Miracle-dz/UniFAR-FTUs huggingface dataset:
Astroglial mediation of fast-acting antidepressant effect in zebrafish

Rapid-acting antidepressants like ketamine hold promise to change the approach to treatment of major depressive disorder (MDD), 1–3 but their cellular and molecular targets remain unclear. Passivity induced by behavioral futility underlies learned helplessness, a process that becomes maladaptive in MDD. 4,5 Antidepressants inhibit futility-induced passivity (FIP) in rodent models such as the forced swimming 6 or tail suspension 7 tasks, but these models lack the throughput and accessibility for screening compounds and investigating their effects on the brain in vivo . Therefore, we adapted a recently discovered FIP behavior 8 in the small and optically accessible larval zebrafish to create a scalable behavioral assay for antidepressant action. We found that rapid-acting antidepressants with diverse pharmacological targets demonstrated a suppression of FIP conserved between fish and rodents. While fast-acting antidepressants are thought to primarily target neurons, 2,9,10 using brain-wide imaging in vivo we found, surprisingly, that ketamine, but not psychedelics or typical antidepressants, drove cytosolic calcium elevation in astroglia lasting many minutes. Blocking neural activity did not prevent ketamine’s effects on FIP or astroglial calcium, suggesting an astroglia-autonomous mechanism of ketamine’s action. Chemogenetic and optogenetic perturbations of astroglia reveal that the aftereffects of calcium elevation are sufficient to suppress FIP by inhibiting astroglial integration of futile swimming. In sum, our work provides evidence that ketamine exerts its antidepressant effects by inhibiting an astroglial population that integrates futility and changes behavioral state. Astroglia play central roles in modulating circuit dynamics, 11 and our work argues that targeting astroglial signaling may be a fruitful strategy for designing new rapid-acting antidepressants.
SOCON: a computer model for analyzing the behavior of sodium-concrete reactions

Guided by experimental evidence available to date, ranging from basic laboratory studies to large scale tests, a mechanistic computer model (the SOCON model) has been developed to analyze the behavior of SOdium-CONcrete reactions. The model accounts for the thermal, chemical and mechanical phenomena which interact to determine the consequences of the reactions. Reaction limiting mechanisms could be any process which reduces water release and sodium transport to fresh concrete; the buildup of the inert reaction product layer would increase the resistance to sodium transport; water dry-out would decrease the bubble agitation transport mechanism. However, stress-induced failure of concrete, such as spalling, crushing and cracking, and a massive release of gaseous products (hydrogen, water vapor and CO/sub 2/) would increase the transport of sodium to the reaction zone. The results of SOCON calculations are in excellent agreement with measurements obtained from large-scale sodium-limestone concrete reaction tests of duration up to 100 hours conducted at the Hanford Engineering Development Laboratory. 8 refs., 7 figs.
Before the fire: Assessing post-wildfire flooding and debris-flow
hazards for pre-disaster mitigation

Abstract. Increasing size and severity of wildfires, and the expanding built environment into the wildland-urban interface makes it imperative that local governments identify, prepare for and reduce risks to people and infrastructure from wildfires and the aftermaths of fires. Here we report on a pre-wildfire assessment of post-fire hazards in Coconino County, Arizona, the mitigation measures identified and implemented as a result of the study, and proposed changes to the assessment methodology for upcoming studies. Results from the Coconino County study indicate that up to 34 % of the buildings, and up to 26 % of the critical facilities countywide are at some level of increased risk of post-fire flooding if no actions are taken to reduce the risk of severe wildfires. As many as 593 homes (2,191 parcels) in Coconino County, as well as 13 dams and other critical facilities, may be impacted by post-fire debris flows. In two smaller areas of detailed study, flood peaks could increase as much as 4–5 times the existing 100-year flood levels, with up to a 350 % increase in the number of buildings in flood-prone areas. Debris flows will likely be limited in aerial extent but could impact a much larger area from following floods and sediment-laden flows. Mitigation measures identified and implemented as a result of this study include County Coconino County coordination with the Kaibab National Forest in regard to forest health projects, development of a post-wildfire emergency action plan for the City of Williams, development of a Post-Fire, Pre-Disaster plan for the City of Williams and educating the City of Williams and County officials and business stakeholders of the post-fire flood risk. Other mitigation measures that are still in the planning stage include installation of additional early flood warning system gages, and increasing building and infrastructure resiliency through channel conveyance improvement and utility protection projects. For two upcoming assessments, we plan to use a new statistical methodology to develop burn severity maps using historical burns, and we plan to employ a new process-based debris-flow model developed for use in Arizona to assess debris-flow inundation limits.
The importance of food quantity and quality for reproductive performance in alpine water pipits (Anthus sp. spinoletta)

