bluuebunny/crossref_metadata_2025_split huggingface dataset:
FRESH-based 3D bioprinting of complex biological geometries using chitosan bioink
Authors: Chaurasia Parul , Singh Richa , Mahto Sanjeev Kumar 
Published: June 2024

Abstract
Traditional three-dimensional (3D) bioprinting has always been associated with the challenge of print fidelity of complex geometries due to the gel-like nature of the bioinks. Embedded 3D bioprinting has emerged as a potential solution to print complex geometries using proteins and polysaccharides-based bioinks. This study demonstrated the Freeform Reversible Embedding of Suspended Hydrogels (FRESH) 3D bioprinting method of chitosan bioink to 3D bioprint complex geometries. 4.5% chitosan was dissolved in an alkali solvent to prepare the bioink. Rheological evaluation of the bioink described its shear-thinning nature. The power law equation was fitted to the shear rate-viscosity plot. The flow index value was found to be less than 1, categorizing the material as pseudo-plastic. The chitosan bioink was extruded into another medium, a thermo-responsive 4.5% gelatin hydrogel. This hydrogel supports the growing print structures while printing. After this, the 3D bioprinted structure was crosslinked with hot water to stabilize the structure. Using this method, we have 3D bioprinted complex biological structures like the human tri-leaflet heart valve, a section of a human right coronary arterial tree, a scale-down outer structure of the human kidney, and a human ear. Additionally, we have shown the mechanical tunability and suturability of the 3D bioprinted structures. This study demonstrates the capability of the chitosan bioink and FRESH method for 3D bioprinting of complex biological models for biomedical applications.
A biopsy-sized 3D skin model with a perifollicular vascular plexus enables studying immune cell trafficking in the skin
Authors: Shah Krutav Rakesh , Garriga-Cerda Laura , Pappalardo Alberto , Sorrells Leila , Jeong Hun Jin , Lee Chang H , Abaci Hasan Erbil 
Published: June 2024

AbstractHuman skin vasculature features a unique anatomy in close proximity to the skin appendages and acts as a gatekeeper for constitutive lymphocyte trafficking to the skin. Approximating such structural complexity and functionality in 3D skin models is an outstanding tissue engineering challenge. In this study, we leverage the capabilities of the digital-light-processing bioprinting to generate an anatomically-relevant and miniaturized 3D skin-on-a-chip (3D-SoC) model in the size of a 6 mm punch biopsy. The 3D-SoC contains a perfusable vascular network resembling the superficial vascular plexus of the skin and closely surrounding bioengineered hair follicles. The perfusion capabilities of the 3D-SoC enables the circulation of immune cells, and high-resolution imaging of the immune cell-endothelial cell interactions, namely tethering, rolling, and extravasation in real-time. Moreover, the vascular pattern in 3D-SoC captures the physiological range of shear rates found in cutaneous blood vessels and allows for studying the effect of shear rate on T cell trafficking. In 3D-SoC, as expected,in vitro-polarized T helper 1 (Th1) cells show a stronger attachment on the vasculature compared to naïve T cells. Both naïve and T cells exhibit higher retention in the low-shear zones in the early stages (<5 min) of T cell attachment. Interestingly, at later stages T cell retention rate becomes independent of the shear rate. The attached Th1 cells further transmigrate from the vessel walls to the extracellular space and migrate toward the bioengineered hair follicles and interfollicular epidermis. When the epidermis is not present, Th1 cell migration toward the epidermis is significantly hindered, underscoring the role of epidermal signals on T cell infiltration. Our data validates the capabilities of 3D-SoC model to study the interactions between immune cells and skin vasculature in the context of epidermal signals. The biopsy-sized 3D-SoC model in this study represents a new level of anatomical and cellular complexity, and brings us a step closer to generating a truly functional human skin with its tissue-specific vasculature and appendages in the presence of circulating immune cells.
Quantum physics: the tranquillity of the Euclidean time circle
Authors: Zaanen Jan 
Published: June 2024

Abstract
It is argued that the standard expositions of quantum weirdness, resting on small atomic physics-inspired systems, is rather pathological since nature is nearly invariably based on the collective behaviour of infinities of microscopic quantum degrees of freedom. A concise and original descriptive explanation of the way that thermal field theory works that governs this physics is presented, culminating in the Euclidean path integral formalism. This exposes in turn the deeply acausal nature of time when this microscopic reality is ‘left alone’, subjected to the principle of unitary time evolution. Upon connecting this to the macroscopic world with its causal time, probability itself emerges through the mystery of the collapse of the wavefunction. Finally, combining this with the Euclidean signature black hole geometry of Chapter 2, the Hawking temperature is very easy to understand. This may be the most challenging chapter of the book for the non-professional physics reader.
ifDEEPre: large protein language-based deep learning enables interpretable and fast predictions of enzyme commission numbers
Authors: Tan Qingxiong , Xiao Jin , Chen Jiayang , Wang Yixuan , Zhang Zeliang , Zhao Tiancheng , Li Yu 
Published: June 2024

