The goods train was carrying iron ore and derailed between Raghavapuram and Ramagundam, officials said. No casualties or injuries were reported in the incident.

The goods train was carrying iron ore and derailed between Raghavapuram and Ramagundam, officials said. No casualties or injuries were reported in the incident.

Museum honored for its project “Search, Discovery, and Interpretation of the African Burial Ground at the John Dickinson Plantation.”

DOVER (April 20, 2017) – The Governor’s Welfare Employment Committee announced the winners of its 2017 TANF Employment Awards of Excellence as it recognized 39 employers in Delaware who hire, train and maintain positive working relationships with employees who receive Temporary Assistance for Needy Families (TANF) benefits, and 47 TANF clients who have succeeded in […]

AT&T cell phone subscribers in Kent and Sussex Counties are experiencing difficulties when dialing and texting 911. In case of an emergency, please call the following emergency numbers: Kent County 911 Admin (302)734-6050 Dover PD Admin (302)736-7111 Sussex County 911 (302)855-2970 Rehoboth Beach (302)227-2577

As Delaware’s Secretary of Education, I’m pleased to share the progress that Delaware students have made in Advanced Placement (AP) programs. This year, our state has seen significant growth in both AP participation and performance, reflecting our commitment to providing every student with access to challenging academic opportunities.

Read the in depth Review of Sony WH-1000XM4 Wireless Noise Cancelling headphones Audio Video. Know detailed info about Sony WH-1000XM4 Wireless Noise Cancelling headphones configuration, design and performance quality along with pros & cons, Digit rating, verdict based on user opinions/feedback.

1. Telangana train accident: Goods train derails near Peddapalli. Check full list of cancelled, diverted trains today | Today News  Mint
2. Goods train derails in Telangana's Peddapalli; 20 trains cancelled, 10 diverted  The Economic Times
3. 11 coaches of goods train derail in Telangana  The Times of India
4. Goods train derailment in Telangana affects rail traffic between Delhi and Chennai  Telangana Today
5. Goods train derails in Telangana's Peddapalli; 30 trains cancelled, several diverted  The Hindu

I am working with innovus on a huge design. I found some cells are placed far away from both timing start points and timing end points. I suspect some other timing paths may be near-critical that results in this sub-optimal cell placement; or innovus has to place the cell far away due to congestion of placement or routing.

Is there a way to see why innovus places/moves the cell during place_opt_design or ccopt_design?

Also, is there a way to highlight all timing start points or timing end points that go through a cell? There may be thousands of timing paths through this cell. I tried using report_timing and timing debugger but it is very painful to click the highlight box and highlight the timing paths one by one.

Thank you for your help!

anyone know what is "cell with Zero maximum clock transition time"  ?

not zero transition, not maximum transtion, it is zero maximum clock transition time.

it means X0 cell? (drive-strength)

can you explain? 

thanks :-)

Hi! 

I am attempting to create a very simple test pcell that contains a single Nmos 4 terminal device (Gate, Source, Drain, Backgate). However, unlike other devices I have used in the past, the backgate terminal of the device I wish to include within the pcell is an inherited connection, and the other 3 are physical terminals. Note that for the pcell master, I do not want any inherited connections, just physical pins. Hence I need to drive this inherited connection with a pin within my pcell. I started implementing the symbol and schematic first, ensuring I could obtain the correct connectivity, extract netlist, etc. I thought I had it hooked up correctly, but alas I am failing to export the CDL. Let me explain my current approach.

Schematic:

Create the 4 physical pins using a combination of dbCreateInst (for the pin isnt), dbMakeNet, dbCreateTerm and dbCreatePin.

Create the device instance using dbCreateInstByMasterName and setting the desired cdf parameters + callbacks.

For the physical terminals of the device, I'm using dbCreateConnByName to make the connection to the appropriate net that was created above.

For the inherited connection, I am creating a netSet property like so: dbCreateProp(newinst deviceTermName "netSet" netName)

Symbol:

Create the 4 physical pins using a combination of dbCreateRect, dbMakeNet, dbCreateTerm, dbCreatePin.

And then create whatever symbol design I wish using the likes of dbCreateRect, dbCreateLine, etc. 

