Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluating the effectiveness of supervised learning models for antibiotic pollution detection from biochip data

A biochip is an array of biosensor spots arranged on a durable substrate that can be used to detect and differentiate between different biochemical analytes. This paper looks at the effectiveness of different supervised learning models to detect analytes using biochip spot patterns using the case-study of antibiotic pollution detection with models generated by extracting RGB values from a chip with sixteen spots. We evaluate the performance and accuracy of four types of model, Decision Trees, Random Forest, Na¨ıve Bayes and Neural Networks, by analysing metrics such as processing time, Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). Our analysis shows that different models have their different strengths and weaknesses for reading biochip data. Decision trees and Na¨ıve Bayes have the advantage of being explainable, so that biologists can understand which particular spot values lead to a given classification, although they are significantly less accurate that other methods. Random Forrest and Neural Networks have the advantage of high accuracy but act like a black box so the biologists have little clue as to what spot patterns lead to a particular classification or how much a reading relies on a small change in value or a small number of spots. This is important in order to assess the reliability of the chip reading and determine if further tests or required or if subsequent action can be taken and help chip-designers to determine if the design of their chips needed to be improved. We also found that Random Forrest classifiers have significantly better computational performance than Neural Networks, which makes them suitable to be used in interfaces that allow users to re-run the classifications to determine how changes in spot values can change the classification. Ultimately the accuracy and computational performance of Random Forrest classifiers would make them the preferred option (to be used with interfaces than can show and allow us to test different values) for biochips of the type described in this paper.
Identification of the CNGC Gene Family in Rice and Mining of Alleles for Application in Rice Improvement

Cyclic nucleotide-gated ion channel (CNGC) gene regulation plays important roles in plant immune and abiotic stress response. Here, we identified 16 CNGC genes in rice (Oryza sativa). Then, we analyzed their chromosomal location, physicochemical properties, subcellular localization, gene functional interaction network, cis-acting elements, phylogenetic relationships, collinearity, expression in tissues under normal conditions and abiotic stresses, and geng-cds-haplotype (gcHap) diversity in 3010 gcHaps. As a result, OsCNGC3 (Os06g0527300) was identified as a gene different from previous report, and OsCNGC genes were found to play important roles in rice population differentiation and rice improvement. Our results revealed their very strong differentiation between subspecies and populations, important roles in response to abiotic stresses, as well as strong genetic bottleneck effects and artificial selection of gcHap diversity in the modern breeding process of Xian (indica) and Geng (japonica) populations. The results also suggested that natural variations in most rice CNGC loci are potentially valuable for improving rice productivity and tolerance to abiotic stresses. The favorable alleles at the CNGC loci should be explored to facilitate their application in future rice improvement.
Global perspectives of the impact of the COVID-19 pandemic on learning science in higher education

The COVID-19 pandemic required higher education institutions to rapidly transition to Emergency Remote Instruction (ERI) with little preparation. Discussions are now underway globally to learn the lessons of COVID-19 and to use this knowledge to shape the future of learning science in higher education. In this study, we examined the experiences of instructors and students to ERI in three universities across three continents–America, Europe, and Australia. We measured the instructional strategies used by instructors including assessment types, and interaction opportunities during and outside class schedules. We also measured the learning challenges experienced by students including planning, distractions, technology, learning resources, their views on educational quality and what characterized quality interactions during ERI. Our findings suggest that most instructional strategies used by instructors changed little during ERI, although the nature of instructor and student interactions during class relied more heavily on technology. Students reported significant learning challenges which included distractions from their physical and social media environments and access to technology. Both instructors and students reported that interactions with each other and their peers were concerningly low, albeit similar to pre COVID-19 pandemic levels. There were differences in the perceptions of instructors and students on whether instructor-student interactions were better or worse online. Common among all universities, there was a large proportion of students reporting mental health and work-related stress. Lessons to be learned from the COVID-19 pandemic include ensuring more support for instructors to implement effective and equitable pedagogies and an increased recognition of the importance of practicals, and the social, interactive and hands-on aspects of learning science in higher education. We predict that the incorporation of active learning pedagogies and strategies which increase student engagement and foster a sense of belonging will be ongoing global challenges for learning science in a post COVID-19 campus.
Metachronous colon cancer metastasis to abdominal wall 7 years after rectosigmoid resection: a case report and literature review

