eIF3 Associates with 80S Ribosomes to Promote Translation Elongation, Mitochondrial Homeostasis, and Muscle Health.

eIF3, a multi-subunit complex with numerous functions in canonical translation initiation, is known to interact with 40S and 60S ribosomal proteins and translation elongation factors, but a direct involvement in translation elongation has never been demonstrated. We found that eIF3 deficiency reduced early ribosomal elongation speed between codons 25 and 75 on a set of ∼2,700 mRNAs encoding proteins associated with mitochondrial and membrane functions, resulting in defective synthesis of their encoded proteins. To promote elongation, eIF3 interacts with 80S ribosomes translating the first ∼60 codons and serves to recruit protein quality-control factors, functions required for normal mitochondrial physiology. Accordingly, eIF3e+/- mice accumulate defective mitochondria in skeletal muscle and show a progressive decline in muscle strength. Hence, eIF3 interacts with 80S ribosomes to enhance, at the level of early elongation, the synthesis of proteins with membrane-associated functions, an activity that is critical for mitochondrial physiology and muscle health.

eIF3 mRNA selectivity profiling reveals eIF3k as a cancer-relevant regulator of ribosome content.

eIF3, whose subunits are frequently overexpressed in cancer, regulates mRNA translation from initiation to termination, but mRNA-selective functions of individual subunits remain poorly defined. Using multiomic profiling upon acute depletion of eIF3 subunits, we observed that while eIF3a, b, e, and f markedly differed in their impact on eIF3 holo-complex formation and translation, they were each required for cancer cell proliferation and tumor growth. Remarkably, eIF3k showed the opposite pattern with depletion promoting global translation, cell proliferation, tumor growth, and stress resistance through repressing the synthesis of ribosomal proteins, especially RPS15A. Whereas ectopic expression of RPS15A mimicked the anabolic effects of eIF3k depletion, disruption of eIF3 binding to the 5'-UTR of RSP15A mRNA negated them. eIF3k and eIF3l are selectively downregulated in response to endoplasmic reticulum and oxidative stress. Supported by mathematical modeling, our data uncover eIF3k-l as a mRNA-specific module which, through controlling RPS15A translation, serves as a rheostat of ribosome content, possibly to secure spare translational capacity that can be mobilized during stress.

Novel Use of a Ferric Salt to Enhance Mainstream Nitrogen Removal from Anaerobically Pretreated Wastewater.

This study aims to demonstrate a new technology roadmap to support the ongoing paradigm shift in wastewater management from pollutant removal to resource recovery. This is achieved by developing a novel use of an iron salt (i.e., FeCl3) in an integrated anaerobic wastewater treatment and mainstream anammox process. FeCl3 was chosen to be dosed in a proposed sidestream unit rather than in a primary settler or a mainstream reactor. This causes acidification of returned activated sludge and enables stable suppression of nitrite-oxidizing bacterial activity and excess sludge reduction. A laboratory-scale system, which comprised an anaerobic baffled reactor, a continuous-flow anoxic-aerobic (A/O) reactor, and a secondary settler, was designed to treat real domestic wastewater, with the performance of the system comprehensively monitored under a steady-state condition. The experimental assessments showed that the system had good effluent quality, with total nitrogen and phosphorus concentrations of 12.6 ± 1.3 mg N/L and 0.34 ± 0.05 mg P/L, respectively. It efficiently retained phosphorus in excess sludge (0.18 ± 0.03 g P/g dry sludge), suggesting its potential for further recovery. About half of influent organic carbon was recovered in the form of bioenergy (i.e., methane). This together with low energy consumption revealed that the system could produce a net energy of about 0.11 kWh/m3-wastewater, assessed by an energy balance analysis.

A 20-Year Journey of Partial Nitritation and Anammox (PN/A): from Sidestream toward Mainstream.

