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Objective: To study the safety and efficacy of algorithmically controlled electroporation (ACE) against spontaneous equine melanoma. Methods: A custom temperature sensing coaxial electrode was paired with a high voltage pulse generation system with integrated temperature feedback controls. Computational modeling and ex vivo studies were conducted to evaluate the system's ability to achieve and maintain target temperatures. Twenty-five equine melanoma tumors were treated with a 2000 V protocol consisting of a 2-5-2 waveform, 45 °C temperature set point, and integrated energized times of 0.005 s, 0.01 s, or 0.02 s (2500x, 5000x, and 10000x 2 μs pulses, respectively). Patients returned 20–50 days post treatment to determine the efficacy of the treatment. Results: ACE temperature control algorithms successfully achieved and maintained target temperatures in a diverse population of spontaneous tumors with significant variation in tissue impedance. All treatments were completed successfully without and without adverse events. Complete response rates greater than 93% were achieved in all treatment groups. Conclusion: ACE is a safe and effective treatment for spontaneous equine melanoma. The temperature control algorithm enabled rapid delivery of electroporation treatments without prior knowledge of tissue electrical or thermal properties and could adjust to real time changes in tissue properties. Significance: Real time temperature control in electroporation procedures enables treatments near critical structures where thermal damage is contraindicated. Unlike standard approaches, ACE protocols do not require extensive pretreatment planning or knowledge of tissue properties to determine an optimal energy delivery rate and they can account for changes in tissue state (e.g., perfusion) in real time to simultaneously minimize treatment time and potential for thermal damage.

Propylene glycol (PG) demonstrates greater efficacy than other sugar polyols. However, the attributes it confers for toxicity and possible co-formulation with other ingredients are unknown. To evaluate this, α-glucosidase and glucose oxidase reactions were performed in Aedes aegypti(L.) (Diptera: Culicidae) to categorize if PG behaves similarly to prior studied sugar alcohols. A combination of no-choice and choice assays was used to determine effective ratios of PG and sucrose, competitiveness against a control of 10% sucrose, and whether mosquitoes recovered from PG consumption. The final trials included β-cyclodextrin encapsulated cinnamon leaf oil, clove stem oil, patchouli oil, garlic oil, cedarwood oil, and papaya seed oil formulated with 5% sucrose + 5% PG. PG functioned as a linear competitive inhibitor of α-glucosidase. The efficacy of PG was synergized by co-ingestion with equivalent ratios of sucrose. Unlike the high diuretic response to other sugar alcohols, PG resulted in diminished excretion regardless of being co-formulated with sucrose or terpenoids. PG is not especially competitive against unadulterated sugar meals but is likewise not clearly repellent. Although mosquitoes did not recover from ingestion of the glycol meals, there was no indication that mortality would continue to accumulate once the treatments were removed. Of the terpenoids tested, cinnamon and patchouli caused ~50% or less mortality; garlic, cedarwood, and clove caused 80-90% mortality; and papaya seed caused 100% mortality, exceeding all other test groups and the formulation blank. PG is a useful supporting ingredient in attractive toxic sugar bait formulations with flexibility in formulation.

Machine learning has the potential to provide tremendous value to life sciences by providing models that aid in the discovery of new molecules and reduce the time for new products to come to market. Chemical reactions play a significant role in these fields, but there is a lack of high-quality open-source chemical reaction data sets for training machine learning models. Herein, we present ORDerly, an open-source Python package for the customizable and reproducible preparation of reaction data stored in accordance with the increasingly popular Open Reaction Database (ORD) schema. We use ORDerly to clean United States patent data stored in ORD and generate data sets for forward prediction, retrosynthesis, as well as the first benchmark for reaction condition prediction. We train neural networks on data sets generated with ORDerly for condition prediction and show that data sets missing key cleaning steps can lead to silently overinflated performance metrics. Additionally, we train transformers for forward and retrosynthesis prediction and demonstrate how non-patent data can be used to evaluate model generalization. By providing a customizable open-source solution for cleaning and preparing large chemical reaction data, ORDerly is poised to push forward the boundaries of machine learning applications in chemistry.

