Symbiotic virus-bacteria interactions in biological treatment of coking wastewater manipulating bacterial physiological activities.

Biological treatment is commonly used in coking wastewater (CWW) treatment. Prokaryotic microbial communities in CWW treatment have been comprehensively studied. However, viruses, as the critical microorganisms affecting microbial processes and thus engineering parameters, still remain poorly understood in CWW treatment context. Employing viromics sequencing, the composition and function of the viral community in CWW treatment were discovered, revealing novel viral communities and key auxiliary metabolic functions. Caudovirales appeared to be the predominant viral order in the oxic-hydrolytic-oxic (OHO) CWW treatment combination, showing relative abundances of 62.47 %, 56.64 % and 92.20 % in bioreactors O1, H and O2, respectively. At the family level, Myoviridae, Podoviridae and Siphoviridae mainly prevailed in bioreactors O1 and H while Phycodnaviridae dominated in O2. A total of 56.23-92.24% of novel viral contigs defied family-level characterization in this distinct CWW habitat. The virus-host prediction results revealed most viruses infecting the specific functional taxa Pseudomonas, Acidovorax and Thauera in the entire OHO combination, demonstrating the viruses affecting bacterial physiology and pollutants removal from CWW. Viral auxiliary metabolic genes (AMGs) were screened, revealing their involvement in the metabolism of contaminants and toxicity tolerance. In the bioreactor O1, AMGs were enriched in detoxification and phosphorus ingestion, where glutathione S-transferase (GSTs) and beta-ketoadipyl CoA thiolase (fadA) participated in biodegradation of polycyclic aromatic hydrocarbons and phenols, respectively. In the bioreactors H and O2, the AMGs focused on cell division and epicyte formation of the hosts, where GDPmannose 4,6-dehydratase (gmd) related to lipopolysaccharides biosynthesis was considered to play an important role in the growth of nitrifiers. The diversities of viruses and AMGs decreased along the CWW treatment process, pointing to a reinforced virus-host adaptive strategy in stressful operation environments. In this study, the symbiotic virus-bacteria interaction patterns were proposed with a theoretical basis for promoting CWW biological treatment efficiency. The findings filled the gaps in the virus-bacteria interactions at the full-scale CWW treatment and provided great value for understanding the mechanism of biological toxicity and sludge activity in industrial wastewater treatment.

The origin of different bending stiffness between double-stranded RNA and DNA revealed by magnetic tweezers and simulations.

The subtle differences in the chemical structures of double-stranded (ds) RNA and DNA lead to significant variations in their biological roles and medical implications, largely due to their distinct biophysical properties, such as bending stiffness. Although it is well known that A-form dsRNA is stiffer than B-form dsDNA under physiological salt conditions, the underlying cause of this difference remains unclear. In this study, we employ high-precision magnetic-tweezer experiments along with molecular dynamics simulations and reveal that the relative bending stiffness between dsRNA and dsDNA is primarily determined by the structure- and salt-concentration-dependent ion distribution around their helical structures. At near-physiological salt conditions, dsRNA shows a sparser ion distribution surrounding its phosphate groups compared to dsDNA, causing its greater stiffness. However, at very high monovalent salt concentrations, phosphate groups in both dsRNA and dsDNA become fully neutralized by excess ions, resulting in a similar intrinsic bending persistence length of approximately 39 nm. This similarity in intrinsic bending stiffness of dsRNA and dsDNA is coupled to the analogous fluctuations in their total groove widths and further coupled to the similar fluctuation of base-pair inclination, despite their distinct A-form and B-form helical structures.

Experimental Study on the Curing Mechanism of Red Mud-Based Stabilized Soil Co-Modified by Nano-SiO2 and Gypsum.

In order to effectively utilize red mud and reduce its occupation of land resources, as well as its impact on the environment, experiments were conducted to develop stabilized soil materials using nano-SiO2 synergistically modified red mud and to investigate the curing mechanism of stabilized soil. The unconfined compressive strength, microscopic morphology, and curing mechanism of the red mud-based stabilized soil materials with different amounts of modified materials were investigated. The test results show that after 7 days of curing, the unconfined compressive strength of red mud-based stabilized soil meets the compressive strength requirement of road base material when nano-SiO2, gypsum, and cement are synergistically modified. In such cases, the soil structure has the lowest fracture rate and the best structural compactness when the amount of nano-SiO2 is 1%. It is found that the needle-like and columnar calcium alumina in the modified red mud-based stabilized soil increases, and the binding energy of hydration product ions in the modified material is improved. The chemical curing mechanism of modified red mud-based stabilized soil includes hydration reaction, pozzolanic reaction, promotion effect of nano-SiO2, and enhancement effect of gypsum. On this base, a model of the early start hydration process of red mud-based stabilized soil promoted by nano-SiO2 is established.