Abstract Distant metastases from colorectal cancer to the abdominal wall are rare presentations of the end-stage of the disease. In this case, we present a female patient treated for Stage I rectosigmoid cancer with the late occurrence of abdominal wall metastasis, 7 years after the primary cancer surgery. The patient was treated with surgical excision and abdominal wall reconstruction with the use of synthetic mesh. Literature research on the abdominal wall recurrence/metastases from colorectal cancer was performed.
Feeding ecology of a lesser‐known arboreal giant: Grizzled Giant Squirrel (Ratufa macroura), Southern Western Ghats, India

Abstract Animal dietary choices help us understand a species' feeding behaviour and are particularly relevant in conservation management. The aim of this study was to gather knowledge on dietary choices and the foraging behaviour of the Grizzled Giant Squirrel (Ratufa macroura) in Chinnar Wildlife Sanctuary, Southern Western Ghats, Kerala, India. The objectives were in particular to determine the food composition, seasonal fluctuations in food selection and feeding technique. Through an observational sampling method, focal animal sampling, the Grizzled Giant Squirrel in Chinnar Wildlife Sanctuary was found to feed on 30 plant species belonging to 18 families. The most used plant family was Fabaceae, with eight species, followed by Moraceae (four species) and Anacardiaceae (two species). The food species consumed included 22 trees, four climbers, one liana, one paraphyte, one shrub and one succulent species. The squirrel spent the most time feeding on Bauhinia racemosa (19.79%), followed by Tamarindus indica (14.08%) and Nothopegia beddomei (9.89%). The squirrel's diet choice was primarily influenced by the availability of food tree species and food resources rather than the season and nontree species were also found in the diet of Grizzled Giant Squirrel. Although the Grizzled Giant Squirrel exhibits some plasticity in its dietary choices, the available diversity of mature trees and plants as food sources appears to be important for its conservation in the fragmented riparian forest of the Western Ghats in Southern India.
Climate factors as determinants of food security in semi-arid Kenya: a longitudinal analysis

Food security in the arid and semi-arid areas (ASALs) of Kenya is linked to climate and socio-economic factors. This study was conducted in two ASAL counties of Kenya, Kajiado and Makueni, inhabited by pastoral communities. Both primary and secondary data were collected on climate and socio-economic aspects from published and unpublished documents. Time series data covering a 31 year period were collected on rainfall amounts, rain days, temperature, beef and maize prices, stocking, livestock sales and land under cultivation to generate descriptive statistics and regression results based on the OLS, GLS and AR models. Livestock contributed 78.2% and 38.3% of total income in Kajiado and Makueni Counties respectively. Crops contributed more to the total income in Makueni County at 52.7% compared to Kajiado County at 6.9%. Of the three models tested, the GLS was found to be the most appropriate based on the number of significant variables and the estimated R2 value. The model showed that rainfall, temperature, rain days, and beef and maize prices influence income in Kajiado and Makueni Counties. Therefore, creation of micro-climates through agroforestry moderates temperatures, attracts rainfall, sequesters carbon and provides services such as food, timber, raw materials and employment. Moreover, initiatives that regulate beef and maize prices ensure predictable markets and income in both counties.
Common types of microdebris affect the physiology of reef-building corals