Metal-Organic Frameworks (MOFs) have gained traction as an adsorbent due to their high surface area and porosity. MIL-101(Fe), a MOF that has been used for removing dyes in water by adsorption, faces the problem of being inseparable from water after use. To get around this difficulty, MIL-101(Fe) was incorporated into composite beads consisting of polymers Chitosan (CS), and Polyvinyl Alcohol (PVA) crosslinked with Glutaraldehyde (GLA) to remove Methyl Orange (MO) from water. The resulting CS/MIL-101(Fe)/PVA beads were optimized based on the right combination of synthesis parameters that gave the highest percent MO removal. It was found that the maximum MO removal can be achieved by beads made of 1500 ppm MIL-101(Fe), 2.0 % PVA, crosslinked in 2.5% GLA. Using FTIR analysis and SEM imaging, the beads exhibited favorable properties for adsorption, as shown by their coarse and porous structure. The beads proved viable for adsorption, exhibiting a percent MO removal of 69.62% upon validation.

ABSTRACTKobi Kabalek interviews Henry (Hank) Greenspan about his sustained collaborations with Holocaust survivors, his questioning of conventional concepts in the field such as ‘testimony’ and ‘trauma,’ his convictions and strategies as a teacher, and his drive to embody some of what he has learned in playwriting and dramatic performance. Among other provocative assertions, Greenspan suggests that all work in Holocaust Studies is inevitably some version of our owntestimony, even though we were not there. That is why scholars must also listen more deeply to each other, interpret collaboratively, and learn together.

Federated learning (FL) is an emerging machine learning paradigm for training models across multiple edge devices holding local data sets, without explicitly exchanging the data. Recently, over-the-air (OTA) FL has been suggested to reduce the bandwidth and energy consumption, by allowing the users to transmit their data simultaneously over a Multiple Access Channel (MAC). However, this approach results in channel noise directly affecting the optimization procedure, which may degrade the accuracy of the trained model. In this paper we jointly exploit the prior distribution of local weights and the channel distribution, and develop an OTA FL algorithm based on a Bayesian approach for signal aggregation. Our proposed technique, dubbed Bayesian Air Aggregation Federated learning (BAAF), is shown to effectively mitigate noise and fading effects induced by the channel. To handle statistical heterogeneity of users data, which is a second major challenge in FL, we extend BAAF to allow for appropriate local updates by the users and develop the Controlled Bayesian Air Aggregation Federated-learning (COBAAF) algorithm. In addition to using a Bayesian approach to average the channel output, COBAAF controls the drift in local updates using a judicious design of correction terms. We analyze the convergence of the learned global model using BAAF and COBAAF in noisy and heterogeneous environments, showing their ability to achieve a convergence rate similar to that achieved over error-free channels. Simulation results demonstrate the improved convergence of BAAF and COBAAF over existing methods in machine learning tasks.

We consider the problem of multi-flow transmission in wireless networks, where data signals from different flows can interfere with each other due to mutual interference between links along their routes, resulting in reduced link capacities. The objective is to develop a multi-flow transmission strategy that routes flows across the wireless interference network to maximize the network utility. However, obtaining an optimal solution is computationally expensive due to the large state and action spaces involved. To tackle this challenge, we introduce a novel algorithm called Dual-stage Interference-Aware Multi-flow Optimization of Network Data-signals (DIAMOND). The design of DIAMOND allows for a hybrid centralized-distributed implementation, which is a characteristic of 5G and beyond technologies with centralized unit deployments. A centralized stage computes the multi-flow transmission strategy using a novel design of graph neural network (GNN) reinforcement learning (RL) routing agent. Then, a distributed stage improves the performance based on a novel design of distributed learning updates. We provide a theoretical analysis of DIAMOND and prove that it converges to the optimal multi-flow transmission strategy as time increases. We also present extensive simulation results over various network topologies (random deployment, NSFNET, GEANT2), demonstrating the superior performance of DIAMOND compared to existing methods.

