Miracle-dz/UniFAR-FTUs huggingface dataset:
Aspects of the ecological flexibility of the Tana mangabey (Cercocebus galeritus) in its fragmented habitat, Tana River, Kenya

“Aspects I investigated how behavior, diet, and ranging of one group have changed over a 27-year period. I determined what forest attributes are most responsible for mangabey abundance throughout its 60-km distribution. My results increase the knowledge of this critically endangered primate, and contribute to the literature of primates in fragmented habitats and of variation in primate behavior and ecology.
Geometry of visual recruitment by seabirds to ephemeral foraging flocks

Using geometric relationships, we calculate theoretical upper (20-30 km) and lower (0.7-6.2 km) limits to horizontal distances over which volant seabirds can be visually recruited to join Type I flocks in the open ocean. These are compared to empirical estimates for recruitment distances, obtained from chumming experiments conducted in the western Atlantic Ocean off the southeastern United States. The product of arrival times and flight speeds for individuals (n = 164) joining 10 flocks indicated that potential recruitment distances were closer to the lower theoretical limits, with a mean distance of 4.5 km when flight time and ground speed were adjusted for detection lags, wind speed, and zigzag flight. Distances and time spans for mutual attraction among seabirds have important consequences for evaluating both intraand inter-specific interactions at the community level, as well as direct implications for sampling independence during distributional surveys that employ consecutive line transects or other sequential counting methods.
Mesenchymal Stem Cells Applications in Alzheimer's Disease

Abstract Alzheimer's disease (AD) is a neurodegenerative disorder that advances gradually and primarily impacts the hippocampus region of the brain. It is defined by a deterioration in cognitive function as well as an observable loss of memory retention. One of the major characteristics of AD is the impairment of neural generation, resulting in the depletion of neurons and synaptic connections within the nervous system. It is unfortunate to say that, at present, no definitive cure is available for AD, and no medication is effective in halting the progression of neurodegeneration associated with it. Nevertheless, it is crucial to highlight that progress has been achieved in addressing the troubling symptoms of AD. The Food and Drug Administration has granted approval for two categories of medications designed to alleviate these symptoms. The scientific community has been inspired by these advancements to investigate alternative therapeutic options, with an emphasis on stem cell therapy in particular. The main focus of this review will be on the potential for the use of a variety of mesenchymal stem cells as a treatment for AD.
Tuberculosis of the Lateral Condyle of the Femur: A Case Report

Tuberculosis in India has been a constant health issue. The revised national tuberculosis control program has suggested antitubercular drug regimens according to WHO guidelines for pulmonary and extrapulmonary tuberculosis. Here is an uncommon case of an adult presenting with a single lytic lesion of the lateral condyle of the femur; he had no history of primary foci of pulmonary tuberculosis and curettage of the lesion and histopathological examination of the bone tissue showed tubercular etiology. The patient, hence, was started on antitubercular drugs and at a six-month follow-up, resolution of the lesion was noticed.
Early Holocene Flint Assemblages from the Bulgarian Black Sea Coast

Presentation de ces assemblages dates de la transition Pleistocene/Holocene, decouverts dans la region de Pobiti Kamani. Ils contiennent 428 microlithes, 900 lames et lamelles et pres de 9 000 eclats. Ils representent la limite orientale de la tradition epigravettienne
Changes in sea floor productivity are crucial to understanding the impact of climate change in temperate coastal ecosystems according to a new size-based model

