Assessing agricultural vulnerability to climate change through dynamic indexing approach.

The present study aims to assess agricultural vulnerability in the context of climate change, focusing on the diverse districts of Odisha. Acknowledging that vulnerability is influenced by exposure, sensitivity, and adaptive capacity, our research incorporates the growth rate and instability of vital performance indicators to evaluate the relative vulnerability of each district. A key strength of this approach is the use of normalized indicators, weighted in accordance with the proportional acreage of major crops in each district relative to the state, culminating in a comprehensive vulnerability index through the aggregation of these weighted components. Our findings reveal significant variability in the vulnerability profiles across districts, thereby necessitating state-level intervention through tailored "Location Performance Vulnerability" based adaptation strategies. These strategies, including early weather warning systems, development of new and early maturing crop varieties, and adjustment of crop planting dates, are crucial for mitigating the adverse effects of climate change on agriculture. The study's methodology and findings offer significant contributions to the field, providing policymakers and stakeholders with a district-specific framework for climate change adaptation. This approach is especially relevant for the international academic and policy-making communities, as it highlights the importance of localized adaptation strategies in the broader context of global climate change resilience.

Navigating the Cardiothoracic Publication Landscape: A Primer for Journal Selection.

Journal selection is very important for any researcher in every field. Publication in a reputable journal not only has a higher reach among a greater number of people but also sets a benchmark for significance and quality. This primer aims to guide researchers in the field of cardiothoracic research, including medicine, surgery, and imaging, to assist in journal selection for their respective articles. Journal selection depends on a variety of factors, such as impact factors, publication charges, review processes, article types that a journal accepts, and indexing. This primer highlights all these factors in detail that are essential for the selection of a suitable journal. The article emphasizes the importance of these factors in making a reasoned decision about journal selection. This article also focuses on different types of publication models and their implications, including hybrid open access, gold open access, and diamond open access. In conclusion, this primer aims to provide insights to researchers in the fields of cardiothoracic surgery, medicine, and imaging about the publication landscapes and guide them to strategically plan their submissions.

Mapping sustainable investing: Exploring ambiguities and consistencies among sustainable indices.

Sustainable investing is perceived as a confusing black-box by many researchers, practitioners, and investors. Terminology, metrics, approaches, disclosure standards are quickly evolving, while the empirical evidence provides a growing number of mixed results. In this paper, we examine the composition, performance, and the risk-return profile of reference sustainable indices for the asset management industry, distinguishing between ethical and socially-responsible, faith-based, ESG and climate ones. Our findings reveal consistent investment trends across various sustainable indices, impacting their overall performance. Specifically, our analysis highlights a prevalent inclination towards large and growth-oriented companies, as well as a persistent focus on technology, financials, and commodity sectors. These results provide valuable insights for investors, asset managers, and index providers regarding the potential misalignment between an investment vehicle's labeling and its actual composition, and the implications this discrepancy might have on investors' expectations.

Enhancing boundary detection of radiofrequency ablation lesions through photoacoustic mapping.

Atrial fibrillation (A-fib) is the most common type of heart arrhythmia, typically treated with radiofrequency catheter ablation to isolate the heart from abnormal electrical signals. Monitoring the formation of ablation-induced lesions is crucial for preventing recurrences and complications arising from excessive or insufficient ablation. Existing imaging modalities lack real-time feedback, and their intraoperative usage is in its early stages. A critical need exists for an imaging-based lesion indexing (LSI) method that directly reflects tissue necrosis formation. Previous studies have indicated that spectroscopic photoacoustic (sPA) imaging can differentiate ablated tissues from their non-ablated counterparts based on PA spectrum variation. In this paper, we introduce a method for detecting ablation lesion boundaries using sPA imaging. This approach utilizes ablation LSI, which quantifies the ratio between the signal from ablated tissue and the total tissue signal. We enhance boundary detection accuracy by adapting a regression model-based compensation. Additionally, the method was cross-validated with clinically used intraoperative monitoring parameters. The proposed method was validated with ex vivo porcine cardiac tissues with necrotic lesions created by different ablation durations. The PA-measured lesion size was compared with gross pathology. Statistical analysis demonstrates a strong correlation (R > 0.90) between the PA-detected lesion size and gross pathology. The PA-detected lesion size also exhibits a moderate to strong correlation (R > 0.75) with local impedance changes recorded during procedures. These results suggest that the introduced PA imaging-based LSI has great potential to be incorporated into the clinical workflow, guiding ablation procedures intraoperatively.

