Minimum Number Research Articles

Opening the species box: What parsimonious microscopic models of speciation have to say about macroevolution.

In the last two decades, lineage-based models of diversification, where species are viewed as particles that can divide (speciate) or die (become extinct) at rates depending on some evolving trait, have been very popular tools to study macroevolutionary processes. Here, we argue that this approach cannot be used to break down the inner workings of species diversification and that "opening the species box" is necessary to understand the causes of macroevolution, but that too detailed speciation models also fail to make robust macroevolutionary predictions. We set up a general framework for parsimonious models of speciation that rely on a minimal number of mechanistic principles: (i) reproductive isolation is caused by excessive dissimilarity between genotypes; (ii) dissimilarity results from a balance between differentiation processes and homogenizing processes; and (iii) dissimilarity can feed back on these processes by decelerating homogenization. We classify such models according to the main homogenizing process : (1) clonal evolution models (ecological drift), (2) models of genetic isolation (gene flow) and (3) models of isolation by distance (spatial drift). We review these models and their specific predictions on macroscopic variables such as species abundances, speciation rates, interfertility relationships or phylogenetic tree structure. We propose new avenues of research by displaying conceptual questions remaining to be solved and new models to address them: the failure of speciation at secondary contact, the feedback of dissimilarity on homogenization, the emergence in space of breeding barriers.

Culture on organ and tissue donation in the community of Medical School, Veracruz Region

Abstract Introduction Organ donation is a life-saving activity, yet there is a disparity between the high number of recipients and the minimal number of donors. Objetives To determine the level of organ and tissue donation culture for transplant purposes among faculty members and undergraduate medical interns at a Medical School Methods A cross-sectional and prospective study was conducted from January 2023 to 2024, including faculty members and undergraduate medical interns from the Medical School at the University of Veracruz - Veracruz Region. The main study variable was the culture of organ and tissue donation for transplant purposes, quantified through three dimensions: knowledge, attitude, and training, assessed using a validated instrument (Cronbach’s alpha: 0.6). The entire described population was included, and they were approached in different areas to invite them to participate in this research. Results 225 subjects (55 faculty members) agreed to participate. 97% of participants expressed support for organ donation. Among those not in favor of donation, 33% feared receiving poor medical care as donors, and 17% viewed donation as a business. Organ donation culture was deemed sufficient in 10.98% and outstanding in 71.76% of participants. The most common level of knowledge was outstanding (52%), 70% of the studied population showed a ‘very positive’ attitude towards organ and tissue donation. Training history was evaluated as an associated factor with knowledge about organ donation (OR/CI95%), identifying an association between lack of training and levels of sufficient knowledge (2.5/1.2-4.9) and outstanding knowledge (0.3/0.1-0.7), as well as with sufficient culture (9.6/1.2-73.0) and outstanding culture (0.1/0.04-0.4) (p < 0.05) Conclusions It is imperative to strengthen training programs for faculty and undergraduate personnel, as quality training enhances knowledge and culture within this essential area of healthcare personnel formation. Key messages • University professors should have accurate information on organ donation and sensitize future physicians on the subject, as this could be a crucial step for future interactions in favor of donation. • It is necessary to eradicate misinformation about organ donation in teachers and students of medical schools, since they are the basis for education and awareness on this issue in the population.

Future Prospects for the Application of Artificial Intelligence in Judicial Management

The problems in judicial management persist regarding the applicability of artificial intelligence (AI) systems. Currently, they have become a challenge as they lack legal foundation and legitimacy, given that to date, only a minimal number of countries have laws addressing the supervision, regulation, management, and control of AI system applicability in judicial settings. The objective of this research is to analyze the future perspectives of the applicability of Artificial Intelligence in Judicial Management. The method employed will be exploratory research to analyze information from reviewed articles, legal reports, and official websites. Possible indicators of issues in AI applicability in judicial management, current and future trends of AI in judicial management, and an appropriate conceptual model for AI applicability in judicial management were identified. It was concluded that the application of artificial intelligence in judicial management remains a challenge and an ongoing issue because it currently does not inspire confidence in decision-making processes.