Chickpea (Cicer arietinumL.)—an important legume crop cultivated in arid and semiarid regions—has limited genetic diversity. Efforts are being undertaken to broaden its diversity by utilizing its wild relatives, which remain largely unexplored. Here, we present the Cicersuper-pangenome based on the de novo genome assemblies of eight annual Cicerwild species. We identified 24,827 gene families, including 14,748 core, 2,958 softcore, 6,212 dispensable and 909 species-specific gene families. The dispensable genome was enriched for genes related to key agronomic traits. Structural variations between cultivated and wild genomes were used to construct a graph-based genome, revealing variations in genes affecting traits such as flowering time, vernalization and disease resistance. These variations will facilitate the transfer of valuable traits from wild Cicerspecies into elite chickpea varieties through marker-assisted selection or gene-editing. This study offers valuable insights into the genetic diversity and potential avenues for crop improvement in chickpea.

We consider the problem of online stochastic optimization in a distributed setting with $M$ clients connected through a central server. We develop a distributed online learning algorithm that achieves order-optimal cumulative regret with low communication cost measured in the total number of bits transmitted over the entire learning horizon. This is in contrast to existing studies which focus on the offline measure of simple regret for learning efficiency. The holistic measure for communication cost also departs from the prevailing approach that separately tackles the communication frequency and the number of bits in each communication round.

Objective: To report the domains of Black mothers’ experiences with preterm infants in the neonatal intensive care unit (NICU) and to determine the frameworks used. Methods: A systematic review of the literature using the PRISMA guideline was performed. An electronic database search of published literature between 2000 and 2022 was conducted based on predetermined search terms and parameters. Results: Twenty-seven articles met the inclusion and exclusion criteria. Eleven articles focused on feeding practices, ten reported on nursing/maternal care experiences, five reported mental health/social support, and only one focused on mother-infant relationships. Only five papers reported using any frameworks, comprising grounded theory framework (n= 2), theory of planned behavior (n= 1), research justice framework (n = 1), and the patient and public engagement protocol (n= 1). Discussion: A holistic approach to understanding the multifactorial experiences of Black mothers with preterm infants needs to be socio-culturally competent to ensure their diverse intersections and identities are accurately represented and understood. Rigorous research at the intersection of Black maternal health and the NICU hold promises for advancing maternal health equity in the United States.

To make things even more complicated, even the Jews who eventually immigrated to Israel were internal outsiders to the nation, as demonstrated in Orly Castel-Bloom's novel I Ha-roman ha-Mizrachi i , which conveys a perpetual feeling of uprootedness, as in yet another diaspora. Victor Magiar's I E venne la note i (2003), for example, illustrates how on a personal level, Jews and Arabs could be friendly neighbors, with an Arab family helping the protagonist during riots against Jews in Tripoli. Miccoli demonstrates that the pasts of the Jews of North Africa and the Middle East are often politicized. [Extracted from the article]

Attractive toxic sugar baits (ATSBs) are an underexploited method for mosquito control. For ATSBs to be more widely accepted, demonstrably effective ingredients need to be verified. We investigated erythritol as a toxic additive in sugar meals against Aedes aegypti(L.) for potential future use in ATSBs. Erythritol is a sugar alcohol that is commonly used as a sugar substitute, while also being toxic to mosquitoes. Our studies tested formulations of erythritol, sucrose, and blends of both. Secondary investigations included combinations with the active ingredients Bacillus thuringiensis israelensis, spinosyn, and boric acid. Adult Ae. aegyptiwere separated into test groups and provided various combinations. Formulations containing erythritol, with or without another toxicant, exhibited 90% mortality within 72 h of observation (P= 0.03192). Additionally, erythritol appeared more effective when combined with sucrose in a 1:1 ratio (5% concentration each). This combination showed a 24% and 85% increase in mortality when combined with boric acid and Bti, respectively, at 48 h compared with equivalent groups containing only 10% sucrose. Erythritol appears to kill adult mosquitoes, even in relatively low concentrations, without another toxicant being required. However, erythritol also effectively enhances kill of main ingredient toxicants such as boric acid and Bti, showing a supporting role. The low concentration of erythritol needed to provide significant kill, its ability to fill in as both a sugar base and toxicant, and its ability to be safely handled by humans makes erythritol a strong candidate for use as a supporting ingredient in future bait formulations.

