Many bioinformatics programming tasks can be automated with ChatGPT

Computer programming is a fundamental tool for life scientists, allowing them to carry out many essential research tasks. However, despite a variety of educational efforts, learning to write code can be a challenging endeavor for both researchers and students in life science disciplines. Recent advances in artificial intelligence have made it possible to translate human-language prompts to functional code, raising questions about whether these technologies can aid (or replace) life scientists' efforts to write code. Using 184 programming exercises from an introductory-bioinformatics course, we evaluated the extent to which one such model -- OpenAI's ChatGPT -- can successfully complete basic- to moderate-level programming tasks. On its first attempt, ChatGPT solved 139 (75.5%) of the exercises. For the remaining exercises, we provided natural-language feedback to the model, prompting it to try different approaches. Within 7 or fewer attempts, ChatGPT solved 179 (97.3%) of the exercises. These findings have important implications for life-sciences research and education. For many programming tasks, researchers no longer need to write code from scratch. Instead, machine-learning models may produce usable solutions. Instructors may need to adapt their pedagogical approaches and assessment techniques to account for these new capabilities that are available to the general public.Comment: 13 pages, 4 figures, to be submitted for publicatio

A single-sample microarray normalization method to facilitate personalized-medicine workflows

AbstractGene-expression microarrays allow researchers to characterize biological phenomena in a high-throughput fashion but are subject to technological biases and inevitable variabilities that arise during sample collection and processing. Normalization techniques aim to correct such biases. Most existing methods require multiple samples to be processed in aggregate; consequently, each sample's output is influenced by other samples processed jointly. However, in personalized-medicine workflows, samples may arrive serially, so renormalizing all samples upon each new arrival would be impractical. We have developed Single Channel Array Normalization (SCAN), a single-sample technique that models the effects of probe-nucleotide composition on fluorescence intensity and corrects for such effects, dramatically increasing the signal-to-noise ratio within individual samples while decreasing variation across samples. In various benchmark comparisons, we show that SCAN performs as well as or better than competing methods yet has no dependence on external reference samples and can be applied to any single-channel microarray platform

Propuesta de virtualización de servidores con Hyper-V en el centro de datos de la Facultad de Ciencias Médicas de la UNAN-Managua

La importancia del crecimiento en la potencia de cómputo y la existencia de problemas relacionados con el uso del hardware, ha hecho de la virtualización la solución más idónea para resolver tales dificultades, dentro de sus propósitos se encuentran hacer uso eficiente de los recursos y disminuir el costo total asociado a los mismos. Este trabajo de investigación fue realizado con la finalidad de proponer una solución para la virtualización servidores. La virtualización es una tecnología que permite la creación de equipos, basados en software, que reproducen el ambiente de una máquina física en sus aspectos de CPU, memoria, almacenamiento y entrada y salida de dispositivos. Se limita a trabajar básicamente con Hyper-V con el fin de acotar y definir la solución de virtualización , debido a la numerosa cantidad de soluciones que existen actualmente, como lo son VMware, Cytrix, entre otros. El enfoque principal se encontrará relacionado principalmente a la virtualización de servidores, a la disposición de Hyper-V para trabajar en cluster y al tipo de cluster que se puede implementar. El objetivo general de este trabajo es entonces, proponer una solución para efectuar la virtualización ya manera explicativa se describe como trabaja un cluster de alta disponibilidad con Hyper-V para efectuar tareas de migración de maquinas virtuales, empleando técnicas propias que vienen incorporadas en el software, como Live Migration ó Quick Migration que facilitan de gran forma la gestión y administración del entorno virtual. También se describirá brevemente los detalles técnicos para la implementación del centro de datos, la disposición de las áreas funcionales, el diagrama de distribución y otros parámetros importantes a tenerse en cuenta para disponer de un centro de datos confiable

