Object and spatial discrimination makes weakly supervised local feature better.

Local feature extraction plays a crucial role in numerous critical visual tasks. However, there remains room for improvement in both descriptors and keypoints, particularly regarding the discriminative power of descriptors and the localization precision of keypoints. To address these challenges, this study introduces a novel local feature extraction pipeline named OSDFeat (Object and Spatial Discrimination Feature). OSDFeat employs a decoupling strategy, training descriptor and detection networks independently. Inspired by semantic correspondence, we propose an Object and Spatial Discrimination ResUNet (OSD-ResUNet). OSD-ResUNet captures features from the feature map that differentiate object appearance and spatial context, thus enhancing descriptor performance. To further improve the discriminative capability of descriptors, we propose a Discrimination Information Retained Normalization module (DIRN). DIRN complementarily integrates spatial-wise normalization and channel-wise normalization, yielding descriptors that are more distinguishable and informative. In the detection network, we propose a Cross Saliency Pooling module (CSP). CSP employs a cross-shaped kernel to aggregate long-range context in both vertical and horizontal dimensions. By enhancing the saliency of keypoints, CSP enables the detection network to effectively utilize descriptor information and achieve more precise localization of keypoints. Compared to the previous best local feature extraction methods, OSDFeat achieves Mean Matching Accuracy of 79.4% in local feature matching task, improving by 1.9% and achieving state-of-the-art results. Additionally, OSDFeat achieves competitive results in Visual Localization and 3D Reconstruction. The results of this study indicate that object and spatial discrimination can improve the accuracy and robustness of local feature, even in challenging environments. The code is available at https://github.com/pandaandyy/OSDFeat.

Improvement in antibacterial use in intensive care units from Argentina: A quality improvement collaborative process evaluation using Normalization Process Theory.

BACKGROUND: Healthcare-associated infections and antibiotic resistance worsen globally. Antibiotic stewardship programs (ASP) aim to optimise infection treatment and curb resistance, yet implementation hurdles persist. This study examined ASP challenges in ICUs. METHODS: This study employed a qualitative methodological design to evaluate the implementation process of an antibiotic stewardship program (ASP) in eight intensive care units (ICUs) across Argentina. Thirty-four semi-structured interviews with healthcare workers (HCWs) were conducted. Interviews were analysed guided by Normalisation Process Theory, examining coherence, cognitive participation, collective action, and reflexive monitoring constructs. RESULTS: Key challenges included insufficient human resources, lack of institutional support, and resistance to change, particularly among staff not initially involved in the study. Despite these challenges, the program saw partial success in improving ICU practices, particularly in antibiotic use and communication across departments. The main strategy implemented in this quality improvement collaborative was the use of improvement cycles, which served as the central component for driving change. However, participation in improvement cycles was inconsistent, and sustainability post-intervention remains uncertain due to workload pressures and the need for continuous education. Concerns about workload and communication barriers persisted. Many participants did not perceive training as a separate component, which led to low engagement. Resistance to change became evident during modifications to clinical guidelines. The intervention had a positive impact on various processes, including communication and record keeping. CONCLUSION: This study underscores the persistent challenges in implementing ASPs in healthcare, emphasising the need for enhanced collaboration, workforce capacity building, and evidence-based practices to overcome barriers and optimize antimicrobial use to improve patient outcomes.

Predicting soil ecological criteria of 17 metal(loid)s in China based on quantitative ion character-activity relationship - Species sensitivity distribution (QICAR-SSD) coupled model.

