Miracle-dz/UniFAR-FTUs huggingface dataset:
External Nutrient Load and Determination of the Trophic Status of Lake Ziway

Lake Ziway is shallow freshwater located in Northern part of Ethiopian Rift Valley. Expansions of the flower industry, fisheries, intensive agricultural activities, fast population growth lead to deterioration of water quality and depletion of aquatic biota. The objectives of the present study are to evaluate the spatial and temporal variations in the external nutrient load and determine the trophic status of Lake Ziway in 2014 and 2015. The nutrients and Chlorophyll-a were measured according to the standard procedures outlined in APHA, 1999. From the result Ketar and Meki Rivers catchment showed the major sources of external nutrient loads to the lake ecosystem. The mean external soluble reactive phosphorus (SRP), total phosphorus (TP), total inorganic nitrogen (TIN) and total nitrogen (TN) loads to Lake Ziway were 230, 2772, 4925 and 24016 kg day-1, respectively. A general trend which was expected that the nutrient loads would be much higher in rainy season than in dry season. The mean concentrations of trophic state variables for TN, TP and Chla were 6700, 212 and 42 mg L-1, respectively. The mean values of TSI-TP, TSI-Chl-a, TSI-TN and TSI-SD were 79, 66, 81 and 83, respectively and the overall evaluation of Carlson Trophic State Index (CTSI) of Lake Ziway was 77. Therefore, the lake is under eutrophic condition. The mean values of TN: TP ratio was 48 which were very high. The trophic state index determined with chlorophyll-a showed lower value than those determined with all trophic state indices values of TN, TP, and SD which indicated that non-algal turbidity affected light attenuation for algal growth. This suggested that phosphorus was the limiting nutrient in Lake Ziway. Due to its importance as being the lake is an intensive agricultural site, management solutions must be urgently developed in order to avoid the destruction of the lake.
Identification of Potential Targets of Stress Cardiomyopathy by a Machine Learning Algorithm

Background: Stress cardiomyopathy (SCM) is a reversible, self-limiting condition that manifests as left ventricular insufficiency. The incidence of stress cardiomyopathy has increased because of increasing mental and social stress, but the exact pathophysiological mechanisms remain unclear. Methods: To elucidate the critical molecules in the pathogenesis of SCM and the functional changes that they mediate, we downloaded data for a healthy control group and stress cardiomyopathy (SCM) group from the Gene Expression Omnibus database, performed differential analysis, and analyzed the results of GO and KEGG enrichment analysis to describe SCM-associated genes and functions. Lasso, random forest, SVM-RFM, and Friends analysis were used to screen hub genes; CIBERSORT and MCPcounter were used to explore the relationship between SCM and immunity; and an animal model of SCM was constructed to conduct bidirectional verification of the obtained results. Results: In total, 21 samples (6 healthy, 15 SCM) were used in this study. Overall, 39 DEGs (absolute fold change ≥ 1; P < 0.05), including 23 upregulated and 16 downregulated genes in SCM, were extracted. Three common hub genes (PLAT, SEMA6B, and CRP) were finally screened. We further confirmed that functional changes in SCM were concentrated in immunity and coagulation functions. Conclusion: Three key genes (PLAT, SEMA6B, and CRP) in SCM were identified by machine learning, and the major functional changes leading to SCM, and relationships of SCM with immunity, were identified.
Formulation Development of Solid Self-Nanoemulsifying Drug Delivery Systems of Quetiapine Fumarate via Hot-Melt Extrusion Technology: Optimization Using Central Composite Design

