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We introduce Viral Phrenology, a new scheme for understanding the genomic composition of spherical viruses based on the locations of their structural protrusions. We used icosahedral point arrays to classify 135 distinct viral capsids collected from over 600 capsids available in the VIPERdb. Using gauge points of point arrays, we found 149 unique structural protrusions. We then show how to use the locations of these protrusions to determine the genetic composition of the virus. We then show that ssDNA, dsDNA, dsRNA and ssRNA viruses use different arrangements for distributing their protrusions. We also found that Triangulation number is also partially dependent on the structural protrusions. This analysis begins to tie together Baltimore Classification and Triangulation number using point arrays.

Since its introduction, the Triangulation number has been the most successful and ubiquitous scheme for classifying spherical viruses. However, despite its many successes, it fails to describe the relative angular orientations of proteins, as well as their radial mass distribution within the capsid. It also fails to provide any critical insight into sites of stability, modifications or possible mutations. We show how classifying spherical viruses using icosahedral point arrays, introduced by Keef and Twarock, unveils new geometric rules and constraints for understanding virus stability and key locations for exterior and interior modifications. We present a modified fitness measure which classifies viruses in an unambiguous and rigorous manner, irrespective of local surface chemistry, steric hinderance, solvent accessibility or Triangulation number. We then use these point arrays to explain the immutable surface loops of bacteriophage MS2, the relative reactivity of surface lysine residues in CPMV and the non-quasi-equivalent flexibility of the HBV dimers. We then explain how point arrays can be used as a predictive tool for site-directed modifications of capsids. This success builds on our previous work showing that viruses place their protruding features along the great circles of the asymmetric unit, demonstrating that viruses indeed adhere to these geometric constraints.

HIV prevalence is increasing rapidly among men who have sex with men (MSM) in China and potentially associated with the number of male sexual partners that each man has on average. This study estimates the distribution of the number of male sexual partners among Chinese MSM through a comprehensive review of English and Chinese published literature. The overall median number of male sexual partners of Chinese MSM in the past 6 months China was estimated to be 1.5 (95% CI, 1.1-1.9) and 3.8 (95% CI 1.5-6.9) sexual partners in the past 6-month and 12-month periods respectively. An estimated 31% of sexual partners of MSM in China are regular partners, 54% are casual partners, and 16% are commercial partners. The reported numbers of sexual partners has not changed over time during the past decade. The numbers of male sexual partners reported by Chinese MSM is consistently lower than other settings and may not be sufficient to explain the recent rapid increase in HIV prevalence.

This study aimed to investigate whether plaque characteristics derived from intravascular optical coherence tomography (IVOCT) could predict a long-term cardiovascular (CV) death. This study was a single-center, retrospective study on 104 patients who had undergone IVOCT-guided percutaneous coronary intervention. Plaque characterization was performed using Optical Coherence TOmography PlaqUe and Stent (OCTOPUS) software developed by our group. A total of 31 plaque features, including lesion length, lumen, calcium, fibrous cap (FC), and vulnerable plaque features (e.g., microchannel), were computed from the baseline IVOCT images. The discriminatory power for predicting CV death was determined using univariate/multivariate logistic regressions. Of 104 patients, CV death was identified in 24 patients (23.1%). Univariate logistic regression revealed that lesion length, calcium angle, calcium thickness, FC angle, FC area, and FC surface area were significantly associated with CV death (p < 0.05). In the multivariate logistic analysis, only the FC surface area (OR 2.38, CI 0.98–5.83, p < 0.05) was identified as a significant determinant for CV death, highlighting the importance of the 3D lesion analysis. The AUC of FC surface area for predicting CV death was 0.851 (95% CI 0.800–0.927, p < 0.05). Patients with CV death had distinct plaque characteristics (i.e., large FC surface area) in IVOCT. Studies such as this one might someday lead to recommendations for pharmaceutical and interventional approaches.

Decades of research have identified genetic and environmental factors involved in age-related neurodegenerative diseases and, to a lesser extent, neuropsychiatric disorders. Genomic instability, i.e., the loss of genome integrity, is a common feature among both neurodegenerative (mayo-trophic lateral sclerosis, Parkinson’s disease, Alzheimer’s disease) and psychiatric (schizophrenia, autism, bipolar depression) disorders. Genomic instability is associated with the accumulation of persistent DNA damage and the activation of DNA damage response (DDR) pathways, as well as pathologic neuronal cell loss or senescence. Typically, DDR signaling ensures that genomic and proteomic homeostasis are maintained in both dividing cells, including neural progenitors, and post-mitotic neurons. However, dysregulation of these protective responses, in part due to aging or environmental insults, contributes to the progressive development of neurodegenerative and/or psychiatric disorders. In this Special Issue, we introduce and highlight the overlap between neurodegenerative diseases and neuropsychiatric disorders, as well as the emerging clinical, genomic, and molecular evidence for the contributions of DNA damage and aberrant DNA repair. Our goal is to illuminate the importance of this subject to uncover possible treatment and prevention strategies for relevant devastating brain diseases.