Marine debris, particularly microdebris (< 1 mm) poses a potential threat to marine life, including reef-building corals. While previous research has mainly focused on the impact of single polymer microplastics, the effects of natural microdebris, composed of a mixture of materials, have not been explored. Therefore, this study aimed to assess the effects of different microdebris, originating from major sources of pollution, on reef-building corals. For this, we exposed two scleractinian coral species, Pocillopora verrucosa and Stylophora pistillata, known to frequently ingest microplastics, to four types of microdebris in an 8-week laboratory experiment: fragmented environmental plastic debris, artificial fibers from clothing, residues from the automobile sector consisting of tire wear, brake abrasion, and varnish flakes, a single polymer microplastic treatment consisting of polyethylene particles, and a microdebris-free control treatment. Specifically, we (I) compared the effects of the different microdebris on coral growth, necrosis, and photosynthesis, (II) investigated the difference between the microdebris mixtures and the exposure to the single polymer treatment, and (III) identified potential mechanisms causing species-specific effects by contrasting the feeding responses of the two coral species on microdebris and natural food. We show that the fibers and tire wear had the strongest effects on coral physiology, with P. verrucosa being more affected than S. pistillata. Both species showed increased volume growth in response to the microdebris treatments, accompanied by decreased calcification in P. verrucosa. Photosynthetic efficiency of the symbionts was enhanced in both species. The species-specific physiological responses might be attributed to feeding reactions, with P. verrucosa responding significantly more often to microdebris than S. pistillata. These findings highlight the effect of different microdebris on coral physiology and the need for future studies to use particle mixtures to better mimic naturally occurring microdebris and assess its effect on corals in more detail. Highlights The effects of major sources of microdebris pollution on coral physiology were compared. Overall, microdebris had only minor impacts on coral physiology. Artificial fibers and tire wear caused the strongest effect on coral physiology. Single polymer and complex microdebris caused similar, yet species-specific effects. Species-specific effects might be due to different feeding behaviors. Graphical abstract
QuComm: Optimizing Collective Communication for Distributed Quantum Computing

Distributed quantum computing (DQC) is a scalable way to build a large-scale quantum computing system. Previous compilers for DQC focus on either qubit-to-qubit inter-node gates or qubit-to-node nonlocal circuit blocks, missing opportunities of optimizing collective communication which consists of nonlocal gates over multiple nodes. In this paper, we observe that by utilizing patterns of collective communication, we can greatly reduce the amount of inter-node communication required to implement a group of nonlocal gates. We propose QuComm, the first compiler framework which unveils and analyzes collective communication patterns hidden in distributed quantum programs and efficiently routes inter-node gates on any DQC architecture based on discovered patterns, cutting down the overall communication cost of the target program. We also provide the first formalization of the communication buffer concept in DQC compiling. The communication buffer utilizes data qubits to store remote entanglement so that we can ensure enough communication resources on any DQC architecture to support the proposed optimizations for collective communication. Experimental results show that, compared to the state-of-the-art baseline, QuComm reduces the amount of inter-node communication by 54.9% on average, over various distributed quantum programs and DQC hardware configurations.CCS CONCEPTS• Computer systems organization → Quantum computing; • Software and its engineering → Compilers.
Organic carbon and total nitrogen topsoil stocks, biogenetic natural reserve ‘Marchesale’ (Calabria region, southern Italy)

ABSTRACT It is essential estimating the spatial distribution of soil organic carbon (SOC) and soil total nitrogen (STN) stocks and their spatial-temporal variations to understand the role of soil in ecosystem services and in the global cycles of carbon and nitrogen. This work was aimed to quantify and map the stocks of SOC and STN in topsoils in an area of the Biogenetic Natural Reserve ‘Marchesale’ (Calabria region, southern Italy). Forest soil samples (0–20 cm depth) were collected at 231 locations and analysed in laboratory for SOC and STN. Moreover, in all samples, bulk density (BD) and soil coarse fragments (SCFs) were determined. Geostatistics was used to map all soil properties (SOC, STN, BD and SCFs) and the stocks of SOC and STN. The mean stock values were 86.3 Mg ha−1 for SOC and 5.1 Mg ha−1 for STN. The total amounts stored in the study area (33.2 ha) were 2865.2 Mg for SOC and 170.1 Mg for STN. Although only the topsoil was considered, the accompanying maps (1:4000 scale) will be useful for the sustainable management of the Biogenetic Natural Reserve ‘Marchesale’ and for undertaking appropriate conservation plans to mitigate the emissions of greenhouse gases.
Superpixels: the end of pixels in OBIA. A comparison of stat-of-the-art superpixel methods for remote sensing data