Everything works fine when using a device without an inherited connection, so I'm guessing I'm missing something along this line... Also, if I copy the contents of the pcell schematic to a regular schematic view, do a check and save, the view extracts just fine. So I wonder if the check and save it fixing the connectivity that I may not have. 

Thanks for any possibly engagement or suggestions 🙂

Keelan

Hello All:

I am looking for help on the following, as I am new to Cadence tools [I have to use Cadence Innovus for Physical Design after Logic Synthesis using Synopsys Design Compiler, using Nangate 45 nm Open Cell Library]: while using Cadence Innovus, I would need to select a few specific nets to be routed through a specific metal layer. How can I do this on Innovus [are there any command(s)]? Also, would writing and sourcing a .tcl script [containing the command(s)] on the Innovus terminal after the Placement Stage of Physical Design be fine for this?

Secondly, is there a way in Innovus to manipulate layout components, such as changing some pin placements, wire directions (say for example, wire direction changed to facing east from west, etc.) or moving specific closely placed cells around (without violating timing constraints of course) using any command(s)/.tcl script? If so, would pin placement changes and constraining some closely placed cells to be moved apart be done after Floorplanning/Powerplanning (that is, prior to Placement) and the wire direction changes be done after Routing? 

While making the necessary changes, could I use the usual Innovus commands to perform Physical Design of the remaining nets/wires/pins/cells, etc., or would anything need modification for the remaining components as well?

I would finally need to dump the entire design containing all of this in a .def file.

I tried looking up but could only find matter on Virtuoso and SKILL scripting, but I'd be using Innovus GUI/terminal with Nangate 45 nm Open Cell Library. I know this is a lot, but I would greatly appreciate your help. Thanks in advance.

Riya

Hi,

I have synthesized a verilog code. When performing the pnr in innovus it is showing the error "Instance g5891__718 (similar for other) of the cell AND2_X6 has no physical library or has wrong dimension  values (<=0). Check your design setup to make sure the physical library is loaded in and attribute specified in library are correct.

When importing synthesized netlist in virtuoso then it says " Module AND2_X6, instantiated in the top module decoder, is not defined. Therefore the top module decoder will be imported as functional."

Please help what's going on here? 

Hi, I would be grateful if you can help me with this error which I get after trying to run an EMX simulation on a PCELL.

I've found very limited information in this forum. Thanks

Hi

I'm learning Hb analysis for the IIP3 simulation of a gilbert mixer. My f_RF=5GHz, f_LO=4GHz and f_IF=1GHz . I'm a bit confused about setting the 1st order and 3rd order harmonic at the Direct plot form. I have set 1st order harmonic = 1GHz and 3rd order harminic = 2*f_LO-f_RF = 3GHz and it gives me the following results.

This is my 1dB compression point result

Hb analysis window

Direct plot window

Please let me know if my harmonic selection is correct and whether I'm getting a correct IIP3 and P1db curve

Che(read more)

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DENPASAR — Several international airlines cancelled flights to and from Indonesia's resort island of Bali on Wednesday, after further eruptions of a volcano that has spewed ash clouds as high as 10 kilometres (32,808 feet) and forced thousands to evacuate.

Toyota has teamed up with JAXA since 2019 to develop the manned lunar rover — which it dubbed the Lunar Cruiser — that they hope can be put on the moon in 2029.

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Animals that hibernate need a way to keep their blood flowing as their body temperature drops, and it seems that the mechanical properties of red blood cells may be key

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They fight invaders, clear debris and tend neural connections, but sometimes microglia go rogue. Preventing this malfunction may offer new treatments for brain conditions including Alzheimer's

