regions interact. To test this hypothesis, we used direct recordings from humans performing multiple memory experiments and a novel analytical framework that measures the directional propagation of oscillations. We found that traveling waves propagated along the cortex in not only plane waves as seen previously, but also in spirals, sources and sinks, and more complex patterns. These traveling wave patterns correlated with various aspects of behavior, with specific propagation patterns reflecting broad cognitive processes, and the identities of specific remembered items. Together, these findings show that complex spatiotemporal patterns of traveling waves underlie human cognition and are relevant practically for neural decoding and brain–computer interfacing.
Whole-Chain Recommendations

With the recent prevalence of Reinforcement Learning (RL), there have been tremendous interests in developing RL-based recommender systems. In practical recommendation sessions, users will sequentially access multiple scenarios, such as the entrance pages and the item detail pages, and each scenario has its specific characteristics. However, the majority of existing RL-based recommender systems focus on optimizing one strategy for all scenarios or separately optimizing each strategy, which could lead to sub-optimal overall performance. In this paper, we study the recommendation problem with multiple (consecutive) scenarios, i.e., whole-chain recommendations. We propose a multi-agent RL-based approach (DeepChain), which can capture the sequential correlation among different scenarios and jointly optimize multiple recommendation strategies. To be specific, all recommender agents (RAs) share the same memory of users' historical behaviors, and they work collaboratively to maximize the overall reward of a session. Note that optimizing multiple recommendation strategies jointly faces two challenges in the existing model-free RL model - (i) it requires huge amounts of user behavior data, and (ii) the distribution of reward (users' feedback) are extremely unbalanced. In this paper, we introduce model-based RL techniques to reduce the training data requirement and execute more accurate strategy updates. The experimental results based on a real e-commerce platform demonstrate the effectiveness of the proposed framework.
Brexit was a huge shock for universities. Now we must regroup and deepen our European links

Universities were shocked and profoundly concerned by the Leave vote. So much collaboration between European universities has arisen from membership of the EU. Norwegian-style membership of the EEA would at least enable them to keep their involvement with programmes like Erasmus+ and Horizon 2020. But, writes Anne Corbett, now is the time to forge and deepen our European links thorough associations like Coimbra and the European University Association.
Environmental Effects of Marine Fisheries

This document is the Royal Society response to both the Royal Commission on Environmental Pollution (RCEP) consultation on the Environmental Effects of Marine Fisheries in May 2003 and the Royal Society of Edinburgh Inquiry into the Scottish Fishing Industry in June 2003. The answers in this response follow the format of the questions as asked in the RCEP invitation to submit evidence. The Society has only responded to issues where it has expertise and as such, not all the questions have been addressed.
30. The Biodemography of Human Health in Contemporary Non-industrial Populations: Insights from the Tsimane Health and Life History Project

The life history of human hunter-gatherers is characterized by an extended post-reproductive lifespan, prolonged juvenile growth, high fertility with multiple dependents and biparental care, and extensive intra-and inter-generational resource and information transfer. Long-term, in-depth study of contemporary non-industrial populations offers important glimpses into how these traits operate, and insights into how they may have evolved. The Tsimane Health and Life History Project is a large-scale bio-behavioural study of the human life course designed to help understand the bidirectional connections between life history, health and social behaviour in a high fertility, kin-based population lacking amenities of modern urban life. It seeks to document the epidemiology of health and mortality across the lifespan, and to understand how growth and investment, social structure, sharing networks and behaviour impact health and aging. It focuses on how pathogen burden influences health and well-being during development and adulthood, and addresses how modernization affects health and sociality. We reflect on the implications of current findings and highlight the need for more joint ethnographic and biomedical studies of subsistence populations to address unresolved questions not only in evolutionary anthropology or biodemography, but in public health, epidemiology, gerontology and medicine.
Revised chart of the submarine canyon and valley systems around the Japanese Islands-on their topographic features and their unsettled questions-

シービーム海底地形図およびJ-EGG500データをもとに日本列島海底谷系図を改訂した．大陸棚外縁から大洋底・海溝まで連続する谷線を持つ海底谷は天竜海底谷，富山海底谷および最上海底谷のみである．曲流を呈するのは釧路海底谷，広尾海底谷および谷末端水深が2000 m以浅の海底峡谷と深海チャネルに限られ，他の海底峡谷は屈曲を主とする適従谷である．ほとんどの海谷とガリ状海底谷および御蔵海底谷，黒瀬海底谷は必従的である．ガリ状海底谷は太平洋側の水深2000 m以浅に分布する．海底谷の縦断面形状は勾配の違いにより7区分される．縦断面は海域によらず1，2の傾斜変換点で連結した単斜谷の組合せからなる．海底谷地形は流動体による作用を示すが，海底谷の形成機構は堆積物重力流の作用だけでなく，海水準変動や地殻変動も併せて考慮するべきであろう．
Measurement and predictors of the financial performance of public health enterprises in Italy