Quetiapine fumarate (QTF) was approved for the treatment of schizophrenia and acute manic episodes. QTF can also be used as an adjunctive treatment for major depressive disorders. QTF oral bioavailability is limited due to its poor aqueous solubility and pre-systemic metabolism. The objective of the current investigation was the formulation development and manufacturing of solid self-nanoemulsifying drug delivery system (S-SNEDDS) formulation through a single-step continuous hot-melt extrusion (HME) process to address these drawbacks. In this study, Capmul® MCM, Gelucire® 48/16, and propylene glycol were selected as oil, surfactant, and co-surfactant, respectively, for the preparation of S-SNEDDS. Soluplus® and Klucel™ EF (1:1) were selected as the solid carrier. Response surface methodology in the form of central composite design (CCD) was utilized in the current experimental design to develop the S-SNEDDS formulations via a continuous HME technology. The developed formulations were evaluated for self-emulsifying properties, particle size distribution, thermal behavior, crystallinity, morphology, physicochemical incompatibility, accelerated stability, and in vitro drug release studies. The globule size and emulsification time of the optimized SNEDDS formulation was 92.27 ± 3.4 nm and 3.4 ± 3.38 min. The differential scanning calorimetry (DSC) and powder X-ray diffraction (PXRD) studies revealed the amorphous nature of the drug within the formulation. There were no drug-excipient incompatibilities observed following the Fourier transform infrared (FTIR) spectroscopy. The optimized formulation showed an extended-release profile for 24 h. The optimized formulation was stable for three months (last time-point tested) at 40 °C/75% RH. Therefore, the developed S-SNEDDS formulation could be an effective oral delivery platform for QTF and could lead to better therapeutic outcomes.
Minor impact of probiotic bacteria and egg white on Tenebrio molitor growth, microbial composition, and pathogen infection

The industrial rearing of the yellow mealworm (Tenebrio molitor) for feed and food purposes on agricultural by-products may expose larvae and adults to entomopathogens used as biocontrol agents in crop production. Bacterial spores/toxins or fungal conidia from species such as Bacillus thuringiensis or Metarhizium brunneum could affect the survival and growth of insects. Therefore, the aim of this study was to investigate the potential benefits of a wheat bran diet supplemented with probiotic bacteria and dried egg white on larval development and survival and its effects on the gut microbiome composition. Two probiotic bacterial species, Pediococcus pentosaceus KVL B19-01 and Lactiplantibacillus plantarum WJB, were added to wheat bran feed with and without dried egg white, as an additional protein source, directly from neonate larval hatching until reaching a body mass of 20 mg. Subsequently, larvae from the various diets were exposed for 72 h to B. thuringiensis, M. brunneum, or their combination. Larval survival and growth were recorded for 14 days, and the bacterial microbiota composition was analyzed using 16S rDNA sequencing prior to pathogen exposure and on days 3 and 11 after inoculation with the pathogens. The results showed increased survival for T. molitor larvae reared on feed supplemented with P. pentosaceus in the case of co-infection. Larval growth was also impacted in the co-infection treatment. No significant impact of egg white or of P. pentosaceus on larval growth was recorded, while the addition of Lb. plantarum resulted in a minor increase in individual mass gain compared with infected larvae without the latter probiotic. On day 14, B. thuringiensis was no longer detected and the overall bacterial community composition of the larvae was similar in all treatments. On the other hand, the relative operational taxonomic unit (OTU) abundance was dependent on day, diet, and probiotic. Interestingly, P. pentosaceus was present throughout the experiments, while Lb. plantarum was not found at a detectable level, although its transient presence slightly improved larval performance. Overall, this study confirms the potential benefits of some probiotics during the development of T. molitor while underlining the complexity of the relationship between the host and its microbiome.
Climate change? Archaeology and Anthropocene

As we pass into an age of the Anthropocene, archaeologists, as scholars of other disciplines, are driven to consider how this physical and ideological climate change affects our craft, or how archaeology can contribute with knowledge and insight of significance in a shifting world. Basing its arguments on research conducted on marine debris and drift beaches in northern Norway and Iceland, the aim of this article is to imagine what kind of alternative ways of doing and thinking archaeology the current climate is calling for. With reference to this material, which conspicuously manifests both obstacles and promises for an ‘Anthropocene archaeology’, the article will question the worth of some perspectives traditionally considered essential to our discipline, while simultaneously building on confidence in a sincerely archaeological imagination.
Bringing Sustainable Development to the Corporate Level: Boards’ Cognitive Biases towards ESG and Relevant Debiasing Interventions