The appearance of dried fruit clearly influences the consumer’s perception of the quality of the product but is a subtle and nuanced characteristic that is difficult to quantitatively measure, especially online. This paper describes a method that combines several simple strategies to assess a suitable surrogate for the elusive quality using imaging, combined with multivariate statistics and machine learning. With such a convenient tool, this study also shows how one can vary the pretreatments and drying conditions to optimize the resultant product quality. Specifically, an image batch processing method was developed to extract color (hue, saturation, and value) and morphological (area, perimeter, and compactness) features. The accuracy of this method was verified using data from a case study experiment on the pretreatment of hot-air-dried kiwifruit slices. Based on the extracted image features, partial least squares and random forest models were developed to satisfactorily predict the moisture ratio (MR) during drying process. The MR of kiwifruit slices during drying could be accurately predicted from changes in appearance without using any weighing device. This study also explored determining the optimal drying strategy based on appearance quality using principal component analysis. Optimal drying was achieved at 60 °C with 4 mm thick slices under ultrasonic pretreatment. For the 70 °C, 6 mm sample groups, citric acid showed decent performance.

Over the past few decades, the food industry has undergone revolutionary changes due to the impacts of globalization, technological advancements, and ever-evolving consumer demands. Artificial intelligence (AI) and big data have become pivotal in strengthening food safety, production, and marketing. With the continuous evolution of AI technology and big data analytics, the food industry is poised to embrace further changes and developmental opportunities. An increasing number of food enterprises will leverage AI and big data to enhance product quality, meet consumer needs, and propel the industry toward a more intelligent and sustainable future. This review delves into the applications of AI and big data in the food sector, examining their impacts on production, quality, safety, risk management, and consumer insights. Furthermore, the advent of Industry 4.0 applied to the food industry has brought to the fore technologies such as smart agriculture, robotic farming, drones, 3D printing, and digital twins; the food industry also faces challenges in smart production and sustainable development going forward. This review articulates the current state of AI and big data applications in the food industry, analyses the challenges encountered, and discusses viable solutions. Lastly, it outlines the future development trends in the food industry.

The ability to quickly discover reliable hits from screening and rapidly convert them into lead compounds, which can be verified in functional assays, is central to drug discovery. The expedited validation of novel targets and the identification of modulators to advance to preclinical studies can significantly increase drug development success. Our SaXPyTM (“SAR by X-ray Poses Quickly”) platform, which is applicable to any X-ray crystallography-enabled drug target, couples the established methods of protein X-ray crystallography and fragment-based drug discovery (FBDD) with advanced computational and medicinal chemistry to deliver small molecule modulators or targeted protein degradation ligands in a short timeframe. Our approach, especially for elusive or “undruggable” targets, allows for (i) hit generation; (ii) the mapping of protein–ligand interactions; (iii) the assessment of target ligandability; (iv) the discovery of novel and potential allosteric binding sites; and (v) hit-to-lead execution. These advances inform chemical tractability and downstream biology and generate novel intellectual property. We describe here the application of SaXPy in the discovery and development of DNA damage response inhibitors against DNA polymerase eta (Pol η or POLH) and apurinic/apyrimidinic endonuclease 1 (APE1 or APEX1). Notably, our SaXPy platform allowed us to solve the first crystal structures of these proteins bound to small molecules and to discover novel binding sites for each target.

To maintain a healthy lifestyle, adults rely on their ability to walk while simultaneously managing multiple tasks that challenge their coordination. This study investigates the impact of cognitive dual tasks on lower-limb muscle activities in 21 healthy young adults during both gait initiation and steady-state gait. We utilized wireless electromyography sensors to measure muscle activities, along with a 3D motion capture system and force plates to detect the phases of gait initiation and steady-state gait. The participants were asked to walk at their self-selected pace, and we compared single-task and dual-task conditions. We analyzed mean muscle activation and coactivation in the biceps femoris, vastus lateralis, gastrocnemius, and tibialis anterior muscles. The findings revealed that, during gait initiation with the dual-task condition, there was a decrease in mean muscle activation and an increase in mean muscle coactivation between the swing and stance limbs compared with the single-task condition. In steady-state gait, there was also a decrease in mean muscle activation in the dual-task condition compared with the single-task condition. When participants performed dual-task activities during gait initiation, early indicators of reduced balance capability were observed. Additionally, during dual-task steady-state gait, the knee stabilizer muscles exhibited signs of altered activation, contributing to balance instability.

