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Saprotrophic wood-decaying abilities of (Polyporales, Basidiomycota) syn. and the structural alterations induced in wood blocks of Muell. Arg were studied. Mass loss of wood blocks was analyzed after 3 and 6 months respectively and the patterns of decay by pathogenic and endophytic isolates of this fungus were investigated using light microscopy. Effects of temperature on growth of the isolates on malt extract agar were also investigated. The isolated from a non- host caused the highest percentage mass loss (27.2% after 6 months), followed by isolates ED310 (21.1%) and M13 (15.7%), both collected from diseased plantations. The isolate initially identified as an endophyte showed very low saprotrophic wood decay capability (4.3% after 6 months). The optimal temperature for growth of the isolates was 30 °C; except for the endophytic isolate which showed highest growth at 25 °C. Wood samples degraded by the isolates showed simultaneous attack of wood cell walls, typical of white rot fungi. Results of the study indicate variability in the wood degrading abilities of the isolates and the potential differences in their physiology are discussed. Our findings further support the need for a taxonomical revision of the genus.Rigidoporus microporusRigidoporus lignosusHevea brasiliensisR. microporusH. brasiliensisH. brasiliensisR. microporusRigidoporus

Kentang (Solanum tuberosum L.) adalah tanaman pangan penting dengan nilai ekonomi yang tinggi dan menyumbang gizi yang besar bagi manusia. Produksi kentang terhambat oleh serangan penyakit dan hama potato beetle (Leptinotarsa decemlineata Say). Hama ini secara signifikan mempengaruhi hasil panen kentang maka perlu penanganan yang efektif untuk mencegah penurunan produksi yang berkelanjutan. Metode yang digunakan untuk mendeteksi dan mengklasifikasikan potato beetle adalah Convolutional Neural Network (CNN) yang merupakan algoritma jaringan syaraf tiruan yang efektif dalam pengolahan citra. Model YOLOv8 (You Only Look Once) diimplementasikan untuk mendeteksi objek pada gambar dengan mengidentifikasi posisi dan kelas dari potato beetle. Data yang digunakan untuk pelatihan adalah menggunakan framework PyTorch yang telah dipecah menjadi data training, data validation, dan data test. Hasil penelitian menunjukkan model yang dikembangkan memiliki tingkat akurasi yang memadai dalam mendeteksi dan mengklasifikasikan potato beetle. Evaluasi model melibatkan pengukuran Mean Average Precision (mAP) dan F1-Score berdasarkan konsep Precision dan Recall. Secara keseluruhan, model ini mencapai mAP50 sebesar 81,8%, yang mengindikasikan tingkat keseluruhan akurasi deteksi yang baik. Namun perlu perbaikan dalam mAP50-95 untuk mengukur akurasi deteksi pada tingkat lebih ketat. Model ini mampu mengklasifikasikan objek dengan precision sebesar 78,1% dan recall sebesar 89,8%. Dalam evaluasi kelas objek individu, model ini berhasil mendeteksi objek guk (potato beetle) dengan tingkat akurasi mencapai precision sebesar 88,1% dan recall sebesar 90,3%. Sedangkan objek lich juga mendapatkan hasil precision 72,8% dan recall 76,8%. Namun, perlu diperhatikan bahwa mAP50-95 pada objek lich menunjukkan penurunan yang lebih tinggi dibandingkan dengan objek guk. Abstract Potato (Solanum tuberosum L.) is a vital food crop with high economic value and a significant source of nutrition for humans. The production of potatoes is hindered by the infestation of the potato beetle (Leptinotarsa decemlineata Say), a pest that significantly impacts potato yields. Effective measures are needed to prevent sustained decreases in production. The Convolutional Neural Network (CNN) algorithm, which is effective in image processing, is utilized for the detection and classification of potato beetles. The YOLOv8 (You Only Look Once) model is implemented to detect objects in images, identifying the position and class of potato beetles. The data used for training is divided into training, validation, and testing datasets using the PyTorch framework. The research results indicate that the developed model achieves an adequate level of accuracy in detecting and classifying potato beetles. Model evaluation involves measurements of Mean Average Precision (mAP) and F1-Score based on Precision and Recall concepts. Overall, the model achieves an mAP50 of 81,8%, indicating a good overall detection accuracy level. However, there is room for improvement in mAP50-95 to measure detection accuracy at stricter levels. This model is capable of classifying objects with a precision of 78,1% and a recall of 89,8%. In the evaluation of individual object classes, the model successfully detects potato beetles with a precision of 88,1% and a recall of 90,3%. Meanwhile, the "lich" object achieves a precision of 72,8% and a recall of 76,8%. It should be noted that the mAP50-95 for the "lich" object shows a higher decrease compared to the "guk" object.

The development of applications and systems for the nuclear domain involves the interplay of many different disciplines and is, therefore, particularly complex. Additionally, these systems and their innovations have to be compliant with strict international regulations and recommendations. The scientific and industrial communities have been studying, developing and applying advanced Artificial Intelligence (AI) techniques and tools in several (non-nuclear) application domains. Their encouraging results have pushed the nuclear community to pay increasing attention to the field of AI. Among the expected benefits of AI is the simplification of complex procedures, the reduction in the execution of time-consuming operations, the increase of safety levels, and the reduction in the overall cost. At the French Atomic Energy Commission (CEA), we have identified and have started to address several open questions, such as: where in the nuclear domain can AI-based techniques be implemented in the most productive way? What do the nuclear standards and recommendations say about its use? Can we identify some core challenges and issues common to multiple areas of the nuclear domain? In this paper we provide a first analysis and answers to the above questions and we conclude by emphasizing some cross-domain high priority challenges.

This paper presents work that has been done on the FIFRELIN Monte-Carlo code. The purpose of the code is to simulate the de-excitation process of fission fragments. Numerous quantity of insterest are calculated (mass yields, prompt particle spectra, mulitiplicities … ). Up to now the code relies on four free parameters which control the initial excitation and total angular momentum of fission fragment. Finding the good set of the free parameters is a diffucult task. In this work, we have developed an optimization algorithm based on Gaussian Process regression.

Summary: CUT&RUN is a recently developed in situ chromatin profiling technique that enables high-resolution chromatin mapping and probing. Herein, we describe our adapted CUT&RUN protocol for transcription factors (TFs). Our protocol outlines all necessary steps for TF profiling including the procedure to obtain proteinA-Mnase, while also outlining the bioinformatic pipeline steps required to process, analyze, and identify novel binding sites and sequences. Due to the small number of cells required, this method will allow the elucidation of cell context-dependent functions of many TFs.For details on the use and execution of this protocol, please refer to Kong et al. (2021).

Energy calibration via correlation has proven to be a powerful calibration method especially in the case of calibration data sets with low counting statistics. This work reports on an evolved version of this approach that uses an adaptive mesh refinement to sample the parameter space which results in the same calibration performance but in a considerably reduced computation time.