Universal Adapter Research Articles

A test of adaptive strategy theory using fifteen years of change in coral abundance

Universal Adaptive Strategy Theory aims to predict how taxa and assemblages respond to disturbances on the basis of adaptive strategy group (ASG) membership. Here, we test such predictions using the adaptive strategy scheme for reef-building corals developed by Darling et al. (Ecol Lett 15:1378–1386, 2012) and a long-term dataset of coral assemblage structure from inshore reefs on the central Great Barrier Reef. Several disturbances including mass bleaching and tropical storms were recorded in this 15-year interval from 1998 to 2013. ASG membership did not predict how a given taxon responded to disturbance. In fact, all ASGs were on average equally affected by bleaching and a period of multiple disturbances. Furthermore, there were no consistent winners at these sites in response to the 1998 bleaching in contrast to previous work suggesting clear hierarchies in susceptibility to bleaching. In conclusion, while further efforts to re-evaluate the utility of ASGs for reef corals should be encouraged our results and a re-examination of the literature suggests that direct trait-based approaches might prove more useful when exploring how corals respond to disturbance.

Purpose Built Catheters for Minimally Invasive Surfactant Therapy: Experience from a Canadian Tertiary Level Neonatal Intensive Care Unit.

Minimally invasive surfactant therapy (MIST), a method of surfactant delivery via a thin catheter during spontaneous breathing, is an increasingly popular alternative to intubation and surfactant administration. Recently, purpose-built catheters for MIST received regulatory approval in Canada and became available for use. However, procedural success and user experience with such catheters have not been described. This retrospective cohort study included neonates who received MIST using purpose-built catheters between January 1, 2021, and March 31, 2022. Two types of purpose-built catheters were used in this period - SurfCath™ and BLEScath™. Procedural success, number of attempts, and adverse events in neonates receiving MIST via the two catheters were compared using chi-square or Fisher's tests. User experience was described using an ease-of-use scale. Thirty-seven neonates met eligibility criteria; 22 received MIST via SurfCath™, whereas 15 received MIST via BLEScath™. Success rates were 91% in SurfCath™ and 93% in BLEScath™ (P> 0.994). Failed attempts were lower in SurfCath™ (23%) in comparison to BLEScath™ (33%), but the difference was not statistically significant (P=0.708). Among operators, 90% found SurfCath™ very easy/relatively easy to use compared to 43% of users reflecting the same degree of use with BLEScath™ (P=.021). There was no difference in adverse events. This is the first study in Canada to report MIST with purpose-built catheters. Overall, the success rate was equally high with both catheters. Users subjectively reported higher ease of use with SurfCath™. Commercially available purpose-built catheters should facilitate universal adaptation of the MIST method.

Meta Bi-classifier Gradient Discrepancy for noisy and universal domain adaptation in intelligent fault diagnosis

Unsupervised domain adaptation fuels knowledge transfer in fault diagnosis, but it depends entirely on a clean source domain and a common label set across domains. Nevertheless, due to the absent knowledge on the label set, the source domain may cover the utmost in classes and unknown classes also come in the target domain, making UDA utterly inapplicable and often impractical. Besides, the ubiquity of incorrect annotations is prone to occur in industrial dataset curations, but it is largely overlooked and left unsolved in UDA. To address these issues, towards a noisy and universal domain adaptation, a Meta Bi-classifier Gradient Discrepancy (MBGD) method is proposed. Specifically, a reliable dual-stage sample selection approach is first initiated to select clean and reusable source samples. Then, to handle private samples on both sides, a coupled divergence criterion is presented to separate target private samples, and a residual bi-classifier with learning constraints is constructed to weaken source private samples. Finally, with a bi-classifier gradient discrepancy, a meta-adversarial domain adaptation is proposed to empower a safe domain alignment on common samples. Comprehensive experiments on various noisy and universal tasks validate the efficacy of MBGD.

