Miracle-dz/UniFAR-FTUs huggingface dataset:
Furnishing the cachexia landscape: A year of research in JCSM

Berlin Institute of Health Center for Regenerative Therapies (BCRT), Berlin, Germany; German Centre for Cardiovascular Research (DZHK), partner site Berlin, Berlin, Germany; Division of Cardiology and Metabolism, Department of Cardiology, Charité—Universitätsmedizin Berlin, Campus Virchow Klinikum, Berlin, Germany; Department of Cardiology and Pneumology, University of Göttingen Medical Center, Göttingen, Germany; German Centre for Cardiovascular Research (DZHK), partner site Göttingen, Göttingen, Germany; Department of Cardiology (CBF), Charité—Universitätsmedizin Berlin, Berlin, Germany
Re: annual report to the nation on the status of cancer (1973 through 1998), featuring cancer with recent increasing trends.

I read with interest the special article by Howe et al. (1). A 14.5% annual increase in peritoneal cancer was observed from 1992 through 1998. Peritoneal cancer was first described by Smerdlow (2) in 1959 and has since been referred to by various terms, including multifocal extra -ovarian carcinoma, serous surface papillary carcinoma, primary carcinoma of the peritoneum, and carcinoma of the coelomic epithelium. In the early 1990s, there was an increased recognition of peritoneal cancer as a distinct entity. In 1994, the Pathology Committee of the Gynecologic Oncology Group (GOG) established a definition of peritoneal carcinoma as a tumor with 5.0 mm or less of ovarian stromal invasion. This definition has been subsequently utilized in all GOG studies, which are conducted in more than 100 major teaching institutions and hospitals in the United States. It is likely that establishing this uniform definition has shifted cases formerly classified as ovarian cancer to peritoneal cancer. In view of the fact that ovarian carcinoma and peritoneal carcinoma are identical in histology, biology , response to therapy, and prognosis , the GOG has included both ovarian and peritoneal cancer patients in their studies since 1996. Given the changing nomenclature, studies evaluating population incidence trends in gynecologic cancers should also combine ovarian and peritoneal carcinomas. Rosenberg HM, Feigal EG, et al. Annual report to the nation on the status of cancer (1973 through 1998), featuring cancers with recent increasing trends. RESPONSE We agree with the comments of Dr. Rose regarding the classification issues related to changing diagnostic practices for ovarian and peritoneal tumors. We did discuss the potential impact of these changes on the findings reported in our article. It is difficult to distinguish a true increase in cancer from changes in cli-nicians' site attribution practices (1). As for Dr. Rose's suggestion that ovarian and peritoneal tumors be combined in a single group for reporting purposes, we believe that it is more meaningful to report statistics for each tumor separately, enabling one to assess information for each group as well as for the combined group. Furthermore, it is unlikely that consolidation of both types of cancer would help clarify the incidence trend, since the number of peritoneal tumors is much smaller than the number of ovar-ian tumors. The downward trend of the more frequent ovarian tumors would dominate the statistics for the consolidated group.
On the greedy heuristic for continuous covering and packing problems

Worst-case bounds are given on the performance of the greedy heuristic for a continuous version of the set covering problem. This generalizes results of Chvatal, Johnson and Lovasz for the 0-1 covering problem. The results for the greedy heuristic and for other heuristics are obtained by treating the covering problem as a limiting case of a generalized location problem for which worst-case results are known. An alternative approach involving dual greedy heuristics leads also to worst-case bounds for continuous packing problems.
De novo design of high-affinity protein binders to bioactive helical peptides

Many peptide hormones form an alpha-helix upon binding their receptors1–4, and sensitive detection methods for them could contribute to better clinical management. De novo protein design can now generate binders with high affinity and specificity to structured proteins5,6. However, the design of interactions between proteins and short helical peptides is an unmet challenge. Here, we describe parametric generation and deep learning-based methods for designing proteins to address this challenge. We show that with the RFdiffusion generative model, picomolar affinity binders can be generated to helical peptide targets either by noising and then denoising lower affinity designs generated with other methods, or completely de novo starting from random noise distributions; to our knowledge these are the highest affinity designed binding proteins against any protein or small molecule target generated directly by computation without any experimental optimization. The RFdiffusion designs enable the enrichment of parathyroid hormone or other bioactive peptides in human plasma and subsequent detection by mass spectrometry, and bioluminescence-based protein biosensors. Capture reagents for bioactive helical peptides generated using the methods described here could aid in the improved diagnosis and therapeutic management of human diseases.7,8
An In Vivo Platform for Rebuilding Functional Neocortical Tissue