The multifaceted effects of climate change on physical and biogeochemical processes are rapidly altering marine ecosystems but often are considered in isolation, leaving our understanding of interactions between these drivers of ecosystem change relatively poor. This is particularly true for shallow coastal ecosystems, which are fuelled by a combination of distinct pelagic and benthic energy pathways that may respond to climate change in fundamentally distinct ways. The fish production supported by these systems is likely to be impacted by climate change differently to those of offshore and shelf ecosystems, which have relatively simpler food webs and mostly lack benthic primary production sources. We developed a novel, multispecies size spectrum model for shallow coastal reefs, specifically designed to simulate potential interactive outcomes of changing benthic and pelagic energy inputs and temperatures and calculate the relative importance of these variables for the fish community. Our model, calibrated using field data from an extensive temperate reef monitoring program, predicts that changes in resource levels will have much stronger impacts on fish biomass and yields than changes driven by physiological responses to temperature. Under increased plankton abundance, species in all fish trophic groups were predicted to increase in biomass, average size, and yields. By contrast, changes in benthic resources produced variable responses across fish trophic groups. Increased benthic resources led to increasing benthivorous and piscivorous fish biomasses, yields, and mean body sizes, but biomass decreases among herbivore and planktivore species. When resource changes were combined with warming seas, physiological responses generally decreased species’ biomass and yields. Our results suggest that understanding changes in benthic production and its implications for coastal fisheries should be a priority research area. Our modified size spectrum model provides a framework for further study of benthic and pelagic energy pathways that can be easily adapted to other ecosystems.
SARS-CoV-2 Infection in the Second Trimester of Pregnancy: A Case Report of Fetal Intraventricular Hemorrhage After Critical COVID-19 Infection and a Brief Review of the Literature

More than three and a half years have passed since the start of the coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and there have been several studies in the literature about the different damage and symptom patterns related to the condition; particular attention has been paid to the transmission of the disease from pregnant mothers to fetus. In this report, we present the case of a 36-year-old patient with a history of two cesarean sections (CS), two miscarriages, and hypothyroidism on replacement therapy, who contracted COVID-19 during the 15th week of gestation. Ultrasound (US) examination at 22 weeks revealed regular fetal biometry and bilateral ventriculomegaly, highly suggestive of massive intracerebral hemorrhage. The patient opted for the interruption of pregnancy. Given the critical maternal COVID-19 complications, especially tracheoesophageal fistula and the patient's two previous cesareans, we decided on an abortive CS at 23 weeks of gestation, and the samples were sent to the Pathology Department. Histologic analysis showed massive intervillous deposition of fibrin and inflammatory infiltration with hotspots of necrotic deciduitis and confirmed massive cerebral hemorrhage in the fetus. This morphological appearance was consistent with COVID-19 infection and probable fetal oxygenation compromise related to deciduitis. Immunoexpression of anti-SARS-CoV-2 S1 antibody was almost entirely positive at the level of syncytiotrophoblast cells and maternal leukocytes in the absence of a clear signal in the fetal circulation. Conversely, in the brain, immunoexpression of angiotensin-converting enzyme 2 (ACE2) and the S1 subunit of the spike protein of SARS-CoV-2, detected by a monoclonal antibody, was almost entirely negative, suggesting that there was no infection in the brain and that the massive intraventricular hemorrhage was probably a secondary effect of placental damage.
Adipose Tissue-Derived Mesenchymal Stem Cells Alter Metabolites of Brain Cholesterol Homeostasis in An Alzheimer’s Model

Objective: Disruption of cholesterol homeostasis in Alzheimer’s disease (AD) plays a crucial role in disease pathogenesis, making it a potential therapeutic target. Mesenchymal stem cells (MSCs) show promise in treating cognitive impairment and provide a novel therapeutic approach. This study aims to investigate the effects of MSCs on specific metabolites associated with brain cholesterol homeostasis in an AD rat model. Materials and Methods: In this experimental study, animals were divided into three groups: control, AD, and AD+MSCs. AD was induced using amyloid beta (Aβ) and confirmed through the Morris water maze (MWM) behavioural test and Congo red staining. MSCs were extracted, characterised via flow cytometry, subjected to osteoblast and adipose differentiation, and injected intraventricularly. The cholesterol metabolite levels were measured using gas chromatography-mass spectrometry (GC)-MS and compared among the groups. Results: Treatment with MSCs significantly improved memory function in the AD+MSCs group compared to the AD group and the number of beta-amyloid plaques decreased according to histological assessment. Disturbances in the brain cholesterol metabolites that included desmosterol, 7-dehydrocholesterol, 24S-hydroxycholesterol, 27-hydroxycholesterol and cholesterol were observed in the AD group compared to the control group. Treatment with MSCs resulted in significant alterations in the levels of these metabolites. Conclusion: The findings indicate that MSC therapy has the potential to improve AD by modulating brain cholesterol homeostasis and promoting the differentiation of stem cells into nerve cells. The results emphasize the importance of investigating the role of cholesterol metabolites in the context of MSC therapy to gain deeper insights into underlying mechanisms of the therapeutic efficacy of MSCs in AD.
Grounded Decoding: Guiding Text Generation with Grounded Models for Embodied Agents

