Miracle-dz/UniFAR-FTUs huggingface dataset:
Thiophene-based N-phenyl pyrazolines: Synthesis, anticancer activity, molecular docking and ADME study

This investigation aimed to synthesize and evaluate the anticancer potential of thiophene-based N-phenyl pyrazoline derivatives containing methoxy groups using both in vitro and in silico assays. The N-phenyl pyrazolines were synthesized by reacting phenylhydrazine with chalcone derivatives. Using the 3-(4,5-dimethyl thiazole-2-yl)- 2,5-diphenyltetrazolium bromide (MTT) assay, we assessed the anticancer activity of the compounds that were synthesized in vitro against four human cancer cell lines (4T1 and T47D for breast cancer, HeLa for cervical cancer, and WiDr for colorectal cancer), as well as a normal cell line (Vero). The in silico evaluation was carried out through molecular docking and absorption, distribution, metabolism, and excretion (ADME) prediction. Among the synthesized pyrazolines, pyrazoline 2 was revealed as the most active anticancer against 4T1, HeLa, and WiDr cancer cell lines, with IC50 values of 9.09, 9.27, and 0.25 μg/ml, respectively. Furthermore, pyrazoline 2 showed high selectivity with selectivity index values of more than 6 toward all tested cancer cell lines. In silico evaluation via molecular docking against epidermal growth factor receptor revealed that pyrazoline 2 had the lowest binding energy with a value of 8.8 kcal/mol, which was consistent with experimental data. The ADME study using the pkCSM webtool indicated that the N-phenyl pyrazoline derivatives had good pharmacokinetic properties. Therefore, the results of this study suggest that thiophene-based N-phenyl pyrazoline derivatives containing methoxy groups, especially pyrazoline 2, could be potential anticancer agents.
Effects of Ecosystem Services Provided by Birds and Bats in Smallholder Cacao Plantations of Central Sulawesi

UNIVERSITAS TADULAKO Cacao agroforestry systems are common in Indonesia, but differences in local management affect biodiversity and related ecosystem services. Importantly, birds and bats alike strongly contribute to biological pest control in cacao, thereby promoting yields and sustainable farming. Our findings from many years of ecological field research, in close collaboration with Indonesian cacao farmers, are summarized and discussed in this bilingual book, written in both English and Indonesian. It is designed to communicate scientific information as well as to facilitate transdisciplinary discussions and more biodiversity-friendly management in tropical agroforestry systems.
Riverine inorganic carbon flux and rate of biological uptake in the Mississippi River plume

Inorganic carbon parameters were studied for the first time in the Mississippi River estuary and plume. Area‐integrated biological uptake rates (1.5–3 gC m−2 d−1) derived from riverine total dissolved inorganic carbon (DIC) and total alkalinity (TAlk) fluxes as well as that based on the consumptions of DIC and TAlk in the plume are comparable to earlier results of 14C incubation in individual water samples. This rate is among the highest in the estuaries and plumes of the world's largest rivers. Fluvial DIC flux (13.5 × 1012 gC yr−1) indicates a 16% increase in the inorganic carbon flux over the earlier estimate and perhaps an increase in the weathering rate in the Mississippi River basin over the past four decades.
In Vitro Microevolution and Co-Selection Assessment of Amoxicillin and Cefotaxime Impact on Escherichia coli Resistance Development

