Nearest-neighbor Method Research Articles

The illusion of the Holy Grail of decoupling: Are there countries with relatively high SDGI and moderately low ecological footprint?

The development of sustainability indicators at the national level emerged due to the limitations of using GDP as a measure of well-being, sustainability, and resilience. Over time, various indicators and rankings have been formulated, with a shift in focus from solely economic growth to a more encompassing perspective. The objective of this study was to create a new ranking, based on the integration of two methodologies, namely UN Sustainable Development Goals Index (SDGI), and the Global Footprint Network's Ecological Footprint (EF), in order to identify meaningful clusters of countries based on both measures. Hierarchical clustering was utilized to group countries, while the Nearest Neighbour method was employed to filter out outliers, and the Ward method determined the final clusters. Additionally, the Pearson's correlation coefficient was calculated to assess the relationship between the 17 SDGIs and the EF. The clustering outcomes revealed that a three-cluster solution can be considered satisfactory. The results obtained with the new method demonstrate that the environmental dimension of SDGI is a sufficient metric for environmental sustainability in terms of ranking countries.

3D attention-focused pure convolutional target detection algorithm for insulator defect detection

To ensure the timely detection of safety hazards in transmission lines and to enhance accurately the detection of insulator defects in complicated environments, this study proposes a 3D attention-focused pure convolutional target detection algorithm (CMYOLOv7) based on you only look once v7 (YOLOv7) for defect detection on insulators in complicated environments. Firstly, to address the incapacity of focusing on the target in the backbone feature extraction networks, this study proposes a focused pure convolutional feature extraction module (ConvSimCB) to enhance the extraction and focus capability on insulator defect features. Secondly, to solve the problem of loss of key detail feature information caused by maximum pooling in spatial pyramid pooling module (SPPCSPC), this study proposes a mixed spatial pyramid pooling module (MIXPCSPC) to retain abundant image texture detail information and increase accuracy in detection of tiny insulator defects. Finally, a lightweight generic upsampling operator (CARAFE) is introduced to enhance the feature map resolution to address image distortion caused by the Nearest Neighbor Method of upsampling. This study proposed CMYOLOv7 achieves 98.37% precision, 90.59% recall, 95.68% mean average precision (MAP), and 94% F1 score, higher than YOLOv7 by 0.61%, 8.64%, 5.41%, and 5%.

Assessments of Gravity Data Gridding Using Various Interpolation Approaches for High-Resolution Geoid Computations

This article investigates the role of different approaches and interpolation methods in gridding terrestrial gravity anomalies. In this regard, first of all, simple and complete Bouguer anomalies are considered in gravity data gridding. In the comparison results of gridding these two Bouguer anomaly datasets, the effect of the high-frequency contribution of topographic gravitation (by means of the terrain correction) is clarified. After that, the role of the used interpolation algorithm on the resulting grid of mean gravity anomalies and hence on the geoid modeling accuracy is inspected. For this purpose, four different interpolation methods including geostatistical Kriging, nearest neighbor, inverse distance to a power (IDP), and artificial neural networks (ANNs) are applied. Here, the IDP and nearest neighbor methods represent simple-structured algorithms among the interpolation methods tested in this study. The ANN method, on the other hand, is preferred as a complex, optimization-based soft computing method that has been applied in recent years. In addition, the geostatistical Kriging method is one of the conventional methods that is mostly applied for gridding gravity data in geodesy and geophysics. The calculated gravity anomalies in grids are employed in high-resolution geoid model computations using the least squares modifications of Stokes formula with additive corrections (LSMSA) technique. The investigations are carried out using the test datasets of Auvergne, France that are provided by the International Service for the Geoid for scientific research. It is concluded that the interpolation algorithms affect the gravity gridding results and hence the geoid model determination. The ANN method does not provide superior results compared to the conventional algorithms in gravity gridding. The geoid model with 4.1 cm accuracy is computed in the test area.