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Retrotransposable elements (RTEs) are common mobile genetic elements comprising ~42% of the human genome. RTEs play critical roles in gene regulation and function, but how they are specifically involved in complex diseases is largely unknown. Here, we investigate the cellular heterogeneity of RTEs using 12 single-cell transcriptome profiles covering three neurodegenerative diseases, Alzheimer's disease (AD), Parkinson's disease, and multiple sclerosis. We identify cell type marker RTEs in neurons, astrocytes, oligodendrocytes, and oligodendrocyte precursor cells that are related to these diseases. The differential expression analysis reveals the landscape of dysregulated RTE expression, especially L1s, in excitatory neurons of multiple neurodegenerative diseases. Machine learning algorithms for predicting cell disease stage using a combination of RTE and gene expression features suggests dynamic regulation of RTEs in AD. Furthermore, we construct a single-cell atlas of retrotransposable elements in neurodegenerative disease (scARE) using these data sets and features. scARE has six feature analysis modules to explore RTE dynamics in a user-defined condition. To our knowledge, scARE represents the first systematic investigation of RTE dynamics at the single-cell level within the context of neurodegenerative diseases.

During B-cell development, cells progress through multiple developmental stages, with the pro-B-cell stage defining commitment to the B-cell lineage. YY1 is a ubiquitous transcription factor that is capable of both activation and repression functions. We found here that knockout of YY1 at the pro-B-cell stage eliminates B lineage commitment. YY1 knockout pro-B cells can generate T lineage cells in vitro using the OP9-DL4 feeder system and in vivo after injection into sublethally irradiated Rag1^–/– mice. These T lineage-like cells lose their B lineage transcript profile and gain a T-cell lineage profile. Single-cell RNA-seq experiments showed that as YY1 knockout pro-B cells transition into T lineage cells in vitro, various cell clusters adopt transcript profiles representing a multiplicity of hematopoietic lineages, indicating unusual lineage plasticity. In addition, YY1 KO pro-B cells in vivo can give rise to other hematopoietic lineages in vivo. Evaluation of RNA-seq, scRNA-seq, ChIP-seq, and scATAC-seq data indicates that YY1 controls numerous chromatin-modifying proteins leading to increased accessibility of alternative lineage genes in YY1 knockout pro-B cells. Given the ubiquitous nature of YY1 and its dual activation and repression functions, YY1 may regulate commitment in multiple cell lineages.

Animal germline development and fertility rely on paralogs of general transcription factors that recruit RNA polymerase II to ensure cell type-specific gene expression. It remains unclear whether gene expression processes downstream from such paralog-based transcription is distinct from that of canonical RNA polymerase II genes. In Drosophila, the testis-specific TBP-associated factors (tTAFs) activate over a thousand spermatocyte-specific gene promoters to enable meiosis and germ cell differentiation. Here, we show that efficient termination of tTAF-activated transcription relies on testis-specific paralogs of canonical polymerase-associated factor 1 complex (PAF1C) proteins, which form a testis-specific PAF1C (tPAF). Consequently, tPAF mutants show aberrant expression of hundreds of downstream genes due to read-in transcription. Furthermore, tPAF facilitates expression of Y-linked male fertility factor genes and thus serves to maintain spermatocyte-specific gene expression. Consistently, tPAF is required for the segregation of meiotic chromosomes and male fertility. Supported by comparative in vivo protein interaction assays, we provide a mechanistic model for the functional divergence of tPAF and the PAF1C and identify transcription termination as a developmentally regulated process required for germline-specific gene expression.

Cellular plasticity in adult multicellular organisms is a protective mechanism that allows certain tissues to regenerate in response to injury. Considering that aging involves exposure to repeated injuries over a lifetime, it is conceivable that cell identity itself is more malleable—and potentially erroneous—with age. In this review, we summarize and critically discuss the available evidence that cells undergo age-related shifts in identity, with an emphasis on those that contribute to age-associated pathologies, including neurodegeneration and cancer. Specifically, we focus on reported instances of programs associated with dedifferentiation, biased differentiation, acquisition of features from alternative lineages, and entry into a preneoplastic state. As some of the most promising approaches to rejuvenate cells reportedly also elicit transient changes to cell identity, we further discuss whether cell state change and rejuvenation can be uncoupled to yield more tractable therapeutic strategies.

