Challenging Recurrence and Management of Squamous Cell Carcinoma in the Calcaneal Region: A Case Report.

Squamous cell carcinoma (SCC) is the second most common type of skin cancer. As ultraviolet exposure represents an important risk factor, SCC commonly occurs on the face, lips, scalp, hands, and heels. The foot is an unusual location to manifest SCC. In this report, we present a case of a 44-year-old woman with severe local recurrence of SCC in the right heel, four years after an initial excision of a primary, small lesion. For various reasons, the patient did not visit the clinic for follow-up assessment during this period. Considering the extent of the lesion and infection risk, the affected leg was amputated at one-third of the lower leg. This case report underlines the importance of educating patients about the risk of SCC and assisting them in attending follow-up visits. In addition, adequate attention should be given to foot lesions with suspicious appearance. Early detection would minimize systemic risks, including metastasis and infection, and maximize preserved function after surgical intervention.

A Theoretical Analysis of DeepWalk and Node2vec for Exact Recovery of Community Structures in Stochastic Blockmodels.

Random-walk-based network embedding algorithms like DeepWalk and node2vec are widely used to obtain euclidean representation of the nodes in a network prior to performing downstream inference tasks. However, despite their impressive empirical performance, there is a lack of theoretical results explaining their large-sample behavior. In this paper, we study node2vec and DeepWalk through the perspective of matrix factorization. In particular, we analyze these algorithms in the setting of community detection for stochastic blockmodel graphs (and their degree-corrected variants). By exploiting the row-wise uniform perturbation bound for leading singular vectors, we derive high-probability error bounds between the matrix factorization-based node2vec/DeepWalk embeddings and their true counterparts, uniformly over all node embeddings. Based on strong concentration results, we further show the perfect membership recovery by node2vec/DeepWalk, followed by K-means/medians algorithms. Specifically, as the network becomes sparser, our results guarantee that with large enough window size and vertex number, applying K-means/medians on the matrix factorization-based node2vec embeddings can, with high probability, correctly recover the memberships of all vertices in a network generated from the stochastic blockmodel (or its degree-corrected variants). The theoretical justifications are mirrored in the numerical experiments and real data applications, for both the original node2vec and its matrix factorization variant.

Preparation of self-assembly silica redox nanoparticles to improve drug encapsulation and suppress the adverse effect of doxorubicin.

Background and Purpose: The utilization of doxorubicin (DOX) in clinal trials is also challenging owing to its adverse effects, including low oral bioavailability, generation of reactive oxygen species (ROS), cardiotoxicity, and epithelial barrier damage. Recently, scavenging of ROS reduced the cytotoxicity of DOX, suggesting a new approach for using DOX as an anticancer treatment. Thus, in this study, non-silica and silica redox nanoparticles (denoted as RNPN and siRNP, respectively) with ROS scavenging features have been designed to encapsulate DOX and reduce its cytotoxicity. Experimental Approach: DOX-loaded RNPN (DOX@RNPN) and DOX-loaded siRNP (DOX@siRNP) were prepared by co-dissolving DOX with RNPN and siRNP, respectively. The size and stability of nanoparticles were characterized by the dynamic light scattering system. Additionally, encapsulation efficiency, loading capacity, and release profile of DOX@RNPN and DOX@siRNP were identified by measuring the absorbance of DOX. Finally, the cytotoxicity of DOX@RNPN and DOX@siRNP against normal murine fibroblast cells (L929), human hepatocellular carcinoma cells (HepG2), and human breast cancer cells (MCF-7) were also investigated. Key results: The obtained result showed that RNPN exhibited a pH-sensitive character while silanol moieties improved the stability of siRNP in physiological conditions. DOX@RNPN and DOX@siRNP were formed at several tens of nanometers in diameter with narrow distribution. Moreover, DOX@siRNP stabilized under different pH buffers, especially gastric pH, and improved encapsulation of DOX owing to the addition of silanol groups. DOX@RNPN and DOX@siRNP maintained anticancer activity of DOX against HepG2, and MCF-7 cells, while their cytotoxicity on L929 cells was significantly reduced compared to free DOX treatment. Conclusion: DOX@RNPN and DOX@siRNP could effectively suppress the adverse effect of DOX, suggesting the potential to become promising nanomedicines for cancer treatments.

Degradation at the Na3SbS4/Anode Interface in an Operating All-Solid-State Sodium Battery.