Adult-type granulosa cell tumor (AGCT) is a rare ovarian malignancy characterized by slow growth and hormonal activity. The prognosis of AGCT is generally favorable, but one-third of patients with low-stage disease experience a late relapse, and over half of them die of AGCT. To identify markers that would distinguish patients at risk for relapse, we performed Lexogen QuantSeq 3′ mRNA sequencing on formalin-fixed paraffin-embedded, archival AGCT tissue samples tested positive for the pathognomonic Forkhead Box L2 (FOXL2) mutation. We compared the transcriptomic profiles of 14 non-relapsed archival primary AGCTs (follow-up time 17–26 years after diagnosis) with 13 relapsed primary AGCTs (follow-up time 1.7–18 years) and eight relapsed tumors (follow-up time 2.8–18.9 years). Non-relapsed and relapsed primary AGCTs had similar transcriptomic profiles. In relapsed tumors three genes were differentially expressed: plasmalemma vesicle associated protein (PLVAP) was upregulated (p = 0.01), whereas argininosuccinate synthase 1 (ASS1) (p = 0.01) and perilipin 4 (PLIN4) (p = 0.02) were downregulated. PLVAP upregulation was validated using tissue microarray RNA in situ hybridization. In our patient cohort with extremely long follow-up, we observed similar gene expression patterns in both primary AGCT groups, suggesting that relapse is not driven by transcriptomic changes. These results reinforce earlier findings that molecular markers do not predict AGCT behavior or risk of relapse.

We used LiDAR metrics and satellite imagery to examine regeneration on forested sites disturbed via harvest or natural means over a 44-year period. We tested the effectiveness of older low-density LiDAR elevation data in producing information related to existing levels of above ground biomass (AGB). To accomplish this, we paired the elevation data with a time series of wetness and greenness indices derived from Landsat satellite imagery to model changes in AGB for sites experiencing different agents of change. Current AGB was determined from high-density LiDAR acquired in northern Minnesota, USA. We then compared high-density LiDAR-based AGB and estimates modeled using Landsat and low-density LiDAR indices for 10,068 sites. Clear differences were found in standing AGB and accumulation rates between sites disturbed by different agents of change. Biomass accumulation following disturbance appears to decrease rapidly following an initial spike as stands 1asZX respond to newly opened growing space. Harvested sites experienced a roughly six-fold increase in the rate of biomass accumulation compared to sites subjected to stand replacing fire or insect and disease, and a 20% increase in productivity when compared to sites subjected to wind mediated canopy loss. Over time, this resulted in clear differences in standing AGB.

Pericoronary adipose tissue (PCAT) features on Computed Tomography (CT) have been shown to reflect local inflammation and increased cardiovascular risk. Our goal was to determine whether PCAT radiomics extracted from coronary CT angiography (CCTA) images are associated with intravascular optical coherence tomography (IVOCT)-identified vulnerable-plaque characteristics (e.g., microchannels (MC) and thin-cap fibroatheroma (TCFA)). The CCTA and IVOCT images of 30 lesions from 25 patients were registered. The vessels with vulnerable plaques were identified from the registered IVOCT images. The PCAT-radiomics features were extracted from the CCTA images for the lesion region of interest (PCAT-LOI) and the entire vessel (PCAT-Vessel). We extracted 1356 radiomic features, including intensity (first-order), shape, and texture features. The features were reduced using standard approaches (e.g., high feature correlation). Using stratified three-fold cross-validation with 1000 repeats, we determined the ability of PCAT-radiomics features from CCTA to predict IVOCT vulnerable-plaque characteristics. In the identification of TCFA lesions, the PCAT-LOI and PCAT-Vessel radiomics models performed comparably (Area Under the Curve (AUC) ± standard deviation 0.78 ± 0.13, 0.77 ± 0.14). For the identification of MC lesions, the PCAT-Vessel radiomics model (0.89 ± 0.09) was moderately better associated than the PCAT-LOI model (0.83 ± 0.12). In addition, both the PCAT-LOI and the PCAT-Vessel radiomics model identified coronary vessels thought to be highly vulnerable to a similar standard (i.e., both TCFA and MC; 0.88 ± 0.10, 0.91 ± 0.09). The most favorable radiomic features tended to be those describing the texture and size of the PCAT. The application of PCAT radiomics can identify coronary vessels with TCFA or MC, consistent with IVOCT. Furthermore, the use of CCTA radiomics may improve risk stratification by noninvasively detecting vulnerable-plaque characteristics that are only visible with IVOCT.