The proposed research is an attempt to view how internationally recognised principles of sustainable development can be implemented at the corporate level, in particular, how the company’s board of directors should respond to climate and environmental challenges. Specifically, the paper examines the problem of reducing board members' repeated cognitive biases towards such a mainstream governance concept as ESG (Environmental, Social and Corporate Governance). The research focuses on the five most well-known cognitive biases of a board towards ESG, often encountered and described conceptually in behavioural literature: over-optimism, status quo effect, confirmation bias, hyperbolic discounting, and groupthink. Subsequently, the paper draws recommendations for overcoming or mitigating these biases by introducing and describing several debiasing interventions and quality control tools. These tools are conditionally divided into three groups: changing incentives, optimising choice architecture, and debiasing training. The author believes that incorporating behavioural insights into an analysis of the board’s ESG commitment would bring a fresh perspective to this research area, which now is typically dominated by a rational choice framework. The paper combines theoretical analysis of the existing literature in law, behavioural economics and psychology with qualitative research application based on data obtained from first-hand observations, board empirical studies, questionnaires, and focus groups.
The Stability of pH and the Concentrations of Iron and Manganese in Acid Mine Drainage Following Coal Fly Ash and Empty Fruit Bunch of Oil Palm Treatments

AMD (acid mine drainage) with low pH and high content of heavy metals is a serious environmental problem in mining activities. Proper AMD management is crucial to ensure compliance with the standards of environmental quality before allowing the flow to the public water system. The objective of this study was to assess the efficacy of increasing pH and decreasing Fe alongside the concentrations of Mn in AMD with the addition of coal fly ash (CFA) and empty fruit bunch of oil palm (EFBOP). A total of four treatments, namely: (1) control (soil without treatment), (2) soil + EFBOP, (3) soil + CFA, and (4) soil + EFBOP + CFA were tested for the ability to improve AMD quality in a batch reactor experiment for 90 days. Weekly observations were carried out for pH, Fe, and Mn concentrations during the experiment, where part of AMD in the reactor was drained and replaced with fresh ones. The results showed that single treatment of EFBOP or CFA caused a pH increase at 2.8 to 5.1–5.4 and 6.3–6.8, respectively. Furthermore, a greater increase occurred from 2.8 to 7.0–7.8 when EFBOP was combined with CFA application. This combination also showed a greater reduction in the concentrations of Fe and Mn compared to the single treatment of EFBOP or CFA. The increase in pH and decrease in Fe alongside the Mn concentrations began in the 3rd week, and this effect was stable during the 90 days of the experiment. The results underscore the potential of EFBOP and CFA as agricultural and industrial wastes in long-term AMD management.
Water chemical relations and water table of North Hungarian mires

The water chemical and physical features (pH, conductivity, Na+, K+, Ca2+, Mg2+ concentration, water table) of fi ve mires (Nyirjes lake at Sirok, Kis-Mohos and Nagy-Mohos at Kelemer, Babtava and Nyires lake in the Bereg region) were investigated in North Hungary. Th e description of water chemical relations of these mires is new result. Th e pH of these mires was low, the means were between 3.29 and 5.92. Th e mean corrected conductivity ranged between 11.08 and 176.6 μScm–1. Th e concentrations of measured cations were high, e.g. the mean Na+ concentration was between 3.47 and 12.71 mgdm–3 and the mean Ca2+ concentration was between 7.96 and 10.21 mgdm–3. Th e mires of Sirok, Kelemer, and the Bereg region are separated on the basis of most variables. Th e mires of Bereg are less acidic and more rich in cations than the mires in Kelemer. Th e mire of Sirok (Nyirjes lake) was more similar to those of Kelemer than to the mires of the Bereg region. Th ere was no clear trend in sampling periods, but the most diverse group structure was found in September.
Deep Interpolation of Remote Sensing Land Surface Temperature Data with Partial Convolutions

Land Surface Temperature (LST) is an important resource for a variety of tasks. The data are mostly free of charge and combine high spatial and temporal resolution with reliable data collection over a historical timeframe. When remote sensing is used to provide LST data, such as the MODA11 product using information from the MODIS sensors attached to NASA satellites, data acquisition can be hindered by clouds or cloud shadows, occluding the sensors’ view on different areas of the world. This makes it difficult to take full advantage of the high resolution of the data. A common solution to interpolating LST data is statistical interpolation methods, such as fitting polynomials or thin plate spine interpolation. These methods have difficulties in incorporating additional knowledge about the research area and learning local dependencies that can help with the interpolation process. We propose a novel approach to interpolating remote sensing LST data in a fixed research area considering local ground-site air temperature measurements. The two-step approach consists of learning the LST from air temperature measurements, where the ground-site weather stations are located, and interpolating the remaining missing values with partial convolutions within a U-Net deep learning architecture. Our approach improves the interpolation of LST for our research area by 44% in terms of RMSE, when compared to state-of-the-art statistical methods. Due to the use of air temperature, we can provide coverage of 100%, even when no valid LST measurements were available. The resulting gapless coverage of high resolution LST data will help unlock the full potential of remote sensing LST data.
UNC5C: Novel Gene Associated with Psychiatric Disorders Impacts Dysregulation of Axon Guidance Pathways

