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Abstract Lions and their prey are threatened across most of their range and especially in West and Central Africa. Prey availability influences carnivore densities, social structure, prey preference and home ranges, and changes in prey are important for carnivore management. Scarcity of large prey in many West and Central African ecosystems has been described as leading to a preference for hunting smaller prey in smaller groups. Here we investigated the changes in prey selection of lions in Zakouma National Park (Chad), a protected area in Central Africa that showed significant recovery in wildlife numbers, by collecting feeding data through observations of lions on kills during monitoring drives and GPS cluster points of lion collars. Compared to similar data collected prior to this significant recovery, lions preferred larger prey and fed in larger groups. Our results show that diet shifts due to prey losses can be reversed with restoration of prey populations thanks to improved management, and we speculate that this may be true across large carnivores and across regions.

IntroductionPrion diseases are deadly neurodegenerative disorders in both animals and humans, causing the destruction of neural tissue and inducing behavioral manifestations. Heat shock proteins (Hsps), act as molecular chaperones by supporting the appropriate folding of proteins and eliminating the misfolded proteins as well as playing a vital role in cell signaling transduction, cell cycle, and apoptosis control. SW02 is a potent activator of Hsp 70 kDa (Hsp70).MethodsIn the current study, the protective effects of SW02 against prion protein 106-126 (PrP106-126)-induced neurotoxicity in human neuroblastoma cells (SH-SY5Y) were investigated. In addition, the therapeutic effects of SW02 in ME7 scrapie-infected mice were evaluated.ResultsThe results showed that SW02 treatment significantly increased Hsp70 mRNA expression levels and Hsp70 ATPase activity (p < 0.01). SW02 also significantly inhibited cytotoxicity and apoptosis induced by PrP106-126 (p < 0.01) and promoted neurite extension. In vivo, intraperitoneal administration of SW02 did not show a statistically significant difference in survival time (p = 0.16); however, the SW02-treated group exhibited a longer survival time of 223.6 ± 6.0 days compared with the untreated control group survival time of 217.6 ± 5.4 days. In addition, SW02 reduced the PrPSc accumulation in ME7 scrapie-infected mice at 5 months post-injection (p < 0.05). A significant difference was not observed in GFAP expression, an astrocyte marker, between the treated and untreated groups.ConclusionIn conclusion, the potential therapeutic role of the Hsp70 activator SW02 was determined in the present study and may be a novel and effective drug to mitigate the pathologies of prion diseases and other neurodegenerative diseases. Further studies using a combination of two pharmacological activators of Hsp70 are required to maximize the effectiveness of each intervention.

Background: Quetiapine (QET) abuse has increased due to its anxiolytic and hedonic effects, necessitating protective adjunct treatments. Acacia saligna (A. saligna) flowers, used in traditional medicine, have potential health benefits. Aim: To investigate the protective role of A. saligna flower extract against QET-induced sexual toxicity, and to elucidate the possible underlying mechanisms through metabolomic and physiological studies. Methods: A. saligna extract was subjected to metabolite profiling via High-Resolution Ultra-Performance Liquid Chromatography-Mass Spectrometry (UPLC-ESI–qTOF-MS). Forty-eight adult male albino rats were assigned into six groups for 30 days. The intracavernosal pressure (ICP), semen, biochemical, hormonal, histological, genetic and Western blot (WB) analyses were determined. Results: A. saligna extract is rich in phenolic compounds, flavonoids, tannins, and unsaturated fatty acids. QET significantly decreased ICP and negatively affected semen parameters. A. saligna mitigated decreased sperm motility and ameliorated overexpressed proinflammatory genes in QET-55 group. A. saligna ameliorated the reduction of the antioxidant biomarkers, testosterone, luteinizing hormone (LH), and follicle-stimulating hormone (FSH), concurrent with downregulation of the nuclear factor kappa B (NF-κB) protein. A. saligna counteracted the disrupted testicular and prostatic structures revealed by histological examination. Conclusion: The extract from A. saligna, which contains a high concentration of antioxidants and anti-inflammatory chemicals, effectively mitigates sexual toxicity caused by QET. This study provided the first known explanation of the hypothesized processes behind the protective properties of A. saligna through biological, biochemical, and histological parameters. The results emphasize the potential of A. saligna as a safeguarding agent against drug-induced sexual toxicity.

