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1. The role of V3 neurons in speed-dependent interlimb coordination during locomotion in mice

   https://doi.org/10.7554/eLife.73424  

   Zhang, Han; Shevtsova, Natalia A; Deska-Gauthier, Dylan; Mackay, Colin; Dougherty, Kimberly J; Danner, Simon M; Zhang, Ying; Rybak, Ilya A (April 2022, eLife)

   Speed-dependent interlimb coordination allows animals to maintain stable locomotion under different circumstances. The V3 neurons are known to be involved in interlimb coordination. We previously modeled the locomotor spinal circuitry controlling interlimb coordination (Danner et al., 2017). This model included the local V3 neurons that mediate mutual excitation between left and right rhythm generators (RGs). Here, our focus was on V3 neurons involved in ascending long propriospinal interactions (aLPNs). Using retrograde tracing, we revealed a subpopulation of lumbar V3 aLPNs with contralateral cervical projections. V3 OFF mice, in which all V3 neurons were silenced, had a significantly reduced maximal locomotor speed, were unable to move using stable trot, gallop, or bound, and predominantly used a lateral-sequence walk. To reproduce this data and understand the functional roles of V3 aLPNs, we extended our previous model by incorporating diagonal V3 aLPNs mediating inputs from each lumbar RG to the contralateral cervical RG. The extended model reproduces our experimental results and suggests that locally projecting V3 neurons, mediating left–right interactions within lumbar and cervical cords, promote left–right synchronization necessary for gallop and bound, whereas the V3 aLPNs promote synchronization between diagonal fore and hind RGs necessary for trot. The model proposes the organization of spinal circuits available for future experimental testing. 

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2. On the Capacity of Vector Linear Computation Over a Noiseless Quantum Multiple-Access Channel With Entangled Transmitters

   https://doi.org/10.1109/TQE.2025.3620628  

   Yao, Yuhang; Jafar, Syed A (October 2025, IEEE Transactions on Quantum Engineering)

   Network function computation is an active topic in network coding, with much recent progress for linear (over a finite field) computations over broadcast (LCBC) and multiple access (LCMAC) channels. Over a quantum multiple access channel (QMAC) with quantum-entanglement shared among transmitters, the linear computation problem (LC-QMAC) is non-trivial even when the channel is noiseless, because of the challenge of optimally exploiting transmit-side entanglement through distributed coding. Given an arbitrary Fd linear function, f(W1,···,WK) = V1W1 +V2W2 +···+VKWK ∈Fm×1 d , the LC- QMAC problem seeks the optimal communication cost (minimum number of qudits ∆1,··· ,∆K that need to be sent by transmitters Alice1, Alice2, ···, AliceK, respectively, to the receiver, Bob, per computation instance) over a noise-free QMAC, when the input data streams Wk ∈Fmk ×1 d ,k ∈[K], originate at the corresponding transmitters, who share quantum entanglement in advance. As our main result, we fully solve this problem for K = 3 transmitters (K ≥4 settings remain open). Coding schemes based on the N-sum box protocol (along with time-sharing and batch-processing) are shown to be information theoretically optimal in all cases. As an example, in the symmetric case where rk(V1) = rk(V2) = rk(V3) ≜ r1, rk([V1,V2]) = rk([V2,V3]) = rk([V3,V1]) ≜ r2, and rk([V1,V2,V3]) ≜ r3 (rk= rank), the minimum total-download cost is max{1.5r1 + 0.75(r3−r2),r3}. 

