Application and Progress of Raman Spectroscopy in Male Reproductive System

Raman spectroscopy is a fast-developing, unmarked, non-invasive, non-destructive technique which allows for real-time scanning and sampling of biological samples in situ, reflecting the subtle biochemical composition alterations of tissues and cells through the variations of spectra. It has great potential to identify pathological tissue and provide intraoperative assistance in clinic. Raman spectroscopy has made many exciting achievements in the study of male reproductive system. In this review, we summarized literatures about the application and progress of Raman spectroscopy in male reproductive system from PubMed and Ovid databases, using MeSH terms associated to Raman spectroscopy, prostate, testis, seminal plasma and sperm. The existing challenges and development opportunities were also discussed and prospected.

Optimal Synthesis and Application of a Si–Ti–Al Ternary Alloy as an Anode Material for Lithium-Ion Batteries

The development of novel anode materials for high energy density is required. Alloying Si with other metals is a promising approach to utilize the high capacity of Si. In this work, we optimized the composition of a Si–Ti–Al ternary alloy to achieve excellent electrochemical performance in terms of capacity, cyclability, and rate capability. The detailed internal structures of the alloys were characterized through their atomic compositions and diffraction patterns. The lithiation process of the alloy was monitored using real-time scanning electron microscopy, revealing that the mechanical stability of the optimized alloy was strongly enhanced compared to that of the pure silicon material.

Sodium Hydroxide Treatment for Cellulose Fiber Accessibility from Corncobs under Microwave Assistive

Corncob is abundantly available lignocellulosic biomass resources obtained from crops harvesting and found to be solid waste accumulation on a field. Less corncob is used as a solid fuel for cooking, and a more significant portion is vanished on the field by burning. Promisingly, corncob contains considerable cellulose as one value-added component potentially utilized as biomaterial or biofuel feedstock. However, the presence of lignin in natural lignocellulosic biomass results in recalcitrant structure and hinders cellulose accessibility. This study aimed to investigate microwave-assisted alkaline treatment to retain cellulose in the solid product while removing other impurities in corncob, especially hemicellulose and lignin. Sodium hydroxide was selected as a chemical with some variations in concentration. The chemical treatment was carried out under 400 W microwave power with various residence times and a 1:10 solid to liquor ratio. The cellulose content upgraded from 26.97% to 71.26% while reducing hemicellulose and lignin from 38.49% to 18.15% and 19.28% to 6.4%, respectively, on chemical treatment using 8% sodium hydroxide concentration for 20 minutes residence time. Scanning electron microscope (SEM) and Fourier transform infrared (FTIR) analysis also confirmed the results. The treated corncob also increased its crystallinity from 30.11% to 52.91%.

Crosscorrelation migration of microseismic source locations with hybrid imaging condition

Locating microseismic sources is critical to monitor the hydraulic fractures that occur during fluid extraction/injection in unconventional oil or gas exploration. Waveform-based seismic location methods can reliably and automatically image weak microseismic source locations without phase picking. Among them, the cross-correlation migration (CCM) method can avoid excitation time scanning by generating virtual gathers. We propose a CCM location method based on the hybrid imaging condition (HIC). There are four main steps in the implementation of this method: 1) selection of receivers with good azimuthal coverage; 2) generation of virtual gathers by correlating the reference receiver with the rest of the receivers; 3) summation of back-projections in the virtual gathers; and 4) multiplication of all summations. The CCM-HIC method was tested on synthetic and field datasets, and the results were compared with those obtained by conventional summation imaging condition (SIC) and multiplication imaging condition (MIC). The comparison results demonstrate that the CCM-HIC is sufficiently robust to obtain better stability and higher spatial resolution image of source location, despite the presence of strong noise.

Chemotherapy roadmaps in pediatric oncology: A digital electronic medical record integrated solution

Background Delivery of antineoplastic regimens in the pediatric setting is facilitated by a paper roadmap. Paper roadmaps are the key safety tool required for safe ordering. Electronic medical record systems offer technological solutions for ordering antineoplastic regimens, however, do not offer a solution that integrates paper roadmaps digitally. Methods A multidisciplinary project team implemented real-time clinician scanning of paper roadmaps into the electronic medical record. Results The rate of missing roadmaps decreased from an average of 1.6 to 0.8 per week. Pharmacists gained 3 h of productivity daily. Providers spend an average of 35–45 s and a total of seven clicks each time a roadmap is scanned. Overall, the clinical systems analyst spent less than 1 h of total build time. Conclusion Implementing roadmap scanning decreased the rate of missing roadmaps, increased pharmacist productivity, and required a nominal amount of analyst and provider time. In addition, this solution allows for concurrent viewing of the roadmap files from any connected computer, facilitating an easier co-signature process for providers, pharmacists, and nurses. Practice Implications These results suggest that implementing real-time scanning of roadmaps can improve oncology care efficiency while maintaining the same safety rigor that paper roadmaps offer.

