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[SAnews.gov.za] With the United Nations Framework Convention on Climate Change (COP29) taking place this week, South Africa expects the COP29 Presidency to enhance efforts to finalise the New Collective Quantified Goal on Finance (NCQG), which is a matter of great importance for developing economies.

Hi all,

I'm trying to model the clock of a clock doubler. The doubler consists of a delay cell and an XOR gate, which generates a pulse on both the rising and falling edge of the input clock. I've created a simple module to evaluate this. In this case, DEL1 and XOR2 are standard library cells. There is a don_touch constraint on both library cells as well as on clk_d.

module top (
input wire clk,
output reg Q);

//Doubler
wire clk_d;
wire clk_2x;
DEL1 u_delay (.I(clk),.Z(clk_d));
XOR2 u_xor (.A1(clk),.A2(clk_d),.Z(clk_2x));

//FF for connecting the clock to some leaf:
always @(posedge clk_2x) Q<=~Q;

endmodule

My SDC looks like this:

create_clock [get_ports {clk}] -name clk_i -period 100
set_clock_latency -rise 0.1 [get_pins u_xor/Z]
set_clock_latency -fall 0.4 [get_pins u_xor/Z]
create_generated_clock -name clk_2x -edges {1 1 2 2 3} -source clk [get_pins u_xor/Z]

The generated clock is correctly generated but the pulse width is zero. I would be expecting that the pulse width is the difference between fall and rise latency but is not applied:

report_clocks:

report_clocks -generated:

clk_2x is disconnected from the FF after syn_generic. What can I do to model some minimum pulse width? Will innovus later on model this correctly with the delay of DEL1?

Wild River Technology (WRT), the leading supplier of signal integrity measurement and optimization test fixtures for high-speed channels at data rates of up to 224G, has announced the availability of a new advanced channel modeling solution that helps achieve extreme signal integrity design to 70GHz. Read the press release. The CMP-70 program continues the industry-first simulation-to-measurement collaboration with Cadence that was initially established with the CMP-50. Significant resources were dedicated to the development of the CMP-70 by Cadence and WRT over almost three years. The CMP-70 will be on display at DesignCon 2025 , January 28-30, in Cadence booth 827 to benchmark the Cadence Clarity 3D Solver . “I am not a fan of hype-based programs that simply get attention,” remarked Alfred P. Neves, WRT’s co-founder and chief technical officer. “Both Cadence and Wild River brought substantial skills to the table in this project as we continued our industry-first simulation-to-measurement collaboration. The result is a proven, robust and accurate platform that brings extreme signal integrity to 70GHz designs. This application package has also been instrumental in demonstrating the robust 3D EM simulation capability of the Cadence Clarity solver.” “We’re delighted to continue the joint development and validation program with WRT that started with the CMP-50,” said Gary Lytle, product management director at Cadence. “The skilled and experienced signal integrity technologists that both companies bring to the program results in a superior signal integrity solution for our mutual customers.” CMP-70 Solution Features The solution is available both in a standard configuration and as a custom solution for customer-specific stackups and fabrication. The primary target application is to support a 3D EM solver analysis modeling versus the time- and frequency-domain measurement methodologies. The solution features include: The CMP-70 platform, assembled and 100% TDR NIST traceable tested, with custom stands Material Identification overview web-based meeting including anisotropic 3D material identification A cross-section PCB report and structures for using as-fabricated geometries Measured S-parameters, pre-tested for quality (passivity/causality and resampled for time domain simulations) A host of novel crosstalk structures suited for 112G HD level project analysis PCB layout design files (NDA required) An EDA starter library including loss models with industry-first accurate surface roughness models Comprehensive training available for 3D EM analysis – correspondence, material ID in X-Y and Z axis for a host of EDA tools Industry-First Hausdorff Technique The WRT application package also includes an industry-first modified Hausdorff (MHD) technique , included as MATLAB code. This algorithmic approach provides an accurate way to compare two sets of measurements in multi-dimensional space to determine how well they match. The technique is used to compare the results simulated by the Clarity solver with those measured on the CMP-70 platform. The methodology and initial results are shown in the figure below, where the figure of merit (FOM) is calculated from 10, 35, and finally to 50GHz. The MHD algorithm requires a MATLAB license, but WRT also accommodates customer data as another option, where WRT provides the comparison between measured and simulated data. Additional Resources If you are attending DesignCon 2025 , be sure to stop by Cadence booth 827 to see WRT’s CMP-70 advanced channel modeling solution in action with the Clarity 3D Solver. Check out our on-demand webinar, " Validating Clarity 3D Solver Accuracy Through Measurement Correlation ." Learn more about the CMP-70 solution and the Clarity 3D Solver . For more information about Cadence’s full suite of integrated multiphysics simulation solutions, download our Multiphysics System Analysis Solutions Portfolio .