All-solid-state sodium batteries utilize earth-abundant elements and are sustainable systems for large-scale energy storage and electric transportation. Replacing flammable carbonate-based electrolytes with solid-state ionic conductors promotes battery safety. Using solid-state electrolytes (SEs) also eliminates the need for packing when fabricating tandem cells, potentially enabling further enhanced energy density. Na3SbS4, a Na+ conductor, remains stable in dry air and shows high Na+ conductivity (σ ≈ 1.0 × 10-3 S/cm) and is thus a promising SE for applications in sodium batteries. However, upon repeated electrochemical cycling, Na3SbS4-containing Na batteries exhibit decaying capacity and limited cycle life, which is likely associated with the decomposition of Na3SbS4 at the electrode/electrolyte interface. This work presents an in-depth analysis of the decomposition chemistry occurring at the Na3SbS4/anode interface using combined in situ Raman and post-mortem characterization. The results indicate that the SbS43- counterion is electrochemically reduced when experiencing Na+ reduction potentials, and this reduction chemistry likely follows multiple pathways. The observed reduction products include SbS33-, the Sb2S74- dimer, the NaSb binary phase, and Na2S. We also observed the irreversibility of the decomposition and, as a consequence, the accumulation of the degradation products over cycles. Also notable is the heterogeneity of this degradation chemistry across the interface. Through the spectroelectrochemical characterizations, we reveal the possible mechanisms of the Na3SbS4 decomposition at the solid electrolyte/anode interface in an operating device.

Nanomechanical Stability of Aß Tetramers and Fibril-like Structures: Molecular Dynamics Simulations.

Alzheimer's disease (AD) is a neurodegenerative disorder and one of the main causes of dementia. The disease is associated with amyloid beta (Aß) peptide aggregation forming initial clusters and then fibril structure and plaques. Other neurodegenerative diseases such as type 2 diabetes, amyotrophic lateral sclerosis, and Parkinson's disease follow a similar mechanism. Therefore, inhibition of Aß aggregation is considered an effective way to prevent AD. Recent experiments have provided evidence that oligomers are more toxic agents than mature fibrils, prompting researchers to investigate various factors that may influence their properties. One of these factors is nanomechanical stability, which plays an important role in the self-assembly of Aß and possibly other proteins. This stability is also likely to be related to cell toxicity. In this work, we compare the mechanical stability of Aß-tetramers and fibrillar structures using a structure-based coarse-grained (CG) approach and all-atom molecular dynamics simulation. Our results support the evidence for an increase in mechanical stability during the Aß fibrillization process, which is consistent with in vitro AFM characterization of Aß42 oligomers. Namely, using a CG model, we showed that the Young modulus of tetramers is lower than that of fibrils and, as follows from the experiment, is about 1 GPa. Hydrogen bonds are the dominant contribution to the detachment of one chain from the Aß fibril fragment. They tend to be more organized along the pulling direction, whereas in the Aß tetramers no preference is observed.

Supervised dimensionality reduction for big data.

To solve key biomedical problems, experimentalists now routinely measure millions or billions of features (dimensions) per sample, with the hope that data science techniques will be able to build accurate data-driven inferences. Because sample sizes are typically orders of magnitude smaller than the dimensionality of these data, valid inferences require finding a low-dimensional representation that preserves the discriminating information (e.g., whether the individual suffers from a particular disease). There is a lack of interpretable supervised dimensionality reduction methods that scale to millions of dimensions with strong statistical theoretical guarantees. We introduce an approach to extending principal components analysis by incorporating class-conditional moment estimates into the low-dimensional projection. The simplest version, Linear Optimal Low-rank projection, incorporates the class-conditional means. We prove, and substantiate with both synthetic and real data benchmarks, that Linear Optimal Low-Rank Projection and its generalizations lead to improved data representations for subsequent classification, while maintaining computational efficiency and scalability. Using multiple brain imaging datasets consisting of more than 150 million features, and several genomics datasets with more than 500,000 features, Linear Optimal Low-Rank Projection outperforms other scalable linear dimensionality reduction techniques in terms of accuracy, while only requiring a few minutes on a standard desktop computer.

On a two-truths phenomenon in spectral graph clustering.

Clustering is concerned with coherently grouping observations without any explicit concept of true groupings. Spectral graph clustering-clustering the vertices of a graph based on their spectral embedding-is commonly approached via K-means (or, more generally, Gaussian mixture model) clustering composed with either Laplacian spectral embedding (LSE) or adjacency spectral embedding (ASE). Recent theoretical results provide deeper understanding of the problem and solutions and lead us to a "two-truths" LSE vs. ASE spectral graph clustering phenomenon convincingly illustrated here via a diffusion MRI connectome dataset: The different embedding methods yield different clustering results, with LSE capturing left hemisphere/right hemisphere affinity structure and ASE capturing gray matter/white matter core-periphery structure.

Rumen fluid, feces, milk, water, feed, airborne dust, and bedding microbiota in dairy farms managed by automatic milking systems.

Microbiota of the gut, milk, and cowshed environment were examined at two dairy farms managed by automatic milking systems (AMS). Feed, rumen fluid, feces, milk, bedding, water, and airborne dust were collected and the microbiota on each was assessed by Illumina MiSeq sequencing. The most abundant taxa in feed, rumen fluid, feces, bedding, and water were Lactobacillaceae, Prevotellaceae, Ruminococcaceae, Ruminococcaceae, and Lactobacillaceae, respectively, at both farms. Aerococcaceae was the most abundant taxon in milk and airborne dust microbiota at farm 1, and Staphylococcaceae and Lactobacillaceae were the most abundant taxa in milk and airborne dust microbiota at farm 2. The three most prevalent taxa (Aerococcaceae, Staphylococcaceae, and Ruminococcaceae at farm 1 and Staphylococcaceae, Lactobacillaceae, and Ruminococcaceae at farm 2) were shared between milk and airborne dust microbiota. Indeed, SourceTracker indicated that milk microbiota was related with airborne dust microbiota. Meanwhile, hierarchical clustering and canonical analysis of principal coordinates demonstrated that the milk microbiota was associated with the bedding microbiota but clearly separated from feed, rumen fluid, feces, and water microbiota. Although our findings were derived from only two case studies, the importance of cowshed management for milk quality control and mastitis prevention was emphasized at farms managed by AMS.

