PENGARUH BERBAGAI RASIO RUMPUT LAPANG FERMENTASI DAN KONSENTRAT TERHADAP KECERNAAN NDF DAN ADF DOMBA EKOR TIPIS

Penelitian ini bertujuan untuk mengetahui pengaruh pemberian berbagai rasio rumput lapang fermentasi dan konsentrat dalam ransum terhadap konsumsi serta kecernaan Neutral Detergent Fiber (NDF) dan Acid Detergent Fiber (ADF) domba ekor tipis jantan. Materi penelitian berupa domba ekor tipis jantan sebanyak 15 ekor yang berumur sekitar 11–15 bulan dengan rata-rata bobot badan awal 25,4±3,65 kg dan bahan pakan yang terdiri dari rumput lapang fermentasi dan konsentrat. Desain penelitian ini menggunakan rancangan acak kelompok dengan tiga macam perlakuan dan lima kelompok bobot badan sebagai ulangan. Setiap ulangan terdiri dari satu ekor domba ekor tipis jantan. Perlakuan dalam ransum terdiri dari P0= 30% RLF + 70% konsentrat, P1= 50% RLF + 50% konsentrat dan P2= 70% RLF + 30% konsentrat. Peubah yang diamati adalah konsumsi NDF, konsumsi ADF, kecernaan NDF dan kecernaan ADF. Data yang diperoleh dianalisis menggunakan analisis variansi untuk mengetahui adanya pengaruh perlakuan terhadap peubah yang diamati. Hasil analisis variansi menunujukkan bahwa pemberian rumput lapang dan konsentrat dalam berbagai rasio tidak berpengaruh terhadap konsumsi NDF, konsumsi ADF, kecernaan NDF dan kecernaan ADF domba ekor tipis. Kesimpulan yang dapat diambil dari penilitian ini adalah konsumsi dan kecernaan NDF serta ADF pada penggunaan rumput lapang fermentasi dan konsentrat rasio 70:30% relatif sama dengan rasio 30:70%. Kata kunci: Domba ekor tipis, Rumput lapang fermentasi, NDF, AD

Dissimilarity-based ensembles for multiple instance learning

In multiple instance learning, objects are sets (bags) of feature vectors (instances) rather than individual feature vectors. In this paper, we address the problem of how these bags can best be represented. Two standard approaches are to use (dis)similarities between bags and prototype bags, or between bags and prototype instances. The first approach results in a relatively low-dimensional representation, determined by the number of training bags, whereas the second approach results in a relatively high-dimensional representation, determined by the total number of instances in the training set. However, an advantage of the latter representation is that the informativeness of the prototype instances can be inferred. In this paper, a third, intermediate approach is proposed, which links the two approaches and combines their strengths. Our classifier is inspired by a random subspace ensemble, and considers subspaces of the dissimilarity space, defined by subsets of instances, as prototypes. We provide insight into the structure of some popular multiple instance problems and show state-of-the-art performances on these data sets

Classification of COPD with Multiple Instance Learning

Chronic obstructive pulmonary disease (COPD) is a lung disease where early detection benefits the survival rate. COPD can be quantified by classifying patches of computed tomography images, and combining patch labels into an overall diagnosis for the image. As labeled patches are often not available, image labels are propagated to the patches, incorrectly labeling healthy patches in COPD patients as being affected by the disease. We approach quantification of COPD from lung images as a multiple instance learning (MIL) problem, which is more suitable for such weakly labeled data. We investigate various MIL assumptions in the context of COPD and show that although a concept region with COPD-related disease patterns is present, considering the whole distribution of lung tissue patches improves the performance. The best method is based on averaging instances and obtains an AUC of 0.742, which is higher than the previously reported best of 0.713 on the same dataset. Using the full training set further increases performance to 0.776, which is significantly higher (DeLong test) than previous results

Dynamic Digital Twin: Diagnosis, Treatment, Prediction, and Prevention of Disease During the Life Course

A digital twin (DT), originally defined as a virtual representation of a physical asset, system, or process, is a new concept in health care. A DT in health care is not a single technology but a domain-adapted multimodal modeling approach incorporating the acquisition, management, analysis, prediction, and interpretation of data, aiming to improve medical decision-making. However, there are many challenges and barriers that must be overcome before a DT can be used in health care. In this viewpoint paper, we build on the current literature, address these challenges, and describe a dynamic DT in health care for optimizing individual patient health care journeys, specifically for women at risk for cardiovascular complications in the preconception and pregnancy periods and across the life course. We describe how we can commit multiple domains to developing this DT. With our cross-domain definition of the DT, we aim to define future goals, trade-offs, and methods that will guide the development of the dynamic DT and implementation strategies in health care