Anaerobic ammonium oxidation (anammox) was discovered as a new microbial reaction in the late 1990s, which led to the development of an innovative energy- and carbon-efficient technology─partial nitritation and anammox (PN/A)─for nitrogen removal. PN/A was first applied to remove the nitrogen from high-strength wastewaters, e.g., anaerobic digestion liquor (i.e., sidestream), and further expanded to the main line of wastewater treatment plants (i.e., mainstream). While sidestream PN/A has been well-established with extensive full-scale installations worldwide, practical application of PN/A in mainstream treatment has been proven extremely challenging to date. A key challenge is achieving stable suppression of nitrite-oxidizing bacteria (NOB). This study examines the progress of NOB suppression in both sidestream- and mainstream PN/A over the past two decades. The successful NOB suppression in sidestream PN/A was reviewed, and these successes were evaluated in terms of their transferability into mainstream PN/A. Drawing on the learning over the past decades, we anticipate that a hybrid process, comprised of biofilm and floccular sludge, bears great potential to achieve efficient mainstream PN/A, while a combination of strategies is entailed for stable NOB suppression. Furthermore, the recent discovery of novel nitrifiers would trigger new opportunities and new challenges for mainstream PN/A.

Wastewater Primary Treatment Using Forward Osmosis Introduces Inhibition to Achieve Stable Mainstream Partial Nitrification.

Achieving stable long-term mainstream nitrite oxidizing bacteria (NOB) suppression is the bottleneck for the novel partial nitrification (PN) process toward energy- and carbon-efficient wastewater treatment. However, long-term PN stability remains a challenge due to NOB adaptation. This study proposed and demonstrated a novel strategy for achieving NOB suppression by the primary treatment of mainstream wastewater with a forward osmosis (FO) membrane process, which facilitated two external NOB inhibition factors (salinity and free nitrous acid, FNA). To evaluate the proposed strategy, a lab-scale sequencing batch reactor was operated for 200 days. A stable PN operation was achieved with a nitrite accumulation ratio of 97.7 ± 2.8%. NOB were suppressed under the combined inhibition effect of NaCl (7.9 ± 0.2 g/L, as introduced by the FO direct filtration) and FNA (0.11 ± 0.02 mg of HNO2-N/L, formed as a result of the increased NH4+-N concentration after the FO process). The two inhibition factors worked in synergy to achieve a more stable PN operation. The microbial analysis showed that the elevated salinity and accumulation of FNA reshaped the microbial community and selectively eliminated NOB. Finally, an economic and feasibility analysis was conducted, which suggests that the integration of an FO unit into PN/A is a feasible and economically viable wastewater treatment process.

An Integrated First Principal and Deep Learning Approach for Modeling Nitrous Oxide Emissions from Wastewater Treatment Plants.

Mathematical modeling plays a critical role toward the mitigation of nitrous oxide (N2O) emissions from wastewater treatment plants (WWTPs). In this work, we proposed a novel hybrid modeling approach by integrating the first principal model with deep learning techniques to predict N2O emissions. The hybrid model was successfully implemented and validated with the N2O emission data from a full-scale WWTP. This hybrid model is demonstrated to have higher accuracy for N2O emission modeling in the WWTP than the mechanistic model or pure deep learning model. Equally important, the hybrid model is more applicable than the pure deep learning model due to the lower requirement of data and the pure mechanistic model due to the less calibration requirement. This superior performance was due to the hybrid nature of the proposed model. It integrated the essential wastewater treatment knowledge as the first principal component and the less understood N2O production processes by the data-driven deep learning approach. The developed hybrid model was also successfully implemented under different circumstances for the prediction of N2O flux, which showed the generalizability of the model. The hybrid model also showed great potential to be applied for the N2O mitigation work. Nevertheless, the capability of the hybrid model in evaluating N2O mitigation strategies still requires validation with experiments. Going beyond N2O modeling in WWTP, the novel hybridization modeling concept can potentially be applied to other environmental systems.

A comprehensive carbon footprint analysis of different wastewater treatment plant configurations.

With the growing concern of global warming, many water utilities are pioneering in mitigating greenhouse gas (GHG) emissions, with some water utilities aiming to achieve net-zero emissions operation in the next decade. However, for wastewater treatment plants (WWTPs), the carbon footprint of different treatment technologies and its contribution among various units within each treatment configuration is still unclear. This study evaluates the impacts of process design on the carbon footprint of WWTPs through the analysis of scope 1 (direct emission), scope 2 (indirect emission), and scope 3 (value chain emission) emissions. The comprehensive configuration design in this work considered three nutrient removal processes including typical aerobic and anaerobic wastewater treatment technologies. Emissions from the sludge management processes are also calculated, including aerobic and anaerobic sludge stabilization processes, short-term and long-term sludge storage, and three sludge disposal options. In total, 45 processes were analysed and the results were compared. The results showed the carbon footprints are highly dependent on the treatment configurations of WWTPs. Analysis suggested scope 2 & 3 emissions can be reduced by selecting suitable processes. In general, anaerobic wastewater and sludge stabilization technologies are more suitable than aerobic technologies to reduce scope 2 & 3 emissions, leading to a lower overall carbon footprint. In comparison, configuration design offers limited opportunities to reduce scope 1 emissions, which may be the future challenge for WWTP to achieve carbon neutrality.