A Comparison Between Local Linear Regression and K- Nearest Neighbor Methods in Estimating the Nonparametric Regression Model with Simulation and Application

In this paper, the nonparametric regression model was estimated using two common estimation methods (Local Linear Regression Estimator (LLRE) and K-Nearest Neighbor Estimator (KNNE). The simulation process was conducted using the statistical programming language, R, for these methods, and they were compared using the Average Root Mean Squares Error (ARMSE) criterion, with three sample sizes (50, 100, 150), three levels of error variance (0.3, 0.7, 1) and two non-linear models. The results for the first model showed the superiority of the nearest neighbor estimator (KNNE) method in most cases, and for the second model the local linear regression estimator (LLRE) was superior at all sample sizes and for all levels of variance. On the applied side, the effect of hemoglobin (Hb) on the packed cell volume (PCV) of 150 patients with chronic kidney disease was studied. The estimation process was made using the two methods, and the comparison between them was made using the root mean square error (RMSE) criterion. It was shown through the results of the applied side that the preference was for the nearest neighbor estimator (KNNE) method.

A Privacy-Aware K-Nearest Neighbor Query Scheme for Location-Based Services

A Privacy-Aware <i>K</i>-Nearest Neighbor Query Scheme for Location-Based Services

Is low-dose computed tomography for lung cancer screening conveniently accessible in China? A spatial analysis based on cross-sectional survey

BackgroundRegular Low-Dose Computed Tomography (LDCT) for lung cancer high-risk population has been proved to improve health outcomes and relieve disease burden efficiently for both individual and society. With geographical impedance becoming the major barrier preventing patients from getting timely healthcare service, this study incorporated health seeking behavior in estimating spatial accessibility of relative scarce LDCT resource in China, thus to provide real-world evidence for future government investment and policy making.MethodsTaking Sichuan Province in southwest China as the study area, a cross-sectional survey was first carried out to collect actual practice and preferences for seeking LDCT services. Using Computed Tomography (CT) registration data reported by owner institutions representing LDCT services capacity, and grided town-level high-risk population as demand, the Nearest Neighbor Method was then utilized to calculate spatial accessibility of LDCT services.ResultsA total of 2,529 valid questionnaires were collected, with only 34.72% of the high-risk populations (746 individuals) followed the recommended annual screening. Participants preferred to travel to municipal-level and above institutions within 60 min for LDCT services. Currently, every thousand high-risk populations own 0.0845 CT scanners in Sichuan Province, with 96.95% able to access LDCT within 60 min and over half within 15 min. Urban areas generally showed better accessibility than rural areas, and the more developed eastern regions were better than the western regions with ethnic minority clusters.ConclusionsSpatial access to LDCT services is generally convenient in Sichuan Province, but disparity exists between different regions and population groups. Improving LDCT capacity in county-level hospitals as well as promoting health education and policy guidance to the public can optimize efficiency of existing CT resources. Implementing mobile CT services and improving rural public transportation may alleviate emerging disparities in accessing early lung cancer detection.

Terminal Embeddings in Sublinear Time

Recently (Elkin, Filtser, Neiman 2017) introduced the concept of a {\it terminal embedding} from one metric space $(X,d_X)$ to another $(Y,d_Y)$ with a set of designated terminals $T\subset X$. Such an embedding $f$ is said to have distortion $\rho\ge 1$ if $\rho$ is the smallest value such that there exists a constant $C&gt;0$ satisfying \begin{equation*} \forall x\in T\ \forall q\in X,\ C d_X(x, q) \le d_Y(f(x), f(q)) \le C \rho d_X(x, q) . \end{equation*} When $X,Y$ are both Euclidean metrics with $Y$ being $m$-dimensional, recently (Narayanan, Nelson 2019), following work of (Mahabadi, Makarychev, Makarychev, Razenshteyn 2018), showed that distortion $1+\epsilon$ is achievable via such a terminal embedding with $m = O(\epsilon^{-2}\log n)$ for $n := |T|$. This generalizes the Johnson-Lindenstrauss lemma, which only preserves distances within $T$ and not to $T$ from the rest of space. The downside of prior work is that evaluating their embedding on some $q\in \mathbb{R}^d$ required solving a semidefinite program with $\Theta(n)$ constraints in~$m$ variables and thus required some superlinear $\mathrm{poly}(n)$ runtime. Our main contribution in this work is to give a new data structure for computing terminal embeddings. We show how to pre-process $T$ to obtain an almost linear-space data structure that supports computing the terminal embedding image of any $q\in\mathbb{R}^d$ in sublinear time $O^* (n^{1-\Theta(\epsilon^2)} + d)$. To accomplish this, we leverage tools developed in the context of approximate nearest neighbor search.

