Sparsest Cut on Bounded Treewidth Graphs: Algorithms and Hardness Results

We give a 2-approximation algorithm for Non-Uniform Sparsest Cut that runs in time $n^{O(k)}$, where $k$ is the treewidth of the graph. This improves on the previous $2^{2^k}$-approximation in time \poly(n) 2^{O(k)} due to Chlamt\'a\v{c} et al. To complement this algorithm, we show the following hardness results: If the Non-Uniform Sparsest Cut problem has a $\rho$-approximation for series-parallel graphs (where $\rho \geq 1$), then the Max Cut problem has an algorithm with approximation factor arbitrarily close to $1/\rho$. Hence, even for such restricted graphs (which have treewidth 2), the Sparsest Cut problem is NP-hard to approximate better than $17/16 - \epsilon$ for $\epsilon > 0$; assuming the Unique Games Conjecture the hardness becomes $1/\alpha_{GW} - \epsilon$. For graphs with large (but constant) treewidth, we show a hardness result of $2 - \epsilon$ assuming the Unique Games Conjecture. Our algorithm rounds a linear program based on (a subset of) the Sherali-Adams lift of the standard Sparsest Cut LP. We show that even for treewidth-2 graphs, the LP has an integrality gap close to 2 even after polynomially many rounds of Sherali-Adams. Hence our approach cannot be improved even on such restricted graphs without using a stronger relaxation

Nitrofurantoin susceptibility profile versus other antibiotics tested in uropathogens- a retrospective study from India

Introduction. Urinary Tract Infection (UTI) is one of the most common bacterial infections encountered by clinicians worldwide. The emergence of multidrug-resistant uropathogens necessitates a review of their susceptibility profiles. This study aims to assess the susceptibility trends of uropathogens to a panel of drugs, with special emphasis on Nitrofurantoin (NFT). Methods. A retrospective analysis was conducted on 2,099 mid-stream clean catch urine samples processed by standard microbiological methods. Clinical and Laboratory Standards Institute (CLSI) guidelines (2019) were followed. Statistical analysis was performed. Results. Out of all samples, 212 were culture positive. Escherichia coli (34.9%) and Enterococcus spp. (15.1%) were the most common Gram-negative and Gram-positive organisms, respectively. Gram-negative isolates were most susceptible to Colistin (97.38%), followed by NFT (69.35%). Gram-positive uropathogens were most sensitive to Linezolid (100%), followed by Vancomycin and NFT, each with 92.45% susceptibility. Conclusion. The increase in antibiotic resistance among various uropathogens underscores the need for surveillance data to inform the appropriate selection of antibiotics. Our study highlights that, among the panel of antibiotics tested, NFT appears to be a viable alternative for treating multidrug-resistant uropathogens

Wearable Tools for Affective Remote Collaboration

Affective computing is the study and development of systems that can recognize human emotions and feelings. Emotions are always an interesting topic of research and these days researchers are trying to develop systems which can recognize, interpret and process emotions based on human physiological and neural changes for the development of well-being. As the market for wearable devices is expanding, it provides more opportunity of research in emotion sharing with remote person. This Master’s thesis investigates the possibility of using wearable devices for affective remote collaboration. Previous research about affective computing, affective communication and remote collaboration using wearable devices is reviewed before starting the design process. Three wearable devices were developed, evaluated and discussed, two for emotion sharing between remote people, and the third for preliminary research to explore if eye gaze information can increase co-presence in remote collaboration. Conclusions and Future work are discussed based on the results from the research evaluation

Human stem cell-derived astrocytes and their application to studying Nrf2-mediated neuroprotective pathways and therapeutics in neurodegeneration

Glia, including astrocytes, are increasingly at the forefront of neurodegenerative research for their role in the modulation of neuronal function and survival. Improved understanding of underlying disease mechanisms, including the role of the cellular environment in neurodegeneration, is central to therapeutic development for these currently untreatable diseases. In these endeavours, experimental models that more closely reproduce the human condition have the potential to facilitate the transition between experimental studies in model organisms and patient trials. In this review we discuss the growing role of astrocytes in neurodegenerative diseases, and how astrocytes generated from human pluripotent stem cells represent a useful tool for analyzing astrocytic signalling and influence on neuronal function