OPTIMIZATION PROBLEM FOR NUMBER OF LOGIC GATES NEEDED TO IMPLEMENT MULTIPLE BOOLEAN FUNCTIONS USING DECODER

Abstract. Background. Computer circuits enable almost every piece of electronics to function. An opportunity for their optimization was found in one of the ways to implement a circuit, which involves a decoder. Decoders are basic circuit components whose behavior makes it easy to implement multiple Boolean functions. Functions are just rules by which informational signals are being processed in the circuit. To implement a function Logic gatEs (LEs) are used. LEs group multiple input signals and process them using binary logic operators like AND or NOR. Purpose. Develop a computer program that, based on provided functions, will find a solution associated with approximately minimal number of logic gates. Relevance. Prevalence of circuits increases the value of small optimizations, since any improvement gets multiplied by millions of circuits produced. Optimization at the logic level is being explored, which will retain value regardless of the physical properties of the circuit. Additionally, the theory of optimizing LE usage for circuits built on decoders is examined, which will alleviate future research. Methods. Development of the theory arose from an understanding that input signals can be grouped using LEs consciously. This way, LE outputs can be used in more than one function, which results in reduction of total number of LEs. It becomes evident that the problem can be restated in terms of sets. Similarity to known NP-hard problems emerges, which prompts usage of existing solution approaches. Among them, the greedy algorithm is chosen for its simplicity in program implementation. The connection between the problem and digital circuitry is weakened by the introduction of special evaluation functions that work with sets. So, the problem is reduced to several mathematical rules, which then are used as the appropriate parts of the greedy algorithm. Development of knowledge necessary to create the target program is thus concluded. Results. The target program is successfully implemented and can be seen in full at https://github.com/Gleb-05/MakeFuncForDC. A test is conducted on 1000 sets of four pseudorandom eight-term functions. It is estimated that the implementations proposed by the program require on average 0.75 of the initial number of LEs. The average running time is estimated at 2 milliseconds. In contrast, brute-force search for the same problem is theorized to run for years. Conclusions. A working program that finds an almost optimal solution is successfully created and tested. Its performance makes it potentially useful in the industrial scale of circuit manufacturing. Since the program essentially offers workload management, its possible applications go beyond the domain of digital electronics. Additionally, the theory of optimizing the use of LEs is presented for the first time. It can be used as the basis for future research on the implementation of multiple Boolean functions using decoder.

Selective collaboration in distributed FxLMS active noise control systems

This paper addresses the selection of the collaboration configuration on distributed active noise control (ANC) systems. ANC systems aim to cancel out acoustic noise within a listening area. In distributed systems, the control task is delegated among multiple acoustic control nodes that generate the control signals by filtering a noise reference signal. The coefficients of each node filter are iteratively calculated using the filtered-X LMS algorithm. The stability is achieved when the adaptive filters computed in each node converge to finite values. However, acoustic coupling among nodes could lead to instability (i.e., divergence). Collaboration among selected nodes may avoid this phenomenon, although not just any collaboration configuration guarantees network stability. On the other hand, a collaborative distributed system presents two drawbacks: the stability assessment is computationally expensive, and communication requirements increase with the number of collaborations among nodes. In this paper, we propose and discuss several methods to establish a collaboration configuration that ensures system stability. The optimal configuration, which is characterized by the minimal number of necessary collaborations between nodes, can be identified through exhaustive search. However, this approach incurs a high computational cost, particularly in networks with many nodes. To address this challenge, we introduce several heuristic methods aimed at efficiently obtaining stable configurations.

Evaluation of the robustness of randomized controlled trials for the treatment modalities of esophageal cancer using the fragility index - a systematic review.

