Treatment of pediatric acute lymphoblastic leukemia (ALL) is based on the concept of tailoring the intensity of therapy to a patient's risk of relapse. To determine whether gene expression profiling could enhance risk assignment, we used oligonucleotide microarrays to analyze the pattern of genes expressed in leukemic blasts from 360 pediatric ALL patients. Distinct expression profiles identified each of the prognostically important leukemia subtypes, including T-ALL, E2A-PBX1, BCR-ABL, TEL-AML1, MLL rearrangement, and hyperdiploid >50 chromosomes. In addition, another ALL subgroup was identified based on its unique expression profile. Examination of the genes comprising the expression signatures provided important insights into the biology of these leukemia subgroups. Further, within some genetic subgroups, expression profiles identified those patients that would eventually fail therapy. Thus, the single platform of expression profiling should enhance the accurate risk stratification of pediatric ALL patients.

Emerging patterns (EPs) are itemsets whose supports change significantly from one dataset to another; they were recently proposed to capture multi-attribute contrasts between data classes, or trends over time. In this paper we propose a new classifier, CAEP, using the following main ideas based on EPs: (i) Each EP can sharply differentiate the class membership of a (possibly small) fraction of instances containing the EP, due to the big difference between its supports in the opposing classes; we define the differentiating power of the EP in terms of the supports and their ratio, on instances containing the EP. (ii) For each instance t, by aggregating the differentiating power of a fixed, automatically selected set of EPs, a score is obtained for each class. The scores for all classes are normalized and the largest score determines t's class. CAEP is suitable for many applications, even those with large volumes of high (e.g. 45) dimensional data; it does not depend on dimension reduction on data; and it is usually equally accurate on all classes even if their populations are unbalanced. Experiments show that CAEP has consistent good predictive accuracy, and it almost always outperforms C4.5 and CBA. By using efficient, border-based algorithms (developed elsewhere) to discover EPs, CAEP scales up on data volume and dimensionality. Observing that accuracy on the whole dataset is too coarse description of classifiers, we also used a more accurate measure, sensitivity and precision, to better characterize the performance of classifiers. CAEP is also very good under this measure.

11 SARS-CoV-2, the newly identified human coronavirus causing severe pneumonia 12 epidemic, was probably originated from Chinese horseshoe bats. However, direct 13 transmission of the virus from bats to humans is unlikely due to lack of direct contact, 14 implying the existence of unknown intermediate hosts. Angiotensin converting enzyme 15 2 (ACE2) is the receptor of SARS-CoV-2, but only ACE2s of certain species can be 16 utilized by SARS-CoV-2. Here, we evaluated and ranked the receptor-utilizing 17 capability of ACE2s from various species by phylogenetic clustering and sequence 18 alignment with the currently known ACE2s utilized by SARS-CoV-2. As a result, we 19 predicted that SARS-CoV-2 tends to utilize ACE2s of various mammals, except 20 murines, and some birds, such as pigeon. This prediction may help to screen the 21 intermediate hosts of SARS-CoV-2. 22 23 2 Keywords: SARS-CoV-2; coronavirus; angiotensin converting enzyme 2 (ACE2); 24 receptor utilization; phylogenetic analysis. 25 26

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