Observability of the Very-High-Energy Emission from GRB 221009A.

The LHAASO Collaboration detected the gamma ray burst GRB 221009A at energies above 500 GeV with a tail extending up to 18 TeV, whose spectral analysis has presently been performed up to 7 TeV for the lower energy instrument LHAASO-WCDA only, with no indication of a cutoff. Soon thereafter, Carpet-2 at Baksan Neutrino Observatory reported the observation of an air shower consistent with being caused by a photon of energy 251 TeV from the same GRB. Given the source redshift z=0.151, the expected attenuation due to the extragalactic background light is very severe so that these detections have proven very hard to explain. In this Letter, we show that the existence of axionlike particles with mass m_{a}≃(10^{-11}-10^{-7}) eV and two-photon coupling g_{aγγ}≃(3-5)×10^{-12} GeV^{-1} strongly reduce the optical depth of TeV photons, thus explaining the observations. Our ALPs meet all available constraints, are consistent with two previous hints at their existence, and are good candidates for cold dark matter. Moreover, we show that Lorentz invariance violation can explain the Carpet-2 result but not the LHAASO observations.

Relevant Features of Polypharmacologic Human-Target Antimicrobials Discovered by Machine-Learning Techniques.

Polypharmacologic human-targeted antimicrobials (polyHAM) are potentially useful in the treatment of complex human diseases where the microbiome is important (e.g., diabetes, hypertension). We previously reported a machine-learning approach to identify polyHAM from FDA-approved human targeted drugs using a heterologous approach (training with peptides and non-peptide compounds). Here we discover that polyHAM are more likely to be found among antimicrobials displaying a broad-spectrum antibiotic activity and that topological, but not chemical features, are most informative to classify this activity. A heterologous machine-learning approach was trained with broad-spectrum antimicrobials and tested with human metabolites; these metabolites were labeled as antimicrobials or non-antimicrobials based on a naïve text-mining approach. Human metabolites are not commonly recognized as antimicrobials yet circulate in the human body where microbes are found and our heterologous model was able to classify those with antimicrobial activity. These results provide the basis to develop applications aimed to design human diets that purposely alter metabolic compounds proportions as a way to control human microbiome.

Heterologous Machine Learning for the Identification of Antimicrobial Activity in Human-Targeted Drugs.

The emergence of microbes resistant to common antibiotics represent a current treat to human health. It has been recently recognized that non-antibiotic labeled drugs may promote antibiotic-resistance mechanisms in the human microbiome by presenting a secondary antibiotic activity; hence, the development of computer-assisted procedures to identify antibiotic activity in human-targeted compounds may assist in preventing the emergence of resistant microbes. In this regard, it is worth noting that while most antibiotics used to treat human infectious diseases are non-peptidic compounds, most known antimicrobials nowadays are peptides, therefore all computer-based models aimed to predict antimicrobials either use small datasets of non-peptidic compounds rendering predictions with poor reliability or they predict antimicrobial peptides that are not currently used in humans. Here we report a machine-learning-based approach trained to identify gut antimicrobial compounds; a unique aspect of our model is the use of heterologous training sets, in which peptide and non-peptide antimicrobial compounds were used to increase the size of the training data set. Our results show that combining peptide and non-peptide antimicrobial compounds rendered the best classification of gut antimicrobial compounds. Furthermore, this classification model was tested on the latest human-approved drugs expecting to identify antibiotics with broad-spectrum activity and our results show that the model rendered predictions consistent with current knowledge about broad-spectrum antibiotics. Therefore, heterologous machine learning rendered an efficient computational approach to classify antimicrobial compounds.

A complete sample of long bright Swift gamma ray bursts.

Complete samples are the basis of any population study. To this end, we selected a complete subsample of Swift long bright gamma ray bursts (GRBs). The sample, made up of 58 bursts, was selected by considering bursts with favourable observing conditions for ground-based follow-up observations and with the 15-150 keV 1 s peak flux above a flux threshold of 2.6 photons cm(-2) s(-1). This sample has a redshift completeness level higher than 90 per cent. Using this complete sample, we investigate the properties of long GRBs and their evolution with cosmic time, focusing in particular on the GRB luminosity function, the prompt emission spectral-energy correlations and the nature of dark bursts.

Modificaciones en los niveles de fosfatasa acida folicular en relación con la madurez ovular en el humano / Folicular acid phosphatase levels changes related to ovular maturity in humans

Los cambios en la actividad enzimática de fosfatasa ácida (FA), tienen una parte importante en los fenómenos de la ovulación y hay algunos datos que parecen indicar que tiene relación con la fertilización del ovocito por el espermatozoide. En el presente trabajo se determinó las actividad enzimática de FA en líquido folicular de 32 pacientes en edad fértil que por diversas razones fueron sometidas a laparoscopia o laparotomía. En todas ellas se puncionaron los folículos ovários y se descartaron aquellos folículos ováricos y se descartaron aquellos folículos sanguinolentos, o que no tuvieron óvulo. El óvulo fue clasificado morfológicamente conforme al complejo ovocito-cúmulo-corona radiada (COCC) en tres grupos: inmaduro, maduro o preovulatorio y atrésico o postovulatorio. Los niveles de FA en líquidos foliculares, conteniendo ovocitos inmaduros (0.5 ñ 0.3 UBL*) fueron significantivamente menores p > 0.05 que los niveles de FA de folículos con ovocitos maduros (3.7 ñ 1.3) y post-maduros (4.8 ñ 3.2) no se encontraron diferencias significativas entre los valores de FA en estos dos últimos grupos. Estos hallazgos indican que estos niveles de FA pueden servir como un indicador de inmadurez folicular y se puede utilizar para escoger el tiempo óptimo de punción folicular para los procedimientos de fertilización in vitro y transferencia de gametos