Functionalization of C–H bonds is a key challenge in medicinal chemistry, particularly for fragment-based drug discovery (FBDD) where such transformations require execution in the presence of polar functionality necessary for protein binding. Recent work has shown the effectiveness of Bayesian optimization (BO) for the self-optimization of chemical reactions; however, in all previous cases these algorithmic procedures have started with no prior information about the reaction of interest. In this work, we explore the use of multitask Bayesian optimization (MTBO) in several in silicocase studies by leveraging reaction data collected from historical optimization campaigns to accelerate the optimization of new reactions. This methodology was then translated to real-world, medicinal chemistry applications in the yield optimization of several pharmaceutical intermediates using an autonomous flow-based reactor platform. The use of the MTBO algorithm was shown to be successful in determining optimal conditions of unseen experimental C–H activation reactions with differing substrates, demonstrating an efficient optimization strategy with large potential cost reductions when compared to industry-standard process optimization techniques. Our findings highlight the effectiveness of the methodology as an enabling tool in medicinal chemistry workflows, representing a step-change in the utilization of data and machine learning with the goal of accelerated reaction optimization.

AbstractIntroduction:The experience of terrorist incidents involving a secondary explosive device that targeted rescue forces led to changes in the safety protocols of these forces in most countries of the world. These protocols are the foundation of the current “Stage-and-Wait” paradigm that prohibits the entry of Emergency Medical Services (EMS) from entering the scene and treating casualties until it is deemed safe. These guidelines were established absent of an evidence-base detailing the risk to responders and the potential consequences to the injured on-scene. The lack of clarity is compounded by the fact that different situations, as well as operational considerations, such as the length of time until bomb squad arrival at the scene versus time of massive bleeding injuries, for example, impact outcomes must be taken into account.Objective:This study sought to shed light on this matter while employing an evidence-based approach exploring the investigations of the frequency of secondary explosion threats in terrorist attacks over the last 20 years and discussing some of the ethical challenges and ramifications ensuing. While this study does not propose an outright change to current guidelines, in light of the evidence gathered, an open review and discussion based on the findings may be beneficial.Methods:The Global Terrorism Database (GTD) was used as the data source of bombing incidents world-wide.Results:The results revealed that approximately 70 per-1,000 bombing incidents involved secondary explosions across regions and countries within the study period.Conclusion:This study emphasizes the need to rethink the current “Stage-and-Wait” paradigm by recommending brainstorming conferences comprised of multi-sectoral experts aimed at deliberating the matter. World-wide experts in emergency medicine, bioethics, and disaster management should cautiously consider all aspects of bomb-related incidents. These brainstorming deliberations should consider the calculated risk of secondary explosions that account for approximately 70 per-1,000 bombing incidents. This study highlights the need to re-examine the current versus new paradigm to achieve a better balance between the need to ensure EMS safety while also providing the necessary and immediate care to improve casualty survival. This ethical dilemma of postponing urgent care needs to be confronted.

Gary Marcus has called for a "CERN for AI" to address issues related to scalability of current iterations of AI foundation models and ensure that AI becomes a public good rather than just the property of a privileged few. In the development and use of Artificial Intelligence (AI) systems it is important to ask questions about who has the power. [Extracted from the article]