Studies relating reproduction to food availability are usually restricted to food quantity, but ignore food quality and the effects of habitat structure on obtaining the food. This is particularly true for insectivorous birds. In this study we relate measures of reproductive success, time of reproduction and nestling size of water pipits (Anthus sp. spinoletta) to biomass, taxonomic composition and nutritional contents of available food, and to vegetation structure and distance to feeding places. Clutch size was correlated positively with the proportion of grass at the feeding places, which facilitates foraging. This suggests that water pipits adapt their clutch size to environmental conditions. Also, pipits started breeding earlier and produced more fledglings when abundant food and a large proportion of grass were available, probably because these conditions allow the birds to gain more energy in less time. The number of fledglings was correlated positively with the energy content of available food. No significant relationships were found between feeding conditions and nestling size or the time that nestlings took to fledge. This suggests that water pipits do not invest more in individual nestlings when food conditions are favourable but rather start breeding earlier and produce more young. Taxonomic composition and nutritional content of prey were not correlated with any of the reproductive parameters, indicating that profitability rather than quality of food affects reproductive success.
Opleiding Informatica Impact of Hyperparameters on Model Performance in Split Learning

This thesis investigates how hyperparameters influence model performance in Split Learning, a decentralized machine learning method. The study conducts thorough testing on numerous datasets to assess the effects of various hyperparameter configurations. This study points out which hyperparameters are significant for model accuracy, and to which degree. Graphical representations provide additional insight on these complex interactions, underlining the significance of fine-tuning hyperparameters in Split Learning. Finally, given this privacy-focused learning framework, our study specifically pinpoints the importance of optimizing the hyperparameters: cut layer, batch size, partition alpha, and total number of clients. Experiments performed over 11 datasets revealed that, in most cases, the partition alpha hyperparameter emerged as the most significant. This offers actionable insights for effective Split Learning model tuning.
A new uncertainty estimation technique for multiple datasets and its
application to various precipitation products

Abstract. The uncertainty among climatological datasets can be characterized as the variance in space and time between various estimates of the same quantity. However, some of the current uncertainty estimates only evaluate variations in one single dimension (time or space) due to the limitation of estimation methodology as averaging variation is necessary for the other dimension. The influence on the uncertainty assessment of the ignorance of variations in one dimension is not well studied. This study introduces a new three-dimensional variance partitioning approach which avoids the averaging and provides an new uncertainty estimation (Ue) technique with consideration of both temporal and spatial variations. Comparisons of Ue to classic uncertainty estimations show that the classic metrics underestimate the uncertainty because of the averaging of variation in the time or space dimension, and Ue is around 20 % higher than classic estimations. The deviation between the new and classic metrics is higher for regions with strong spatial heterogeneity and where the spatial and temporal variations significantly differ. Decomposing of the new metric demonstrates that Ue is a comprehensive assessment of model uncertainty which has been included the model variations identified by the classic metrics. Multiple precipitation products of different types (gauge-based, merged products and GCMs) are used to better explain and understand the peculiarity of the new methodology. The new uncertainty estimation technique is flexible in its structure and particularly suitable for a comprehensive assessment of multiple datasets over a large regions within any given period.
A note on the annual wobble excitation due to the seasonal atmospheric loading on continents

