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Reply to Krefeld-Schwalb et al.: Measuring population heterogeneity requires upholding good scientific practice

Design and experiment of a bionic drag-reducing digger for tuberous crops under heavy soil conditions

Aiming at the problems of high working resistance and high energy consumption in potato crop harvesting in sticky soil, this paper designs a potato bionic drag-reducing digging shovel based on the streamline shape of catfish head. Based on the theoretical analysis and discrete element method (DEM) simulation, the main factors affecting the digging resistance are the angle of entry, forward speed and vibration frequency, and the digging resistance increases with the increase of forward speed, and decreases with the increase of vibration frequency. Through the orthogonal test in the field, the optimal working parameters of the drag reduction performance are determined with the digging resistance as the test index, and the comparative test of the different shovel shapes is carried out with this parameter. The results show that the optimal solution is to use bionic shovel, with an entry angle of 15°, an operating speed of 0.27m/s, and a vibration frequency of 6Hz. The average digging resistance of the bionic shovel is 3612.86N, and the bionic digging shovel reduces resistance by 17.76% relative to an ordinary flat shovel, and 21.09% relative to the plane triangle shovel. The effect of drag reduction is remarkable, and the structure of the digging shovel bionic is reasonable, which can satisfy the requirements of resistance reduction and consumption reduction of potato harvesting under the conditions of sticky and heavy soils.

Multi-target detection and tracking based on CRF network and spatio-temporal attention for sports videos

NPF binding to Arp2 is allosterically linked to the release of ArpC5’s N-terminal tail and conformational changes in Arp2/3 complex

Arp2/3 complex generates branched actin networks essential for numerous motile functions of the cell. It comprises seven subunits: actin-related proteins (Arps) 2 and 3 and five scaffolding subunits (ArpC1-5). The complex adopts two major conformations: inactive, with the Arps interacting end-to-end, and active, with the Arps aligned side-by-side like subunits in the actin filament. Activation involves several cofactors, including ATP, WASP-family nucleation-promoting factors (NPFs), actin monomers, and the mother actin filament. NPFs bind to two sites, one on Arp2–ArpC1 and one on Arp3, delivering actin subunits at the barbed end of the Arps to initiate branch elongation. However, the mechanisms by which each NPF drives the equilibrium toward activation remain unclear. We present two cryo-electron microscopy (cryo-EM) structures of Arp2/3 complex at 2.9-Å resolution: one with NPFs bound to Arp3 and ArpC1 but not Arp2 and another with NPFs bound to Arp3 and Arp2–ArpC1. The structures reveal that NPF binding to Arp2 is allosterically linked to the release of ArpC5’s N-terminal tail from Arp2 and conformational changes in Arp2, including closure of its ATP-binding cleft and partial rotation and translation toward its position in the active complex at the branch. Previous work identified another allosteric switch linking NPF binding to Arp3 with the release of its inhibitory C-terminal tail, which we also observe. In summary, both NPF-binding sites induce allosteric changes in Arp2/3 complex, collectively shifting the equilibrium toward activation.

Correction: Sensory focused exercise improves anxiety in Parkinson’s disease: A randomized controlled trial

Implementation of two-roller scheduling path planning under road construction scenarios

Short-term exposure to filter-bubble recommendation systems has limited polarization effects: Naturalistic experiments on YouTube

An enormous body of literature argues that recommendation algorithms drive political polarization by creating “filter bubbles” and “rabbit holes.” Using four experiments with nearly 9,000 participants, we show that manipulating algorithmic recommendations to create these conditions has limited effects on opinions. Our experiments employ a custom-built video platform with a naturalistic, YouTube-like interface presenting real YouTube videos and recommendations. We experimentally manipulate YouTube’s actual recommendation algorithm to simulate filter bubbles and rabbit holes by presenting ideologically balanced and slanted choices. Our design allows us to intervene in a feedback loop that has confounded the study of algorithmic polarization—the complex interplay between supply of recommendations and user demand for content—to examine downstream effects on policy attitudes. We use over 130,000 experimentally manipulated recommendations and 31,000 platform interactions to estimate how recommendation algorithms alter users’ media consumption decisions and, indirectly, their political attitudes. Our results cast doubt on widely circulating theories of algorithmic polarization by showing that even heavy-handed (although short-term) perturbations of real-world recommendations have limited causal effects on policy attitudes. Given our inability to detect consistent evidence for algorithmic effects, we argue the burden of proof for claims about algorithm-induced polarization has shifted. Our methodology, which captures and modifies the output of real-world recommendation algorithms, offers a path forward for future investigations of black-box artificial intelligence systems. Our findings reveal practical limits to effect sizes that are feasibly detectable in academic experiments.