Mainstream short-cut N removal modelling: current status and perspectives.

This work gives an overview of the state-of-the-art in modelling of short-cut processes for nitrogen removal in mainstream wastewater treatment and presents future perspectives for directing research efforts in line with the needs of practice. The modelling status for deammonification (i.e., anammox-based) and nitrite-shunt processes is presented with its challenges and limitations. The importance of mathematical models for considering N2O emissions in the design and operation of short-cut nitrogen removal processes is considered as well. Modelling goals and potential benefits are presented and the needs for new and more advanced approaches are identified. Overall, this contribution presents how existing and future mathematical models can accelerate successful full-scale mainstream short-cut nitrogen removal applications.

Exploring the malnutrition status and impact of total parenteral nutrition on the outcome of patients with advanced stage ovarian cancer.

BACKGROUND: Ovarian cancer is a common cancer type in women and is often associated with onset of malnutrition. Total parenteral nutrition (TPN) is a nutritional intervention method that has been reported to have controversial effect on cancer patients. In the present retrospective study, we sought to explore the prevalence of malnutrition assessed by the Nutritional Risk Index (NRI) and its association with survival in advanced stage ovarian cancer patients. We also compared the post-operative outcome of the malnourished patients treated with either TPN or conservative management. RESULTS: A total of 415 patients with advanced stage ovarian cancer were separated into 4 nutrition groups based on the NRI scores. We found that a number of factors were significantly different among the 4 nutrition groups, including age, serum albumin level, BMI and NRI; among which serum albumin level and NRI were identified to be independent predictors of progression-free and overall survival. In the moderately and severely malnourished patients, those who were treated with TPN had significantly shorter hospitalization period, lower serum albumin level and lower BMI after surgery. In addition, serum albumin level, use of TPN and number of patients with complications were closely related to the hospital stay duration. CONCLUSION: Malnutrition status is closely associated with survival of advanced stage ovarian cancer patients. These patients may benefit from TPN treatment for reduced hospitalization, especially with the onset of hypoalbuminemia.

Insights into Nitrous Oxide Mitigation Strategies in Wastewater Treatment and Challenges for Wider Implementation.

Nitrous oxide (N2O) emissions account for the majority of the carbon footprint of wastewater treatment plants (WWTPs). Many N2O mitigation strategies have since been developed while a holistic view is still missing. This article reviews the state-of-the-art of N2O mitigation studies in wastewater treatment. Through analyzing existing studies, this article presents the essential knowledge to guide N2O mitigations, and the logics behind mitigation strategies. In practice, mitigations are mainly carried out by aeration control, feed scheme optimization, and process optimization. Despite increasingly more studies, real implementation remains rare, which is a combined result of unclear climate change policies/incentives, as well as technical challenges. Five critical technical challenges, as well as opportunities, of N2O mitigations were identified. It is proposed that (i) quantification methods for overall N2O emissions and pathway contributions need improvement; (ii) a reliable while straightforward mathematical model is required to quantify benefits and compare mitigation strategies; (iii) tailored risk assessment needs to be conducted for WWTPs, in which more long-term full-scale trials of N2O mitigation are urgently needed to enable robust assessments of the resulting operational costs and impact on nutrient removal performance; (iv) current mitigation strategies focus on centralized WWTPs, more investigations are warranted for decentralised systems, especially decentralized activated sludge WWTPs; and (v) N2O may be mitigated by adopting novel strategies promoting N2O reduction denitrification or microorganisms that emit less N2O. Overall, we conclude N2O mitigation research is reaching a maturity while challenges still exist for a wider implementation, especially in relation to the reliability of N2O mitigation strategies and potential risks to nutrient removal performances of WWTPs.

Effects of pH, Temperature, Suspended Solids, and Biological Activity on Transformation of Illicit Drug and Pharmaceutical Biomarkers in Sewers.