Soil pollution caused by metal(loid)s is increasingly serious and poses unexpected risks to terrestrial organisms. Establishing soil quality standards is essential for assessing ecological risks of metal(loid)s and protecting soil ecosystems. However, the limited availability of metal(loid) ecotoxicological data has hampered the development of soil quality standards due to financial and practical constraints on toxicity testing. This study collected 77 normalization equations and 58 cross-species extrapolation equations to calculate the normalized EC10 (the added concentration causing a 10 % inhibition effect) of metal(loid)s under a representative scenario. A set of quantitative ion character-activity relationship (QICAR) models were then constructed using normalized EC10 and nine critical ionic characters (AR, AR/AW, BP, MP, Z/r2, Z/r, Xm, σp, and |Log(KOH)|). Subsequently, these QICAR models were employed to predict ecotoxicological EC10 of 17 metal(loid)s to 12 soil species and coupled with species sensitivity distribution (SSD) to determine Predicted No Effect Concentration (PNEC). The results demonstrated the coupled QICAR-SSD model could effectively derive terrestrial PNEC for data-poor metal(loid)s, with errors between the predicted PNEC and reported soil standards (excluding soil background levels) from different countries mostly <0.3 orders of magnitude. Finally, soil ecological criteria (SEC) for 17 metal(loid)s were calculated using an added risk approach based on PNEC and national soil background concentration. Overall, the coupled model proposed here can provide a valuable supplement to the development of soil quality standards for numerous metal(loid)s in soil components.

Taxanorm: a novel taxa-specific normalization approach for microbiome data.

BACKGROUND: In high-throughput sequencing studies, sequencing depth, which quantifies the total number of reads, varies across samples. Unequal sequencing depth can obscure true biological signals of interest and prevent direct comparisons between samples. To remove variability due to differential sequencing depth, taxa counts are usually normalized before downstream analysis. However, most existing normalization methods scale counts using size factors that are sample specific but not taxa specific, which can result in over- or under-correction for some taxa. RESULTS: We developed TaxaNorm, a novel normalization method based on a zero-inflated negative binomial model. This method assumes the effects of sequencing depth on mean and dispersion vary across taxa. Incorporating the zero-inflation part can better capture the nature of microbiome data. We also propose two corresponding diagnosis tests on the varying sequencing depth effect for validation. We find that TaxaNorm achieves comparable performance to existing methods in most simulation scenarios in downstream analysis and reaches a higher power for some cases. Specifically, it balances power and false discovery control well. When applying the method in a real dataset, TaxaNorm has improved performance when correcting technical bias. CONCLUSION: TaxaNorm both sample- and taxon- specific bias by introducing an appropriate regression framework in the microbiome data, which aids in data interpretation and visualization. The 'TaxaNorm' R package is freely available through the CRAN repository https://CRAN.R-project.org/package=TaxaNorm and the source code can be downloaded at https://github.com/wangziyue57/TaxaNorm .

Enhanced efficacy of combined VEGFR peptide-drug conjugate and anti-PD-1 antibody in treating hepatocellular carcinoma.

This study aimed to design a VEGFR-targeting peptide-drug conjugate with the ability to decrease tumor burden and suppress tumor angiogenesis, and to further evaluate the therapeutic effect of anti-PD-1 antibody in HCC therapy. A VEGFR-targeting peptide VEGF125 - 136 (QR) was conjugated with a lytic peptide (KLU) to form a peptide-drug conjugate QR-KLU. And the efficacy of QR-KLU in combination with anti-PD-1 antibody for HCC therapy in vivo and in vitro were evaluated. QR-KLU inhibited the proliferation and migration of mouse HCC cell line (Hepa1-6) cells under normoxic and hypoxic conditions in a dose-dependent manner. In the subcutaneous Hepa1-6 tumor model, QR-KLU combined with the anti-PD-1 antibody substantially inhibited tumor growth, promoted tumor necrosis, and prolonged the survival time of tumor-bearing mice. QR-KLU substantially inhibited hypoxia-induced expression of VEGF, promoted tumor vascular normalization, and increased cluster of differentiation 8+ (CD8+) T cell infiltration in the tumor. In addition, QR-KLU and anti-PD-1 antibody demonstrated a strong synergistic effect in promoting the activation of intratumoral CD8+ T cells, reducing the expression of immune-inhibitory factors, and increasing the expression of immune-stimulatory factors. This study proposed a novel approach for enhancing the efficacy of anti-PD-1 antibody using a VEGFR-targeting peptide-drug conjugate in HCC therapy.