DDR5 DIMM Architectures The DDR5 generation of Double Data Rate DRAM memories has experienced rapid adoption in recent years. In particular, the JEDEC-defined DDR5 Dual Inline Memory Module (DIMM) cards have become a mainstay for systems looking for high-density, high-bandwidth, off-chip random access memory[1]. Within a short time, the DIMM architecture evolved from an interconnected hierarchy of only SDRAM memory devices (UDIMM[2]) to complex subsystems of interconnected components (RDIMM/LRDIMM/MRDIMM[3]). DIMM Designs and Popular Verification Use Cases The growing complexity of the DIMMs presented a challenge for pre-silicon verification engineers who could no longer simply validate against single DDR5 SDRAM memory models. They needed to consider how their designs would perform against DIMMs connected to each channel and operating at gigahertz clock speeds. To address this verification gap, Cadence developed DDR5 DIMM Memory Models that encapsulated all of the architectural complexities presented by real-world DIMMs based on a robust, easy-to-use, easy-to-debug, and easy-to-reconfigure methodology. This memory-subsystem-in-a-single-instance model has seen explosive adoption among the traditional IP Developer and SOC Integrator customers of Cadence Memory Models. The Cadence DIMM models act as a single unit with all of the relevant DIMM components instantiated and interconnected within, and with all AC/Timing parameters among the various components fully matched out-of-the-box, based on JEDEC specifications as well as datasheets of actual devices in the market. The typical use-case for the DIMM models has been where the DUT is a DDR5 Memory Controller + PHY IP stack, and the validation plan mandated compliance with the JEDEC standards and Memory Device vendor datasheets. Unique Use Case for the DIMM Discrete Component Models Although the Cadence DIMM models have enjoyed tremendous proliferation because of their cohesive implementation and unified user API, the actual DIMM Models are built on top of powerful, flexible discrete component models, each of which was designed to stand on its own as a complete SystemVerilog UVM-based VIP. All of these discrete component models exist in the Cadence VIP Catalog as standalone VIPs, complete with their own protocol compliance checking capabilities and their own configuration mappings comprehensively modeling individual AC/Timing parameters. Because of this deliberate design decision, the Cadence DIMM Discrete Component Models can support a unique use-case scenario. Some users seek to develop IC Designs for the various DIMM components. Such users need verification environments that can model the individual components of a DIMM and allow them the option to replace one or another component with their Component Design IP. They can then validate that their component design is fully compatible with the rest of the components on the DIMM and meets the integrity of the overall DIMM compliance with JEDEC standards or Memory Vendor datasheets. The Cadence Memory VIP portfolio today includes various examples that demonstrate how customers can create DIMM “wrappers” by selecting from among the available DIMM discrete component models and “stitching” them together to build their own custom testbench around their specific Component Design IP. A Solution for Unique Component Scenarios The Cadence DDR5 DIMM Memory Models and DIMM Discrete Component Models can provide users with a flexible approach to validating their specific component designs with a fully populated pre-silicon environment. Augmented Verification Capabilities When the DIMM “wrapper” model is augmented with the Cadence DFI VIP[4] that can simulate an MC+PHY stack and offers a SystemVerilog UVM test API to the verification engineer, the overall testbench transforms into a formidable pre-silicon validation vehicle. The DFI VIP is designed as a combination of an independent DFI MC VIP and a DFI PHY VIP connected to each other via the DFI Standard Interface and capable of operating seamlessly as a single unit. It presents a UVM Sequence API to the user