Esophageal cancer remains a significant global health challenge. Several treatment modalities were explored in randomized controlled trials (RCTs) in recent decades. This study evaluates the robustness of RCTs focusing on esophageal cancer treatment using the fragility index (FI) and reverse fragility index (RFI). A systematic review of RCTs studying different treatment modalities for esophageal cancer from 2000 to 2023 was conducted. The FI and RFI were utilized to gauge the robustness of statistically significant and non-significant outcomes, respectively. The FI represents the minimal number of patient outcomes that would need to alter to overturn a trial's statistical significance, while RFI indicates the minimal changes required to achieve significance in non-significant results. Out of 4028 studies retrieved, 21 RCTs were included for final analysis. The studies spanned 2001 to 2023 with a mean followup of 66 months (range, 29-108 months) and median number of patients of 194 (range, 45-802). The most common treatment modalities examined in these studies were neoadjuvant chemoradiotherapy (n = 7, 33.3%), neoadjuvant chemotherapy (n = 4, 19.0%), and neoadjuvant immunotherapy (n = 2, 9.5%). Only 5 studies (23.8%) had a statistically significant primary outcome result with a median FI of 6 (IQR, 2.5-8.5). Non-significant primary outcomes were seen in 16 studies (76.2%) with a median RFI of 4 (IQR 1-11) and lost to followup of 0 (IQR 0-4). In the study with the highest FI (10), the FI was lower than the number of patients lost to followup (13). Our findings demonstrate that most RCTs on esophageal cancer treatments did not report significant primary outcomes. The few studies that reported significant results had a low fragility index, suggesting a vulnerability in their findings.

Companion Diagnostics (CDx) Based on Molecular Biology Techniques

Molecular profiling based on genomic mutations provides clinically important diagnostic and prognostic information. Companion diagnostic (CDx) testing, which is based on targeted drug therapy, is being applied to a variety of molecular diagnostic techniques (e.g., fluorescent in situ hybridization—FISH; polymerase chain reaction—PCR; and next-generation sequencing—NGS) to diagnose complex etiologies using a minimal number of specimens, replacing immunohistochemical analysis, which may show bias at certain stages. The safety and effectiveness of CDx testing using molecular diagnostic technology in precision medicine is an important factor in determining the treatment outcome and prognosis of patients. Meeting minimum safety and effectiveness performance standards is essential for CDx testing, and a thorough understanding of regulatory considerations is necessary to plan and design the optimal product. In this review, we focus on the diagnostic field of precision medicine and discuss the safety and effectiveness that each molecular diagnostic technology must meet according to CDx testing diversity.

A Cooperative game theory-based feature selection for efficient hand grasp classification using minimal number of sEMG signals

Deploying bio-electrical signals and image processing (visual) techniques are the two popular means to provide input to generate grasp control for robotic and prosthetic devices. Visual perception-based techniques rely on computationally expensive image processing algorithms and are affected by lighting conditions. In contrast, grasp control based on bio-electric signals such as surface electromyography (sEMG) is invariant to lighting conditions. It can reflect human intent to hand motion or grasp with lesser computational costs. In this article, we propose an efficient machine-learning pipeline to classify hand grasp using a minimal number of sEMG sensors. A cooperative game theory-based feature selection technique is applied to find the representative feature subset. The feature selection method uses a modified marginal contribution based on the class distribution coefficient to generate feature ranking. This feature ranking is further used to find the most representative feature subset from the extracted feature set. Our proposed pipeline has been evaluated on a benchmark dataset and has achieved a classification accuracy of 98.20%, using single-channel EMG when coupled with the XGBoost classifier. Thorough assessments were conducted to confirm the reliability of the results obtained. Our proposed pipeline holds the potential to facilitate the development of cost-effective sEMG prosthetics.

A keep-it-simple embolisation approach to treat pelvic congestion syndrome without compromising clinical effectiveness.