Transcriptomic analysis reveals novel desiccation tolerance mechanism of Cronobacter based on type VI secretion system inhibition

Cronobacter malonaticus (C. malonaticus) is a food-borne pathogen inducing severe infections both in infants and adults, and it could survive in dry powdered infant formula (PIF) for a long time, implying its strong tolerance to desiccation. However, the thorough molecular mechanism of resistance to desiccation remains elusive. When C. malonaticus was exposed to desiccation conditions (7, 15, and 30 d), transcriptomic analysis provided a universal adaptation strategy to withstand desiccation with the increased compatible solutes accumulation, activated stress resistance-related regulators, suppressed protein export and bacterial secretion system, and reduced other unessential survival functions including adhesion, invasion, virulence, and flagellar motility. Importantly, type VI secretion system (T6SS) genes exhibited significantly downregulated expressions, as well as markedly increased survival and viability of their mutants after desiccation treatment, revealing the negative regulation of T6SS in desiccation tolerance. Meanwhile, the decreased expressions of T6SS structure genes in other six species further confirmed the vital role of T6SS in desiccation tolerance of Cronobacter spp. Thus, our studies present a novel hypothesis of desiccation resistance in Cronobacter based on type VI secretion system inhibition, causing the reduction of macromolecule secretion such as effectors and hyperosmolality development within the cytomembrane, which allow Cronobacter to survive in desiccation.

Prediction of common labels for universal domain adaptation

Universal domain adaptation (UniDA) is an unsupervised domain adaptation that selectively transfers the knowledge between different domains containing different label sets. However, the existing methods do not predict the common labels of different domains and manually set a threshold to discriminate private samples, so they rely on the target domain to finely select the threshold and ignore the problem of negative transfer. In this paper, to address the above problems, we propose a novel classification model named Prediction of Common Labels (PCL) for UniDA, in which the common labels are predicted by Category Separation via Clustering (CSC). It is noted that we devise a new evaluation metric called category separation accuracy to measure the performance of category separation. To weaken negative transfer, we select source samples by the predicted common labels to fine-tune model for better domain alignment. In the test process, the target samples are discriminated by the predicted common labels and the results of clustering. Experimental results on three widely used benchmark datasets indicate the effectiveness of the proposed method.

Researchers engineer a universal receptor system for cell immunotherapies

Chimeric antigen receptor (CAR) T-cell therapy is a promising immunotherapy against cancer. The idea is to fortify a person’s immune system to fight its own battle by boosting T cells with the ability to ferret out tumor-associated antigens. But engineering CAR-T cells is an arduous process, and they are usually customized to recognize only one antigen. In a new paper, one research group reports broadening the versatility of engineered CAR-T cells by bringing into the fold functionalized antibodies that act as universal adapters between the CAR-T cell and various tumor antigens ( Nat. Commun. 2023, DOI: 10.1038/s41467-023-37863-5 ). “An antibody is . . . easier to chemically modify than an entire cell,” says Alexander Deiters, a University of Pittsburgh chemist and a coauthor of the study. The researchers engineered CAR-T cells with receptors that carry a SNAPtag label, a modified O 6 -methylguanine-DNA methyltransferase. This SNAPtag can covalently bind with

Neighborhood-based credibility anchor learning for universal domain adaptation

Universal domain adaptation (UniDA) aims to transfer knowledge from the source domain to the target domain in the presence of distribution shift and class mismatch. Most existing works design threshold-relied methods to reject target private classes by carefully-proposed uncertainty scoring functions which are very sensitive to thresholds. To overcome this problem, a few threshold-free methods are proposed but ignore the neighborhood structure information of the target domain, leading to poor performance. In this paper, we propose Neighborhood-based Credibility Anchor Learning (NCAL), a new threshold-free framework that fully mines the neighborhood structure information to explore better target representations. NCAL contains three key components: a class anchor learning module to learn target class distribution, a credibility-weighted conditional adversarial module to learn class-invariant features of common classes, and an open-set neighborhood clustering module to learn well-clustered features. Extensive experiments demonstrate that our method outperforms the state-of-the-art.

DeepAstroUDA: semi-supervised universal domain adaptation for cross-survey galaxy morphology classification and anomaly detection

Artificial intelligence methods show great promise in increasing the quality and speed of work with large astronomical datasets, but the high complexity of these methods leads to the extraction of dataset-specific, non-robust features. Therefore, such methods do not generalize well across multiple datasets. We present a universal domain adaptation method, DeepAstroUDA, as an approach to overcome this challenge. This algorithm performs semi-supervised domain adaptation (DA) and can be applied to datasets with different data distributions and class overlaps. Non-overlapping classes can be present in any of the two datasets (the labeled source domain, or the unlabeled target domain), and the method can even be used in the presence of unknown classes. We apply our method to three examples of galaxy morphology classification tasks of different complexities (three-class and ten-class problems), with anomaly detection: (1) datasets created after different numbers of observing years from a single survey (Legacy Survey of Space and Time mock data of one and ten years of observations); (2) data from different surveys (Sloan Digital Sky Survey (SDSS) and DECaLS); and (3) data from observing fields with different depths within one survey (wide field and Stripe 82 deep field of SDSS). For the first time, we demonstrate the successful use of DA between very discrepant observational datasets. DeepAstroUDA is capable of bridging the gap between two astronomical surveys, increasing classification accuracy in both domains (up to on the unlabeled data), and making model performance consistent across datasets. Furthermore, our method also performs well as an anomaly detection algorithm and successfully clusters unknown class samples even in the unlabeled target dataset.