Variability, stability, and resilience of fecal microbiota in dairy cows fed whole crop corn silage.

The microbiota of whole crop corn silage and feces of silage-fed dairy cows were examined. A total of 18 dairy cow feces were collected from six farms in Japan and China, and high-throughput Illumina sequencing of the V4 hypervariable region of 16S rRNA genes was performed. Lactobacillaceae were dominant in all silages, followed by Acetobacteraceae, Bacillaceae, and Enterobacteriaceae. In feces, the predominant families were Ruminococcaceae, Bacteroidaceae, Clostridiaceae, Lachnospiraceae, Rikenellaceae, and Paraprevotellaceae. Therefore, Lactobacillaceae of corn silage appeared to be eliminated in the gastrointestinal tract. Although fecal microbiota composition was similar in most samples, relative abundances of several families, such as Ruminococcaceae, Christensenellaceae, Turicibacteraceae, and Succinivibrionaceae, varied between farms and countries. In addition to the geographical location, differences in feeding management between total mixed ration feeding and separate feeding appeared to be involved in the variations. Moreover, a cow-to-cow variation for concentrate-associated families was demonstrated at the same farm; two cows showed high abundance of Succinivibrionaceae and Prevotellaceae, whereas another had a high abundance of Porphyromonadaceae. There was a negative correlation between forage-associated Ruminococcaceae and concentrate-associated Succinivibrionaceae and Prevotellaceae in 18 feces samples. Succinivibrionaceae, Prevotellaceae, p-2534-18B5, and Spirochaetaceae were regarded as highly variable taxa in this study. These findings help to improve our understanding of variation and similarity of the fecal microbiota of dairy cows with regard to individuals, farms, and countries. Microbiota of naturally fermented corn silage had no influence on the fecal microbiota of dairy cows.

Diversity of lactic acid bacteria in vegetable-based and meat-based fermented foods produced in the central region of Vietnam.

The diversity of lactic acid bacteria (LAB) in naturally fermented foods produced in Hue, a city in the central region of Vietnam, was examined. From local markets, a total of 25 samples of three vegetable-based fermented products, specifically dua gia (bean sprouts), dua cai (cabbage), and mang chua (bamboo shoots), and two meat-based fermented products, specifically nem chua (uncooked pork) and tre (cooked pork) were obtained. The LAB diversity was assessed by quantitative real-time polymerase chain reaction (PCR) and qualitative denaturing gradient gel electrophoresis. Lactic and acetic acid contents were greater in meat-based products than in vegetable-based products. Major LAB species found in vegetable-based products (Lactobacillus plantarum, Lactobacillus fermentum, and Lactobacillus helveticus) were different from those identified in meat-based products (Pediococcus pentosaceus, Weissella cibaria, and Lactococcus lactis). The total bacterial population was approximately 109-10 copies/g regardless of the food item, with the proportion of Lactobacillus spp. determined to be from 78% (dua cai) to 94% (nem chua).

Coxiella burnetii Infection in a Community Operating a Large-Scale Cow and Goat Dairy, Missouri, 2013.

Coxiella burnetii is a zoonotic pathogen that causes Q fever in humans and is transmitted primarily from infected goats, sheep, or cows. Q fever typically presents as an acute febrile illness; however, individuals with certain predisposing conditions, including cardiac valvulopathy, are at risk for chronic Q fever, a serious manifestation that may present as endocarditis. In response to a cluster of Q fever cases detected by public health surveillance, we evaluated C. burnetii infection in a community that operates a large-scale cow and goat dairy. A case was defined as an individual linked to the community with a C. burnetii phase II IgG titer ≥ 128. Of 135 participants, 47 (35%) cases were identified. Contact with or close proximity to cows, goats, and their excreta was associated with being a case (relative risk 2.7, 95% confidence interval 1.3-5.3). Cases were also identified among individuals without cow or goat contact and could be related to windborne spread or tracking of C. burnetii on fomites within the community. A history of injection drug use was reported by 26/130 (20%) participants; follow-up for the presence of valvulopathy and monitoring for development of chronic Q fever may be especially important among this population.

Consistent latent position estimation and vertex classification for random dot product graphs.

In this work, we show that using the eigen-decomposition of the adjacency matrix, we can consistently estimate latent positions for random dot product graphs provided the latent positions are i.i.d. from some distribution. If class labels are observed for a number of vertices tending to infinity, then we show that the remaining vertices can be classified with error converging to Bayes optimal using the $(k)$-nearest-neighbors classification rule. We evaluate the proposed methods on simulated data and a graph derived from Wikipedia.