There are two approaches to treating pelvic congestion syndrome (PCS): (i) the keep-it-simple (KIS) approach, which involves embolising only the refluxing vein(s), typically the left ovarian vein (LOV) unless the right ovarian vein (ROV) or left/right internal iliac vein (IIV) tributaries are also refluxing; and (ii) the extensive (EXT) approach, which empirically embolises almost all of the LOV, ROV, and left and right IIV tributaries. The aim of this study is to determine whether the KIS approach can effectively treat PCS while minimising the number of treated veins and coils used, without the need for injecting sclerosing agents into pelvic veins or the use of occlusion balloons. This is a single-institution retrospective cohort study. Our records identified 154 women who underwent venograms for possible PCS, with the intent to proceed with embolisation. Refluxing veins were treated using the KIS approach, deploying minimal number of coils, 'sandwiching' sclerosing foam. Short-term follow-up was conducted at 6 weeks; long-term follow-ups (between 12 and 60 months) were conducted via an electronic survey consisting of 19 questions assessing pelvic pain/pressure, leg and back pain, fatigue, and bladder and menstrual symptoms. Fifteen women had negative venogram; 139 women had one or more refluxing veins on venogram. Most women (73%) required unilateral ovarian vein (OV) embolisation, 14% required bilateral OV embolisation, and 12% underwent pelvic vein embolisation. Most cases required only four pushable coils. Clinical success was 89% at 6 weeks and 84% at 1-5 years. A visual analogue scale reduction of 5.2 points (from 7.8 to 2.7) was achieved. There were no instances of coil dislodgement or other complications. The keep-it-simple approach, embolising only the refluxing ovarian and/or iliac veins, can achieve a successful clinical outcome for pelvic congestion syndrome. The extensive approach of empirically embolising all ovarian veins and internal iliac veins may not be necessary. This carries implications for potential savings in procedure time, cost and radiation dose.

Automated analysis of flow cytometry data with minimal training files: Research evaluation of an elastic image registration algorithm for TBNK, stem cell enumeration, and lymphoid screening tube assays.

Automated analysis of flow cytometry data can improve objectivity and reduce analysis time but has generally required work by software and algorithm experts. Here, we investigated the performance of BD ElastiGate™ Software (hereafter ElastiGate), which allows users to automate gating by selecting gated training files, then uses elastic image registration to gate new files. Three assays of increasing complexity were examined: TBNK, stem cell enumeration (SCE), and lymphoid screening tube (LST). For TBNK analysis, 60 peripheral blood (PB) samples from normal, HIV+, and controls were tested with ground truth analysis by an existing automated method. For SCE, 128 samples including bone marrow (BM), cord blood (CB), and apheresis were tested with analysis by multiple manual analysts. For LST, 80 PB and 28 BM samples were tested with manual analysis. For ElastiGate, a minimal number of training files was selected. Results were compared by Bland-Altman or F1 score analysis. For TBNK, ElastiGate using three training files (1 control, 1 normal, 1 HIV+) showed mean %bias across all reported populations between -1.48% and 7.13% (average 2.08%). For SCE, ElastiGate using three BM and two CB training files showed median F1 scores >0.93 in comparison to >0.94 and >0.92 for two other manual analysts. For LST, ElastiGate using four training files for each of PB and BM showed median F1 scores >0.945 for 13 of 14 PB populations and 10 of 14 BM populations, with generally similar or better performance for normal samples compared to abnormal; populations with lower scores were often associated with lower agreement between manual analysts. Based on analysis of three assays with four sample types of increasing complexity, ElastiGate with minimal training files may perform as an automated gating assistant. The results reported here are for research use only, not for use in diagnostic or therapeutic procedures.

