Dominic Lepage, Amin Yaghoobi, Heidi Tremblay
et al.
This work unlocks the manufacturing of nanophotonic quantum systems that exploit the unique material properties of single-crystal diamond (SCD). We achieve this by introducing a semiconductor-compatible process for the direct bonding of multiple high-quality, ultrathin diamond films onto a carrier wafer, enabling the subsequent parallel nanofabrication of optoelectronic integrated circuits. Central to this approach is a new diamond surface-preparation method that avoids boiling tri-acid mixtures while producing exceptionally clean 20 um thin single crystals. These platelets are bonded side-by-side to 100 mm silica wafers and exhibit a record shear strength of 45.1 MPa for (100)-oriented diamond, surpassing all previously reported bonding attempts. Evidence indicates that the bonding is dominated by van der Waals interactions, likely arising from mismatched protonation mechanisms between Si-OH and C-OH surface terminations, rather than from covalent-bond-driven mechanisms. Despite this non-molecular nature, the heterostructures remain stable through liquid immersions and standard nanofabrication steps. Because the method depends primarily on surface cleanliness and roughness rather than specific chemistries, it is broadly transferable across wafer materials. This capability to parallel-bond ultrathin SCD films onto large-area substrates provides a scalable route to high-performance platforms spanning nanophotonic quantum technologies, high-power electronics, MEMS, and biotechnology.
After AlphaFold won the Nobel Prize, protein prediction with deep learning once again became a hot topic. We comprehensively explore advanced deep learning methods applied to protein structure prediction and design. It begins by examining recent innovations in prediction architectures, with detailed discussions on improvements such as diffusion based frameworks and novel pairwise attention modules. The text analyses key components including structure generation, evaluation metrics, multiple sequence alignment processing, and network architecture, thereby illustrating the current state of the art in computational protein modelling. Subsequent chapters focus on practical applications, presenting case studies that range from individual protein predictions to complex biomolecular interactions. Strategies for enhancing prediction accuracy and integrating deep learning techniques with experimental validation are thoroughly explored. The later sections review the industry landscape of protein design, highlighting the transformative role of artificial intelligence in biotechnology and discussing emerging market trends and future challenges. Supplementary appendices provide essential resources such as databases and open source tools, making this volume a valuable reference for researchers and students.
The biotech venture market faces intense capital demands and regulatory scrutiny, yet academic research on VC networks remains rooted in software and consumer-tech contexts. This dissertation investigates how repeated co-investment ties and domain-expertise homophily influence a venture's exit likelihood, timing, and route amid the sector's pronounced technological and market uncertainty. Using a novel panel of 11,680 biotechnology start-ups from the United States, Canada, and Europe (2010-2024), we apply pooled logit, Cox proportional-hazards, multinomial logit, and Fine-Gray competing-risk models. Our findings show that both average prior co-investment and investor homophily exhibit robust inverted-U relationships with exit outcomes. Moderate familiarity and scientific overlap maximize exit probability, while either sparse or excessive embedding reduces success. Governance mechanisms also play a crucial role: participation of a pharmaceutical corporate VC or a highly independent board flattens the negative effects of over-embedding, enabling syndicates to sustain exit momentum at higher levels of familiarity or homogeneity. Furthermore, the optimal degree of embeddedness is route-specific: IPOs require deeper coordination than trade sales, while acquisitions peak earlier and are less sensitive to homophily. These findings refine network-embeddedness theory in the life-science context, identify governance contingencies, and offer practitioners quantitative metrics to balance trust, expertise, and oversight in biotech financing.