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3. High resolution diel transcriptomes of autotetraploid potato reveal expression and sequence conservation among rhythmic genes

   https://doi.org/10.5061/dryad.x69p8czwp  

   Farre, Eva M; Feke, Ann; Vaillancourt, Brieanne; Acheson, Kaitlyn; Brose, Julia; Hamilton, John P; Martin, Dionne; Wang, Yi-Wen; Wood, Joshua C; Buell, C Robin (September 2025, Dryad)

   Background Photoperiodic changes in diel cycles of gene expression are pervasive in plants. The timing of circadian regulators, together with light signals, regulate multiple photoperiod-dependent responses such as growth, flowering or tuber formation. However, for most genes, the importance of cyclic mRNA levels is less clear. We analyzed the diel transcriptome of modern cultivated potato, a highly heterozygous autotetraploid. Clonal propagation and limited meiosis have led to the accumulation of deleterious alleles, making tetraploid potato an ideal model system to investigate the conservation of cyclic expression and cyclic genes during artificial selection and clonal propagation. Results Our results indicate that rhythmic alleles of cultivated potato were more highly expressed than non-rhythmic genes and were highly co-expressed not only under diel cycles but also across tissues, developmental stages, and stress conditions. Moreover, the smaller ratio of non-synonymous to synonymous differences within rhythmic versus non-rhythmic allelic groups indicates that cyclic genes, in general, have more conserved core functions than non-cyclic genes. In accordance with this observation, fully rhythmic allelic groups were highly enriched in photosynthesis and ribosome biogenesis genes, which have core functions in plants. Furthermore, we investigated differences in cyclic expression patterns between photoperiod identifying potential regulators of the strong photoperiodic change in phase of expression for ribosome biogenesis and pathogen response genes. Finally, analyses of genes involved in tuber formation suggests that the regulation of CO gene transcription is not the only factor enabling tuberization under long days in modern cultivated potato. Conclusions This study not only provides high quality diel transcriptomic datasets of cultivated potato but also provides important insight on the role of allelic diversity in rhythmic expression in plants.    # High resolution diel transcriptomes of autotetraploid potato reveal expression and sequence conservation among rhythmic genes #### Principle Investigator Contact Information ``` Name: Eva M. Farre Institution: Michigan State University Email: farre@msu.edu ``` ## Dataset Overview This repository contains the large datasets used in the manuscript titled 'High resolution diel transcriptomes of autotetraploid potato reveal expression and sequence conservation among rhythmic genes' by Feke et al., accepted in the BMC Genomics. The code used to generate the plots on the publication can be found here: [https://github.com/efarre/Autotetraploid_potato_diel_transcriptome/tree/main](https://github.com/efarre/Autotetraploid_potato_diel_transcriptome/tree/main). This repository contains the allelic groups for *S. tuberosum* cv. Atlantic used in this study (Dataset S1). It contains the normalized expression of *S. tuberosum* cv. Atlantic leaf tissue under short and long days, and tuber tissue under short days (Dataset S2). As well as the normalized expression of the Atlantic Developmental Gene Expression Atlas(Datasets S3 and S4). It also includes the rhythmic expression analysis results under diel conditions for leaf and tuber tissue (Datasets S5, S6, S7). The repository also contains the pairwise allelic expression correlations of the diel expression datasets (Datasets S8 and S9) and the Atlantic Developmental Gene Expression Atlas (Datasets S10 and S11), as well as the results of the differential expression anlysis between photoperiods of leaf tissue (Dataset S12) and between leaf and tuber tissues (Dataset S13). Dataset S14 contains the MapMan functional annnotation results. ## Description of the data and file structure #### File: Dataset_S1_Allelic_groups.csv **Description:** Synthenic allelic groups in *Solanum tuberosum* cv. Atlantic v3 and S. tuberosum Group Phureja DM 1-3 516 R44 (DM) v6.1. ##### Variables * Syntelog: allelic groups * geneID: gene ID #### File: Dataset_S2_Diel_expression_rlog_long.csv **Description:** Gene expression in Atlantic leaves under short (12 h light/12 h dark) and long days (16 h light/8 h dark) and tubers under short days normalized using DEseq, rlog values are provided. Original data from BioProjects PRJNA957457 (short day) and PRJNA1093480 (long day). ##### Variables * geneID: based on Atlantic Genome Assembly (v3) ([https://spuddb.uga.edu](https://spuddb.uga.edu)) * ZT: time after dawn * SD: short day, light period ZT0-ZT12 * LD: long day, light period ZT0-ZT16 * Expression: in rlog (generated using DEseq) #### File: Dataset_S3_Expression_of_Tissue_samples_from_the_Developmental_Gene_Expression_Atlas.csv **Description:** Gene expression was normalized using DEseq and rlog values are provided. Atlantic Developmental Gene Expression Atlas data were obtained from NCBI under BioProject PRJNA753086. Plant growth and tissue harvest methods are described in doi: [https://doi.org/10.1101/2025.06.26.661617](https://doi.org/10.1101/2025.06.26.661617). ##### Variables * geneID: based on Atlantic Genome Assembly (v3) ([https://spuddb.uga.edu](https://spuddb.uga.edu)) * Sample column labels: Samplename_replicate. Abbreviations: * R#: replicate number * TuberS#: tuber developmental stages S1-S4 were collected for this experiment. * YL: young leaf: * ImmFruit: immature fruit. #### File: Dataset_S4_Expression_of_Stress_samples_from_the_Developmental_Gene_Expression_Atlas.csv **Description:** Gene expression was normalized using DEseq and rlog values are provided. Atlantic Developmental Gene Expression Atlas data were obtained from NCBI under BioProject PRJNA753086. Plant growth and tissue harvest methods are described in doi: [https://doi.org/10.1101/2025.06.26.661617](https://doi.org/10.1101/2025.06.26.661617). ##### Variables * geneID: based on Atlantic Genome Assembly (v3) ([https://spuddb.uga.edu](https://spuddb.uga.edu)) * Sample column labels: SampleName_replicate. Abbreviations: * R#: replicate number * Meja: methyl jasmonate * BTH: benzothiodiazole #### File: Dataset_S5_Leaf_short_day_cycling_parameters_as_determined_per_JTK.csv **Description:** Rhythmic parameters determined using JTK implemented in MetaCycle using rlog normalized data from short day time course in leaf tissue. ##### Variables * CycID: gene ID based on Atlantic Genome Assembly (v3) ([https://spuddb.uga.edu](https://spuddb.uga.edu)) * BH.Q:Benjamini–Hochberg q-value * ADJ.P: Adjusted p=value * PER: period * LAG: phase * AMP: amplitude #### File: Dataset_S6_Leaf_long_day_cycling_parameters_as_determined_per_JTK.csv **Description:** Rhythmic parameters determined using JTK implemented in MetaCycle using rlog normalized data from long day time course in leaf tissue. ##### Variables * CycID: gene ID based on Atlantic Genome Assembly (v3) ([https://spuddb.uga.edu](https://spuddb.uga.edu)) * BH.Q: Benjamini–Hochberg q-value * ADJ.P: Adjusted p=value * PER: period * LAG: phase * AMP: amplitude #### File: Dataset_S7_Tuber_short_day_cycling_parameters_as_determined_per_JTK.csv **Description:** Rhythmic parameters determined using JTK implemented in MetaCycle using rlog normalized data from short day time course in tuber tissue. ##### Variables * CycID: gene ID based on Atlantic Genome Assembly (v3) ([https://spuddb.uga.edu](https://spuddb.uga.edu)) * BH.Q:Benjamini–Hochberg q-value * ADJ.P: Adjusted p=value * PER: period * LAG: phase * AMP: amplitude #### File: Dataset_S8_Pairwise_allelic_expression_correlations_in_short_days.csv **Description:** The Pearson correlation of the z-scored expression values (rlog) was calculated for each pair of alleles. Leaf short day expression data from Dataset S2 was used to generate these correlations. ##### Variables * Syntelog: allelic group * Haplotype_1: allele 1, gene ID based on Atlantic Genome Assembly (v3) ([https://spuddb.uga.edu](https://spuddb.uga.edu)) * Haplotype_2: allele 2, gene ID based on Atlantic Genome Assembly (v3) ([https://spuddb.uga.edu](https://spuddb.uga.edu)) * Correlation: Pearson correlation, no correlation value is provided for pairs in which one haplotype is not expressed. #### File: Dataset_S9_Pairwise_allelic_expression_correlations_in_long_days.csv **Description:** The Pearson correlation of the z-scored expression values (rlog) was calculated for each pair of alleles.Leaf long day expression data from Dataset S2 was used to generate these correlations. ##### Variables * Syntelog: allelic group * Haplotype_1: allele 1, gene ID based on Atlantic Genome Assembly (v3) ([https://spuddb.uga.edu](https://spuddb.uga.edu)) * Haplotype_2: allele 2, gene ID based on Atlantic Genome Assembly (v3) ([https://spuddb.uga.edu](https://spuddb.uga.edu)) * Correlation: Pearson correlation, no correlation value is provided for pairs in which one haplotype is not expressed. #### File: Dataset_S10_Pairwise_allelic_expression_correlations_in_Tissue_samples_from_the_Developmental_Gene_Expression_Atlas.csv **Description:** The Pearson correlation of the z-scored expression values (rlog) was calculated for each pair of alleles. The rlog expression values are from Dataset S3. Atlantic Developmental Gene Expression Atlas data were obtained from NCBI under BioProject PRJNA753086. Plant growth and tissue harvest methods are described in doi: [https://doi.org/10.1101/2025.06.26.661617](https://doi.org/10.1101/2025.06.26.661617). ##### Variables * Syntelog: allelic group * Haplotype_1: allele 1, gene ID based on Atlantic Genome Assembly (v3) ([https://spuddb.uga.edu](https://spuddb.uga.edu)) * Haplotype_2: allele 2, gene ID based on Atlantic Genome Assembly (v3) ([https://spuddb.uga.edu](https://spuddb.uga.edu)) * Correlation: Pearson correlation, no correlation value is provided for pairs in which one haplotype is not expressed. #### File: Dataset_S11_Pairwise_allelic_expression_correlations_in_Stress_samples_from_the_Developmental_Gene_Expression_Atlas.csv **Description:** The Pearson correlation of the z-scored expression values (rlog) was calculated for each pair of alleles.The rlog expression values are from Dataset S4. Atlantic Developmental Gene Expression Atlas data were obtained from NCBI under BioProject PRJNA753086. Plant growth and tissue harvest methods are described in doi: [https://doi.org/10.1101/2025.06.26.661617](https://doi.org/10.1101/2025.06.26.661617). ##### Variables * Syntelog: allelic group * Haplotype_1: allele 1, gene ID based on Atlantic Genome Assembly (v3) ([https://spuddb.uga.edu](https://spuddb.uga.edu)) * Haplotype_2: allele 2, gene ID based on Atlantic Genome Assembly (v3) ([https://spuddb.uga.edu](https://spuddb.uga.edu)) * Correlation: Pearson correlation, no correlation value is provided for pairs in which one haplotype is not expressed. #### File: Dataset_S12_Differential_expression_short_vs._long_day_determined_by_DEseq.csv **Description:** Differential gene expression between short and long days in leaves as determined by DEseq. For this analysis, the time component of RNAseq samples was disregarded. Note that genes with a lower than a threshold value of counts or outlier expression values have no output in DEseq (empty cells). ##### Variables * V1: gene ID based on Atlantic Genome Assembly (v3) ([https://spuddb.uga.edu](https://spuddb.uga.edu)) * baseMean: The average of the normalized count values, dividing by size factors, taken over all samples. * log2FoldChange: the effect size estimate. This value indicates how much the gene or transcript's expression seems to have changed between the comparison and control groups. This value is reported on a logarithmic scale to base 2 * lfcSE: The standard error estimate for the log2 fold change estimate * stat: The value of the test statistic for the gene or transcript. * pvalue: P-value of the test for the gene or transcript. * padj: Adjusted P-value for multiple testing for the gene or transcript. #### File: Dataset_S13_Differential_expression_leaf_vs._tuber_under_short_days_determined_by_DEseq.csv **Description:** Differential gene expression between leaf and tuber in short days as determined by DEseq. For this analysis, the time component of RNAseq samples was disregarded. Note that genes with a lower than a threshold value of counts or outlier expression values have no output in DEseq (empty cells). ##### Variables * V1: gene ID based on Atlantic Genome Assembly (v3) ([https://spuddb.uga.edu](https://spuddb.uga.edu)) * baseMean: The average of the normalized count values, dividing by size factors, taken over all samples. * log2FoldChange: he effect size estimate. This value indicates how much the gene or transcript's expression seems to have changed between the comparison and control groups. This value is reported on a logarithmic scale to base 2 * lfcSE: The standard error estimate for the log2 fold change estimate * stat: The value of the test statistic for the gene or transcript. * pvalue: P-value of the test for the gene or transcript. * padj: Adjusted P-value for multiple testing for the gene or transcript. #### File: Dataset_S14_Functional_annotation_of_Atlantic_using_MapMan.txt **Description:** Mercator4 v7.0 ([www.plabipd.de/mercator_main.html](http://www.plabipd.de/mercator_main.html)) and the S. tuberosum cv. Atlantic v3 high confidence representative gene models from SpudDB were used. ##### Variables * BINCODE: Code for functional group * NAME: Name of functional group * IDENTIFIER: gene ID based on Atlantic Genome Assembly (v3) ([https://spuddb.uga.edu](https://spuddb.uga.edu)) * DESCRIPTION: description of functional group * TYPE: The type of the item (T=Transcript, M=Metabolite, P=Protein, E= Enzyme) 