FedKNN: Secure Federated k-Nearest Neighbor Search

Nearest neighbor search is a fundamental task in various domains, such as federated learning, data mining, information retrieval, and biomedicine. With the increasing need to utilize data from different organizations while respecting privacy regulations, private data federation has emerged as a promising solution. However, it is costly to directly apply existing approaches to federated k-nearest neighbor (kNN) search with difficult-to-compute distance functions, like graph or sequence similarity. To address this challenge, we propose FedKNN, a system that supports secure federated kNN search queries with a wide range of similarity measurements. Our system is equipped with a new Distribution-Aware kNN (DANN) algorithm to minimize unnecessary local computations while protecting data privacy. We further develop DANN*, a secure version of DANN that satisfies differential obliviousness. Extensive evaluations show that FedKNN outperforms state-of-the-art solutions, achieving up to 4.8× improvement on federated graph kNN search and up to 2.7× improvement on federated sequence kNN search. Additionally, our approach offers a trade-off between privacy and efficiency, providing strong privacy guarantees with minimal overhead.

SeRF: Segment Graph for Range-Filtering Approximate Nearest Neighbor Search

Effective vector representation models, e.g., word2vec and node2vec, embed real-world objects such as images and documents in high dimensional vector space. In the meanwhile, the objects are often associated with attributes such as timestamps and prices. Many scenarios need to jointly query the vector representations of the objects together with their attributes. These queries can be formalized as range-filtering approximate nearest neighbor search (ANNS) queries. Specifically, given a collection of data vectors, each associated with an attribute value whose domain has a total order. The range-filtering ANNS consists of a query range and a query vector. It finds the approximate nearest neighbors of the query vector among all the data vectors whose attribute values fall in the query range. Existing approaches suffer from a rapidly degrading query performance when the query range width shifts. The query performance can be optimized by a solution that builds an ANNS index for every possible query range; however, the index time and index size become prohibitive -- the number of query ranges is quadratic to the number n of data vectors. To overcome these challenges, for the query range contains all attribute values smaller than a user-provided threshold, we design a structure called the segment graph whose index time and size are the same as a single ANNS index, yet can losslessly compress the n ANNS indexes, reducing the indexing cost by a factor of Ω(n). To handle general range queries, we propose a 2D segment graph with average-case index size O(n log n) to compress n segment graphs, breaking the quadratic barrier. Extensive experiments conducted on real-world datasets show that our proposed structures outperformed existing methods significantly; our index also exhibits superior scalability.

Small intestinal submucosa graft bulbar urethroplasty is a viable technique: results compared to buccal mucosa graft urethroplasty after propensity score matching.