ZENG Wenjuan, ZHU Youpeng, CHEN Jiaxin, LI Hongyu, WANG Shuanghui, GONG Yihui, CHEN Zhiyin
Codon usage bias serves as an important driving force for gene expression regulation and molecular evolution, and is of particular importance in the study of plant organellar genomes. Camellia sinensis cv. ‘Zhuyeqi’, an important tea cultivar in China, has not yet received a systematic report on the codon usage patterns of its organellar genomes. This study was systematic bioinformatic analysis of the 52 chloroplast-encoded genes and 29 mitochondrial-encoded genes of ‘Zhuyeqi’. The results reveal that: (1) both the chloroplast genome (ENC=44.64±3.25) and the mitochondrial genome (ENC=51.98±3.47) exhibit weak codon usage bias, with the chloroplast bias primarily driven by natural selection (GC3s and ENC correlation R2=0.482). While the mitochondrial bias is jointly influenced by natural selection and mutational pressure (R2=0.312). (2) Relative synonymous codon usage (RSCU) analysis demonstrates that both organellar genomes significantly prefer synonymous codons ending in A/U, and the highly expressed chloroplast genes (rpoC2, psbA) exhibit stronger codon preferences. (3) a multi-parameter screening approach identified 20 optimal chloroplast codons (GCA, GCU) and 23 optimal mitochondrial codons (GCC, AGG). This study provided elucidation of the codon usage characteristics and evolutionary driving forces in the organellar genomes of Camellia sinensis cv. ‘Zhuyeqi’, offering crucial theoretical guidance for the optimization of the tea molecular breeding system and the efficient expression of exogenous genes.
The present study focuses on developing novel gradient thin films for surface-based magnetic resonance imaging of fluids such as water. Four types of magnetic-polymer colloids were investigated as T2 contrast agents, including Janus magnetic-polystyrene and core–shell magnetic-poly(styrene/divinylbenzene/methacrylic acid) particles. These colloids were coated with amino dextran to enhance their performance. Key factors such as emulsion composition, particle size, and surface properties were systematically examined. Gradient thin films were fabricated on glass slides using a layer-by-layer self-assembled multilayer (LbL-SAMu) technique. The films consisted of positively charged poly(dimethyl diallyl ammonium chloride) and negatively charged magnetic-polymer colloids. The developed colloids and thin films were characterized by their surface wettability, surface morphology, and zeta potential. These films exhibited relatively improved hydrophilicity and T2 contrast. The utilization of such gradient thin films as molecular probes could enhance clinical MRI for in vitro diagnosis. This study indicated that thin-film gradients can offer a facile technique for unique cellular imaging via a lab-on-chip device to enable effective point-of-care molecular diagnostics.
Tafesse Kibatu, Sebsebe Demissew, Diriba Muleta
et al.
Enset is a staple food for approximately 25% of Ethiopia’s population. It is threatened by a range of biotic and abiotic stress, of which bacterial wilt is the most significant. This study investigated the enset bacterial wilt (EBW) status on farms in Gedeo, Kembata Tembaro, Gurage, Hadiya zones, and the Basketo special woreda of Southern Ethiopia. In addition, infected enset plant samples were collected from Hadiya, Kembata Tembaro, and Gedeo zones to assess bacterial strain diversity using physicochemical and morphological approaches. Representative Kebeles were selected using purposive sampling based on their agroecological conditions. Data was collected through in-depth interviews, questionnaires, group discussions, and field observation. The morphology of bacterial wilt isolates was characterized by color, texture, form, elevation, margin, and motility. In addition, a combination of oxidase, aesculin hydrolysis, catalase, gram reaction, hydrogen sulfide (H2S), gelatin liquefaction, and fructose, lactose, mannitol, and sorbitol utilization tests were conducted to capture physiochemical differences. Tolerance to salt and high temperatures was also evaluated. The bacterial wilt impact varies significantly across enset growing regions, with highlands experiencing the highest. This research emphasizes the importance of assessing both spatial and temporal variation, as well as integrating local knowledge and robust scientific approaches for effective bacterial wilt management and enset landrace conservation efforts. The research also provides valuable insights into the characteristics of bacterial wilt isolates in Southern Ethiopia. Analyses of morphology, potassium hydroxide solubility, catalase activity, and carbohydrate utilization were consistent, however, variations in bacterial isolates response to tests of easculin, oxidase, gelatin liquid, H2S tests and response to osmotic and temperature exposures. This study reveals a strong association between the bacterial wilt effect and the enset growing regions. EBW exhibits seasonal fluctuations. Bacterial wilt isolates displayed consistent morphological characteristics. All isolates similarly utilized sorbitol, mannitol, lactose, and fructose carbohydrates. All isolates exhibited positive potassium hydroxide solubility and catalase activity. However, the isolates displayed variations in responses to easculin, oxidase, gelatin liquefaction, and H2S production. The isolates also displayed variations in tolerance to salt and high temperatures. These variations can be valuable for understanding disease epidemiology and management.