Abstract
Accurate understanding of the biological functions of enzymes is vital for various tasks in both pathologies and industrial biotechnology. However, the existing methods are usually not fast enough and lack explanations on the prediction results, which severely limits their real-world applications. Following our previous work, DEEPre, we propose a new interpretable and fast version (ifDEEPre) by designing novel self-guided attention and incorporating biological knowledge learned via large protein language models to accurately predict the commission numbers of enzymes and confirm their functions. Novel self-guided attention is designed to optimize the unique contributions of representations, automatically detecting key protein motifs to provide meaningful interpretations. Representations learned from raw protein sequences are strictly screened to improve the running speed of the framework, 50 times faster than DEEPre while requiring 12.89 times smaller storage space. Large language modules are incorporated to learn physical properties from hundreds of millions of proteins, extending biological knowledge of the whole network. Extensive experiments indicate that ifDEEPre outperforms all the current methods, achieving more than 14.22% larger F1-score on the NEW dataset. Furthermore, the trained ifDEEPre models accurately capture multi-level protein biological patterns and infer evolutionary trends of enzymes by taking only raw sequences without label information. Meanwhile, ifDEEPre predicts the evolutionary relationships between different yeast sub-species, which are highly consistent with the ground truth. Case studies indicate that ifDEEPre can detect key amino acid motifs, which have important implications for designing novel enzymes. A web server running ifDEEPre is available at https://proj.cse.cuhk.edu.hk/aihlab/ifdeepre/ to provide convenient services to the public. Meanwhile, ifDEEPre is freely available on GitHub at https://github.com/ml4bio/ifDEEPre/.
Conway–Bromage–Lyndon (CBL): an exact, dynamic representation of k-mer sets
Authors: Martayan Igor , Cazaux Bastien , Limasset Antoine , Marchet Camille 
Published: June 2024

Abstract

Summary
In this article, we introduce the Conway–Bromage–Lyndon (CBL) structure, a compressed, dynamic and exact method for representing k-mer sets. Originating from Conway and Bromage’s concept, CBL innovatively employs the smallest cyclic rotations of k-mers, akin to Lyndon words, to leverage lexicographic redundancies. In order to support dynamic operations and set operations, we propose a dynamic bit vector structure that draws a parallel with Elias-Fano’s scheme. This structure is encapsulated in a Rust library, demonstrating a balanced blend of construction efficiency, cache locality, and compression. Our findings suggest that CBL outperforms existing dynamic k-mer set methods. Unique to this work, CBL stands out as the only known exact k-mer structure offering in-place set operations. Its different combined abilities position it as a flexible Swiss knife structure for k-mer set management.

Availability and implementation
https://github.com/imartayan/CBL.
Learning locality-sensitive bucketing functions
Authors: Yuan Xin , Chen Ke , Li Xiang , Shi Qian , Shao Mingfu 
Published: June 2024

Abstract

Motivation
Many tasks in sequence analysis ask to identify biologically related sequences in a large set. The edit distance, being a sensible model for both evolution and sequencing error, is widely used in these tasks as a measure. The resulting computational problem—to recognize all pairs of sequences within a small edit distance—turns out to be exceedingly difficult, since the edit distance is known to be notoriously expensive to compute and that all-versus-all comparison is simply not acceptable with millions or billions of sequences. Among many attempts, we recently proposed the locality-sensitive bucketing (LSB) functions to meet this challenge. Formally, a (d1,d2)-LSB function sends sequences into multiple buckets with the guarantee that pairs of sequences of edit distance at most d1 can be found within a same bucket while those of edit distance at least d2 do not share any. LSB functions generalize the locality-sensitive hashing (LSH) functions and admit favorable properties, with a notable highlight being that optimal LSB functions for certain (d1,d2) exist. LSB functions hold the potential of solving above problems optimally, but the existence of LSB functions for more general (d1,d2) remains unclear, let alone constructing them for practical use.

Results
In this work, we aim to utilize machine learning techniques to train LSB functions. With the development of a novel loss function and insights in the neural network structures that can potentially extend beyond this specific task, we obtained LSB functions that exhibit nearly perfect accuracy for certain (d1,d2), matching our theoretical results, and high accuracy for many others. Comparing to the state-of-the-art LSH method Order Min Hash, the trained LSB functions achieve a 2- to 5-fold improvement on the sensitivity of recognizing similar sequences. An experiment on analyzing erroneous cell barcode data is also included to demonstrate the application of the trained LSB functions.