Northern Eurasian continent has been regarded to contribute as the major source area, of which seasonal atmospheric pressure loading and unloading cycle leads to the annual wobble of the Earth. In the early days when reliable data of global coverage were not accessible, this dominance has remained as only a hypothesis. Nowadays, however, European Centre for Medium-Range Weather Forecasts and National Centers for Environmental Prediction produce reliable datasets, therefore, such unique feature has become clearer with quantitative evidences. Having both the Earth’s polar motion and the global atmospheric state known with unprecedented accuracy, we hereby identify and scrutinize whether Siberia and Manchuria dominate the annual polar motion of the Earth. Article history: Received 23 February 2018
Fresh Water River discharges as observed by SMOS in the Arabian Sea and the Bay of Bengal

The Bay of Bengal (BoB) and the Arabian Sea (AS) are two peculiar regions in the Indian Ocean exhibiting a wide range of Sea Surface Salinity (SSS) values. In the BoB, the strong summer monsoon rainfall and the continental run-offs into these semi-enclosed basins result in an intense dilution of the surface seawater in the northern part of the Bay, thereby inducing some of the lowest SSS water masses found in the tropical belt. In the AS, because of the intense variability associated with the monsoon cycle, water mass structure in the upper layers of the AS shows enormous variability in the space and time. As such, the role of the salinity in these regions is crucial in the ocean dynamics of these regions. After the continues provide a series of salinity data that could be used to monitor the SSS variations in these climatically relevant regions, provided that systematic errors due to land contamination are reduced. algorithms for SSS retrieval [2] improved the ﬁltering criteria and the mitigation of the systematic bias, providing coherent SSS retrievals close to the land masses. the SSS in 2-degree located at the of the main rivers in the BoB: Ganges-Brahmaputra, Irrawady, Mahanadi, Godovari; and the AS: We the SMOS salinity retrievals with in situ measurements. there is few available in situ data, we also the climatological SSS behavior derived from SMOS the by the World Ocean Atlas We compared the SMOS SSS data with historical data of discharges
Impact of Tom and Jerry Cartoon on Chemotherapy-Induced Anxiety

Background: Anxiety is a significant psychological challenge for patients newly diagnosed with cancer and scheduled for chemotherapy, potentially affecting their treatment adherence. This study aimed to evaluate the impact of watching the Tom and Jerry cartoon on reducing anxiety in individuals undergoing chemotherapy for the first time. Method: This pilot study employed a prospective, non-randomized, observational, two-arm, pre-and post-test design involving literate English-speaking patients (test group) and non-literate patients (control group) who were about to receive cytotoxic chemotherapy for their cancer. The test group viewed Tom and Jerry cartoons via video systems, whereas the control group was exposed to Kannada movies. An 11-point Likert scale featuring facial expressions assessed patients' anxiety levels three times: twenty minutes before, 30 minutes after, and following the chemotherapy session. Data analysis included frequency, percentage, chi-square tests for patient and treatment data, and repeated measures ANOVA with Bonferroni correction for anxiety scores at three-time intervals. A P-value of <0.05 was considered statistically significant. Results: The anxiety assessment outcomes indicated a significant reduction in anxiety levels among the test group patients who watched Tom and Jerry compared to the control group (P ≤ 0.001). Inter-group comparisons, analyzed using the Bonferroni test, revealed significant differences in anxiety reduction thirty minutes post-viewing (P = 0.001) and after the treatment (P = 0.04). Conclusion: To the authors' knowledge, this is the inaugural study demonstrating the efficacy of the Tom and Jerry cartoon in alleviating anxiety among cancer patients undergoing chemotherapy, suggesting broad clinical applicability.
Challenges of Automated Identiﬁcation of Access to Education and Training in Germany