Deep neural policies have recently been installed in a diverse range of settings, from biotechnology to automated financial systems. However, the utilization of deep neural networks to approximate the value function leads to concerns on the decision boundary stability, in particular, with regard to the sensitivity of policy decision making to indiscernible, non-robust features due to highly non-convex and complex deep neural manifolds. These concerns constitute an obstruction to understanding the reasoning made by deep neural policies, and their foundational limitations. Hence, it is crucial to develop techniques that aim to understand the sensitivities in the learnt representations of neural network policies. To achieve this we introduce a theoretically founded method that provides a systematic analysis of the unstable directions in the deep neural policy decision boundary across both time and space. Through experiments in the Arcade Learning Environment (ALE), we demonstrate the effectiveness of our technique for identifying correlated directions of instability, and for measuring how sample shifts remold the set of sensitive directions in the neural policy landscape. Most importantly, we demonstrate that state-of-the-art robust training techniques yield learning of disjoint unstable directions, with dramatically larger oscillations over time, when compared to standard training. We believe our results reveal the fundamental properties of the decision process made by reinforcement learning policies, and can help in constructing reliable and robust deep neural policies.
Melissa Kosovari, Thierry Buffeteau, Laurent Thomas
et al.
Biomaterial surface engineering and integrating cell-adhesive ligands are crucial in biological research and biotechnological applications. The interplay between cells and their microenvironment, influenced by chemical and physical cues, impacts cellular behavior. Surface modification of biomaterials profoundly affects cellular responses, especially at the cell-surface interface. This work focuses on enhancing cellular activities through material manipulation, emphasizing silanization for further functionalization with bioactive molecules like RGD peptides to improve cell adhesion. The grafting of three distinct silanes onto silicon wafers using both spin coating and immersion methods was investigated. This study sheds light on the effects of different alkyl chain lengths and protecting groups on cellular behavior, providing valuable insights into optimizing silane-based self-assembled monolayers (SAMs) before peptide or protein grafting for the first time. Specifically, it challenges the common use of APTES molecules in this context. These findings advance our understanding of surface modification strategies, paving the way for tailoring biomaterial surfaces to modulate cellular behavior for diverse biotechnological applications.
Proteins are the fundamental macromolecules that play diverse and crucial roles in all living matter and have tremendous implications in healthcare, manufacturing, and biotechnology. Their functions are largely determined by the sequences of amino acids that compose them and their unique three-dimensional structures when folded. The recent surge in highly accurate computational protein structure prediction tools has equipped scientists with the means to derive preliminary structural insights without the onerous costs of experimental structure determination. These breakthroughs hold profound promise for building robust and efficient in silico protein design systems. While the prospect of designing de novo proteins with precise computational accuracy remains a grand challenge in biochemical engineering, conventional assembly-based and rational design methods often grapple with the expansive design space, resulting in suboptimal design success rates. Despite recently emerged deep learning-based models have shown promise in improving the efficiency of the computational protein design process, a significant gap persists between current design paradigms and their experimental realization. This thesis will investigate the potential of deep generative models in refining protein structure and sequence design methods, aiming to develop frameworks capable of crafting novel protein sequences with predetermined structures or specific functionalities. By harnessing extensive protein databases and cutting-edge neural architectures, this research aims to enhance precision and robustness in current protein design paradigms, potentially paving the way for advancements across various scientific fields.
Absolute quantity imaging of biomolecules on a single cell level is critical for measurement assurance in biosciences and bioindustries. While infrared (IR) transmission microscopy is a powerful label-free imaging modality capable of chemical quantification, its applicability to hydrated biological samples remains challenging due to the strong water absorption. We overcome this challenge by applying a solvent absorption compensation (SAC) technique to a home-built quantum cascade laser IR microscope. SAC-IR microscopy improves the chemical sensitivity considerably by adjusting the incident light intensity to pre-compensate the IR absorption by water while retaining the full dynamic range. We demonstrate the label-free chemical imaging of key biomolecules of a cell, such as protein, fatty acid, and nucleic acid, with sub-cellular spatial resolution. By imaging live fibroblast cells over twelve hours, we monitor the mass change of the three molecular species of single cells at various phases, including cell division. While the current live-cell imaging demonstration involved three wavenumbers, more wavenumber images could measure more biomolecules in live cells with higher accuracy. As a label-free method to measure absolute quantities of various molecules in a cell, SAC-IR microscopy can potentially become a standard chemical characterization tool for live cells in biology, medicine, and biotechnology.