Recent progress in cortical stem cell transplantation has demonstrated its potential to repair the brain. However, current transplant models have yet to demonstrate that the circuitry of transplant-derived neurons can encode useful function to the host. This is likely due to missing cell types within the grafts, abnormal proportions of cell types, abnormal cytoarchitecture, and inefficient vascularization. Here, we devised a transplant platform for testing neocortical tissue prototypes. Dissociated mouse embryonic telencephalic cells in a liquid scaffold were transplanted into aspiration-lesioned adult mouse cortices. The donor neuronal precursors differentiated into upper and deep layer neurons that exhibited synaptic puncta, projected outside of the graft to appropriate brain areas, became electrophysiologically active within one month post-transplant, and responded to visual stimuli. Interneurons and oligodendrocytes were present at normal densities in grafts. Grafts became fully vascularized by 1-week post-transplant and vessels in grafts were perfused with blood. With this paradigm, we could also organize cells into layers. Overall, we have provided proof of concept for an in vivo platform that can be used for developing and testing neocortical-like tissue prototypes.
Positions of pivot points in quadrupedal locomotion: limbs and trunk global control in four different dog breeds

Dogs (Canis familiaris) prefer the walk at lower speeds and the more economical trot at speeds ranging from 0.5 Fr up to 3 Fr. Important works were carried out to understand these gaits at the levels of center of mass, joint mechanics, and muscular control. However, less is known about the global control goals for limbs and overall locomotion, and of whether these global control goals are gait or breed specific. For walk and trot, we analyzed dog global dynamics based on motion capture and single leg kinetic data recorded from treadmill locomotion of French Bulldog (N = 4), Whippet (N = 5), Malinois (N = 4) and Beagle (N = 5). Dogs displayed two virtual pivot points (VPP) during walk and trot each. One resembles control of both thoracic (fore) limbs and is roughly located above and caudally to the scapular pivot, while the second is located roughly above and cranially to the hip and mirrors the control of the pelvic (hind-) limbs. The positions of VPPs and the patterns of the legs‘ axial and tangential functions were gait and breed related. However, breed related changes were mainly exposed by the French Bulldog. The position of VPPs relative to the proximal pivots explains the propulsive and breaking forces observed in quadrupedal locomotion and may help to reduce limb work. In combination with former work, from the present study the VPP template emerges as the expression of a simple and general global control rule for both bipeds and quadrupeds.
Learning the Drug-Target Interaction Lexicon

Sequence-based prediction of drug-target interactions has the potential to accelerate drug discovery by complementing experimental screens. Such computational prediction needs to be generalizable and scalable while remaining sensitive to subtle variations in the inputs. However, current computational techniques fail to simultaneously meet these goals, often sacrificing performance on one to achieve the others. We develop a deep learning model, ConPLex, successfully leveraging the advances in pre-trained protein language models (“PLex”) and employing a novel protein-anchored contrastive co-embedding (“Con”) to outperform state-of-the-art approaches. ConPLex achieves high accuracy, broad adaptivity to unseen data, and specificity against decoy compounds. It makes predictions of binding based on the distance between learned representations, enabling predictions at the scale of massive compound libraries and the human proteome. Furthermore, ConPLex is interpretable, which enables us to visualize the drug-target lexicon and use embeddings to characterize the function of human cell-surface proteins. We anticipate ConPLex will facilitate novel drug discovery by making highly sensitive and interpretable in-silico drug screening feasible at genome scale. Con-PLex is available open-source at https://github.com/samsledje/ConPLex. Significance Statement In time and money, one of the most expensive steps of the drug discovery pipeline is the experimental screening of small molecules to see which will bind to a protein target of interest. Therefore, accurate high-throughput computational prediction of drug-target interactions would unlock significant value, guiding and prioritizing promising candidates for experimental screening. We introduce ConPLex, a machine learning method for predicting drug-target binding which achieves state-of-the-art accuracy on many types of targets by using a pre-trained protein language model. The approach co-locates the proteins and the potential drug molecules in a shared feature space while learning to contrast true drugs from similar non-binding “decoy” molecules. ConPLex is extremely fast, which allows it to rapidly shortlist candidates for deeper investigation.
Strategic control of location and ordinal context in visual working memory