'A void in our community': exploring the complexities of delivering and implementing primary care services for transgender individuals in Northern Ontario.

AIM: To understand how the implementation of primary care services for transgender individuals is undertaken and delivered by practitioners in Northern Ontario. BACKGROUND: Northern Ontario, Canada, has a shortage of primary care health practitioners, and of these, there are a limited number providing transgender primary care. Transgender people in Northern Ontario must also negotiate a lack of allied and specialty services related to transgender health and travel over long distances to access those services that do exist. METHODS: A convergent mixed methods design was guided by normalization process theory (NPT) to explore transgender primary care delivery and implementation by nurses, nurse practitioners, physicians, social workers, and psychotherapists. A survey measuring implementation processes was elaborated through qualitative interviews with participants. Analysis of key themes emerging using the NPT framework informed understanding of primary care successes, barriers, and gaps in Northern Ontario. FINDINGS: Key themes included the need for more education on transgender primary care practice, increased need for training and awareness on transgender resources, identification of unique gaps and barriers to access in Northern Ontario transgender care, and the benefits of embedding and normalizing transgender care in clinical practice to practitioners and transgender patients. These findings are key to understanding and improving access and eliminating healthcare barriers for transgender people in Northern Ontario.

Combined manifestation of two geometric visual illusions.

The present study continued to investigate whether the effects of length misperception caused by cross-shaped (formed by two pairs of the oppositely oriented Müller-Lyer wings) contextual distractors can be explained by the combined manifestation of two different (i.e., the Müller-Lyer and filled-space) geometric illusions of extent. In psychophysical experiments, the luminance of one pair of wings was randomly changed, while the luminance of the other pair remained constant. Two different distractor orientations were used-when the wings with constant luminance formed the right side of the cross or the left side, otherwise. To separately evaluate the manifestation of the Müller-Lyer illusion under different luminance conditions, two distracting crosses of the same orientation were attached to the lateral stimulus terminators in the first series of experiments. In the following four series, a single distracting cross (with different orientation) was attached to one of the lateral stimulus terminators and various combinations of the constant and background luminance were used. To interpret the experimental data, we used the basic computational principles of previously developed quantitative models of hypothetical visual mechanisms underlying the emergence of the Müller-Lyer illusion and the filled-space illusion. It was shown that the results of theoretical calculations adequately approximate the experimental curves obtained for all modifications of stimuli, which strongly supports the suggestion that the joint manifestations of these two illusions can be considered among the main factors determining the features of the illusion investigated.

Stability and expression patterns of housekeeping genes in Mediterranean mussels (Mytilus galloprovincialis) under field investigations.

The use of marine mussels as biological models encompasses a broad range of research fields, in which the application of RNA analyses disclosed novel biomarkers of environmental stress and investigated biochemical mechanisms of action. Quantitative real-time PCR (qPCR) is the gold standard for these studies, and despite its wide use and available protocols, it may be affected by technical flaws requiring reference gene data normalization. In this study, stability of housekeeping genes commonly employed as reference genes in qPCR analyses with Mytilus galloprovincialis was explored under field conditions. Mussels were collected from farms in the Northwestern Adriatic Sea. The sampling strategy considered latitudinal gradients of environmental parameters (proxied by location), gender, and their interactions with seasonality. Analyses of gene stability were performed using different algorithms. BestKeeper and geNorm agreed that combination of the ribosomal genes 18S ribosomal RNA (18S) and 28S ribosomal RNA (28S) was the best normalization strategy in the conditions tested, which agrees with available evidence. NormFinder provided different normalization strategies, involving combinations of tubulin (TUB)/28S (Gender/Season effect) or TUB/helicase (HEL) (Location/Season effect). Since NormFinder considers data grouping and computes both intra- and inter-group stability variations, it should work better with complex experimental designs and dataset structuring. Under the selected normalization strategies, expressions of the variable housekeeping genes actin (ACT) and elongation factor-1α (EF1) correlated with seasonal and latitudinal changes of abiotic environmental factors and mussel physiological status. Results point to consider ACT and EF1 expressions as molecular biomarkers of mussel general physiological status in field studies.