Efficient Ensemble Adversarial Attack for a Deep Neural Network (DNN)-Based Unmanned Aerial Vehicle (UAV) Vision System

In recent years, unmanned aerial vehicles (UAVs) vision systems based on deep neural networks (DNNs) have made remarkable advancements, demonstrating impressive performance. However, due to the inherent characteristics of DNNs, these systems have become increasingly vulnerable to adversarial attacks. Traditional black-box attack methods typically require a large number of queries to generate adversarial samples successfully. In this paper, we propose a novel adversarial attack technique designed to achieve efficient black-box attacks with a minimal number of queries. We define a perturbation generator that first decomposes the image into four frequency bands using wavelet decomposition and then searches for adversarial perturbations across these bands by minimizing a weighted loss function on a set of fixed surrogate models. For the target victim model, the perturbation images generated by the perturbation generator are used to query and update the weights in the loss function, as well as the weights for different frequency bands. Experimental results show that, compared to state-of-the-art methods on various image classifiers trained on ImageNet (such as VGG-19, DenseNet-121, and ResNext-50), our method achieves a success rate over 98% for targeted attacks and nearly a 100% success rate for non-targeted attacks with only 1–2 queries per image.

Board assignment heuristics for nail laminated out-of-grade timber

ABSTRACT Structural sawn timber, sourced from softwood plantations, is widely used in lightweight timber-framed residential housing and in engineered wood products for contemporary mass timber construction. However, a significant proportion of milled timber boards are considered ‘out-of-grade’, failing to attain a structural grade under existing classifications systems due to various stiffness, strength, and/or utility-limiting features. This study investigates the potential of controlled lamination techniques to produce structurally usable laminated timber products from out-of-grade timber, using a minimal number of requisite boards. Numerical and experimental methods were employed to compare different board assignment heuristics, in terms of reducing the stiffness variability in laminated board populations. The results indicate that controlled board locations produced remarkably consistent laminated products, achieving very low variability even with a minimum lamination of only two boards. The findings of this paper suggest that controlled lamination techniques have the potential to transform low-value out-of-grade timber into a valuable resource for value-added applications, challenging the existing market perception that out-of-grade timber lacks structural usability.

Design and Analysis of a Novel Bidirectional DC‐DC Converter With Ultra‐Conversion Ratio and Reduced Current Stress

ABSTRACTConventional nonisolated bidirectional DC‐DC converters are limited because of their low conversion voltage ratio. To overcome the limitation, this paper proposed a new bidirectional DC‐DC converter to introduce the novel regenerative configuration merged with the switched inductor technique for high voltage conversion applications. This design enables extreme DC‐voltage conversion with a minimal number of semiconductors and passive components. As a result, the converter's cost is reduced, and it can attain a high voltage gain with less duty ratio. In general, ultra‐gain converters suffer from high current stress across the input and output side switches in the step‐up and step‐down modes. To reduce high current stress, an active network configuration is imposed in the proposed converter. This paper describes the operating principle, steady‐state analysis of the voltage gain, and presents the performance matrices of the proposed converter. Furthermore, a comparative analysis is made with conventional and recent literature converters. Finally, a 350‐V, 500‐W, and 20‐kHz experimental prototype has been fabricated to validate the operation and corroborate the theoretical waveforms with experimental results.

