Ordered k-Median with Outliers

We study a natural generalization of the celebrated ordered k-median problem, named robust ordered k-median, also known as ordered k-median with outliers. We are given facilities ? and clients ? in a metric space (???,d), parameters k,m ? ?_+ and a non-increasing non-negative vector w ? ?_+^m. We seek to open k facilities F ? ? and serve m clients C ? ?, inducing a service cost vector c = {d(j,F):j ? C}; the goal is to minimize the ordered objective w^?c^?, where d(j,F) = min_{i ? F}d(j,i) is the minimum distance between client j and facilities in F, and c^? ? ?_+^m is the non-increasingly sorted version of c. Robust ordered k-median captures many interesting clustering problems recently studied in the literature, e.g., robust k-median, ordered k-median, etc. We obtain the first polynomial-time constant-factor approximation algorithm for robust ordered k-median, achieving an approximation guarantee of 127. The main difficulty comes from the presence of outliers, which already causes an unbounded integrality gap in the natural LP relaxation for robust k-median. This appears to invalidate previous methods in approximating the highly non-linear ordered objective. To overcome this issue, we introduce a novel yet very simple reduction framework that enables linear analysis of the non-linear objective. We also devise the first constant-factor approximations for ordered matroid median and ordered knapsack median using the same framework, and the approximation factors are 19.8 and 41.6, respectively

Ex Vivo Expanded Hematopoietic Stem Cells Overcome the MHC Barrier in Allogeneic Transplantation

SummaryThe lack of understanding of the interplay between hematopoietic stem cells (HSCs) and the immune system has severely hampered the stem cell research and practice of transplantation. Major problems for allogeneic transplantation include low levels of donor engraftment and high risks of graft-versus-host disease (GVHD). Transplantation of purified allogeneic HSCs diminishes the risk of GVHD but results in decreased engraftment. Here we show that ex vivo expanded mouse HSCs efficiently overcame the major histocompatibility complex barrier and repopulated allogeneic-recipient mice. An 8-day expansion culture led to a 40-fold increase of the allograft ability of HSCs. Both increased numbers of HSCs and culture-induced elevation of expression of the immune inhibitor CD274 (B7-H1 or PD-L1) on the surface of HSCs contributed to the enhancement. Our study indicates the great potential of utilizing ex vivo expanded HSCs for allogeneic transplantation and suggests that the immune privilege of HSCs can be modulated

Exploring Unsupervised Cell Recognition with Prior Self-activation Maps

The success of supervised deep learning models on cell recognition tasks relies on detailed annotations. Many previous works have managed to reduce the dependency on labels. However, considering the large number of cells contained in a patch, costly and inefficient labeling is still inevitable. To this end, we explored label-free methods for cell recognition. Prior self-activation maps (PSM) are proposed to generate pseudo masks as training targets. To be specific, an activation network is trained with self-supervised learning. The gradient information in the shallow layers of the network is aggregated to generate prior self-activation maps. Afterward, a semantic clustering module is then introduced as a pipeline to transform PSMs to pixel-level semantic pseudo masks for downstream tasks. We evaluated our method on two histological datasets: MoNuSeg (cell segmentation) and BCData (multi-class cell detection). Compared with other fully-supervised and weakly-supervised methods, our method can achieve competitive performance without any manual annotations. Our simple but effective framework can also achieve multi-class cell detection which can not be done by existing unsupervised methods. The results show the potential of PSMs that might inspire other research to deal with the hunger for labels in medical area.Comment: MICCAI 2023. arXiv admin note: substantial text overlap with arXiv:2210.0786

CDDSA: Contrastive Domain Disentanglement and Style Augmentation for Generalizable Medical Image Segmentation

