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15 pages, 297 KiB  
Article
Sorting Permutations on an nBroom
by Ranjith Rajesh, Rajan Sundaravaradhan and Bhadrachalam Chitturi
Mathematics 2024, 12(17), 2620; https://doi.org/10.3390/math12172620 - 24 Aug 2024
Viewed by 535
Abstract
With applications in computer networks, robotics, genetics, data center network optimization, cryptocurrency exchange, transportation and logistics, cloud computing, and social network analysis, the problem of sorting permutations on transposition trees under various operations is highly relevant. The goal of the problem is to [...] Read more.
With applications in computer networks, robotics, genetics, data center network optimization, cryptocurrency exchange, transportation and logistics, cloud computing, and social network analysis, the problem of sorting permutations on transposition trees under various operations is highly relevant. The goal of the problem is to sort or rearrange the markers in a predetermined order by swapping them out at the vertices of a tree in the fewest possible swaps. Only certain classes of transposition trees, like path, star, and broom, have computationally efficient algorithms for sorting permutations. In this paper, we examine the so-called nbroom transposition trees. A single broom or simply a broom is a spanning tree formed by joining the center of the star graph with one end of the path graph. A generalized version of a broom known as an nbroom is created by joining the ends of n brooms to one vertex, known as the nbroom center. By using the idea of clear path markers, we present a novel algorithm for sorting permutations on an nbroom for n>2 that reduces to a novel 2broom algorithm and that further reduces to two instances of a 1broom algorithm. Our single-broom algorithm is similar to that of Kawahara et al.; however, our proof of optimality for the same is simpler. Full article
(This article belongs to the Special Issue Graph Theory: Advanced Algorithms and Applications)
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Figure 1

Figure 1
<p>A broom with star leaf vertices <math display="inline"><semantics> <mrow> <msub> <mi>v</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>v</mi> <mn>2</mn> </msub> <mo>,</mo> <mspace width="4.pt"/> <mi>and</mi> <mspace width="3.33333pt"/> <msub> <mi>v</mi> <mn>3</mn> </msub> </mrow> </semantics></math>, center <math display="inline"><semantics> <msub> <mi>v</mi> <mn>4</mn> </msub> </semantics></math> and path vertices <math display="inline"><semantics> <mrow> <msub> <mi>v</mi> <mn>5</mn> </msub> <mo>,</mo> <msub> <mi>v</mi> <mn>6</mn> </msub> <mo>,</mo> <mspace width="3.33333pt"/> <mi>and</mi> <mspace width="3.33333pt"/> <msub> <mi>v</mi> <mn>7</mn> </msub> </mrow> </semantics></math>. All markers except 7 are un-homed.</p>
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<p>Illustration of <math display="inline"><semantics> <msup> <mi>A</mi> <mo>*</mo> </msup> </semantics></math>. (<b>a</b>) Homing <math display="inline"><semantics> <mrow> <msub> <mi>P</mi> <mrow> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> </msub> <mo>=</mo> <mn>6</mn> </mrow> </semantics></math>, swaps = 3. (<b>b</b>) Homing the star leaf marker 2 followed by 3, swaps = 2. (<b>c</b>) Homing <math display="inline"><semantics> <mrow> <msub> <mi>P</mi> <mrow> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> </msub> <mo>=</mo> <mn>5</mn> </mrow> </semantics></math> followed by 1, swaps = 2, (<b>d</b>) sorted broom. Total number of swaps = 7.</p>
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<p>A <math display="inline"><semantics> <mrow> <mn>2</mn> <mo>−</mo> <mi>b</mi> <mi>r</mi> <mi>o</mi> <mi>o</mi> <mi>m</mi> </mrow> </semantics></math> with left star leaf vertices <math display="inline"><semantics> <mrow> <msub> <mi>v</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>v</mi> <mn>2</mn> </msub> <mo>,</mo> <mspace width="3.33333pt"/> <mi>and</mi> <mspace width="3.33333pt"/> <msub> <mi>v</mi> <mn>3</mn> </msub> </mrow> </semantics></math>, path vertices <math display="inline"><semantics> <mrow> <msub> <mi>v</mi> <mn>4</mn> </msub> <mo>,</mo> <msub> <mi>v</mi> <mn>5</mn> </msub> <mo>,</mo> <msub> <mi>v</mi> <mn>6</mn> </msub> <mo>,</mo> <msub> <mi>v</mi> <mn>7</mn> </msub> <mo>,</mo> <mspace width="3.33333pt"/> <mi>and</mi> <mspace width="3.33333pt"/> <msub> <mi>v</mi> <mn>8</mn> </msub> </mrow> </semantics></math> and right star leaf vertices <math display="inline"><semantics> <mrow> <msub> <mi>v</mi> <mn>9</mn> </msub> <mo>,</mo> <msub> <mi>v</mi> <mn>10</mn> </msub> <mo>,</mo> <mspace width="3.33333pt"/> <mi>and</mi> <mspace width="3.33333pt"/> <mo>,</mo> <msub> <mi>v</mi> <mn>11</mn> </msub> </mrow> </semantics></math>. All markers except 11 are un-homed.</p>