SeqImprove: Machine-Learning-Assisted Curation of Genetic Circuit Sequence Information.

The progress and utility of synthetic biology is currently hindered by the lengthy process of studying literature and replicating poorly documented work. Reconstruction of crucial design information through post hoc curation is highly noisy and error-prone. To combat this, author participation during the curation process is crucial. To encourage author participation without overburdening them, an ML-assisted curation tool called SeqImprove has been developed. Using named entity recognition, called entity normalization, and sequence matching, SeqImprove creates machine-accessible sequence data and metadata annotations, which authors can then review and edit before submitting a final sequence file. SeqImprove makes it easier for authors to submit sequence data that is FAIR (findable, accessible, interoperable, and reusable).

Automated registration-based skull stripping procedure for feline neuroimaging.

Skull stripping is a fundamental preprocessing step in modern neuroimaging analyses that consists of removing non-brain voxels from structural images. When performed entirely manually, this laborious step can be rate-limiting for analyses, with the potential to influence the population size chosen. This emphasizes the need for a fully- or semi-automated masking procedure to decrease man-hours without an associated decline in accuracy. These algorithms are plentiful in human neuroimaging but are relatively lacking for the plethora of animal species used in research. Unfortunately, software designed for humans cannot be easily transformed for animal use due to the high amount of tailoring required to accurately account for the considerable degree of variation within the highly folded human cortex. As most animals have a relatively less complex cerebral morphology, intersubject variability is consequently decreased, presenting the possibility to simply warp the brain mask of a template image into subject space for the purpose of skull stripping. This study presents the use of the Cat Automated Registration-based Skull Stripper (CARSS) tool on feline structural images. Validation metrics revealed that this method was able to perform on par with manual raters on >90 % of scans tested, and that its consistency across multiple runs was superior to that of masking performed by two independent raters. Additionally, CARSS outperformed three well-known skull stripping programs on the validation dataset. Despite a handful of manual interventions required, the presented tool reduced the man-hours required to skull strip 60 feline images over tenfold when compared to a fully manual approach, proving to be invaluable for feline neuroimaging studies, particularly those with large population sizes.

Taohong Siwu decoction enhances the chemotherapeutic efficacy of doxorubicin by promoting tumor vascular normalization.

BACKGROUND: Instead of completely suppressing blood vessels inside tumors, vascular normalization therapy is proposed to normalize and prune the abnormal vasculature in tumor microenvironment (TME) to acquire a normal and stable blood flow and perfusion. The theoretical basis for the use of "blood-activating and stasis-resolving" formulas in Traditional Chinese Medicine to treat cancer is highly consistent with the principle of vascular normalization therapy, suggesting the potential application of these traditional formulas in vascular normalization therapy. PURPOSE: To study the underlying mechanisms of a classical "blood-activating and stasis-resolving" formula, Taohong Siwu decoction (TSD), in enhancing the efficacy of chemotherapy for breast cancer treatment. STUDY DESIGN: HUVECs and transgenic zebrafish embryos were used as the major model in vitro. A 4T1 mouse breast cancer model was applied to study tumor vasculature normalization of TSD and the combination effects with DOX. RESULTS: Our data showed that TSD exhibited anti-angiogenic potential in HUVECs and transgenic zebrafish embryos. After 20 days treatment, TSD significantly normalized the tumor vasculature by remodeling vessel structure, reducing intratumoral hypoxia and vessel leakage, and promoting vessel maturation and blood perfusion in 4T1 breast tumor-bearing mice. Moreover, the anti-tumor efficacy of doxorubicin liposome in 4T1 breast tumors was significantly improved by TSD, including the suppression of tumor cell proliferation, angiogenesis, hypoxia, and the increase of cell apoptosis, which is likely through the vascular normalization induced by TSD. TSD also shifted the macrophage polarization from M2 to M1 phenotype in TME during the combination therapy, as evidenced by the reduced number of CD206+ macrophages and increased number of CD86+ macrophages. Additionally, TSD treatment protected against doxorubicin-induced cardiotoxicity in animals, as evidenced by the reduced cardiomyocytes apoptosis and improved heart function. CONCLUSION: This study demonstrated for the first time that TSD as a classical Chinese formula can enhance the drug efficacy and reduce the side effects of doxorubicin. These findings can support that TSD could be used as an adjuvant therapy in combination with conventional chemotherapy for the future breast cancer treatment.