into the DFI MC VIP with the Memory Interface of the PHY VIP connected to the DIMM “wrapper” model. With this testbench in hand, the user can then fully take advantage of the UVM Sequence Library that comes with the DFI VIP to enable deep validation of their Component Design inside the DIMM “wrapper” model. Verification Capabilities Further Enhanced A possible further enhancement comes with the potential addition of an instance of the Cadence DIMM Memory Model in a Passive Monitor mode at the DRAM Memory Interface. The DIMM Passive Monitor consumes the same configuration describing the DIMM “wrapper” in the testbench, and thus can act as a reference model for the DIMM wrapper. If the DIMM Passive Monitor responds successfully to accesses from the DFI VIP, but the DIMM wrapper does not, then it exposes potential bugs in the DUT Components or in the settings of their AC/Timing parameters inside the DIMM wrapper. Debuggability, Interface Visibility, and Protocol Compliance One of the key benefits of the DIMM Discrete Component Models that become manifest, whether in terms of the unique use-case scenario described here, or when working with the wholly unified DDR5 DIMM Memory Models, is the increased debuggability of the protocol functionality. The intentional separation of the discrete components of a DIMM allows the user to have full visibility of the memory traffic at every datapath landmark within a DIMM structure. For example, in modeling an LRDIMM or MRDIMM, the interface between the RCD component and the SDRAM components, the interface between the RCD component and the DB components, and the interface between the SDRAM components and the DB components—all are visible and accessible to the user. The user has full access to dump the values and states of the wire interconnects at these interfaces to the waveform viewer and thus can observe and correlate the activity against any protocol violations flagged in the trace logs by any one or more of the DIMM Discrete Component Models. Access to these interfaces is freely available when using the DIMM Discrete Component Models. On the unified DDR5 DIMM Memory Models, a feature called Debug Ports enables the same level of visibility into the individual interconnects amidst the SDRAM components, RCD components, and DB components. When combined with the Waveform Debugger[5] capability that comes built-in with the VIPs and Memory Models offered by Cadence and used with the Cadence Verisium Debug[6] tool, the enhanced debuggability becomes a powerful platform. With these debug accesses enabled, the user can pull out transaction streams, chip state and bank state streams, mode register streams, and error message streams all right next to their RTL signals in the same Verisium Debug waveform viewer window to debug failures all in one place. The Verisium Debug tool also parses all of the log files to probe and extract messages into a fully integrated Smart Log in a tabbed window fully hyperlinked to the waveform viewer, all at your fingertips. A Solution for Every Scenario Cadence's DDR5 DIMM Memory Models and DIMM Discrete Component Models , partnered with the Cadence DFI VIP, can provide users with a robust and flexible approach to validating their designs thoroughly and effectively in pre-silicon verification environments ahead of tapeout commitments. The solution offers unparalleled latitude in debuggability when the Debug Ports and Waveform Debugger functions of the Memory Models are switched on and boosted with the use of the Cadence Verisium Debug tool. [1] Shyam Sharma, DDR5 DIMM Design and Verification Considerations , 13 Jan 2023. [2] Shyam Sharma, DDR5 UDIMM Evolution to Clock Buffered DIMMs (CUDIMM) , 23 Sep 2024. [3] Kos Gitchev, DDR5 12.8Gbps MRDIMM IP: Powering the Future of AI, HPC, and Data Centers , 26 Aug 2024. [4] Chetan Shingala and Salehabibi Shaikh, How to Verify JEDEC DRAM Memory Controller, PHY, or Memory Device? , 29 Mar 2022. [5] Rahul Jha, Cadence Memory Models - The Gold Standard , 15 Apr 2024. [6] Manisha Pradhan, Accelerate Design Debugging Using Verisium Debug , 11 Jul 2023.