: The German labor market relies heavily on vocational training, retraining, and continuing education. In order to match training seekers with training offers and to make the available data interoperable, we present a novel approach to automatically detect access to education and training in German training offers and advertisements and identify open research questions and areas for further research. In particular, we focus on (a) general education and school leaving certiﬁcates, (b) work experience, (c) previous apprenticeship, and (d) a list of skills provided by the German Federal Employment Agency. This novel approach combines several methods: First, we provide technical terms and classes of the education system that are used synonymously, combining different qualiﬁcations and adding obsolete terms. Second, we provide rule-based matching to identify the need for work experience or education. However, not all qualiﬁcation requirements can be matched due to incompatible data schemas or non-standardized requirements such as initial tests or interviews. Although there are several shortcomings, the presented approach shows promising results for two data sets
IOT ENABLED SMART ADVERTISEMENTS RECOMMENDER FOR SHOPPING MARTS

Product advertisement is very crucial in a successful business for several reasons, Advertisement helps increase the visibility of a product. With so many products available in the market, effective advertisement ensures that your product stands out and reaches a wider audience. Advertising a product is also a way to build and reinforce brand awareness. Consistent advertisement helps in creating a strong brand image in the minds of consumers, also the consumers will remember and choose their products when making a purchase. Continuous and strategic product advertisement is essential for the long-term success of a business. It helps in maintaining visibility, adapting to market changes, and staying relevant in the eyes of consumers. Product advertisement can indeed present various challenges, and addressing these challenges effectively is crucial for a successful marketing campaign that is 1) Small company budgets can restrict the range and effectiveness of products advertisement activities 2) Evolving consumer preferences and behaviours can make it difficult to predict how to effectively reach your audience 3) If your brand is not well-known, getting attention for your product can be a hurdle 4) Not fully understanding your target audience can lead to ineffective advertisement strategies. However, the Internet of Things (IoT) plays a significant role in product advertisement and overcome the above listed issues by providing innovative ways to enhance customer engagement, streamline marketing efforts, and gather valuable data for targeted campaigns. This Research investigate the real-time product advertisements using IoT devices and allowing different companies businesses to monitor the advertisement campaigns instantly and remotely for targeted products over different marts and different city locations. We achieved successful results on each remote location as well as targeted mart using ESP-8266 WiFi deployed on each product shelf. In this architecture the overall communication held over the http protocol using RestFul API and Arduino.
Density Optimization of Lithium Lanthanum Titanate Ceramics for Lightweight Lithium-Air Batteries

Abstract : The US Army has an urgent need for robust, high-energy-density, lightweight energy storage devices, and the lithium (Li)-air battery is one promising solution due to its high-energy density and lower weight compared with Li-ion batteries. The ceramic electrolytic membrane is currently the bottleneck for successful implementation of Li-air batteries. The density of the electrolytic membrane material, Li0.35La0. 57TiO3 (LLTO), must be optimized for LLTO to attain its maximum ionic conductivity because previous work done by the US Army Research Laboratory has demonstrated a relationship between density and conductivity in LLTO. The sintering behavior of LLTO requires comprehensive study to synthesize dense ceramics with high ionic conductivity. Therefore, the sintering temperature was varied from 1,100 C to 1,350 C, and the density of LLTO pellets was measured using 3 different techniques X-ray diffraction, optical microscopy, and scanning electron microscopy, which were also used to determine the phases, crystal structure, crystallite size, and microstructure of the sintered LLTO pellets. It was found that sintering at 1,200 C produced tetragonal LLTO ceramics with the highest density more than 95% of the theoretical density. Lower sintering temperatures yielded LLTO samples with lower densities and smaller crystallite sizes. Sintering at 1,300 C produced concave pellets with slight preferred crystallographic orientation.
Elevated stresses and creep rates beneath the brittle‐ductile transition caused by seismic faulting in the upper crust