Agriculture faces critical challenges from population growth, resource scarcity, and climate change, driving a shift toward advanced, technology-integrated farming. Mechanization has transformed agriculture, enhancing sustainability and crop productivity. Now, technologies like artificial intelligence (AI), robotics, biotechnology, blockchain, and the Internet of Things (IoT) are advancing precision agriculture. The concept of the Internet of Everything (IoE) has gained traction due to its holistic approach to integrating various IoT specializations, called IoXs with X referring to a specific domain. This paper explores the transformative role of IoE in agriculture, expanding beyond traditional IoT applications to integrate niche subdomains like molecular communication (MC), the Internet of Nano Things (IoNT), the Internet of Bio-Nano Things (IoBNT), designer phages, and the Internet of Fungus (IoF). Our study provides a detailed review of how these IoE subdomains, in conjunction with 6G, blockchain, and machine learning (ML), can enhance precision farming in areas like crop monitoring, resource management, and disease control. Unlike prior IoT centric reviews, this work uniquely focuses on IoEs potential to advance agriculture at molecular and biological scales, achieving more precise resource utilization and resilience. Key contributions include an exploration of these technologies applicability, associated challenges, and recommendations for future research directions within precision agriculture.
Valentina Aparicio, Daniel Gordon, Sebastian G. Huayamares
et al.
Large language models (LLMs) are deep learning algorithms being used to perform natural language processing tasks in various fields, from social sciences to finance and biomedical sciences. Developing and training a new LLM can be very computationally expensive, so it is becoming a common practice to take existing LLMs and finetune them with carefully curated datasets for desired applications in different fields. Here, we present BioFinBERT, a finetuned LLM to perform financial sentiment analysis of public text associated with stocks of companies in the biotechnology sector. The stocks of biotech companies developing highly innovative and risky therapeutic drugs tend to respond very positively or negatively upon a successful or failed clinical readout or regulatory approval of their drug, respectively. These clinical or regulatory results are disclosed by the biotech companies via press releases, which are followed by a significant stock response in many cases. In our attempt to design a LLM capable of analyzing the sentiment of these press releases,we first finetuned BioBERT, a biomedical language representation model designed for biomedical text mining, using financial textual databases. Our finetuned model, termed BioFinBERT, was then used to perform financial sentiment analysis of various biotech-related press releases and financial text around inflection points that significantly affected the price of biotech stocks.
Amino acids (AAs) have broad nutritional, therapeutic, and medical significance and thus are one of the most common active ingredients of nutritional supplements. Analytical strategies for determining AAs are high-priced and often limited to methods that require modification of AA polarity or incorporation of an aromatic moiety. The aim of this work was to develop a new method for the determination of L-arginine, L-ornithine, and L-lysine on low-cost microchip electrophoresis instrumentation conjugated with capacitively coupled contactless conductivity detection. A solution consisting of 0.3 M acetic acid and 1 × 10<sup>−5</sup> M iminodiacetic acid has been identified as the optimal background electrolyte, ensuring the shortest possible analysis time. The short migration times of amino acids (t ≤ 64 s) and method simplicity resulted in high analysis throughput with high precision and linearity (R<sup>2</sup><inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>≥</mo></mrow></semantics></math></inline-formula> 0.9971). The limit of detection values ranged from 0.15 to 0.19 × 10<sup>−6</sup> M. The accuracy of the proposed method was confirmed by recovery measurements. The results were compared with CE-UV-VIS and HPLC-DAD methods and showed good agreement. This work represents the first successful demonstration of the ME-C<sup>4</sup>D analysis of L-arginine, L-ornithine, and L-lysine in real samples.
A. S. Zoricheva, E. A. Zvonova, L. S. Agapova
et al.