Introduction: In the healthcare sector, hospitals are by far the largest producers. As their costs continue to rise, sustaining long-term financial sustainability is becoming increasingly difficult. The measurement of healthcare providers’ financial performance is less developed in European countries compared to others; structured information on financial performance is especially scarce when providers are publicly financed. Methods: This study constructs and validates a multidimensional financial performance measure (common factor approach) by the example of Public Health Enterprises (PHEs) in Italy, based on publicly available data. In a second step, several predictor variables are examined in a PLS-SEM model (partial least squares - structural equation model), including the environmental factor umbrella institutions' financial performance (Region), structural factors size (absolute), size class, teaching/scientific function and specialization , operational factor occupancy and complexity of treatment and the staffing factor non-medical staff rate. Results: The proposed measure aggregates multiple dimensions of financial performance (e.g. profitability, liquidity, capital structure) and satisfies all necessary conditions of construct reliability and validity. For PHEs in Italy, financial performance is significantly negatively influenced by the Region's financial performance (financial recovery plans) and the complexity of treatment, whereas medium-sized
THERE IS NO ‘I’ IN TEAM: CAREER CONCERNS, GAMBLING INCENTIVES AND TEAM OUTCOMES

National Basketball Association (NBA) contracting rules provide plausibly exogenous variation in career concerns near contract end. We use this setting to study how individual career concerns affect risk-taking behavior and can sabotage team performance. Using the frequency and duration of player injury data from 1991 to 2013 to measure individual risk-taking behavior, we find that the average player’s likelihood of missing a game due to injury falls by 73% in the final three months of his contract, and when missing games due to injury is unavoidable, his recovery time drops by 17 days. However, elite players with virtually no career concerns actually miss more games due to injury. Finally, we find that elite players missing too many games and average players playing before healthy combine to hurt team performance. For each additional player in the last three months on contract, the win probability for that team falls by over 2%. JEL Classification: M5, M12, G21, L83
2014 G AL S IM : The modular galaxy image simulation toolkit

G AL S IM is a collaborative, open-source project aimed at providing an image simulation tool of enduring beneﬁt to the astronomical community. It provides a software library for generating images of astronomical objects such as stars and galaxies in a variety of ways, efﬁciently handling image transformations and operations such as convolution and rendering at high precision. We describe the G AL S IM software and its capabilities, including necessary theoretical background. We demonstrate that the performance of G AL S IM meets the stringent requirements of high precision image analysis applications such as weak gravitational lensing, for current datasets and for the Stage IV dark energy surveys of the Large Synoptic Survey Telescope, ESA’s Euclid mission, and NASA’s WFIRST-AFTA mission. The G AL S IM project repository is public and includes the full code history, all open and closed issues, installation instructions, documentation, and wiki pages (including a Frequently Asked Questions section). The G AL S IM repository can be found at https://github.com/GalSim-developers/GalSim .
Dual Dense Uncertainty Embedding for Iris Recognition

Pixelwise dense representations are more prevalent in the field of iris recognition, a.k. a. iris templates or IrisCodes. Almost all previous works of this kind are deterministic. To be specific, pixel-level representations are exclusively derived from certain point-by-point modeling, including filter response, phase correlations, ordinal relations and so on. Moreover, the binary mask indicating valid iris regions is solely determined by a fixed threshold or the output of standalone segmentation and localization algorithms. Uncertainty acquisition factors in the process of iris imagery formation are not considered. In this paper, we propose a simple yet effective building block termed as dual dense uncertainty embedding (D 2 UE), which can be seamlessly incorporated into any deep learning (DL) frameworks that extract dense representations for iris recognition. D 2 UE has two pathways wherein both take dense feature maps of the backbone network as input. One pathway of D 2 UE predicts a variance-scaling map (VSM) and then applies it to an adaptive threshold masking operation on the iris image. The dynamic threshold for each pixel in this manner is dependent on not only the intensity distribution of the iris image but also each pixel’s low-level uncertainty. The other pathway of D 2 UE adopts over-parameterization technique and extracts uncertainty embedded dense representation (UEDR) by modeling each pixel’s contextual uncertainty. Extensive experiments on several iris datasets demonstrate that recognition performance under both within-database and cross-database settings can be significantly improved by incorporating D 2 UE into the baseline method. D 2
Prospect forecast for drilling ultra-deep wells in difficult geological conditions of western Turkmenistan