Working memory (WM) requires encoding stimulus identity and context (e.g., where or when stimuli were encountered). To explore the neural bases of the strategic control of context binding in WM, we acquired fMRI while subjects performed delayed recognition of three orientation patches presented serially and at different locations. The recognition probe was an orientation patch with a superimposed digit, and pretrial instructions directed subjects to respond according to its location (location-relevant), to the ordinal position corresponding to its digit (order-relevant), or to just its orientation (relative to all three samples; context-irrelevant). Delay-period signal in PPC was greater for context-relevant than for context-irrelevant trials, and multivariate decoding revealed strong sensitivity to context binding requirements (relevant vs. irrelevant) and to context domain (location- vs. order-relevant) in both occipital cortex and PPC. At recognition, multivariate inverted encoding modeling revealed markedly different patterns in these two regions, suggesting different context-processing functions. In occipital cortex, an active representation of the location of each of the three samples was reinstated, regardless of trial type. The pattern in PPC, in contrast, suggested a trial type-dependent filtering of sample information. These results indicate that PPC exerts strategic control over the representation of stimulus context in visual WM.
Baseline Acute Myeloid Leukemia Prognosis Models using Transcriptomic and Clinical Profiles by Studying the Impacts of Dimensionality Reductions and Gene Signatures on Cox-Proportional Hazard

Gene marker extraction to evaluate risk in cancer can refine the diagnosis process and lead to adapted therapies and better survival. These survival analyses can be done through computer systems and Machine Learning (ML) algorithms such as the Cox-Proportional-Hazard model from gene expression (GE) RNA-Seq data. However, optimal tuning of CPH from genome-wide GE data is challenging and poorly assessed so far. In this work we propose to interrogate an Acute Myeloid Leukemia (AML) dataset (Leucegene) to derive key components of the CPH driving down its performance and discovering its sensitivity to various factors in hoping to ameliorate the system. In this study, we compare the projection and selection data reduction techniques, mainly the PCA and LSC17 gene signature in combination with the CPH in a ML framework. Results reveals that CPH performs better with a combination of clinical and genetic expression features. We determine that projections performs better than selections without clinical information. We ascertain that CPH is affected by overfitting and that this overfitting is linked to the number and the content of input covariables. We show that PCA links clinical features via ability to learn from the input data directly and generalizes better than LSC17 on Leucegene. We postulate that projection are preferred than selection on harder task such as assessing risk in the intermediate subset of Leucegene. We extrapolate that these findings apply in the more general context of risk detection via machine learning in cancer. We see that higher capacity models such as CPH-DNNs systems can be improved via survival-derived projections and combat overfitting through heavy regularization. Author summary This study aims to investigate the feasibility of using gene expression to evaluate risk in cancer, and to compare the projection and selection data reduction techniques. The study used the Leucegene dataset to compare the PCA method and a previously published 17 genes signature in combination with the Cox-Proportional-Hazard model in a machine learning framework. Results showed that CPH was affected by overfitting and that this overfitting was linked to the number and the content of input covariables. The study found that PCA links clinical features via ability to learn from the input data directly and generalizes better than LSC17 on Leucegene. The study concluded that projections are preferred than selection on harder task such as assessing risk in the intermediate subset of Leucegene and can be tuned to improve their performance. Data availability statement Source code for pipelines and algorithms, as well as gene expression matrices, are available here: https://github.com/lemieux-lab/dimensions_coxph. Access to the Leucegene cohort’s survival times can be granted upon request and following ethical review.
TWAS coupled with eQTL analysis reveals the genetic connection between gene expression and flowering time in Arabidopsis

Genome-wide association study (GWAS) has improved our understanding of complex traits, but challenges exist in distinguishing causation versus association caused by linkage disequilibrium. Instead, the transcriptome-wide association study (TWAS) detects direct associations between expression levels and phenotypic variations, providing an opportunity to better prioritize candidate genes. To assess the feasibility of TWAS, we investigated the association among transcriptomes, genomes, and various traits, including flowering time in Arabidopsis. First, the associated genes formerly known to regulate growth allometry or metabolite production were identified by TWAS. Then, for flowering time, six TWAS-newly identified genes were functionally validated. Analysis of expression quantitative trait locus (eQTL) further revealed a trans-regulatory hotspot affecting the expression of several TWAS-identified genes. The hotspot covers the FRIGIDA (FRI) gene body, which possesses multiple haplotypes differentially affecting the expression of downstream genes, such as FLOWERING LOCUS C (FLC) and SUPPRESSOR OF OVEREXPRESSION OF CO 1 (SOC1). We also revealed multiple independent paths towards the loss of FRI function in natural accessions. Altogether, this study demonstrates the potential of combining TWAS with eQTL analysis to identify important regulatory modules of the FRI-FLC-SOC1 for quantitative traits in natural populations. Highlight Combining TWAS with eQTL analyses identifies haplotypes connecting flowering genes with their physiological trait, strengthening the importance of FRI-FLC-SOC1 regulatory module on flowering time among the Arabidopsis natural population.
Limits to evolutionary rescue by conjugative plasmids