Analysis of the Synergistic Effect on the Strength Characteristics of Modified Red Mud-Based Stabilized Soil.

Based on the existing research results, this research team developed roadbed stabilized soil materials using nano-SiO2 synergistically modified red mud in order to study whether the strength of the stabilized soil materials meets the strength requirements of the roadbed materials, and at the same time, analyze its strength characteristics to make the feasibility of it being used as a roadbed material clear. Through different combination schemes, the effects of different nano-SiO2 and cement contents on the strength of the stabilized materials were explored. The test results show the following: In the synergistic modification of nano-SiO2 and cement, nano-SiO2 can significantly improve the early unconfined compressive strength of red mud-based stabilized soil. In the synergistic modification of nano-SiO2, gypsum, and cement, the 7 d unconfined compressive strength of red mud-based stabilized soil is greater than 2 MPa, which meets the strength requirements of road base materials and shows the superiority of synergism. The nominal stress-strain curves are divided into five stages: compressed and compacted stage, elastic deformation stage, plastic deformation stage, damage deformation stage, and residual deformation stage. The macroscopic compressive damage pattern of the specimens shows that the modified red mud-based stabilized soil mostly exhibits brittle damage. Tests have shown that the strength of modified terracotta-based stabilized soil meets the requirements of roadbed strength.

Modeling Coil-Globule-Helix Transition in Polymers by Self-Interacting Random Walks.

Random walks (RWs) have been important in statistical physics and can describe the statistical properties of various processes in physical, chemical, and biological systems. In this study, we have proposed a self-interacting random walk model in a continuous three-dimensional space, where the walker and its previous visits interact according to a realistic Lennard-Jones (LJ) potential uLJr=εr0/r12-2r0/r6. It is revealed that the model shows a novel globule-to-helix transition in addition to the well-known coil-to-globule collapse in its trajectory when the temperature decreases. The dependence of the structural transitions on the equilibrium distance r0 of the LJ potential and the temperature T were extensively investigated. The system showed many different structural properties, including globule-coil, helix-globule-coil, and line-coil transitions depending on the equilibrium distance r0 when the temperature T increases from low to high. We also obtained a correlation form of kBTc = λε for the relationship between the transition temperature Tc and the well depth ε, which is consistent with our numerical simulations. The implications of the random walk model on protein folding are also discussed. The present model provides a new way towards understanding the mechanism of helix formation in polymers like proteins.

Fourier-transform ghost imaging with super-Rayleigh speckles.

Ghost imaging based on the high-order correlation of optical field has developed rapidly and has been extended to the x-ray region. However, the limited flux leads to severe image deterioration. Here, an approach of Fourier-transform ghost imaging with super-Rayleigh speckles is proposed to realize high quality ghost imaging at low photon flux level. The super-Rayleigh speckles are designed by optimizing binary modulating screens based on the direct binary search algorithm. The experimental results show that the speckle contrast can be greatly enhanced and high visibility Fourier-transform diffraction pattern of the sample can be obtained. The sample's image in spatial domain is successfully achieved even if the detected photon level decreases to 0.1 photons/pixel. This method is of great importance for high-resolution imaging in the photon-limited scenarios, especially for laboratory x-ray systems.

RNA 3D Structure Prediction: Progress and Perspective.

Ribonucleic acid (RNA) molecules play vital roles in numerous important biological functions such as catalysis and gene regulation. The functions of RNAs are strongly coupled to their structures or proper structure changes, and RNA structure prediction has been paid much attention in the last two decades. Some computational models have been developed to predict RNA three-dimensional (3D) structures in silico, and these models are generally composed of predicting RNA 3D structure ensemble, evaluating near-native RNAs from the structure ensemble, and refining the identified RNAs. In this review, we will make a comprehensive overview of the recent advances in RNA 3D structure modeling, including structure ensemble prediction, evaluation, and refinement. Finally, we will emphasize some insights and perspectives in modeling RNA 3D structures.