I am looking for LM117 Pspice model. Can someone send me the file. Thank you

With the latest release (Sigrity and Systems Analysis 2022.1 HF2) of Clarity  3D Solver, support for encrypted component models is now available. With this functionality, vendors that supply 3D components, such as connectors, can now merge their...(read more)

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Janet Temitope Adetu is a certified corporate etiquette and brand analyst as well as a professional image and international protocol consultant. The founder of JSK Etiquette Consortium, a thriving business etiquette, behavourial change and professional image enhancement firm, talks about her profession and how she juggles this with her marriage in this interview with QISMAT YINUS. Excerpts: What exactly do you do at JSK Etiquette Consortium? JSK Etiquette Consortium is a Corporate Etiquette, Professional Image and International Protocol Consultancy. We help corporate organisations leverage their human capital through personal and professional image projection, branding, impression management and leadership. Our objective […]

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Title: Scientists Grow 'Model Brain' From Stem Cells
Category: Health News
Created: 8/28/2013 2:35:00 PM
Last Editorial Review: 8/29/2013 12:00:00 AM

Pulmonary hypertension (PH) is highly prevalent in patients with interstitial lung disease (ILD) and is associated with increased morbidity and mortality. Widely available noninvasive screening tools are warranted to identify patients at risk for PH, especially severe PH, that could be managed at expert centres. This review summarises current evidence on noninvasive diagnostic modalities and prediction models for the timely detection of PH in patients with ILD. It critically evaluates these approaches and discusses future perspectives in the field. A comprehensive literature search was carried out in PubMed and Scopus, identifying 39 articles that fulfilled inclusion criteria. There is currently no single noninvasive test capable of accurately detecting and diagnosing PH in ILD patients. Estimated right ventricular pressure (RVSP) on Doppler echocardiography remains the single most predictive factor of PH, with other indirect echocardiographic markers increasing its diagnostic accuracy. However, RVSP can be difficult to estimate in patients due to suboptimal views from extensive lung disease. The majority of existing composite scores, including variables obtained from chest computed tomography, pulmonary function tests and cardiopulmonary exercise tests, were derived from retrospective studies, whilst lacking validation in external cohorts. Only two available scores, one based on a stepwise echocardiographic approach and the other on functional parameters, predicted the presence of PH with sufficient accuracy and used a validation cohort. Although several methodological limitations prohibit their generalisability, their use may help physicians to detect PH earlier. Further research on the potential of artificial intelligence may guide a more tailored approach, for timely PH diagnosis.

Exercise significantly alters human physiological functions, such as increasing cardiac output and muscle blood flow and decreasing glomerular filtration rate (GFR) and liver blood flow, thereby altering the absorption, distribution, metabolism, and excretion of drugs. In this study, we aimed to establish a database of human physiological parameters during exercise and to construct equations for the relationship between changes in each physiological parameter and exercise intensity, including cardiac output, organ blood flow (e.g., muscle blood flow and kidney blood flow), oxygen uptake, plasma pH and GFR, etc. The polynomial equation P = a_iHRⁱ was used for illustrating the relationship between the physiological parameters (P) and heart rate (HR), which served as an index of exercise intensity. The pharmacokinetics of midazolam, quinidine, digoxin, and lidocaine during exercise were predicted by a whole-body physiologically based pharmacokinetic (WB-PBPK) model and the developed database of physiological parameters following administration to 100 virtual subjects. The WB-PBPK model simulation results showed that most of the observed plasma drug concentrations fell within the 5^th–95^th percentiles of the simulations, and the estimated peak concentrations (C_max) and area under the curve (AUC) of drugs were also within 0.5–2.0 folds of observations. Sensitivity analysis showed that exercise intensity, exercise duration, medication time, and alterations in physiological parameters significantly affected drug pharmacokinetics and the net effect depending on drug characteristics and exercise conditions. In conclusion, the pharmacokinetics of drugs during exercise could be quantitatively predicted using the developed WB-PBPK model and database of physiological parameters.