Antifolates are important for chemotherapy in non–small cell lung cancer (NSCLC). They mainly rely on reduced folate carrier (RFC) and proton-coupled folate transporter (PCFT) to enter cells. PCFT is supposed to be the dominant transporter of the two in tumors, as it operates optimally at acidic pH and has limited transport activity at physiological pH, whereas RFC operates optimally at neutral pH. In this study, we found RFC showed a slightly pH-dependent uptake of antifolates, with similar affinity values at pH 7.4 and 6.5. PCFT showed a highly pH-dependent uptake of antifolates, with an optimum pH of 6.0 for pemetrexed and 5.5 for methotrexate. The Michaelis-Menten constant (K_m) value of PCFT for pemetrexed at pH 7.4 was more than 10 times higher than that at pH 6.5. Interestingly, we found that antifolate accumulations mediated by PCFT at acidic pH were significantly affected by the efflux transporter, breast cancer resistance protein (BCRP). The highest pemetrexed concentration was observed at pH 7.0–7.4 after a 60-minute accumulation in PCFT-expressing cells, which was further evidenced by the cytotoxicity of pemetrexed, with the IC₅₀ value of pemetrexed at pH 7.4 being one-third of that at pH 6.5. In addition, the in vivo study indicated that increasing PCFT and RFC expression significantly enhanced the antitumor efficacy of pemetrexed despite the high expression of BCRP. These results suggest that both RFC and PCFT are important for antifolates accumulation in NSCLC, although there is an acidic microenvironment and high BCRP expression in tumors.

Pregnane X receptor (PXR) belongs to the nuclear receptor superfamily that plays a crucial role in hepatic physiologic and pathologic conditions. Phase separation is a process in which biomacromolecules aggregate and condense into a dense phase as liquid condensates and coexist with a dilute phase, contributing to various cellular and biologic functions. Until now, whether PXR could undergo phase separation remains unclear. This study aimed to investigate whether PXR undergoes phase separation. Analysis of the intrinsically disordered regions (IDRs) using algorithm tools indicated a low propensity of PXR to undergo phase separation. Experimental assays such as hyperosmotic stress, agonist treatment, and optoDroplets assay demonstrated the absence of phase separation for PXR. OptoDroplets assay revealed the inability of the fusion protein of Cry2 with PXR to form condensates upon blue light stimulation. Moreover, phase separation of PXR did not occur even though the mRNA and protein expression levels of PXR target, cytochrome P450 3A4, changed after sorbitol treatment. In conclusion, for the first time, these findings suggested that exogenous PXR does not undergo phase separation following activation or under hyperosmotic stress in nucleus of cells.

People with sickle cell disease (SCD) often experience chronic pain as well as unpredictable episodes of acute pain, which significantly affects their quality of life and life expectancy. Current treatment strategies for SCD-associated pain primarily rely on opioid analgesics, which have limited efficacy and cause serious adverse effects. Cannabis has emerged as a potential alternative, yet its efficacy remains uncertain. In this study, we investigated the antinociceptive effects of ⁹-tetrahydrocannabinol (THC), cannabis’ intoxicating constituent, in male HbSS mice, which express >99% human sickle hemoglobin, and male HbAA mice, which express normal human hemoglobin A, as a control. Acute THC administration (0.1–3 mg/kg^–1, i.p.) dose-dependently reduced mechanical and cold hypersensitivity in human sickle hemoglobin (HbSS) but not human normal hemoglobin A (HbAA) mice. In the tail-flick assay, THC (1 and 3 mg/kg^–1, i.p.) produced substantial antinociceptive effects in HbSS mice. By contrast, THC (1 mg/kg^–1, i.p.) did not alter anxiety-like behavior (elevated plus maze) or long-term memory (24-hour novel object recognition). Subchronic THC treatment (1 and 3 mg/kg^–1, i.p.) provided sustained relief of mechanical hypersensitivity but led to tolerance in cold hypersensitivity in HbSS mice. Together, the findings identify THC as a possible therapeutic option for the management of chronic pain in SCD. Further research is warranted to elucidate its mechanism of action and possible interaction with other cannabis constituents.

