Miracle-dz/UniFAR-FTUs huggingface dataset:
Influence of ketamine, propofol or isoflurane on intraocular pressure, heart rate and blood pressure in healthy dogs premedicated with medetomidine and midazolam

Abstract Background According to the findings of several studies, sedatives and anaesthetics have different effects on the functioning of the cardiovascular system and intraocular pressure (IOP). For accurate diagnosis, treatment and surgery with minimal complications, it is necessary to be aware of the effects of sedatives and anaesthetics on the cardiovascular system and IOP. Objectives The aim of this study was to evaluate the effects of sedatives (medetomidine and midazolam) and anaesthetics (ketamine, propofol and isoflurane) on IOP, heart rate (HR) and blood pressure in dogs. Methods In this study, 10 dogs participated in three treatments using a randomised cross‐over design, with a 1‐week washout period between each treatment. Dogs in all treatments were premedicated with medetomidine and midazolam. Anaesthesia was induced using ketamine, propofol, or isoflurane and maintained for 60 min with the appropriate doses of each drug. The cardiovascular variables (heart rate, and systolic, diastolic and mean arterial pressures) and IOP were measured at different timepoints: before premedication (baseline values, T‐Bas), 15 min after medetomidine administration (T‐Med), 20 min after midazolam administration (T‐Mid) and at 15 (T‐15), 30 (T‐30), 45 (T‐45) and 60 (T‐60) min after anaesthesia induction. Results Medetomidine significantly reduced the IOP and HR and did not significantly change the mean arterial pressure (MAP). Midazolam significantly reduced the IOP while did not significantly change the HR and MAP. Ketamine and isoflurane significantly increased the IOP and HR while did not significantly change the MAP. Propofol significantly increased the HR, but did not cause significant changes in IOP and MAP. Conclusions Considering that anaesthetics are typically administered in conjunction with pre‐anaesthetic drugs, the increases in IOP induced by ketamine and isoflurane are not important, as the IOP did not exceed the baseline values. However, further studies are required to investigate these effects in patients with elevated IOP.
The integration of information and communication technology into rural schools of South Africa :a case study of schools in Malamulele /

Thesis (MPhil (Information Science)) – University of Stellenbosch, 2007. %%%%The world has advanced and grown to a stage where a person without a basic computer literacy finds it almost impossible to function comfortably in society. To address the situation governments all over the world are encouraging their citizens to become technologically literate. Countries are spending large sums of money to integrate ICTs in education. The South African government on its part has realized and acknowledged the importance of Information and Communication Technology in education. Hence, its White paper on e-Education, which provides for the integration of ICTs into schools. This study looks at efforts being made by the Department of Education and other education stakeholders to address the ICT situation in some schools based in a rural area of the Limpopo Province. A well-resourced school in so far as ICTs are concerned was visited to assess the functionality and applicability of ICT within the school system and to make comparisons to less resourced schools. The study highlights the plight of schools in rural areas where the lack of basic amenities such as adequate accommodation, furniture, water, etc. is the order of the day. It is however pointed out that dwelling on these issues is no solution as the question of ICT integration into schools is a non negotiable one, if South African learners are to be kept abreast with trends in other parts of the world. That is, without adequate immersion into ICT, learners would not be globally competitive. The ICT situation in schools in the Limpopo Province has been analysed within a framework developed under this study. The framework is set to help the Department of Education determine the suitable intervention programmes for each level of ICT integration. In addition, the study concludes with a recommendation. The success or failure to integrate ICT into schools, especially those in rural areas, depends to a large extent on the kind of intervention the government adopts. Since all schools have not attained the same level of ICT integration, there should be a clearly defined audit to determine what is needed and how it should be provided. This study raises some of the key issues for integration of ICTs in schools within a rural setting.
Historical Seismicity of the Stable Continental Regions (SCRs) In the Arabian Plate (Preliminary Study)

