Polylogarithmic Approximation Algorithm for k-Connected Directed Steiner Tree on Quasi-Bipartite Graphs

In the k-Connected Directed Steiner Tree problem (k-DST), we are given a directed graph G = (V,E) with edge (or vertex) costs, a root vertex r, a set of q terminals T, and a connectivity requirement k > 0; the goal is to find a minimum-cost subgraph H of G such that H has k edge-disjoint paths from the root r to each terminal in T. The k-DST problem is a natural generalization of the classical Directed Steiner Tree problem (DST) in the fault-tolerant setting in which the solution subgraph is required to have an r,t-path, for every terminal t, even after removing k-1 vertices or edges. Despite being a classical problem, there are not many positive results on the problem, especially for the case k ? 3. In this paper, we present an O(log k log q)-approximation algorithm for k-DST when an input graph is quasi-bipartite, i.e., when there is no edge joining two non-terminal vertices. To the best of our knowledge, our algorithm is the only known non-trivial approximation algorithm for k-DST, for k ? 3, that runs in polynomial-time Our algorithm is tight for every constant k, due to the hardness result inherited from the Set Cover problem

Toward Transparent Sequence Models with Model-Based Tree Markov Model

In this study, we address the interpretability issue in complex, black-box Machine Learning models applied to sequence data. We introduce the Model-Based tree Hidden Semi-Markov Model (MOB-HSMM), an inherently interpretable model aimed at detecting high mortality risk events and discovering hidden patterns associated with the mortality risk in Intensive Care Units (ICU). This model leverages knowledge distilled from Deep Neural Networks (DNN) to enhance predictive performance while offering clear explanations. Our experimental results indicate the improved performance of Model-Based trees (MOB trees) via employing LSTM for learning sequential patterns, which are then transferred to MOB trees. Integrating MOB trees with the Hidden Semi-Markov Model (HSMM) in the MOB-HSMM enables uncovering potential and explainable sequences using available information

Two-Year Clinical and Functional Outcomes of an Asian Cohort at Ultra-High Risk of Psychosis

Background: To determine the 2-year clinical and functional outcomes of an Asian cohort at ultra-high risk (UHR) of psychosis.Method: This was a longitudinal study with a follow-up period of 2 years on 255 help-seeking adolescents and young adults at UHR of psychosis managed by a multi-disciplinary mental health team in Singapore. Clients received case management, psychosocial, and pharmacological treatment as appropriate. Data comprising symptom and functional outcomes were collected over the observation period by trained clinicians and psychiatrists.Results: The 2-year psychosis transition rate was 16.9%, with a median time to transition of 168 days. After 2 years, 14.5% of the subjects had persistent at-risk symptoms while 7.5% developed other non-psychotic psychiatric disorders. 38.4% of the cohort had recovered and was discharged from mental health services. The entire cohort's functioning improved as reflected by an increase in the score of the Social and Occupational Functioning Assessment Scale during the follow-up period. Predictors to psychosis transition included low education level, baseline unemployment, a history of violence, and brief limited intermittent psychotic symptoms, while male gender predicted the persistence of UHR state, or the development of non-psychotic disorders.Conclusion: Use of the current UHR criteria allows us to identify individuals who are at imminent risk of developing not just psychosis, but also those who may develop other mental health disorders. Future research should include identifying the needs of those who do not transition to psychosis, while continuing to refine on ways to improve the UHR prediction algorithm for psychosis

OMMA enables population-scale analysis of complex genomic features and phylogenomic relationships from nanochannel-based optical maps.

BackgroundOptical mapping is an emerging technology that complements sequencing-based methods in genome analysis. It is widely used in improving genome assemblies and detecting structural variations by providing information over much longer (up to 1 Mb) reads. Current standards in optical mapping analysis involve assembling optical maps into contigs and aligning them to a reference, which is limited to pairwise comparison and becomes bias-prone when analyzing multiple samples.FindingsWe present a new method, OMMA, that extends optical mapping to the study of complex genomic features by simultaneously interrogating optical maps across many samples in a reference-independent manner. OMMA captures and characterizes complex genomic features, e.g., multiple haplotypes, copy number variations, and subtelomeric structures when applied to 154 human samples across the 26 populations sequenced in the 1000 Genomes Project. For small genomes such as pathogenic bacteria, OMMA accurately reconstructs the phylogenomic relationships and identifies functional elements across 21 Acinetobacter baumannii strains.ConclusionsWith the increasing data throughput of optical mapping system, the use of this technology in comparative genome analysis across many samples will become feasible. OMMA is a timely solution that can address such computational need. The OMMA software is available at https://github.com/TF-Chan-Lab/OMTools

Growth mechanism and magnon excitation in NiO nanowalls

The nanosized effects of short-range multimagnon excitation behavior and short-circuit diffusion in NiO nanowalls synthesized using the Ni grid thermal treatment method were observed. The energy dispersive spectroscopy mapping technique was used to characterize the growth mechanism, and confocal Raman scattering was used to probe the antiferromagnetic exchange energy J2 between next-nearest-neighboring Ni ions in NiO nanowalls at various growth temperatures below the Neel temperature. This study shows that short spin correlation leads to an exponential dependence of the growth temperatures and the existence of nickel vacancies during the magnon excitation. Four-magnon configurations were determined from the scattering factor, revealing a lowest state and monotonic change with the growth temperature

Metabolic Stress-Induced Phosphorylation of KAP1 Ser473 Blocks Mitochondrial Fusion in Breast Cancer Cells

Mitochondrial dynamics during nutrient starvation of cancer cells likely exert profound effects on their capability for metastatic progression. Here, we report that KAP1 (TRIM28), a transcriptional coadaptor protein implicated in metastatic progression in breast cancer, is a pivotal regulator of mitochondrial fusion in glucose-starved cancer cells. Diverse metabolic stresses induced Ser473 phosphorylation of KAP1 (pS473-KAP1) in a ROS- and p38-dependent manner. Results from live-cell imaging and molecular studies revealed that during the first 6 to 8 hours of glucose starvation, mitochondria initially underwent extensive fusion, but then subsequently fragmented in a pS473-KAP1-dependent manner. Mechanistic investigations using phosphorylation-defective mutants revealed that KAP1 Ser473 phosphorylation limited mitochondrial hyperfusion in glucose-starved breast cancer cells, as driven by downregulation of the mitofusin protein MFN2, leading to reduced oxidative phosphorylation and ROS production. In clinical specimens of breast cancer, reduced expression of MFN2 corresponded to poor prognosis in patients. In a mouse xenograft model of human breast cancer, there was an association in the core region of tumors between MFN2 downregulation and the presence of highly fragmented mitochondria. Collectively, our results suggest that KAP1 Ser473 phosphorylation acts through MFN2 reduction to restrict mitochondrial hyperfusion, thereby contributing to cancer cell survival under conditions of sustained metabolic stress