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<p>Illustration of <math display="inline"><semantics> <msubsup> <mi>A</mi> <mi>n</mi> <mo>*</mo> </msubsup> </semantics></math>. (<b>a</b>) Solving the independent <math display="inline"><semantics> <mrow> <mi>b</mi> <mi>r</mi> <mi>o</mi> <mi>o</mi> <mi>m</mi> </mrow> </semantics></math> (star) formed by <math display="inline"><semantics> <mrow> <mo>{</mo> <msub> <mi>v</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>v</mi> <mn>2</mn> </msub> <mo>,</mo> <msub> <mi>v</mi> <mn>3</mn> </msub> <mo>,</mo> <msub> <mi>v</mi> <mn>4</mn> </msub> <mo>}</mo> </mrow> </semantics></math>, swaps = 3. (<b>b</b>) Homing the star leaf marker 10 and moving the markers 14 and 16 to the path, swaps = 3. (<b>c</b>) Homing the maximum clear path marker 5 followed by 11, swaps = 5. (<b>d</b>) Homing the maximum clear path marker 8, swaps = 5. (<b>e</b>) Homing the maximum clear path marker 6 followed by 14 and 16, swaps = 6. (<b>f</b>) Sorted <math display="inline"><semantics> <mrow> <mn>3</mn> <mo>−</mo> <mi>b</mi> <mi>r</mi> <mi>o</mi> <mi>o</mi> <mi>m</mi> </mrow> </semantics></math>.</p>
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18 pages, 3577 KiB  
Article
Immunogenic Effects of Dietary Terminalia arjuna Bark Powder in Labeo rohita, a Fish Model: Elucidated by an Integrated Biomarker Response Approach
by Dharmendra Kumar Meena, Soumya Prasad Panda, Amiya Kumar Sahoo, Prem Prakash Srivastava, Narottam Prasad Sahu, Mala Kumari, Smruti Samantaray, Simanku Borah and Basanta Kumar Das
Animals 2023, 13(1), 39; https://doi.org/10.3390/ani13010039 - 22 Dec 2022
Viewed by 2001
Abstract
Utilizing agro-industrial waste and herbal products to create a circular bioeconomy is becoming increasingly popular. Terminalia arjuna is a significant ethnomedicinal plant that has not yet been exploited in animal feed. In the present study, nutritional Terminalia arjuna bark powder-based fish feed was [...] Read more.
Utilizing agro-industrial waste and herbal products to create a circular bioeconomy is becoming increasingly popular. Terminalia arjuna is a significant ethnomedicinal plant that has not yet been exploited in animal feed. In the present study, nutritional Terminalia arjuna bark powder-based fish feed was created and supplied to a candidate fish species Labeo rohita at varied levels: 0% (0 g/kg), 0.5% (5 g/kg), 1% (10 g/kg), and 1.5% (15 g/kg). These treatment groups are denoted as CT, T1, T2, and T3, respectively. Utilizing a contemporary comprehensive biomarker response strategy, the study clarified the genomic influence of dietary herb inclusion. In response to bacterial infection, the immunogenic genes, STAT 1 (signal transducer and activator of transcription 1), ISG 15 (interferon stimulating gene), and Mx “myxovirus resistance gene”, were shown to be elevated. The results of densitometry demonstrated a dose-dependent increase in STAT 1 and ISG 15, with Mx exhibiting maximal values at 1 g/kg TABP (Terminalia arjuna bark powder-based feed). This is the first study to identify TABP as an immunomodulator in fish and established the IBR (Integrated Bio-marker Response) as a reliable marker in evaluating the impact of multiple drivers in a holistic manner. Thus, the present study cleared the path for TABP to be utilized as an effective feed additive which enhances the specific adaptive immune system of the fish for the production of the Green fish product for a sustainable circular bioeconomy. Full article
(This article belongs to the Special Issue Functional Feeds to Improve Shrimp and Fish Aquaculture)
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Figure 1
<p>(<b>a</b>–<b>c</b>) show the semi-quantitative PCR of targeted genes and housekeeping genes. Legends. (<b>a</b>) Showing semi-quantitative PCR of ISG15. (<b>b</b>) Showing semi-quantitative PCR of Mx gene. (<b>c</b>) Showing semi-quantitative PCR of STAT1 gene. L-1, 11, and 19: 1Kb molecular marker; L-2, 4, 6, 8, 11,13, 15, 17, 20, 22, 24, and 26: β-actin (housekeeping gene); L-3: CT90; L-5: T1-90; L-7: T2-90; L-9: T3-90; L-12: CT100-Ah; L-14: T1-100Ah; L-16: T2-100Ah; L-18: T3-100Ah; L-21: CT100-Et; L-23: T1-100Et; L-25: T2-100Et; L-27: T3-100Et.</p>
Full article ">Figure 1 Cont.