The surface appearance of milk powders is a crucial quality property since the roughness of the milk powder determines its functional properties, and especially the purchaser perception of the milk powder. Unfortunately, powder produced from similar spray dryers, or even the same dryer but in different seasons, produces powder with a wide variety of surface roughness. To date, professional panelists are used to quantify this subtle visual metric, which is time-consuming and subjective. Consequently, developing a fast, robust, and repeatable surface appearance classification method is essential. This study proposes a three-dimensional digital photogrammetry technique for quantifying the surface roughness of milk powders. A contour slice analysis and frequency analysis of the deviations were performed on the three-dimensional models to classify the surface roughness of milk powder samples. The result shows that the contours for smooth-surface samples are more circular than those for rough-surface samples, and the smooth-surface samples had a low standard deviation; thus, milk powder samples with the smoother surface have lower Q (the energy of the signal) values. Lastly, the performance of the nonlinear support vector machine (SVM) model demonstrated that the technique proposed in this study is a practicable alternative technique for classifying the surface roughness of milk powders.

Targeted nanotheranostic systems offer significant benefits due to the integration of diagnostic and therapeutic functionality, promoting personalized medicine. In recent years, prostate-specific membrane antigen (PSMA) has emerged as an ideal theranostic target, fueling multiple new drug approvals and changing the standard of care in prostate cancer (PCa). PSMA-targeted nanosystems such as self-assembled nanoparticles (NPs), liposomal structures, water-soluble polymers, dendrimers, and other macromolecules are under development for PCa theranostics due to their multifunctional sensing and therapeutic capabilities. Herein, we discuss the significance and up-to-date development of “PSMA-targeted nanocarrier systems for radioligand imaging and therapy of PCa”. The review also highlights critical parameters for designing nanostructured radiopharmaceuticals for PCa, including radionuclides and their chelators, PSMA-targeting ligands, and the EPR effect. Finally, prospects and potential for clinical translation is discussed.

The lithium (Li) isotopic composition of carbonates is considered to be a reliable archive of past seawater Li isotopic compositions, which are useful as a tracer of silicate weathering. However, δ7Li values have been shown to be dependent on either pH or DIC in two studies using similar species of large, benthic foraminifera from the genus Amphistegina. To resolve this issue, we conducted culture experiments on Amphistegina lessonii in decoupled pH–DIC conditions, under two different light treatments, and with normal or Li-enriched seawater. The δ7Li values and Li/Ca ratios in the foraminifera tests were analysed by ion microprobe and LA-ICP-MS, respectively. No links between either the pH or DIC and δ7Li or Li/Ca values were observed for any of the treatments, and growth rates also did not seem to influence the Li incorporation or isotopic fractionation, contrary to observations from inorganic carbonate-precipitation experiments. Overall, these findings appear to support the use of Li isotopes in large benthic foraminifera to reconstruct past seawater chemistry and to infer changes in chemical weathering during carbon-cycle perturbations.

This retrospective case series summarized the primary causes of death in 857 dairy cattle necropsied from 2008 to 2019 at the Utah Veterinary Diagnostic Laboratory, from dairy farms in Utah (76%), Idaho (16%) or other states (8%), primarily in the U.S. Intermountain West. Of cattle with age provided, 74% matched with body weight based estimates for those with no age stated. Cattle ranged from fetuses at 60 days of gestation to 9 years old. Primary cause of mortality was diagnosed in 833 cattle (97%); no cause was evident in 24 cattle (3%). Sexes were female 620 (72%), male 214 (25%), not recorded 23 (3%). Seven diseases killed 80% of the animals: gastrointestinal disease (most enteritis/colitis) 318 (37%); pneumonia 166 (19%); abortion 96 (11%), peritonitis 30 (4%), omphalophlebitis (navel ill) 27 (3%), abomasitis 23 (3%), and metritis 23 (3%). Etiologic agents and specific causes varied with age categories of the animals. Young calves that died from dystocia, omphalophlebitis, or congenital abnormality often presented with no suspicion of those causes by the owners because of no external signs. Some important fatal diseases of adult dairy cows that are often diagnosed ante-mortem were diagnosed at necropsy with no suspicion by those submitting the carcasses: metritis, hardware disease, and displaced abomasum. Multicentric lymphoma was a relatively important cause of death in cows more than 4 years old. Despite use of a toxicology laboratory, toxicity was only diagnosed as causing 1% of the deaths across all ages of dairy cattle. There were numerous other causes of mortality diagnosed as well. Necropsy is a vital tool to diagnose causes of death in dairy cattle and can guide changes in management or preventive practices to reduce the rate of deaths in dairy herds.