The dynamic architecture of Map1- and NatB-ribosome complexes coordinates the sequential modifications of nascent polypeptide chains.

Cotranslational modification of the nascent polypeptide chain is one of the first events during the birth of a new protein. In eukaryotes, methionine aminopeptidases (MetAPs) cleave off the starter methionine, whereas N-acetyl-transferases (NATs) catalyze N-terminal acetylation. MetAPs and NATs compete with other cotranslationally acting chaperones, such as ribosome-associated complex (RAC), protein targeting and translocation factors (SRP and Sec61) for binding sites at the ribosomal tunnel exit. Yet, whereas well-resolved structures for ribosome-bound RAC, SRP and Sec61, are available, structural information on the mode of ribosome interaction of eukaryotic MetAPs or of the five cotranslationally active NATs is only available for NatA. Here, we present cryo-EM structures of yeast Map1 and NatB bound to ribosome-nascent chain complexes. Map1 is mainly associated with the dynamic rRNA expansion segment ES27a, thereby kept at an ideal position below the tunnel exit to act on the emerging substrate nascent chain. For NatB, we observe two copies of the NatB complex. NatB-1 binds directly below the tunnel exit, again involving ES27a, and NatB-2 is located below the second universal adapter site (eL31 and uL22). The binding mode of the two NatB complexes on the ribosome differs but overlaps with that of NatA and Map1, implying that NatB binds exclusively to the tunnel exit. We further observe that ES27a adopts distinct conformations when bound to NatA, NatB, or Map1, together suggesting a contribution to the coordination of a sequential activity of these factors on the emerging nascent chain at the ribosomal exit tunnel.

Genetic investigation of 124 SNPs in a Myanmar population using the Precision ID Identity Panel and the Illumina MiSeq

Single nucleotide polymorphisms (SNPs) have become popular in forensic genetics as an alternative to short tandem repeats (STRs). The Precision ID Identity Panel (Thermo Fisher Scientific), consisting of 90 autosomal SNPs and 34 Y-chromosomal SNPs, enabled human identification studies on global populations through next-generation sequencing (NGS). However, most previous studies on the panel have used the Ion Torrent platform, and there are few reports on the Southeast Asian population. Here, a total of 96 unrelated males from Myanmar (Yangon) were analyzed with the Precision ID Identity Panel on a MiSeq (Illumina) using an in-house TruSeq compatible universal adapter and a custom variant caller, Visual SNP. The sequencing performance evaluated by locus balance and heterozygote balance was comparable to that of the Ion Torrent platform. For 90 autosomal SNPs, the combined match probability (CMP) was 6.994 × 10−34, lower than that of 22 PowerPlex Fusion autosomal STRs (3.130 × 10−26). For 34 Y-SNPs, 14 Y-haplogroups (mostly O2 and O1b) were observed. We found 51 cryptic variations (42 haplotypes) around target SNPs, of which haplotypes corresponding to 33 autosomal SNPs decreased CMP. Interpopulation analysis revealed that the Myanmar population is genetically closer to the East and Southeast Asian populations. In conclusion, the Precision ID Identity Panel can be successfully analyzed on the Illumina MiSeq and provides high discrimination power for human identification in the Myanmar population. This study broadened the accessibility of the NGS-based SNP panel by expanding the available NGS platforms and adopting a robust NGS data analysis tool.