<p>(<b>a</b>–<b>c</b>) show the semi-quantitative PCR of targeted genes and housekeeping genes. Legends. (<b>a</b>) Showing semi-quantitative PCR of ISG15. (<b>b</b>) Showing semi-quantitative PCR of Mx gene. (<b>c</b>) Showing semi-quantitative PCR of STAT1 gene. L-1, 11, and 19: 1Kb molecular marker; L-2, 4, 6, 8, 11,13, 15, 17, 20, 22, 24, and 26: β-actin (housekeeping gene); L-3: CT90; L-5: T1-90; L-7: T2-90; L-9: T3-90; L-12: CT100-Ah; L-14: T1-100Ah; L-16: T2-100Ah; L-18: T3-100Ah; L-21: CT100-Et; L-23: T1-100Et; L-25: T2-100Et; L-27: T3-100Et.</p>
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<p>(<b>a</b>). Showing the correlation between the densiometric parameters of the Mx gene. (<b>b</b>). Showing the correlation between the densiometric parameters of the ISG15 gene. (<b>c</b>). Showing the correlation between the densiometric parameters of the STAT1 gene.</p>
Full article ">Figure 2 Cont.
<p>(<b>a</b>). Showing the correlation between the densiometric parameters of the Mx gene. (<b>b</b>). Showing the correlation between the densiometric parameters of the ISG15 gene. (<b>c</b>). Showing the correlation between the densiometric parameters of the STAT1 gene.</p>
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<p>(<b>a</b>–<b>c</b>) Showing networking among treatment groups based on genes densiometric parameters. Here, treatment groups followed by a challenge study are being represented as follows: CT, T1, T2, and T3 are the treatment groups, suffix-90 indicates a 90-day feeding trial of these treatment groups, and Et and Ah represent the bacterial isolates <span class="html-italic">Edwardsiella tarda</span> and <span class="html-italic">Aeromonas hydrophila</span>, respectively.</p>
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<p>qPCR analyses of three immunogenic genes (Mx, STAT1, and ISG15) during the feeding trial followed by the challenge study by two bacterial isolates, <span class="html-italic">Edwardsiella tarda</span> and <span class="html-italic">Aeromonas hydrophila.</span> Here, we conducted statistical analysis between and within the treatments. The superscript above the same color column shows a comparison between the treatments, and significance with an arrow (<span class="html-italic">p</span> &lt; 0.05 representing significance at 5 % level of significance and ns represents insignificance) shows comparison among the treatments. CT, T1, T2, and T3 suffixed with 90 shows the feed trial of 90 days with treatment groups, and 100Ah and 100Et shows the challenge study with bacterial isolates, <span class="html-italic">Edwardsiella tarda</span> and <span class="html-italic">Aeromonas hydrophila</span>, for 10 days (90–100 days).</p>
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<p>Integrated biomarker response approach plot of gene biomarkers in a feeding trial followed by a challenge study with two bacterial isolates, <span class="html-italic">Edwardsiella tarda</span> and <span class="html-italic">Aeromonas hydrophila</span>, for 10 days (90–100 days). Here, (<b>A</b>): CT-90-control treatment at 90 days; (<b>B</b>): T1-90-T1 treatment at 90 days; (<b>C</b>): T2-90-T2 treatment at 90 days; (<b>D</b>): T3-90-T3 treatment at 90 days; (<b>E</b>): CT-Ah-Control fish challenge with <span class="html-italic">A. hydrophila</span> at 100 days; (<b>F</b>): T1-Ah-T1 fish challenge with <span class="html-italic">A. hydrophila</span> at 100 days; (<b>G</b>): T2-Ah-T2 fish challenge with <span class="html-italic">A. hydrophila</span> at 100 days; (<b>H</b>): T3-Ah-T3 fish challenge with <span class="html-italic">A. hydrophila</span> at 100 days; (<b>I</b>): CT-Et-Control fish challenge with <span class="html-italic">E. tarda</span> at 100 days; (<b>J</b>): T1-Et-T1 fish Control fish challenge with <span class="html-italic">E. tarda</span> at 100 days; (<b>K</b>): T2-Et-T2 fish challenge with <span class="html-italic">E.Tarda</span> at 100 days; (<b>L</b>): T3-Et-T3 fish challenge with <span class="html-italic">E. tarda</span> at 100 days. STAT: STAT1 gene; ISG1: ISG 15.</p>