Microvessels in vascular plaque are associated with plaque progression and are found in plaque rupture and intra-plaque hemorrhage. To analyze this characteristic of vulnerability, we developed an automated deep learning method for detecting microvessels in intravascular optical coherence tomography (IVOCT) images. A total of 8403 IVOCT image frames from 85 lesions and 37 normal segments were analyzed. Manual annotation was performed using a dedicated software (OCTOPUS) previously developed by our group. Data augmentation in the polar (r,θ) domain was applied to raw IVOCT images to ensure that microvessels appear at all possible angles. Pre-processing methods included guidewire/shadow detection, lumen segmentation, pixel shifting, and noise reduction. DeepLab v3+ was used to segment microvessel candidates. A bounding box on each candidate was classified as either microvessel or non-microvessel using a shallow convolutional neural network. For better classification, we used data augmentation (i.e., angle rotation) on bounding boxes with a microvessel during network training. Data augmentation and pre-processing steps improved microvessel segmentation performance significantly, yielding a method with Dice of 0.71 ± 0.10 and pixel-wise sensitivity/specificity of 87.7 ± 6.6%/99.8 ± 0.1%. The network for classifying microvessels from candidates performed exceptionally well, with sensitivity of 99.5 ± 0.3%, specificity of 98.8 ± 1.0%, and accuracy of 99.1 ± 0.5%. The classification step eliminated the majority of residual false positives and the Dice coefficient increased from 0.71 to 0.73. In addition, our method produced 698 image frames with microvessels present, compared with 730 from manual analysis, representing a 4.4% difference. When compared with the manual method, the automated method improved microvessel continuity, implying improved segmentation performance. The method will be useful for research purposes as well as potential future treatment planning.

Oxidative stress occurs at various phases of spinal cord injury (SCI), promoting detrimental processes such as free radical injury of proteins, nucleic acids, lipids, cytoskeleton, and organelles. Oxidative DNA damage is likely a major contributor to the pathogenesis of SCI, as a damaged genome cannot be simply turned over to avert detrimental molecular and cellular outcomes, most notably cell death. Surprisingly, the evidence to support this hypothesis is limited. There is some evidence that oxidative DNA damage is increased following SCI, mainly using comet assays and immunohistochemistry. However, there is great variability in the timing and magnitude of its appearance, likely due to differences in experimental models, measurement techniques, and the rigor of the approach. Evidence indicates that 8-oxodG is most abundant at 1 and 7 days post-injury (dpi), while DNA strand breaks peak at 7 and 28 dpi. The DNA damage response seems to be characterized by upregulation of PCNA and PARP1 but downregulation of APEX1. Significant improvements in the analysis of oxidative DNA damage and repair after SCI, including single-cell analysis at time points representative for each phase post-injury using new methodologies and better reporting, will uncover the role of DNA damage and repair in SCI.

Milk powders produced from similar spray dryers have different visual appearances, while the surface appearance of the powder is a key quality attribute because the smoothness of the milk powder also affects flowability and handling properties. Traditionally quantifying this nuanced visual metric was undertaken using sensory panelists, which is both subjective and time consuming. Therefore, it is advantageous to develop an on-line quick and robust appearance assessment tool. The aim of this work is to develop a classification model which can classify the milk powder samples into different surface smoothness groups. This work proposes a strategy for quantifying the relative roughness of commercial milk powder from 3D images. Photogrammetry equipment together with the software RealityCapture were used to build 3D models of milk powder samples, and a surface normal analysis which compares the area of the triangle formed by the 3 adjacent surface normals or compares the angle between the adjacent surface normals was used to quantify the surface smoothness of the milk powder samples. It was found that the area of the triangle of the smooth-surface milk powder cone is smaller than the area of the triangle of the rough-surface milk powder cone, and the angle between the adjacent surface normals of the rough-surface milk powder cone is larger than the angle between the adjacent surface normals of the smooth-surface milk powder cone, which proved that the proposed area metrics and angle metrics can be used as tools to quantify the smoothness of milk powder samples. Finally, the result of the support vector machine (SVM) classifier proved that image processing can be used as a preliminary tool for classifying milk powder into different surface texture groups.