SIGNIFICANCE STATEMENT

This study simulated real-time changes of human physiological parameters during exercise in the WB-PBPK model and comprehensively investigated pharmacokinetic changes during exercise following oral and intravenous administration. Furthermore, the factors affecting pharmacokinetics during exercise were also revealed.

Early detection of drug-drug interactions (DDIs) can facilitate timely drug development decisions, prevent unnecessary restrictions on patient enrollment, resulting in clinical study populations that are not representative of the indicated study population, and allow for appropriate dose adjustments to ensure safety in clinical trials. All of these factors contribute to a streamlined drug approval process and enhanced patient safety. Here we describe a new approach for early prediction of the magnitude of change in exposure for cytochrome P450 (P450) CYP3A4-related DDIs of small-molecule anticancer drugs based on the model-based extrapolation of human-CYP3A4-transgenic mice pharmacokinetics to humans. Victim drugs brigatinib and lorlatinib were evaluated with the new approach in combination with the perpetrator drugs itraconazole and rifampicin. Predictions of the magnitude of change in exposure deviated at most 0.99- to 1.31-fold from clinical trial results for inhibition with itraconazole, whereas exposure predictions for the induction with rifampicin were less accurate, with deviations of 0.22- to 0.48-fold. Results for the early prediction of DDIs and their clinical impact appear promising for CYP3A4 inhibition, but validation with more victim and perpetrator drugs is essential to evaluate the performance of the new method.

SIGNIFICANCE STATEMENT

The described method offers an alternative for the early detection and assessment of potential clinical impact of CYP3A4-related drug-drug interactions. The model was able to adequately describe the inhibition of CYP3A4 metabolism and the subsequent magnitude of change in exposure. However, it was unable to accurately predict the magnitude of change in exposure of victim drugs in combination with an inducer.

ABSTRACTWe document the effort over the last 30 years to respond to the call by women advocates at the International Conference on Population and Development for more woman-initiated single or dual-purpose contraceptive methods by developing the Caya contoured diaphragm, an innovative diaphragm designed to meet the needs of women and their partners and expand options for nonhormonal barrier contraception. We describe the complex and interrelated set of activities undertaken to develop the product using a human-centered design process and how we are working to create a corollary sustainable market. This review includes the evidence generated around improved acceptability among couples in low- and middle-income countries and depicts challenges and practical actions on how to dispel misconceptions about diaphragm use. Importantly, we share programmatic lessons learned on increasing universal access to this new sexual and reproductive health technology. Following our new model for increasing access to new and underutilized methods, Caya is now registered and being marketed in nearly 40 countries worldwide.

ABSTRACTBackground: This analysis aimed to evaluate the effectiveness of eliciting sexual partners from HIV-positive clients using the elicitation box model (where an HIV-positive index can report sexual contacts on paper and insert in a box for a health care provider to contact at a later time) compared to the conventional model (in which a health care provider elicits sexual contacts directly from clients) in Akwa Ibom, Southern Nigeria.Methods: Between March 2021 and April 2022, data were collected from index testing registers at 4 health facilities with a high volume of HIV clients currently on treatment in 4 local government areas in Akwa Ibom State. Primary outcome analyzed was the elicitation ratio (number of partners elicited per HIV-index offered index testing services). Secondary outcomes were the index testing acceptance (index HIV-positive clients accepted index testing service), testing coverage (partners tested for HIV from a list of partners elicited from HIV-index accepted index testing services), testing yield (index partners identified HIV positive from index partners HIV-tested), and linkage rate (index partners identified HIV positive and linked to antiretroviral therapy).Results: Of the total 2,705 index clients offered index testing services, 91.9% accepted, with 2,043 and 439 indexes opting for conventional elicitation and elicitation box models, respectively. A total of 3,796 sexual contacts were elicited: 2,546 using the conventional model (elicitation ratio=1:1) and 1,250 using the elicitation box model (elicitation ratio=1:3). Testing coverage was significantly higher in the conventional compared to the elicitation box model (P<.001). However, there was no significant difference in the testing yield (P=.81) and linkage rate using the conventional compared to elicitation box models (P=.13).Conclusion: The implementation of the elicitation box model resulted in an increase in partner elicitation compared to the conventional model. Increasing the testing coverage by implementing the elicitation box model should be considered.