[One more step in the quality of 'Farmacéuticos Comunitarios': indexing]. / Un paso más en la calidad de 'Farmacéuticos Comunitarios': indexación.

The reason for existence of a scientific journal is to disseminate knowledge of science. The objective is a continuous improvement in the quality of what is published and an increase in the number of readers. In this sense, the impact factor is an indicator that helps us visualize the improvement in the quality of our journal. To improve this impact factor, indexing in the most used databases is very important.Farmacéuticos Comunitarios is indexed in several index journals but we were missing the most used ones. In this last year we have requested indexing in the most important ones, SCOPUS and Medline. In November we received SCOPUS approval and are close to getting it on Medline. In September we were approved by the editorial criteria and in December by the scientists. We only need the technical criteria to be able to appear in Medline and in its online version PubMed. We are working on it and it is very likely that within a year we will be indexed in PubMed.

Indexing neutron transmission spectra of a rotating crystal.

Neutron time-of-flight transmission spectra of mosaic crystals contain Bragg dips, i.e., minima at wavelengths corresponding to diffraction reflections. The positions of the dips are used for investigating crystal lattices. By rotating the sample around a fixed axis and recording a spectrum at each rotation step, the intensity of the transmitted beam is obtained as a function of the rotation angle and wavelength. The questions addressed in this article concern the determination of lattice parameters and orientations of centrosymmetric crystals from such data. It is shown that if the axis of sample rotation is inclined to the beam direction, the reflection positions unambiguously determine reciprocal-lattice vectors, which is not the case when the axis is perpendicular to the beam. Having a set of such vectors, one can compute the crystal orientation or lattice parameters using existing indexing software. The considerations are applicable to arbitrary Laue symmetry. The work contributes to the automation of the analysis of diffraction data obtained in the neutron imaging mode.

TORO Indexer: a PyTorch-based indexing algorithm for kilohertz serial crystallography.

Serial crystallography (SX) involves combining observations from a very large number of diffraction patterns coming from crystals in random orientations. To compile a complete data set, these patterns must be indexed (i.e. their orientation determined), integrated and merged. Introduced here is TORO (Torch-powered robust optimization) Indexer, a robust and adaptable indexing algorithm developed using the PyTorch framework. TORO is capable of operating on graphics processing units (GPUs), central processing units (CPUs) and other hardware accelerators supported by PyTorch, ensuring compatibility with a wide variety of computational setups. In tests, TORO outpaces existing solutions, indexing thousands of frames per second when running on GPUs, which positions it as an attractive candidate to produce real-time indexing and user feedback. The algorithm streamlines some of the ideas introduced by previous indexers like DIALS real-space grid search [Gildea, Waterman, Parkhurst, Axford, Sutton, Stuart, Sauter, Evans & Winter (2014). Acta Cryst. D70, 2652-2666] and XGandalf [Gevorkov, Yefanov, Barty, White, Mariani, Brehm, Tolstikova, Grigat & Chapman (2019). Acta Cryst. A75, 694-704] and refines them using faster and principled robust optimization techniques which result in a concise code base consisting of less than 500 lines. On the basis of evaluations across four proteins, TORO consistently matches, and in certain instances outperforms, established algorithms such as XGandalf and MOSFLM [Powell (1999). Acta Cryst. D55, 1690-1695], occasionally amplifying the quality of the consolidated data while achieving superior indexing speed. The inherent modularity of TORO and the versatility of PyTorch code bases facilitate its deployment into a wide array of architectures, software platforms and bespoke applications, highlighting its prospective significance in SX.

Evaluation of adherence to STARD for abstracts in a diverse sample of diagnostic accuracy abstracts published in 2012 and 2019 reveals suboptimal reporting practices.

OBJECTIVES: To evaluate the completeness of reporting in a sample of abstracts on diagnostic accuracy studies before and after the release of Standards for Reporting of Diagnostic Accuracy Studies (STARD) for abstracts in 2017. METHODS: We included 278 diagnostic accuracy abstracts published in 2012 (N = 138) and 2019 (N = 140) and indexed in EMBASE. We analyzed their adherence to 10 items of the 11-item STARD for abstracts checklist, and we explored variability in reporting across abstract characteristics using multivariable Poisson modeling. RESULTS: Most of the 278 abstracts (75%) were published in discipline-specific journals, with a median impact factor of 2.9 (IQR: 1.9-3.7). The majority (41%) of abstracts reported on imaging tests. Overall, a mean of 5.4/10 (SD: 1.4) STARD for abstracts items was reported (range: 1.2-9.7). Items reported in less than one-third of abstracts included 'eligible patient demographics' (24%), 'setting of recruitment' (30%), 'method of enrollment' (18%), 'estimates of precision for accuracy measures' (26%), and 'protocol registration details' (4%). We observed substantial variability in reporting across several abstract characteristics, with higher adherence associated with the use of a structured abstract, no journal limit for abstract word count, abstract word count above the median, one-gate enrollment design, and prospective data collection. There was no evidence of increase in the number of reported items between 2012 and 2019 (5.2 vs 5.5 items; adjusted reporting ratio: 1.04 [95% CI: 0.98-1.10]). CONCLUSION: This sample of diagnostic accuracy abstracts revealed suboptimal reporting practices without improvement between 2012 and 2019. The test evaluation field could benefit from targeted knowledge translation strategies to improve completeness of reporting in abstracts.