Small intestinal submucosa (SIS) graft urethroplasty has been employed to decrease buccal mucosa morbidity and facilitate the procedure. The first published series had a short follow-up, inhomogeneous patient selection, and a lack of a control group. Our purpose is to report treatment outcomes at 13years in a propensity score-matched cohort comparing bulbar urethroplasty with SIS (SISU) or buccal mucosa (BMU). From our institutional database of 1132 bulbar urethroplasties, we used propensity score matching with the nearest-neighbor method without replacement to generate a study sample of 25 BMU and 25 SISU. Failure was defined as any treatment after urethroplasty. Survival analyses were used to analyze treatment failure occurrence with data censored at 156mo. Matching resulted in a complete correction of bias between the two samples except for the follow-up duration, which was slightly longer in the SIS group. The cumulative treatment success probability of BMU and SISU at 156mo was 83.4% and 68%, respectively. At multivariable Cox regression, SIS graft, previous urethrotomy, stricture length, and lower postoperative Qmax (within 2mo after catheter removal) were predictors of failure. Stricture length had a more remarkable effect in SISU, with estimated survival probabilities from the Cox model lower than 80% in strictures > = 3cm. SIS has poorer outcomes compared to BM but may still be useful when BM grafting is not possible. The best candidates for SISU, with similar success to BMU, are patients with strictures shorter than 3cm, preferably without a history of DVIU.

Weight telemonitoring of heart failure versus standard of care in a real-world setting: Results on mortality and hospitalizations in a 6-month nationwide matched cohort study.

Evaluating the benefit of telemonitoring in heart failure (HF) management in real-world settings is crucial for optimizing the healthcare pathway. The aim of this study was to assess the association between a 6-month application of the telemonitoring solution Chronic Care Connect™ (CCC) and mortality, HF hospitalizations, and associated costs compared with standard of care (SOC) in patients with a diagnosis of HF. From February 2018 to March 2020, a retrospective cohort study was conducted using the largest healthcare insurance system claims database in France (Système National des Données de Santé) linked to the CCC telemonitoring database of adult patients with an ICD-10-coded diagnosis of HF. Patients from the telemonitoring group were matched with up to two patients from the SOC group based on their high-dimensional propensity score, without replacement, using the nearest-neighbour method. A total of 1358 telemonitored patients were matched to 2456 SOC patients. The cohorts consisted of high-risk patients with median times from last HF hospitalization to index date of 17.0 (interquartile range: 7.0-66.0) days for the telemonitoring group and 27.0 (15.0-70.0) days for the SOC group. After 6 months, telemonitoring was associated with mortality risk reduction (hazard ratio [HR] 0.71, 95% confidence interval [CI] 0.56-0.89), a higher risk of first HF hospitalization (HR 1.81, 95% CI 1.55-2.13), and higher HF healthcare costs (relative cost 1.38, 95% CI 1.26-1.51). Compared with the SOC group, the telemonitoring group experienced a shorter average length of overnight HF hospitalization and fewer emergency visits preceding HF hospitalizations. The results of this nationwide cohort study highlight a valuable role for telemonitoring solutions such as CCC in the management of high-risk HF patients. However, for telemonitoring solutions based on weight and symptoms, consideration should be given to implement additional methods of assessment to recognize imminent worsening of HF, such as impedance changes, as a way to reduce mortality risk and the need for HF hospitalizations. Further studies are warranted to refine selection of patients who could benefit from a telemonitoring system and to confirm long-term benefits in high-risk and stable HF patients.

Determinants of Mortality and Mid-Term Outcomes After Transcarotid Artery Revascularization and Transfemoral Carotid Artery Stenting.