A systems analysis and improvement approach to optimizing syringe services programs’ delivery of HIV testing and referrals: Study protocol for a parallel-group randomized controlled trial (SAIA-SSP-HIV)

With changing drug supplies and associated drug consumption behaviors, HIV transmission has increased among people who inject drugs in the United States. HIV testing and referrals to effective prevention and treatment services are critical for individual and population health, yet multilevel barriers limit access to HIV testing for this population, even within syringe services programs (SSPs). In this organizational-level interrupted time series randomized controlled trial, we will assess the effectiveness and cost-effectiveness of an implementation strategy, the Systems Analysis and Improvement Approach (SAIA), in optimizing HIV testing and referrals to appropriate clinical services among U.S. SSPs. From 01/12/2023 to 01/07/2025, we will recruit a diverse sample of 32 SSPs nationally that directly provide HIV testing to participants. SSPs will be randomized to the active implementation arm (i.e., SAIA-SSP-HIV) or an implementation-as-usual arm (n = 16 organizations per arm). SAIA-SSP-HIV is a flexible, data-driven implementation strategy designed to help optimize SSPs’ delivery of HIV testing and referrals to appropriate clinical services for HIV prevention (e.g., pre-exposure prophylaxis) and treatment. In the active implementation arm, trained SAIA specialists will guide SSPs through three cyclical steps over 12 months: (1) process mapping to identify organization-specific needs, (2) cascade analysis and prioritization of areas for improvement, and (3) testing solutions through continuous quality improvement. In both arms, we will collect outcome data over 21 months (3-month lead-in period, 12-month implementation period, 6-month sustainment period). We will assess the initial and sustained effectiveness of SAIA and calculate its cost and cost-effectiveness. This trial presents a novel opportunity to test the effectiveness of an organization-level implementation strategy for optimizing the delivery of HIV screening and referrals in community settings that are frequented by an at-risk population. If successful, SAIA-SSP-HIV could be adapted for other infectious or chronic disease care cascades within SSPs. Trial registration: ClinicalTrials.gov: NCT06025435.

LncRNA-Gm17586 inhibits S.typhimurium mediated pyroptosis by promoting NLRP3 and Tnip1 binding

A limitation lifted: A conditional knockdown system reveals essential roles for Polo-like kinase and Aurora kinase 1 in <i>Trypanosoma cruzi</i> cell division

While advances in genome editing technologies have simplified gene disruption in many organisms, the study of essential genes requires development of conditional disruption or knockdown systems that are not available in most organisms. Such is the case for Trypanosoma cruzi , a parasite that causes Chagas disease, a severely neglected tropical disease endemic to Latin America that is often fatal. Our knowledge of the identity of essential genes and their functions in T. cruzi has been severely constrained by historical challenges in very basic genetic manipulation and the absence of RNA interference machinery. Here, we describe the development and use of self-cleaving RNA sequences to conditionally regulate essential gene expression in T. cruzi . Using these tools, we identified essential roles for Polo-like and Aurora kinases in T. cruzi cell division, mirroring their functions in Trypanosoma brucei . Importantly, we demonstrate conditional knockdown of essential genes in intracellular amastigotes, the disease-causing stage of the parasite in its human host. This conditional knockdown system enables the efficient and scalable functional characterization of essential genes in T. cruzi and provides a framework for the development of conditional gene knockdown systems for other nonmodel organisms.

