Nutrient Digestibility and Performances of Frisian Holstein Calves Fed with Pennisetum Purpureum and Inoculated with Buffalo's Rumen Bacteria

Buffalo's rumen bacteria (BRB) are potential in digesting fiber feed. BRB already adapted well with low quality forages and agricultural byproducts. The aim of this study was to determine the effect of buffalo's rumen bacteria (BRB) consortium inoculated into preweaning Frisian Holstein calves on nutrient digestibility, physiological status, mineral uptake, and blood profile. This study used 14 isolates of bacteria isolated from rumen fluid of four local buffalos. The research units consisted of seven Frisian Holstein calves at two weeks old with the average body weight of 43.6±4.5 kg. Calves were inoculated with 20 mL of buffalo's rumen bacteria isolates [4.56 x 109 cfu/mL] every morning for 10 weeks. The calves were divided into two groups i.e., three calves received bacterial inoculation and four calves without any inoculation. The variables which were analyzed in the preweaning and weaning period were feed intake, digestibility, average daily gain (ADG), feed conversion ratio (FCR), rumen fermentation characteristics, body weight, physiological status, blood profile, and mineral status. Data were analyzed statistically using t-test. The results showed that inoculation of buffalo's rumen bacteria into Frisian Holstein calves effectively increased feed intake, characteristics of leukocytes and neutrophils, and cobalt (Co) uptake during the weaning period. Inoculation of rumen bacteria improved rumen pH during preweaning and weaning periods. Inoculation of rumen bacteria also had no negative effects on digestibility, feed conversion (FCR), average daily gain (ADG), and physiological status

Aspek Juridis Terhadap Tindakan Aborsi Pada Kehamilan Akibat Perkosaan

Tujuan dilakukan penelitian ini adalah untuk mengetahui bagaimanakah larangan melakukan aborsi sesuai dengan peraturan Perundang-undangan yang berlaku dan bagaimanakah pengecualian atas larangan melakukan aborsi untuk kehamilan akibat perkosaan. Penelitian ini menggunakan metode penelitian yuridis normatif dan dapat disimpulkan, bahwa: 1. Larangan melakukan aborsi diberlakukan sesuai dengan peraturan Perundang-undangan yang berlaku, menyatakan bahwa setiap orang dilarang melakukan aborsi. Larangan tersebut dapat dikecualikan berdasarkan indikasi kedaruratan medis yang dideteksi sejak usia dini kehamilan, baik yang mengancam nyawa ibu dan/atau janin, yang menderita penyakit genetik berat dan/atau cacat bawaan, maupun yang tidak dapat diperbaiki sehingga menyulitkan bayi tersebut hidup di luar kandungan; atau kehamilan akibat perkosaan yang dapat menyebabkan trauma psikologis bagi korban perkosaan. Tindakan sebagaimana dimaksud hanya dapat dilakukan setelah melalui konseling dan/atau penasehatan pra tindakan dan diakhiri dengan konseling pasca tindakan yang dilakukan oleh konselor yang kompeten dan berwenang. 2. Pengecualian atas larangan melakukan aborsi untuk kehamilan akibat perkosaan, karena perkosaan dapat menyebabkan trauma psikologis bagi korban dan hal ini dilakukan untuk mencegah perempuan melakukan aborsi yang tidak bermutu, tidak aman, dan tidak dan tidak bertanggung jawab serta bertentangan dengan norma agama dan ketentuan peraturan Perundang-undangan

Quasinormal Modes of Asymptotically (A)dS Black Hole in Lovelock Background

We study the quasinormal modes of the massless scalar field in asymptotically (A)dS black holes in Lovelock spacetime by using the sixth order of the WKB approximation. We consider the effects of the second and third order of Lovelock coupling constants on quasinormal frequencies spectrum as well as cosmological constant.Comment: 20 pages, 3 Tables, 13 figures, the caption of the tables modifie

Encoding Multi-Resolution Brain Networks Using Unsupervised Deep Learning

The main goal of this study is to extract a set of brain networks in multiple time-resolutions to analyze the connectivity patterns among the anatomic regions for a given cognitive task. We suggest a deep architecture which learns the natural groupings of the connectivity patterns of human brain in multiple time-resolutions. The suggested architecture is tested on task data set of Human Connectome Project (HCP) where we extract multi-resolution networks, each of which corresponds to a cognitive task. At the first level of this architecture, we decompose the fMRI signal into multiple sub-bands using wavelet decompositions. At the second level, for each sub-band, we estimate a brain network extracted from short time windows of the fMRI signal. At the third level, we feed the adjacency matrices of each mesh network at each time-resolution into an unsupervised deep learning algorithm, namely, a Stacked De- noising Auto-Encoder (SDAE). The outputs of the SDAE provide a compact connectivity representation for each time window at each sub-band of the fMRI signal. We concatenate the learned representations of all sub-bands at each window and cluster them by a hierarchical algorithm to find the natural groupings among the windows. We observe that each cluster represents a cognitive task with a performance of 93% Rand Index and 71% Adjusted Rand Index. We visualize the mean values and the precisions of the networks at each component of the cluster mixture. The mean brain networks at cluster centers show the variations among cognitive tasks and the precision of each cluster shows the within cluster variability of networks, across the subjects.Comment: 6 pages, 3 figures, submitted to The 17th annual IEEE International Conference on BioInformatics and BioEngineerin

Cloud cover, cloud liquid water and cloud attenuation at Ka and V bands over equatorial climate

Cloud cover statistics and their diurnal variation have been obtained from in situ and satellite measurements for three equatorial locations. Cloud liquid water content, 0 °C isotherm height and cloud attenuation have also been obtained from radiosonde measurement using the so-called Salonen model at Kuala Lumpur (Malaysia). The results show a strong seasonal variation of cloud cover and cloud liquid water content on the two monsoon seasons. The Liquid Water Content (LWC) obtained from radiosonde and the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) is higher during the Northeast Monsoon season, which corresponds to the period of higher percentage cloud cover and high rainfall accumulation. The International Telecommunication Union—Region (ITU-R) model underestimates the cumulative distribution of LWC values at the present station. The relationship of the cloud attenuation, derived from the profiles of liquid water density and temperature within the cloud, shows an underestimate by the data obtained from the ITU-R model. The cloud attenuation at Kuala Lumpur is somewhat underestimated by the ITU-R model up to about 1.2 dB at Ka (30 GHz) and 3.4 dB at V (50 GHz) bands. The results of the specific attenuation can be used for the estimation of cloud attenuation at microwave and millimetre wave over earth-space paths. The present data are important for planning and design of satellite communications at Ka and V bands on the Earth–space path in the equatorial region