The aim of this study was to validate a previously developed deep learning model in 5 independent clinical trials. The predictive performance of this model was compared with the international prognostic index (IPI) and 2 models incorporating radiomic PET/CT features (clinical PET and PET models). Methods: In total, 1,132 diffuse large B-cell lymphoma patients were included: 296 for training and 836 for external validation. The primary outcome was 2-y time to progression. The deep learning model was trained on maximum-intensity projections from PET/CT scans. The clinical PET model included metabolic tumor volume, maximum distance from the bulkiest lesion to another lesion, SUV_peak, age, and performance status. The PET model included metabolic tumor volume, maximum distance from the bulkiest lesion to another lesion, and SUV_peak. Model performance was assessed using the area under the curve (AUC) and Kaplan–Meier curves. Results: The IPI yielded an AUC of 0.60 on all external data. The deep learning model yielded a significantly higher AUC of 0.66 (P < 0.01). For each individual clinical trial, the model was consistently better than IPI. Radiomic model AUCs remained higher for all clinical trials. The deep learning and clinical PET models showed equivalent performance (AUC, 0.69; P > 0.05). The PET model yielded the highest AUC of all models (AUC, 0.71; P < 0.05). Conclusion: The deep learning model predicted outcome in all trials with a higher performance than IPI and better survival curve separation. This model can predict treatment outcome in diffuse large B-cell lymphoma without tumor delineation but at the cost of a lower prognostic performance than with radiomics.

This single-center, single-arm, phase II trial (ChiCTR2100050057) investigated the ability of ¹⁸F-labeled fibroblast activation protein inhibitor ([¹⁸F]AlF-NOTA-FAPI-04, denoted as ¹⁸F-FAPI) PET/CT to predict the response to neoadjuvant camrelizumab plus chemotherapy (nCC) in locally advanced esophageal squamous cell carcinoma (LA-ESCC). Methods: This study included 32 newly diagnosed LA-ESCC participants who underwent ¹⁸F-FAPI PET/CT at baseline, of whom 23 also underwent scanning after 2 cycles of nCC. The participants underwent surgery after 2 cycles of nCC. Recorded PET parameters included maximum, peak, and mean SUVs and tumor-to-background ratios (TBRs), metabolic tumor volume, and total lesion FAP expression. PET parameters were compared between patient groups with good and poor pathologic responses, and the predictive performance for treatment response was analyzed. Results: The good and poor response groups each included 16 participants (16/32, 50.0%). On ¹⁸F-FAPI PET/CT, the posttreatment SUVs were significantly lower in good responders than in poor responders, whereas the changes in SUVs with treatment were significantly higher (all P < 0.05). SUV_max (area under the curve [AUC], 0.87; P = 0.0026), SUV_peak (AUC, 0.89; P = 0.0017), SUV_mean (AUC, 0.88; P = 0.0021), TBR_max (AUC, 0.86; P = 0.0031), and TBR_mean (AUC, 0.88; P = 0.0021) after nCC were significant predictors of pathologic response to nCC, with sensitivities of 63.64%–81.82% and specificities of 83.33%–100%. Changes in SUV_max (AUC, 0.81; P = 0.0116), SUV_peak (AUC, 0.82; P = 0.0097), SUV_mean (AUC, 0.81; P = 0.0116), and TBR_mean (AUC, 0.74; P = 0.0489) also were significant predictors of the pathologic response to nCC, with sensitivities and specificities in similar ranges. Conclusion: ¹⁸F-FAPI PET/CT parameters after treatment and their changes from baseline can predict the pathologic response to nCC in LA-ESCC participants.

We present a unique case of a single patient presenting with two mutationally distinct, PD-L1⁺ diffuse large B-cell lymphomas (DLBCLs). One of these DLBCLs demonstrated exceptionally high mutational burden (eight disease-associated variants and 41 variants of undetermined significance) with microsatellite instability (MSI) and an acquired PMS2 mutation with loss of PMS2 protein expression, detected postchemotherapy. This report, while highlighting the extent of possible tumor heterogeneity across separate clonal expansions as well as possible syndromic B-cell neoplasia, supports the notion that, although rare, PD-L1 expression and associated states permissive of high mutational burden (such as mismatch repair gene loss of function/MSI) should be more routinely considered in DLBCLs. Appropriate testing may be predictive of outcome and inform the utility of targeted therapy in these genetically diverse and historically treatment-refractory malignancies.