Remote sensing tree classification with a multilayer perceptron

To accelerate scientific progress on remote tree classification—as well as biodiversity and ecology sampling—The National Institute of Science and Technology created a community-based competition where scientists were invited to contribute informatics methods for classifying tree species and genus using crown-level images of trees. We classified tree species and genus at the pixel level using hyperspectral and LiDAR observations. We compared three algorithms that have been implemented extensively across a broad range of research applications: support vector machines, random forests, and multilayer perceptron. At the pixel level, the multilayer perceptron algorithm classified species or genus with high accuracy (92.7% and 95.9%, respectively) on the training data and performed better than the other two algorithms (85.8–93.5%). This indicates promise for the use of the multilayer perceptron (MLP) algorithm for tree-species classification based on hyperspectral and LiDAR observations and coincides with a growing body of research in which neural network-based algorithms outperform other types of classification algorithm for machine vision. To aggregate patterns across the images, we used an ensemble approach that averages the pixel-level outputs of the MLP algorithm to classify species at the crown level. The average accuracy of these classifications on the test set was 68.8% for the nine species

Multiplicity: an organizing principle for cancers and somatic mutations

<p>Abstract</p> <p>Background</p> <p>With the advent of whole-genome analysis for profiling tumor tissue, a pressing need has emerged for principled methods of organizing the large amounts of resulting genomic information. We propose the concept of multiplicity measures on cancer and gene networks to organize the information in a clinically meaningful manner. Multiplicity applied in this context extends Fearon and Vogelstein's multi-hit genetic model of colorectal carcinoma across multiple cancers.</p> <p>Methods</p> <p>Using the Catalogue of Somatic Mutations in Cancer (COSMIC), we construct networks of interacting cancers and genes. Multiplicity is calculated by evaluating the number of cancers and genes linked by the measurement of a somatic mutation. The Kamada-Kawai algorithm is used to find a two-dimensional minimum energy solution with multiplicity as an input similarity measure. Cancers and genes are positioned in two dimensions according to this similarity. A third dimension is added to the network by assigning a maximal multiplicity to each cancer or gene. Hierarchical clustering within this three-dimensional network is used to identify similar clusters in somatic mutation patterns across cancer types.</p> <p>Results</p> <p>The clustering of genes in a three-dimensional network reveals a similarity in acquired mutations across different cancer types. Surprisingly, the clusters separate known causal mutations. The multiplicity clustering technique identifies a set of causal genes with an area under the ROC curve of 0.84 versus 0.57 when clustering on gene mutation rate alone. The cluster multiplicity value and number of causal genes are positively correlated via Spearman's Rank Order correlation (<it>r_s</it>(8) = 0.894, Spearman's <it>t </it>= 17.48, <it>p </it>< 0.05). A clustering analysis of cancer types segregates different types of cancer. All blood tumors cluster together, and the cluster multiplicity values differ significantly (Kruskal-Wallis, <it>H </it>= 16.98, <it>df </it>= 2, <it>p </it>< 0.05).</p> <p>Conclusion</p> <p>We demonstrate the principle of multiplicity for organizing somatic mutations and cancers in clinically relevant clusters. These clusters of cancers and mutations provide representations that identify segregations of cancer and genes driving cancer progression.</p

Combating subclonal evolution of resistant cancer phenotypes

Metastatic breast cancer remains challenging to treat, and most patients ultimately progress on therapy. This acquired drug resistance is largely due to drug-refractory sub-populations (subclones) within heterogeneous tumors. Here, we track the genetic and phenotypic subclonal evolution of four breast cancers through years of treatment to better understand how breast cancers become drug-resistant. Recurrently appearing post-chemotherapy mutations are rare. However, bulk and single-cell RNA sequencing reveal acquisition of malignant phenotypes after treatment, including enhanced mesenchymal and growth factor signaling, which may promote drug resistance, and decreased antigen presentation and TNF-α signaling, which may enable immune system avoidance. Some of these phenotypes pre-exist in pre-treatment subclones that become dominant after chemotherapy, indicating selection for resistance phenotypes. Post-chemotherapy cancer cells are effectively treated with drugs targeting acquired phenotypes. These findings highlight cancer's ability to evolve phenotypically and suggest a phenotype-targeted treatment strategy that adapts to cancer as it evolves

Engaging terminally ill patients in end of life talk: How experienced palliative medicine doctors navigate the dilemma of promoting discussions about dying