INTRODUCTION. The current cell-based cartilage repair methods, such as autologous chondrocyte transplantation, are not sufficiently effective, and the surgery is painful and traumatic. Therefore, there is a need for a more effective cell therapy product with a minimally invasive surgical procedure for its implantation into the patient.AIM. This study aimed to develop a manufacturing technology for the production of an autologous cell-based medicinal product (CBMP) comprising three-dimensional structures (3D-spheroids) based on chondrocytes isolated from the patient’s cartilage tissue, as well as to evaluate its clinical efficacy.MATERIALS AND METHODS. Autologous chondrocytes isolated from the patient’s cartilage biopsy were propagated in monolayer culture to obtain the required number of cells. Subsequently, the chondrocytes were cultivated on plates with a non-adhesive coating to form 3D spheroids. All CBMP production steps were performed under aseptic conditions in cell culture isolators. The authors used phase-contrast microscopy and immunohistochemical staining with specific fluorescence-labelled antibodies to characterise chondrocyte phenotypes at different stages of cultivation. Genetic stability was controlled by karyotyping. The efficacy of Easytense® was evaluated in a clinical trial using specialised functional tests and the Magnetic Resonance Observation of Cartilage Repair Tissue (MOCART) score. The primary efficacy endpoint was a change in the overall score on the Knee Injury and Osteoarthritis Outcome Score (KOOS).RESULTS. A manufacturing technology without using animal sera, growth factors, cytokines, or other additives was developed for the production of the autologous CBMP Easytense®. Karyological data confirmed that the chondrocytes retained genetic stability for 3 passages in monolayer culture. When cultured as 3D spheroids, the chondrocytes produced cartilage extracellular matrix proteins (type II collagen, aggrecan), thus acquiring the ability to repair damaged cartilage. The clinical trial demonstrated a statistically significant improvement in knee cartilage 12 months after the transplantation of 3D spheroids derived from autologous chondrocytes. The mean change in the overall KOOS score was 23.8±15.9.CONCLUSIONS. The clinical trial results indicate that Easytense® is highly effective for cartilage repair. Based on these results, the CBMP has been granted marketing authorisation and introduced into clinical practice in the Russian Federation. Easytense® has the potential to replace endoprosthetics and expensive surgeries abroad.
Efrida Basri, Istie Sekartining Rahayu, Saefudin Saefudin
et al.
Lactic acid (LA), citric acid (CA), and glycerol (G) are renewable and environmentally friendly chemicals that could improve the qualities of short-rotation teak (SRT) woods. This study investigated the effect of thermal and chemical modification using 20% aqueous solutions (w/w) of LA, CA, and G and their mixtures in the same composition on physical and mechanical properties of SRT teak wood. The impregnation process was initiated by vacuum process for 1 h and pressure (12.2 bar) for 2 h, followed by thermal (150 °C) treatment for 6 h on the SRT wood samples after being removed from the vacuum-pressure tube. Retention (R), weight percent gain (WPG), density (D), anti-swelling efficiency (ASE), leachability (WL), modulus of elasticity (MOE), and modulus of rupture (MOR) were measured. FTIR spectrometry and SEM analyses were performed. The wood impregnated with a mixture of 10% LA + 10% CA provided the highest ASE values of 50.1%, and the lowest leaching resistance of 1.54%. Based on wood strengths (MOE and MOR) and physical properties, as well as supported by FTIR and SEM analysis, the use of 10% LA + 10% CA is the most prospective as an impregnant formula for SRT wood modification of this research.
Emily H. Soice, Rafael Rocha, Kimberlee Cordova
et al.
Large language models (LLMs) such as those embedded in 'chatbots' are accelerating and democratizing research by providing comprehensible information and expertise from many different fields. However, these models may also confer easy access to dual-use technologies capable of inflicting great harm. To evaluate this risk, the 'Safeguarding the Future' course at MIT tasked non-scientist students with investigating whether LLM chatbots could be prompted to assist non-experts in causing a pandemic. In one hour, the chatbots suggested four potential pandemic pathogens, explained how they can be generated from synthetic DNA using reverse genetics, supplied the names of DNA synthesis companies unlikely to screen orders, identified detailed protocols and how to troubleshoot them, and recommended that anyone lacking the skills to perform reverse genetics engage a core facility or contract research organization. Collectively, these results suggest that LLMs will make pandemic-class agents widely accessible as soon as they are credibly identified, even to people with little or no laboratory training. Promising nonproliferation measures include pre-release evaluations of LLMs by third parties, curating training datasets to remove harmful concepts, and verifiably screening all DNA generated by synthesis providers or used by contract research organizations and robotic cloud laboratories to engineer organisms or viruses.