Historical earthquakes catalog which were felt in the Stable Continental Regions (SCRs) of the Arabian plate for the period (600 to 1900 A.D.) was compiled. The spatial distribution of these earthquakes appears as clusters of events in the northern parts and absence of events in the other parts. This may be attributed to concentration of the population centers in the northern parts relative to other parts and / or to that the seismicity level is very low. These events have a shallow focal 22
Workplace Health Promotion Programs Targeting Smoking, Nutrition, Physical Activity, and Obesity in Men: A Systematic Review and Meta-Analysis of Randomized Controlled Trials

The workplace has been highlighted as a potential setting to deliver health promotion programs to target modifiable health behaviors that contribute to chronic disease. This review evaluated the effectiveness of interventions implemented within the workplace that targeted either smoking, nutrition, alcohol, physical activity, and/or overweight and obesity in men. A review protocol was prospectively registered through PROSPERO (CRD42021293398). Five electronic bibliographic databases were searched for randomized controlled trials conducted in the workplace assessing chronic disease risk factors in men from January 2010 to August 2021. Eleven studies were included, reporting on overweight and obesity n = 8, physical activity n = 7, nutrition n = 4, alcohol n = 3, smoking n = 3, with eight studies assessing multiple outcomes. Results were mixed. Narrative synthesis highlighted studies reporting improvements to snacking frequency, sugar sweetened beverage consumption, and physical activity (METs and Vo2 max). Meta-analysis highlighted pooled mean decrease in body weight of −0.28 kg up to 3 months; −1.38 kg for >3 months, and pooled mean decrease in body mass index 0.06 kg/m2 up to 3 months; −0.27 kg/m2 for >3 months. Despite the encouraging direction of the relationship, results were not statistically significant (p > .05). Findings underscore the potential of workplace health promotion programs targeting certain chronic disease risk factors in men; however, future research should consider long-term study designs to assess the efficacy of workplace health programs as a solution to the growing burden of global disease.
Impact of early cognitive impairment on outcome trajectory in patients with intracerebral hemorrhage

Abstract Objective To assess the prevalence and factors associated with early cognitive impairment in intracerebral hemorrhage (ICH) patients and to describe short‐term recovery trajectories among ICH patients with early cognitive impairment. Methods We prospectively enrolled ICH patients without baseline dementia in our institutions. Cognitive function was assessed using mini‐mental state examination (MMSE), and functional outcome was evaluated at discharge, 3, and 6 months after symptoms onset using the modified Rankin Scale (mRS). We used multinomial logistic regression models to investigate potential risk factors and generalized linear models to analyze the functional outcome data. Results Out of 181 patients with ICH, 167 were included in the final analysis. Early cognitive impairment occurred in 60.48% of patients with ICH. Age (odds ratio [OR] per 1‐year increase, 1.037; 95% confidence interval [CI], 1.003–1.071; p = 0.034), National Institutes of Health Stroke Scale (NIHSS) score (OR per 1‐point increase, 1.146; 95% CI, 1.065–1.233; p < 0.001) and lobar ICH location (OR, 4.774; 95% CI, 1.810–12.593; p = 0.002) were associated with early cognitive impairment in ICH patients. Patients with ≥10 years of education were less likely to experience early cognitive impairment (OR, 0.323; 95% CI, 0.133–0.783; p = 0.012). Participants with early cognitive impairment had a higher risk of poor outcome (OR, 4.315; 95% CI, 1.503–12.393; p = 0.005) than those without. Furthermore, there was a significantly faster functional recovery rate for those without early cognitive impairment compared with those with at 3 and 6 months (p < 0.05). Interpretation Early cognitive impairment was prevalent and associated with poor outcomes in ICH patients, which decelerated short‐term functional recovery.
Propofol anesthesia destabilizes neural dynamics across cortex