Clinical outcome of Bioball universal adapter in revision hip arthroplasty

BackgroundRemoval of a well-fixed uncemented femoral component in revision hip arthroplasty is challenging. A modular head–neck adapter provides an option to optimise the femoral offset and anteversion, avoiding the need for femoral stem revision. AimTo present the clinical results following revision arthroplasty with the Bioball head-neck adapter in the elderly American Society of Anaesthesiologists (ASA) Grade II, III & IV patients. Materials and methodsA retrospective review of our database was performed, and all patients classed as ASA grades II, III, & IV treated with the Bioball Universal Adapter (BUA) for 10 years were included. The indication for revision, stem retention, type of adapter, and head size were identified. Patients were contacted by a research nurse to assess the Forgotten Joint Score (FJS), the Oxford Hip Score (OHS), and any symptoms of instability at a minimum of one year post revision surgery. ResultsOur study included 47 patients. 5 (10.6%) were ASA II, 19 (40.4%) were ASA III and 23 (49%) were ASA IV. The mean age was 74 years. The mean follow up was 52 months ± 28.4 SD. The median FJS was 86 ± 11.6 SD. The median OHS was 43 ± 6.2 SD. One patient (2.1%) developed recurrent dislocation following lumbar spinal fusion. None of the other patients experienced instability. The survival rate for the adapter was 98%. ConclusionThe BUA gives good clinical outcomes with very low post revision instability. It is a valuable option for the elderly because it avoids the morbidity and risks associated with the removal of a well-fixed femoral stem. Level of EvidenceLevel IV

A novel localization-free approach to system identification for underwater vehicles using a Universal Adaptive Stabilizer

In this work, we propose a new approach to the otherwise lengthy and involved process of system identification for underwater vehicles. The algorithm is based on Universal Adaptive Stabilization (UAS). The proposed methodology does not require localization equipment as it is based on the more easily attainable linear and angular velocity measurements. It also does not rely on accurate initial estimates to converge. The algorithm is proven to be stable, and its capability to yield the correct parameters of the vehicle is established. In simulation, multiple classes of vehicles are tested to verify the ability of the algorithm to identify varying arrangements of parameters depending on the class and shape of a given underwater vehicle. The algorithm is validated using simulation and experimental data. The simulation tests show very promising identification results. The algorithm is additionally validated on a publicly available Autonomous Underwater Vehicle (AUV) dataset. Experimental results establish the capacity of the algorithm to realize accurate estimates using nothing but generally available state measurements using sensors available on commercial underwater vehicles. In both testing environments, the algorithm produced parameter estimates with accuracy upwards of 95% in a Normalized Mean Absolute Error (NMAE) sense.

Evolutionary and functional conservation of myeloid differentiation factor 88 (MyD88) in amphibian Xenopus tropicalis

As a universal adaptor used by most TLR members, the myeloid differentiation factor 88 (MyD88) plays essential roles in TLR-mediated inflammatory response of invertebrate and vertebrate animals, and functional features of MyD88 remain largely unknown in amphibians. In this study, a MyD88 gene named Xt-MyD88 was characterized in the Western clawed frog (Xenopus tropicalis). Xt-MyD88 and MyD88 in other species of vertebrates share similar structural characteristics, genomic structures, and flanking genes, suggesting that MyD88 is structurally conserved in different phyla of vertebrates ranging from fish to mammals. Moreover, Xt-MyD88 was widely expressed in different organs/tissues, and was induced by poly(I:C) in spleen, kidney, and liver. Importantly, overexpression of Xt-MyD88 triggered a marked activation of both NF-κB promoter and interferon-stimulated response elements (ISREs), implying that it may be play important roles in inflammatory responses of amphibians. The research represents the first characterization on the immune functions of amphibian MyD88, and reveals considerable functional conservation of MyD88 in early tetrapods.

In-vivo Comparison of Planned and Measured Rectal Doses during Cobalt-60 HDR CT-based Intracavitary Brachytherapy Applications of Cervical Cancer Using the PTW 9112 Semiconductor Probe.

A semiconductor rectal probe was used to compare planned and measured rectal doses during Co-60 high dose rate (HDR) CT-based intracavitary brachytherapy applications (ICBT) of cervical cancer. A total of 22 HDR brachytherapy applications were included from 11 patients who were first treated with EBRT to the whole pelvis with a total prescribed dose of 50 Gy in 25 fractions. During each application, a PTW 9112 probe rectal probe having a series of five semiconductor diodes (R1 to R5) was inserted into the patient's rectum and a CT-based HDR ICBT application with a prescribed dose per fraction of 7 or 7.5 Gy to HRCTV was performed. Measurements were carried in water phantom using PTW rectal and universal adaptor plugs. Doses measured in phantom and with patients were compared to those calculated by the treatment planning system. The mean percentage dose difference ΔD (%) between calculated and measured values from phantom study were -5.29%, 1.89%, -2.72%, -4.76, and 0.72% for R1, R2, R3, R4, and R3 diodes, respectively and the overall mean ΔD (%) value with standard deviation (SD) was -2.03%±9.6%. From the patient study, a ΔD (%) that ranged from -19.5% to 24.0%, which corresponded to dose disparities between -0.77 Gy and 0.66 Gy. The median ΔD (%) ranged from 0.4% to 1.3%, or -0.03 to 0.05 Gy, respectively. ΔD (%) values exceeded 10% in approximately 26.4% of measurements (29 out of 110 in 22 applications). The location of Rmax in computed and measured values differs in 5 of 22 applications might be due to possible displacement of rectal probe between simulation and treatment. Despite the likely geometrical shift of measuring detectors between insertion and treatment, in-vivo dosimetry is feasible and can be used to estimate the dose to the rectum during HDR ICBT.