Recent progress in large language models (LLMs) has demonstrated the ability to learn and leverage Internet-scale knowledge through pre-training with autoregressive models. Unfortunately, applying such models to settings with embodied agents, such as robots, is challenging due to their lack of experience with the physical world, inability to parse non-language observations, and ignorance of rewards or safety constraints that robots may require. On the other hand, language-conditioned robotic policies that learn from interaction data can provide the necessary grounding that allows the agent to be correctly situated in the real world, but such policies are limited by the lack of high-level semantic understanding due to the limited breadth of the interaction data available for training them. Thus, if we want to make use of the semantic knowledge in a language model while still situating it in an embodied setting, we must construct an action sequence that is both likely according to the language model and also realizable according to grounded models of the environment. We frame this as a problem similar to probabilistic filtering: decode a sequence that both has high probability under the language model and high probability under a set of grounded model objectives. We demonstrate how such grounded models can be obtained across three simulation and real-world domains, and that the proposed decoding strategy is able to solve complex, long-horizon embodiment tasks in a robotic setting by leveraging the knowledge of both models. The project's website can be found at grounded-decoding.github. io.
The Gaussian-Linear Hidden Markov model: a Python package

We propose the Gaussian-Linear Hidden Markov model (GLHMM), a generalisation of different types of HMMs commonly used in neuroscience. In short, the GLHMM is a general framework where linear regression is used to flexibly parameterise the Gaussian state distribution, thereby accommodating a wide range of uses -including unsupervised, encoding and decoding models. GLHMM is implemented as a Python toolbox with an emphasis on statistical testing and out-of-sample prediction -i.e. aimed at finding and characterising brain-behaviour associations. The toolbox uses a stochastic variational inference approach, enabling it to handle large data sets at reasonable computational time. Overall, the approach can be applied to several data modalities, including animal recordings or non-brain data, and applied over a broad range of experimental paradigms. For demonstration, we show examples with fMRI, electrocorticography, magnetoencephalo-graphy and pupillometry.
Dynamic Adversarial Attacks on Autonomous Driving Systems

This paper introduces an attacking mechanism to challenge the resilience of autonomous driving systems. Specifically, we manipulate the decision-making processes of an autonomous vehicle by dynamically displaying adversarial patches on a screen mounted on another moving vehicle. These patches are optimized to deceive the object detection models into misclassifying targeted objects, e.g., traffic signs. Such manipulation has significant implications for critical multi-vehicle interactions such as intersection crossing and lane changing, which are vital for safe and efficient autonomous driving systems. Particularly, we make four major contributions. First, we introduce a novel adversarial attack approach where the patch is not co-located with its target, enabling more versatile and stealthy attacks. Moreover, our method utilizes dynamic patches displayed on a screen, allowing for adaptive changes and movement, enhancing the flexibility and performance of the attack. To do so, we design a Screen Image Transformation Network (SIT-Net), which simulates environmental effects on the displayed images, narrowing the gap between simulated and real-world scenarios. Further, we integrate a positional loss term into the adversarial training process to increase the success rate of the dynamic attack. Finally, we shift the focus from merely attacking perceptual systems to influencing the decision-making algorithms of self-driving systems. Our experiments demonstrate the first successful implementation of such dynamic adversarial attacks in real-world autonomous driving scenarios, paving the way for advancements in the field of robust and secure autonomous driving.
JWST Observations of Young protoStars (JOYS+): Detection of icy complex organic molecules and ions. I. CH$_4$, SO$_2$, HCOO$^-$, OCN$^-$, H$_2$CO, HCOOH, CH$_3$CH$_2$OH, CH$_3$CHO, CH$_3$OCHO, CH$_3$COOH