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<p>(<b>a</b>). Treatment biomarkers in the feeding trial followed by the challenge study. (<b>b</b>). Treatment biomarkers in the feeding trial followed by the challenge study. Here, CT, T1, T2, and T3 are the treatment groups, Mx, ISG15, and STAT1 are the targeted immunogenic genes, the suffix with 90 shows the feeding trial duration of 90 days, and the suffixes with Et and Ah represent the challenge study with two bacterial isolates, <span class="html-italic">Ewardsiella tarda</span> and <span class="html-italic">Aeromonas hydrophila</span>, for 10 days (90–100 days).</p>
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13 pages, 4382 KiB  
Article
Autophagy Is a Crucial Path in Chondrogenesis of Adipose-Derived Mesenchymal Stromal Cells Laden in Hydrogel
by Elena Gabusi, Enrico Lenzi, Cristina Manferdini, Paolo Dolzani, Marta Columbaro, Yasmin Saleh and Gina Lisignoli
Gels 2022, 8(12), 766; https://doi.org/10.3390/gels8120766 - 24 Nov 2022
Cited by 4 | Viewed by 2588
Abstract
Autophagy is a cellular process that contributes to the maintenance of cell homeostasis through the activation of a specific path, by providing the necessary factors in stressful and physiological situations. Autophagy plays a specific role in chondrocyte differentiation; therefore, we aimed to analyze [...] Read more.
Autophagy is a cellular process that contributes to the maintenance of cell homeostasis through the activation of a specific path, by providing the necessary factors in stressful and physiological situations. Autophagy plays a specific role in chondrocyte differentiation; therefore, we aimed to analyze this process in adipose-derived mesenchymal stromal cells (ASCs) laden in three-dimensional (3D) hydrogel. We analyzed chondrogenic and autophagic markers using molecular biology, immunohistochemistry, and electron microscopy. We demonstrated that ASCs embedded in 3D hydrogel showed an increase expression of typical autophagic markers Beclin 1, LC3, and p62, associated with clear evidence of autophagic vacuoles in the cytoplasm. During ASCs chondrogenic differentiation, we showed that autophagic markers declined their expression and autophagic vesicles were rare, while typical chondrogenic markers collagen type 2, and aggrecan were significantly increased. In line with developmental animal models of cartilage, our data showed that in a 3D hydrogel, ASCs increased their autophagic features. This path is the fundamental prerequisite for the initial phase of differentiation that contributes to fueling the cells with energy and factors necessary for chondrogenic differentiation. Full article
(This article belongs to the Special Issue Hydrogel-Based Scaffolds with a Focus on Medical Use)
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<p>Flow cytometry analysis of IgG1 (isotype negative control), CD73, CD90, CD105, and CD146 markers (<b>a</b>). Quantitative analysis of hASCs embedded in VG-RGD hydrogel at days 2 and 7 (<b>b</b>). Data are expressed as percentage of positive cells. Data presented as mean ± SD, <span class="html-italic">n</span> = 5.</p>
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<p>Experimental scheme.</p>
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<p>Real-time PCR analysis of <span class="html-italic">COL2A1</span> and <span class="html-italic">ACAN</span> genes of hASCs embedded in VG-RGD hydrogel at days 2, 10, and 28 (<b>a</b>). Data were expressed as % <span class="html-italic">GAPDH</span> (housekeeping gene) and represented as a boxplot with median, minimum, and maximum. Kruskal–Wallis with Dunn’s multiple comparisons test was used for statistical analysis: * indicates differences along time points (day 10 to day 28); *** <span class="html-italic">p</span> &lt; 0.0005, **** <span class="html-italic">p</span> &lt; 0.0001. Representative images of immunohistochemical analysis at day 28: negative control (control) and collagen type 2 (<b>b</b>). Positive areas are pink. Bar = 100 µm.</p>
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<p>Real-time PCR analysis of <span class="html-italic">BECN1, MAP1LC3B, SQSTM1</span> genes of hASCs embedded in VG-RGD hydrogel at day 2. Data were expressed as % <span class="html-italic">GAPDH</span> (housekeeping gene) and represented as a boxplot with median, minimum, and maximum. Kruskal–Wallis with Dunn’s multiple comparisons test was used for statistical analysis: * indicates differences between autophagic markers; * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.005, **** <span class="html-italic">p</span> &lt; 0.0001.</p>
