Supplementary Material.Wei Y, Yang M, Liu J, Wang Y, Wang G. Associations between Sensitivity to Thyroid Hormones and Visceral Adiposity in Euthyroid Adults. J Clin Endocrinol Metab. 2024 Nov 18:dgae806. doi: 10.1210/clinem/dgae806. PMID: 39556484.
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Introduction: The co-existence of hypertension with diabetes mellitus among diabetic patients is a setback to public health. About 40–75% of diabetic patients present with hypertension. The co-existence of hypertension and diabetes can accelerate complications such as stroke, myocardial infarction, nephropathy, and mortality. Available data indicate the devastating effects of hypertension and diabetes on individuals, families, and the economy as catastrophic. Therefore, knowing the predictors of hypertension among diabetic patients would inform the lifestyle and management of the two conditions. Objective: The study focused on predictors of hypertension among diabetic patients in the Ejisu Municipality of Ghana. Methods: The study employed a quantitative approach with a sample size of 120. Data were collected on sociodemographic characteristics, family history, 24-hour dietary recall, blood pressure, fasting blood glucose, glycated haemoglobin, total lipid profile, and anthropometrics. Data were analyzed using SPSS version 27. Results: Out of 120 respondents, 85% were females with 77.5% above 50 years of age. A majority (66.7%) had a family history of diabetes with 76.7% having hypertension as a comorbidity. Fasting blood glucose was found to be 8.519 times more likely to present with hypertension. Systolic blood pressure, carbohydrate, and sodium intakes were 6.1%, 2.9%, and 0.1% respectively. However, diabetic patients with high HbA1c were 97% less likely not to present with hypertension. Conclusion: Hypertension was found to be the most common comorbidity among diabetic patients in Ghana. Glycaemic control, systolic blood pressure, and dietary factors specifically carbohydrate and sodium intake were significant predictors of hypertension among the study participants.
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handle: 10481/87555
UNED
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Kingdom: PlantaeDivision: MagnoliophytaClass: MonocotsOrder: PoalesFamily: PoaceaeScientific name: Schismus barbatus (Loefl. ex L.) Thell.Specimen barcode: 198986
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Kingdom: PlantaeDivision: MagnoliophytaClass: MonocotsOrder: PoalesFamily: PoaceaeScientific name: Stipagrostis ciliata var. capensis (Trin. & Rupr.) De WinterSpecimen barcode: 200243
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This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/11062553. AlphaFold Meets Flow Matching for Generating Protein Ensembles https://arxiv.org/abs/2402.04845 Summary Major progress has been made using ML methods to predict single static structures of proteins given their primary sequence (e.g. using AlphaFold or ESMFold). But, given that proteins exist as an ensemble of conformations, there is a need for methods that can predict these ensembles given the sequence or a single structure. Although there are some computational methods available for this task, in this paper, the authors take a new approach by converting Alphafold/ESMfold, which are regressive models, into generative models using flow matching. Flow matching is a generalization of diffusion modeling and in this case, works by iteratively sampling from a harmonic prior and interpolating with a data point to create a noisy input template for Alphafold/ESMfold along with a constant sequence. The authors compare their method to an increasingly popular method for generating diversity from AF predictions: MSA subsampling. They evaluate the ensemble outputs from Alphaflow against pseudo-ensembles of previously solved structures deposited in PDB and ensembles of structures generated using MD. They use a combination of informative metrics such as diversity, recall, and precision of the Alphaflow and MSA subsampling ensembles as compared with the ground truth ensembles and show that their method is better than MSA subsampling in the diversity/recall v. precision trade-off frontiers. Unlike existing diffusion methods, flow matching is also able to handle missing residues in the input structure and is invariant to structure size. As mentioned by the authors, the limitations to their approach remain that it cannot yet be used to generate temporally ordered ensembles, it does not aim to sample the whole Boltzmann distribution of the protein of interest, and cannot be used for any kinetic studies. There is also conformational diversity they are not able to recapitulate. We