The potential benefit of transcarotid artery revascularization (TCAR) over transfemoral carotid artery stenting (tfCAS) has been studied in the perioperative period with lower rates of stroke and death; however, data on mid-term outcomes are limited. We aimed to evaluate 3-year outcomes after TCAR and tfCAS and determine the primary predictors of 30-day and 1-year mortality following TCAR. Data from the Vascular Quality Initiative for patients undergoing TCAR or tfCAS from January 2016 to December 2022 were analyzed. 1:1 propensity score matching using the nearest-neighbor method was used to adjust baseline demographics and clinical characteristics. Kaplan-Meier survival analysis and Cox Proportional Hazard Regression were used to evaluate long-term outcomes. Iterative stepwise multiple logistic regression analysis and Cox Proportional Hazard Regression were used to identify predictors of 30-day and 1-year mortality, respectively, based upon preoperative, intraoperative, and postoperative factors. A total of 70 237 patients were included in analysis (TCAR=58.7%, tfCAS=41.3%). Transcarotid artery revascularization patients were older and had higher rates of comorbid conditions and high-risk medical and anatomic features than tfCAS patients. Propensity score matching yielded 22 322 pairs with no major differences between groups except that TCAR patients were older (71.6 years vs 70.8 years). At 3 years, TCAR was associated with a 24% reduction in hazard of death compared with tfCAS (hazard ratio [HR]=0.76, 95% confidence interval [CI]=0.71-0.82, p<0.001), for both symptomatic and asymptomatic patients. This survival advantage was established in the first 6 months (HR=0.59, 95% CI=0.53-0.62, p<0.001), with no difference in mortality risk from 6 months to 36 months (HR=0.95, 95% CI=0.86-1.05, p=0.31). Transcarotid artery revascularization was also associated with decreased hazard for 3-year stroke (HR=0.81, 95% CI=0.66-0.99, p=0.04) and stroke or death (HR=0.81, 95% CI=0.76-0.87, p<0.001) compared with tfCAS. The top predictors for 30-day and 1-year mortality were postoperative complications. The primary independent predictor was the occurrence of postoperative stroke. Transcarotid artery revascularization had a sustained mid-term survival advantage associated over tfCAS, with the benefit being established primarily within the first 6 months. Notably, our findings highlight the importance of postoperative stroke as the primary independent predictor for 30-day and 1-year mortal. The ongoing debate over the superiority of TCAR compared to tfCAS and CEA has been limited by a lack of comparative studies examining the impact of pre-operative symptoms on outcomes. Furthermore, data are scarce on mid-term outcomes for TCAR beyond the perioperative period. As a result, it remains uncertain whether the initial benefits of stroke and death reduction observed with TCAR over tfCAS persist beyond one year. Our study addresses these gaps in the literature, offering evidence to enable clinicians to assess the efficacy of TCAR for up to three years. Additionally, our study seeks to identify risk factors for postoperative mortality following TCAR, facilitating optimal patient stratification.

Metode Data Mining untuk Seleksi Calon Mahasiswa Baru pada Penerimaan Mahasiswa Baru di Universitas Muhammadiyah Gresik

Muhammadiyah University of Gresik is one of the educational institutions in Gresik. Every year, the ratio of new students to graduates is not the same, which can affect the accreditation of the campus. To address this issue, a prediction is made on the data of prospective new students to detect whether they can graduate on time or not. A comparison of the classification results is performed using the K-Nearest Neighbor and Naive Bayes methods. From the implementation and testing, Naive Bayes achieves an accuracy of 72%, while the K- Nearest Neighbor method achieves an accuracy of 64%. Therefore, Naive Bayes is better at classifying the data of prospective new students compared to K-Nearest Neighbor.

ANNProof: Building a verifiable and efficient outsourced approximate nearest neighbor search system on blockchain

Data-as-a-service is increasingly prevalent, with outsourced K-approximate nearest neighbors search (K-ANNS) gaining popularity in applications like similar image retrieval and anti-money laundering. However, malicious search service providers and dataset providers in current outsourced query systems cause incorrect user query results. To address this, we propose ANNProof, a novel framework supporting verifiable outsourced K-ANNS on the blockchain. ANNProof utilizes two innovative authenticated data structures (ADS), the Merkle HNSW node tree, and the Merkle vector identifier tree, for efficient K-ANNS query verification. Additionally, we employ the Merkle sharding tree as an ADS optimization technique, reducing the overhead of delivering verifiable queries. We implement the ADS construction protocol based on blockchain smart contracts to ensure tamper-evident datasets and enhance execution efficiency via a contract state consistency checking scheme. Extensive evaluations show that ANNProof reduces VO generation time, result verification time, and VO size by 160, 120, and 28×, respectively, compared to the state-of-the-art systems. Moreover, ADS construction using ANNProof takes at most 2% of the index construction time, resulting in a negligible overhead for implementing verifiable queries. Meanwhile, the sharding optimization accelerates ADS updates by 53×.