Chronic pain conditions affect nearly 20% of the population in the United States. Current medical interventions, such as opioid drugs, are effective at relieving pain but are accompanied by many undesirable side effects. This is one reason increased numbers of chronic pain patients have been turning to Cannabis for pain management. Cannabis contains many bioactive chemical compounds; however, current research looking into lesser-studied minor cannabinoids in Cannabis lacks uniformity between experimental groups and/or excludes female mice from investigation. This makes it challenging to draw conclusions between experiments done with different minor cannabinoid compounds between laboratories or parse out potential sex differences that could be present. We chose five minor cannabinoids found in lower quantities within Cannabis: cannabinol (CBN), cannabidivarin (CBDV), cannabigerol (CBG), 8-tetrahydrocannabinol (8-THC), and 9-tetrahydrocannabivarin (THCV). These compounds were then tested for their cannabimimetic and pain-relieving behaviors in a cannabinoid tetrad assay and a chemotherapy-induced peripheral neuropathy (CIPN) pain model in male and female CD-1 mice. We found that the minor cannabinoids we tested differed in the cannabimimetic behaviors evoked, as well as the extent. We found that CBN, CBG, and high-dose 8-THC evoked some tetrad behaviors in both sexes, while THCV and low-dose 8-THC exhibited cannabimimetic tetrad behaviors only in females. Only CBN efficaciously relieved CIPN pain, which contrasts with reports from other researchers. Together these findings provide further clarity to the pharmacology of minor cannabinoids and suggest further investigation into their mechanism and therapeutic potential.

SIGNIFICANCE STATEMENT

Minor cannabinoids are poorly studied ligands present in lower levels in Cannabis than cannabinoids like THC. In this study, we evaluated five minor cannabinoids (CBN, CBDV, CBG, THCV, and 8-THC) for their cannabimimetic and analgesic effects in mice. We found that four of the five minor cannabinoids showed cannabimimetic activity, while one was efficacious in relieving chronic neuropathic pain. This work is important in further evaluating the activity of these drugs, which are seeing wider public use with marijuana legalization.

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.

SIGNIFICANCE STATEMENT

The study explores ⁹-tetrahydrocannabinol (THC)’s efficacy in alleviating pain in sickle cell disease (SCD) using a humanized mouse model. Findings indicate that acute THC administration reduces mechanical and cold hypersensitivity in SCD mice without impacting emotional and cognitive dysfunction. Subchronic THC treatment offers sustained relief of mechanical hypersensitivity but leads to cold hypersensitivity tolerance. These results offer insights into THC's potential as an alternative pain management option in SCD, highlighting both its benefits and limitations.

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.

The role of gastrin-releasing peptide receptor (GRPR) in various diseases, including cancer, has been extensively studied and has emerged as a promising therapeutic target. In this study, we successfully achieved the use of [²¹²Pb]Pb-DOTAM-GRPR1, comprising the α-particle generator, ²¹²Pb, combined with a GRPR-targeting peptide, GRPR1, in a prostate cancer model. Methods: Pharmacokinetics, toxicity, radiation dosimetry, and efficacy were assessed in GRPR-positive prostate tumor–bearing mice after intravenous administration of [²¹²Pb]Pb-DOTAM-GRPR1 (where DOTAM is 1,4,7,10-tetrakis(carbamoylmethyl)-1,4,7,10-tetraazacyclododecane). Results: Preclinical studies have shown tumor targeting of up to 5 percent injected dose per gram over 24 h, and optimization of the drug formulation and quantity has led to minimized oxidation and off-target binding, respectively. Particularly, an increase in peptide amount from 28 to 280 ng was shown to reduce off-target uptake, especially at the level of the pancreas, by about 30%. Furthermore, dosimetry studies confirmed the kidney as the dose-limiting organ, and toxicity studies revealed that a nontoxic dose of up to 1,665 kBq could be injected into mice. Efficacy studies indicated a median survival time of 9 wk in the control group, which received only a buffer solution, compared with 19 wk in the group that received 4 injections of 370 kBq at 3-wk intervals. Conclusion: Taken together, these combined data demonstrate the safety, tolerability, and efficacy of [²¹²Pb]Pb-DOTAM-GRPR1, thus warranting further exploration in clinical trials.