The global spread of antimicrobial resistance has become a prominent issue in both veterinary and public health in the 21st century. The extensive use of amoxicillin, a beta-lactam antibiotic, and consequent resistance development are particularly alarming in food-producing animals, with a focus on the swine and poultry sectors. Another beta-lactam, cefotaxime, is widely utilized in human medicine, where the escalating resistance to third- and fourth-generation cephalosporins is a major concern. The aim of this study was to simulate the development of phenotypic and genotypic resistance to beta-lactam antibiotics, focusing on amoxicillin and cefotaxime. The investigation of the minimal inhibitory concentrations (MIC) of antibiotics was performed at 1×, 10×, 100×, and 1000× concentrations using the modified microbial evolution and growth arena (MEGA-plate) method. Our results indicate that amoxicillin significantly increased the MIC values of several tested antibiotics, except for oxytetracycline and florfenicol. In the case of cefotaxime, this increase was observed in all classes. A total of 44 antimicrobial resistance genes were identified in all samples. Chromosomal point mutations, particularly concerning cefotaxime, revealed numerous complex mutations, deletions, insertions, and single nucleotide polymorphisms (SNPs) that were not experienced in the case of amoxicillin. The findings suggest that, regarding amoxicillin, the point mutation of the acrB gene could explain the observed MIC value increases due to the heightened activity of the acrAB-tolC efflux pump system. However, under the influence of cefotaxime, more intricate processes occurred, including complex amino acid substitutions in the ampC gene promoter region, increased enzyme production induced by amino acid substitutions and SNPs, as well as mutations in the acrR and robA repressor genes that heightened the activity of the acrAB-tolC efflux pump system. These changes may contribute to the significant MIC increases observed for all tested antibiotics. The results underscore the importance of understanding cross-resistance development between individual drugs when choosing clinical alternative drugs. The point mutations in the mdtB and emrR genes may also contribute to the increased activity of the mdtABC-tolC and emrAB-tolC pump systems against all tested antibiotics. The exceptionally high mutation rate induced by cephalosporins justifies further investigations to clarify the exact mechanism behind.
Revisión bibliográfica de las iniciativas de protección de ríos y los diferentes enfoques implementados a nivel mundial

Rapid population growth, combined with inadequate and sometimes nonexistent territorial planning exert enormous pressure on river ecosystems in the world; contamination of water sources by domestic waste and industry, changes in the flow, uncontrolled fishing or diverting water for farmland irrigation, often compromise the ecological integrity of these ecosystems and their ability to provide ecosystem services (SE) to society. Current efforts to protect rivers have achieved important results in the protection of these ecosystems, however, the need to strengthen the legislative framework under which protection is sought is evident. This article presents a literature review of efforts to protect rivers in the world; seeks to contextualize the reader about different approaches on river ecosystems management, and emphasizes the need to strengthen environmental legislation to achieve lasting commitments and safeguard these ecosystems and the cultural, spiritual and social values associated .
Uremic Toxins and Inflammation: Metabolic Pathways Affected in Non-Dialysis-Dependent Stage 5 Chronic Kidney Disease

Chronic kidney disease (CKD) affects approximately 12% of the global population, posing a significant health threat. Inflammation plays a crucial role in the uremic phenotype of non-dialysis-dependent (NDD) stage 5 CKD, contributing to elevated cardiovascular and overall mortality in affected individuals. This study aimed to explore novel metabolic pathways in this population using semi-targeted metabolomics, which allowed us to quantify numerous metabolites with known identities before data acquisition through an in-house polar compound library. In a prospective observational design with 50 patients, blood samples collected before the initial hemodialysis session underwent liquid chromatography and high-resolution mass spectrometer analysis. Univariate (Mann–Whitney test) and multivariate (logistic regression with LASSO regularization) methods identified metabolomic variables associated with inflammation. Notably, adenosine-5′-phosphosulfate (APS), dimethylglycine, pyruvate, lactate, and 2-ketobutyric acid exhibited significant differences in the presence of inflammation. Cholic acid, homogentisic acid, and 2-phenylpropionic acid displayed opposing patterns. Multivariate analysis indicated increased inflammation risk with certain metabolites (N-Butyrylglycine, dimethylglycine, 2-Oxoisopentanoic acid, and pyruvate), while others (homogentisic acid, 2-Phenylpropionic acid, and 2-Methylglutaric acid) suggested decreased probability. These findings unveil potential inflammation-associated biomarkers related to defective mitochondrial fatty acid beta oxidation and branched-chain amino acid breakdown in NDD stage 5 CKD, shedding light on cellular energy production and offering insights for further clinical validation.
A Hybrid Framework Leveraging Whale Optimization and Deep Learning With Trust-Index for Attack Identification in IoT Networks