Amplicon-Based Bisulfite Conversion-NGS DNA Methylation Analysis Protocol.

DNA methylation is an important epigenetic modification that regulates chromatin structure and the cell-type-specific expression of genes. The association of aberrant DNA methylation with many diseases, as well as the increasing interest in modifying the methylation mark in a directed manner at genomic sites using epigenome editing for research and therapeutic purposes, increases the need for easy and efficient DNA methylation analysis methods. The standard approach to analyze DNA methylation with a single-cytosine resolution is bisulfite conversion of DNA followed by next-generation sequencing (NGS). In this chapter, we describe a robust, powerful, and cost-efficient protocol for the amplification of target regions from bisulfite-converted DNA, followed by a second PCR step to generate libraries for Illumina NGS. In the two consecutive PCR steps, first, barcodes are added to individual amplicons, and in the second PCR, indices and Illumina adapters are added to the samples. Finally, we describe a detailed bioinformatics approach to extract DNA methylation levels of the target regions from the sequencing data. Combining barcodes with indices enables a high level of multiplexing allowing to sequence multiple pooled samples in the same sequencing run. Therefore, this method is a robust, accurate, quantitative, and cheap approach for the readout of DNA methylation patterns at defined genomic regions.

Risk factors and management countermeasures for obstructive sleep apnea hypoventilation syndrome in children.

BACKGROUND: Obstructive sleep apnea hypoventilation syndrome (OSAHS) in children is a sleep respiratory disorder characterized by a series of pathophysiologic changes. Statistics in recent years have demonstrated an increasing yearly incidence. AIM: To investigate the risk factors for OSAHS in children and propose appropriate management measures. METHODS: This study had a case-control study design. Altogether, 85 children with OSAHS comprised the case group, and healthy children of the same age and sex were matched at 1:1 as the control group. Basic information, including age, sex, height, weight and family history, and medical history data of all study participants were collected. Polysomnography was used to detect at least 8 h of nocturnal sleep. All participants were clinically examined for the presence of adenoids, enlarged tonsils, sinusitis, and rhinitis. RESULTS: The analysis of variance revealed that the case group had a higher proportion of factors such as adenoid grading, tonsil indexing, sinusitis, and rhinitis than the control group. CONCLUSION: A regression model was established, and glandular pattern grading, tonsil indexing, sinusitis, and pharyngitis were identified as independent risk factors affecting OSAHS development.

Ideas of lattice-basis reduction theory for error-stable Bravais lattice determination and abinitio indexing.

In ab initio indexing, for a given diffraction/scattering pattern, the unit-cell parameters and the Miller indices assigned to reflections in the pattern are determined simultaneously. `Ab initio' means a process performed without any good prior information on the crystal lattice. Newly developed ab initio indexing software is frequently reported in crystallography. However, it is not widely recognized that use of a Bravais lattice determination method, which is tolerant of experimental errors, can simplify indexing algorithms and increase their success rates. One of the goals of this article is to collect information on the lattice-basis reduction theory and its applications. The main result is a Bravais lattice determination algorithm for 2D lattices, along with a mathematical proof that it works even for parameters containing large observational errors. It uses two lattice-basis reduction methods that seem to be optimal for different symmetries, similarly to the algorithm for 3D lattices implemented in the CONOGRAPH software. In indexing, a method for error-stable unit-cell identification is also required to exclude duplicate solutions. Several methods are introduced to measure the difference in unit cells known in crystallography and mathematics.

MEM-based pangenome indexing for k-mer queries.