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<p>Immunohistochemical analysis of Beclin 1, LC3 and p62 proteins of hASCs embedded in VG-RGD hydrogel at day 2. Bar = 100 µm. Arrows indicate positive stained cells. Quantification of the percentage of positive cells. Data were expressed as percentage of positive cells and represented as a boxplot with median, minimum, and maximum. Kruskal–Wallis with Dunn’s multiple comparisons test was used for statistical analysis: * indicates differences between autophagic markers; **** <span class="html-italic">p</span> &lt; 0.0001.</p>
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<p>Real-time PCR analysis of the <span class="html-italic">BECN1</span>, <span class="html-italic">MAP1LC3B</span>, and <span class="html-italic">SQSTM1</span> genes on chondrogenic differentiated hASCs embedded in VG-RGD hydrogel, at days 10 and 28. Data were expressed as % <span class="html-italic">GAPDH</span> (housekeeping gene) and represented as a boxplot with median, minimum, and maximum. Kruskal–Wallis with Dunn’s multiple comparisons test was used for statistical analysis: * indicates differences along time points (day 10 to day 28); * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span>&lt; 0.005, *** <span class="html-italic">p</span> &lt; 0.0005.</p>
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<p>Immunohistochemical analysis of Beclin 1, LC3, and p62 proteins on chondrogenic differentiated hASCs embedded in VG-RGD at days 10 and 28. Bar = 100 µm. Arrows indicate positive stained cells. Quantification of the percentage of positive cells was performed at days 10 and 28. Data were expressed as percentage of positive cells and represented as a boxplot with median, minimum, and maximum. Kruskal–Wallis with Dunn’s multiple comparisons test was used for statistical analysis: * indicates differences along time points (day 10 to day 28); ** <span class="html-italic">p</span> &lt; 0.005, **** <span class="html-italic">p</span> &lt; 0.0001.</p>
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<p>Immunohistochemical analysis of Beclin 1, LC3, and p62 proteins on chondrogenic differentiated hASCs embedded in VG-RGD at days 2, 10, and 28. Quantification of the percentage of positive cells. Data were expressed as percentage of positive cells and represented as a boxplot with median, minimum, and maximum. Kruskal–Wallis with Dunn’s multiple comparisons test was used for statistical analysis: * indicates differences along time points; ** <span class="html-italic">p</span> &lt; 0.005, *** <span class="html-italic">p</span> &lt; 0.0005, **** <span class="html-italic">p</span> &lt; 0.0001.</p>
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<p>Representative transmission electron microscopy images of chondrogenic differentiated hASCs embedded in VG-RGD at days 2 (<b>A</b>–<b>C</b>), 10 (<b>D</b>–<b>F</b>), and 28 (<b>G</b>–<b>I</b>). In the images, s indicates VG-RGD hydrogel, c indicates dispersed chromatin, rer indicates rough endoplasmic reticulum, ld indicates lipid droplet, m indicates mitochondria, v indicates vacuoles, ev indicates empty vacuole, lv indicates degradative vacuole, black arrow indicates double membrane, AP indicates autophagosome, coll indicates collagen, and tv indicates transport vesicles. (<b>A</b>,<b>G</b>) bar 5 μm; (<b>E</b>,<b>H</b>) bar 2 μm; (<b>D</b>) bar 10 μm; (<b>B</b>,<b>C</b>,<b>F</b>,<b>I</b>) bar 1 μm.</p>
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<p>Summary of the main results of autophagy in chondrogenic differentiation of hASCs laden in VG-RGD hydrogel. Green arrows indicate induced markers, red arrows indicate reduced markers.</p>
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16 pages, 6862 KiB  
Article
Profiles of Motor-Cognitive Interference in Parkinson’s Disease—The Trail-Walking-Test to Discriminate between Motor Phenotypes
by Thomas J. Klotzbier, Nadja Schott and Quincy J. Almeida
Brain Sci. 2022, 12(9), 1217; https://doi.org/10.3390/brainsci12091217 - 9 Sep 2022
Cited by 3 | Viewed by 2133
Abstract
Background and Aims. Most research on Parkinson’s disease (PD) focuses on describing symptoms and movement characteristics. Studies rarely focus on the early detection of PD and the search for suitable markers of a prodromal stage. Early detection is important, so treatments that may [...] Read more.
