Miracle-dz/UniFAR-FTUs huggingface dataset:
Antifungal Therapies for Aspergillus spp.: Present and Future

Abstract Currently available and recommended options for the treatment of pulmonary aspergillosis include the triazoles, echinocandins, and amphotericin B products. These therapies have significant limitations. Only the azoles are available orally, but their use is often limited by toxicities, drug–drug interactions, pharmacokinetic variability, and emerging resistance. While the echinocandins are safe agents and may have a role in combination therapy, they are unproven as monotherapy. Amphotericin B preparations are toxic and require intensive monitoring. Finally, aspergillosis continues to be a disease conferring substantial morbidity and mortality, and clinical trials have not identified a therapeutic approach clearly associated with improved outcomes. As a result, there is a great need for new options in the treatment of invasive aspergillosis. Ideally, such options would be safe, have high oral bioavailability, have favorable pharmacokinetics to sequestered sites and retain activity against azole-resistant isolates. Reassuringly, there is a robust pipeline of novel therapies in development. Rezafungin (a once-weekly dosed echinocandin) and ibrexafungerp (oral agent with same mechanism of action as echinocandins) will likely be reserved for combination therapy or refractory/intolerance scenarios with no other options. Inhaled opelconazole is an attractive option for combination therapy and prophylaxis of pulmonary aspergillosis. Development of an oral form of amphotericin B that avoids nephrotoxicity and electrolyte disturbances is an exciting development. Finally, olorofim and fosmanogepix, two agents with novel mechanisms of action and oral formulations, hold significant potential to challenge the triazole antifungals place as preferred therapies. However, many questions remain regarding these novel agents, and at the time of this writing, none of these agents have been robustly studied in Phase III studies of aspergillosis, and so their promise remains investigational.
Compte rendu de l'expédition «Balbuzard» sur les côtes méditerranéennes du Maroc

Expedition realisee entre le Cap Negre et le Cap des Trois Fourches entre le 20 mai et la fin juin 1983. Statut reproducteur des especes identifiees. Un projet pour la protection et la gestion de la zone ornithologiquement la plus interessante est considere
An Examination of the Role of Silicate in Sodium Transport and Salinity Tolerance in Rice (O. sativa)

Silicate (Si)-mediated alleviation of salinity toxicity in rice is proposed to occur through a decrease in apoplastic sodium (Na+) transport. This is said to decrease shoot Na+ accumulation, a commonly proposed salt tolerance strategy. To investigate this, salttolerant (Pokkali) and salt-sensitive (IR29) rice cultivars were grown in 0 or 1.67 mM Si over a range of Na+ concentrations, and cultivar-specific effective concentrations of Na+ at which shoot fresh weight was decreased by 50% were determined. Within this experimental framework, salinity tolerance strategies were characterized using the radiotracer 24Na+ along with electrophysiological, photometric, and fluorescent dye measurements. Si treatment was found have no effect on transmembrane ion currents while decreasing shoot Na+ accumulation by over 50% in both cultivars. Si-treated IR29 exhibited lower apoplastic bypass and shoot Na+ flux. In Pokkali, examinations of Na+ influx, efflux, and shoot-to-root recirculation were unable to reconcile differences between shoot Na+ content and tracer-flux analyses.
Head mouse control system for people with disabilities

In this paper, a human–machine interface for disabled people with spinal cord injuries is proposed. The designed human–machine interface is an assistive system that uses head movements and blinking for mouse control. In the proposed system, by moving one's head, the user moves the mouse pointer to the required coordinates and then blinks to send commands. The considered head mouse control is based on image processing including facial recognition, in particular, the recognition of the eyes, mouth, and nose. The proposed recognition system is based on the convolutional neural network, which uses the low‐quality images that are captured by a computer's camera. The convolutional neural network (CNN) includes convolutional layers, a pooling layer, and a fully connected network. The CNN transforms the head movements to the actual coordinates of the mouse. The designed system allows people with disabilities to control a mouse pointer with head movements and to control mouse buttons with blinks. The results of the experiments demonstrate that this system is robust and accurate. This invention allows people with disabilities to freely control mouse cursors and mouse buttons without wearing any equipment.
Using a workbook to support cognitive behavioural therapy with young people: a survey of psychological practitioners