A novel tree structure-based multi-prototype clustering algorithm

Density-peak clustering (DPC) is a novel clustering algorithm that has received much attention from researchers. Its principle is to identify the cluster center of the data by density and distance. However, the density-peak clustering algorithm still suffers from poor clustering results due to irrational choices of cluster centers and misallocating samples. To tackle these problems, a novel tree structure-based multi-prototype clustering algorithm (MPCTS) is proposed in this paper. Firstly, a division-merger method is adopted to divide the dataset into multiple peak clusters based on multi-prototype clustering, which could minimize the impact of high-density sample points on the subsequent sample point assignments. Secondly, the KNN nearest-neighbor search strategy is improved to quickly obtain the sub-cluster center in each peak cluster to overcome the defect of irrational selection of initial cluster centers. Afterward, a tree-based decay strategy is designed to evaluate the correlation between sample points, and a tree-based association strategy is designed to achieve the merging of multiple peak clusters to tackle the sample points misallocation problem. Finally, the extensive experiments on clustering task are conducted over several benchmark synthetic and real datasets to verify the efficiency and superiority of the proposed MPCTS.

An Interpretable Machine Learning Model for Heart Arrhythmia Classification

Machine learning (ML) has been a very effective tool for arrhythmia detection and classification using electrocardiograms (ECGs). However, in order for patients and healthcare professionals to trust the ML models, the models have to be interpretable to show how they arrived at a certain conclusion. We present an ML model that uses the Deep k-Nearest Neighbors framework in order to produce example-based explanations and uncertainty estimations. These examples are ECGs similar to the input derived from conducting a nearest neighbor search on encoded samples (which are values of a layer of the neural network after passing dataset samples through it). We introduce a new technique of using these example-based explanations in conjunction with saliency maps, and also use a neighbor-based uncertainty estimation technique. We show that the saliency maps provide good explanations, but the neighbor examples are needed to assess the credibility of those saliency maps. Our uncertainty estimations increase the accuracy of the model from 86% to 93% (when measured with coverage of 76%). Overall, our novel methods prove to be a promising solution in the field of interpretable ML for arrhythmia classification.

Estimation of an Extent of Sinusoidal Voltage Waveform Distortion Using Parametric and Nonparametric Multiple-Hypothesis Sequential Testing in Devices for Automatic Control of Power Quality Indices

Deviations of power quality indices (PQI) from standard values in power supply systems of industrial consumers lead to defective products, complete shutdown of production processes, and significant damage. At the same time, the PQI requirements vary depending on the industrial consumer, which is due to different kinds, types, and composition of essential electrical loads. To ensure their reliable operation, it is crucial to introduce automatic PQI control devices, which evaluate the extent of distortion of the sinusoidal voltage waveform of a three-phase system. This allows the power dispatchers of grid companies and industrial enterprises to quickly make decisions on the measures to be taken in external and internal power supply networks to ensure that the PQI values are within the acceptable range. This paper proposes the use of an integrated indicator to assess the extent of distortion of the sinusoidal voltage waveform in a three-phase system. This indicator is based on the use of the magnitude of the ratio of complex amplitudes of the forward and reverse rotation of the space vector. In the study discussed, block diagrams of algorithms and flowcharts of automatic PQI control devices are developed, which implement parametric and nonparametric multiple-hypothesis sequential analysis using an integrated indicator. In this case, Palmer’s algorithm and the nearest neighbor method are used. The calculations demonstrate that the developed algorithms have high speed and high performance in detecting deviations of the electrical power quality.