Spatial prediction of dynamic interactions in rats

Animals and humans receive the most critical information from parts of the environment that are immediately inaccessible and highly dynamic. The brain must effectively process potential interactions between elements in such an environment to make appropriate decisions in critical situations. We trained male Long-Evans rats to discriminate static and dynamic spatial stimuli and to generalize novel dynamic spatial stimuli displayed on an inaccessible computer screen. We provide behavioral evidence indicating that rats encode dynamic visuospatial situations by constructing internal static representations that capture meaningful future interactions between objects. These observations support previous findings in humans that such internal static representations can encapsulate relevant spatiotemporal information of dynamic environments. This mechanism would allow animals and humans to process complex time-changing situations neatly.

High precision water quality retrieval in Dianchi Lake using Gaofen 5 data and machine learning methods

Toward equitable major histocompatibility complex binding predictions

Deep learning tools that predict peptide binding by major histocompatibility complex (MHC) proteins play an essential role in developing personalized cancer immunotherapies and vaccines. In order to ensure equitable health outcomes from their application, MHC binding prediction methods must work well across the vast landscape of MHC alleles observed across human populations. Here, we show that there are alarming disparities across individuals in different racial and ethnic groups in how much binding data are associated with their MHC alleles. We introduce a machine learning framework to assess the impact of this data imbalance for predicting binding for any given MHC allele, and apply it to develop a state-of-the-art MHC binding prediction model that additionally provides per-allele performance estimates. We demonstrate that our MHC binding model successfully mitigates much of the data disparities observed across racial groups. To address remaining inequities, we devise an algorithmic strategy for targeted data collection. Our work lays the foundation for further development of equitable MHC binding models for use in personalized immunotherapies.

Identification of suitable qPCR reference genes for the normalization of gene expression in the BL10-mdx and D2-mdx mouse models of Duchenne muscular dystrophy

Duchenne muscular dystrophy (DMD) is an X-linked disorder that is caused by mutations in the DMD gene, leading to progressive muscle wasting and weakness. There is currently no cure for DMD. The BL10-mdx mouse is the most commonly used model in preclinical DMD studies, but it exhibits a mild disease phenotype compared to DMD patients, limiting research translatability. The newer D2-mdx mouse has a more severe phenotype at an early age and may better recapitulate human disease. To compare these mouse models on a transcriptional level with quantitative RT-PCR, stable and reliable reference genes are indispensable. We aimed to evaluate the stability and reliability of a panel of nine candidate reference genes (Actb, Ap3d1, Gapdh, Hmbs, Htatsf1, Pak1ip1, Rpl13a, Sdha and Zfp91) in the gastrocnemius, diaphragm and heart of mice from both strains and their corresponding wild types aged 4 to 52 weeks. Data was analyzed using geNorm, BestKeeper, deltaCt and NormFinder. We found that Htatsf1, Pak1ip1 and Zfp91 are suitable reference genes for normalization of gene expression in dystrophic and healthy mice, regardless of the tissue type or age. In our hands, Actb, Gapdh and Rpl13a were not suitable as reference genes, exhibiting tissue-, age-, or disease specific changes in expression. This study highlights the importance of the selection of suitable reference genes, as their stability can differ between specific experimental setups.

Comprehensive analysis of Metacrinus rotundus full length transcriptome

Tracking proteasome degradation: A cross-organ analysis via intact degradomics mass spectrometry

The proteasome is a multisubunit degradation machinery that is essential for maintaining protein homeostasis by breaking down unnecessary or damaged proteins into peptides. While most of these peptides are further processed into amino acids, a subset evades complete degradation and plays key roles in biological processes such as antigen presentation, signaling, and apoptosis. However, the variability in peptide lengths and the diverse composition of proteasomes make their comprehensive identification and characterization particularly challenging. Here, we present a method that enables real-time identification of generated peptides, as well as uncleaved and partially cleaved protein substrates, revealing the processive nature of protein proteasomal degradation. Our intact degradomics workflow is based on intact mass spectrometry measurements and treats the enzymatically produced peptides as if they were generated within the mass spectrometer, akin to top–down products. We applied this approach to determine the kinetic profile of proteasome degradation and compare the real-time activity of proteasomes isolated from different mouse organs, uncovering distinct functionalities of the complex. Overall, this method offers a valuable tool for studying peptide degradation products across various proteasome configurations, while also enabling the investigation of how interacting proteins, inhibitors, and activators influence proteasome activity. Furthermore, its adaptability makes it applicable to a wide range of other proteolytic complexes, broadening its potential impact in the field.