A Comparative Study on the Effects of Different Sources of Carboxymethyl Poria Polysaccharides on the Repair of DSS-Induced Colitis in Mice.

Carboxymethyl poria polysaccharide plays important anti-tumor, antioxidant, and anti-inflammatory roles. Therefore, this study aimed to compare the healing impacts of two different sources of carboxymethyl poria polysaccharides [Carboxymethylat Poria Polysaccharides I (CMP I) and Carboxymethylat Poria Polysaccharides II (CMP II)] on ulcerative colitis in mice caused by dextran sulfate sodium (DSS). All the mice were arbitrarily split into five groups (n = 6): (a) control (CTRL), (b) DSS, (c) SAZ (sulfasalazine), (d) CMP I, and (e) CMP II. The experiment lasted for 21 days, and the body weight and final colon length were monitored. A histological analysis of the mouse colon tissue was carried out using H&E staining to assess the degree of inflammatory infiltration. The levels of inflammatory cytokines [interleukin-1ß (IL-1ß), interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α), and interleukin-4 (IL-4)] and enzymes [superoxide dismutase (SOD) and myeloperoxidase (MPO)] in the serum were examined using ELISA. Additionally, 16S ribosomal RNA sequencing was used to analyze the microorganisms in the colon. The results indicated that both CMP I and CMP II alleviated weight loss, colonic shortening, and inflammatory factor infestation in colonic tissues caused by DSS (p < 0.05). Furthermore, the ELISA results revealed that both CMP I and CMP II reduced the expression of IL-1ß, IL-6, TNF-α, and MPO, and elevated the expression of IL-4 and SOD in the sera of the mice (p < 0.05). Moreover, 16S rRNA sequencing showed that CMP I and CMP II increased the plenitude of microorganisms in the mouse colon relative to that in the DSS group. The results also indicated that the therapeutic effect of CMP I on DSS-induced colitis in the mice was superior to that of CMP II. This study demonstrated that carboxymethyl poria polysaccharide from Poria cocos had therapeutic effects on DSS-induced colitis in mice, with CMP I being more effective than CMP II.

Universality in RNA and DNA deformations induced by salt, temperature change, stretching force, and protein binding.

Nucleic acid deformations play important roles in many biological processes. The physical understanding of nucleic acid deformation by environmental stimuli is limited due to the challenge in the precise measurement of RNA and DNA deformations and the complexity of interactions in RNA and DNA. Magnetic tweezers experiments provide an excellent opportunity to precisely measure DNA and RNA twist changes induced by environmental stimuli. In this work, we applied magnetic tweezers to measure double-stranded RNA twist changes induced by salt and temperature changes. We observed RNA unwinds when lowering salt concentration, or increasing temperature. Our molecular dynamics simulations revealed the mechanism: lowering salt concentration or increasing temperature enlarges RNA major groove width, which causes twist decrease through twist-groove coupling. Combining these results with previous results, we found some universality in RNA and DNA deformations induced by three different stimuli: salt change, temperature, and stretching force. For RNA, these stimuli first modify the major groove width, which is transduced into twist change through twist-groove coupling. For DNA, these stimuli first modify diameter, which is transduced into twist change through twist-diameter coupling. Twist-groove coupling and twist-diameter coupling appear to be utilized by protein binding to reduce DNA and RNA deformation energy cost upon protein binding.

cgRNASP: coarse-grained statistical potentials with residue separation for RNA structure evaluation.

Knowledge-based statistical potentials are very important for RNA 3-dimensional (3D) structure prediction and evaluation. In recent years, various coarse-grained (CG) and all-atom models have been developed for predicting RNA 3D structures, while there is still lack of reliable CG statistical potentials not only for CG structure evaluation but also for all-atom structure evaluation at high efficiency. In this work, we have developed a series of residue-separation-based CG statistical potentials at different CG levels for RNA 3D structure evaluation, namely cgRNASP, which is composed of long-ranged and short-ranged interactions by residue separation. Compared with the newly developed all-atom rsRNASP, the short-ranged interaction in cgRNASP was involved more subtly and completely. Our examinations show that, the performance of cgRNASP varies with CG levels and compared with rsRNASP, cgRNASP has similarly good performance for extensive types of test datasets and can have slightly better performance for the realistic dataset-RNA-Puzzles dataset. Furthermore, cgRNASP is strikingly more efficient than all-atom statistical potentials/scoring functions, and can be apparently superior to other all-atom statistical potentials and scoring functions trained from neural networks for the RNA-Puzzles dataset. cgRNASP is available at https://github.com/Tan-group/cgRNASP.