In computer vision, using superpixels or perceptually meaningful atomic regions to speed up later-stage processing are becoming increasingly popular in many applications. Superpixels are used as a pre-processing stage to organize an image into a low-level grouping process through oversegmentation, thus simplifying the computation in later stages. However, in remote sensing domain few studies use superpixels. Even so, there is no comparison between superpixel methods and their suitability for remote sensing images. In this study, we compare four state-of-the-art superpixel methods: Simple Linear Iterative Clustering (SLIC and SLICO), Superpixels Extracted via Energy-Driven Sampling (SEEDS) and Linear Spectral Clustering (LSC). We applied them to very high resolution remote sensing data of different characteristics (extent, spatial resolution and landscape complexity) in order to see how superpixels are affected by these factors. The four algorithms were compared regarding their computational time, ability to adhere to image boundaries and the accuracy of the resulted superpixels. Furthermore, we discuss the individual strengths and weaknesses of each algorithm and draw further applications of superpixels in OBIA.
Comparing the efficacy of electronic cognitive behavioral therapy to medication and combination therapy for generalized anxiety disorder: a quasi-experimental clinical trial

Background Generalized anxiety disorder (GAD) is a debilitating mental health disorder with first-line treatments include cognitive behavioral therapy (CBT) and pharmacotherapy. CBT is costly, time-consuming, and inaccessible. Electronic delivery (e-CBT) is a promising solution to address these barriers. However, due to the novelty of this intervention, more research testing the e-CBT efficacy independently and in conjunction with other treatments is needed. Objective This study investigated the efficacy of e-CBT compared to and in conjunction with pharmacotherapy for GAD. Methods This study employed a quasi-experimental design where patients selected their preferred treatment modality. Patients with GAD were enrolled in either e-CBT, medication, or combination arms. The 12-week e-CBT program was delivered through a digital platform. The medications followed clinical guidelines. The efficacy of each arm was evaluated using questionnaires measuring depression, anxiety, and stress severity, as well as quality of life. Results There were no significant differences between arms (N e-CBT = 41; N Medication = 41; N Combination = 33) in the number of weeks completed or baseline scores. All arms showed improvements in anxiety scores after treatment. The medication and combination arms improved depression scores. The e-CBT and Combination arms improved quality of life, and the combination arm improved stress scores. There were no differences between the groups in depression, anxiety, or stress scores post-treatment. However, the combination arm had a significantly larger improvement in quality of life. Gender and treatment arm were not predictors of dropout, whereas younger age was. Conclusion Incorporating e-CBT on its own or in combination with pharmaceutical interventions is a viable option for treating GAD. Treating GAD with e-CBT or medication appears to offer significant improvements in symptoms, with no meaningful difference between the two. Combining the treatments also offer significant improvements, while not necessarily superior to either independently. The findings suggest that all options are viable. Taking the patient’s preferred treatment route based on their lifestyle, personality, and beliefs into account when deciding on treatment should be a priority for care providers.
Tissue properties and respiratory kinematics of the tongue base and soft palate in the obese OSA minipig