The rise of smart cities, smart homes, and smart health powered by the Internet of Things (IoT) presents significant challenges in design, deployment, and security. The seamless data processing across a complex network of interconnected devices in unprotected conditions makes it vulnerable to potential breaches, underscoring the need for robust security at various levels of the network. Traditional security methods based on statistics often struggle to comprehend data patterns and provide the desired level of security. This work proposes a novel hybrid framework that combines Whale Optimization and Deep Learning with a trust-index to identify malicious nodes engaging in various attacks such as DoS, DDoS, Drop attack, and Tamper Attacks, thus enhancing IoT node security. The developed framework first calculates a trust-index score for IoT nodes based on drop attack, tamper attack, replay attack, and multiple-max attack. Subsequently, it utilizes the trust index score in the Optimized Neural Network model to effectively identify the malicious IoT node. The neural network optimization is achieved through a fitness function that determines optimal weights using the Whale Optimization Algorithm. The proposed framework has been tested across varying network sizes, comprising 100, 500, and 1000 nodes. The resulting outcomes were evaluated against benchmark security methods such as Logical regression, Random Forest, Support Vector Machine, Bayesian models, ANN, Elephant herding optimization, and Lion algorithm using metrics like specificity, sensitivity, accuracy, precision, False Positive Rate, False Negative Rate, False Discovery Rate, Error, F1 score, Matthews Correlation Coefficient, and Negative Predictive Value. The results reveal a notable enhancement in accuracy (26.63%, 13.04%, 17.78%, 30.52%, 22.45%, 4.26%, and 2.24%) for a 100-node network when compared to the benchmark security methods. Furthermore, the proposed framework consistently demonstrates strong performance even when applied to larger IoT networks with a higher node count.
GEOINFORMATION MAPPING OF THE DYNAMICS OF THE FOREST FORMATION COMPOSITION AT THE STOLBY RESTRICTED NATURE RESERVES

The results of geoinformation mapping of the forests formational composition at the Stolby territory based on the forest inventory data between 1948 and 1977 are presented. The GIS model of the Stolby Reserve consists of many different and diverse long-term environmental monitoring data. The vectorization of the forest inventory plan since 1948 has been performed. The methods of adjustment of the GIS model of the reserve to a unified cartographic basis are described. The editing has been done in the software ESRI ArcGIS by using the built-in tool "Spatial Adjustment". For the first time, the changes in the forests formational composition have been determined on the basis of the geoinformation analysis. To evaluate the changes, the two different sets of data, which are similar semantically and describing the reserve territory at two stages of vegetation developing, have been used. The definition of the resulting changes was made by means of the overlay functions built into the GIS. The dynamic of forests formation composition is considered within the bounds of the areas separated within the mountain landscape based on natural features – geology, lithology, climate – which are reflected in the pattern of the vegetation. This allowed us to see the differentiation of the process associated with the restoration and development of the forest cover at the reserve territory. The key role of the altitudinal zonation of the mountain landscape is revealed. The terrains can be separated into two groups: subtaiga pine-larch forests (on the slopes) and mountain taiga larchsprucesiberian pine-fir (on the watersheds and shady slopes). The results of the analysis visually represented in two variants – tabular data and a set of analytical maps have a practical importance.
Impact of Length of Dataset on Streamflow Calibration Parameters and Performance of APEX Model

Due to resource constraints, long‐term monitoring data for calibration and validation of hydrologic and water quality models are rare. As a result, most models are calibrated and, if possible, validated using limited measured data. However, little research has been done to determine the impact of length of available calibration data on model parameterization and performance. The main objective of this study was to evaluate the impact of length of calibration data (LCD) on parameterization and performance of the Agricultural Policy Environmental eXtender model for predicting daily, monthly, and annual streamflow. Long‐term (1984‐2015) measured daily streamflow data from Rock Creek watershed, an agricultural watershed in northern Ohio, were used for this study. Data were divided into five Short (5‐year), two Medium (15‐year), and one Long (25‐year) streamflow calibration data scenarios. All LCD scenarios were calibrated and validated at three time steps: daily, monthly, and annual. Results showed LCD affected the ability of the model to accurately capture temporal variability in simulated streamflow. However, overall average streamflow, water budgets, and crop yields were simulated reasonably well for all LCD scenarios.
An Overview of OpenAI's Sora and Its Potential for Physics Engine Free Games and Virtual Reality