In-sewer stability of biomarkers is a critical factor for wastewater-based epidemiology, as it could affect the accuracy of the estimated prevalence of substances in the community. The spatiotemporal variations of environmental and biological conditions in sewers can influence the transformation of biomarkers. To date, the relationship between environmental variables and biomarker stability in sewers is poorly understood. Therefore, this study evaluated the transformation of common illicit drug and pharmaceutical biomarkers in laboratory sewer reactors with different levels of pH, temperature, and suspended solids. The correlations between degradation rates of 14 biomarkers, 3 controlled environmental variables (pH, temperature, and suspended solids concentration), and 3 biological activity indicators (sulfide production rate, methane production rate, and the removal rate of soluble chemical oxygen demand (SCOD)) were assessed using correlation matrix, stepwise regression method, and principal component analysis. The consistent results affirmed the dominant effects of biological activities and pH on biomarker transformation in sewers, particularly for labile compounds, whereas the impact of temperature or suspended solids was less significant. This study enhances the understanding of factors affecting the fate of micropollutants in sewer systems and facilitates the interpretation of WBE results for assessing drug use and public health in communities.

Responses of fungal communities along a chronosequence succession in soils of a tailing dam with reclamation by Heteropogon contortus.

Phytoremediation can obviously change the fungal communities in the soils, which will significantly impact carbon (C) and nitrogen (N) cycling in ecological system. So far, the relationship between soil fungal communities and environmental factors is still poorly understood along a long chronosequence. In this study, fungal communities in the surface and rhizosphere soils of a tailing dam with Heteropogon contortus phytoremediation were investigated to explore the evolution of fungal community in a span of 50 years. The results showed that microbial community diversity increases along with time series of Heteropogon contortus phytoremediation. The dominant Dothideomycetes (20.86%), Agaricomycetes (18.09%), and Arthoniomycetes (1.69%) in rhizosphere soils were relatively higher than those in topsoil (13.9%, 2.65%, and 0.20%) at class level. Spearman correction analysis by phylum level was conducted to detect whether microflora was related to soil Physico-chemical properties, which affecting the composition of fungal communities along with the Heteropogon contortus phytoremediation. The nitrogen cycle indicators represented good linear correlations as chronosequence goes on, the indexes in the rhizosphere soil were much higher than those in the surface soils and the highest level has occurred in the 47-year-old Heteropogon contortus phytoremediation. The relative abundance of plant pathogen, wood saprotroph, dung saprotroph, and Arbuscular Mycorrhizal showed an upward tendency in rhizosphere soils along with the Heteropogon contortus phytoremediation. The highest soil fungal communities abundance and diversity were possibly attributed to the high-quality Heteropogon contortus litter returning to the ground and artificial disturbance treatments. Such changes in soil fungal communities might demonstrate a significant step forward and provided theoretical support for the biological governance of Heteropogon contortus phytoremediation in 50 years. Our study provides an insight on microbial communities connecting with soil C, N, P and S cycles and community functions in a complex plant-fungal-soil system along a long chronosequence in mine micro-ecology.

Overcoming Nitrite Oxidizing Bacteria Adaptation through Alternating Sludge Treatment with Free Nitrous Acid and Free Ammonia.

Stable suppression of nitrite oxidizing bacteria (NOB) is one of the major bottlenecks for achieving mainstream nitrite shunt or partial nitritation/anammox (PN/A). It is increasingly experienced that NOB could develop resistance to suppressions over an extended time, leading to failure of nitrite shunt or PN/A. This study reports and demonstrates the first effective strategy to overcome NOB adaptation through alternating sludge treatment with free nitrous acid (FNA) and free ammonia (FA). During over 650 days of reactor operation, NOB adaptation to both FNA and FA was observed, but the adaptation was successfully overcome by deploying the alternate treatment strategy. Microbial community analysis showed Nitrospira and Nitrobacter, the key NOB populations in the reactor, have the ability to adapt to FNA and FA, respectively, but do not adapt to the alternation. Stable nitrite shunt with nitrite accumulation ratio over 95% and excellent nitrogen removal were maintained for the last 10 months with only one alternation applied. N2O emission increased initially as the attainment of nitrite shunt but exhibited a declining trend during the study. By using on-site-produced nitrite and ammonium, the proposed strategy is feasible and sustainable. This study brings the mainstream nitrite shunt and PN/A one step closer to wide applications.

Self-Sustained Nitrite Accumulation at Low pH Greatly Enhances Volatile Solids Destruction and Nitrogen Removal in Aerobic Sludge Digestion.