This review explores the concept of synergy in pharmacology, emphasizing its importance in optimizing treatment outcomes through the combination of drugs with different mechanisms of action. Synergy, defined as an effect greater than the expected additive effect elicited by individual agents according to specific predictive models, offers a promising approach to enhance therapeutic efficacy while minimizing adverse events. The historical evolution of synergy research, from ancient civilizations to modern pharmacology, highlights the ongoing quest to understand and harness synergistic interactions. Key concepts, such as concentration-response curves, additive effects, and predictive models, are discussed in detail, emphasizing the need for accurate assessment methods throughout translational drug development. Although various mathematical models exist for synergy analysis, selecting the appropriate model and software tools remains a challenge, necessitating careful consideration of experimental design and data interpretation. Furthermore, this review addresses practical considerations in synergy assessment, including preclinical and clinical approaches, mechanism of action, and statistical analysis. Optimizing synergy requires attention to concentration/dose ratios, target site localization, and timing of drug administration, ensuring that the benefits of combination therapy detected bench-side are translatable into clinical practice. Overall, the review advocates for a systematic approach to synergy assessment, incorporating robust statistical analysis, effective and simplified predictive models, and collaborative efforts across pivotal sectors, such as academic institutions, pharmaceutical companies, and regulatory agencies. By overcoming critical challenges and maximizing therapeutic potential, effective synergy assessment in drug development holds promise for advancing patient care.

Significance Statement

Combining drugs with different mechanisms of action for synergistic interactions optimizes treatment efficacy and safety. Accurate interpretation of synergy requires the identification of the expected additive effect. Despite innovative models to predict the additive effect, consensus in drug-drug interactions research is lacking, hindering the bench-to-bedside development of combination therapies. Collaboration among science, industry, and regulation is crucial for advancing combination therapy development, ensuring rigorous application of predictive models in clinical settings.

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The software used to control a robot is normally highly adapted to its specific physical set up. But now researchers have created a single general-purpose robotic control policy that can operate robotic arms, wheeled robots, quadrupeds, and even drones.

One of the biggest challenges when it comes to applying machine learning to robotics is the paucity of data. While computer vision and natural language processing can piggyback off the vast quantities of image and text data found on the Internet, collecting robot data is costly and time-consuming.

To get around this, there have been growing efforts to pool data collected by different groups on different kinds of robots, including the Open X-Embodiment and DROID datasets. The hope is that training on diverse robotics data will lead to “positive transfer,” which refers to when skills learned from training on one task help to boost performance on another.

The problem is that robots often have very different embodiments—a term used to describe their physical layout and suite of sensors and actuators—so the data they collect can vary significantly. For instance, a robotic arm might be static, have a complex arrangement of joints and fingers, and collect video from a camera on its wrist. In contrast, a quadruped robot is regularly on the move and relies on force feedback from its legs to maneuver. The kinds of tasks and actions these machines are trained to carry out are also diverse: The arm may pick and place objects, while the quadruped needs keen navigation.

That makes training a single AI model for robots on these large collections of data challenging, says Homer Walke, a Ph.D. student at the University of California, Berkeley. So far, most attempts have either focused on data from a narrower selection of similar robots or researchers have manually tweaked data to make observations from different robots more similar. But in research to be presented at the Conference on Robot Learning (CoRL) in Munich in November, they unveiled a new model called CrossFormer that can train on data from a diverse set of robots and control them just as well as specialized control policies.