Every day, hundreds of thousands of people undergo general anesthesia. One hypothesis is that anesthesia disrupts dynamic stability, the ability of the brain to balance excitability with the need to be stable and thus controllable. We tested this hypothesis using a new method for quantifying population-level dynamic stability in complex systems, Delayed Linear Analysis for Stability Estimation (DeLASE). Propofol was used to transition animals between the awake state and anesthetized unconsciousness. DeLASE was applied to macaque cortex local field potentials (LFPs). We found that neural dynamics were more unstable in unconsciousness compared to the awake state. Cortical trajectories mirrored predictions from destabilized linear systems. We mimicked the effect of propofol in simulated neural networks by increasing inhibitory tone. Paradoxically, increased inhibition also destabilized the networks. Our results suggest that anesthesia disrupts dynamical stability that is required for consciousness.
Habitat selection during dispersal reduces the energetic cost of transport when making large displacements

When dispersing, individuals typically have to make large displacements. While previously thought to be costly, recent work has suggested that individuals can largely mitigate these costs by expressing distinct movement strategies—moving faster and straighter—during dispersal. Several studies have also found that individuals express differences in their habitat preferences during dispersal. These preferences often involve an increased use of open habitats relative to their non-dispersing habitat selection, within which individuals typically achieve larger displacements per unit of time. As a result, it has been suggested, but never explicitly tested, that changes in habitat selection could reflect an increased preference for habitats that facilitate greater energetic efficiency in terms of movement. Here, we combine step-selection analysis with models of the energetics of movement to determine whether actively dispersing vulturine guineafowl (Acryllium vulturinum) select different habitats relative to their non-dispersing counterparts, and whether their habitat preferences correspond to a decrease in the cost of transport. Our results confirm that fine-scale differences in habitat preferences expressed when making large displacements during the transient stage of dispersal correspond with an increase in the energetic efficiency of movement. These findings highlight the ability for animals to exhibit adaptive movement strategies across different axes of decision-making (e.g., where and how to move) to overcome different ecological challenges.
MolLM: A Unified Language Model for Integrating Biomedical Text with 2D and 3D Molecular Representations

Motivation The current paradigm of deep learning models for the joint representation of molecules and text primarily relies on 1D or 2D molecular formats, neglecting significant 3D structural information that offers valuable physical insight. This narrow focus inhibits the models’ versatility and adaptability across a wide range of modalities. Conversely, the limited research focusing on explicit 3D representation tends to overlook textual data within the biomedical domain. Results We present a unified pre-trained language model, MolLM, that concurrently captures 2D and 3D molecular information alongside biomedical text. MolLM consists of a text Transformer encoder and a molecular Transformer encoder, designed to encode both 2D and 3D molecular structures. To support MolLM’s self-supervised pre-training, we constructed 160K molecule-text pairings. Employing contrastive learning as a supervisory signal for cross-modal information learning, MolLM demonstrates robust molecular representation capabilities across 4 downstream tasks, including cross-modality molecule and text matching, property prediction, captioning, and text-prompted molecular editing. Through ablation, we demonstrate that the inclusion of explicit 3D representations improves performance in these downstream tasks. Availability and implementation Our code, data, and pre-trained model weights are all available at https://github.com/gersteinlab/MolLM.
Rapid evaluation of habitat connectivity change to safeguard multispecies persistence in human-transformed landscapes