Background and Aims. Most research on Parkinson’s disease (PD) focuses on describing symptoms and movement characteristics. Studies rarely focus on the early detection of PD and the search for suitable markers of a prodromal stage. Early detection is important, so treatments that may potentially change the course of the disease can be attempted early on. While gait disturbances are less pronounced in the early stages of the disease, the prevalence, and severity increase with disease progression. Therefore, postural instability and gait difficulties could be identified as sensitive biomarkers. The aim was to evaluate the discriminatory power of the Trail-Walking Test (TWT; Schott, 2015) as a potential diagnostic instrument to improve the predictive power of the clinical evaluation concerning the severity of the disease and record the different aspects of walking. Methods. A total of 20 older healthy (M = 72.4 years, SD = 5.53) adults and 43 older adults with PD and the motor phenotypes postural instability/gait difficulty (PIGD; M = 69.7 years, SD = 8.68) and tremor dominant (TD; M = 68.2 years, SD = 8.94) participated in the study. The participants performed a motor-cognitive dual task (DT) of increasing cognitive difficulty in which they had to walk a given path (condition 1), walk to numbers in ascending order (condition 2), and walk to numbers and letters alternately and in ascending order (condition 3). Results. With an increase in the cognitive load, the time to complete the tasks (seconds) became longer in all groups, F(1.23, 73.5) = 121, p < 0.001, ɳ2p = 0.670. PIGD showed the longest times in all conditions of the TWT, F(2, 60) = 8.15, p < 0.001, ɳ2p = 0.214. Mutual interferences in the cognitive and motor domain can be observed. However, clear group-specific patterns cannot be identified. A differentiation between the motor phenotypes of PD is especially feasible with the purely motor condition (TWT-M; AUC = 0.685, p = 0.44). Conclusions. PD patients with PIGD must be identified by valid, well-evaluated clinical tests that allow for a precise assessment of the disease’s individual fall risk, the severity of the disease, and the prognosis of progression. The TWT covers various aspects of mobility, examines the relationship between cognitive functions and walking, and enables differentiation of the motor phenotypes of PD. Full article
(This article belongs to the Special Issue From Bench to Bedside: Motor-Cognitive Interactions)
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Figure 1
<p>Mean and standard deviation of groups (PIGD, TD &amp; control) and TWT conditions (TWT-A, TWT-B &amp; TWT-M) based on times.</p>
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<p>(<b>a</b>) Pattern of motor-cognitive DTC in TWT-A based on times in PIGD, TD, and control. (<b>b</b>) Pattern of motor-cognitive DTC in TWT-B based on times in PIGD, TD, and control. Motor and cognitive DTCs are calculated using the following formula: DTC (%) = ((performance DT − performance in ST)/performance in ST) ×100).</p>
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18 pages, 286 KiB  
Review
Macrophages/Microglia in the Glioblastoma Tumor Microenvironment
by Jun Ma, Clark C. Chen and Ming Li
Int. J. Mol. Sci. 2021, 22(11), 5775; https://doi.org/10.3390/ijms22115775 - 28 May 2021
Cited by 33 | Viewed by 5140
Abstract
The complex interaction between glioblastoma and its microenvironment has been recognized for decades. Among various immune profiles, the major population is tumor-associated macrophage, with microglia as its localized homolog. The present definition of such myeloid cells is based on a series of cell [...] Read more.
The complex interaction between glioblastoma and its microenvironment has been recognized for decades. Among various immune profiles, the major population is tumor-associated macrophage, with microglia as its localized homolog. The present definition of such myeloid cells is based on a series of cell markers. These good sentinel cells experience significant changes, facilitating glioblastoma development and protecting it from therapeutic treatments. Huge, complicated mechanisms are involved during the overall processes. A lot of effort has been dedicated to crack the mysterious codes in macrophage/microglia recruiting, activating, reprogramming, and functioning. We have made our path. With more and more key factors identified, a lot of new therapeutic methods could be explored to break the ominous loop, to enhance tumor sensitivity to treatments, and to improve the prognosis of glioblastoma patients. However, it might be a synergistic system rather than a series of clear, stepwise events. There are still significant challenges before the light of truth can shine onto the field. Here, we summarize recent advances in this field, reviewing the path we have been on and where we are now. Full article
(This article belongs to the Special Issue Macrophages in the Glioblastoma Tumor Microenvironment)
6 pages, 202 KiB  
Editorial
Behavioral Graded Activity+ (BGA+) for Osteoarthritis: A Paradigm Shift from Disease-Based Treatment to Personalized Activity Self-Management
by Jo Nijs, Kelly Ickmans, David Beckwée and Laurence Leysen
J. Clin. Med. 2020, 9(6), 1793; https://doi.org/10.3390/jcm9061793 - 9 Jun 2020
Cited by 3 | Viewed by 3748
Abstract
Three promising directions for improving care for osteoarthritis (OA) include novel education strategies to target unhelpful illness and treatment beliefs; methods to enhance the efficacy of exercise interventions; and innovative, brain-directed treatments. Here we explain that each of those three promising directions can [...] Read more.