Plasmids may carry genes coding for beneficial traits and thus contribute to adaptation of bacterial populations to environmental stress. Conjugative plasmids can horizontally transfer between cells, which a priori facilitates the spread of adaptive alleles. However, if the potential recipient cell is already colonized by another incompatible plasmid, successful transfer may be prevented. Competition between plasmids can thus limit horizontal transfer. Previous modeling has indeed shown that evolutionary rescue by a conjugative plasmid is hampered by incompatible resident plasmids in the population. If the rescue plasmid is a mutant variant of the resident plasmid, both plasmids transfer at the same rates. A high conjugation rate then has two, potentially opposing, effects – a direct positive effect on spread of the rescue plasmid and an increase in the fraction of resident plasmid cells. This raises the question whether a high conjugation rate always benefits evolutionary rescue. In this article, we systematically analyse three models of increasing complexity to disentangle the benefits and limits of increasing horizontal gene transfer in the presence of plasmid competition and plasmid costs. We find that the net effect can be positive or negative and that the optimal transfer rate is thus not always the highest one. These results can contribute to our understanding of the many facets of plasmid-driven adaptation and the wide range of transfer rates observed in nature.
Fingerprint SRS Imaging Unveils Ergosteryl Ester as a Metabolic Signature of Azole-Resistant Candida albicans

Candida albicans (C. albicans), a major fungal pathogen, causes life-threatening infections in immunocompromised individuals. Fluconazole (FLC) is recommended as first-line therapy for treatment of invasive fungal infections. Yet, the widespread use of FLC has resulted in increased antifungal resistance among different strains of Candida, especially C. albicans, which is a leading source of hospital-acquired infections. Here, by hyperspectral stimulated Raman scattering (hSRS) imaging of single fungal cells in the fingerprint window and pixel-wise spectral unmixing, we report aberrant ergosteryl ester accumulation in azole-resistant C. albicans compared to azole-susceptible species. This accumulation was a consequence of de novo lipogenesis. Lipid profiling by mass spectroscopy identified ergosterol oleate to be the major species stored in azole-resistant C. albicans. Blocking ergosterol esterification by oleate and suppressing sterol synthesis by FLC synergistically suppressed the viability of C. albicans in vitro and limited the growth of biofilm on mouse skin in vivo. Our findings highlight a metabolic marker and a new therapeutic strategy for targeting azole-resistant C. albicans by interrupting the esterified ergosterol biosynthetic pathway. Significance Statement Invasive fungal infections and increasing antifungal resistance are emerging threats to public health with high morbidity and mortality. Despite the advances in azole resistance mechanisms, it remains unclear why some fungal species are intrinsically resistant to or easily acquire resistance to multiple antifungal drugs. Here, using fingerprint SRS microscopy, we uncovered a molecular signature, aberrant ergosteryl ester accumulation, linked to the azole resistance of Candida species. An antifungal treatment strategy combining oleate (inhibitor of ersgosteryl esterification) and azole significantly attenuates the azole resistance and the viability of C. albicans in vitro and in vivo. Our work opens a new way to detect and treat azole-resistant fungal infections by targeting ergosterol metabolism.
Dark matter particles as inhibitors of the solar core pulsations

L'influence des particules constituant la matiere sombre froide est examinee vis-a-vis de la stabilite des pulsations radiales du noyau solaire. Les instabilites vibrationnelles dans les etoiles sont passees en revue. Le critere pour l'amortissement (ou l'excitation) des pulsations radiales est presente en terme de leur variation d'energie durant chaque oscillation. La contribution specifique des particules de la matiere sombre froide a cette variation d'energie est etudiee. La limite conductive et le regime des grands nombres de Knudsen sont examines separement
A novel approach for tetrahedral-element-based finite element simulations of anisotropic hyperelastic intervertebral disc behavior

Intervertebral discs are microstructurally complex spinal tissues that add greatly to the flexibility and mechanical strength of the human spine. Attempting to provide an adjustable basis for capturing a wide range of mechanical characteristics and to better address known challenges of numerical modeling of the disc, we present a robust finite-element-based model formulation for spinal segments in a hyperelastic framework using tetrahedral elements. We evaluate the model stability and accuracy using numerical simulations, with particular attention to the degenerated intervertebral discs and their likely skewed and narrowed geometry. To this end, 1) annulus fibrosus is modeled as a fiber-reinforced Mooney-Rivlin type solid for numerical analysis. 2) An adaptive state-variable dependent explicit time step is proposed and utilized here as a computationally efficient alternative to theoretical estimates. 3) Tetrahedral-element-based FE models for spinal segments under various loading conditions are evaluated for their use in robust numerical simulations. For flexion, extension, lateral bending, and axial rotation load cases, numerical simulations reveal that a suitable framework based on tetrahedral elements can provide greater stability and flexibility concerning geometrical meshing over commonly employed hexahedral-element-based ones for representation and study of spinal segments in various stages of degeneration.