Hardness of longest common subsequence for sequences with bounded run-lengths

International audienceThe longest common subsequence (LCS) problem is a classic and well-studied problem in computer science with extensive applications in diverse areas ranging from spelling error corrections to molecular biology. This paper focuses on LCS for fixed alphabet size and fixed run-lengths (i.e., maximum number of consecutive occurrences of the same symbol). We show that LCS is NP-complete even when restricted to (i) alphabets of size 3 and run-length at most 1, and (ii) alphabets of size 2 and run-length at most 2 (both results are tight). For the latter case, we show that the problem is approximable within ratio 3/5

Evolution, structure and emerging roles of C1ORF112 in DNA replication, DNA damage responses, and cancer

The C1ORF112 gene initially drew attention when it was found to be strongly co‐expressed with several genes previously associated with cancer and implicated in DNA repair and cell cycle regulation, such as RAD51 and the BRCA genes. The molecular functions of C1ORF112 remain poorly understood, yet several studies have uncovered clues as to its potential functions. Here, we review the current knowledge on C1ORF112 biology, its evolutionary history, possible functions, and its potential relevance to cancer. C1ORF112 is conserved throughout eukaryotes, from plants to humans, and is very highly conserved in primates. Protein models suggest that C1ORF112 is an alpha-helical protein. Interestingly, homozygous knockout mice are not viable, suggesting an essential role for C1ORF112 in mammalian development. Gene expression data show that, among human tissues, C1ORF112 is highly expressed in the testes and overexpressed in various cancers when compared to healthy tissues. C1ORF112 has also been shown to have altered levels of expression in some tumours with mutant TP53. Recent screens associate C1ORF112 with DNA replication and reveal possible links to DNA damage repair pathways, including the Fanconi anaemia pathway and homologous recombination. These insights provide important avenues for future research in our efforts to understand the functions and potential disease relevance of C1ORF112

Actas de las V Jornadas ScienCity 2022. Fomento de la Cultura Científica, Tecnológica y de Innovación en Ciudades Inteligentes

ScienCity es una actividad que viene siendo continuada desde 2018 con el objetivo de dar a conocer los conocimientos y tecnologías emergentes siendo investigados en las universidades, informar de experiencias, servicios e iniciativas puestas ya en marcha por instituciones y empresas, llegar hasta decisores políticos que podrían crear sinergias, incentivar la creación de ideas y posibilidades de desarrollo conjuntas, implicar y provocar la participación ciudadana, así como gestar una red internacional multidisciplinar de investigadores que garantice la continuación de futuras ediciones. En 2022 se recibieron un total de 48 trabajos repartidos en 25 ponencias y 24 pósteres pertenecientes a 98 autores de 14 instituciones distintas de España, Portugal, Polonia y Países Bajos.Fundación Española para la Ciencia y la Tecnología-Ministerio de Ciencia, Innovación y Universidades; Consejería de la Presidencia, Administración Pública e Interior de la Junta de Andalucía; Estrategia de Política de Investigación y Transferencia de la Universidad de Huelva; Cátedra de Innovación Social de Aguas de Huelva; Cátedra de la Provincia; Grupo de investigación TEP-192 de Control y Robótica; Centro de Investigación en Tecnología, Energía y Sostenibilidad (CITES

Time to Switch to Second-line Antiretroviral Therapy in Children With Human Immunodeficiency Virus in Europe and Thailand.