Maintaining a robust, stable source of energy for doing chemical and physical work is essential to all living organisms. In eukaryotes, metabolic energy (ATP) production and consumption occurs in two separate compartments, the mitochondrial matrix and the cytosol. As a result, understanding eukaryotic metabolism requires knowledge of energy metabolism in each compartment and how metabolism in the two compartments is coordinated. Central to energy metabolism is the adenylate energy state ([ATP]/[ADP][Pi]). ATP is synthesized by oxidative phosphorylation (mitochondrial matrix) and glycolysis (cytosol) and each compartment provides the energy to do physical work and to drive energetically unfavorable chemical syntheses. The energy state in the cytoplasmic compartment has been established by analysis of near equilibrium metabolic reactions localized in that compartment. In the present paper, analysis is presented for energy-dependent reactions localized in the mitochondrial matrix using data obtained from both isolated mitochondria and intact tissues. It is concluded that the energy state ([ATP]f/[ADP]f[Pi]) in the mitochondrial matrix, calculated from the free (unbound) concentrations, is not different from the energy state in the cytoplasm. Corollaries are: (1) ADP in both the cytosol and matrix is selectively bound and the free concentrations are much lower than the total measured concentrations; and (2) under physiological conditions, the adenylate energy states in the mitochondrial matrix and cytoplasm are not substantially different.

The computational fluid dynamic method has been widely used to quantify the hemodynamic alterations in a diseased artery and investigate surgery outcomes. The artery model reconstructed based on optical coherence tomography (OCT) images generally does not include the side branches. However, the side branches may significantly affect the hemodynamic assessment in a clinical setting, i.e., the fractional flow reserve (FFR), defined as the ratio of mean distal coronary pressure to mean aortic pressure. In this work, the effect of the side branches on FFR estimation was inspected with both idealized and optical coherence tomography (OCT)-reconstructed coronary artery models. The electrical analogy of blood flow was further used to understand the impact of the side branches (diameter and location) on FFR estimation. Results have shown that the side branches decrease the total resistance of the vessel tree, resulting in a higher inlet flowrate. The side branches located at the downstream of the stenosis led to a lower FFR value, while the ones at the upstream had a minimal impact on the FFR estimation. Side branches with a diameter larger than one third of the main vessel diameter are suggested to be considered for a proper FFR estimation. The findings in this study could be extended to other coronary artery imaging modalities and facilitate treatment planning.

Neurological complications directly impact the lives of hundreds of millions of people worldwide. While the precise molecular mechanisms that underlie neuronal cell loss remain under debate, evidence indicates that the accumulation of genomic DNA damage and consequent cellular responses can promote apoptosis and neurodegenerative disease. This idea is supported by the fact that individuals who harbor pathogenic mutations in DNA damage response genes experience profound neuropathological manifestations. The review article here provides a general overview of the nervous system, the threats to DNA stability, and the mechanisms that protect genomic integrity while highlighting the connections of DNA repair defects to neurological disease. The information presented should serve as a prelude to the Special Issue “Genome Stability and Neurological Disease”, where experts discuss the role of DNA repair in preserving central nervous system function in greater depth.

Alternatives to routine antibiotic treatment of dairy cattle during the dry period before their next calving are of interest. This was a preliminary study of whether intramammary infusion of casein hydrolysate, administered alone or combined with standard dry treatment, accelerated the rate of mammary involution early in the dry period. Four treatments were studied in a split udder design. One udder half was assigned a treatment, and the contralateral half was administered dry cow treatment + internal teat sealant as a control. Treatments were casein hydrolysate, casein hydrolysate + dry cow treatment, casein hydrolysate + teat sealant and casein hydrolysate + dry cow treatment + teat sealant. Cows (n = 16) were blocked by a number of intramammary infections per udder half (0 or 1+) and randomized to treatments. Milk production was not different between control or treated udder halves post-calving. A generalized linear mixed model tested for differences between the treatment groups in the concentration of mammary involution indicators in milk: somatic cell count, bovine lactoferrin and bovine serum albumin. At 7 days, dry udder halves treated with casein hydrolysate had higher milk concentrations of lactoferrin than those treated with casein hydrolysate + dry cow treatment, casein hydrolysate + teat sealant and control. At 10 days dry, bovine serum albumin was higher in udder halves treated with casein hydrolysate than in those treated with casein hydrolysate + dry cow treatment, casein hydrolysate + dry cow treatment + teat sealant and control. Post-calving, casein hydrolysate-treated udder halves produced 51% of total milk, unchanged from before dry-off. There were seven total intramammary infections entering the dry period, all caused by coagulase-negative staphylococci. Cure rates (3/7, 43%) were not different among all treatments and control, partly because of the small sample size. Intramammary infusion of casein hydrolysate at the end of lactation may be an alternative or possible adjunct to antibiotic dry cow therapy.