find this to be an important paper with very interesting results that have been well presented and well written. Some of the key limitations we think the current study has is a lack of demonstration of its ability to generate biologically relevant conformations as part of the ensembles and comparison to existing generative diffusion-like methods. We expand on these points and other questions we have below: Major points Out of the 563 proteins that satisfy all the criteria in the test set, the authors say they sub-sampled 100 structures to form the test set. It is unclear why they chose to do this. Did they do any bootstrapping on this subsample? Also, the chain length range chosen by the authors (265-765) seems strange given that a large number of proteins have length under their lower limit. The authors fixed the number of flow-matching steps to 10. However, it will be useful and interesting to see the relationship between diversity/recall v. precision tradeoff and the number of flow-matching steps. The authors mention that they perform RMSD Alignment as one of the tweaks to make this method work in the Quotient space (in section A2, before equation 15). But the motivation behind this decision is not obvious or made clear to the reader. Although the authors have shown improvement at the ensemble level over MSA subsampling, some MSA subsampling methods have been able to sample biologically important conformations such as the open and closed state in kinases (for example, as shown in https://www.biorxiv.org/content/10.1101/2023.07.25.550545v3). Have the authors had success in generating such biologically relevant conformational transitions? Also, given the low level of aggregate recall, is the Alphaflow-generated ensemble heterogeneity representative of biologically relevant heterogeneity that is not present in the PDB or MD simulations? Or the other way around, is Alphaflow not generating the biologically relevant type of heterogeneity(e.g. not around equilibrium), which is present in the PDB We understand that in order to generalize the results of the comparison between Alphaflow ensembles and MD ensembles, the authors have resorted to using the ATLAS database since it has trajectories of a representative set of proteins. However, as an extension of the previous point, to truly evaluate Alphaflow's ability to sample biologically relevant conformations, it will be interesting to compare Alphaflow outputs to some long MD simulations as shown for example by Riccabona et al, 2024. With long duration simulations, it is possible to plot a free energy landscape of the protein and project the Alphaflow predicted structures onto the landscape and directly visualize the extent of sampling achieved by the method. We were wondering why the mentioned training cutoff of May 1, 2018, appears to coincide with the Alphafold1 training cutoff, suggesting the use of Alphafold1. Even though it is mentioned that Openfold (Ahdritz et al, 2022) is used for finetuning, which is modeled to be analogous to Alphafold 2 with its corresponding 2020 cutoff date. We pose that Alphaflow is in fact trained by finetuning the Alphafold2 model and that the early cutoff date may have been chosen to offer a larger potential testing protein dataset, does that hold? Besides MSA subsampling, the authors have not compared their method to other (iterative denoising) models used for structure prediction and ensemble generation - e.g. comparing performance to a Distributional Graphormer (Zheng et al, 2023) or Eigenfold (Jing et al, 2023) for a wider range of protein sizes. Ahdritz et al, 2022 show with Openfold that Alphafold - colloquially - greedily reduces FAPE loss during training by forming a rudimentary PCA-like representation of the input structure and solving the structure for that in an iterative dimension-increasing fashion. How does using a FAPE^2 loss influence this behavior? We would expect this kind of greedy PCA representation behavior to increase. We would also be interested in the diversity inherent in the PDB set and how that compares to the diversity found in the compared methods. Minor points The current method takes only the positions of the Carbon-beta (Cβ) atoms as input, and consequently, the optimization process is performed solely over these positions. However, it is important to note that the selection of the best-fitting overall structure is not limited to Cβ information alone. The method incorporates whole structure information, including the positions of Cβ and other residue atoms, when calculating the RMSD-aligned loss function. This allows for a more comprehensive evaluation of the predicted structure's quality but raises a question about the impact of incorporating