Protecting habitat connectivity in fragmented landscapes is essential for safeguarding biodiversity and nature’s contributions to people. Following the Post-2020 Kunming-Montreal Global Biodiversity Framework (KM-GBF) of the Convention on Biological Diversity (CBD) there is a clear science-policy need to assess habitat connectivity and track its change over time to inform conservation planning. In response to this need we describe an analytical, multi-indicator and multispecies approach for the rapid assessment of habitat connectivity at fine spatial grain and at the extent of an entire ecoregion. Out of 69 connectivity indicators we found through a literature review, we identified a key-set of nine indicators that align with the Essential Biodiversity Variables framework and that are suitable to guide rapid action for connectivity and conservation targets in the KM-GBF. Using these selected indicators, we mapped and evaluated connectivity change from 2011 to 2021 across the ecoregion of the St-Lawrence Lowlands in Quebec (∼30,000 km2) for seven ecoprofile species representing regional forest habitat needs. For the majority of these ecoprofile species, trends over the last decade indicate a decline in effective connected area and metapopulation carrying capacity, mainly via a division of large contiguous habitat into smaller fragments, whereas total habitat area largely remained unchanged. These results highlight that trends in habitat area and connectivity are not necessarily correlated and the urgent need to conserve and restore connectivity in the St-Lawrence Lowlands, in order to meet regional targets under the KM-GBF. Our general approach enables a comprehensive evaluation of connectivity for regional spatial planning for biodiversity. We develop an R-tool to support this analysis and that can be extended to other conservation planning efforts for connectivity.
Hurricanes Pose Substantial Risk to New England Forest Carbon Stocks

Nature-based climate solutions are championed as a primary tool to mitigate climate change, especially in forested regions capable of storing and sequestering vast amounts of carbon. New England is one of the most heavily forested regions in the United States (>75% forested by land area), and forest carbon is a significant component of regional climate mitigation strategies. Large infrequent disturbances, such as hurricanes, are a major source of uncertainty and risk for policies that rely on forest carbon for climate mitigation, especially as climate change is projected to alter the intensity and geographic extent of hurricanes. To date, most research into disturbance impacts on forest carbon stocks has focused on fire. Here we show that a single hurricane in the region can down between 121-250 MMTCO2e or 4.6-9.4% of the total aboveground forest carbon, much greater than the carbon sequestered annually by New England’s forests (16 MMTCO2e yr-1). However, the emissions from the storms are not instantaneous; it takes approximately 19 years for the downed carbon to become a net emission, and 100 years for 90% of the downed carbon to be emitted. Using hurricane reconstructions across a range of historical and projected wind speeds, we find that an 8% and 16% increase in hurricane wind speeds leads to a 10.7 and 24.8 fold increase in the extent of high-severity damaged areas (widespread tree mortality). Increased wind speed also leads to unprecedented geographical shifts in damage; both inland and northward into heavily forested regions traditionally unaffected by hurricanes. Given that a single hurricane can emit the equivalent of 10+ years of carbon sequestered by forests in New England, the status of these forests as a durable carbon sink is uncertain. Understanding the risks to forest carbon stocks from large infrequent disturbances is necessary for decision-makers relying on forests as a nature-based climate solution.
Catalyst: Optimizing Cache Management for Large In-memory Key-value Systems

In-memory key-value cache systems, such as Memcached and Redis, are essential in today's data centers. A key mission of such cache systems is to identify the most valuable data for caching. To achieve this, the current system design keeps track of each key-value item's access and attempts to make accurate estimation on its temporal locality. All it aims is to achieve the highest cache hit ratio. However, as cache capacity quickly increases, the overhead of managing metadata for a massive amount of small key-value items rises to an unbearable level. Put it simply, the current fine-grained, heavy-cost approach cannot continue to scale. In this paper, we have performed an experimental study on the scalability challenge of the current key-value cache system design and quantitatively analyzed the inherent issues related to the metadata operations for cache management. We further propose a key-value cache management scheme, called Catalyst , based on a highly efficient metadata structure, which allows us to make effective caching decisions in a scalable way. By offloading non-essential metadata operations to GPU, we can further dedicate the limited CPU and memory resources to the main service operations for improved throughput and latency. We have developed a prototype based on Memcached. Our experimental results show that our scheme can significantly enhance the scalability and improve the cache system performance by a factor of up to 4.3.
Query Refinement for Diversity Constraint Satisfaction