Selection and Validation of Reference Genes for Quantitative Real-Time PCR Analysis in Cockroach Parasitoid Tetrastichus hagenowii (Ratzeburg).

Parasitoid wasps play a crucial role in the efficient control of pests, a substantial menace to human health and well-being. Tetrastichus hagenowii (Ratzeburg) stands out as the most effective egg parasitoid wasp for controlling American cockroaches, but accurate and stable reference genes for quantitative real-time polymerase chain reaction of T. hagenowii genes are still lacking. In this study, we assessed seven candidate nuclear genes, including α-tubulin (α-TUB), elongation factor-1-alpha (EF-1α), ß-actin (Actin), ribosomal protein 49 (RP49), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), nicotinamide adenine dinucleotide (NADH), and elongation factor 2 (EF2) of T. hagenowii. By analyzing expression stability with four algorithms (Delta Ct, geNorm, NormFinder, and BestKeeper), as well as comprehensive ranking with RefFinder, we identified α-TUB as the most stable reference gene for the larval, pupal, female adult, and male adult stages. Subsequently, we estimated the transcript levels of vitellogenin (Vg) and cuticle protein (CP) after normalization with α-TUB across various developmental stages. Significantly higher expression levels of CP and Vg were observed in pupae and female adults, respectively, consistent with previous findings in other insects. This study offers a reliable reference gene for normalizing transcription levels of T. hagenowii genes.

Behavioral state and stimulus strength regulate the role of somatostatin interneurons in stabilizing network activity.

Inhibition stabilization enables cortical circuits to encode sensory signals across diverse contexts. Somatostatin-expressing (SST) interneurons are well-suited for this role through their strong recurrent connectivity with excitatory pyramidal cells. We developed a cortical circuit model predicting that SST cells become increasingly important for stabilization as sensory input strengthens. We tested this prediction in mouse primary visual cortex by manipulating excitatory input to SST cells, a key parameter for inhibition stabilization, with a novel cell-type specific pharmacological method to selectively block glutamatergic receptors on SST cells. Consistent with our model predictions, we find antagonizing glutamatergic receptors drives a paradoxical facilitation of SST cells with increasing stimulus contrast. In addition, we find even stronger engagement of SST-dependent stabilization when the mice are aroused. Thus, we reveal that the role of SST cells in cortical processing gradually switches as a function of both input strength and behavioral state.

RGeasy: a reference gene analysis tool for gene expression studies via RT-qPCR.

Gene expression through RT-qPCR can be performed by the relative quantification method, which requires the expression normalization through reference genes. Therefore, it is essential to validate, experimentally, the candidate reference genes. Thus, although there are several studies that are performed to identify the most stable reference genes, most them validate genes for very specific conditions, not exploring the whole potential of the research since not all possible combinations of treatments and/or conditions of the study are explored. For this reason, new experiments must be conducted by researchers that have interest in analyzing gene expression of treatments and/or conditions present, but not explored, in these studies. Here, we present the RGeasy tool, which aims to facilitate the selection of reference genes, allowing the user to choose genes for a greater number of combinations of treatments/conditions, compared to the ones present in the original articles, through just a few clicks. RGeasy was validated with RT-qPCR data from gene expression studies performed in two coffee species, Coffea arabica and Coffea canephora, and it can be used for any animal, plant or microorganism species. In addition to displaying a rank of the most stable reference genes for each condition or treatment, the user also has access to the primer pairs for the selected reference genes.

