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A human in the loop approach to applying large language models for farm management insight
Abstract Sustainable food production and security depend on increasing agricultural productivity within existing arable land. This necessitates the effective translation of complex agronomic research into actionable, field-specific crop management recommendations. Despite substantial advances in agricultural research, a persistent knowledge-practice gap continues to impede the widespread adoption of evidence-based management practices. We evaluate whether large language models (LLMs) can bridge this gap by generating crop management recommendations from scientific literature. Using US soybean production as a case study, we developed a semi-automated, human-in-the-loop pipeline (hereafter called “our system”) adhering to systematic review protocols. Our system demonstrated high accuracy for literature screening, outperforming standalone models. However, when generating a general soybean management plan, expert evaluations rated two commercial LLMs’ output more favorably than the plan from our system. This work highlights the need to develop systems that address user trust and provide tailored, field-specific advice that is both trustworthy and practically useful for farming communities.
Correction for Cai et al., SLC39A8-mediated zinc dyshomeostasis potentiates kidney disease
Whole genome of petroleum hydrocarbon degrading Rhodococcus indonesiensis isolated from Nacharam, Hyderabad, India
Elevated risk of infectious diseases in adulthood after prenatal or early postnatal exposure to the Great Chinese Famine
Although prenatal exposure to famine is known to increase the risk of various noncommunicable conditions, its effects on infectious diseases remain poorly understood. We examined the effect of prenatal exposure to the Great Chinese Famine on risks for 11 notifiable infectious diseases in two consecutive generations through the analysis of >4.4 million surveillance records in Sichuan Province, China. Using an approach combining cohort models with counterfactual effect imputation, we estimated the ratio of observed to expected incidence rates (incidence rate ratio, IRR) in the absence of famine among both the directly affected birth cohort (F1) and their possible offspring (F2). We further examined whether there was a dose–response relationship between famine severity and infectious disease outcomes using meta-regression at the prefecture level. We found that the risk of acquiring any of the 11 infectious diseases studied increased significantly among both the F1 (IRR: 1.13, 95% CI: 1.04 to 1.21) and the F2 cohort (IRR: 1.08, 95% CI: 1.00 to 1.15). IRRs for the F1 generation were higher in prefectures that experienced more severe famine, with one interquartile range increase in famine severity associated with a 3.95% (95% CI: 1.87 to 6.05%) increase in F1 IRR. Positive associations of varying magnitudes were estimated between increased risks in the F1 cohort and famine intensity for most individual diseases. Prenatal exposure to famine may have long-term and potential intergenerational impacts on the risk of a broad range of infectious diseases. Ensuring adequate prenatal nutrition may be crucial in reducing the burden of infectious diseases across generations.
On the use of TabPFN on mass spectrometry analysis of volatile organic compounds
Protein kinase A fuels positive feedback loops during Th1 cell differentiation
Roles of administration route, expectation, and belief in placebos in a randomized controlled trial with open-label placebos
Abstract Recent reports challenge the view that placebos need to be given in deception. Numerous studies report remarkable effects when giving placebos honestly in an open way. However, the mechanisms of open-label placebos still remain to be cleared. For example, while expectation is often regarded as an important mechanism for concealed placebos, it is unclear whether this process may also account for open-label placebo effects. Thus, alternative theories based on, for example, the embodiment theory are also discussed. Here we examined possible mechanisms of open-label placebos including administration route (to address rituals or sensorimotor embodiment mechanisms), expectation, and belief in placebos. Healthy participants viewed emotional distressing pictures and were given an open-label placebo beforehand or were in the control group. Results replicated previous studies by showing that participants in the open-label placebo conditions felt less emotional distress. Route of administration did not affect the placebo response. Treatment expectancy did not predict the placebo effect but was linked to the strength of the placebo effect that participants self-reported (which was not related to the actual placebo effect). Belief in open-label placebos was related to the strength of the placebo effect. Interestingly, an affirmative attitude towards homeopathic treatments was linked to a positive treatment expectation, but not to the actual placebo effect. We discuss the results with respect to different theories explaining open-label placebo mechanisms and argue that open-label placebos may work predominantly in an unconscious way.