Predicting 3D structures and stabilities for complex RNA pseudoknots in ion solutions.

RNA pseudoknots are a kind of important tertiary motif, and the structures and stabilities of pseudoknots are generally critical to the biological functions of RNAs with the motifs. In this work, we have carefully refined our previously developed coarse-grained model with salt effect through involving a new coarse-grained force field and a replica-exchange Monte Carlo algorithm, and employed the model to predict structures and stabilities of complex RNA pseudoknots in ion solutions beyond minimal H-type pseudoknots. Compared with available experimental data, the newly refined model can successfully predict 3D structures from sequences for the complex RNA pseudoknots including SARS-CoV-2 programming-1 ribosomal frameshifting element and Zika virus xrRNA, and can reliably predict the thermal stabilities of RNA pseudoknots with various sequences and lengths over broad ranges of monovalent/divalent salts. In addition, for complex pseudoknots including SARS-CoV-2 frameshifting element, our analyses show that their thermally unfolding pathways are mainly dependent on the relative stabilities of unfolded intermediate states, in analogy to those of minimal H-type pseudoknots.

Investigation of the nitrogen removal performance and microbial community structure in a full-scale A/O1/H/O2 coking wastewater treatment system.

Biological treatment processes are an effective method for removing the nitrogen-containing contaminants that exist in coking wastewater. However, little is known about microbial composition and keystone taxa involved in biological nitrogen removal processes. In order to improve the removal efficiency of nitrogen-containing contaminants in anaerobic-aerobic-hydrolytic-aerobic (A/O1/H/O2) system, the microbial composition and interactions of keystone taxa should be clarified. The present work clarifies the removal performance of nitrogen-containing contaminants in the A/O1/H/O2 system, identifies the microbial community involved in various bioreactors, and reveals the keystone taxa within the microbial communities. Combined the processes of ammoniation, denitrification, and nitrification, total nitrogen decreased from 248 to 31 mg L-1 and achieved a removal efficiency of 87.5% in the full-scale A/O1/H/O2 system. High-throughput MiSeq sequencing revealed that Proteobacteria was the most abundant phylum in the A/O1/H/O2 system with relative abundances of 24%-50%. Thiobacillus dominated in bioreactors A and O1 with relative abundances of 2.90% and 4.44%, respectively, while Nitrospira was identified as the most dominant genus in bioreactors H and O2, accounting for 13.33% and 18.38%, respectively. The microbial community composition and co-occurrence network analysis showed that the keystone taxa belonged to Thiobacillus, Nitrospira, Bdellovibrio, Planctomyces, Desulfotomaculum, and Sphingobium, which are related to nitrogen degradation.

Comparative and phylogenetic analysis of the complete chloroplast genomes of Uncaria (Rubiaceae) species.

The genus Uncaria is famous for its high medicinal value. However, the high morphological similarities and unclear interspecific genetic relationships have posed challenges to the classification and identification of Uncaria species. Here, we newly sequenced six chloroplast genomes of Uncaria species: U. hirsuta, U. rhynchophylla, U. rhynchophylloides, U. homomalla, U. sinensis, and U. lancifolia. Comparisons among the chloroplast genomes of Uncaria species showed their conservation in structure, gene content, and order. Ten highly variable loci could be potentially used as specific molecular markers in the identification of Uncaria species. The third position of codons tended to use A/U base, and natural selection contributed more to the formation of codon usage bias in comparison to mutation pressure. Four genes (rbcL, ndhF, rps8, and ycf2) were detected to be subjected to positive selection. Phylogenetic analysis showed that the genus Uncaria was a monophyletic group, belonging to the tribe Naucleeae. Moreover, U. sinensis was not a variant of U. rhynchophylla. U. rhynchophylloides and U. rhynchophylla were not the same species. The results of the comparative and phylogenetic analysis provide valuable references for further research studies of classification, identification, breeding improvement, and phylogenetic relationships in Uncaria species.

5-Methyl-cytosine stabilizes DNA but hinders DNA hybridization revealed by magnetic tweezers and simulations.