Pangenomes are growing in number and size, thanks to the prevalence of high-quality long-read assemblies. However, current methods for studying sequence composition and conservation within pangenomes have limitations. Methods based on graph pangenomes require a computationally expensive multiple-alignment step, which can leave out some variation. Indexes based on k-mers and de Bruijn graphs are limited to answering questions at a specific substring length k. We present Maximal Exact Match Ordered (MEMO), a pangenome indexing method based on maximal exact matches (MEMs) between sequences. A single MEMO index can handle arbitrary-length queries over pangenomic windows. MEMO enables both queries that test k-mer presence/absence (membership queries) and that count the number of genomes containing k-mers in a window (conservation queries). MEMO's index for a pangenome of 89 human autosomal haplotypes fits in 2.04 GB, 8.8× smaller than a comparable KMC3 index and 11.4× smaller than a PanKmer index. MEMO indexes can be made smaller by sacrificing some counting resolution, with our decile-resolution HPRC index reaching 0.67 GB. MEMO can conduct a conservation query for 31-mers over the human leukocyte antigen locus in 13.89 seconds, 2.5x faster than other approaches. MEMO's small index size, lack of k-mer length dependence, and efficient queries make it a flexible tool for studying and visualizing substring conservation in pangenomes.

Geographic Barriers to Indexing: Examining Appalachia.

The objective of this study was to examine the accuracy of indexing for "Appalachian Region"[Mesh]. Researchers performed a search in PubMed for articles published in 2019 using "Appalachian Region"[Mesh] or "Appalachia" or "Appalachian" in the title or abstract. Only 17.88% of the articles retrieved by the search were about Appalachia according to the ARC definition. Most articles retrieved appeared because they were indexed with state terms that were included as part of the mesh term. Database indexing and searching transparency is of growing importance as indexers rely increasingly on automated systems to catalog information and publications.

Flow-cytometry Assessment of DNA content and Immunophenotyping of Immune-cells in Lymph-node-specimens as a Potential Diagnostic Signature of Aggressiveness in B-Non-Hodgkin Lymphomas.

Flow-cytometry (FC) is a powerful tool that can assist in lymphoma diagnosis in lymph node (LN) specimens. Although lymphoma diagnosis and classification are mainly based on tumor cell characteristics, surrounding cells are less employed in this process. We retrospectively investigated alterations in the ploidy status, proliferative cell fraction (PF) and the percentages of surrounding immune cells in 62 consecutive LN specimens with B-Cell Non-Hodgkin Lymphoma (B-NHL) that were submitted for FC evaluation between 2019-2022. Compared with indolent B-NHLs, aggressive B-NHLs show increased DNA aneuploidy and PF, increased monocytes, immature-granulocytes, mature granulocytes, CD8+ T-cells, Double-Negative-T-cells and Double-Positive-T-cells, and decreased total CD45+ cells, total lymphocytes, CD4+ T-cells and CD4/CD8 ratio. Receiver operating characteristic analysis determined PF > 6.8% and immature-granulocytes > 0.9% as optimal cutoffs with highest specificity and sensitivity in differentiating aggressive and indolent B-NHLs. These findings further strength the diagnostic value of DNA content analysis by FC and suggest the utilization of tumor surrounding immune cells in NHL diagnosis and classification.

The Healthy Fatty Index Allows for Deeper Insights into the Lipid Composition of Foods of Animal Origin When Compared with the Atherogenic and Thrombogenicity Indexes.

The aim of this research was to validate the effectiveness of the Healthy Fatty Index (HFI) regarding some foods of animal origin (meat, processed, fish, milk products, and eggs) typical of the Western diet and to compare these results with two consolidated indices (atherogenic-AI, and thrombogenic-TI) in the characterization of the nutritional features of their lipids. The fatty acids profile (% of total fatty acids and mg/100 g) of 60 foods, grouped in six subclasses, was used. The AI, TI, and HFI indexes were calculated, and the intraclass correlation coefficients and the degree of agreement were evaluated using different statistical approaches. The results demonstrated that HFI, with respect to AI and TI, seems better able to consider the complexity of the fatty acid profile and the different fat contents. HFI and AI are the two most diverse indices, and they can provide different food classifications. AI and IT exhibit only a fair agreement in regards to food classification, confirming that such indexes are always to be considered indissolubly and never separately, in contrast to the HFI, which can stand alone.

scifi-ATAC-seq: massive-scale single-cell chromatin accessibility sequencing using combinatorial fluidic indexing.