“We want to be able to train on all of this data to get the most capable robot,” says Walke. “The main advance in this paper is working out what kind of architecture works the best for accommodating all these varying inputs and outputs.”

How to control diverse robots with the same AI model

The team used the same model architecture that powers large language model, known as a transformer. In many ways, the challenge the researchers were trying to solve is not dissimilar to that facing a chatbot, says Walke. In language modeling, the AI has to to pick out similar patterns in sentences with different lengths and word orders. Robot data can also be arranged in a sequence much like a written sentence, but depending on the particular embodiment, observations and actions vary in length and order too.

“Words might appear in different locations in a sentence, but they still mean the same thing,” says Walke. “In our task, an observation image might appear in different locations in the sequence, but it’s still fundamentally an image and we still want to treat it like an image.”

UC Berkeley/Carnegie Mellon University

Most machine learning approaches work through a sequence one element at a time, but transformers can process the entire stream of data at once. This allows them to analyze the relationship between different elements and makes them better at handling sequences that are not standardized, much like the diverse data found in large robotics datasets.

Walke and his colleagues aren’t the first to train transformers on large-scale robotics data. But previous approaches have either trained solely on data from robotic arms with broadly similar embodiments or manually converted input data to a common format to make it easier to process. In contrast, CrossFormer can process images from cameras positioned above a robot, at head height or on a robotic arms wrist, as well as joint position data from both quadrupeds and robotic arms, without any tweaks.

The result is a single control policy that can operate single robotic arms, pairs of robotic arms, quadrupeds, and wheeled robots on tasks as varied as picking and placing objects, cutting sushi, and obstacle avoidance. Crucially, it matched the performance of specialized models tailored for each robot and outperformed previous approaches trained on diverse robotic data. The team even tested whether the model could control an embodiment not included in the dataset—a small quadcopter. While they simplified things by making the drone fly at a fixed altitude, CrossFormer still outperformed the previous best method.

“That was definitely pretty cool,” says Ria Doshi, an undergraduate student at Berkeley. “I think that as we scale up our policy to be able to train on even larger sets of diverse data, it’ll become easier to see this kind of zero shot transfer onto robots that have been completely unseen in the training.”

The limitations of one AI model for all robots

The team admits there’s still work to do, however. The model is too big for any of the robots’ embedded chips and instead has to be run from a server. Even then, processing times are only just fast enough to support real-time operation, and Walke admits that could break down if they scale up the model. “When you pack so much data into a model it has to be very big and that means running it for real-time control becomes difficult.”

One potential workaround would be to use an approach called distillation, says Oier Mees, a postdoctoral research at Berkley and part of the CrossFormer team. This essentially involves training a smaller model to mimic the larger model, and if successful can result in similar performance for a much smaller computational budget.

But of more importance than the computing resource problem is that the team failed to see any positive transfer in their experiments, as CrossFormer simply matched previous performance rather than exceeding it. Walke thinks progress in computer vision and natural language processing suggests that training on more data could be the key.

Others say it might not be that simple. Jeannette Bohg, a professor of robotics at Stanford University, says the ability to train on such a diverse dataset is a significant contribution. But she wonders whether part of the reason why the researchers didn’t see positive transfer is their insistence on not aligning the input data. Previous research that trained on robots with similar observation and action data has shown evidence of such cross-overs. “By getting rid of this alignment, they may have also gotten rid of this significant positive transfer that we’ve seen in other work,” Bohg says.

It’s also not clear if the approach will boost performance on tasks specific to particular embodiments or robotic applications, says Ram Ramamoorthy, a robotics professor at Edinburgh University. The work is a promising step towards helping robots capture concepts common to most robots, like “avoid this obstacle,” he says. But it may be less useful for tackling control problems specific to a particular robot, such as how to knead dough or navigate a forest, which are often the hardest to solve.

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