The relevance of the study is related to the growing need for efficient production of hydrocarbons in the difficult geological conditions of western Turkmenistan to ensure energy security and economic development of the region in the changing global oil and gas market. The purpose of the study is to conduct a comprehensive analysis of the geological, technical, and economic aspects of drilling ultra-deep wells in difficult geological conditions of western Turkmenistan to assess their potential feasibility and contribution to the energy infrastructure of the region. The study used analytical, functional, and statistical methods, classification, and synthesis. The prospects for drilling wells in difficult and extreme geological conditions of western Turkmenistan were identified. The analysis of the current state of prospecting and exploration in this region was carried out, while the main attention was paid to the investigation of the main difficulties faced by drillers when working with deep horizons of the reedbeds. As a result of the conducted research, general principles and recommendations aimed at overcoming these problems were developed. Despite the successful introduction of modern technical and technological innovations that meet international standards when drilling deep and ultra-deep wells in the West Turkmen depression, the study emphasized that the existing drilling problems have not yet been eliminated. This study will provide valuable recommendations and information for making informed decisions when developing and implementing projects for drilling ultra-deep wells in difficult geological conditions, contributing to effective resource management and development of the oil and gas industry in western Turkmenistan.
attribut-Predicting methane emissions of individual grazing dairy cows from spectral analyses of their milk samples

Data on the enteric methane emissions of individual cows are useful not just in assisting management decisions and calculating herd inventories but also as inputs for animal genetic evaluations. Data generation for many animal characteristics, including enteric methane emissions, can be expensive and time consuming, so being able to extract as much information as possible from available samples or data sources is worthy of investigation. The objective of the present study was to attempt to predict individual cow methane emissions from the information contained within milk samples, specifically the spectrum of light transmittance across different wavelengths of the mid-infrared (MIR) region of the electromagnetic spectrum. A total of 93,888 individual spot measures of methane (i.e., individual samples of an animal’s breath when using the GreenFeed technology) from 384 lactations on 277 grazing dairy cows were collapsed into weekly averages expressed as grams per day; each weekly average coincided with a MIR spectral analysis of a morning or evening individual cow milk sample. Associations between the spectra and enteric methane measures were performed separately using partial least squares regression or neural networks with different tuning parameters evaluated. Several alternative definitions of the enteric methane phenotype (i.e., average enteric methane in the 6 d preceding or 6 d following taking the milk sample or the average of the 6 d before and after the milk sample, all of which also included the enteric methane emitted on the day of milk sampling), the candidate model features (e.g., milk yield, milk composition, and milk MIR) as well as validation strategy (i.e., cross-validation or leave-one-experimental treatment-out) were evaluated. Irrespective of the validation method, the prediction accuracy was best when the average of the milk MIR from the morning and evening milk sample was used and the prediction model was developed using neural networks; concurrently including
Improving Image-based Generation of Implicit Texture Fields for 3D Objects

Transferring the surface texture from an image of an object to a 3D model of an object is a long-standing problem at the intersection of vision and graphics. One new technology that has recently been applied to this problem is neural implicit fields: learning a neural network which takes as input the 3D object to be textured, the image, and a point in space, and returns an RGB texture value at that point. However, current texture field methods produce low-frequency output textures which do not faithfully represent the input image. In this paper, we present an improved method for generating implicit texture fields based on an input image. We leverage the power of modern single-image 3D reconstruction methods to unproject pixels from the conditioning image into points in 3D. As the image object may be geometrically different from the 3D object, we leverage unsupervised implicit dense correspondences to warp these points toward the shape of the 3D object. Our method then makes use of these points in two ways. First, it defines an initial texture field as an average of nearby image points, which gives high quality texture in regions that are visible in the input image. We improve coverage further by evaluating distances in a feature space produced by a shape segmentation network, in which points on semantically-related parts of an object (e.g. symmetric parts) tend to be close. To produce texture in uncovered regions, it then trains a neural implicit network to predict a residual texture field on top of the initial field. We compare our method to the prior state-of-the-art in image-based implicit texture field generation, showing that it produces texture fields that more faithfully reflect the input image and adhere to the object’s local geometry.