Bridging Software-Hardware for CXL Memory Disaggregation in Billion-Scale Nearest Neighbor Search

We propose CXL-ANNS , a software-hardware collaborative approach to enable scalable approximate nearest neighbor search (ANNS) services. To this end, we first disaggregate DRAM from the host via compute express link (CXL) and place all essential datasets into its memory pool. While this CXL memory pool allows ANNS to handle billion-point graphs without an accuracy loss, we observe that the search performance significantly degrades because of CXL’s far-memory-like characteristics. To address this, CXL-ANNS considers the node-level relationship and caches the neighbors in local memory, which are expected to visit most frequently. For the uncached nodes, CXL-ANNS prefetches a set of nodes most likely to visit soon by understanding the graph traversing behaviors of ANNS. CXL-ANNS is also aware of the architectural structures of the CXL interconnect network and lets different hardware components collaborate with each other for the search. Furthermore, it relaxes the execution dependency of neighbor search tasks and allows ANNS to utilize all hardware in the CXL network in parallel. Our evaluation shows that CXL-ANNS exhibits 93.3% lower query latency than state-of-the-art ANNS platforms that we tested. CXL-ANNS also outperforms an oracle ANNS system that has unlimited local DRAM capacity by 68.0%, in terms of latency.

Validation of the Kidney Failure Risk Equation in the Colombian Population.

Chronic kidney disease prevention programs must identify patients at risk of early progression to provide better treatment and prolong kidney replacement therapy-free survival. Risk equations have been developed and validated in cohorts outside of Colombia, so this study aims to evaluate the discrimination and calibration of the four-variable kidney failure risk equation in a Colombian population where it has yet to be validated. External validation study of a kidney failure risk equation using a historical cohort of patients with CKD stages 3, 4, and 5, adults without a history of dialysis or kidney transplantation with a two-year follow-up, belonging to the Baxter Renal Care Services Colombia network. The discriminatory capacity of the model was evaluated by the concordance index using Harrell's C statistic, and the time-dependent area under the receiver operating characteristic (ROC) curve was estimated using the nearest neighbor method, as well as the optimal cut-off point for sensitivity and specificity. Calibration was determined by the degree of agreement between the observed outcome and the probabilities predicted by the model using the Hosmer-Lemeshow statistic. A total of 5,477 patients were included, with a mean age of 72 years, 36.4% diabetic, and a mean baseline eGFR of 36 ml/min/1.73 m2. The rate of dialysis initiation was three events per 100 patient-years, 95% CI (2.9-3.6). The optimal cutoff for sensitivity was 0.94, for specificity, 0.76, and the area under the ROC curve was 0.92. Harrell's C-statistic was 0.88 for the total population, 0.88 for diabetic patients, and 0.93 for those 65 years or older. The validation of the model showed good calibration. In this Colombian cohort, the four-variable KFRE with a two-year prediction horizon has excellent calibration and discrimination, and its use in the care of CKD Colombian patients is recommended.

CRISPR-COPIES: an in silico platform for discovery of neutral integration sites for CRISPR/Cas-facilitated gene integration.

The CRISPR/Cas system has emerged as a powerful tool for genome editing in metabolic engineering and human gene therapy. However, locating the optimal site on the chromosome to integrate heterologous genes using the CRISPR/Cas system remains an open question. Selecting a suitable site for gene integration involves considering multiple complex criteria, including factors related to CRISPR/Cas-mediated integration, genetic stability, and gene expression. Consequently, identifying such sites on specific or different chromosomal locations typically requires extensive characterization efforts. To address these challenges, we have developed CRISPR-COPIES, a COmputational Pipeline for the Identification of CRISPR/Cas-facilitated intEgration Sites. This tool leverages ScaNN, a state-of-the-art model on the embedding-based nearest neighbor search for fast and accurate off-target search, and can identify genome-wide intergenic sites for most bacterial and fungal genomes within minutes. As a proof of concept, we utilized CRISPR-COPIES to characterize neutral integration sites in three diverse species: Saccharomyces cerevisiae, Cupriavidus necator, and HEK293T cells. In addition, we developed a user-friendly web interface for CRISPR-COPIES (https://biofoundry.web.illinois.edu/copies/). We anticipate that CRISPR-COPIES will serve as a valuable tool for targeted DNA integration and aid in the characterization of synthetic biology toolkits, enable rapid strain construction to produce valuable biochemicals, and support human gene and cell therapy applications.