Energy Constrained Depth First Search

AbstractDepth first search is a natural algorithmic technique for constructing a closed route that visits all vertices of a graph. The length of such a route equals, in an edge-weighted tree, twice the total weight of all edges of the tree and this is asymptotically optimal over all exploration strategies. This paper considers a variant of such search strategies where the length of each route is bounded by a positive integer B (e.g. due to limited energy resources of the searcher). The objective is to cover all the edges of a tree T using the minimum number of routes, each starting and ending at the root and each being of length at most B. To this end, we analyze the following natural greedy tree traversal process that is based on decomposing a depth first search traversal into a sequence of limited length routes. Given any arbitrary depth first search traversal R of the tree T, we cover R with routes $$R_1,\ldots ,R_l$$ R 1 , … , R l , each of length at most B such that: $$R_i$$ R i starts at the root, reaches directly the farthest point of R visited by $$R_{i-1}$$ R i - 1 , then $$R_i$$ R i continues along the path R as far as possible, and finally $$R_i$$ R i returns to the root. We call the above algorithm piecemeal-DFS and we prove that it achieves the asymptotically minimal number of routes l, regardless of the choice of R. Our analysis also shows that the total length of the traversal (and thus the traversal time) of piecemeal-DFS is asymptotically minimum over all energy-constrained exploration strategies. The fact that R can be chosen arbitrarily means that the exploration strategy can be constructed in an online fashion when the input tree T is not known in advance. Each route $$R_i$$ R i can be constructed without any knowledge of the yet unvisited part of T. Surprisingly, our results show that depth first search is efficient for energy constrained exploration of trees, even though it is known that the same does not hold for energy constrained exploration of arbitrary graphs.

Synthesis of Hetaryl Substituted Pyrazolo[3,4-b]Quinolinone Systems by Multicomponent Cyclocondensation Reaction

In this study, the synthesis of pyrazolo[3,4-b]quinolinone compounds was carried out via one-pot three-component reactions. These reactions proceed as domino processes, making them easier to occur than the conventional multistep organic reactions. By employing this method, new organic molecules can be synthesized in a single step, using minimal time and number of trials. In the first phase of this two-step study, heteroaromatic carbaldehydes (quinoline-8-carboxaldehyde and quinoline-4-carboxaldehyde) were prepared to be used as substrates in subsequent reactions by oxidation of 8-methylquinoline and 4-methylquinoline with selenium dioxide, a mild oxidant. In the second step, heteroaromatic carbaldehyde reacted with aminopyrazole and dimedone in anhydrous ethanol by one-pot multicomponent condensation method to synthesize six new compounds with heteryl-substituted pyrazolo[3,4-b]quinolinone ring system. The crude products were obtained in excellent yields and further purified by crystallization. The structures of the compounds, which were found to be completely pure as a result of chromatographic studies, were elucidated by spectroscopic methods and elemental analysis.

Conditions for tractability of the weighted $$L_p$$-discrepancy and integration in non-homogeneous tensor product spaces

AbstractWe study tractability properties of the weighted $$L_p$$ L p -discrepancy, where the weights model the influence of different coordinates. A discrepancy is said to be tractable if the minimal number N of points, such that the discrepancy is less than the initial discrepancy times an error threshold $$\varepsilon $$ ε , does not grow exponentially fast with the dimension and $$\varepsilon ^{-1}$$ ε - 1 . In this case there are various notions of tractabilities used in order to classify the exact rate. In the present paper we prove matching sufficient conditions (upper bounds) and neccessary conditions (lower bounds) for polynomial and weak tractability for all $$p \in (1, \infty )$$ p ∈ ( 1 , ∞ ) and we prove a necessary condition for strong polynomial tractability. The proofs of the lower bounds are based on a powerful general result for the information complexity of integration with positive quadrature rules for tensor product spaces. As a second application of this general result we consider the integration of polynomials of degree at most 2.

Multiplexed Random Access Approach to DNA Microspheres for High‐Capacity Data Storage

AbstractWith the rapid growth of modern information data, DNA has emerged as a novel medium for data storage due to its durability, replicability, sustainability, and high density. This study focuses on the compatibility, high capacity, and random access of DNA data storage systems. A calcium phosphate (CaP) mineralized fluorescent SiO2 DNA (FSD@CaP) microspheres is developed by encapsulating fluorescence‐labeled encoding DNA files onto the surface of the SiO2 microspheres that are intrinsically sized and fluorescent as addressed two‐dimensional (2D) barcodes. The various sizes of SiO2 and various fluorescence‐labeled encoding DNA files forming 2D barcodes of FSD@CaP. The 2D barcodes enhance the system's compatibility while allowing multiplexed random access to DNA files. The high‐capacity DNA storage permits effective single access to the DNA file using a minimal number of FSD@CaP microspheres, without impacting the matrix of the system. Ultimately, random access to arbitrary DNA files within a complex pool of distinct 2D barcode‐tagged FSD@CaP microspheres is demonstrated by utilizing a flow cytometer for multiplexed sorting. Consequently, the strategy of this storage system provides a scalable concept for compatibility and multiplexed random access to DNA data sets.