5-Methyl-cytosine (5mC) is one of the most important DNA modifications and plays versatile biological roles. It is well known that 5mC stabilizes DNA duplexes. However, it remains unclear how 5mC affects the kinetics of DNA melting and hybridization. Here, we studied the kinetics of unzipping and rezipping using a 502-bp DNA hairpin by single-molecule magnetic tweezers. Under constant loading rates, 5mC increases the unzipping force but counterintuitively decreases the rezipping force at various salt and temperature conditions. Under constant forces, the non-methylated DNA hops between metastable states during unzipping and rezipping, which implies low energy barriers. Surprisingly, the 5mC DNA can't rezip after fully unzipping unless much lower forces are applied, where it rezips stochastically in a one-step manner, which implies 5mC kinetically hinders DNA hybridization and high energy barriers in DNA hybridization. All-atom molecular dynamics simulations reveal that the 5mC kinetically hinders DNA hybridization due to steric effects rather than electrostatic effects caused by the additional methyl groups of cytosines. Considering the possible high speed of DNA unzipping and zipping during replication and transcription, our findings provide new insights into the biological roles of 5mC.

FebRNA: An automated fragment-ensemble-based model for building RNA 3D structures.

Knowledge of RNA three-dimensional (3D) structures is critical to understanding the important biological functions of RNAs. Although various structure prediction models have been developed, the high-accuracy predictions of RNA 3D structures are still limited to the RNAs with short lengths or with simple topology. In this work, we proposed a new model, namely FebRNA, for building RNA 3D structures through fragment assembly based on coarse-grained (CG) fragment ensembles. Specifically, FebRNA is composed of four processes: establishing the library of different types of non-redundant CG fragment ensembles regardless of the sequences, building CG 3D structure ensemble through fragment assembly, identifying top-scored CG structures through a specific CG scoring function, and rebuilding the all-atom structures from the top-scored CG ones. Extensive examination against different types of RNA structures indicates that FebRNA consistently gives the reliable predictions on RNA 3D structures, including pseudoknots, three-way junctions, four-way and five-way junctions, and RNAs in the RNA-Puzzles. FebRNA is available on the Web site: https://github.com/Tan-group/FebRNA.

Analysis of Engineering Characteristics and Microscopic Mechanism of Red Mud-Bauxite Tailings Mud Foam Light Soil.

In order to effectively utilize aluminum industrial waste-red mud and bauxite tailings mud-and reduce the adverse impact of waste on the environment and occupation of land resources, a red mud-bauxite tailings mud foam lightweight soil was developed based on the existing research results. Experiments were conducted to investigate the mechanical properties and microscopic characteristics of the developed materials with different proportions of red mud and bauxite tailings mud. Results show that with the increase in red mud content, the wet density and fluidity of the synthetic sample was increased. With 16% red mud content, the water stability coefficient of the synthetic sample reached its maximum of 0.826, as well as the unconfined compressive strength (UCS) of the sample cured for 28 d (1.056 MPa). SEM images reveal that some wastes of the sample without red mud were agglomerated, the peripheral hydration products were less wrapped, and when the amount of red mud was 16%, the hydration products tightly wrapped the waste particles and increased the structural compactness. The final concentration of alkali leaching of samples increased with the addition of red mud. The maximum concentration of alkali leaching was 384 mg/L for the group with the addition of red mud of 16%. Based on the obtained mechanical strength and alkali release analysis, the sample B24R16 was selected as the optimum among all tested groups. This study explored a way to reuse aluminum industrial waste, and the results are expected to be applied to roadbed and mining filling.

Twist-diameter coupling drives DNA twist changes with salt and temperature.

DNA deformations upon environmental changes, e.g., salt and temperature, play crucial roles in many biological processes and material applications. Here, our magnetic tweezers experiments observed that the increase in NaCl, KCl, or RbCl concentration leads to substantial DNA overwinding. Our simulations and theoretical calculation quantitatively explain the salt-induced twist change through the mechanism: More salt enhances the screening of interstrand electrostatic repulsion and hence reduces DNA diameter, which is transduced to twist increase through twist-diameter coupling. We determined that the coupling constant is 4.5 ± 0.8 kBT/(degrees∙nm) for one base pair. The coupling comes from the restraint of the contour length of DNA backbone. On the basis of this coupling constant and diameter-dependent DNA conformational entropy, we predict the temperature dependence of DNA twist Δωbp/ΔT ≈ -0.01 degree/°C, which agrees with our and previous experimental results. Our analysis suggests that twist-diameter coupling is a common driving force for salt- and temperature-induced DNA twist changes.

