In this task, you're given a paragraph from the research paper and your task is to generate a suitable title for the research paper based on the given paper. Under 100 words is a good title length.

In trial 1, phase 1, 48 male Holstein calves initially 2 to 4 d of age were transported 3.5 h to the research facility. Calves were randomly selected to either receive a s.c. injection of Zuprevo (Merck Animal Health, Summit, NJ; 4 mg of tildipirosin/kg of BW; TIL) the day after arrival (d 0) and again at weaning (d 42) or receive no injections (CON). Calves were fed 0.66 kg of milk replacer DM daily for 39 d and then 0.33 kg daily for 3 d. A starter was fed free choice for the 56 d of phase 1. In trial 1, phase 2, the same calves from phase 1 grouped by CON and TIL were moved to group pens (4 pens per treatment, 4 calves per pen) for the next 56 d. The starter was blended with 5% chopped grass hay and fed free choice. Trial 2 was similar to trial 1, phase 2 and used 48 two-month-old male Holstein calves. Calves were randomly selected to receive either a s.c. injection of Zuprevo (4 mg of tildipirosin/kg of BW; TIL) on d 0 or no injections (CON). In trial 1, phase 1, preweaning ADG and BCS change; postweaning starter intake and hip width change; overall starter intake, ADG, and hip width change; final hip width; and final BCS were greater for TIL than CON. During phase 2 of trial 1 and trial 2, calf ADG and hip width change were greater for TIL than CON. Overall, in transported Holstein calves, TIL improved ADG and structural growth by approximately 13%.
Case Study: Control of respiratory disease in male Holstein calves with tildipirosin and effect on health and growth from 0 to 4 months of age

Many examples of the emergence or re-emergence of infectious diseases involve the adaptation of zoonotic viruses to new amplification hosts or to humans themselves. These include several instances of simple mutational adaptations, often to hosts closely related to the natural reservoirs. However, based on theoretical grounds, arthropod-borne viruses, or arboviruses, may face several challenges for adaptation to new hosts. Here, we review recent findings regarding adaptive evolution of arboviruses and its impact on disease emergence. We focus on the zoonotic alphaviruses Venezuelan equine encephalitis and chikungunya viruses, which have undergone adaptive evolution that mediated recent outbreaks of disease, as well as the flaviviruses dengue and West Nile viruses, which have emerged via less dramatic adaptive mechanisms.
Factors shaping the adaptive landscape for arboviruses: implications for the emergence of disease

Genome-wide transcriptional profiling provides a global view of cellular state and how this state changes under different treatments (e.g. drugs) or conditions (e.g. healthy and diseased). Here, we present ProTINA (Protein Target Inference by Network Analysis), a network perturbation analysis method for inferring protein targets of compounds from gene transcriptional profiles. ProTINA uses a dynamic model of the cell-type specific protein-gene transcriptional regulation to infer network perturbations from steady state and time-series differential gene expression profiles. A candidate protein target is scored based on the gene network's dysregulation, including enhancement and attenuation of transcriptional regulatory activity of the protein on its downstream genes, caused by drug treatments. For benchmark datasets from three drug treatment studies, ProTINA was able to provide highly accurate protein target predictions and to reveal the mechanism of action of compounds with high sensitivity and specificity. Further, an application of ProTINA to gene expression profiles of influenza A viral infection led to new insights of the early events in the infection. Figure 1. Protein target prediction by ProTINA. (A) The protein-gene network describes direct and indirect regulations of gene expression by transcription factors (TF) and their protein partners (P), respectively. A drug interaction with a protein is expected to cause differential expression of the downstream genes in the PGN. (B) Based on a kinetic model of gene transcriptional process, ProTINA infers the weights of the protein-gene regulatory edges, denoted by a kj , using gene expression data. The variable a kj describes the regulation of protein j on gene k, where the magnitude and sign of a kj indicate the strength and mode (+a kj : activation, -a kj : repression) of the regulatory interaction, respectively. (C) A candidate protein target is scored based on the deviations in the expression of downstream genes from the PGN model prediction (P j : log 2 FC expression of protein j, G k : log 2 FC expression of gene k). The colored dots in the plots illustrate the log 2 FC data of a particular drug treatment, while the lines show the predicted expression of gene k by the (linear) PGN model. The variable z k denotes the z-score of the deviation of the expression of gene k from the PGN model prediction. A drug-induced enhancement of protein-gene regulatory interactions is indicated by a positive (negative) z k in the expression of genes that are activated (repressed) by the protein (i.e. a kj z k > 0). Vice versa, a drug-induced attenuation is indicated by a negative (positive) z k in the expression of genes that are activated (repressed) by the protein (i.e. a kj z k < 0). (D) The score of a candidate protein target is determined by combining the z-scores of the set of regulatory edges associated with the protein in the PGN. A positive (negative) score indicates a drug-induced enhancement (attenuation). The larger the magnitude of the score, the more consistent is the drug induced perturbations (enhancement/attenuation) on the protein-gene regulatory edges.

Network perturbation analysis of gene transcriptional profiles reveals protein targets and mechanism of action of drugs and influenza A viral infection