Background: Data on durability of first-line antiretroviral therapy (ART) in children with human immunodeficiency virus (HIV) are limited. We assessed time to switch to second-line therapy in 16 European countries and Thailand. Methods: Children aged <18 years initiating combination ART (≥2 nucleoside reverse transcriptase inhibitors [NRTIs] plus nonnucleoside reverse transcriptase inhibitor [NNRTI] or boosted protease inhibitor [PI]) were included. Switch to second-line was defined as (i) change across drug class (PI to NNRTI or vice versa) or within PI class plus change of ≥1 NRTI; (ii) change from single to dual PI; or (iii) addition of a new drug class. Cumulative incidence of switch was calculated with death and loss to follow-up as competing risks. Results: Of 3668 children included, median age at ART initiation was 6.1 (interquartile range (IQR), 1.7-10.5) years. Initial regimens were 32% PI based, 34% nevirapine (NVP) based, and 33% efavirenz based. Median duration of follow-up was 5.4 (IQR, 2.9-8.3) years. Cumulative incidence of switch at 5 years was 21% (95% confidence interval, 20%-23%), with significant regional variations. Median time to switch was 30 (IQR, 16-58) months; two-thirds of switches were related to treatment failure. In multivariable analysis, older age, severe immunosuppression and higher viral load (VL) at ART start, and NVP-based initial regimens were associated with increased risk of switch. Conclusions: One in 5 children switched to a second-line regimen by 5 years of ART, with two-thirds failure related. Advanced HIV, older age, and NVP-based regimens were associated with increased risk of switch

A Computational Geometry Approach to Digital Image Contour Extraction

We present a method for extracting contours from digital images, using techniques from computational geometry. Our approach is different from traditional pixel-based methods in image processing. Instead of working directly with pixels, we extract a set of oriented feature points from the input digital images, then apply classical geometric techniques, such as clustering, linking, and simplification, to find contours among these points. Experiments on synthetic and natural images show that our method can effectively extract contours, even from images with considerable noise; moreover, the extracted contours have a very compact representation

On the Complexity of Collecting Items With a Maximal Sliding Agent

We study the computational complexity of collecting items inside a grid map with obstacles, using an agent that always slides to the maximal extend, until it is stopped by an obstacle. An agent could be, for example, a robot or a vehicle, while obstacles could be walls or other immovable objects, and items could be packages that need to be picked up. This problem has very natural applications in robotics. The restricted type of motion of the agent naturally models movement on a frictionless surface, and movement of a robot with limited sensing capabilities and thus limited localization. For example, if a robot cannot determine the distance traveled once it starts moving, then it makes sense to keep moving until an obstacle is reached, even if the robot has a map of the environment. With today’s technology it is possible to create sophisticated robots but, since the complexity and the costs of such robots are high, it is sometimes better to use simple inexpensive robots that can still solve relatively complex tasks. In fact, simple robots are quite common and usually built using simple sensors that have limited capabilities, but that are easy to use and are considerably cheaper than more sophisticated ones. The computational complexity of numerous problems with movable objects has been extensively studied before. However, only a few of them have maximal sliding agents, and they usually do not have the goal of collecting items. We show that the problem of deciding if all the items can be collected by a maximal sliding agent can be solved efficiently when the agent is the only moving object in the map. However, we show that optimization problems such as determining the minimum number of moves required to collect all the items, and also variants in more complex environments are computationally intractable. Hence, for those problems it is better to focus on using heuristics than on finding optimal solutions

On the Complexity of Collecting Items With a Maximal Sliding Agent

We study the computational complexity of collecting items inside a grid map with obstacles, using an agent that always slides to the maximal extend, until it is stopped by an obstacle. An agent could be, for example, a robot or a vehicle, while obstacles could be walls or other immovable objects, and items could be packages that need to be picked up. This problem has very natural applications in robotics. The restricted type of motion of the agent naturally models movement on a frictionless surface, and movement of a robot with limited sensing capabilities and thus limited localization. For example, if a robot cannot determine the distance traveled once it starts moving, then it makes sense to keep moving until an obstacle is reached, even if the robot has a map of the environment. With today’s technology it is possible to create sophisticated robots but, since the complexity and the costs of such robots are high, it is sometimes better to use simple inexpensive robots that can still solve relatively complex tasks. In fact, simple robots are quite common and usually built using simple sensors that have limited capabilities, but that are easy to use and are considerably cheaper than more sophisticated ones. The computational complexity of numerous problems with movable objects has been extensively studied before. However, only a few of them have maximal sliding agents, and they usually do not have the goal of collecting items. We show that the problem of deciding if all the items can be collected by a maximal sliding agent can be solved efficiently when the agent is the only moving object in the map. However, we show that optimization problems such as determining the minimum number of moves required to collect all the items, and also variants in more complex environments are computationally intractable. Hence, for those problems it is better to focus on using heuristics than on finding optimal solutions