Single-cell ATAC-seq has emerged as a powerful approach for revealing candidate cis-regulatory elements genome-wide at cell-type resolution. However, current single-cell methods suffer from limited throughput and high costs. Here, we present a novel technique called scifi-ATAC-seq, single-cell combinatorial fluidic indexing ATAC-sequencing, which combines a barcoded Tn5 pre-indexing step with droplet-based single-cell ATAC-seq using the 10X Genomics platform. With scifi-ATAC-seq, up to 200,000 nuclei across multiple samples can be indexed in a single emulsion reaction, representing an approximately 20-fold increase in throughput compared to the standard 10X Genomics workflow.

Blockchain-Based Method for Spatial Retrieval and Verification of Remote Sensing Images.

Remote sensing image is a vital basis for land management decisions. The protection of remote sensing images has seen the application of blockchain's notarization function by many scholars. Yet, research on efficient retrieval of such images on the blockchain remains sparse. Addressing this issue, this paper introduces a blockchain-based spatial index verification method using Hyperledger Fabric. It linearizes the spatial information of remote sensing images via Geohash and integrates it with LSM trees for effective retrieval and verification. The system also incorporates IPFS as an underlying storage unit for Hyperledger Fabric, ensuring the safe storage and transmission of images. The experiments indicate that this method significantly reduces the latency in data retrieval and verification without impacting the write performance of Hyperledger Fabric, enhancing throughput and providing a solid foundation for efficient blockchain-based verification of remote sensing images in land registry systems.

Multiple Object Tracking Without Pre-attentive Indexing.

Multiple object tracking (MOT) involves simultaneous tracking of a certain number of target objects amongst a larger set of objects as they all move unpredictably over time. The prevalent explanation for successful target tracking by humans in MOT involving visually identical objects is based on the Visual Indexing Theory. This assumes that each target is indexed by a pointer using a non-conceptual mechanism to maintain an object's identity even as its properties change over time. Thus, successful tracking requires successful indexing and the absence of identification errors. Identity maintenance and successful tracking are measured in terms of identification (ID) and tracking accuracy respectively, with higher accuracy indicating better identity maintenance or better tracking. Existing evidence suggests that humans have high tracking accuracy despite poor identification accuracy, suggesting that it might be possible to perform MOT without indexing. Our work adds to existing evidence for this position through two experiments, and presents a computational model of multiple object tracking that does not require indexes. Our empirical results show that identification accuracy is aligned with tracking accuracy in humans for tracking up to three, but is lower when tracking more objects. Our computational model of MOT without indexing accounts for several empirical tracking accuracy patterns shown in earlier studies, reproduces the dissociation between tracking and identification accuracy produced earlier in the literature as well as in our experiments, and makes several novel predictions.

Pfp-fm: an accelerated FM-index.

FM-indexes are crucial data structures in DNA alignment, but searching with them usually takes at least one random access per character in the query pattern. Ferragina and Fischer [1] observed in 2007 that word-based indexes often use fewer random accesses than character-based indexes, and thus support faster searches. Since DNA lacks natural word-boundaries, however, it is necessary to parse it somehow before applying word-based FM-indexing. In 2022, Deng et al. [2] proposed parsing genomic data by induced suffix sorting, and showed that the resulting word-based FM-indexes support faster counting queries than standard FM-indexes when patterns are a few thousand characters or longer. In this paper we show that using prefix-free parsing-which takes parameters that let us tune the average length of the phrases-instead of induced suffix sorting, gives a significant speedup for patterns of only a few hundred characters. We implement our method and demonstrate it is between 3 and 18 times faster than competing methods on queries to GRCh38, and is consistently faster on queries made to 25,000, 50,000 and 100,000 SARS-CoV-2 genomes. Hence, it seems our method accelerates the performance of count over all state-of-the-art methods with a moderate increase in the memory. The source code for PFP - FM is available at https://github.com/AaronHong1024/afm .

Determining Protein Structures Using X-Ray Crystallography.

X-ray crystallography is a robust and widely used technique that facilitates the three-dimensional structure determination of proteins at an atomic scale. This methodology entails the growth of protein crystals under controlled conditions followed by their exposure to X-ray beams and the subsequent analysis of the resulting diffraction patterns via computational tools to determine the three-dimensional architecture of the protein. However, achieving high-resolution structures through X-ray crystallography can be quite challenging due to complexities associated with protein purity, crystallization efficiency, and crystal quality.In this chapter, we provide a detailed overview of the gene to structure determination pipeline used in X-ray crystallography, a crucial tool for understanding protein structures. The chapter covers the steps in protein crystallization, along with the processes of data collection, processing, structure determination, and refinement. The most commonly faced challenges throughout this procedure are also addressed. Finally, the importance of standardized protocols for reproducibility and accuracy is emphasized, as they are crucial for advancing the understanding of protein structure and function.