Introduction: The most successful programme for secondary fracture prevention is the FLS (fracture liaison service) model. Our orthopaedic department carried out a prospective randomised study to measure the effectiveness of a 4-step intervention programme. The findings in this study reveal important additional clinical benefits to having an orthopaedic-based FLS programme and evaluates the usefulness of fracture risk tools.Methods: We carried out a prospective study to evaluate patients with a fragility fracture of the hip. There were 2 groups, intervention and control (each 100 patients). Of these, 20 were either removed from the study or dropped out, leaving 180 for analysis. In addition to routine preoperative blood tests, albumin and thyroid function levels were obtained and PTH (parathyroid hormone) levels when indicated. The intervention group (83 patients) had a dual-energy x-ray absorptiometry (DEXA) scan performed and fracture risk (FRAX) was calculated.Results: 12 patients (6.7%) had blood results which showed a potentially treatable cause for osteoporosis and 36 (20%) had blood results that changed their medical care. FRAX scores (180 patients) showed that the major osteoporotic fracture score correctly predicted the hip fracture in only 49%. The hip fracture score correctly predicted the hip fracture in 83%. DEXA scores (65 patients) showed osteoporosis in only 46% of hips and in only 26% of spines. An abnormal FRAX score or DEXA scan would have predicted a fragility fracture 93% of the time.Conclusions: In addition to reducing secondary fractures, FLS programmes can provide fundamental benefits to the health of the patient. The intervention programme in this study identified patients with underlying treatable causes, correctable clinical conditions and patients with an unusually low bone density. When used together, FRAX and DEXA are more sensitive predictors for hip fracture risk than either are individually.Trial registry: 201497CTIL (https://clinicaltrials.gov/ct2/show/NCT02239523)

Objectives: Alcohol misuse remains a major concern in college student populations and rates of risky and problematic drinking are specifically on the rise for women. One important factor that has been shown to be positively associated with substance use/misuse for women from historically marginalized or minoritized racial/ethnic backgrounds is exposure to racial discrimination and possible resulting traumatic stress reactions. Questions remain about the relationship between race-based traumatic stress and risky drinking particularly among diverse female college students who are at greater risk due to their marginalized status in their racial/ethnic and gender groups. Design: The current study examined race-based traumatic stress as a unique predictor of risky drinking when controlling for negative affectivity and general trauma symptoms, additional risk factors for risky drinking in women. Results: Hierarchical linear regressions revealed that race-based traumatic stress made a significant and unique contribution to the amount of variance in risky drinking, above negative affectivity, and general trauma symptoms. Conclusion: These findings highlight the importance of considering experiences of racism as risk factors in substance use prevention and intervention, specifically for female college students from marginalized or minoritized racial/ethnic backgrounds.

We consider the problem of dynamic spectrum access (DSA) in cognitive wireless networks, consisting of primary users (PUs) and secondary users (SUs), where only partial observations are available at the SUs due to narrowband sensing and transmissions. The network operates in a time-slotted regime, where the traffic patterns of the PUs are modeled as finite-memory Markov chains, that are unknown to the SUs. Since observations are partial, then both channel sensing and access actions affect the throughput. Focusing on the case in which there is a single SU, our objective is to maximize the SU’s long-term throughput. To that aim, we develop a novel algorithm that learns both access and sensing policies via deep Q-learning, dubbed Double Deep Q-network for Sensing and Access (DDQSA). To the best of our knowledge, this is the first work that jointly optimizes both sensing and access policies for DSA via deep Q-learning. Next, we consider wireless networks with access policy which implements a fixed channel hopping dynamics, for which we analytically determine the optimal SU sensing and access policy and its associated throughput. Then, we demonstrate that indeed, the proposed DDQSA algorithm can achieve near-optimal performance for the considered network. Our results show that the proposed DDQSA algorithm learns a policy that implements both sensing and channel access, which significantly outperforms existing approaches, and can achieve the optimal performance in certain scenarios.

Detection of anomalies among a large number of processes is a fundamental task that has been studied in multiple research areas, with diverse applications spanning from spectrum access to cyber-security. Anomalous events are characterized by deviations in data distributions, and thus can be inferred from noisy observations based on statistical methods. In some scenarios, one can often obtain noisy observations aggregated from a chosen subset of processes. Such hierarchical search can further minimize the sample complexity while retaining accuracy. An anomaly search strategy should thus be designed based on multiple requirements, such as maximizing the detection accuracy; efficiency, be efficient in terms of sample complexity; and be able to cope with statistical models that are known only up to some missing parameters (i.e., composite hypotheses). In this paper, we consider anomaly detection with observations taken from a chosen subset of processes that conforms to a predetermined tree structure with partially known statistical model. We propose Hierarchical Dynamic Search (HDS), a sequential search strategy that uses two variations of the Generalized Log Likelihood Ratio (GLLR) statistic, and can be used for detection of multiple anomalies. HDS is shown to be order-optimal in terms of the size of the search space, and asymptotically optimal in terms of detection accuracy. An explicit upper bound on the error probability is established for the finite sample regime. In addition to extensive experiments on synthetic datasets, experiments have been conducted on the DARPA intrusion detection dataset, showing that HDS is superior to existing methods.