First validity testing of GluciQuizz, a French self-questionnaire evaluating carb-counting for patients with type 1 diabetes

Objective Carbohydrate intake remains one of the most influential factors affecting post-meal glucose levels in type 1 diabetes (T1D). To individualize and optimize patient education on carbohydrate counting, it is essential to assess their knowledge of carbohydrate counting. For this purpose, we aimed to produce and validate a questionnaire for French-speaking individuals based on the American AdultCarbQuiz questionnaire (43 items). Design The translation and cross-cultural adaptation of the American questionnaire were followed by an analysis conducted by patient and diabetologist expert groups. Participants 190 participants living with T1D, Diabetologist expert group. Main outcome measure(s) Internal consistency and reliability were verified by administering the adapted French version of the questionnaire to 190 participants living with T1D. Analysis Clinical validity was verified by examining the correlation between the questionnaire score and the time in range (TIR) of these participants. Results Translation and back-translation demonstrated good consistency with the original questionnaire. Typical American food items were replaced with foods commonly consumed in France. All items were validated by both patient and diabetologist experts. After analyzing the responses of 190 participant, seven items were considered non-discriminatory and were therefore removed. Internal consistency, assessed by Cronbach’s α, was high, with α =  0.785, 95 CI [0.738–0.826]. Finally, we produced a 36 item French questionnaire named GluciQuizz. TIR correlated with the GluciQuizz score (r =  0.3; p  &lt;  0.0001). Conclusions and implications This study validates a French-language self-administered questionnaire (GluciQuizz), which evaluates different domains of carbohydrate knowledge in a 15-minute quiz for people living with T1D.

Pollen–pistil interactions in divergent wide crosses lead to spatial and temporal pre-fertilization reproductive barrier in flax (Linum usitatissimum L.)

Deep-water corals indicate the Red Sea survived the last glacial lowstand

The Red Sea, a nascent ocean basin connected to the Indian Ocean via a shallow strait, is assumed to have experienced significant environmental changes during the last glacial period due to a sea-level drop likely exceeding 110 m. This study investigates the hypothesis that hydrodynamic restriction led to severe ecological impacts, including basin-wide extinction due to elevated salinity followed by a short time of oxygen depletion. Uranium–Thorium dating of deep-water corals (DWCs) from 26 northern Red Sea sites reveals coral growth during and after the Last Glacial sea-level lowstand, indicating tolerable seawater chemistry. Additional geochemical data show no significant difference in Red Sea chemistry or temperature between the Latest Pleistocene and Holocene. A meta-analysis of 27 deep-sea cores reveals that while planktonic foraminifera experienced local extinction, other microfossil groups seemingly persisted. These findings suggest that the Red Sea survived the last sea-level lowstand, challenging the paradigm of a complete ecological collapse and providing insights into the resilience of marine ecosystems.

Community emergency management policy in China using a policy text tool

Systematically analyzing the current status and problems of the Community Emergency Management Policy (CEMP) system in China and proposing practical suggestions that are conducive to subsequent policy formulation and improvement are important for improving community emergency management capabilities and levels and promoting sustainable community development. Based on the two-dimensional analysis framework of “policy tools–policy objectives”, this paper draws from content analysis and social network analysis, quantitatively analyzing the CEMP texts at the national level from 2004 to 2024. The results show that the CEMP system in China has essentially taken shape, but there are still some problems, such as the uneven use of policy tools, the unbalanced internal structure of policy tools, the large distribution gap of policy objective elements, and the adaptability between policy tools and policy objectives, which needs to be improved. Given these results, we suggest 1) Appropriately increasing the frequency of using demand-type policy tools and effectively optimizing the internal structure of the three main types of policy tools; 2) Comprehensively deepening the reform of CEMP objectives; and 3) Reasonably improving the adaptability between policy tools and objectives.