BSNEU-net: Block Feature Map Distortion and Switchable Normalization-Based Enhanced Union-net for Acute Leukemia Detection on Heterogeneous Dataset.

Acute leukemia is characterized by the swift proliferation of immature white blood cells (WBC) in the blood and bone marrow. It is categorized into acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML), depending on whether the cell-line origin is lymphoid or myeloid, respectively. Deep learning (DL) and artificial intelligence (AI) are revolutionizing medical sciences by assisting clinicians with rapid illness identification, reducing workload, and enhancing diagnostic accuracy. This paper proposes a DL-based novel BSNEU-net framework to detect acute leukemia. It comprises 4 Union Blocks (UB) and incorporates block feature map distortion (BFMD) with switchable normalization (SN) in each UB. The UB employs union convolution to extract more discriminant features. The BFMD is adapted to acquire more generalized patterns to minimize overfitting, whereas SN layers are appended to improve the model's convergence and generalization capabilities. The uniform utilization of batch normalization across convolution layers is sensitive to the mini-batch dimension changes, which is effectively remedied by incorporating an SN layer. Here, a new dataset comprising 2400 blood smear images of ALL, AML, and healthy cases is proposed, as DL methodologies necessitate a sizeable and well-annotated dataset to combat overfitting issues. Further, a heterogeneous dataset comprising 2700 smear images is created by combining four publicly accessible benchmark datasets of ALL, AML, and healthy cases. The BSNEU-net model achieved excellent performance with 99.37% accuracy on the novel dataset and 99.44% accuracy on the heterogeneous dataset. The comparative analysis signifies the superiority of the proposed methodology with comparing schemes.

Joint coordinate attention mechanism and instance normalization for COVID online comments text classification.

Background: The majority of extant methodologies for text classification prioritize the extraction of feature representations from texts with high degrees of distinction, a process that may result in computational inefficiencies. To address this limitation, the current study proposes a novel approach by directly leveraging label information to construct text representations. This integration aims to optimize the use of label data alongside textual content. Methods: The methodology initiated with separate pre-processing of texts and labels, followed by encoding through a projection layer. This research then utilized a conventional self-attention model enhanced by instance normalization (IN) and Gaussian Error Linear Unit (GELU) functions to assess emotional valences in review texts. An advanced self-attention mechanism was further developed to enable the efficient integration of text and label information. In the final stage, an adaptive label encoder was employed to extract relevant label information from the combined text-label data efficiently. Results: Empirical evaluations demonstrate that the proposed model achieves a significant improvement in classification performance, outperforming existing methodologies. This enhancement is quantitatively evidenced by its superior micro-F1 score, indicating the efficacy of integrating label information into text classification processes. This suggests that the model not only addresses computational inefficiencies but also enhances the accuracy of text classification.

Bi-stochastically normalized graph Laplacian: convergence to manifold Laplacian and robustness to outlier noise.

Bi-stochastic normalization provides an alternative normalization of graph Laplacians in graph-based data analysis and can be computed efficiently by Sinkhorn-Knopp (SK) iterations. This paper proves the convergence of bi-stochastically normalized graph Laplacian to manifold (weighted-)Laplacian with rates, when [Formula: see text] data points are i.i.d. sampled from a general [Formula: see text]-dimensional manifold embedded in a possibly high-dimensional space. Under certain joint limit of [Formula: see text] and kernel bandwidth [Formula: see text], the point-wise convergence rate of the graph Laplacian operator (under 2-norm) is proved to be [Formula: see text] at finite large [Formula: see text] up to log factors, achieved at the scaling of [Formula: see text]. When the manifold data are corrupted by outlier noise, we theoretically prove the graph Laplacian point-wise consistency which matches the rate for clean manifold data plus an additional term proportional to the boundedness of the inner-products of the noise vectors among themselves and with data vectors. Motivated by our analysis, which suggests that not exact bi-stochastic normalization but an approximate one will achieve the same consistency rate, we propose an approximate and constrained matrix scaling problem that can be solved by SK iterations with early termination. Numerical experiments support our theoretical results and show the robustness of bi-stochastically normalized graph Laplacian to high-dimensional outlier noise.