Objective: To examine how palliative medicine doctors engage patients in end-of-life (hereon, EoL) talk. To examine whether the practice of “eliciting and responding to cues”, which has been widely advocated in the EoL care literature, promotes EoL talk. Design: Conversation analysis of video- and audio-recorded consultations. Participants: Unselected terminally ill patients and their companions in consultation with experienced palliative medicine doctors. Setting: Outpatient clinic, day therapy clinic, and inpatient unit of a single English hospice. Results: Doctors most commonly promoted EoL talk through open elaboration solicitations; these created opportunities for patients to introduce Ð then later further articulate Ð EoL considerations in such a way that doctors did not overtly ask about EoL matters. Importantly, the wording of elaboration solicitations avoided assuming that patients had EoL concerns. If a patient responded to open elaboration solicitations without introducing EoL considerations, doctors sometimes pursued EoL talk by switching to a less participatory and more presumptive type of solicitation, which suggested the patient might have EoL concerns. These more overt solicitations were used only later in consultations, which indicates that doctors give precedence to patients volunteering EoL considerations, and offer them opportunities to take the lead in initiating EoL talk. There is evidence that doctors treat elaboration of patients’ talk as a resource for engaging them in EoL conversations. However, there are limitations associated with labelling that talk as “cues” as is common in EoL communication contexts. We examine these limitations and propose “possible EoL considerations” as a descriptively more accurate term. Conclusions: Through communicating Ð via open elaboration solicitations Ð in ways that create opportunities for patients to volunteer EoL considerations, doctors navigate a core dilemma in promoting EoL talk: giving patients opportunities to choose whether to engage in conversations about EoL whilst being sensitive to their communication needs, preferences and state of readiness for such dialogue

The extent and variability of storm-induced temperature changes in lakes measured with long-term and high-frequency data

The intensity and frequency of storms are projected to increase in many regions of the world because of climate change. Storms can alter environmental conditions in many ecosystems. In lakes and reservoirs, storms can reduce epilimnetic temperatures from wind-induced mixing with colder hypolimnetic waters, direct precipitation to the lake's surface, and watershed runoff. We analyzed 18 long-term and high-frequency lake datasets from 11 countries to assess the magnitude of wind- vs. rainstorm-induced changes in epilimnetic temperature. We found small day-to-day epilimnetic temperature decreases in response to strong wind and heavy rain during stratified conditions. Day-to-day epilimnetic temperature decreased, on average, by 0.28 degrees C during the strongest windstorms (storm mean daily wind speed among lakes: 6.7 +/- 2.7 m s(-1), 1 SD) and by 0.15 degrees C after the heaviest rainstorms (storm mean daily rainfall: 21.3 +/- 9.0 mm). The largest decreases in epilimnetic temperature were observed >= 2 d after sustained strong wind or heavy rain (top 5(th) percentile of wind and rain events for each lake) in shallow and medium-depth lakes. The smallest decreases occurred in deep lakes. Epilimnetic temperature change from windstorms, but not rainstorms, was negatively correlated with maximum lake depth. However, even the largest storm-induced mean epilimnetic temperature decreases were typicallyPeer reviewe

A method for detergent-free isolation of membrane proteins in their local lipid environment.

Despite the great importance of membrane proteins, structural and functional studies of these proteins present major challenges. A significant hurdle is the extraction of the functional protein from its natural lipid membrane. Traditionally achieved with detergents, purification procedures can be costly and time consuming. A critical flaw with detergent approaches is the removal of the protein from the native lipid environment required to maintain functionally stable protein. This protocol describes the preparation of styrene maleic acid (SMA) co-polymer to extract membrane proteins from prokaryotic and eukaryotic expression systems. Successful isolation of membrane proteins into SMA lipid particles (SMALPs) allows the proteins to remain with native lipid, surrounded by SMA. We detail procedures for obtaining 25 g of SMA (4 d); explain the preparation of protein-containing SMALPs using membranes isolated from Escherichia coli (2 d) and control protein-free SMALPS using E. coli polar lipid extract (1-2 h); investigate SMALP protein purity by SDS-PAGE analysis and estimate protein concentration (4 h); and detail biophysical methods such as circular dichroism (CD) spectroscopy and sedimentation velocity analytical ultracentrifugation (svAUC) to undertake initial structural studies to characterize SMALPs (∼2 d). Together, these methods provide a practical tool kit for those wanting to use SMALPs to study membrane proteins