Spatial modulation of electron beams is an essential tool for various applications such as nanolithography and imaging, yet its conventional implementations are severely limited and inherently non-tunable. Conversely, proposals of light-driven electron spatial modulation promise tunable electron wavefront shaping, for example, using the mechanism of photon-induced near-field electron microscopy. Here we present tunable photon-induced spatial modulation of electrons through their interaction with externally controlled surface plasmon polaritons (SPPs). Using recently developed methods of shaping SPP patterns, we demonstrate a dynamic control of the electron beam with a variety of electron distributions and verify their coherence through electron diffraction. Finally, the nonlinearity stemming from energy post-selection provides us with another avenue for controlling the electron shape, generating electron features far below the SPP wavelength. Our work paves the way to on-demand electron wavefront shaping at ultrafast timescales, with prospects for aberration correction, nanofabrication and material characterization.

AbstractIntroduction:Asymmetric warfare and the reaction to its threats have implications in the way far-forward medical assistance is provided in such settings. Investments in far-forward emergency resuscitation and stabilization can contribute to saving lives and increase the resilience of health systems. Thus, it is proposed to extend the use of the Haddon Matrix to determine a set of strategies to better understand and prioritize activities to prepare for and set-up frontline care in the form of Trauma Stabilization Points (TSPs).Methods:An expert consensus methodology was used to achieve the research aim. A small subject matter experts’ group was convened to create and validate the content of the Haddon Matrix.Results:The result of the expert group consultations presented an overview of TSP Preparedness and Operational Readiness activities within a Haddon Matrix framework. Main strategies to be adopted within the cycle from pre- to post-event had been identified and presented considering the identified opportunities in the context of the possibility of implementation. Of particular importance was the revision of a curriculum that fits the civilian medical system and facilitates its adaptation to the context and available resources.Conclusion:The new framework to enhance frontline care preparedness and response using the Haddon Matrix facilitated the identification of a set of strategies to support frontline health care workers in a more efficient manner. Since the existing approach and tools are insufficient for modern warfare, additional research is needed.

This article deals with the first groups (convoys) of Jewish tourists who traveled to Eretz Israel at the end of the Ottoman period and examines them in light of the typology of pilgrimage, tourism and Zionism. The participants in the convoys felt they had a mission to become familiar with Palestine, in order for them to express their opinion regarding the chances of settling there in the future. The interest that the participants in the convoys showed in the Jewish enterprise in Palestine were also milestone in the efforts to unite the factions of Jewish Yishuv in Palestine. [ABSTRACT FROM AUTHOR]

Imaging evanescent waves is of crucial importance for sub-wavelength-scale investigation of various phenomena. However, frequently used techniques for near-field imaging require either a strong perturbation of the field, long acquisition times or complex electron-based tools. Here, we introduce nonlinear near-field optical microscopy (NNOM), which is capable of real-time evanescent wave imaging by nonlinear wave mixing while using only standard optical components. As a proof-of-concept, we present non-perturbative, single-shot mapping of evanescent plasmonic patterns, utilizing the nonlinearity of the host metal, and monitor in real time the externally controlled changes to the patterns. We further demonstrate the ability to extract the full field information—the amplitude and phase of all electric-field components—in a polarization-sensitive, spin-selective manner. This simple and highly tunable technique could be extended to deep sub-wavelength imaging of polaritons in two-dimensional materials or other nanophotonic guided modes, for swift photonic device characterization and optimized light−matter interactions.