Complex organic molecules (COMs) detected in the gas phase are thought to be mostly formed on icy grains, but no unambiguous detection of icy COMs larger than CH3OH has been reported so far. Exploring this matter in more detail has become possible with the JWST the critical 5-10 $\mu$m range. In the JOYS+ program, more than 30 protostars are being observed with the MIRI/MRS. This study explores the COMs ice signatures in the low and high-mass protostar, IRAS 2A and IRAS 23385, respectively. We fit continuum and silicate subtracted observational data with IR laboratory ice spectra. We use the ENIIGMA fitting tool to find the best fit between the lab data and the observations and to performs statistical analysis of the solutions. We report the best fits for the spectral ranges between 6.8 and 8.6 $\mu$m in IRAS 2A and IRAS 23385, originating from simple molecules, COMs, and negative ions. The strongest feature in this range (7.7 $\mu$m) is dominated by CH4 and has contributions of SO2 and OCN-. Our results indicate that the 7.2 and 7.4 $\mu$m bands are mostly dominated by HCOO-. We find statistically robust detections of COMs based on multiple bands, most notably CH3CHO, CH3CH2OH, and CH3OCHO. The likely detection of CH3COOH is also reported. The ice column density ratios between CH3CH2OH and CH3CHO of IRAS 2A and IRAS 23385, suggests that these COMs are formed on icy grains. Finally, the derived ice abundances for IRAS 2A correlate well with those in comet 67P/GC within a factor of 5. Based on the MIRI/MRS data, we conclude that COMs are present in interstellar ices, thus providing additional proof for a solid-state origin of these species in star-forming regions. The good correlation between the ice abundances in comet 67P and IRAS 2A is in line with the idea that cometary COMs can be inherited from the early protostellar phases.
Emergent Gauge Symmetry in Active Brownian Matter

We investigate a two-dimensional system of Active Brownian Particles at finite density. Using the Martin-Siggia-Rose-Janssen-de Dominicis formalism, we built up the generating functional for correlation functions. We study in detail the hydrodynamic regime with a constant density stationary state. Our findings reveal that, within a small density fluctuations regime, an emergent $U(1)$ gauge symmetry arises, originated from the conservation of fluid vorticity. Consequently, the interaction between the orientational order parameter and density fluctuations can be cast into a gauge theory, where the concept of ``electric charge density"aligns with the local vorticity of the original fluid. We study in detail the case of a microscopic local two-body interaction. We show that, upon integrating out the gauge fields, the stationary states of the rotational degrees of freedom satisfy a non-local Frank free energy. We give explicit expressions for the splay and bend elastic constants as a function of the P\'eclet number (${\rm Pe}$) and the diffusion interaction constant ($k_d$).
Probabilistic Lookahead Strong Branching via a Stochastic Abstract Branching Model

Strong Branching (SB) is a cornerstone of all modern branching rules used in the Branch-and-Bound (BnB) algorithm, which is at the center of Mixed-Integer Programming solvers. In its full form, SB evaluates all variables to branch on and then selects the one producing the best relaxation, leading to small trees, but high runtimes. State-of-the-art branching rules therefore use SB with working limits to achieve both small enough trees and short run times. So far, these working limits have been established empirically. In this paper, we introduce a theoretical approach to guide how much SB to use at each node within the BnB. We first define an abstract stochastic tree model of the BnB algorithm where the geometric mean dual gains of all variables follow a given probability distribution. This model allows us to relate expected dual gains to tree sizes and explicitly compare the cost of sampling an additional SB candidate with the reward in expected tree size reduction. We then leverage the insight from the abstract model to design a new stopping criterion for SB, which fits a distribution to the dual gains and, at each node, dynamically continues or interrupts SB. This algorithm, which we refer to as Probabilistic Lookahead Strong Branching, improves both the tree size and runtime over MIPLIB instances, providing evidence that the method not only changes the amount of SB, but allocates it better.
Transformer-based No-Reference Image Quality Assessment via Supervised Contrastive Learning