Geometric quantum complexity of bosonic oscillator systems

According to the pioneering work of Nielsen and collaborators, the length of the minimal geodesic in a geometric realization of a suitable operator space provides a measure of the quantum complexity of an operation. Compared with the original concept of complexity based on the minimal number of gates required to construct the desired operation as a product, this geometrical approach amounts to a more concrete and computable definition, but its evaluation is nontrivial in systems with a high-dimensional Hilbert space. The geometrical formulation can more easily be evaluated by considering the geometry associated with a suitable finite-dimensional group generated by a small number of relevant operators of the system. In this way, the method has been applied in particular to the harmonic oscillator, which is also of interest in the present paper. However, subtle and previously unrecognized issues of group theory can lead to unforeseen complications, motivating a new formulation that remains on the level of the underlying Lie algebras for most of the required steps. Novel insights about complexity can thereby be found in a low-dimensional setting, with the potential of systematic extensions to higher dimensions as well as interactions. Specific examples include the quantum complexity of various target unitary operators associated with a harmonic oscillator, inverted harmonic oscillator, and coupled harmonic oscillators. The generality of this approach is demonstrated by an application to an anharmonic oscillator with a cubic term.

Intractability results for integration in tensor product spaces

We prove lower bounds on the worst-case error of numerical integration in tensor product spaces. The information complexity is the minimal number N of function evaluations that is necessary such that the N-th minimal error is less than a factor ε times the initial error, i.e., the error for N=0, where ε belongs to (0,1). We are interested to which extent the information complexity depends on the number d of variables of the integrands. If the information complexity grows exponentially fast in d, then the integration problem is said to suffer from the curse of dimensionality.Under the assumption of the existence of a worst-case function for the uni-variate problem, we present two methods for providing lower bounds on the information complexity. The first method is based on a suitable decomposition of the worst-case function and can be seen as a generalization of the method of decomposable reproducing kernels. The second method, although only applicable for positive quadrature rules, does not require a suitable decomposition of the worst-case function. Rather, it is based on a spline approximation of the worst-case function and can be used for analytic functions. Several applications of both methods are presented.

Machine Learning-Aided Ultra-Low-Density Single Nucleotide Polymorphism Panel Helps to Identify the Tharparkar Cattle Breed: Lessons for Digital Transformation in Livestock Genomics.

Cattle breed identification is crucial for livestock research and sustainable food systems, and advances in genomics and artificial intelligence present new opportunities to address these challenges. This study investigates the identification of the Tharparkar cattle breed using genomics tools combined with machine learning (ML) techniques. By leveraging data from the Bovine SNP 50K chip, we developed a breed-specific panel of single nucleotide polymorphisms (SNPs) for Tharparkar cattle and integrated data from seven other Indian cattle populations to enhance panel robustness. Genome-wide association studies (GWAS) and principal component analysis were employed to identify 500 SNPs, which were then refined using ML models-AdaBoost, bagging tree, gradient boosting machines, and random forest-to determine the minimal number of SNPs needed for accurate breed identification. Panels of 23 and 48 SNPs achieved accuracy rates of 95.2-98.4%. Importantly, the identified SNPs were associated with key productive and adaptive traits, thus attesting to the value and potentials of digital transformation in livestock genomics. The ML-aided ultra-low-density SNP panel approach reported here not only facilitates breed identification but also contributes to preserving genetic diversity and guiding future breeding programs.