additional information beyond Cβ positions. It remains unclear how the inclusion of more detailed structural data, such as the positions of other backbone or side-chain atoms, could influence the prediction outcomes. Is it possible to sample MSA in numbers that are not powers of 2? We ask this because, in certain metrics shown in Table 1, based on the trends in MSA subsampling, it seems like choosing an intermediate number might make the method match the performance of Alphaflow. For instance, if the MSA was subsampled at 48 instead of 32 or 64, maybe the pairwise RMSD of the MSA subsample ensemble might match that of the MD ensemble. To us, it is clear why the authors would use the Frame Aligned Point Error loss. But we are unsure why the loss is squared. We think that, unlike a single-structure-predicting Alphafold, we are now looking at ensembles and expect the predictions to be quite close to each other. Therefore one would want to penalize relatively small differences more severely than usual. It will be good to know the authors' thought process behind this choice. The schematic shown in Figure 1 is quite helpful. However, we would like to know if there is any reason behind the authors' choice to show a symmetric bifurcation in the flowfield. Is this indicative of some underlying "sub-classes" of structures in the predicted ensembles? Is there any roadmap on how to adapt Alphaflow so that it could be used for studying the elusive class of membrane proteins? To us, it is unclear how in inference time the stepsize/number of steps has been chosen. "When necessary, we subsample or replicate by the appropriate power of 2 to ensure all analyses operate on 256 frames (important for finite-sample Wasserstein distances)." What do the authors mean by replicate? Is it a duplication of the output structures or something else? The distillation procedure is described poorly in the appendix. What is X and Y? To us, it is unclear why in panel "PC > 0.5" of Figure 4 all the metrics go down in the last increase step of allotted GPU time. Repeated use of "the" in Section 3.2, below function (5), in the sentence "..discussed previously to be immediately used as the the denoising model xˆ1(x, t; θ), with x as the noisy input and t as an additional time embedding." Reviewed by Flip Jansen, Ashraya Ravikumar, Stephanie Wankowicz, James Fraser UCSF Competing interests The authors declare that they have no competing interests.
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handle: 20.500.12259/267156
Babesia spp. pirmuonys yra paplitę visame pasaulyje ir gali paveikti daugelį žinduolių, įskaitant žmones, naminius gyvūnus ir laukinius gyvulius. Šie pirmuonys užkrečia žinduolių (šeimininkų) eritrocitus ir sukelia babeziozę, kuri yra vis didėjanti problema visame pasaulyje, nes erkių buveinių plėtra ir padidėjęs gyvūnų mobilumas skatina parazitų plitimą į naujas geografines sritis. Šio darbo tikslas – nustatyti ir identifikuoti Babesia spp. pirmuonis lapėse ir jų ektoparazituose. Babesia spp. buvo nustatytos 2,3 % (1 iš 43) tirtų šuninių erkių (Ixodes ricinus) mėginių lizdinės polimerazės grandinės reakcijos metodu. Siekiant nustatyti Babesia spp. pirmuonis lapėse, pasitelkti du skirtingi tyrimo metodai. Atlikus tikro laiko polimerazinę grandininę reakciją Babesia spp. patogenų nustatymui rudosiose lapėse (Vulpes vulpes), matome, kad užsikrėtimas patogenu siekė 87,14 % (61 iš 70) tirtų mėginių. Pasitelkus lizdinės PGR metodą tiriant rudąsias lapes, gauti 2,85 % (2 iš 70) teigiamų mėginių. Babesia spp. patogenų filogenetinės analizės metu nustatyta, jog sekvenuotuose rudųjų lapių mėginiuose aptikta Babesia vulpes rūšis. Babesia spp. protozoans are distributed worldwide and can affect a lot of mammals including humans as well as domestic and wild animals. These protozoans can infect mammals (hosts) and cause babesiosis which is a problem that is increasingly growing worldwide as the increased animal mobility encourages the spread of parasites to new geographic areas. The aim of this work is to determine and identify Babesia spp. protozoans in foxes and their ectoparasites. Babesia spp. was detected in 2.3% (1 out of 43) of Ixodes ricinus tick samples tested by the nested PCR method. In order to identify Babesia spp. in foxes two different research methods were used. A real-time PCR used for the detection of Babesia spp. in red foxes (Vulpes vulpes) showed that infection with the pathogen reached 87,14% (61 out of 70) of the tested samples. Nested PCR in brown foxes yielded 2,85% (2 out of 70) positive samples. During the phylogenetic analysis of Babesia spp. pathogens it was established that the Babesia vulpes species were detected in the sequenced samples of red foxes.