Abstract Practitioners have mixed views about the value of cognitive behavioural therapy (CBT) manuals, with some preferring to work based on professional judgement. The workbook represents a compromise, providing guidance and resources without prescribing standardised procedures. Workbooks have not previously been widely addressed in the CBT literature. This exploratory study analysed how practitioners use a CBT workbook (Think Good – Feel Good, TGFG) to support therapeutic work with young people (YP). Practitioners (n=238) completed an online survey about how TGFG is used and how it supports CBT. A convergent mixed-methods design was pursued. Qualitative and quantitative data were analysed using content analysis, descriptive statistics, and chi-squared tests before themes were defined to summarise the dataset. When deciding whether to use TGFG, practitioners consider a YP’s presenting difficulty, level of understanding, motivation, and availability of systemic support. Practitioners use TGFG inside and outside the therapeutic space to plan sessions, revise CBT concepts, and complete worksheets (particularly those with a cognitive focus). Practitioners use TGFG flexibly and pragmatically (combining it with other therapeutic approaches), and they adapt resources to suit a YP’s understanding and interests. TGFG appears to be a widely used resource for practitioners across the range of experience. Key learning aims (1) To expand upon the concept of the workbook as a therapeutic adjunct within the CBT literature. (2) To explore how a popular CBT workbook is useful to practitioners, how it is typically used, and the breadth of usage. (3) To consider whether and how workbooks are used flexibly by practitioners. (4) To encourage practitioners across the range of experience to reflect on how workbooks support planning and delivery of therapeutic interventions.
Defense responses of the green microalgae Chlorella vulgaris to the vanadium pentoxide nanoparticles

Abstract Although vanadium-based nanomaterials have found extensive use in industry, their influence on ecosystems and living organisms is not yet well investigated. In this study, hydrothermal methods were utilized for the synthesis of vanadium pentoxide nanoparticles (V2O5 NPs). The gained NPs were characterized using XRD, FT-IR, EDS, DLS, SEM and TEM techniques. Subsequently, the toxic effects of V2O5 NPs on the model green microalgae Chlorella vulgaris were evaluated. According to the obtained results, V2O5 NPs caused a significant reduction in cell number and biomass production of algae in a dose and time dependent manner. Moreover, flow cytometric analysis confirmed a reduction in the quantity of living cells. Scanning electron microscopy showed plasmolysis and deformation of the cells after exposure to nanoparticles. The photosynthetic pigments and phenolics content exhibited a decrease in comparison with the control sample. Although, non-enzymatic antioxidant system in C. vulgaris displayed an average action, antioxidant enzymes, including superoxide dismutase (SOD), catalase (CAT), and ascorbate peroxidase (APX) showed a dose dependent increasing trend. These intercellular reactions designated the activation of the antioxidant defense system in response to the induced oxidative stress by V2O5 NPs.
An ichthyofaunal amendment with the length-weight relations and condition factors of some endemic and invasive freshwater fishes from Western Anatolia

Abstract In the scope of this study, fish fauna of Küçük Menderes River and its tributaries, updated with the comparison of the recent ichthyofaunal studies, as well as the length-weight relations (LWR) and condition factors (CF) were estimated for 7 freshwater fish species belonging to six families from the river basin: endemic Oxynoemacheilus eliasi, Cobitis fahireae; invasive, Carassius gibelio, Atherina boyeri, transloce Perca fluviatilis and the native Squalius fellowesii, Cyprinus carpio. The fish samples were collected with various fishnets and DC electro-fishing devices from six stations in 2018 and 2019. The LWR of the fishes was studied based on 379 specimens. The estimated values of parameter b ranged from 2.884 (A. boyeri) to 3.176 (C. fahireae). The coefficient of determination (R2) was changed between 0.792 to 0.980 for all sampling localities. In the study, Fulton’s condition factor ranged between 0.391 (S. fellowesii) to 3.080 (S. fellowesii); the relative condition factor ranged between 0.346 (O. eliasi) to 2.746 (S. fellowesii), respectively. This research is anticipated to contribute valuable insights for the conservation of the species, while also furnishing essential data to inform future fisheries management studies in the region.
Stressed Speech Recognition Using Smartphone and Embedded Device Integration

Stress is a state of emotional tension generated by a variety of factors such as work, study, family, and others. Stress can worsen and have an impact on health if it is not managed soon. Several studies have presented methods for detecting people's emotions through their voices. The goal of this study is to determine whether someone is stressed and how much stress he is under by listening to his voice. It is expected to be able to assess the amount of stress through sound utilizing MFCC feature extraction and artificial neural network machine learning. This system is powered by a Raspberry Pi 4 connected through Bluetooth to a microphone and an application on an Android phone. The smartphone application was designed to integrate with the embedded system and to display the prediction result. The dataset used in this research was SUSAS (Speech Under Simulated and Acute Stress) consisting of 1860 utterances. During the development of the artificial neural network model using 70% as training dataset, the accuracy only achieved 76%. However, the accuracy of the overall integrated system by utilizing 30 data taken from dataset reached 90%. Meanwhile, the test's average computing time is 2 seconds.
Use of the TTA-DIVAYANA Concept to Improve the Weight of Decision Makers in the TOPSIS Method as an Effort to Optimize Evaluation Results