The UNC-5 family of netrin receptor genes, predominantly expressed in brain tissues, plays a pivotal role in various neuronal processes. Mutations in genes involved in axon development contribute to a wide spectrum of human diseases, including developmental, neuropsychiatric, and neurodegenerative disorders. The NTN1/DCC signaling pathway, interacting with UNC5C, plays a crucial role in central nervous system axon guidance and has been associated with psychiatric disorders during adolescence in humans. Whole-exome sequencing analysis unveiled two compound heterozygous causative mutations within the UNC5C gene in a patient diagnosed with psychiatric disorders. In silico analysis demonstrated that neither of the observed variants affected the allosteric linkage between UNC5C and NTN1. In fact, these mutations are located within crucial cytoplasmic domains, specifically ZU5 and the region required for the netrin-mediated axon repulsion of neuronal growth cones. These domains play a critical role in forming the supramodular protein structure and directly interact with microtubules, thereby ensuring the functionality of the axon repulsion process. We emphasize that these mutations disrupt the aforementioned processes, thereby associating the UNC5C gene with psychiatric disorders for the first time and expanding the number of genes related to psychiatric disorders. Further research is required to validate the correlation of the UNC5C gene with psychiatric disorders, but we suggest including it in the genetic analysis of patients with psychiatric disorders.
High-intensity interval training on unstable vs stable surfaces: effects on explosive strength, balance, agility, and Tsukahara vault performance in gymnastics

Background and Study Aim. This study compares the effects of high-intensity interval training (HIIT) on unstable and stable surfaces on explosive strength, balance, agility, and Tsukahara vault performance in gymnastics. Material and Methods. A nonrandomized trial study was conducted on twenty-seven well-trained male vault players who were assigned into 3 equal groups, a HIIT unstable group (UG), a HIIT stable group (SG), and the control group (CG). The training period for the subjects lasted eight weeks, with 3 sessions each week. All of the aforementioned variables were assessed both before and after the training period. Results. The main findings indicate that both UG and SG showed greater improvement than the CG on explosive strength, balance, agility, and Tsukahara vault performance in gymnastics (ES= 0.52 to 0.68, P<0.05). In addition, there were significant differences between the UG and SG in explosive strength, balance and Tsukahara vault performance (P<0.05) favoring UG, while no significant differences between UG and SG in agility. However, the UG as compared to the SG in agility had only limited additional effects. The UG and SG significantly increased all tests from pre- to post-test (ES= 1.10 to 4.78, 0.98 to 3.53), respectively (P<0.05). The CG significantly increased the explosive strength and Tsukahara vault performance tests from pre- to post-test ((ES= 0.77 to 0.78, P <0.05), while there were no significant differences in the balance and agility tests (P > 0.05). Conclusions. Accordingly, HIIT with unstable surfaces can be used as an alternative method to improve explosive strength and balance. Also, it can be used alongside stable surface exercises when developing agility.
Phonocardiogram Classification by Learning From Positive and Unlabeled Examples