Aerobic sludge digestion of waste activated sludge (WAS) is widely used as a stabilization option in small- and midsized wastewater treatment plants. However, the digestion process is often limited by low volatile solids (VS) destruction and poor pathogen removal efficiency. This study presents a novel operational strategy that achieves enhanced VS destruction and nitrogen removal by inducing sustained nitrite accumulation via a single spike of nitrite to aerobic digester operated at a natively low pH (<5.5). The strategy was demonstrated through the use of three laboratory aerobic sludge digesters, each continuously operated for over 300 days. Compared to control reactors, the strategy enhanced volatile solids destruction by 35.0-38.4%, nitrogen removal by 58.5-70.8%, and pathogen reduction by approximately 1 log. The standard oxygen uptake rate (SOUR) was reduced to 0.49 ± 0.03 mgO2/gVS/h, compared to 0.85 ± 0.01-1.68 ± 0.02 mgO2/gVS/h in the control, indicating enhanced stabilization. Free nitrous acid formed from nitrite at low pH, rather than nitrite itself, was identified to be the cause of improved digestion performance. Since the nitrite production is self-supporting, no additional ongoing costs are incurred.

A novel free ammonia based pretreatment technology to enhance anaerobic methane production from primary sludge.

This study proposed a novel free ammonia (FA, i.e., NH3 ) pretreatment technology to enhance anaerobic methane production from primary sludge for the first time. The solubilization of primary sludge was substantially enhanced following 24 h FA pretreatment (250-680 mg NH3 -N/L), by which the release of soluble chemical oxygen demand (SCOD) (i.e., 0.4 mg SCOD/mg VS added; VS: volatile solids) was approximately 10 times as much as that without pretreatment (i.e., 0.03 mg SCOD/mg VS added). Then, biochemical methane potential (BMP) tests demonstrated that FA pretreatment of 250-680 mg NH3 -N/L was capable of enhancing anaerobic methane production while the digestion time was more than 7 days. Model based analysis indicated that the improved anaerobic methane production was due to an increased biochemical methane potential (B0 ) of 8-17% (i.e., from 331 to 357-387 L CH4 /kg VS added), with the highest B0 achieved at 420 mg NH3 -N/L pretreatment. However, FA pretreatment of 250-680 mg NH3 -N/L decreased hydrolysis rate (k) by 24-38% compared with control (i.e., from 0.29 d-1 to 0.18-0.22 d-1 ), which explained the lower methane production over the first 7 days' digestion period. Economic analysis and environmental evaluation demonstrated that FA pretreatment technology was environmentally friendly and economically favorable. Biotechnol. Bioeng. 2017;114: 2245-2252. © 2017 Wiley Periodicals, Inc.

Achieving Stable Mainstream Nitrogen Removal via the Nitrite Pathway by Sludge Treatment Using Free Ammonia.

Biological nitrogen removal through the nitrite pathway (NH4+ → NO2- → N2) is favorable for wastewater treatment plants without sufficient carbon sources. This study demonstrates an innovative approach for attaining the nitrite pathway based on sludge treatment using free ammonia (FA, i.e., NH3). This approach is based on our innovative discovery in this study that FA at 210 mg NH3-N/L is far less biocidal to ammonium-oxidizing bacteria (AOB) than to nitrite-oxidizing bacteria (NOB). A total of 22% of the activated sludge from the sequencing batch reactor (SBR) receiving synthetic domestic wastewater was treated in an FA treatment unit at 210 mg NH3-N/L for 1 day. The FA-treated sludge was afterward recirculated back to the SBR. A nitrite accumulation ratio of above 90% was quickly achieved (in 40 days) and maintained stably in the SBR, indicating the establishment of the nitrite pathway. The NOB population and activity after implementing FA treatment was less than 5% of those without FA treatment, suggesting the washout of NOB. In contrast, the AOB population and activity in the SBR were not affected. The nitrogen-removal performance was significantly improved after incorporating the FA approach. The FA approach is a closed-loop approach and is economically and environmentally attractive.

Prototype Correlation Matching and Class-Relation Reasoning for Few-Shot Medical Image Segmentation.