This study aimed to demonstrate the calculation process to obtain an equivalent score by improving the decision-making weight in the TOPSIS method using the TTA (Tat Twam Asi)-DIVAYANA (Description-Input-Verification-Action-Yack-Analysis-Nominate-Actualization) concept. The equivalent of decision-making weight scores can support the optimal evaluation results of blended learning implementation. This study used a quantitative approach by simulating the calculation from the decision maker's weighted improvement scores in the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) method. The number of experts as decision-makers involved in giving a weighted score to each evaluation indicator was four experts. Questionnaires were used as a tool by experts to provide weighted scores for decision-makers. The Analysis of weighting results was by comparing the quality standards of the eleven scales with the quality percentage from the decision maker's assessment of the evaluation indicators. The results showed that the decision-making weight was very good because the quality percentage obtained was 89.03%.
Electrocardiogram interpretation skills among healthcare professional and related factors: A Review on Myocardial Infraction cases

Acute Coronary Syndrome (ACS) is a major cause of death and disability worldwide. In spite of the advance of many other diagnostic techniques of biomarkers and imaging, the 12-lead electrocardiogram (ECG) remains the gold standard for early identification and management of ACS. The aim of this study was to determine the HCPs interpretation skills on 12 lead ECG especially in Myocardial Infarction cases. Primary research of ECG interpretation skill of HCPs and related factors were identiﬁed by searching electronic databases and bibliographies. Sixteen articles were identified based on inclusion and exclusion criteria. Variations were found on ECG interpretations among HCPs. Moreover it was found that training, experience seniority and AMI mimicking pattern influence ECG interpretation skills among HCPs and decision making for thrombolytic therapy.
Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN) on Melanoma Skin Cancer Detection

Neural networks and image enhancement have become significant in oncology by aiding in the early diagnosis of various cancer types, including Melanoma, the most lethal type of skin cancer. The conventional approach to diagnosing melanoma includes using dermoscopic tools to capture skin lesions. However, these captured skin lesions have limitations, such as low resolution, artifacts on the skin, and variations in lighting conditions. One promising method for improving the resolution of these dermoscopic images is the Enhanced Super-Resolution Generative Adversarial Network (ESRGAN). To evaluate the effectiveness of ESRGAN in melanoma skin cancer detection, researchers trained CNN models with ESRGAN-enhanced images and not enhanced images and compared their performance. They used the ISIC 2020 dataset and balanced it with random undersampling and data augmentation. The study utilized two deep learning models, VGG16 and ResNet50, to compare their performance with and without ESRGAN enhancement. The results showed that the enhanced dataset outperformed the unprocessed dataset, with ResNet50 achieving an impressive accuracy of 98.2% and VGG16 achieving 94.74%. Additionally, training with the enhanced dataset took 5 minutes longer in VGG16 and 18 minutes longer in ResNet50 which led to significantly better results. In conclusion, the study shows that ESRGAN can improve the performance of deep learning models in melanoma skin cancer detection.
A Social Community Sensor for Natural Disaster Monitoring in Indonesia Using Hybrid 2D CNN LSTM

One of the most notable advantages of utilizing social media is its ability to aid communication during natural disasters, specifically in regards to disseminating information. Social media users serve as community monitors by collecting public messages from a multitude of social media platforms. The implementation of artificial intelligence can facilitate the automatic recognition of messages concerning natural disasters. Deep learning-based artificial intelligence models for text classification, such as Convolutional Neural Network (CNN) and Long Short-Term Memory networks (LSTM), each possess their own unique strengths and weaknesses. Nevertheless, the utilization of word padding techniques presents a further challenge to accurately classify texts, as its implementation may negatively impact classification performance. In this study, feature extraction based on word embedding and word padding techniques were employed, utilizing a maximum number of words to be processed by a hybrid 2D CNN LSTM model. The results indicate the highest level of accuracy in cases of floods, with an 81.27% accuracy rate, forest fires with an 86.14% accuracy rate, and earthquakes with an 80.16% accuracy rate. This outcome represents a significant advancement over the accuracy attained by the classification model constructed with feature extraction based on word embedding and word padding based on mean and using solely 2D CNN.
Artificially Ripeness Identification of Indonesian Banana Cultivar Using Convolution Neural Network

Detection of bananas that ripen naturally and artificially using digital images is quite difficult because they physically have almost the same characteristics. Feature identification requires very detailed characteristic information so that a diverse feature extraction process is needed, but not necessarily the extracted ones are the optimal features. In this study, the convolution neural network (CNN) method was used and the best features were learned automatically during the classification process. Two CNN-based methods, VGG based model and ResNet18, were tested to obtain the optimal identification. The results of testing on six Indonesian banana cultivars showed that VGG based model produced better accuracy of 88.67%. However, ResNest18 achieved convergence faster.
Implementation of Summary-Connection-Prediction (SCP) Instructional Strategy in Flipped Classroom with Moodle: Investigate Preservice Teachers' Acceptance and Beliefs

