Pengaruh Penambahan Tepung Gaplek dan Isolat Bakteri Asam Laktat dari Cairan Rumen Sapi PO terhadap Kualitas Silase Rumput Gajah (Pennisetum Purpureum)

Penelitian tentang pengaruh penambahan tepung gaplek dan isolat bakteri laktat (LAB) diinkubasi dari cairan rumen sapi terhadap silase rumput gajah (Pennisetum purpureum) telah dilaksanakan di Desa Ujung Ujung Pabelan, Kabupaten Semarang. Analisis nilai gizi dilaksanakan di Laboratorium Biokimia Pangan Gizi, Fakultas Ilmu Makanan Ternak, Universitas Gadjah Mada. Penelitian ini menggunakan rancangan acak lengkap terdiri dari 4 perlakuan dan 3 ulangan. Perlakuannya adalah T0 = kontrol silase, T1 = silase rumput gajah dengan penambahan tepung gaplek 1% dan inokulum bakteri asam laktat dari cairan rumen sapi PO 106 cfu/g hijauan, T2 = silase rumput gajah dengan penambahan tepung gaplek 3%, dan inokulum bakteri asam laktat dari cairan rumen sapi PO 106 cfu/g hijauan, T3 = silase rumput gajah dengan penambahan tepung gaplek 5% dan inokulum bakteri asam laktat dari cairan rumen sapi PO 106 cfu/g hijauan. Parameter yang diamati adalah asam laktat, pH, bahan kering, protein kasar, dan serat kasar. Untuk mengetahui pengaruh perlakuan, analisis data dilanjutkan dengan Uji Duncan. Hasil penelitian ini menunjukkan bahwa penambahan tepung gaplek memberikan pengaruh yang nyata terhadap asam laktat, pH, NH3, protein kasar dan serat kasar. Sedang pada bahan kering pengaruhnya tidak nyata. Perlakuan terbaik adalah adalah silase rumput gajah dengan 5% tepung gaplek

A nonparametric self-adjusting control for joint learning and optimization of multi-product pricing with finite resource capacity

We study a multi-period network revenue management problem where a seller sells multiple products, made from multiple resources with infinite capacity, in an environment where the underlying demand function is a priori unknown (in the nonparametric sense). The objective of the seller is to simultaneously learn the unknown demand function and dynamically price his products to minimize the expected revenue loss. For the problem where the number of selling periods and initial capacity are scaled by k > 0, it is known that the expected revenue loss of any non-anticipating pricing policy is (pk). However, there is a considerable gap between this theoretical lower bound and the performance bound of the best known heuristic control in the literature. In this paper, we propose a Nonparametric Self-adjusting Control and show that its expected revenue loss is O(k1=2+ log k) for any arbitrarily small >0, provided that the underlying demand function is sufficiently smooth. This is the tightest bound of its kind for the problem setting that we consider in this paper and it significantly improves the performance bound of existing heuristic controls in the literature; in addition, our intermediate results on the large deviation bounds for spline estimation and nonparametric stability analysis of constrained optimization are of independent interest and are potentially useful for other applications

Real-time spatial-intertemporal dynamic pricing for balancing supply and demand in a ride-hailing network: near-optimal policies and the value of dynamic pricing

Motivated by the growth of ride-hailing services in urban areas, we study a (tactical) real-time spatial–intertemporal dynamic pricing problem where a firm uses a pool of homogeneous servers (e.g., a fleet of taxis) to serve price-sensitive customers (i.e., a rider requesting a trip from an origin to a destination) within a finite horizon (e.g., a day). We consider a revenue maximization problem in a model that captures the stochastic and nonstationary nature of demands, and the nonnegligible travel time from one location to another location. We first show that the relative revenue loss of any static pricing policy is at least in the order of n−1/2 in a large system regime where the demand arrival rate and the number of servers scale linearly with n, which highlights the limitation of static pricing control. We also propose a static pricing control with a matching performance (up to a multiplicative logarithmic term). Next, we develop a novel state-dependent dynamic pricing control with a reduced relative revenue loss of order n−2/3. The key idea is to dynamically adjust the prices in a way that reduces the impact of past “errors” on the balance of future distributions of servers and customers across the network. Our extensive numerical studies using both a synthetic data set and a real data set from the New York City Taxi and Limousine Commission, confirm our theoretical findings and highlight the benefit of dynamic pricing over static pricing, especially when dealing with nonstationary demands. Interestingly, we also observe that the revenue improvement under our proposed policy primarily comes from an increase in the number of customers served instead of from an increase in the average prices compared with the static pricing policy. This suggests that dynamic pricing can be potentially used to simultaneously increase both revenue and the number of customers served (i.e., service level). Finally, as an extension, we discuss how to generalize the proposed policy to a setting where the firm can also actively relocate some of the available servers to different locations in the network in addition to implementing dynamic pricing

Technical note - Joint learning and optimization of multi-product pricing with finite resource capacity and unknown demand parameters

We consider joint learning and pricing in network revenue management (NRM) with multiple products, multiple resources with finite capacity, parametric demand model, and a continuum set of feasible price vectors. We study the setting with a general parametric demand model and the setting with a well-separated demand model. For the general parametric demand model, we propose a heuristic that is rate-optimal (i.e., its regret bound exactly matches the known theoretical lower bound under any feasible pricing control for our setting). This heuristic is the first rate-optimal heuristic for a NRM with a general parametric demand model and a continuum of feasible price vectors. For the well-separated demand model, we propose a heuristic that is close to rate-optimal (up to a multiplicative logarithmic term). Our second heuristic is the first in the literature that deals with the setting of a NRM with a well-separated parametric demand model and a continuum set of feasible price vectors