The advent of deep learning has rekindled research in computer-aided auscultation for the classification of phonocardiogram (PCG) signals. Deep-learning techniques require a large labeled dataset for training. However, labeling large datasets is a formidable task. To create the large labeled dataset, the labeled PCG signal records are segmented cycle-wise, and the labels of the records are passed on to all the cycles of the record. Although this label inheritance may be appropriate for segments of the normal PCG signals, it may be inappropriate for abnormal PCGs and, therefore, may result in the wrong labeling of cycles of abnormal PCG signals. To address this issue, we propose positive unlabeled (PU) learning based on a two-step technique deep-learning model for the classification of PCG signals where PCG segments of normal records are considered positive exemplars and PCG segments of abnormal records are considered unlabeled. To attain the final results, a voting method is employed using a differential evolution (DE) algorithm and majority voting. The system was evaluated using a dataset from the 2016 Physionet/Computing in Cardiology Challenge. The proposed system achieved exceptional record classification performance with a score of about 0.95. Regardless of an imbalanced dataset, the method achieved balanced specificity and sensitivity values of about 0.94 and 0.95, respectively. Additionally, the proposed system outperformed existing human PCG binary classification systems. In conclusion, utilizing PU learning and convolutional neural network (CNN) techniques for diagnosing heart sounds can lead to effective and efficient classification.
CALIBRAÇÃO DE UM EQUIPAMENTO TDR EM CONDIÇÕES DE CAMPO

CALIBRACAO DE UM EQUIPAMENTO TDR EM CONDICOES DE CAMPO Rosangela Villwock; Maria Herminia Ferreira Tavares; Marcio Antonio Vilas BoasCentro de Ciencias Exatas e Tecnologicas, Universidade Estadual do Oeste do Parana, Cascavel , PR, mhstavar@certto.com. br 1 RESUMO A utilizacao da Reflectometria no Dominio do Tempo (TDR) tornou-se um metodo muito bem aceito nas avaliacoes de teores de agua no solo. Todavia, para utilizacao do equipamento ha a necessidade de calibra-lo para que se obtenha valores acurados de umidade volumetrica, devendo-se sempre respeitar as peculiaridades de cada solo para que o processo de calibracao ocorra de forma correta. O objetivo deste trabalho foi calibrar o equipamento TDR em condicoes de campo, especificamente para um Latossolo Vermelho distroferrico, localizado no Nucleo Experimental de Engenharia Agricola, Universidade Estadual do Oeste do Parana, Campus de Cascavel. O modelo polinomial cubico foi o que melhor se ajustou aos dados para a calibracao do equipamento. UNITERMOS: constante dieletrica aparente, modelo polinomial cubico, umidade do solo. VILLWOCK, R. ; TAVARES, M. H. F. ; VILLAS BOAS, M. A.TDR EQUIPMENT CALIBRATION IN FIELD CONDITIONS 2 ABSTRACT The use of Time Domain Reflectometry (TDR) has become a very well accepted method to evaluate soil water content. However, for the equipment utilization there is the need to calibrate it to obtain accurate values of volumetric soil water content, always respecting the soil characteristics. The objective of this research was to calibrate the TDR equipment in field conditions, specifically for a Rhodic Hapludox, at the UNIOESTE Experimental Station of Agricultural Engineering, in the campus of Cascavel, State of Parana. The cubic polynomial model was the model that best adjusted the data for the calibration of the equipment. KEYWORDS: apparent dielectric constant, cubic polynomial model, soil moisture.
Effects of fabrication parameters on porous silicon structure with some potential applications

Porous silicon (PS) is a sponge-like structure of silicon with a high surface to volume ratio and properties that distinguish it from bulk silicon. The structure is suitable for many applications such as biosensors, optoelectronic, drug delivery and solar cell devices. Although there are many reports available in the literature regarding the PS and its application but there is no work done systematically to investigate the fabrication of the PS for various applications. In this work we are investigating the PS production under various fabrication conditions. This work produces instructions to fabricate PS structures for any specific application. We have used electrochemical technique to fabricate samples of PS and undertaken studies the effects of several physical parameters like, materials doping level, light illumination, etching time, applied electric current and silicon surface defects, and chemical parameters like, impurities, and PH factor of the electrolyte during the PS preparation. In order to investigate the effect of silicon surface defects some textured surfaces of silicon samples were prepared by anisotropic etching of silicon in TMAH prior to their electrochemical etching in HF solutions. Structures were characterized using SEM, AFM and Raman spectroscopy. For the first time, fabricated pyramidal porous silicon was used to prepare granular metallic films using electrodeposition techniques. We have also used the pyramidal porous structures to fabricate a miniaturized gas ionization sensor (GIS). GIS works based on measuring the breakdown voltages for various gases. The sensor was fabricated using a parallel plate model having PS as one of the electrodes.