A longitudinal mixed methods evaluation of a facilitation training intervention to build implementation capacity.

Background: There is a demand for facilitators who can ease the collaboration within a team or an organization in the implementation of evidence-based interventions (EBIs) and who are positioned to build the implementation capacity in an organization. This study aimed to evaluate the results the Building implementation capacity for facilitation (BIC-F) intervention had on the participants' perceived knowledge, skills, and self-efficacy to facilitate implementation and the normalization of a systematic implementation model into their work routines, and its use into their respective organizations. Methods: The BIC-F intervention was delivered to 37 facilitators in six workshops, which focused on teaching participants to apply a systematic implementation model and various facilitation tools and strategies. A longitudinal mixed methods design was used to evaluate the intervention. Data was collected pre- and post-intervention using questionnaires and semi-structured interviews grounded on the Normalization Process Theory (NPT). Quantitative data were analyzed using descriptive (mean, SD) and inferential (paired t-tests) methods. Qualitative data were analyzed using deductive content analysis according to NPT. Results: An increase in the participants' perceived knowledge, skills, and self-efficacy was observed post-intervention. Normalization of the systematic implementation model in the participants' work routines was in an early phase, facilitated by high coherence, however, other NPT mechanisms were not sufficiently activated yet to contribute to full normalization. In the organizations where participants initiated the normalization process, they were still working towards achieving coherence and cognitive participation among relevant stakeholders. Conclusion: The intervention had positive results on the participants' perceived knowledge, skills, and self-efficacy and these recognized the value of a systematic implementation model for their practice. However, further efforts are needed to apply it consistently as a part of their work routines and in the organization. Future interventions should provide long-term support for facilitators, and include methods to transfer training between organizational levels and to overcome contextual barriers.

Selection and Validation of Reference Genes in Dendrocalamus brandisii for Quantitative Real-Time PCR.

Dendrocalamus brandisii (Munro) Kurz is a sympodial bamboo species with a wide distribution in tropical and subtropical regions. Due to its remarkable regenerative ability and exceptional flavor, this species plays a pivotal role in bolstering the economies of numerous nations across these regions. We recently published a high-quality genome of this species. To date, no study results have identified the optimal reference genes for quantitative real-time polymerase chain reaction (qRT-PCR) normalization in Dendrocalamus brandisii. qRT-PCR offers a highly accurate and effective approach to analyzing gene expression. However, the precision of the resulting quantitative data hinges on the correct choice of reference genes. Twenty-one potential reference genes were identified from the D. brandisii transcriptomes. Their expression in 23 samples, including 8 different tissue organs and 15 samples of D. brandisii under various treatment conditions, were evaluated through qRT-PCR. Subsequently, four software programs-Delta CT, geNorm, NormFinder, and RefFinder-were employed to compare their expression stability. The results revealed that the selection of optimal reference genes varied based on the particular organ and condition being examined. EF-1-α-2 consistently exhibits the most stable expression across diverse tissues, while ACTIN-1, TUBULIN-1, and EF-1-α-2 were the most consistent reference genes in roots, culms, and leaves under various treatments, respectively. In this study, we identified and characterized appropriate internal genes utilized for calibrating qRT-PCR analyses of D. brandisii across different tissue organs and under various treatments. This research will provide key insights for advancing the study of gene functionality and molecular biology in D. brandisii and related species.