Three promising directions for improving care for osteoarthritis (OA) include novel education strategies to target unhelpful illness and treatment beliefs; methods to enhance the efficacy of exercise interventions; and innovative, brain-directed treatments. Here we explain that each of those three promising directions can be combined through a paradigm-shift from disease-based treatments to personalized activity self-management for patients with OA. Behavioral graded activity (BGA) accounts for the current understanding of OA and OA pain and allows a paradigm shift from a disease-based treatment to personalized activity self-management for patients with OA. To account for the implementation barriers of BGA, we propose adding pain neuroscience education to BGA (referred to as BGA+). Rather than focusing on the biomedical (and biomechanical) disease characteristics of OA, pain neuroscience education implies teaching people about the underlying biopsychosocial mechanisms of pain. To account for the lack of studies showing that BGA is “safe” with respect to disease activity and the inflammatory nature of OA patients, a trial exploring the effects of BGA+ on the markers of inflammation is needed. Such a trial could clear the path for the required paradigm shift in the management of OA (pain) and would allow workforce capacity building that de-emphasizes biomedical management for OA. Full article
(This article belongs to the Special Issue Rehabilitation for Persistent Pain Across the Lifespan)
4402 KiB  
Communication
Easily Fabricated Microfluidic Devices Using Permanent Marker Inks for Enzyme Assays
by Coreen Gallibu, Chrisha Gallibu, Ani Avoundjian and Frank A. Gomez
Micromachines 2016, 7(1), 6; https://doi.org/10.3390/mi7010006 - 12 Jan 2016
Cited by 24 | Viewed by 6557
Abstract
In this communication, we describe microfluidic paper analytical devices (μPADs) easily fabricated from commercially available Sharpie ink permanent markers on chromatography paper to colorimetrically detect glucose using glucose oxidase (GOx). Here, solutions of horseradish peroxidase (HRP), GOx, and potassium iodide (KI)were directly spotted [...] Read more.
In this communication, we describe microfluidic paper analytical devices (μPADs) easily fabricated from commercially available Sharpie ink permanent markers on chromatography paper to colorimetrically detect glucose using glucose oxidase (GOx). Here, solutions of horseradish peroxidase (HRP), GOx, and potassium iodide (KI)were directly spotted onto the center of the μPAD and flowed into samples of glucose that were separately spotted on the μPAD. Using an XY plotter (Roland DGA Corporation, Irvine, CA USA), several ink marks drawn in the paper act as the hydrophobic barriers, thereby, defining the hydrophilic fluid flow paths of the solutions. Two paper devices are described that act as independent assay zones. The glucose assay is based on the enzymatic oxidation of iodide to iodine whereby a color change from clear to brownish-yellow is associated with the presence of glucose. In these experiments, two designs are highlighted that consist of circular paper test regions fabricated for colorimetric and subsequent quantification detection of glucose. The use of permanent markers for paper patterning is inexpensive and rapid and does not require special laboratory equipment or technical skill. Full article
(This article belongs to the Special Issue Paper-Based Microfluidic Devices for Point-of-Care Diagnostics)
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Figure 1

Figure 1
<p>(<b>a</b>) Cloverleaf (four-channel) and (<b>b</b>) shamrock (three-channel) μPADs used in this study; (<b>c</b>) Representation of samples spotted on cloverleaf μPAD.</p>
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<p>Digital photographic images of the glucose assay for the cloverleaf four-channel μPADs using a range (<b>i</b>) 0.6; (<b>ii</b>) 1.9; (<b>iii</b>) 2.5; (<b>iv</b>) 3.0; (<b>v</b>) 3.3; (<b>vi</b>) 4.4; (<b>vii</b>) 6.25; (<b>viii</b>) 9.20; and (<b>ix</b>) 12.1 mM of concentrations of glucose.</p>
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<p>(<b>a</b>) Saturation curve of average yellow intensity as a function of glucose concentration for the cloverleaf chip. The error bars represent the relative standard deviation of three independent measurements; (<b>b</b>) Saturation curve of average yellow intensity as a function of glucose concentration for the shamrock chip. The error bars represent the relative standard deviation of three independent measurements.</p>
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<p>Digital photographic images of the glucose assay for the three-channel μPADs using a range (<b>i</b>) 0.0; (<b>ii</b>) 0.6; (<b>iii</b>) 1.9; (<b>iv</b>) 2.5; (<b>v</b>) 3.0; (<b>vi</b>) 3.3; (<b>vii</b>) 4.4; (<b>viii</b>) 6.25; (<b>ix</b>) 9.20; and (<b>x</b>) 12.1 mM of concentrations of glucose.</p>
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2201 KiB  
Article
Image-Based Navigation for the SnowEater Robot Using a Low-Resolution USB Camera
by Ernesto Rivas, Koutarou Komagome, Kazuhisa Mitobe and Genci Capi
Robotics 2015, 4(2), 120-140; https://doi.org/10.3390/robotics4020120 - 8 Apr 2015
Cited by 5 | Viewed by 8352
Abstract
This paper reports on a navigation method for the snow-removal robot called SnowEater. The robot is designed to work autonomously within small areas (around 30 m2 or less) following line segment paths. The line segment paths are laid out so as [...] Read more.