Sora, OpenAI's latest text-to-video model, is particularly skilled at understanding the physical world, and all of the content it generates mostly consistent with the laws of physics. This indicates that Sora already has the beginnings of a world model and has the potential to become an excellent physics engine in the near future. This paper analyses and explains in detail the potential applications of Sora in physics engines and virtual reality. In addition, its advantages and disadvantages over traditional physics engines are compared based on its unique behavioural characteristics. Finally, it looks forward to the application of Sora in other fields.
Advanced Millimeter-Wave Radar System for Real-Time Multiple Human Tracking and Fall Detection

This study explores an indoor system for tracking multiple humans and detecting falls, employing three Millimeter-Wave radars from Texas Instruments placed on x-y-z surfaces. Compared to wearables and camera methods, Millimeter-Wave radar is not plagued by mobility inconvenience, lighting conditions, or privacy issues. We establish a real-time framework to integrate signals received from these radars, allowing us to track the position and body status of human targets non-intrusively. To ensure the overall accuracy of our system, we conduct an initial evaluation of radar characteristics, covering aspects such as resolution, interference between radars, and coverage area. Additionally, we introduce innovative strategies, including Dynamic DBSCAN clustering based on signal energy levels, a probability matrix for enhanced target tracking, target status prediction for fall detection, and a feedback loop for noise reduction. We conduct an extensive evaluation using over 300 minutes of data, which equates to approximately 360,000 frames. Our prototype system exhibits remarkable performance, achieving a precision of 98.9% for tracking a single target and 96.5% and 94.0% for tracking two and three targets in human tracking scenarios, respectively. Moreover, in the field of human fall detection, the system demonstrates a high accuracy of 98.2%, underscoring its effectiveness in distinguishing falls from other statuses.
CSCNet: Class-Specified Cascaded Network for Compositional Zero-Shot Learning

Attribute and object (A-O) disentanglement is a fundamental and critical problem for Compositional Zero-shot Learning (CZSL), whose aim is to recognize novel A-O compositions based on foregone knowledge. Existing methods based on disentangled representation learning lose sight of the contextual dependency between the A-O primitive pairs. Inspired by this, we propose a novel A-O disentangled framework for CZSL, namely Class-specified Cascaded Network (CSCNet). The key insight is to firstly classify one primitive and then specifies the predicted class as a priori for guiding another primitive recognition in a cascaded fashion. To this end, CSCNet constructs Attribute-to-Object and Object-to-Attribute cascaded branches, in addition to a composition branch modeling the two primitives as a whole. Notably, we devise a parametric classifier (ParamCls) to improve the matching between visual and semantic embeddings. By improving the A-O disentanglement, our framework achieves superior results than previous competitive methods.
XYLITOL PRODUCTION BY Candida guilliermondiiFROM FERMENTATION OF WASTE OIL PALM

1 Ingeniera de Alimentos, Candidata Doctorado en Biotecnología, Facultad de Ciencias, Escuela de Química. Universidad Nacional de Colombia, calle 59a # 63-20, Medellín, Antioquia, e-mail: jkmanjarresp@unal.edu. co; 2 Ingeniero Químico, Ph.D, Profesor Titular, Facultad de Ciencias, Escuela de Química. Universidad Nacional de Colombia, calle 59a # 63-20, Medellín, Antioquia, e-mail: marioari@unal.edu. co; 3 Biotecnóloga, Facultad Ciencias de la Salud, Institución Universitaria Colegio Mayor de Antioquia, Carrera 78 # 65-46 Medellín, Antioquia, e-mail: yuly.molina@colmayor.edu. co; 4 Ingeniero Químico, MSc, Facultad de Ciencias Agrarias, Departamento de Ingeniería Agrícola y Alimentos, calle 59a # 63-20. Universidad Nacional de Colombia, Medellín, Antioquia, e-mail: labcca_med@unal.edu. co; 5 Ingeniero Químico, Ph.D, Profesor Asociado, Facultad de Ciencias Agrarias, Departamento de Ingeniería Agrícola y Alimentos, calle 59a # 63-20. Universidad Nacional de Colombia, Medellín, Antioquia, e-mail: edrodriguezs@unal.edu. co
EarthLoc: Astronaut Photography Localization by Indexing Earth from Space