The growing demand for metabolism-specific imaging techniques has rekindled interest in Deuterium (2H) Metabolic Imaging (DMI), a robust method based on administration of a substrate (glucose, acetate, fumarate, etc.) labeled with the stable isotope of hydrogen and the observation of its metabolic fate in three-dimensions. This technique allows the investigation of multiple metabolic processes in both healthy and diseased states. Despite its low natural abundance, the short relaxation time of deuterium allows for rapid radiofrequency (RF) pulses without saturation and efficient image acquisition. In this review, we provide a comprehensive picture of the evolution of DMI over the course of recent decades, with a special focus on its potential clinical applications.

Stonefish are regarded as one of the most venomous fish in the world. Research on stonefish venom has chiefly focused on the in vitro and in vivo neurological, cardiovascular, cytotoxic and nociceptive effects of the venom. The last literature review on stonefish venom was published over a decade ago, and much has changed in the field since. In this review, we have generated a global map of the current distribution of all stonefish (Synanceia) species, presented a table of clinical case reports and provided up-to-date information about the development of polyspecific stonefish antivenom. We have also presented an overview of recent advancements in the biomolecular composition of stonefish venom, including the analysis of transcriptomic and proteomic data from Synanceia horrida venom gland. Moreover, this review highlights the need for further research on the composition and properties of stonefish venom, which may reveal novel molecules for drug discovery, development or other novel physiological uses.

Cone snails are venomous marine predators that rely on fast-acting venom to subdue their prey and defend against aggressors. The conotoxins produced in the venom gland are small disulfide-rich peptides with high affinity and selectivity for their pharmacological targets. A dominant group comprises α-conotoxins, targeting nicotinic acetylcholine receptors. Here, we report on the synthesis, structure determination and biological activity of a novel α-conotoxin, CIC, found in the predatory venom of the piscivorous species Conus catus and its truncated mutant Δ-CIC. CIC is a 4/7 α-conotoxin with an unusual extended N-terminal tail. High-resolution NMR spectroscopy shows a major influence of the N-terminal tail on the apparent rigidity of the three-dimensional structure of CIC compared to the more flexible Δ-CIC. Surprisingly, this effect on the structure does not alter the biological activity, since both peptides selectively inhibit α3β2 and α6/α3β2β3 nAChRs with almost identical sub- to low micromolar inhibition constants. Our results suggest that the N-terminal part of α-conotoxins can accommodate chemical modifications without affecting their pharmacology.

The chemical and physical properties of instant whole milk powder (IWMP), such as morphology, protein content, and particle size, can affect its functionality and performance. Bulk density, which directly determines the packing cost and transportation cost of milk powder, is one of the most important functional properties of IWMP, and it is mainly affected by physical properties, e.g., morphology and particle size. This work quantified the relationship between morphology and bulk density of IWMP and developed a predictive model of bulk density for IWMP. To obtain milk powder samples with different particle size fractions, IWMP samples of four different brands were sieved into three different particle size range groups, before using the simplex-centroid design (SCD) method to remix the milk powder samples. The bulk densities of these remixed milk powder samples were then measured by tap testing, and the particles’ shape factors were extracted by light microscopy and image processing. The number of variables was decreased by principal component analysis and partial least squares models and artificial neural network models were built to predict the bulk density of IWMP. It was found that different brands of IWMP have different morphology, and the bulk density trends versus the shape factor changes were similar for the different particle size range groups. Finally, prediction models for bulk density were developed by using the shape factors and particle size range fractions of the IWMP samples. The good results of these models proved that predicting the bulk density of IWMP by using shape factors and particle size range fractions is achievable and could be used as a model for online model-based process monitoring.

Conopeptides belonging to the A-superfamily from the venomous molluscs, Conus, are typically α-conotoxins. The α-conotoxins are of interest as therapeutic leads and pharmacological tools due to their selectivity and potency at nicotinic acetylcholine receptor (nAChR) subtypes. Structurally, the α-conotoxins have a consensus fold containing two conserved disulfide bonds that define the two-loop framework and brace a helical region. Here we report on a novel α-conotoxin Pl168, identified from the transcriptome of Conus planorbis, which has an unusual 4/8 loop framework. Unexpectedly, NMR determination of its three-dimensional structure reveals a new structural type of A-superfamily conotoxins with a different disulfide-stabilized fold, despite containing the conserved cysteine framework and disulfide connectivity of classical α-conotoxins. The peptide did not demonstrate activity on a range of nAChRs, or Ca2+ and Na+ channels suggesting that it might represent a new pharmacological class of conotoxins.