Obesity is a common finding and a major pathogenetic factor in obstructive sleep apnea (OSA) in adults. To understand the mechanisms behind this, the present study investigated the tissue properties and respiratory kinematics of the tongue base and soft palate in the obese OSA minipig model. In 4 verified obese/OSA and 3 non-obese/non-OSA control minipigs, MRI fat-weighted images, ultrasound elastography (USE), and sleep video-fluoroscopy (SVF) were performed to quantify the fat composition, tissue stiffness, and respiratory kinematics of the tongue base and soft palate during sedated sleep. The results indicated that the fat composition gradually increased from the rostral to caudal tongue base, particularly in the posterior 1/3 of the tongue base, regardless of the presence of obesity and OSA. However, this trend was not seen in the soft palate and pharyngeal wall. The pharyngeal wall presented the highest fat composition as compared with the tongue base and soft palate. Overall, obese OSA minipigs showed stiffer tongue tissue than the controls, particularly in the rostral region of the tongue in obese Yucatan minipigs. The respiratory moving ranges of the soft palate were greater in both dorsal-ventral and rostral-caudal directions and during both respiratory and expiratory phases in OSA obese than control minipigs, and the largest moving ranges were seen in OSA obese Panepinto minipigs. The moving range of the tongue base was significantly smaller. These results suggest more fat infiltration in the caudal region of the tongue base regardless of the presence of obesity and/or OSA. The greater tissue stiffness of the tongue in obese OSA minipigs may result from altered neuromuscular drive.
Weight and sight: Association between body mass index and visual efficiency skills of university students

PURPOSE: The aim of this study is to investigate the association between body mass index (BMI) and visual efficiency skills of university students. METHODS: This study adopted an analytical and descriptive methodology whereby a comprehensive screening of 385 university students was conducted. Ultimately, 116 subjects were selected to ensure judicious distribution of subjects across the four categories of BMI. These 116 subjects were subjected to visual efficiency testing parameters, as referenced from “Clinical Management of Binocular Vision,” by Scheiman and Wick. RESULTS: BMI and visual efficiency anomalies were observed to be correlated with statistical significance to each other as indicated by a P = 0.018 and χ2 = 15.22. It was also observed that the obese category exhibited the highest prevalence of accommodation and vergence anomalies, followed by the underweight category, as discerned from a detailed analysis of subjects classified according to the BMI categories. These findings imply a noteworthy and potentially deleterious impact of BMI on the visual system and underscore the importance of regular screening for visual efficiency skills in individuals across various BMI categories. CONCLUSION: This study established a compelling association between BMI and visual efficiency skills anomalies. Consistent with prior research, the findings of this study suggest that individual with higher BMI is more prone to experiencing such anomalies.
Comparison of multimodal attract-and-kill formulations for managing Drosophila suzukii: Behavioral and lethal effects

Attract-and-kill (A&K) is a potential alternative control tactic for managing the invasive spotted-wing drosophila, Drosophila suzukii Matsumura. Here, we compared the efficacy of two novel A&K formulations based on proprietary blends–ACTTRA SWD OR1 (henceforth OR1) and ACTTRA SWD TD (henceforth TD)–in managing D. suzukii. Using two-choice bioassays, we compared OR1 and TD for their relative attractiveness to adult D. suzukii. Additionally, we tested how the addition of (1) a red dye (visual cue) and (2) the insecticide spinosad (Entrust™) to the OR1 and TD formulations influenced the attraction of adult D. suzukii in the presence of blueberry fruits. Finally, complementary laboratory efficacy (no-choice) bioassays were conducted to assess the mortality of adult D. suzukii exposed to OR1 and TD. A direct comparison between TD and OR1 formulations indicated the TD formulation was ~8 times more attractive than OR1. Adding a red dye to the TD or OR1 formulation did not significantly alter the attraction or mortality of adult D. suzukii compared to the formulation without a dye. Similarly, irrespective of dye status, adding spinosad to either the TD or OR1 formulation did not alter the adult D. suzukii behavioral response to these formulations but resulted in significantly higher D. suzukii mortality. Overall, the TD formulations resulted in significantly higher, or at least comparable, mortality to the OR1 formulations. In summary, our laboratory results demonstrated the higher efficacy of a TD-based A&K product in managing D. suzukii over its well-tested predecessor, the OR1 formulation, confirming its potential as a new behavioral tactic against this pest.