This paper reports on a navigation method for the snow-removal robot called SnowEater. The robot is designed to work autonomously within small areas (around 30 m2 or less) following line segment paths. The line segment paths are laid out so as much snow as possible can be cleared from an area. Navigation is accomplished by using an onboard low-resolution USB camera and a small marker located in the area to be cleared. Low-resolution cameras allow only limited localization and present significant errors. However, these errors can be overcome by using an efficient navigation algorithm to exploit the merits of these cameras. For stable robust autonomous snow removal using this limited information, the most reliable data are selected and the travel paths are controlled. The navigation paths are a set of radially arranged line segments emanating from a marker placed in the environment area to be cleared, in a place where it is not covered by snow. With this method, by using a low-resolution camera (640 × 480 pixels) and a small marker (100 × 100 mm), the robot covered the testing area following line segments. For a reference angle of 4.5° between line paths, the average results are: 4° for motion on hard floor and 4.8° for motion on compacted snow. The main contribution of this study is the design of a path-following control algorithm capable of absorbing the errors generated by a low-cost camera. Full article
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Graphical abstract

Graphical abstract
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<p><span class="html-italic">SnowEater</span> robot prototype.</p>
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<p>Line paths that cover the snow-removal area.</p>
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<p>Monochromatic square marker and small version of the <span class="html-italic">SnowEater</span> robot.</p>
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<p>Experimental setup and different camera-marker position during the outdoor experiments.</p>
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<p>Localization position <span class="html-italic">vs.</span> actual robot-marker distance.</p>
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<p>Orientation angle <span class="html-italic">vs.</span> robot-marker distance.</p>
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<p>Recognized marker position in the camera image.</p>
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<p>Recognized marker position <span class="html-italic">vs.</span> robot-marker distance.</p>
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<p>Motion model of the tracked robot.</p>
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<p>Recursive back-forward algorithm.</p>
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<p>Orientation angle of the robot in the marker coordinate system and in the camera image.</p>
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<p>Curved path to reach the marker.</p>
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<p>Control system diagram for the <span class="html-italic">SnowEater</span> robot.</p>
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<p><span class="html-italic">Motion 2</span> experimental setup on a hard floor, and on a slippery floor of polystyrene beads.</p>
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<p>Robot position throughout the experiment.</p>
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<p><math display="inline"> <semantics> <mrow> <msub> <mi>α</mi> <mrow> <mi>r</mi> <mi>e</mi> <mi>f</mi> </mrow> </msub> </mrow> </semantics> </math> experimental results when <span class="html-italic">Motion 2</span> is executed.</p>
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<p>Test area setup.</p>
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<p><span class="html-italic">Motion 1</span> experimental results on snow in outdoor conditions.</p>
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<p>Motion 2 on snow.</p>
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<p>Recursive back-forward motion experimental results in indoor conditions.</p>
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<p>Recursive back-forward motion experimental results in indoor conditions.</p>
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<p>Recursive back-forward motion experimental results in outdoor conditions.</p>
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<p>Localization and recognized marker position errors for different cameras.</p>
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<p>Localization and recognized marker position errors for different patterns.</p>
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<p>Localization and recognized marker position errors for different marker size.</p>
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<p>Track response to different angular velocity commands.</p>
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<p>Direction controller (recognized marker position) <span class="html-italic">vs</span>. PI controller (inaccurate localization signals).</p>
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