Spatiotemporal epidemiology of cryptosporidiosis in the Republic of Ireland, 2008–2017: development of a space–time “cluster recurrence” index

Background Ireland frequently reports the highest annual Crude Incidence Rates (CIRs) of cryptosporidiosis in the EU, with national CIRs up to ten times the EU average. Accordingly, the current study sought to examine the spatiotemporal trends associated with this potentially severe protozoan infection. Methods Overall, 4509 cases of infection from January 2008 to December 2017 were geo-referenced to a Census Small Area (SA), with an ensemble of geo-statistical approaches including seasonal decomposition, Local Moran’s I, and space–time scanning used to elucidate spatiotemporal patterns of infection. Results One or more confirmed cases were notified in 3413 of 18,641 Census SAs (18.3%), with highest case numbers occurring in the 0–5-year range (n = 2672, 59.3%). Sporadic cases were more likely male (OR 1.4) and rural (OR 2.4), with outbreak-related cases more likely female (OR 1.4) and urban (OR 1.5). Altogether, 55 space–time clusters (≥ 10 confirmed cases) of sporadic infection were detected, with three “high recurrence” regions identified; no large urban conurbations were present within recurrent clusters. Conclusions Spatiotemporal analysis represents an important indicator of infection patterns, enabling targeted epidemiological intervention and surveillance. Presented results may also be used to further understand the sources, pathways, receptors, and thus mechanisms of cryptosporidiosis in Ireland.

Articles That Use Artificial Intelligence for Ultrasound: A Reader’s Guide

Artificial intelligence (AI) transforms medical images into high-throughput mineable data. Machine learning algorithms, which can be designed for modeling for lesion detection, target segmentation, disease diagnosis, and prognosis prediction, have markedly promoted precision medicine for clinical decision support. There has been a dramatic increase in the number of articles, including articles on ultrasound with AI, published in only a few years. Given the unique properties of ultrasound that differentiate it from other imaging modalities, including real-time scanning, operator-dependence, and multi-modality, readers should pay additional attention to assessing studies that rely on ultrasound AI. This review offers the readers a targeted guide covering critical points that can be used to identify strong and underpowered ultrasound AI studies.

Inverse Material Search and Synthesis Verification by Hand Drawings via Transfer Learning and Contour Detection

Nanomaterials of various morphologies and chemistry have an extensive use <a>as photonic devices, advanced catalysts, sorbents for water purification, agrochemicals, platforms for drug delivery</a> as well as imaging systems to name a few. However, search for synthesis routes giving custom nanomaterials for particular needs with the desired structure, shape, and size remains a challenge and is often implemented by manual research articles screening. Here, we develop for the first time scanning and transmission electron microscopy (SEM/TEM) reverse image search and hand drawing-based search <i>via</i> transfer learning (TL), namely, VGG16 convolutional neural network (CNN) repurposing for image features extraction and subsequent image similarity determination. Moreover, we demonstrate case use of this platform on calcium carbonate system, where sufficient amount of data was acquired by random high throughput multiparametric synthesis, as well as on Au nanoparticles (NPs) data extracted from the articles. This approach can be not only used for advanced nanomaterials search and synthesis procedure verification, but also can be further combined with machine learning (ML) solutions to provide data-driven novel nanomaterials discovery.

Inverse Material Search and Synthesis Verification by Hand Drawings via Transfer Learning and Contour Detection

Nanomaterials of various morphologies and chemistry have an extensive use <a>as photonic devices, advanced catalysts, sorbents for water purification, agrochemicals, platforms for drug delivery</a> as well as imaging systems to name a few. However, search for synthesis routes giving custom nanomaterials for particular needs with the desired structure, shape, and size remains a challenge and is often implemented by manual research articles screening. Here, we develop for the first time scanning and transmission electron microscopy (SEM/TEM) reverse image search and hand drawing-based search <i>via</i> transfer learning (TL), namely, VGG16 convolutional neural network (CNN) repurposing for image features extraction and subsequent image similarity determination. Moreover, we demonstrate case use of this platform on calcium carbonate system, where sufficient amount of data was acquired by random high throughput multiparametric synthesis, as well as on Au nanoparticles (NPs) data extracted from the articles. This approach can be not only used for advanced nanomaterials search and synthesis procedure verification, but also can be further combined with machine learning (ML) solutions to provide data-driven novel nanomaterials discovery.