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Cette fiche est issue de l’enquête menée par Jean Delumeau sur l’activité du port de Saint-Malo au 17e et 18e siècle. Cette fiche correspond à l’activité de l’année 1699 avec le port "Dahouët" Catégories de marchandises arrivées à Saint-Malo : produits agroalimentaires ; céréales ; matériaux de construction ; produits combustibles ; alcool Bibliographic citation: LE MENÉ, Michel. Le Commerce maritime malouin en 1699. DES : Histoire : Rennes, 1959 (Bibliothèque François-Lebrun, MH 1012) Marchandises arrivées à Saint-Malo : froment ; bois à merrain ; bois à brûler ; bois ; miel ; vin date: Date de numérisation : 2021 à 2023 created: Années 1950-1960 Coverage: Petit cabotage
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Kingdom: PlantaeDivision: MagnoliophytaClass: MonocotsOrder: PoalesFamily: PoaceaeScientific name: Aira cupaniana Guss.Specimen barcode: 198125
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Supplementary Material.Wei Y, Yang M, Liu J, Wang Y, Wang G. Associations between Sensitivity to Thyroid Hormones and Visceral Adiposity in Euthyroid Adults. J Clin Endocrinol Metab. 2024 Nov 18:dgae806. doi: 10.1210/clinem/dgae806. PMID: 39556484.
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Introduction: The co-existence of hypertension with diabetes mellitus among diabetic patients is a setback to public health. About 40–75% of diabetic patients present with hypertension. The co-existence of hypertension and diabetes can accelerate complications such as stroke, myocardial infarction, nephropathy, and mortality. Available data indicate the devastating effects of hypertension and diabetes on individuals, families, and the economy as catastrophic. Therefore, knowing the predictors of hypertension among diabetic patients would inform the lifestyle and management of the two conditions. Objective: The study focused on predictors of hypertension among diabetic patients in the Ejisu Municipality of Ghana. Methods: The study employed a quantitative approach with a sample size of 120. Data were collected on sociodemographic characteristics, family history, 24-hour dietary recall, blood pressure, fasting blood glucose, glycated haemoglobin, total lipid profile, and anthropometrics. Data were analyzed using SPSS version 27. Results: Out of 120 respondents, 85% were females with 77.5% above 50 years of age. A majority (66.7%) had a family history of diabetes with 76.7% having hypertension as a comorbidity. Fasting blood glucose was found to be 8.519 times more likely to present with hypertension. Systolic blood pressure, carbohydrate, and sodium intakes were 6.1%, 2.9%, and 0.1% respectively. However, diabetic patients with high HbA1c were 97% less likely not to present with hypertension. Conclusion: Hypertension was found to be the most common comorbidity among diabetic patients in Ghana. Glycaemic control, systolic blood pressure, and dietary factors specifically carbohydrate and sodium intake were significant predictors of hypertension among the study participants.
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handle: 10481/87555
UNED
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Kingdom: PlantaeDivision: MagnoliophytaClass: MonocotsOrder: PoalesFamily: PoaceaeScientific name: Schismus barbatus (Loefl. ex L.) Thell.Specimen barcode: 198986
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Kingdom: PlantaeDivision: MagnoliophytaClass: MonocotsOrder: PoalesFamily: PoaceaeScientific name: Stipagrostis ciliata var. capensis (Trin. & Rupr.) De WinterSpecimen barcode: 200243