Generalization to previously unseen images with potential domain shifts and different styles is essential for clinically applicable medical image segmentation, and the ability to disentangle domain-specific and domain-invariant features is key for achieving Domain Generalization (DG). However, existing DG methods can hardly achieve effective disentanglement to get high generalizability. To deal with this problem, we propose an efficient Contrastive Domain Disentanglement and Style Augmentation (CDDSA) framework for generalizable medical image segmentation. First, a disentangle network is proposed to decompose an image into a domain-invariant anatomical representation and a domain-specific style code, where the former is sent to a segmentation model that is not affected by the domain shift, and the disentangle network is regularized by a decoder that combines the anatomical and style codes to reconstruct the input image. Second, to achieve better disentanglement, a contrastive loss is proposed to encourage the style codes from the same domain and different domains to be compact and divergent, respectively. Thirdly, to further improve generalizability, we propose a style augmentation method based on the disentanglement representation to synthesize images in various unseen styles with shared anatomical structures. Our method was validated on a public multi-site fundus image dataset for optic cup and disc segmentation and an in-house multi-site Nasopharyngeal Carcinoma Magnetic Resonance Image (NPC-MRI) dataset for nasopharynx Gross Tumor Volume (GTVnx) segmentation. Experimental results showed that the proposed CDDSA achieved remarkable generalizability across different domains, and it outperformed several state-of-the-art methods in domain-generalizable segmentation.Comment: 14 pages, 8 figure

Study on overlying strata containing primary fractures migration and spatial-temporal characteristics of water gushing (leaching) caused by mining field disturbance

The super-thick, high-pressure, medium-strong water-rich Luohe Formation aquifer is overlying in the Binchang mining area of Shanxi Province, and the fractures in the overlying rock are developed, it makes the water channel easier to communicate with the aquifer and stope of Luohe Formation, resulting in the increase of water inflow and area in the stope. In order to study the morphological characteristics of water inrush induced by the network of water-conducting channels formed by primary fractures communicating with the aquifer of the thick Luohe Formation under the influence of mining, the solid-flow coupling similar material simulation test was carried out based on the similar simulation physical experiment system of water-sand inrush in overburden rock. The results show that when the working face is advanced to 140 m, the lower strata of the bed separation are broken in advance due to the influence of the primary fractures. The left incomplete bed separation space and the triangular space formed by the right cantilever beam support form the “Z” bed separation space. When the working face is advanced to 160 m, two “Z-type” bed separation spaces are developed in the overlying strata, which are interconnected with the primary fractures and mining-induced fractures to form a water channel network. The form of gushing (leaching) water in the stope changed from ‘ drip-drip and flow-flow-multi-state ’, and the overall gushing (leaching) water volume increased first and then decreased. The water pressure of overlying strata and the advancing distance of the working face show a segmented evolution characteristic of decreasing first and then increasing. The minimum interval and the position of the inflection point of the segmentation increase with the increase of the distance between the monitoring point and the open-off cut. The final water pressure values near the central area of the goaf are greater than the two boundary monitoring points. The analysis results show that the existence of primary fractures promotes the development of water-conducting fracture channel network, accelerates the process of water transport, and induces the formation and development of water gushing (leaching) in the stope. The research results clarify the influence of primary fractures on the distribution characteristics of water conduction channel network and the evolution law of water gushing (leaching) form morphology, and explain the conduction mechanism of thick and high confined aquifer water to stope water inrush

In situ Raman quantitative monitoring of methanogenesis: Culture experiments of a deep-sea cold seep methanogenic archaeon

Gas production from several metabolic pathways is a necessary process that accompanies the growth and central metabolism of some microorganisms. However, accurate and rapid nondestructive detection of gas production is still challenging. To this end, gas chromatography (GC) is primarily used, which requires sampling and sample preparation. Furthermore, GC is expensive and difficult to operate. Several researchers working on microbial gases are looking forward to a new method to accurately capture the gas trends within a closed system in real-time. In this study, we developed a precise quantitative analysis for headspace gas in Hungate tubes using Raman spectroscopy. This method requires only a controlled focus on the gas portion inside Hungate tubes, enabling nondestructive, real-time, continuous monitoring without the need for sampling. The peak area ratio was selected to establish a calibration curve with nine different CH4–N2 gaseous mixtures and a linear relationship was observed between the peak area ratio of methane to nitrogen and their molar ratios (A(CH4)/A(N2) = 6.0739 × n(CH4)/n(N2)). The results of in situ quantitative analysis using Raman spectroscopy showed good agreement with those of GC in the continuous monitoring of culture experiments of a deep-sea cold seep methanogenic archaeon. This method significantly improves the detection efficiency and shows great potential for in situ quantitative gas detection in microbiology. It can be a powerful complementary tool to GC