Availability and implementation
The code for the training process and the structure of trained models are freely available at https://github.com/Shao-Group/lsb-learn.
An algorithm for decoy-free false discovery rate estimation in XL-MS/MS proteomics
Authors: Peng Yisu , Jain Shantanu , Radivojac Predrag 
Published: June 2024

Abstract

Motivation
Cross-linking tandem mass spectrometry (XL-MS/MS) is an established analytical platform used to determine distance constraints between residues within a protein or from physically interacting proteins, thus improving our understanding of protein structure and function. To aid biological discovery with XL-MS/MS, it is essential that pairs of chemically linked peptides be accurately identified, a process that requires: (i) database search, that creates a ranked list of candidate peptide pairs for each experimental spectrum and (ii) false discovery rate (FDR) estimation, that determines the probability of a false match in a group of top-ranked peptide pairs with scores above a given threshold. Currently, the only available FDR estimation mechanism in XL-MS/MS is the target-decoy approach (TDA). However, despite its simplicity, TDA has both theoretical and practical limitations that impact the estimation accuracy and increase run time over potential decoy-free approaches (DFAs).

Results
We introduce a novel decoy-free framework for FDR estimation in XL-MS/MS. Our approach relies on multi-sample mixtures of skew normal distributions, where the latent components correspond to the scores of correct peptide pairs (both peptides identified correctly), partially incorrect peptide pairs (one peptide identified correctly, the other incorrectly), and incorrect peptide pairs (both peptides identified incorrectly). To learn these components, we exploit the score distributions of first- and second-ranked peptide-spectrum matches for each experimental spectrum and subsequently estimate FDR using a novel expectation-maximization algorithm with constraints. We evaluate the method on ten datasets and provide evidence that the proposed DFA is theoretically sound and a viable alternative to TDA owing to its good performance in terms of accuracy, variance of estimation, and run time.

Availability and implementation
https://github.com/shawn-peng/xlms
Scalable de novo classification of antibiotic resistance of Mycobacterium tuberculosis
Authors: Serajian Mohammadali , Marini Simone , Alanko Jarno N , Noyes Noelle R , Prosperi Mattia , Boucher Christina 
Published: June 2024

Abstract

Motivation
World Health Organization estimates that there were over 10 million cases of tuberculosis (TB) worldwide in 2019, resulting in over 1.4 million deaths, with a worrisome increasing trend yearly. The disease is caused by Mycobacterium tuberculosis (MTB) through airborne transmission. Treatment of TB is estimated to be 85% successful, however, this drops to 57% if MTB exhibits multiple antimicrobial resistance (AMR), for which fewer treatment options are available.

Results
We develop a robust machine-learning classifier using both linear and nonlinear models (i.e. LASSO logistic regression (LR) and random forests (RF)) to predict the phenotypic resistance of Mycobacterium tuberculosis (MTB) for a broad range of antibiotic drugs. We use data from the CRyPTIC consortium to train our classifier, which consists of whole genome sequencing and antibiotic susceptibility testing (AST) phenotypic data for 13 different antibiotics. To train our model, we assemble the sequence data into genomic contigs, identify all unique 31-mers in the set of contigs, and build a feature matrix M, where M[i, j] is equal to the number of times the ith 31-mer occurs in the jth genome. Due to the size of this feature matrix (over 350 million unique 31-mers), we build and use a sparse matrix representation. Our method, which we refer to as MTB++, leverages compact data structures and iterative methods to allow for the screening of all the 31-mers in the development of both LASSO LR and RF. MTB++ is able to achieve high discrimination (F-1 &gt;80%) for the first-line antibiotics. Moreover, MTB++ had the highest F-1 score in all but three classes and was the most comprehensive since it had an F-1 score &gt;75% in all but four (rare) antibiotic drugs. We use our feature selection to contextualize the 31-mers that are used for the prediction of phenotypic resistance, leading to some insights about sequence similarity to genes in MEGARes. Lastly, we give an estimate of the amount of data that is needed in order to provide accurate predictions.

Availability
The models and source code are publicly available on Github at https://github.com/M-Serajian/MTB-Pipeline.
CODEX: COunterfactual Deep learning for the in silico EXploration of cancer cell line perturbations
Authors: Schrod Stefan , Zacharias Helena U , Beißbarth Tim , Hauschild Anne-Christin , Altenbuchinger Michael 
Published: June 2024

Abstract

Motivation
High-throughput screens (HTS) provide a powerful tool to decipher the causal effects of chemical and genetic perturbations on cancer cell lines. Their ability to evaluate a wide spectrum of interventions, from single drugs to intricate drug combinations and CRISPR-interference, has established them as an invaluable resource for the development of novel therapeutic approaches. Nevertheless, the combinatorial complexity of potential interventions makes a comprehensive exploration intractable. Hence, prioritizing interventions for further experimental investigation becomes of utmost importance.