Sarcasm is a complex linguistic phenomenon involving humor, criticism, or phrases that convey the opposite meaning, mask true feelings, and play pivotal roles in various aspects of communication. Therefore, identifying sarcasm is essential for sentiment analysis, social media monitoring, and customer service, as it enables a better understanding of public sentiment. Moreover, social media has become a primary platform for people to express their feelings and opinions and provide feedback to businesses and service providers. Misinterpreting sarcasm in customer feedback can lead to incorrect responses and actions. However, accurately detecting sarcasm is challenging because it depends on context, cultural factors, and inherent ambiguity. Despite the plenty of research and resources in Machine Learning (ML) for detecting sarcasm in English, including Deep Learning (DL) techniques, there is still a shortage of research in sarcasm detection in Arabic, particularly in DL methodologies and available sarcastic datasets. This paper constructed a new Arabic sarcastic corpus and fine-tuned three pre-trained Arabic transformer-based Language Models (LM) for Arabic sarcasm detection. We also proposed a hybrid DL approach for sarcasm detection that combines static and contextualized representations using pre-trained LM, such as Word2Vec word embeddings and Bidirectional Encoder Representations from Transformers (BERT) models pretrained on Arabic resources. The proposed enhanced hybrid deep learning approach outperforms state-of-the-art models by 8% on a shared benchmark dataset and achieves a 5% improvement in F1-score on another.

Incorporating distributed renewable energy sources into heating, power, and cooling systems is facilitating the drive toward intelligent building energy solutions. However, the inherent uncertainties associated with controllable renewable resources pose challenges for the thermo-economic scheduling of smart buildings. Nonetheless, the flexibility inherent in smart buildings can be leveraged through various market mechanisms. Hence, this research introduces a sustainable energy-building system driven by an autonomous solar/dish Stirling engine (SDSE) and wind turbine combined with a sophisticated control strategy and battery storage, which is designed to provide heat and power requirements. The proposed control strategy is also devised to optimize energy generation, ensuring prudent conservation and minimal waste, thereby amplifying overall efficiency and financial viability. The meticulous design of the system is accomplished through advanced MATLAB/Simulink® modeling. A thorough technical sensitivity analysis meticulously hones design parameters, revealing optimal operational thresholds. Simulation outcomes unveil a consistent Stirling engine (SE) efficiency, achieving a pinnacle of 35 %, while the SDSE attains over 28 %, respectively. The horizontal axis wind turbine, encompassing 100 kW and 500 kW modules, demonstrates power coefficients spanning from 0.18 to 0.09, with corresponding land area requirements ranging from 4.22 m2 to 21.10 m2, emphasizing the pivotal roles of module power and land area optimization. This paper also casts a spotlight on the environmental repercussions of the system, illustrating its potential to avert up to 30,000 kg of CO2 emissions per kWh/year. Moreover, the levelized cost of electricity ranges from 0.13 to 0.16 $/kWh, accompanied by an hourly cost that fluctuates between 3 $/h and 40 $/h, respectively. Conclusively, the developed modeling can be regarded as a significant stride in the realm of hybrid renewable energy systems, replacing the conventional photovoltaic/wind models with cutting-edge solar/wind configurations managed by a sophisticated control strategy and battery storage system.

Green synthesis of magnetic gamma alumina nanoparticles (MGAl-NPs) for heavy metal adsorption: A novel approach for wastewater treatment. This study investigates the potential of magnetically modified gamma alumina nanoparticles (MGAl-NPs) for removing copper (II) ions from aqueous solutions. The synthesis of MGAl-NPs involves the mixture of aluminum hydroxide and magnetic iron oxide, resulting in a composite material with both magnetic and adsorptive properties. The adsorption capacity of MGAl-NPs was found to be 34.48 mg/g at optimal conditions of pH 5.0, initial concentration of 50 mg/L, and adsorbent dosage of 0.1 g.The adsorption kinetics followed the pseudo-second-order model, indicating that chemisorption was the dominant mechanism. The Langmuir isotherm model best described the adsorption data with an R2 value of 0.995, suggesting that the adsorption occurred through monolayer coverage. The adsorption efficiency increased with decreasing pH, confirming that electrostatic attraction played a significant role in adsorption.Our findings demonstrate that the green synthesis of MGAl-NPs is an effective and eco-friendly method for removing heavy metals from wastewater. The magnetic properties of MGAl-NPs enable easy separation and recovery of the adsorbed heavy metals, making them a promising candidate for practical applications. This study provides valuable insights into developing sustainable adsorbents for water pollution remediation.

In response to the pandemic spread of COVID-19, universities all over the world have shifted to e-teaching. The Department of English, Mustaqbal University, College of Administration and Humanities, Saudi Arabia, used the Microsoft Teams App for teaching all courses in virtual classrooms. However, reading as a receptive skill, was difficult to teach remotely because it was not so interactive for a teacher to know whether the students have performed the assigned tasks or not. Therefore, there was a need to search for a strategy that could be integrated while using technology to help EFL students improve their reading comprehension. This strategy was think-aloud strategy which could potentially make use of participants' mental data while interpreting and responding to a text. During reading, this strategy is capable of making the mental processing covert showing direct evidence of internal reading processes. A quasi-experimental design with an experimental group and a control group was used to determine the effectiveness of this think-aloud strategy in improving the reading comprehension of 72 EFL female students majoring in English, Business, and Law. Moreover, the study also explored the instructors' and the students' perceptions towards this strategy. Results indicated that all participants viewed the think-aloud strategy very positively. The study recommends adopting a highly customized think-aloud strategy to facilitate readers' comprehension.