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4. Experience Migrating a Pipeline for the C-MĀIKI gateway from Tapis v2 to Tapis v3

   https://doi.org/10.1145/3491418.3535139  

   Wong, Yick Ching; Cleveland, Sean B.; Jacobs, Gwen A. (July 2022, Practice and Experience in Advanced Research Computing)

   The C-MĀIKI gateway is a science gateway that leverages a computational workload management API called Tapis to support modern, interoperable, and scalable microbiome data analysis. This project is focused on migrating an existing C-MĀIKI gateway pipeline from Tapis v2 to Tapis v3 so that it can take advantage of the new robust Tapis v3 features and stay modern. This requires three major steps: 1) Containerization of each existing microbiome workflow. 2) Create a new app definition for each of the workflows. 3) Enabling the ability to submit jobs to a SLURM scheduler inside of a singularity container to support the Nextflow workflow manager. This work presents the experience and challenges in upgrading the pipeline. 

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5. Probing adsorption of methane onto vanadium cluster cations via vibrational spectroscopy

   https://doi.org/10.1063/5.0169118  

   Kozubal, Justine; Heck, Tristan; Metz, Ricardo B (November 2023, The Journal of Chemical Physics)

   Photofragment spectroscopy is used to measure the vibrational spectra of V2(+)(CH4)n (n = 1–4), V3(+)(CH4)n (n = 1–3), and Vx(+)(CH4) (x = 4–8) in the C–H stretching region (2550–3100 cm−1). Spectra are measured by monitoring loss of CH4. The experimental spectra are compared to simulations at the B3LYP+D3/6-311++G(3df,3pd) level of theory to identify the geometry of the ions. Multi-reference configuration interaction with Davidson correction (MRCI+Q) calculations are also carried out on V2(+) and V3(+). The methane binding orientation in V2(+)(CH4)n (n = 1–4) evolves from η3 to η2 as more methane molecules are added. The IR spectra of metal-methane clusters can give information on the structure of metal clusters that may otherwise be hard to obtain from isolated clusters. For example, the V3(+)(CH4)n (n = 1–3) experimental spectra show an additional peak as the second and third methane molecules are added to V3(+), which indicates that the metal atoms are not equivalent. The Vx(+)(CH4) show a larger red shift in the symmetric C–H stretch for larger clusters with x = 5–8 than for the small clusters with x = 2, 3, indicating increased covalency in the interaction of larger vanadium clusters with methane. 

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