Results
We propose CODEX (COunterfactual Deep learning for the in silico EXploration of cancer cell line perturbations) as a general framework for the causal modeling of HTS data, linking perturbations to their downstream consequences. CODEX relies on a stringent causal modeling strategy based on counterfactual reasoning. As such, CODEX predicts drug-specific cellular responses, comprising cell survival and molecular alterations, and facilitates the in silico exploration of drug combinations. This is achieved for both bulk and single-cell HTS. We further show that CODEX provides a rationale to explore complex genetic modifications from CRISPR-interference in silico in single cells.

Availability and implementation
Our implementation of CODEX is publicly available at https://github.com/sschrod/CODEX. All data used in this article are publicly available.
Hide and seek: chemical cues drive site preference among potential mates and intraguild competitors
Authors: Dionisio-da-Silva Welton , Araujo Rocha-da-Silva Katarina Luisa , Veloso Helena Maria Gonçalves , DaSilva Marcio Bernardino 
Published: June 2024

Abstract
Chemical perception is essential among arthropods for mate recognition, prey search, and predator avoidance, especially for solitary predators which are often aggressive. Such mechanisms may be intensified in environments like the Caatinga, a seasonally dry tropical forest in Brazil, characterized by low habitat complexity and high seasonal variation. Thus, we investigated chemical perception between two scorpion species from this environment, involved in intraguild competition. Experiments assessed their response to chemical cues from prey, predators, and potential mates. We use Y-mazes to test the time spent by the scorpions between Y-arms with or without a given substrate-borne chemical cue (site preference) and the number of active individuals during trials (presence of activity). Scorpions’ activity was not influenced by chemical stimuli, although they clearly exhibit site preferences. The smaller predators avoided sites with the larger species’ chemical cues, while the larger predators preferred sites with the smaller species’ cues. Additionally, both species trailed female chemical cues. These findings suggest a dual-oriented arms race where prey and predator modulate their behaviour to avoid and hunt heterospecifics, respectively. This study provides the first evidence of a scorpion using chemical cues to detect a heterospecific scorpion and highlights the importance of this trait in arachnids.
CPC03 Notch your average dermatitis
Authors: Long Amy , Murphy Lisa , Quigley Claire , Leonard Niamh , Clowry Julieanne 
Published: June 2024

Abstract
We present the case of triangular nasal notch sign in a White woman with previous exposure to tumour necrosis factor (TNF) inhibition for Crohn disease, which persisted throughout a biologic-free period. The 33-year-old woman presented with symptoms of nasal rash, itch and cosmetic concern for bilateral indentations in the nasal soft triangle, ongoing for 6 years. Medical history was notable for childhood atopic dermatitis, herpes labialis and Crohn disease. Her Crohn disease was well controlled on ustekinumab, commenced in the last year. She commenced adalimumab 6 years before the rash first appeared, but had discontinued this agent 4 years prior to her presentation to dermatology. On examination she had patchy erythema with scale and crust affecting her nasal tip, septum, alar sidewalls and columella, with loss of the normal nostril contour and cicatricial indentation at the apex of the nasal soft triangle bilaterally. She had evidence of acute herpes simplex virus (HSV)-1 infection of the upper lip. A punch biopsy from the left nasal tip showed acanthosis and spongiosis with a dermal mixed inflammatory infiltrate including lymphocytes, histiocytes and neutrophils. There was focal ulceration and intraepithelial small abscesses, without features of granulomatous inflammation. Triangular nasal notch sign is a clinical sign that was first described in 2019 (Mesnard C, Aubert H, Bourreille A et al. The triangular nasal notch sign in patients with Crohn disease treated with tumour necrosis factor inhibitors. Br J Dermatol 2019; 181: 1103–4). This group reported eight patients with Crohn disease exposed to TNF inhibitors, presenting with a cicatricial triangular nasal notch, and hypothesized that TNF inhibition specifically was implicated in the development of the phenomenon. Similarly to these patients, our case had exposure to anti-TNF; however, the persistence of nasal dermatitis throughout a ‘biologic-free’ period is novel. The pathophysiology of TNF inhibitor-induced nasal dermatitis and triangular nasal notch sign is not fully understood. It has been hypothesized that it is within the scope of paradoxical reactions seen with TNF inhibitors and persistent Staphylococcal aureus infection. An association with HSV-1 has not been previously reported in the context of this condition. Nasal swabs in this case showed commensals only; however, clinical improvement of nasal inflammation was achieved with topical mupirocin, prophylactic valaciclovir and a short course of oral azithromycin. The singular association with Crohn disease and limited involvement of the nose is also as yet unexplained and requires further research. We encourage greater reporting of this phenomenon, to increase awareness among clinicians and to help identify the exact pathomechanism and optimal treatment.