Imputation of ancient canid genomes reveals inbreeding history over the past 10,000 years
The multi-millennia-long history between dogs and humans has placed them at the forefront of archaeological and genomic research. Despite ongoing efforts including the analysis of ancient dog and wolf genomes, many questions remain regarding the evolutionary processes that led to the diversity of breeds today. Although ancient genome sequences provide valuable information about these processes, their utility is hindered by low depths of coverage and postmortem damage, which inhibits confident genotype calling. In the present study, we assess how genotype imputation of ancient dog and wolf genomes, using a large reference panel, can increase the amount of information provided by ancient datasets. We evaluated imputation accuracy by down-sampling high-coverage dog and wolf genomes to 0.05 to 2× coverage and compared concordance between imputed and high-coverage genotypes. We measured the impact of imputation on principal component analyses and runs of homozygosity (ROH). Our findings show high (R 2 > 0.9) imputation accuracy for dogs with coverage as low as 0.5× and for wolves as low as 1.0×. We then imputed a dataset of 90 ancient dog and wolf genomes to assess changes in inbreeding during the last 10,000 y of dog evolution. Ancient dog and wolf populations generally exhibit lower inbreeding levels than present-day individuals. Regions with low ROH density maintained across ancient and present-day dogs were significantly associated with genes related to immunity and chemosensory receptors. Our study indicates that imputing ancient canine genomes is a viable strategy that allows for the use of analytical methods previously limited to high-quality genetic data.
Secure edge-guided adaptive image steganography using HED-based attention maps and CNN
A high precision and lightweight method for steel surface defect detection based on improved YOLOv5
Clinical significance of lipoprotein(a) as a residual risk factor for atherosclerotic coronary plaque progression in statin-treated patients with coronary artery disease
Exploring community and expert perceptions of the acceptability of an oropharyngeal gonorrhoea controlled human infection model in Australia
Abstract The development of an oropharyngeal gonorrhoea controlled human infection model (CHIM) could result in important translational outcomes, including accelerated development of new drugs and vaccines. Ethical study design for such a model requires community consultation, including assessment of the acceptability of the proposed CHIM among key stakeholders. This qualitative study involved: (i) semi-structured interviews and focus groups with individuals who would be eligible for participation in the proposed oropharyngeal gonorrhoea CHIM, defined as healthy men who have sex with men (MSM) aged 18–50 years living in Victoria (Australia); and, (ii) semi-structured interviews with gonorrhoea experts. Data were analysed using inductive thematic analysis supported by NVivo. Twenty-seven semi-structured interviews and one focus group were undertaken with 32 individuals between July and November, 2024, comprising 22 potential CHIM participants, and 10 experts. Overall, an oropharyngeal gonorrhoea CHIM was acceptable to most participants. Financial compensation and including only MSM who do not have sex with women were highlighted as key areas of debate. Participants highlighted that recruitment strategies should be sensitive to the stigma associated with gonorrhoea and history of stigma experienced by MSM. An oropharyngeal gonorrhoea CHIM is acceptable to key stakeholders but must be carefully designed to avoid exacerbation of stigma.
Cartesian equivariant representations for learning and understanding molecular orbitals
Qualitative and quantitative orbital properties such as bonding/antibonding character, localization, and orbital energies are critical to how chemists understand reactivity, catalysis, and excited-state behavior. Despite this, representations of orbitals in deep learning models have been very underdeveloped relative to representations of molecular geometries and Hamiltonians. Here, we apply state-of-the-art equivariant deep learning architectures to the task of assigning global labels to orbitals, namely energies characterizations, given the molecular coefficients from Hartree–Fock or density functional theory. The architecture we have developed, the Cartesian Equivariant Orbital Network ( CEONET ), shows how molecular orbital coefficients are readily featurized as equivariant node features common to all graph-based machine-learned potentials. We find that CEONET performs well at predicting difficult quantitative labels such as the orbital energy and orbital entropy. Furthermore, we find that the CEONET representation provides an intuitive latent space for differentiating orbital character for the qualitative assignment of e.g. bonding or antibonding character. In addition to providing a useful representation for further integrating deep learning with electronic structure theory, we expect CEONET to be useful for automatizing and interpreting the results of advanced electronic structure methods such as complete active space self-consistent field theory. In particular, the ability of CEONET to infer multireference character via the orbital entropy paves the way toward the machine-learned selection of active spaces.