Real-Time Dynamic Pricing with Minimal and Flexible Price Adjustment

We study a standard dynamic pricing problem where the seller (a monopolist) possesses a finite amount of inventories and attempts to sell the products during a finite selling season. Despite the potential benefits of dynamic pricing, many sellers still adopt a static pricing policy because of (1) the complexity of frequent reoptimizations, (2) the negative perception of excessive price adjustments, and (3) the lack of flexibility caused by existing business constraints. In this paper, we develop a family of pricing heuristics that can be used to address all these challenges. Our heuristic is computationally easy to implement; it requires only a single optimization at the beginning of the selling season and automatically adjusts the prices over time. Moreover, to guarantee a strong revenue performance, the heuristic only needs to adjust the prices of a small number of products and do so infrequently. This property helps the seller focus his effort on the prices of the most important products instead of all products. In addition, in the case where not all products are equally admissible to price adjustment (due to existing business constraints such as contractual agreement, strategic product positioning, etc.), our heuristic can immediately substitute the price adjustment of the original products with the price adjustment of similar products and maintain an equivalent revenue performance. This property provides the seller with extra flexibility in managing his prices

Functional complementation of UvsX and UvsY mutations in the mediation of T4 homologous recombination

Bacteriophage T4 homologous recombination events are promoted by presynaptic filaments of UvsX recombinase bound to single-stranded DNA (ssDNA). UvsY, the phage recombination mediator protein, promotes filament assembly in a concentration-dependent manner, stimulating UvsX at stoichiometric concentrations but inhibiting at higher concentrations. Recent work demonstrated that UvsX-H195Q/A mutants exhibit decreased ssDNA-binding affinity and altered enzymatic properties. Here, we show that unlike wild-type UvsX, the ssDNA-dependent ATPase activities of UvsX-H195Q/A are strongly inhibited by both low and high concentrations of UvsY protein. This inhibition is partially relieved by UvsY mutants with decreased ssDNA-binding affinity. The UvsX-H195Q mutant retains weak DNA strand exchange activity that is inhibited by wild-type UvsY, but stimulated by ssDNA-binding compromised UvsY mutants. These and other results support a mechanism in which the formation of competent presynaptic filaments requires a hand-off of ssDNA from UvsY to UvsX, with the efficiency of the hand-off controlled by the relative ssDNA-binding affinities of the two proteins. Other results suggest that UvsY acts as a nucleotide exchange factor for UvsX, enhancing filament stability by increasing the lifetime of the high-affinity, ATP-bound form of the enzyme. Our findings reveal new details of the UvsX/UvsY relationship in T4 recombination, which may have parallels in other recombinase/mediator systems

EMSY links breast cancer gene 2 to the 'Royal Family'

Although the role of the breast cancer gene 2 (BRCA2) tumor suppressor gene is well established in inherited breast and ovarian carcinomas, its involvement in sporadic disease is still uncertain. The recent identification of a novel BRCA2 binding protein, EMSY, as a putative oncogene implicates the BRCA2 pathway in sporadic tumors. Furthermore, EMSY's binding to members of the 'Royal Family' of chromatin remodeling proteins may lead to a better understanding of the physiological function of BRCA2 and its role in chromatin remodeling

Entamoeba lysyl-tRNA Synthetase Contains a Cytokine-Like Domain with Chemokine Activity towards Human Endothelial Cells

Immunological pressure encountered by protozoan parasites drives the selection of strategies to modulate or avoid the immune responses of their hosts. Here we show that the parasite Entamoeba histolytica has evolved a chemokine that mimics the sequence, structure, and function of the human cytokine HsEMAPII (Homo sapiens endothelial monocyte activating polypeptide II). This Entamoeba EMAPII-like polypeptide (EELP) is translated as a domain attached to two different aminoacyl-tRNA synthetases (aaRS) that are overexpressed when parasites are exposed to inflammatory signals. EELP is dispensable for the tRNA aminoacylation activity of the enzymes that harbor it, and it is cleaved from them by Entamoeba proteases to generate a standalone cytokine. Isolated EELP acts as a chemoattractant for human cells, but its cell specificity is different from that of HsEMAPII. We show that cell specificity differences between HsEMAPII and EELP can be swapped by site directed mutagenesis of only two residues in the cytokines' signal sequence. Thus, Entamoeba has evolved a functional mimic of an aaRS-associated human cytokine with modified cell specificity

BRCA1 tumor suppression depends on BRCT phosphoprotein binding, but not its E3 ligase activity

Germline mutations of the breast cancer 1 (BRCA1) gene are a major cause of familial breast and ovarian cancer. The BRCA1 protein displays E3 ubiquitin ligase activity, and this enzymatic function is thought to be required for tumor suppression. To test this hypothesis, we generated mice that express an enzymatically defective Brca1. We found that this mutant Brca1 prevents tumor formation to the same degree as does wild-type Brca1 in three different genetically engineered mouse (GEM) models of cancer. In contrast, a mutation that ablates phosphoprotein recognition by the BRCA C terminus (BRCT) domains of BRCA1 elicits tumors in each of the three GEM models. Thus, BRCT phosphoprotein recognition, but not the E3 ligase activity, is required for BRCA1 tumor suppression