Group testing with Random Pools: Phase Transitions and Optimal Strategy

The problem of Group Testing is to identify defective items out of a set of objects by means of pool queries of the form "Does the pool contain at least a defective?". The aim is of course to perform detection with the fewest possible queries, a problem which has relevant practical applications in different fields including molecular biology and computer science. Here we study GT in the probabilistic setting focusing on the regime of small defective probability and large number of objects, $p \to 0$ and $N \to \infty$. We construct and analyze one-stage algorithms for which we establish the occurrence of a non-detection/detection phase transition resulting in a sharp threshold, $\bar M$, for the number of tests. By optimizing the pool design we construct algorithms whose detection threshold follows the optimal scaling $\bar M\propto Np|\log p|$. Then we consider two-stages algorithms and analyze their performance for different choices of the first stage pools. In particular, via a proper random choice of the pools, we construct algorithms which attain the optimal value (previously determined in Ref. [16]) for the mean number of tests required for complete detection. We finally discuss the optimal pool design in the case of finite $p$

Spatial partitioning of the regulatory landscape of the X-inactivation centre

In eukaryotes transcriptional regulation often involves multiple long-range elements and is influenced by the genomic environment. A prime example of this concerns the mouse X-inactivation centre (Xic), which orchestrates the initiation of X-chromosome inactivation (XCI) by controlling the expression of the non-protein-coding Xist transcript. The extent of Xic sequences required for the proper regulation of Xist remains unknown. Here we use chromosome conformation capture carbon-copy (5C) and super-resolution microscopy to analyse the spatial organization of a 4.5-megabases (Mb) region including Xist. We discover a series of discrete 200-kilobase to 1 Mb topologically associating domains (TADs), present both before and after cell differentiation and on the active and inactive X. TADs align with, but do not rely on, several domain-wide features of the epigenome, such as H3K27me3 or H3K9me2 blocks and lamina-associated domains. TADs also align with coordinately regulated gene clusters. Disruption of a TAD boundary causes ectopic chromosomal contacts and long-range transcriptional misregulation. The Xist/Tsix sense/antisense unit illustrates how TADs enable the spatial segregation of oppositely regulated chromosomal neighbourhoods, with the respective promoters of Xist and Tsix lying in adjacent TADs, each containing their known positive regulators. We identify a novel distal regulatory region of Tsix within its TAD, which produces a long intervening RNA, Linx. In addition to uncovering a new principle of cis-regulatory architecture of mammalian chromosomes, our study sets the stage for the full genetic dissection of the X-inactivation centre

Objective Evaluation of Multiple Sclerosis Lesion Segmentation using a Data Management and Processing Infrastructure

We present a study of multiple sclerosis segmentation algorithms conducted at the international MICCAI 2016 challenge. This challenge was operated using a new open-science computing infrastructure. This allowed for the automatic and independent evaluation of a large range of algorithms in a fair and completely automatic manner. This computing infrastructure was used to evaluate thirteen methods of MS lesions segmentation, exploring a broad range of state-of-theart algorithms, against a high-quality database of 53 MS cases coming from four centers following a common definition of the acquisition protocol. Each case was annotated manually by an unprecedented number of seven different experts. Results of the challenge highlighted that automatic algorithms, including the recent machine learning methods (random forests, deep learning, …), are still trailing human expertise on both detection and delineation criteria. In addition, we demonstrate that computing a statistically robust consensus of the algorithms performs closer to human expertise on one score (segmentation) although still trailing on detection scores

Adjuvant hysterectomy for treatment of residual disease in patients with cervical cancer treated with radiation therapy

The objective of this retrospective study was to determine the efficacy of adjuvant hysterectomy for treatment of residual disease in cervical carcinoma treated with radiation therapy. Between 1971 and 1996, 1590 patients with carcinoma of the uterine cervix (stages I–IIIb) were treated with radiation therapy. Three months after completion of radiation therapy, the status of local control was investigated, and total abdominal hysterectomy was performed in cases in which central residual disease existed in the cervix. Of the 1590 patients, residual disease was identified in 162 patients. Among these patients, 35 showed an absence of distant metastasis or lateral parametrial invasion and underwent hysterectomy. The overall 5- and 10-year survival rates for these patients were 68.6 and 65.7%, respectively. There was no significant difference in survival between patients with squamous cell carcinoma and those with non-squamous cell carcinoma or between patients with stage I/II carcinoma and those with stage III carcinoma. With respect to treatment-related morbidity, five (14.3%) patients suffered grade III or IV complications after hysterectomy. Adjuvant hysterectomy is an effective addition to radiation therapy in the treatment of cervical cancer, even in patients with stage III disease and in those with non-squamous cell carcinoma

Accurate prediction of X-ray pulse properties from a free-electron laser using machine learning

Free-electron lasers providing ultra-short high-brightness pulses of X-ray radiation have great potential for a wide impact on science, and are a critical element for unravelling the structural dynamics of matter. To fully harness this potential, we must accurately know the X-ray properties: intensity, spectrum and temporal profile. Owing to the inherent fluctuations in free-electron lasers, this mandates a full characterization of the properties for each and every pulse. While diagnostics of these properties exist, they are often invasive and many cannot operate at a high-repetition rate. Here, we present a technique for circumventing this limitation. Employing a machine learning strategy, we can accurately predict X-ray properties for every shot using only parameters that are easily recorded at high-repetition rate, by training a model on a small set of fully diagnosed pulses. This opens the door to fully realizing the promise of next-generation high-repetition rate X-ray lasers