Astronaut photography, spanning six decades of human spaceflight, presents a unique Earth observations dataset with immense value for both scientific research and disaster response. Despite its significance, accurately localizing the geographical extent of these images, crucial for effective utilization, poses substantial challenges. Current manual localization efforts are time-consuming, motivating the need for automated solutions. We propose a novel approach - leveraging image retrieval - to address this challenge efficiently. We introduce innovative training techniques, including Year-Wise Data Augmentation and a Neutral-Aware Multi-Similarity Loss, which contribute to the development of a high-performance model, EarthLoc. We develop six evaluation datasets and perform a comprehensive benchmark comparing EarthLoc to existing methods, showcasing its superior efficiency and accuracy. Our approach marks a significant advancement in automating the localization of astronaut photography, which will help bridge a critical gap in Earth observations data. Code and datasets are available at https://github.com/gmberton/EarthLoc
Health risk assessment along wastewater recovery and reuse systems in Kampala, Uganda and Hanoi, Vietnam

Reuse of wastewater in agriculture is a common feature in the developing world. While this strategy might contribute to the livelihood of farming communities, there are health risks associated with the management and reuse of wastewater and faecal sludge. We visualise here an assessment of health risks along the major wastewater channel in Kampala, Uganda. The visualization brings to bear the context of wastewater reuse activities in the Nakivubo wetlands and emphasises interconnections to disease transmission pathways. The contextual features are complemented with findings from environmental sampling and a cross-sectional epidemiological survey in selected exposure groups. Our documentation can serve as a case study for a step-by-step implementation of risk assessment and management as described in the World Health Organization’s 2006 guidelines for the safe use of wastewater, greywater and excreta in light of the forthcoming sanitation safety planning approach.
Nanoparticles alleviate non-alcoholic steatohepatitis via ER stress sensor-mediated intestinal barrier damage and gut dysbiosis

Introduction The gut microbiota plays an important role in the development of non-alcoholic steatohepatitis (NASH), but the underlying mechanism is unclear. It has been found that the transcription factor XBP1s plays an important role in regulating inflammation and lipid metabolism and maintaining the integrity of intestinal barrier. However, whether XBP1s modulates the development of NASH by regulating the integrity of the intestinal barrier and altering the composition of the gut microbiota remains unknown. Methods Mice fed with a fat-, fructose-, cholesterol-rich (FFC) diet for 24 weeks successfully established the NASH model, as demonstrated by significant hepatic steatosis, inflammation, hepatocyte injury and fibrosis. The profile of gut microbiota dynamically changed with the different stages of NAFLD via 16S rDNA sequencing the feces from mice fed with FFC diet for 0, 12, or 24 weeks or NASH mice treated with siRNA-loaded folic acid-modified TPGS (hereafter named FT@XBP1). Results NASH mice had significantly higher abundance of Firmicutes, Blautia and Bacteroides, and lower abundance of Bifidobacterium and GCA-900066575. FT@XBP1 supplementation had a significantly attenuated effect on FFC diet-induced weight gain, hepatic fat accumulation, dyslipidemia, inflammatory cytokines, ER stress and fibrosis. In particularly, FT@XBP1 modulates the composition of the intestinal flora; for example, NASH mice demonstrated higher abundance of Blautia and Bacteroides, and lower abundance of Actinobacteriota, Muribaculaceae and Bifidobacterium, which were partially restored by FT@XBP1 treatment. Mechanistically, FT@XBP1 increased the expression of ZO-1 in the intestine and had the potential to restore intestinal barrier integrity and improve antimicrobial defense to alleviate enterogenic endotoxemia and activation of inflammatory signaling pathways. Discussion Regulation of the key transcription factor XBP1s can partially restore the intestinal microbiota structure, maintain the integrity of intestinal mucosal barrier, and prevent the progression of NASH, providing new evidence for treating NASH.