Few-shot medical image segmentation has achieved great progress in improving accuracy and efficiency of medical analysis in the biomedical imaging field. However, most existing methods cannot explore inter-class relations among base and novel medical classes to reason unseen novel classes. Moreover, the same kind of medical class has large intra-class variations brought by diverse appearances, shapes and scales, thus causing ambiguous visual characterization to degrade generalization performance of these existing methods on unseen novel classes. To address the above challenges, in this paper, we propose a Prototype correlation Matching and Class-relation Reasoning (i.e., PMCR) model. The proposed model can effectively mitigate false pixel correlation matches caused by large intra-class variations while reasoning inter-class relations among different medical classes. Specifically, in order to address false pixel correlation match brought by large intra-class variations, we propose a prototype correlation matching module to mine representative prototypes that can characterize diverse visual information of different appearances well. We aim to explore prototypelevel rather than pixel-level correlation matching between support and query features via optimal transport algorithm to tackle false matches caused by intra-class variations. Meanwhile, in order to explore inter-class relations, we design a class-relation reasoning module to segment unseen novel medical objects via reasoning inter-class relations between base and novel classes. Such inter-class relations can be well propagated to semantic encoding of local query features to improve few-shot segmentation performance. Quantitative comparisons illustrates the large performance improvement of our model over other baseline methods.

Developing a conceptual model for understanding caregiving experience and their impacts on quality of life for Chinese breast cancer family caregivers: A qualitative study.

AIM: The purpose of this study was to understand the caregiving experiences of breast cancer family caregivers and explore the profound impacts of those experiences on their quality of life. DESIGN: A qualitative research method was used. METHODS: We extended invitations to 23 family caregivers of outpatients and inpatients receiving breast surgery and oncology treatments in Taiyuan, China, to participate in semi-structured interviews. The interviews were audio-recorded and transcribed verbatim. Thematic analysis was employed to analyse the interview data. RESULTS: Four themes and associated categories were identified: (1) changes in family dynamics, (2) the socio-medical context, (3) interactions between family and society, (4) self-efficacy and nine subthemes and their related categories, where virtually all participants expressed future uncertainty, emotional contagion, and personal challenges, and self-efficacy had a moderating influence on the first three themes. PATIENT OR PUBLIC CONTRIBUTION: This study did not involve direct participation of patients or the public. However, their experiences and perspectives on caregiving were indirectly reflected through the insights provided by the family caregivers who participated in the interviews. Their valuable input contributed to a deeper understanding of the caregiving experience and its impact on the quality of life for Chinese breast cancer family caregivers.

MRL-Seg: Overcoming Imbalance in Medical Image Segmentation With Multi-Step Reinforcement Learning.

Medical image segmentation is a critical task for clinical diagnosis and research. However, dealing with highly imbalanced data remains a significant challenge in this domain, where the region of interest (ROI) may exhibit substantial variations across different slices. This presents a significant hurdle to medical image segmentation, as conventional segmentation methods may either overlook the minority class or overly emphasize the majority class, ultimately leading to a decrease in the overall generalization ability of the segmentation results. To overcome this, we propose a novel approach based on multi-step reinforcement learning, which integrates prior knowledge of medical images and pixel-wise segmentation difficulty into the reward function. Our method treats each pixel as an individual agent, utilizing diverse actions to evaluate its relevance for segmentation. To validate the effectiveness of our approach, we conduct experiments on four imbalanced medical datasets, and the results show that our approach surpasses other state-of-the-art methods in highly imbalanced scenarios. These findings hold substantial implications for clinical diagnosis and research.

UniHead: Unifying Multi-Perception for Detection Heads.

The detection head constitutes a pivotal component within object detectors, tasked with executing both classification and localization functions. Regrettably, the commonly used parallel head often lacks omni perceptual capabilities, such as deformation perception (DP), global perception (GP), and cross-task perception (CTP). Despite numerous methods attempting to enhance these abilities from a single aspect, achieving a comprehensive and unified solution remains a significant challenge. In response to this challenge, we develop an innovative detection head, termed UniHead, to unify three perceptual abilities simultaneously. More precisely, our approach: 1) introduces DP, enabling the model to adaptively sample object features; 2) proposes a dual-axial aggregation transformer (DAT) to adeptly model long-range dependencies, thereby achieving GP; and 3) devises a cross-task interaction transformer (CIT) that facilitates interaction between the classification and localization branches, thus aligning the two tasks. As a plug-and-play method, the proposed UniHead can be conveniently integrated with existing detectors. Extensive experiments on the COCO dataset demonstrate that our UniHead can bring significant improvements to many detectors. For instance, the UniHead can obtain + 2.7 AP gains in RetinaNet, + 2.9 AP gains in FreeAnchor, and + 2.1 AP gains in GFL. The code is available at https://github.com/zht8506/UniHead.