Promoted peroxydisulfate activation by nitrogen-doped carbon embedding iron on a nickel foam cathode: Performance, mechanism and relationship between C[dbnd]O and 1O2 generation

Nitrogen doped carbon embedding iron on a nickel foam (xN-C@Fe/NF) cathode was prepared and used to activate peroxydisulfate (PDS) for organic pollutant degradation. Benefiting from N doping-induced CO content increasing, which possessed good electron-withdrawing properties from Fe and the cathode, the optimum 3 NC@Fe/NF cathode displayed superior model organic pollutant (sulfamethoxazole, SMX) removal efficiency. Specifically, almost 100 % SMX removal efficiency was achieved within 15 min. It was found that the contribution of singlet oxygen (1O2) to SMX degradation gradually became more important with increasing N doping. On the basis of the semiquantitative and quantitative structure–activity relationship and density functional theory (DFT) calculation, it is conclusively identified that CO in the NC@Fe/NF tuned by the doped pyrrolic N and graphitic N are the core catalytic active sites for PDS activation, which was further evidenced by the high linear correlation (R2 of 0.983) between CO content and 1O2 yield. Furthermore, the 3 NC@Fe/NF cathode displayed attractive practical application prospect because of its wide pH applicability, good anti-interference ability, stable catalytic performance and universal adaptability for multiple organics removal. This study not only established a high-efficiency method for green remediation of actual polluted water but also developed valuable insight for discriminating the catalytic active sites of the cathode for PDS activation.

Universal source-free domain adaptation method for cross-domain fault diagnosis of machines

Cross-domain machinery fault diagnosis aims to transfer enriched diagnosis knowledge from a labeled source domain to a new unlabeled target domain. Most existing methods assume that the prior information on the fault modes of the target domain is known in advance. However, in engineering practice, prior knowledge of fault modes is rare in a new domain, in which there may be only partial source fault modes or some new fault modes. Furthermore, up to the present, almost all existing cross-domain fault diagnosis methods require the labeled source data during the model training process, which restricts their deployment on certain devices with limited computing resources. To this end, we propose a universal source-free domain adaptation method that can handle cross-domain fault diagnosis scenarios without access to the source data and is free of explicit assumptions about the target fault modes. More specifically, we develop a convolutional network with a Transformer as the attention module to extract discriminative feature information from the source data and then send the model and parameters to the target domain. In target domain training, we first propose a supervised contrastive learning strategy based on source class prototypes, which utilizes high-confident predictions to achieve source-free domain alignment and class alignment. Then, we also introduce a threshold-based entropy max–min loss to further align known class samples in the target domain or reject target outlier samples as an unknown class. Furthermore, we introduce self-supervised learning to further learn feature representations of the target domain to reduce the previous misclassification. A series of experiments on two rotating machine datasets demonstrate the effectiveness and practicability of the proposed method.

Optimization for Device Figure of Merit of Ferroelectric Tunnel FET using Genetic Algorithm

Tunnel FET is a gate-controlled, field effect transistor, followed band to band tunneling (BTBT) transport of charge carriers, having low subthreshold swing (SS < 60 Mv decade−1|T = 300 K). With tunnel FET, low-ION is a built-in problem, that limits its universal adaptability high-speed low-power uses. To overcome, this limitation of tunnel FET, a conventional double gate TFET has acquired for analysis having ferroelectric (BaTiO3)/HfO2 gate materials and source/channel region with Si1−xGex/Si semiconductor channel composition.The present device design techniques enhanced the ION and put down the subthreshold swing(SS). The analysis results by using the Silvaco simulator shows improvement in switching current(ION) approximately ∼103 times better than conventional DGTFET,without affecting the IOFF. Ultimately the change in ION∼order of 10−8 A μm−1 to 10−5 A μ has been measured for VDS ∼ 0.5 V at room temperature. The IOFF ( ∼10−20 A μm−1) has been measured. In addition to this, first time genetic algorithm has been used for the optimization of ferroelectric tunnel FET (Fe-Tunnel FET) device design parameters like a subthreshold swing (SS), ambipolar current (Iamb) and IONby using device deign parameters, doping (NS, ND), dielectric (εOX) and work function (WF).The research conclusion shows that Fe-Tunnel can play in lead backgroundfor super low power applications in advanced VLSI circuit and system.