This paper extends the concept of Complex Conjugate Control (CCC) of linear wave energy converters (WECs) to nonlinear WECs by designing optimal limit cycles with Hamiltonian Surface Shaping and Power Flow Control (HSSPFC). It will be shown that CCC for a regular wave is equivalent to a power factor of one in electrical power networks, equivalent to mechanical resonance in a mass-spring-damper (MSD) system, and equivalent to a linear limit cycle constrained to a Hamiltonian surface defined in HSSPFC. Specifically, the optimal linear limit cycle is defined as a second-order center in the phase plane projection of the constant energy orbit across the Hamiltonian surface. This concept of CCC described by a linear limit cycle constrained to a Hamiltonian surface will be extended to nonlinear limit cycles constrained to a Hamiltonian surface for maximum energy harvesting by the nonlinear WEC. The case studies presented confirm increased energy harvesting which utilizes nonlinear geometry realization for reactive power generation.

Graphs have become ubiquitous structures to encode geographic knowledge online. The Semantic Web’s linked open data, folksonomies, wiki websites and open gazetteers can be seen as geo-knowledge graphs, that is labeled graphs whose vertices represent geographic concepts and whose edges encode the relations between concepts. To compute the semantic similarity of concepts in such structures, this article defines the network-lexical similarity measure (NLS). This measure estimates similarity by combining two complementary sources of information: the network similarity of vertices and the semantic similarity of the lexical definitions. NLS is evaluated on the OpenStreetMap Semantic Network, a crowdsourced geo-knowledge graph that describes geographic concepts. The hybrid approach outperforms both network and lexical measures, obtaining very strong correlation with the similarity judgments of human subjects.

A family of tungstated zirconia solid acid catalysts were synthesised via wet impregnation and subsequent thermochemical processing for the transformation of glucose to 5-hydroxymethylfurfural (HMF). Acid strength increased with tungsten loading and calcination temperature, associated with stabilisation of tetragonal zirconia. High tungsten dispersions of between 2 and 7 W atoms·nm−2 were obtained in all cases, equating to sub-monolayer coverages. Glucose isomerisation and subsequent dehydration via fructose to HMF increased with W loading and calcination temperature up to 600 °C, indicating that glucose conversion to fructose was favoured over weak Lewis acid and/or base sites associated with the zirconia support, while fructose dehydration and HMF formation was favoured over Brönsted acidic WOx clusters. Aqueous phase reforming of steam exploded rice straw hydrolysate and condensate was explored heterogeneously for the first time over a 10 wt% WZ catalyst, resulting in excellent HMF yields as high as 15% under mild reaction conditions.

Unclassified variants (UV) of BRCA1 can affect normal pre-mRNA splicing. Here, we investigate the UV c.693G>A, a “silent” change in BRCA1 exon 11, which we have found induces aberrant splicing in patient carriers and in vitro. Using a minigene assay, we show that the UV c.693G>A has a strong effect on the splicing isoform ratio of BRCA1. Systematic site-directed mutagenesis of the area surrounding the nucleotide position c.693G>A induced variable changes in the level of exon 11 inclusion/exclusion in the mRNA, pointing to the presence of a complex regulatory element with overlapping enhancer and silencer functions. Accordingly, protein binding analysis in the region detected several splicing regulatory factors involved, including SRSF1, SRSF6 and SRSF9, suggesting that this sequence represents a composite regulatory element of splicing (CERES).

The amount of biomass stored in forest ecosystems is a result of past natural disturbances, forest management activities, and current structure and composition such as age class distributions. Although natural disturbances are projected to increase in their frequency and severity on a global scale in the future, forest management and timber harvesting decisions continue to be made at local scales, e.g., the ownership or stand level. This study simulated potential changes in natural disturbance regimes and their interaction with timber harvest goals across the Superior National Forest (SNF) in northeastern Minnesota, USA. Forest biomass stocks and stock changes were simulated for 120 years under three natural disturbance and four harvest scenarios. A volume control approach was used to estimate biomass availability across the SNF and a smaller project area within the SNF (Jeanette Project Area; JPA). Results indicate that under current harvest rates and assuming disturbances were twice that of normal levels resulted in reductions of 2.62 to 10.38% of forest biomass across the four primary forest types in the SNF and JPA, respectively. Under this scenario, total biomass stocks remained consistent after 50 years at current and 50% disturbance rates, but biomass continued to decrease under a 200%-disturbance scenario through 120 years. In comparison, scenarios that assumed both harvest and disturbance were twice that of normal levels and resulted in reductions ranging from 14.18 to 29.85% of forest biomass. These results suggest that both natural disturbances and timber harvesting should be considered to understand their impacts to future forest structure and composition. The implications from simulations like these can provide managers with strategic approaches to determine the economic and ecological outcomes associated with timber harvesting and disturbances.