This study investigated the implementation of summary-connection-prediction (SCP) instructional strategy on students’ report tasks before the class under the flipped classroom learning design with Moodle. A total of 106 undergraduate students who enrolled in the Teaching and Learning Theory course participated in this study. A questionnaire based on the technology acceptance model (TAM) and open-ended interview questions were used to collect the data. Following the results, it is known that the preservice teachers have high positive acceptance of SCP strategy. Further, perceived ease of use and perceived usefulness significantly predict behavioral intention, which follows the relationship direction in TAM. Regarding the thematic analysis of interview data, three themes are elaborated to obtain in-depth information about what preservice teachers’ beliefs when implementing this strategy.
Flank vents and graben as indicators of Late Amazonian volcanotectonic activity on Olympus Mons

Previous studies have focused on large‐scale features on Olympus Mons, such as its flank terraces, the summit caldera complex, and the basal escarpment and aureole deposits. Here we identify and characterize previously unrecognized and unmapped small scale features to help further understand the volcanotectonic evolution of this enormous volcano. Using Context Camera, High Resolution Imaging Science Experiment, Thermal Emission Imaging System, High Resolution Stereo Camera Digital Terrain Model, and Mars Orbiter Laser Altimeter data, we identified and characterized the morphology and distribution of 60 flank vents and 84 grabens on Olympus Mons. We find that effusive eruptions have dominated volcanic activity on Olympus Mons in the Late Amazonian. Explosive eruptions were rare, implying volatile‐poor magmas and/or a lack of magma‐water interactions during the Late Amazonian. The distribution of flank vents suggests dike propagation of hundreds of kilometers and shallow magma storage. Small grabens, not previously observed in lower‐resolution data, occur primarily on the lower flanks of Olympus Mons and indicate late‐stage extensional tectonism. Based on superposition relationships, we have concluded two stages of development for Olympus Mons during the Late Amazonian: (1) primarily effusive resurfacing and formation of flank vents followed by (2) waning effusive volcanism and graben formation and/or reactivation. This developmental sequence resembles that proposed for Ascraeus Mons and other large Martian shields, suggesting a similar geologic evolution for these volcanoes.
Substorms and Solar Eclipses: A Mutual Information Based Study

Solar eclipses present a rare glimpse into the impact of ionospheric electrodynamics on the magnetosphere independent of other well studied seasonal influences. Despite decades of study, we still do not have a complete description of the conditions for geomagnetic substorm onset. We present herein a mutual information based study of previously published substorm onsets and the past two decades of eclipses which indicates the likelihood of co‐occurrence is greater than random chance. A plausible interpretation for this relation suggests that the abrupt fluctuations in ionospheric conductivity during an eclipse may influence the magnetospheric preconditions of substorm initiation. While the mechanism remains unclear, this study presents strong evidence of a link between substorm onset and solar eclipses.
Short‐term dynamics of drainage density based on a combination of channel flow state surveys and water level measurements

Headwater streams often experience intermittent flow. Consequently, the flowing drainage network expands and contracts and the flowing drainage density (DD) varies over time. Monitoring the DD dynamics is essential to understand the processes controlling it. However, our knowledge of the event‐scale DD dynamics is limited because high spatial and temporal resolution data on the DD remain sparse. Therefore, our team monitored the DD dynamics and hydrologic variables in two 5‐ha headwater catchments in the Swiss pre‐Alps in the summer of 2021, through mapping surveys of the flow state and a wireless streamwater level sensor network. We combined the two data sources to calculate the DD at the event‐time scale. Our so‐called CEASE method assumes that flow in a channel reach occurs above a set of water level thresholds, and it determined the DDs with accuracies >94%. DD responses to events differed for the two catchments, despite their proximity and similar size. DD ranged from 2.7 to 32.2 km km−2 in the flatter catchment (average slope: 15°). For this catchment, the discharge‐DD relationship became steeper when DD exceeded 20 km km−2 and DD increased substantially with relatively small increases in discharge. For rainfall events during dry conditions, the discharge‐DD relationship showed counterclockwise hysteresis, likely due to initially high groundwater discharge from the area near the catchment outlet; once rainfall stopped, DD remained high during the streamflow recession due to rising groundwater levels throughout the catchment. For events during wet conditions, the discharge and DD responded synchronously. In the steeper catchment (average slope: 24°), the DD varied only from 7.8 to 14.6 km km−2 and there was no hysteresis or threshold behaviour in the discharge‐DD relationship, likely because multiple groundwater springs maintained streamflow throughout the network during the monitoring period. These results highlight the high variability in DD and its dynamics across small headwater catchments.