Lucas M. Ferroni, Moira I. Dolz, María Florencia Guerra
et al.
The physical pre-treatment of seeds is a growing field of research in agricultural biotechnology. Among its possibilities, magnetopriming of seeds has received increasing attention in the last two decades. Inspired by remarkable reports of the literature on the effects of static magnetic fields (SMF) on maize seeds, we performed similar experiments, though expanding the range of SMF up to 350 mT, higher than most of the reported studies. With exposure durations of 1 h, we tested 7 different SMF intensities, from 50 to 350 mT at increments of 50 mT. We challenged our findings with an exhaustive analysis of the background static and alternated magnetic fields inside the stove where the germinations took place, in order to rule out background fields as a confounding variable. We found a maximum effect of 108.9 % increase (more than double) in the average total length of plantules at 150 mT, at the 10th day of germination. All other intensities, except 350 mT, also induced a significant growth stimulation. While not seeming to represent a determinant factor, our analysis calls the attention to the possible relevance of different conditions in different levels and zones of standard stoves. Our findings are in line with numerous studies pointing to magnetopriming of maize seeds as a novel, viable and reproducible physical treatment for the enhancement of germination.
Mass spectrometry-based proteomics is a key enabler for personalized healthcare, providing a deep dive into the complex protein compositions of biological systems. This technology has vast applications in biotechnology and biomedicine but faces significant computational bottlenecks. Current methodologies often require multiple hours or even days to process extensive datasets, particularly in the domain of spectral clustering. To tackle these inefficiencies, we introduce SpecHD, a hyperdimensional computing (HDC) framework supplemented by an FPGA-accelerated architecture with integrated near-storage preprocessing. Utilizing streamlined binary operations in an HDC environment, SpecHD capitalizes on the low-latency and parallel capabilities of FPGAs. This approach markedly improves clustering speed and efficiency, serving as a catalyst for real-time, high-throughput data analysis in future healthcare applications. Our evaluations demonstrate that SpecHD not only maintains but often surpasses existing clustering quality metrics while drastically cutting computational time. Specifically, it can cluster a large-scale human proteome dataset-comprising 25 million MS/MS spectra and 131 GB of MS data-in just 5 minutes. With energy efficiency exceeding 31x and a speedup factor that spans a range of 6x to 54x over existing state of-the-art solutions, SpecHD emerges as a promising solution for the rapid analysis of mass spectrometry data with great implications for personalized healthcare.
Sheela Katuwal, Nur-Al-Sarah Rafsan, Amanda J. Ashworth
et al.
Poultry litter is a useful product as a fertilizer, energy feedstock for thermochemical conversion, and a precursor for synthesis of adsorbents and catalysts. Detailed characterization of baseline properties is necessary for enhanced environmental and economic utilization of this valuable resource. Baseline physicochemical characterization was carried out at two broiler production facilities (Arkansas, PL1, and North Carolina, PL2). Greater concentrations of inorganic nitrogen, phosphorus, and potassium were obtained for PL1, suggesting greater nutrient value compared to PL2. PL2 had greater carbon content and water-holding capacity than PL1. X-ray photoelectron spectroscopy (XPS) of PL1 and PL2 indicated a similarity between litters in terms of the presence of carbon, nitrogen, and oxygen bonds. Both poultry litters had oxygen, nitrogen, sulfur, and phosphorous functional groups, as confirmed by infrared spectroscopy. Time of flight - secondary ion mass spectroscopy of negative ions also indicated similarity of the surface charge distribution between PL1 and PL2. Overall, poultry litters evaluated had similar surface chemistries, with nutrient composition varying based on rearing conditions, which has implications for downstream use in thermochemical conversion and other value-added products.