Intelligent bearing fault diagnosis method based on a domain aligned clustering network

In recent years, intelligent fault diagnosis algorithms based on domain adaptation have provided a feasible solution to the problem of diagnosing performance degradation caused by different data distributions and a lack of target labels. However, most of the existing domain adaptation fault diagnosis algorithms are highly dependent on the label space and prior knowledge of the source and target domain, which greatly limits their application in practical scenarios. In this paper, faced with the circumstances that fault information and label space of the target mechanical device are completely unknown, a novel intelligent diagnostic method based on universal domain adaptation—the domain aligned clustering network (DACN)—is proposed. On the one hand, the number of clusters is determined by domain clustering analysis, and the public class and private class samples in both domains are identified. On the other hand, in order to achieve high accuracy of model identification on common class samples, this paper introduces the contrast domain difference and realizes class alignment between different domains by maximizing the inter-class difference and minimizing the intra-class difference. Finally, the effectiveness of the DACN is verified on the bearing datasets from Case Western Reserve University and Paderborn University. A one-dimensional class gradient activation map is calculated to explain the performance of the fault diagnosis model.

Uni3DA: Universal 3D Domain Adaptation for Object Recognition

Traditional 3D point cloud classification tasks focus on training a classifier in the closed-set scenario, where training and test data have the same label set and the same data distribution. In this work, we focus on a more challenging and realistic scenario in 3D point cloud classification task: universal domain adaptation (UniDA), where 1) data distributions for training and test data are different; and 2) for given label sets of training data and test data, they may have the shared classes and keep the private classes respectively, introducing an extra label set discrepancy. To solve UniDA problem, researchers have designed many methods based on 2D image datasets. However, due to the difficulty in capturing discriminative local geometric structures brought by the unordered and irregular 3D point cloud data, we cannot directly deploy the existing methods based on 2D image datasets to the 3D scenarios. To address UniDA in 3D scenarios, we develop a 3D universal domain adaptation framework, which consists of three modules: Self-Constructed Geometric (SCG) module, Local-to-Global Hypersphere Reasoning (LGHR) module and Self-Supervised Boundary Adaptation (SBA) module. SCG and LGHR generate the discriminative representation, which is used to acquire domain-invariant knowledge for training and test data. SBA is designed to automatically recognize whether a given label is from the shared label set or private label set, and adapts training and test data from the shared label set. To our best knowledge, this work is the first exploration of UniDA for 3D scenarios. Extensive experiments on public 3D point cloud datasets verify that the proposed method outperforms the existing UniDA methods.

СОВЕРШЕНСТВОВАНИЕ ПРОЦЕССА АДАПТАЦИИ ПЕРСОНАЛА В ОРГАНИЗАЦИИ

The problem of personnel adaptation is relevant in the theory and practice of management. An effective adaptation process in an organization requires the joint efforts of management, personnel management specialists and the employee himself. As a result of the implementation of an individual personnel adaptation program, an organization can receive a number of socio-economic effects: a stable professional staff, an increase in the financial component of the organization, a decrease in staff turnover, an improvement in the sociopsychological climate, organizational culture and an increase in staff loyalty to the organization. The adaptation process helps the employee to get used to the production team much faster, which helps to reduce staff turnover in the first year of staff work in the organization. The author notes that the Russian Railways company has considerable experience in organizing the process of adaptation of workers and specialists. A number of tools for activating and accelerating the adaptation of personnel are listed and characterized. It is emphasized that there can be no universal adaptation, a personalized adaptation program requires taking into account the characteristics of the position, the personality of the employee and the characteristics of the organization itself. The article analyzes the results of a survey of young workers about the organization of mentoring in the workforce. The article concludes that there is a need for systematic work on organizing the adaptation of personnel, about the informal use of the entire arsenal of adaptation tools in personnel management.