Multivalent Cations Reverse the Twist-Stretch Coupling of RNA.

When stretched, both DNA and RNA duplexes change their twist angles through twist-stretch coupling. The coupling is negative for DNA but positive for RNA, which is not yet completely understood. Here, our magnetic tweezers experiments show that the coupling of RNA reverses from positive to negative by multivalent cations. Combining with the previously reported tension-induced negative-to-positive coupling reversal of DNA, we propose a unified mechanism of the couplings of both RNA and DNA based on molecular dynamics simulations. Two deformation pathways are competing when stretched: shrinking the radius causes positive couplings but widening the major groove causes negative couplings. For RNA whose major groove is clamped by multivalent cations and canonical DNA, their radii shrink when stretched, thus exhibiting positive couplings. For elongated DNA whose radius already shrinks to the minimum and canonical RNA, their major grooves are widened when stretched, thus exhibiting negative couplings.

rsRNASP: A residue-separation-based statistical potential for RNA 3D structure evaluation.

Knowledge-based statistical potentials have been shown to be rather effective in protein 3-dimensional (3D) structure evaluation and prediction. Recently, several statistical potentials have been developed for RNA 3D structure evaluation, while their performances are either still at a low level for the test datasets from structure prediction models or dependent on the "black-box" process through neural networks. In this work, we have developed an all-atom distance-dependent statistical potential based on residue separation for RNA 3D structure evaluation, namely rsRNASP, which is composed of short- and long-ranged potentials distinguished by residue separation. The extensive examinations against available RNA test datasets show that rsRNASP has apparently higher performance than the existing statistical potentials for the realistic test datasets with large RNAs from structure prediction models, including the newly released RNA-Puzzles dataset, and is comparable to the existing top statistical potentials for the test datasets with small RNAs or near-native decoys. In addition, rsRNASP is superior to RNA3DCNN, a recently developed scoring function through 3D convolutional neural networks. rsRNASP and the relevant databases are available to the public.

Application of a Machine Learning Algorithms in a Wrist-Wearable Sensor for Patient Health Monitoring during Autonomous Hospital Bed Transport.

Smart sensors, coupled with artificial intelligence (AI)-enabled remote automated monitoring (RAMs), can free a nurse from the task of in-person patient monitoring during the transportation process of patients between different wards in hospital settings. Automation of hospital beds using advanced robotics and sensors has been a growing trend exacerbated by the COVID crisis. In this exploratory study, a polynomial regression (PR) machine learning (ML) RAM algorithm based on a Dreyfusian descriptor for immediate wellbeing monitoring was proposed for the autonomous hospital bed transport (AHBT) application. This method was preferred over several other AI algorithm for its simplicity and quick computation. The algorithm quantified historical data using supervised photoplethysmography (PPG) data for 5 min just before the start of the autonomous journey, referred as pre-journey (PJ) dataset. During the transport process, the algorithm continued to quantify immediate measurements using non-overlapping sets of 30 PPG waveforms, referred as in-journey (IJ) dataset. In combination, this algorithm provided a binary decision condition that determined if AHBT should continue its journey to destination by checking the degree of polynomial (DoP) between PJ and IJ. Wrist PPG was used as algorithm's monitoring parameter. PPG data was collected simultaneously from both wrists of 35 subjects, aged 21 and above in postures mimicking that in AHBT and were given full freedom of upper limb and wrist movement. It was observed that the top goodness-of-fit which indicated potentials for high data accountability had 0.2 to 0.6 cross validation score mean (CVSM) occurring at 8th to 10th DoP for PJ datasets and 0.967 to 0.994 CVSM at 9th to 10th DoP for IJ datasets. CVSM was a reliable metric to pick out the best PJ and IJ DoPs. Central tendency analysis showed that coinciding DoP distributions between PJ and IJ datasets, peaking at 8th DoP, was the precursor to high algorithm stability. Mean algorithm efficacy was 0.20 as our proposed algorithm was able to pick out all signals from a conscious subject having full freedom of movement. This efficacy was acceptable as a first ML proof of concept for AHBT. There was no observable difference between subjects' left and right wrists.