[1] Large faults in the brittle upper continental crust are envisaged to terminate near the brittle-ductile transition zone, at a depth that is a complex function of material properties, temperature, stress, and strain rate. Here we investigate the effect of coseismic displacement along a fault in the upper crust on the adjacent middle and lower crust using a two-dimensional numerical model of faulting within an elastic-frictional-viscous rheology. Results demonstrate that a major displacement event can cause a dramatic increase in differential stress and creep strain rates in the crust near and below the lower termination of the fault, a transient deflection of the brittle-ductile transition zone, and a zone of elevated strain rates down to the base of the crust. The perturbation decays over timescales consistent with postseismic relaxation. The stresses and strain rates predicted in such models are in accord with those inferred from microstructural damage analysis of exhumed metamorphic rocks, indicating short-term deformation at decaying stress and strain rates.
Joint downlink user association and interference avoidance with a load balancing approach in backhaul-constrained HetNets

In heterogeneous networks (HetNets), different lower-power base stations are added in a typically unplanned manner to the well-planned macro-only network, bringing new challenges to the network functions. Small cells experience limited backhaul capacity since cost-effective backhaul is not easily accessible to them. This study focuses on the issue of user association in backhaul-constrained HetNets. It shows that it is necessary to associate users with cells using a load balancing approach in order to fully leverage the addition of small cells. The cell association needs to be done jointly with an interference management technique that protects offloaded users and those prone to harmful interference. After modeling the system and describing the interference model, the problem of cell and subband allocation is formulated. We first examine the problem in a time-sharing mode and present a centralized heuristic solution to the cell and subband allocation problem. This is accomplished by solving the convex problem using the gradual removal method. The importance of providing distributed algorithms for HetNets leads to the development of a new algorithm through the application of the dual decomposition method to a reformulated problem and the use of an admission control mechanism. In the achieved algorithm, all computations are performed locally, with each user and base station relying only on local information. This algorithm obtains near-optimal answers, as confirmed by the simulation results. Compared with conventional cell allocation methods, our distributed algorithm prevents intensive interference for all users and achieves better load balance between network tiers, resulting in improved network utility.
CD62E- and ROS-Responsive ETS Improves Cartilage Repair by Inhibiting Endothelial Cell Activation through OPA1-Mediated Mitochondrial Homeostasis

Background: In the environment of cartilage injury, the activation of vascular endothelial cell (VEC), marked with excessive CD62E and reactive oxygen species (ROS), can affect the formation of hyaluronic cartilage. Therefore, we developed a CD62E- and ROS-responsive drug delivery system using E-selectin binding peptide, Thioketal, and silk fibroin (ETS) to achieve targeted delivery and controlled release of Clematis triterpenoid saponins (CS) against activated VEC, and thus promote cartilage regeneration. Methods: We prepared and characterized ETS/CS and verified their CD62E- and ROS-responsive properties in vitro. We investigated the effect and underlying mechanism of ETS/CS on inhibiting VEC activation and promoting chondrogenic differentiation of bone marrow stromal cells (BMSCs). We also analyzed the effect of ETS/CS on suppressing the activated VEC-macrophage inflammatory cascade in vitro. Additionally, we constructed a rat knee cartilage defect model and administered ETS/CS combined with BMSC-containing hydrogels. We detected the cartilage differentiation, the level of VEC activation and macrophage in the new tissue, and synovial tissue. Results: ETS/CS was able to interact with VEC and inhibit VEC activation through the carried CS. Coculture experiments verified ETS/CS promoted chondrogenic differentiation of BMSCs by inhibiting the activated VEC-induced inflammatory cascade of macrophages via OPA1-mediated mitochondrial homeostasis. In the rat knee cartilage defect model, ETS/CS reduced VEC activation, migration, angiogenesis in new tissues, inhibited macrophage infiltration and inflammation, promoted chondrogenic differentiation of BMSCs in the defective areas. Conclusions: CD62E- and ROS-responsive ETS/CS promoted cartilage repair by inhibiting VEC activation and macrophage inflammation and promoting BMSC chondrogenesis. Therefore, it is a promising therapeutic strategy to promote articular cartilage repair.