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Techniques for automatic query expansion have been extensively studied in information research as a means of addressing the word mismatch between queries and documents. These techniques can be categorized as either global or local. While global techniques rely on analysis of a whole collection to discover word relationships, local techniques emphasize analysis of the top-ranked documents retrieved for a query. While local techniques have shown to be more effective that global techniques in general, existing local techniques are not robust and can seriously hurt retrieved when few of the retrieval documents are relevant. We propose a new technique, called local context analysis, which selects expansion terms based on cooccurrence with the query terms within the top-ranked documents. Experiments on a number of collections, both English and non-English, show that local context analysis offers more effective and consistent retrieval results.

E ective retrieval in a distributed environment is an important but di cult problem. Lack of e ectiveness appears to have three causes. First, collection selection based on word histograms is not appropriate for heterogeneous collections. Second, relevant documents are scattered over many collections and searching a few collections misses many relevant documents. Third, most existing collection selection metrics lack sound theoretical justi cations and hence may not bewell tuned to the problem. We propose a new approach to distributed retrieval based on document clustering and language modeling. Document clustering is used to organize collections around topics. Language modeling is used to properly represent topics and e ectively select the right topics for a query. Based on these ideas, three methods are proposed to suit di erent e n vironments. We show that all three methods improve e ectiveness of distributed retrieval.

PURPOSE Patients with relapsed or refractory T-cell acute lymphoblastic leukemia (r/r T-ALL) have few options and poor prognosis. The aim was to assess donor-derived anti-CD7 chimeric antigen receptor (CAR) T-cell safety and efficacy in patients with r/r T-ALL. METHODS In this single-center, phase I trial, we administered anti-CD7 CAR T cells, manufactured from either previous stem-cell transplantation donors or new donors, to patients with r/r T-ALL, in single infusions at doses of 5 × 105 or 1 × 106 (±30%) cells per kilogram of body weight. The primary end point was safety with efficacy secondary. RESULTS Twenty participants received infusions. Adverse events including cytokine release syndrome grade 1-2 occurred in 90% (n = 18) and grade 3-4 in 10% (n = 2), cytopenia grade 3-4 in 100% (n = 20), neurotoxicity grade 1-2 in 15% (n = 3), graft-versus-host disease grade 1-2 in 60% (n = 12), and viral activation grade 1-2 in 20% (n = 4). All adverse events were reversible, except in one patient who died through pulmonary hemorrhage related to fungal pneumonia, which occurred at 5.5 months, postinfusion. Ninety percent (n = 18) achieved complete remission with seven patients proceeding to stem-cell transplantation. At a median follow-up of 6.3 months (range 4.0-9.2), 15 remained in remission. CAR T cells were still detectable in five of five patients assessed in month 6, postinfusion. Although patients' CD7-positive normal T cells were depleted, CD7-negative T cells expanded and likely alleviated treatment-related T-cell immunodeficiency. CONCLUSION Among 20 patients with r/r T-ALL enrolled in this trial, donor-derived CD7 CAR T cells exhibited efficient expansion and achieved a high complete remission rate with manageable safety profile. A multicenter, phase II trial of donor-derived CD7 CAR T cells is in progress ( NCT04689659 ).

Stemming is used in many information retrieval (IR) systems to reduce variant word forms to common roots. It is one of the simplest applications of natural-language processing to IR and is one of the most effective in terms of user acceptance and consistency, though small retrieval improvements. Current stemming techniques do not, however, reflect the language use in specific corpora, and this can lead to occasional serious retrieval failures. We propose a technique for using corpus-based word variant cooccurrence statistics to modify or create a stemmer. The experimental results generated using English newspaper and legal text and Spanish text demonstrate the viability of this technique and its advantages relative to conventional approaches that only employ morphological rules.

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This paper evaluates the retrieval effectiveness of distributed information retrieval systems in realistic environments.We find that when a large number of collections are available, the retrieval effectiveness is significantly worse than that of centralized systems, mainly because typical queries are not adequate for the purpose of choosing the right collections. We propose two techniques to address the problem. One is to use phrase information in the collection selection index and the other is query expansion. Both techniques enhance the discriminatory power of typical queries for choosing the right collections and hence significantly improve retrieval results. Query expansion, in particular, brings the effectiveness of searching a large set of distributed collections close to that of searching a centralized collection.

Pan and colleagues report one of the first prospective evaluations of planned sequential chimeric antigen receptor (CAR) T-cell therapy targeting CD19 and then CD22 in a phase 1 trial, indicating acceptable toxicity and encouraging durable efficacy in pediatric patients with relapsed/refractory (r/r) acute lymphoblastic leukemia (ALL).