Diversity, group representation, and similar needs often apply to query results, which in turn require constraints on the sizes of various subgroups in the result set. Traditional relational queries only specify conditions as part of the query predicate(s), and do not support such restrictions on the output. In this paper, we study the problem of modifying queries to have the result satisfy constraints on the sizes of multiple subgroups in it. This problem, in the worst case, cannot be solved in polynomial time. Yet, with the help of provenance annotation, we are able to develop a query refinement method that works quite efficiently, as we demonstrate through extensive experiments.
ShadowAQP: Efficient Approximate Group-by and Join Query via Attribute-oriented Sample Size Allocation and Data Generation

Approximate query processing (AQP) is one of the key techniques to cope with big data querying problem on account that it obtains approximate answers efficiently. To address non-trivial sample selection and heavy sampling cost issues in AQP, we propose ShadowAQP, an efficient and accurate approach based on attribute-oriented sample size allocation and data generation. We select samples according to group-by and join attributes, and determine the sample size for each group of unique value combinations to improve query accuracy. We design a conditional variational autoencoder model with automatic table data encoding and model update strategies. To further improve accuracy and efficiency, we propose a set of extensions, including parallel multi-round sampling aggregation, data outlier-aware sampling, and dimension reduction optimization. Evaluation results on diversified datasets show that, compared with SOTA approaches, ShadowAQP achieves 5.8× query speed performance improvement on average (up to 12.8×), while reducing query error by 74% on average (up to 95%) at the same time.
The "UFSC sem papel" system in the innovation perspective

The present study intends to analyze the “UFSC SEM PAPEL” system in the innovation perspective. For this purpose, it was done a descriptive research and a case study, in a qualitative and transversal approach, considering the period between October and December of 2011. It is a bibliographic and a field research too. The individual that was researched was the responsible for the Superintendencia de Governanca Eletronica e Tecnologia da Informacao e Comunicacao (SeTIC), of the Federal University of Santa Catarina (UFSC) and he was chosen by the typicit criterion. For the date collect, it was utilized the semi-structured interview with a script. The date were analysed using the pattern-matching technique. The studied system is a technical and radical innovation, having effects over the management context of the University. It is divided in several modules for the management of the administrative process (protocols) of purchasing and public tenders, of patrimony, and of materials and stockroom.
Confidence Is All You Need for MI Attacks

In this evolving era of machine learning security, membership inference attacks have emerged as a potent threat to the confidentiality of sensitive data. In this attack, adversaries aim to determine whether a particular point was used during the training of a target model. This paper proposes a new method to gauge a data point's membership in a model's training set. Instead of correlating loss with membership, as is traditionally done, we have leveraged the fact that training examples generally exhibit higher confidence values when classified into their actual class. During training, the model is essentially being 'fit' to the training data and might face particular difficulties in generalization to unseen data. This asymmetry leads to the model achieving higher confidence on the training data as it exploits the specific patterns and noise present in the training data. Our proposed approach leverages the confidence values generated by the machine learning model. These confidence values provide a probabilistic measure of the model's certainty in its predictions and can further be used to infer the membership of a given data point. Additionally, we also introduce another variant of our method that allows us to carry out this attack without knowing the ground truth(true class) of a given data point, thus offering an edge over existing label-dependent attack methods.
TD-Net: A Tri-domain network for sparse-view CT reconstruction

Sparse-view CT reconstruction, aimed at reducing X-ray radiation risks, frequently suffers from image quality degradation, manifested as noise and artifacts. Existing post-processing and dual-domain techniques, although effective in radiation reduction, often lead to over-smoothed results, compromising diagnostic clarity. Addressing this, we introduce TD-Net, a pioneering tri-domain approach that unifies sinogram, image, and frequency domain optimizations. By incorporating Frequency Supervision Module(FSM), TD-Net adeptly preserves intricate details, overcoming the prevalent over-smoothing issue. Extensive evaluations demonstrate TD-Net's superior performance in reconstructing high-quality CT images from sparse views, efficiently balancing radiation safety and image fidelity. The enhanced capabilities of TD-Net in varied noise scenarios highlight its potential as a breakthrough in medical imaging.