Image Quality Assessment (IQA) has long been a research hotspot in the field of image processing, especially No-Reference Image Quality Assessment (NR-IQA). Due to the powerful feature extraction ability, existing Convolution Neural Network (CNN) and Transformers based NR-IQA methods have achieved considerable progress. However, they still exhibit limited capability when facing unknown authentic distortion datasets. To further improve NR-IQA performance, in this paper, a novel supervised contrastive learning (SCL) and Transformer-based NR-IQA model SaTQA is proposed. We first train a model on a large-scale synthetic dataset by SCL (no image subjective score is required) to extract degradation features of images with various distortion types and levels. To further extract distortion information from images, we propose a backbone network incorporating the Multi-Stream Block (MSB) by combining the CNN inductive bias and Transformer long-term dependence modeling capability. Finally, we propose the Patch Attention Block (PAB) to obtain the final distorted image quality score by fusing the degradation features learned from contrastive learning with the perceptual distortion information extracted by the backbone network. Experimental results on six standard IQA datasets show that SaTQA outperforms the state-of-the-art methods for both synthetic and authentic datasets. Code is available at https://github.com/I2-Multimedia-Lab/SaTQA.
SSTA: Salient Spatially Transformed Attack

Extensive studies have demonstrated that deep neural networks (DNNs) are vulnerable to adversarial attacks, which brings a huge security risk to the further application of DNNs, especially for the AI models developed in the real world. Despite the significant progress that has been made recently, existing attack methods still suffer from the unsatisfactory performance of escaping from being detected by naked human eyes due to the formulation of adversarial example (AE) heavily relying on a noise-adding manner. Such mentioned challenges will significantly increase the risk of exposure and result in an attack to be failed. Therefore, in this paper, we propose the Salient Spatially Transformed Attack (SSTA), a novel framework to craft imperceptible AEs, which enhance the stealthiness of AEs by estimating a smooth spatial transform metric on a most critical area to generate AEs instead of adding external noise to the whole image. Compared to state-of-the-art baselines, extensive experiments indicated that SSTA could effectively improve the imperceptibility of the AEs while maintaining a 100\% attack success rate.
Robotics Applications in Neurology: A Review of Recent Advancements and Future Directions

Robotic technology has the potential to revolutionize the field of neurology by providing new methods for diagnosis, treatment, and rehabilitation of neurological disorders. In recent years, there has been an increasing interest in the development of robotics applications for neurology, driven by advances in sensing, actuation, and control systems. This review paper provides a comprehensive overview of the recent advancements in robotics technology for neurology, with a focus on three main areas: diagnosis, treatment, and rehabilitation. In the area of diagnosis, robotics has been used for developing new imaging techniques and tools for more accurate and non-invasive mapping of brain structures and functions. For treatment, robotics has been used for developing minimally invasive surgical procedures, including stereotactic and endoscopic approaches, as well as for the delivery of therapeutic agents to specific targets in the brain. In rehabilitation, robotics has been used for developing assistive devices and platforms for motor and cognitive training of patients with neurological disorders. The paper also discusses the challenges and limitations of current robotics technology for neurology, including the need for more reliable and precise sensing and actuation systems, the development of better control algorithms, and the ethical implications of robotic interventions in the human brain. Finally, the paper outlines future directions and opportunities for robotics applications in neurology, including the integration of robotics with other emerging technologies, such as neuroprosthetics, artificial intelligence, and virtual reality. Overall, this review highlights the potential of robotics technology to transform the field of neurology and improve the lives of patients with neurological disorders.